Editor-In-Chief Chair Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel

CEO, Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Additional Director, Technocrats Institute of Technology and Science, Bhopal (MP), India

Associated Editor-In-Chief Members Dr. Hitesh Kumar

Ph.D.(ME), M.E.(ME), B.E. (ME)

Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science, Technology and Maritime

Transport, Egypt

Associated Editor-In-Chief Members Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India

Executive Editor Dr. Deepak Garg

Professor, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Technical Program Committee Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Manager Chair Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Editorial Members Dr. J. Gladson Maria Britto

Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education University, Amman- Jordan

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

Dr. Mohammad Mahdi Mansouri

Associate Professor, Department of High Voltage Substation Design & Development, Yazd Regional Electric Co., Yazd Province,

Iran.

Dr. Kaushik Pal

Youngest Scientist Faculty Fellow (Independent Researcher), (Physicist & Nano Technologist), Suite.108 Wuhan University, Hubei,

Republic of China.

Dr. Wan Aezwani Wan Abu Bakar

Lecturer, Faculty of Informatics & Computing, Universiti Sultan Zainal Abidin (Uni SZA), Terengganu, Malaysia.

Dr. P. Sumitra

Professor, Vivekanandha College of Arts and Sciences for Women (Autonomous), Elayampalayam, Namakkal (DT), Tiruchengode

(Tamil Nadu), India.

Dr. S. Devikala Rameshbabu

Principal & Professor, Department of Electronics and Electrical Engineering, Bharath College of Engineering and Technology for

Women Kadapa, (Andra Pradesh), India.

Dr. V. Lakshman Narayana

Associate Professor, Department of Computer Science and Engineering, Vignan’s Nirula Institute of Technology & Science for

women, Guntur, (Andra Pradesh), India.

S. No

Volume-9 Issue-7S, May 2020, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Archana N. Mahajan, Maulika S. Patel

Paper Title: Computational Identification of Biomarkers

Abstract: Early detection of diseases and personalized medicine are gaining huge attention as a result of the

advancements in the fields of bioinformatics and computational biology. Computational Biology is an

interdisciplinary area involving Biomolecular data understanding and analysis by using and development of

various tools, algorithms and methods. Disease detection and prediction is mostly carried out through the

symptoms reported by patient and clinical test performed on the basis of it. There is a need to detect disease before

the symptoms progress. Biomarkers are biological markers which are implicitly available in biomolecular data.

There are different types of biomarkers which can help in early disease detection and finding correlation of the

disease with other changes at the cellular level. The biomolecular data exploration is the key to identify various

biomarkers. This paper presents a summary of types of biomarkers and biomarker identification techniques.

Keywords: Biomarker, Computational biology, Decision Trees, Genomics, k-means clustering, Proteomics,

Support vector machines, Transcriptomics

References: 1. 1 Procter, R. (2009). Definition of Health Informatics.,from http://www.nlm.nih.gov/hsrinfo/informatics.html 2. Nadri H, Rahimi B, Timpka T, Sedghi S (August 2017). "The Top 100 Articles in the Medical Informatics: a Bibliometric Analysis",

Journal of Medical Systems. 41 (10): 150. doi:10.1007/s10916-017-0794-4. PMID 28825158.

3. Strimbu K, Tavel JA. What are biomarkers?. Curr Opin HIV AIDS.2010;5(6):463–466. doi:10.1097/COH.0b013e32833ed177 4. Ich, “ICH Topic E15 Definitions for genomic biomarkers, pharmacogenomics, pharmacogenetics, genomic data and sample coding

categories ,” EMEA, November, p. 1-8, 2007.

5. M. Gosho, K. Nagashima, and Y. Sato, “Study Designs and statistical analyses for biomarker research,” Sensors (Switzerland), vol. 12, no. 7, pp. 8966–8986, 2012.

6. I. H. Witten, E. Frank, and M. a Hall, Data Mining: Practical Machine Learning Tools and Techniques (Google eBook). 2011.

7. Carl Kingsford and Steven L Salzberg ,”What are decision trees?,Public Access,” Bone, vol. 23, no. 1, pp. 1–7, 2008. 8. E. Raczko and B. Zagajewski, “Comparison of support vector machine, random forest and neural network classifiers for tree species

classification on airborne hyperspectral APEX images,” Eur. J. Remote Sens., vol. 50, no. 1, pp. 144–154, 2017.

9. Y. Qi, “Random forest for bioinformatics,” Ensemble Mach. Learn. Methods Appl., pp. 307–323, 2012. 10. J.Y. Wang, “Applications of Support Vector Machines in bioinformatics,” Bioinformatics, pp. 1–56, 2002.

11. N. Toschi et al., “Biomarker-guided clustering of Alzheimer’s disease clinical syndromes,” Neurobiol. Aging, vol. 83, pp. 42–53,

2019. 12. C. Cheng et al., “Identification of differentially expressed genes, associated functional terms pathways, and candidate diagnostic

biomarkers in inflammatory bowel diseases by bioinformatics analysis,” Exp. Ther. Med., pp. 278–288, 2019.

13. Jingsun Wei et al., ”Identification of biomarkers and their functions in dasatinib-resistant pancreatic cancer using bioinformatics

analysis”,ONCOLOGY LETTERS 18: 197-206, April 2019

14. J. Chen, Z. Wang, X. Shen, X. Cui, and Y. Guo, “Identification of novel biomarkers and small molecule drugs in human colorectal

cancer by microarray and bioinformatics analysis,” Mol. Genet. Genomic Med., vol. 7, no. 7, pp. 1–15, 2019. 15. H. Kim, Y. Kim, B. Han, J. Y. Jang, and Y. Kim, “Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic

Data,” J. Proteome Res., vol. 18, no. 8, pp. 3195–3202, 2019.

16. X. Li, J. Wu, E. Z. Chen, and H. Jiang, “From Deep Learning Towards Finding Skin Lesion Biomarkers,” 2019 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 2797–2800, 2019.

17. S. Akter et al., “Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data,” Front. Genet., vol.

10, 2019. 18. B. Gao, S. Li, Z. Tan, L. Ma, and J. I. A. Liu, “ACTG1 and TLR3 are biomarkers for alcohol-associated hepatocellular carcinoma,”

Oncol. Lett., vol. 17, no. 2, pp. 1714–1722, 2019.

19. A. J. Rosato et al., “Salivary microRNAs identified by small RNA sequencing and machine learning as potential biomarkers of alcohol dependence,” Epigenomics, vol. 11, no. 7, pp. 739–749, 2019.

20. A. B. Niculescu et al., “Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs,” Mol. Psychiatry, vol.

24, no. 4, pp. 501–522, 2019. 21. X. Feng et al., “Age is important for the early-stage detection of breast cancer on both transcriptomic and methylomic biomarkers,”

Front. Genet., vol. 10, no. MAR, pp. 1–13, 2019.

22. K. Ni and G. Sun, “The identification of key biomarkers in patients with lung adenocarcinoma based on bioinformatics,” Math. Biosci. Eng., vol. 16, no. 6, pp. 7671–7687, 2019.

23. T. Ris et al., “Inflammatory biomarkers in infective endocarditis: machine learning to predict mortality,” Clin. Exp. Immunol., vol.

196, no. 3, pp. 374–382, 2019. 24. N. Giangreco et al., “Exosome Proteomics and Machine Learning Identify Novel Biomarkers of Primary Graft Dysfunction,” J.

Hear. Lung Transplant., vol. 38, no. 4, p. S137, 2019.

25. Y. Wang, Z. Wang, and H. Zhang, “Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome-wide gene expression and pattern recognition,” J. Cell. Biochem., vol. 120, no. 2, pp. 1503–1510, 2019.

26. M. M. Ivancic et al., “Noninvasive Detection of Colorectal Carcinomas Using Serum Protein Biomarkers,” J. Surg. Res., vol. 246,

no. 205, pp. 160–169, 2019.

1-5

2.

Authors:

Dhruvil Parmar, Mit Suthar, Dhaval Bhoi

Paper Title: Pattern Recognition in Games

Abstract: In this paper, we discuss how pattern recognition plays a vital role in games [3]. Pattern recognition is

used to gather necessary details related to the gaming world and that will provide us input to the decision-making

system, which produces actions for the game world [1]. Before pattern recognition how the decision is making

and implementing it. We also define emotion-based recognition in affect-aware games [11]. Then we discuss the

6-8

usefulness of pattern recognition in games, how and where it can be implemented and what is expected from it

and how it can affect relations between the human player and synthetic player [1].

Keywords: Computer generated player(Bots), Decision-making, Operational, Strategical, Tactical.

References: 1. Kaukoranta, Timo & Smed, Jouni & Hakonen, Harri. (2003). “Role of Pattern Recognition in Computer Games.”

2. M. Capps, P. McDowell, and M. Zyda. A future for entertainment-defense research collaboration. IEEE Computer Graphics and Applications.

3. G. Costikyan. I have no words & I must design: Toward a critical vocabulary for games. In F. Mayr ̈ an (editor), ̈ Computer Games

and Digital Cultures Conference Proceedings, pp. 9–33, Tampere, Finland. 4. Federation Internationale de Football Association. Laws of the game. Web page, Nov. 2002. http://www.fifa.com/refs/laws E.html.

5. J.E. Laird and M. van Lent. Human-level AI’s killer application: interactive computer games. AI Magazine. 6. RoboCup. Web page, Nov. 2002. http://www.robocup.org/.

7. R. Schalkoff. Pattern Recognition: Statistical, Structural and Neural .u.Approaches. John Wiley & Sons, 0New York, NY, 1992.

8. S. Rabin, editor. AI Game Programming Wisdom. Charles River Media, Hingham, MA, 2002. 9. International Game Developers Association. Web page, Nov. 2002. http://www.igda.org/

10. Marill and Green, 1960 Marill T., Green D.M.Statistical recognition functions and the design of pattern recognizers IRE Trans. E

(Mariusz Szwoch, 2015)electron. Computers, EC-9 (1960), pp. 472-477 11. Mariusz Szwoch, Wioleta Szwoch, “Emotion Recognition for Affect Aware Video Games”, in Image Processing &

Communications Challenges 6, 2015, Volume 313, (ISBN: 978-3-319-10661-8)

12. Ugur Demir, Esam Ghaleb, Hazım Kemal Ekenel, “A Face Recognition Based Multiplayer Mobile Game Application”, in Artificial Intelligence Applications and Innovations, 2014, Volume 436, (ISBN: 978-3-662-44653-9)

3.

Authors: Mala Sinnoor, Shanthi K J

Paper Title: Survey on Filtering Techniques Applied to ECG Signal

Abstract: This Analysis of ECG signals is crucial for early detection of cardiac ailments. With the sensors

becoming more and more predominant and available in all wearable forms for measuring multiple physiological

parameters, the importance of algorithms to detect the abnormalities are also gaining importance. The

physiological signals are measured through noninvasive means unless very critical and hence are very weak in

nature. Bio-amplifiers are applied during signal acquisition. The signals are weak and hence susceptible for the

electrical disturbances in the environment. Selection of Filtering techniques is very important and is decided by

many factors. This paper is a survey which presents digital filtering techniques the researchers have applied in the

last decade. The study reveals the different types FIR/IIR filters the researchers have worked and also the

performance metrics adapted by the researchers electronic document is a “live” template and already

Keywords: FIR, IIR, De-noising, ECG, Noise, Power line interference

References: 1. Gandham Sreedevi, Bhuma Anuradha, “Using Of Fir And IIR Filters For Noise Removal From ECG Signal: A Performance

Analysis”, International Journal of Electronics and Communication Engineering and Technology (IJECET) Volume 7, Issue 4,

July-August 2016, pp. 91–99, Article ID: IJECET_07_04_011.

2. Shubhankar Saxena ; Rohan Jais ; Malaya Kumar Hota, “Removal of Powerline Interference from ECG Signal using FIR, IIR, DWT and NLMS Adaptive Filter” IEEE International Conference on Communication and Signal Processing, April 4-6, 2019, India.

3. Julien Oster∗, Joachim Behar, Omid Sayadi, Shamim Nemati, Alistair E. W. Johnson, “Semisupervised ECG Ventricular Beat

Classification With Novelty Detection Based on Switching Kalman Filters”, IEEE TRANSACTIONS on Biomedical Engineering,

vol. 62, no. 9, September 201.5 International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-

3075, Volume-9 Issue-7S, May 2020 11 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: G10030597S20/2020©BEIESP DOI: 10.35940/ijitee.G1003.0597S20

4. Sarang L. Joshi , Rambabu A.Vatti , Rupali V.Tornekar, “A Survey on ECG Signal Denoising Techniques”, IEEE International Conference on Communication Systems and Network Technologies 2013.

5. Thion Ming Chieng, Yuan wen Hau, Zaid Omar, “The study and comparison between various digital filters for ECG De-noising”,

IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) 2018. 6. P. Raphisak, S.C. Schuckers, A.J. Curry, An algorithm for EMG noise detection in large ECG data, Comput”, Cardiol, 31 (2004)

369– 372.

7. Md. Asadur Rahman ; Md. Mahmudul Haque Milu ; Anika Anjum ; Farzana Khanam ; Mohiuddin Ahmad , “Baseline wandering removal from ECG signal by wandering path finding algorithm”, IEEE 3rd International Conference on Electrical Information and

Communication Technology (EICT) 2017.

8. Mohammed Tali Almalchy, Vlad Ciobanu, Nirvana Popescu, „Noise Removal from ECG Signal Based on Filtering Techniques”, IEEE 22nd International Conference On Control Systems And Computer Science (CSCS), 2019.

9. Akanksha Mittal, Amit Rege, “ Design of Digital FIR Filter Implemented with Window Techniques for Reduction of Power line

Interference from ECG Signal”, IEEE International Conference on Computer, Communication and Control (IC4-2015). 10. K.Sravan Kumar, Babak Yazdanpanah, P Rajesh Kumar, “Removal of Noise from Electrocardiogram Using Digital FIR and IIR

Filters with Various Methods”, the IEEE ICCSP 2015 conference.

11. Sande Seema Bhogeshwar, M.K.Soni, Dipali Bansal, “Design of Simulink Model to denoise ECG signal using various IIR & FIR filters”, IEEE 2014 International Conference on Reliability, Optimization and Information Technology -ICROIT 2014, India, Feb

6-8 2014 12. Nauman Razzaq, Shafa-At Ali Sheikh, Muhammad Salman, And Tahir Zaidi , “An Intelligent Adaptive Filter for

Elimination of Power Line Interference From High Resolution Electrocardiogram”, IEEE Access ( Volume: 4 ), Electronic ISSN: 2169-3536,page(s): 1676 – 1688, Date of Publication: 31 March 2016.

12. Tanuj Yadav , Rajesh Mehra ,” Denoising and SNR Improvement of ECG Signals Using Wavelet Based Techniques”, IEEE 2nd

International Conference on Next Generation Computing Technologies (NGCT-2016) 13. Bharati Sharma, Jenkin Suji, “Analysis of Various Window Techniques used for denoising ECG signal”, IEEE Symposium on

Colossal Data Analysis and Networking (CDAN), 2016

14. Eminaga, Y., Coskun, A. and Kale, I., “IIR Wavelet Filter Banks for ECG Signal Denoising”, 22nd IEEE International Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). Poznan, Poland, 19 - 21 Sep 2018.

15. Tapash Karmaker, Md. Shamim Anower, Muhammad Abdul Goffar Khan, Md. Ahasan Habib , “A New Adjustable Window

Function to Design FIR Filter and Its Application in Noise Reduction From Contaminated ECG Signal” 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) 21 - 23 Dec 2017, Dhaka, Bangladesh

9-11

16. Yaprak Eminaga, Adem Coskun, Sterghios A. Moschos, and Izzet Kale1, “Low Complexity All-Pass Based Polyphase Decimation Filters for ECG Monitoring”, 2015 11th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME).

17. Sujan Sarkar ; Shubham Sarkar ; Jishan MehediDept. “Comprehensive Study for Selection of proper IIR filter: Specifications

Dependant Approach”, 2018 3rd International Conference for Convergence in Technology (I2CT) The Gateway Hotel, XION Complex, Wakad Road, Pune, India. Apr 06-08, 2018.

18. Nilotpal Das, Monisha Chakraborty, “Performance Analysis of FIR and IIR Filters for ECG Signal Denoising based on SNR”,

Third International Conference On Research In Computational Intelligence And Communication Networks (ICRCICN), 2017. 19. 20. Thandar Oo ; Pornchai Phukpattaranont ; Prapakorn Klabklay “Effects of SNR on removing ECG noise from EMG signal using

DSWT” IEEE 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and

Information Technology (ECTI-CON), 2018.

4.

Authors: Rinal Patel, Hetal Gaudani

Paper Title: Toxic Comments Classification using Neural Network

Abstract: Humans have built broad models of expressing their thoughts via several appliances. The internet has

not only become a credible method for expressing one's thoughts, but is also rapidly becoming the single largest

means of doing so. In this context, one area of focus is the study of negative online behaviors of users like, toxic

comments that are threat, obscenity, insults and abuse. The task of identifying and removing toxic communication

from public forums is critical. The undertaking of analyzing a large corpus of comments is infeasible for human

moderators. Our approach is to use Natural Language Processing (NLP) techniques to provide an efficient and

accurate tool to detect online toxicity. We apply TF-IDF feature extraction technique, Neural Network models to

tackle a toxic comment classification problem with a labeled dataset from Wikipedia Talk Page.

Keywords: Natural language processing, neural network, TF-IDF feature extraction, Toxic comments

References: 1. CJ Adams and Lucas Dixon. Better discussions with imperfect models. url: https: / / medium . com / the - false - positive / better -

discussions - with - imperfect - models-91558235d442. 2. Duggan, M., Rainie, L., Smith, A., Fuck, C., Lenhart, A., & Madden, M. (2014). Online harassment. Pew research center.

3. Conversation AI Team. https://conversationai.github.io/

4. Perspective API. https://perspectiveapi.com/#/ 5. “Dataset” URL: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-chall enge/data

6. CJ Adams and Lucas Dixon. Better discussions with imperfect models. URL: https://medium.com/the- false-

positive/betterdiscussions- with- imperfect-models 91558235d442. 7. Reis, Julio CS, et al. "Supervised Learning for Fake News Detection." IEEE Intelligent Systems 34.2 (2019): 76-81.

8. Ying Liu, Han Tong Loh, and Aixin Sun. \Imbalanced text classification: A term weighting approach". In: Expert Systems with

Applications (2009). URL: http : / /scholarbank.nus.edu.sg/handle/10635/60483. 9. Mohammad, Fahim. "Is preprocessing of text really worth your time for online comment classification?." arXiv preprint

arXiv:1806.02908 (2018).

10. Barnaghi, Peiman, Parsa Ghaffari, and John G. Breslin. "Opinion mining and sentiment polarity on twitter and correlation between events and sentiment." 2016 IEEE Second International Conference on Big Data Computing Service and Applications

(BigDataService). IEEE, 2016.

11. Hamida, Chady Ben, Victoria Ge, and Nolan Miranda. "Toxic Comment Classification and Unintended Bias." (2019).

12. Jiang, Chuntao, et al. "Text classification using graph mining-based feature extraction." Research and Development in Intelligent

Systems XXVI. Springer, London, 2010. 21-34.

13. Chakrabarty, Navoneel. "A Machine Learning Approach to Comment Toxicity Classification." Computational Intelligence in Pattern Recognition. Springer, Singapore, 2020. 183-193.

14. “Feed forward and back propagation technique for NLP” URL: https://www.guru99.com/backpropogation-neural-network.html

15. “Analyticsvidhya blog for neural network” URL : https://www.analyticsvidhya.com/blog/2017/05/neural-network-fromscratch-in python-and-r/ 16. “Analyticsvidhya blog on Bag-of-Word feature extraction technique” URL:https://stackabuse.com/python-for-

nlp-creating-bag-of-words-m odel-from-scratch/

16. Li, Yang, and Tao Yang. "Word embedding for understanding natural language: a survey." Guide to Big Data Applications. Springer, Cham, 2018. 83-104.

17. Liu, Bing. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5.1 (2012): 1-167.

18. Ibrahim, Mai, Marwan Torki, and Nagwa El-Makky. "Imbalanced Toxic Comments Classification Using Data Augmentation and Deep Learning." 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018.

19. Hinton, Geoffrey, et al. "Deep neural networks for acoustic modeling in speech recognition." IEEE Signal processing magazine 29

(2012). 20. “Deep learning approach to classifying types of toxicity in Wikipedia comments” Stanford education.URL:

https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1184/reports /6838795.pdf.

21. 22. “Detecting and Classifying Toxic Comments” Stanford education.URL:https://web.stanford.edu/class/archive/cs/cs224n/cs22 4n.1184/reports/6837517.pdf

12-15

5.

Authors: Jyoti Rana, Nidhi Arora

Paper Title: Effect of Carrying Load on Gait Recognition using J48 with Knee Joint Movements

Abstract: Background: Gait patterns are influenced by various factors. Every person walks differently in

different scenarios and it becomes difficult to identify the person correctly by his walking style. Research question:

Is it possible to correctly recognize a person through his gait while he walks carrying load? What is the effect of

distance on a person’s gait while he walks with carrying load? Objective: The paper is an attempt to study the

effect of load on gait of a person. As the knee angle varies from person to person in varying conditions, we have

studied the effect of load on knee angle and thus on gait recognition. Methods: Experimentation is done on 41

subjects of age group 18-30 years carrying bilateral weights of 2.5kg in both hands. Data collected from

accelerometer has been studied using J48 decision tree classification algorithm. Results: Results of experiments

shows 86.12% accuracy in recognizing subjects carrying load up to a distance of 60 meters on a flat surface. The

FAR and FRR are found to be 0.77% and 27.52% respectively. Conclusion: Carrying load affects the gait of the

16-20

subject. This makes it difficult to recognize the subject while he walks carrying load. After walking for some

distance, the gait pattern and knee angle of the subject shows significant variations.

Keywords: Gait, FAR, FRR, EER, ROC

References: 1. Kinoshita, H., “Effects of different loads and carrying systems on selected biomechanical parameters describing walking gait,”

Ergonomics, vol. 28(9), 1985, pp.1347-1362.

2. Martin, P. E., and Nelson, R. C., “The effect of carried loads on the combative movement performance of men and women,”

Military medicine, vol. 150(7), 1985, pp. 357-362. 3. Winsmann, F. R., and Goldman, R. F., “Methods for evaluation of load-carriage systems,” Perceptual and Motor Skills, vol.

43(3_suppl), 1976, pp. 1211-1218.

4. Zarrugh, M.Y. and Radcliffe, C.W., “Predicting metabolic cost of level walking,” European Journal of Applied Physiology, vol. 38, 1978, pp. 215-223.

5. Hughes, A.L. and Goldman, R.F., “Energy cost of hard work”. Journal of Applied Physiology, vol. 29, 1970, pp. 570- 572.

6. Goh, J.H., Thambyah, A. and Bose, K. “Effects of varying backpack loads on peak forces in the lumbosacral spine during walking,” Clinical Biomechanics, vol.13, 1988, pp.26-31.

7. Singh, T. and Koh, M., “Effects of backpack load position on spatiotemporal parameters and trunk forward lean,” Gait & Posture,

vol. 29, 2009, pp. 49-53. 8. J.Bobet and R.W.Norman, “Effects of load placement on back muscle activity in load carriage,” European Journal of Applied

Physiology and Occupational Physiology, vol. 53, no. 1, 1984, pp. 71– 75.

9. C. Schulze, T. Lindner, K. Schulz, S. Woitge, W. Mittelmeier, and R. Bader, “Influence of increased load wearing on human posture and muscle activation of trunk and lower limb,” Swiss Medical Weekly, vol. 142, supplement 193, 2012, pp. 4–5.

10. Pascoe DD, Pascoe DE, and Wang YT, “Influence of carrying book bags on gait cycle and posture of youths,” Ergonomics, vol.

40, 1997, pp. 631–641.

11. Yu JH, Lee DY, and Hong JH, “The effects of a backpack on the ground reaction force in a normal gait,” IJACT, vol.5, 2013, pp.

593– 598.

12. Son S and Noh H, “Gait changes caused by the habits and methods of carrying a handbag,” J Phys Ther Sci, vol.25, 2013, pp. 969–971.

13. Yoon JG, “Correlations between muscle activities and strap length and types of school bag during walking,” J Phys Ther Sci, vol.26,

2014, pp. 1937–1939. 0 0.1 0.2 0.3 0.4 0.5 0.6 0 0.005 0.009 0.012 0.015 FRR FAR International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7S, May 2020 20 Retrieval Number:

G10060597S20/2020©BEIESP DOI: 10.35940/ijitee.G1006.0597S20 Published By: Blue Eyes Intelligence Engineering &

Sciences Publication 14. Gorodnichy, D. O., & Hoshino, R., “Calibrated confidence scoring for biometric identification,” In NIST International Biometric

Performance Conference, Vol.

15. 15, 2010, pp. 2-4 15. Martin, P. E., and Nelson, R. C., “The effect of carried loads on the walking patterns of men and women,” Ergonomics, vol. 29(10), 1986, pp. 1191-1202.

16. Majumdar, D., Pal, M. S., and Majumdar, D., “Effects of military load carriage on kinematics of gait,” Ergonomics, vol. 53(6), 2010, 782-791.

17. J. Knapik, E. Harman, and K. Reynolds, “Load carriage using packs: a review of physiological, biomechanical and medical aspects,”

Applied Ergonomics, vol.27, no.3, pp.207–216, 1996. 18. 18. Rana, J., Arora, N. and Hiran, D. (2018), “Gait Recognition using J48 Based Identification with Knee Joint Moments,” Presented

at International Conference on Soft Computing and Signal Processing, Hyderabad, 22-23 June. Advance Intelligent Systems and

Computing (AISC): Springer Publication.

6.

Authors: Priyanka Israni, Maulika S. Patel

Paper Title: Medical Image Analysis (MedIA) using Deep Learning

Abstract: Medical Image analysis has gained momentum in the research since last ten years. Medical images of

different modalities like X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound

etc. are generated with an increase of 15% to 20% every year. Medical image analysis requires high processing

power and huge memory for storing the medical images, processing them, extracting features for useful

information and segment the interested area for analysis. Thus, here comes the role of deep learning which proves

to be promising for medical image analysis. The major focus of the paper is on exploring the literature on the

broad areas of medical image analysis like Image Classification, Tumor/lesion classification and detection,

Organ/Sub-structure Segmentation, Image Registration and Image Construction/ Enhancement using deep

learning. Paper also highlights the physiological and medical challenges to be taken care, while analyzing medical

images. It also discusses the technical challenges of using deep learning for medical image analysis and its

solutions.

Keywords: Convolutional Neural Network, MRI, CT-Scan, Transfer learning.

References: 1. “Breast cancer survival statistics”, Cancer Research UK. (https://www.cancerresearchuk.org/healthprofessional/cancer-

statistics/statistics-by-cancer-type/breast-cancer) assessed on 9th January 2020.

2. J. S. Duncan and N. Ayache, “Medical Image Analysis: Progress over Two Decades and the Challenges Ahead”, IEEE Transactions

on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 85-106, 2000. 3. T. Brosch and R. Tam, “Manifold learning of brain MRIs by deep learning”, in the proceedings of the Medical Image Computing and

Computer-Assisted Intervention, in Lecture Notes in Computer Science, pp. 633–640, 2013, doi: 10.1007/ 978- 3- 642- 40763- 5 _ 78.

4. V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venu- gopalan, K. Widner,T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega and D. R. Webster, “Development and validation of a deep learning algorithm for detection

of diabetic retinopathy in retinal fundus photographs”, Journal of American Medical Association, pp.2402–2410, 2016. doi:

10.1001/jama.2016.17216 5. A. Esteva, B. Kuprel, R.A Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, “Dermatologist-level classification of skin cancer with

deep neural networks”, Nature, pp. 115–118, 2017. doi: 10.1038/nature21056

6. A. Menegola, M. Fornaciali, R. Pires, S. Avila and E. Valle, “Towards automated melanoma screening: exploring transfer learning schemes”. arXiv:1609.01228,2016.

21-25

7. A. Payan and G. Montana, “Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks”, arxiv: 1502.02506, 2015.

8. E. Hosseini-Asl, G. Gimel’farb and A. El-Baz, “Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional

network. arXiv: 1607.00556 ,2016. 9. Y. Pan, W. Huang,Z. Lin, W. Zhu, J. Zhou, J. Wong, Z. Ding, “Brain tumor grading based on neural networks and convolutional

neural networks”, in proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 699–702, 2015. doi:

10.1109/EMBC.2015.7318458 . 10. Q. Dou, H. Chen , L. Yu, L. Shi, D. Wang, V. C. Mok, P. A. Heng, “ Automatic cerebral microbleeds detection from MR images via independent subspace anal- ysis based hierarchical features” , in Proceedings of the IEEE

Engineering in Medicine and Biology Society, pp. 7933–7936, 2015. doi: 10.1109/EMBC.2015.7320232 . 11. Q. Dou,H. Chen,L. Yu,

L. Zhao, J. Qin, D. Wang, V.C. Mok , L. Shi ,P.A. Heng , “Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks”, IEEE Transaction of Medical Imaging, Vol.35, pp.1182–1195, 2016b

10. J. Kawahara, A. BenTaieb and G. Hamarneh, “Deep features to classify skin lesions.”, in the proceedings of the IEEE International

Symposium on Biomedical Imaging, pp. 1397–1400. doi: 10.1109/ISBI.2016.7493528. 11. G. E. Humpire-Mamani, A. A. Setio, B. van Ginneken and C. Jacobs, “Efficient organ localization using multi-label convolutional

neural networks in thorax-abdomen CT scans”, Journal of Physics in Medicine & Biology, Vol. 63, No.8, 2018.

12. S. Pereira, A. Pinto, V. Alves, V. and C. A. Silva, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images”, IEEE Transactions on Medical Imaging, Vol.35, No.5, pp.1240-1251, 2016.

13. M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, P. Diao, C. Igel and M. Lillholm“Unsupervised deep learning applied to breast

density segmentation and mammographic risk scoring”, IEEE transactions on medical imaging, Vol.35, No.5, pp.1322-1331,2016. 14. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P. M. Jodoin, and H. Larochelle, “Brain tumor

segmentation with deep neural networks.”, Medical Image Analysis, Vol.35, pp.18- 31, 2017.

15. X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang and Y. Fan, “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.”, Medical Image Analysis, Vol.43, pp.98-111, 2018.

16. X. Cheng, L. Zhang and Y. Zheng, “Deep similarity learning for multimodal medical images.”, Computer Methods in Biomechanics

and Biomedical Engineering: Imaging & Visualization, Vol.6, No.3, pp.248-52 , 2018, doi: 10.1080/ 21681163.2015.1135299 . 17. M. Simonovsky, B. Gutiérrez-Becker, D. Mateus, N. Navab and N. Komodakis, “A deep metric for multimodal registration.” in

proceedings of the Medical Image Computing and Computer-Assisted Intervention. In: Lecture Notes in Computer Science, pp. 10–

18, 2016, doi: 10.1007/978- 3- 319- 46726- 9_ 2. 18. S. Miao, Z. J. Wang and R. Liao, “ A CNN regression approach for real-time 2D/3D registration.” IEEE Transactions of Medical

Imaging, vol.35, no.5, pp.1352–1363,2016, doi: 10.1109/TMI.2016. 2521800.

19. R. Li, W. Zhang, H. I. Suk, L. Wang, J. Li, D. Shen and S. Ji, “Deep learning based imaging data completion for improved brain disease diagnosis”, in proceedings of the Medical Image Computing and Computer- Assisted Intervention. In: Lecture Notes in

Computer Science, pp. 305–312, 2014.

20. K. Bahrami, F. Shi, I. Rekik and D. Shen, “Convolutional neural network for re- construction of 7T-like images from 3T MRI using appearance and anatomical features.” in proceedings of the Deep Learning in Medical Image Analysis (DLMIA). In: Lecture Notes in

Computer Science, 10 0 08, pp. 39–47, 2016, doi: 10.1007/ 978- 3- 319- 46976- 8 _ 5.

21. V. Sevetlidis, M. V. Giuffrida and S. A. Tsaftaris, “Whole image synthesis using a deep encoder–decoder network”, in proceedings of the Simulation and Synthesis in Medical Imaging. In: Lecture Notes in Computer Science, pp. 127–137, 2016, doi: 10.1007/978- 3-

319- 46630- 9 _ 13. 22. A. Y. Hannun, P. Rajpurkar, M. Haghpanahi, G. H. Tison, C. Bourn, M. P. Turakhia and A. Y. Ng, “Cardiologist-level arrhythmia

detection and classification in ambulatory electrocardiograms using a deep neural network”, Nature medicine, Vol.25, No.1, 2019.

23. P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya and M. P. Lungren, “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.”, arXiv preprint arXiv:1711.05225, 2017.

24. “Google researchers find trained AI detects diabetic retinopathy on par with experts”,

(https://www.mobihealthnews.com/content/google-researchers-find-tr ained-ai-detects-diabetic-retinopathy- par-experts) assessed on 9th January 2020.

25. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken and C. I. Sánchez,

“A survey on deep learning in medical image analysis.”, Medical Image Analysis, Vol.42, pp.60-88, 2017. 26. A. Janowczyk and A. Madabhushi, “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use

cases.”, Journal of pathology informatics 7, 2016. 29. S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang

and P. H. Torr, “Conditional random fields as recurrent neural networks.”, in Proceedings of the IEEE International Conference on Computer Vision, pp. 1529-1537, 2015.

27. P. Israni, “Breast Cancer Diagnosis (BCD) Model Using Machine Learning”, International Journal of Innovative Technology and

Exploring Engineering (IJITEE), vol.8, no.10, pp.4456-4463 ,2019. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7S, May 2020 25 Published By: Blue Eyes Intelligence Engineering &

Sciences Publication Retrieval Number: G10070597S20/2020©BEIESP DOI: 10.35940/ijitee.G1007.0597S20

28. 31. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems, pp. 2672–2680, 2014. 32. R. C. Gonzalez and R. E. Woods, R.E., “Digital

image processing”, 2002

7.

Authors: Dhwani Trivedi, Bhargav Goradiya, Ghanshyam Rathod

Paper Title: AI Enabled Self Diagnosis Predictor Tool using Tongue Image Capture with Automatic Prescription

Generation

Abstract: : WHO data shows that half of the people in the world suffer due to basic health care needs as there

are not enough medical facilities available in many parts of the world. It is difficult for the refugees to have all the

basic health care needs and not enough doctors available for primary diagnosis. To diagnose the person there are

many methods by which the doctor can predict what type disease one might be suffering from. One of those factors

includes the first diagnosis done by just observing the tongue, as it’s the only visible part of the body and one of

the factors which helps for primary diagnosis and widely accepted by doctors in TCM, diagnosis. It addresses for

an aid to people to do primary diagnosis from tongue using AI device, like Raspberry Pi with camera, which is

trained using tongue dataset of different types of tongue images like strawberry tongue, Black tongue, normal

tongue, Red tongue, Swallowed tongue etc. for various symptoms of various diseases to identify the type of the

tongue and based on that it will generate the prescription. The proposed research work is based on the edge

computing and does not need any internet or cloud support and best suitable for installing as portable kiosk in

affected areas where primary medical facility is not available. The report generated by system has primary

predicted suggestions based on the tongue diagnosis using AI.

Keywords: ICT in Health, Artificial Intelligence, Primary Diagnosis, Health prediction, Edge Computing

26-29

References: 1. Tongue diagnoses -https://www.nhp.gov.in/uploadfiles/microsite/635846561062513669 _1.pdf 2. TensorFlow for poets for developing the model on

3. Raspberry Pi hardware. https://codelabs.developers.google.com/codelabs/tensorflow-for-poets -2/

4. TensorFlow for Poets 2: TFLite Android 5. https://codelabs.developers.google.com/codelabs/tensorflow-for-poets -2-tflite/#0

6. 4 Google’s Inception model V3.

7. https://www.tensorflow.org/tutorials/images/hub_with_keras 8. NVIDIA Jetson Nano https://developer.nvidia.com/embedded/jetson-nano-developer-kit

9. L.Yao et al., “Discovering treatment pattern in traditional Chinese medicine clinical cases by exploiting supervised topic model

and domain knowledge,” J. Biomed. Informant., vol. 58, pp. 260–267, Dec. 2015. 10. A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An ensemble of fine-tuned convolutional neural networks for medical

image classification,” IEEE J. Biomed. Health Informant. vol. 21, no. 1, pp. 31–40, Jan. 2017. 11. S. S. Girija. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. [Online]. Available:

https://www.tensorflow.org/

12. 12. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2014.

8.

Authors: Vraj Baxi, Justin Patel, Vaibhavi Lakhani, Priyang Bhatt

Paper Title: Re-Vision: Spectacles for the Visually Challenged

Abstract: The primary issue faced by all types of visually challenged people around the globe is their self-

independence. They feel dependent on every task they want to perform in their daily lives and this acts as an

obstacle to the exciting things which they would otherwise want to do. This paper proposes a solution in the form

of wearable smart spectacles which works on the Raspberry Pi Platform for making the visually challenged people

self-sufficient and move freely in their known as well as unknown surroundings. This smart Spectacle uses a USB

(Universal Serial Bus) camera and detects real-time objects in the vicinity using the SSD-MobileNets object

detection algorithm and provides vision in the form of audio through the use of Headphones. The Smart Spectacles

also combines the use of the OCR algorithm for Text Detection and proposes the module for quick and accurate

detection of currency by the visually challenged.

Keywords: Smart Spectacles, Object Detectin, Text Detection, Currency Detection

References: 1. Blindness and Visual Impairment: Global Facts: https://www.iapb.org/vision-2020/who-facts/ 2. R. Velázquez, “Wearable Assistive Devices for the Blind”. Chapter 17 in A. Lay-Ekuakille & S.C. Mukhopadhyay (Eds.),

“Wearable and Autonomous Biomedical Devices and Systems for Smart Environment: Issues and Characterization”, LNEE 75,

Springer, pp 331-349, 2010. 3. Illinois Library: Blind/Visual Impairment: Common Assistive Technology: https://guides.library.illinois.edu/c.php?g=526852&p=3602299

3. Sejal Gianani, Abhishek Mehta, Twinkle Motwani, Rohan Shende. “JUVO - An Aid for the Visually Impaired”, International

Conference on Smart City and Emerging Technology (ICSCET), January 2018. 4. Esra Ali Hassan, Tong Boon Tang. “Smart Glasses for the Visually Impaired People”, from book Computers Helping People with

Special Needs: 15th International Conference, ICCHP 2016, Linz, Austria, July 13-15, 2016, Proceedings, Part II (pp.579-582).

5. The Macular Degeneration Foundation, “Low Vision Aids & Technology”, Sydney, Australia: The Macular Degeneration Foundation, July 2012.

6. Velázquez, R. “Wearable assistive devices for the blind. In: Lay-Ekuakille, A., Mukhopadhyay”, S.C. (eds.) Wearable and

Autonomous Systems. LNEE, vol. 75, pp. 331–349. Springer, Heidelberg (2010). 7. OrCam, OrCam. http://www.orcam.com.

8. Esight. http://esighteyewear.com/

9. Python Package Index: https://pypi.org/project/pyttsx3/2.7/ 10. Yundong Zhang, Haomin Peng, Pan Hu. “Towards Real-time Detection and Camera Triggering”. Available:

http://cs231n.stanford.edu/reports/2017/pdfs/808.pdf

11. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”. Available:

https://arxiv.org/pdf/1704.04861.pdf

12. OSCAR ALSING, “Mobile Object Detection using TensorFlow Lite and Transfer Learning”, STOCKHOLM, SWEDEN 2018, https://kth.diva-portal.org/smash/get/diva2:1242627/FULLTEXT01.p df

13. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott E. Reed. “SSD: Single Shot MultiBox Detector.” In:

CoRR abs/1512.02325 (2015). 14. SMITH, R.,”An Overview of the Tesseract OCR Engine. In proceedings of Document analysis and Recognition.”, ICDAR 2007.

IEEE Ninth International Conference.

15. Y. WEN, Y. L. 2011., “An Algorithm for License Plate Recognition Applied to Intelligent Transportation System.”, IEEE

Transactions on Intelligent Systems, pp.1-16.

16. Zhang, Q., & Yan, W. Q. (2018), “Currency Detection and Recognition Based on Deep Learning”, 2018 15th IEEE International

Conference on Advanced Video and Signal Based Surveillance (AVSS). 17. Whitney Huang, Hunter McNamara, Diana Molodan, Amol Pasarkar,”Smart Cane”,”

https://soe.rutgers.edu/sites/default/files/imce/pdfs/gset-2014/Smart+ Cane+Final.pdf”.

18. 19. L. Kay, “A sonar aid to enhance spatial perception of the blind: Engineering design and evaluation”, Radio Electron. Eng., vol. 44, no. 11, pp. 605-627, 1974.

30-37

9.

Authors: Bhavika. N. Patel, Binal kaka, Dweepna Garg, Rima Patel

Paper Title: Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in

Plants

Abstract: India is an agricultural country where most of people are depends on the agriculture. When Plants

are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of

that, plants production is decreased also economy of the country is decreased. The farmer has to identify the

disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect

the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and

38-42

information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So,

to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network,

Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants

disease than manual method.

Keywords: K Means Clustering; texture feature; Neural Network; GLCM; Leaf

References: 1. L. Sherly Puspha Annabel, Member, IEEE, T. Annapoorani and P. Deepalakshmi, (2019), “Machine Learning for Plant Leaf Disease

Detection and Classification – A Review”, International Conference on Communication and Signal Processing, April 4-6, 2019, India,

IEEE, pp – 0538 – 0542. 2. Shima Ramesh, Mr. Ramachandra Hebbar, Niveditha M, Pooja R, Prasad Bhat N, Shashank N and Mr. P V Vinod, (2018), “Plant

Disease Detection Using Machine Learning “, International Conference on Design Innovations for 3Cs Compute Communicate

Control, DOI 10.1109/ICDI3C.2018.00017, pp- 41 – 45. 3. S.Santhana Hari, M.Sivakumar, Dr. P.Renuga, S.karthikeyan and S.Suriya, (2019), “ DETECTION OF PLANT DISEASE BY LEAF

IMAGE USING CONVOLUTIONAL NEURAL NETWORK”, International Conference on Vision Towards Emerging Trends in

Communication International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7S, May 2020 42 Retrieval Number: G10120597S20/2020©BEIESP DOI: 10.35940/ijitee.G1012.0597S20 Published By: Blue

Eyes Intelligence Engineering & Sciences Publication and Networking (ViTECoN), pp – 1 – 5.

4. Siddharth Singh Chouhan, Ajay Kaul, Uday Pratap Singh and Sanjeev Jain, (2018) “Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards

Plant Pathology”, IEEE Access, DOI : 10.1109/ACCESS.2017.

5. Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong and Zhongfu Sun, (2018), “A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network”, Computers and Electronics in

Agriculture, /doi.org/10.1016/j.compag.2018.08.048, pp – 18-24.

6. P. Mohanainah, P. Sathyanarayana and L. Gurukumar, (2013), “Image Texture Feature Extraction using GLCM Approach”, International Journal of Scientific and Research Publications, pp. 1-5, vol. 3, Issue 5.

7. Konstantinos G. Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson and Dionysis Bochtis, (2018), “Machine Learning in

Agriculture: A Review’, MDPI Sensors, doi:10.3390/s18082674. 8. Sukhvir Kaur, Shreelekha Pandey and Shivani Goel, (2018), “Plants Disease Identification and Classification Through Leaf Images:

A Survey”, Archives of Computational Methods in Engineering, Springer, DOI: /10.1007/s11831-018-9255-6.

9. Anandhakrishnan MG Joel Hanson, Annette Joy and Jerin Francis, (2017), “Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network”, International Journal of Engineering Science and Computing.

10. 10. Harshal Waghmare, Yogesh Dandawate, and Radha Kokare (2016), “Detection and Classification of Diseases of Grape Plant Using

Opposite Colour Local Binary Pattern Feature and Machine Learning for Automated Decision Support System”, 3rd International Conference on Signal Processing and Integrated Networks (SPIN).

10.

Authors: Priyank S. Yadav, Kiran R. Trivedi

Paper Title: Development of Indian Spoken Language Identification System for Two Languages using MFCC

Feature with Deep Neural Network

Abstract: Language is the ability to communicate with any person. Approximate number of spoken languages

are 6500 in the world. Different regions in a world have different languages spoken. Spoken language recognition

is the process to identify the language spoken in a speech sample. Most of the spoken language identification is

done on languages other than Indian. There are many applications to recognize a speech like spoken language

translation in which the fundamental step is to recognize the language of the speaker. This system is specifically

made to identify two Indian languages. The speech data of various news channels is used that is available online.

The Mel Frequency Cepstral Coefficients (MFCC) feature is used to collect features from the speech sample

because it provides a particular identity to the different classes of audio. The identification is done by using MFCC

feature in the Deep Neural Network. The objective of this work is to improve the accuracy of the classification

model. It is done by making changes in several layers of the Deep Neural Network.

Keywords: Mel frequency cepstral coefficients, Convolutional Neural Network, Language Identification System.

References: 1. Mel Frequency Cepstral Coefficient (MFCC) tutorial. (n.d.). Retrieved from practicalcryptography: accessed on 23 August 2019

http://www.practicalcryptography.com/miscellaneous/machine-learni ng/guide-mel-frequency-cepstral-coefficients-mfccs/

2. S. D. Joge and A. S. Shirsat, "Different language recognition model," 2016 International Conference on Automatic Control and

Dynamic Optimization Techniques (ICACDOT), Pune, 2016, pp. 1096-1101. doi: 10.1109/ICACDOT.2016.7877756 3. Mwiti, D. (2018, May 8). Convolutional Neural Networks: An Intro Tutorial. Retrieved from heartbeat: accessed on 23 August 2019

https://heartbeat.fritz.ai/a-beginners-guide-to-convolutional-neural-ne tworks-cnn-cf26c5ee17ed

4. Prabhu. (2018, March 4). Understanding of Convolutional Neural Network (CNN) — Deep Learning. Retrieved from Medium: accessed on 23 August 2019 https://medium.com/@RaghavPrabhu/understanding-of-convolutiona l-neural-network-cnn-deep-

learning-99760835f148

5. Rosebrock, A. (2018, December 31). Keras Conv2D and Convolutional Layers. Retrieved from pyimagesearch: accessed on 28 August 2019 https://www.pyimagesearch.com/2018/12/31/keras-conv2d-and-conv olutional-layers/

6. 6. Hargrave, M. (2019, April 30). Deep Learning. Retrieved from Investopedia: accessed on 23 August 2019

https://www.investopedia.com/terms/d/deep-learning.asp

43-45

11.

Authors: Dharmesh Visaroliya, Helly Patel, Bhavya Dhoru, Karan Dewani, Ajay Patel

Paper Title: Military Based Automated Guided Vehicle System

Abstract: The interest of this research is to provide a solution to the use of Unmanned Ground Vehicles in

military applications. This paper model such an Automated Guided Vehicle (AGV) that can not only be used as

load carriers but also as transportation and surveillance vehicles with an ability to communicate, too. From border

surveillance to carrying arms & ammunitions for the camps, there are a lot many applications of this AGV.

Furthermore, changing the intelligence of the system to fully automated possible applications are explored in this

46-49

paper. With this intelligence system, vehicles can provide constant updates about its surroundings, and the data

shared with peer vehicles will include information on potential and sudden obstacles on the route. Communication

of an Unmanned Aerial Vehicle or any other AGV can be achieved. The study concludes that the objective of an

autonomous vehicle system in military and other applications can be achieved by this model.

Keywords: Automated guided vehicle, military vehicles, sensor fusion, swarm technology,unmanned grounded

vehicle.

References: 1. P. W. Singer, “Drones don’t die - A history of military robots”, Available: https://www.historynet.com/drones-dont-die-a-history-

ofmilitary-robotics.htm 2. Christopher McFadden, 6th Nov, 2018; “ A brief history on military vehicles including autonomous vehicles”, Available:

https://interestingengineering.com/a-brief-history-ofmilitary-robots-including-autonomous-systems

3. “iRobot 510 PackBot Multi-Mission Robot”, Available: https://www.army-technology.com/projects/irobot-510-packbotmulti-mission-robot/

4. “10 military robots of the future” Available: https://www.designnews.com/content/10-military-robots-future

5. Sydney J. FreedbergJr.on August 20, 2018 “Army Wants 70 Self-Driving Supply Trucks By 2020”, Available: https://breakingdefense.com/2018/08/army-wants-70-self-drivingsupply-trucks-by-2020/ Military Based Automated Guided Vehicle

System 49 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number:

G10150597S20/2020©BEIESP DOI: 10.35940/ijitee.G1015.0597S20 6. Naveed Ur Rehman ; Kundan Kumar ; Ghulam e Mustafa Abro, 19 April 2018, “Implementation of an autonomous path planning &

obstacle avoidance UGV using SLAM”, Available: DOI: 10.1109/ICEET1.2018.8338628

7. Demim Fethi, AbdelkrimNemra, KahinaLouadj and Mustapha Hamerlain, “Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control”, Available: https://doi.org/10.1177/1687814017736653

8. Xia Hua, Xinqing Wang , Dong Wang, Jie Huang and Xiaodong Hu, “Military Object Real-Time Detection Technology Combined

with Visual Salience and Psychology”, Available: https://doi.org/10.3390/electronics7100216 9. Alessio Carullo and Marco Parvis, An ultrasonic sensor for distance measurement in automotive applications, Politecnico di Torino,

Available: doi: 10.1109/JSEN.2001.936931 10. Seong-Soo Kim, Christina Young, Boris Mizaikoff, Miniaturized mid infrared sensor

technologies, Ulm -University, Available: doi: 10.1007/s00216- 007-1673-5 10. Tolga Özer, Sinan Kivrak, Yüksel Oğuz, “H Brıdge DC Motor Drıver Desıgn and Implementatıon with usıng dsPIC30f4011”,

Available: https://www.researchgate.net/publication/317225711_H_Bridge_DC_

Motor_Driver_Design_and_Implementation_with_Using_dsPIC30f40 11 11. Luis Ramos, Alejandro Diaz, Daniel Reyes, “Unmanned Ariel Vehicle”, Florida International University, Available:

https://mme.fiu.edu/wp-content/uploads/2013/04/T7_TBD.pdf

12. Seongkyun Han,JisangYoo, and Soonchul Kwon, “Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery”, Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767679/

13. Xie Yang and Cheng Wushan, AGV path planning based on smoothing A* algorithm, Shanghai University of Engineering (2016),

Available: doi: 10.5121/ijsea.2015.6501 14. Guo Qing, Zhang Zheng, Xu Yue, Path planning of Automated Guided Vehicle based on improved Dijkstra algorithm, Beijing

University of Chemical Technology (2017), Available: https://www.semanticscholar.org/paper/Path-planning-of-automatedguided-

vehicle-based-on-QingZheng/f67aa43cc778836b8d7390f4399be7896c9bd661/figure/3 15. Dae Hwan Kim, Nguyen Trong Hai, Sang Kwun Jeong, A guide to selecting path planning algorithm for automated guided vehicle

Pukyong National University, Available: doi: 10.1007/978-3-319- 69814-4_56

16. Menyhart Jozsef, “Concept of an UGV with Arduino device”, Available:

https://www.researchgate.net/publication/273449017_CONCEPT_OF _AN_UGV_WITH_ARDUINO_DEVICE

17. Dr. Daisy Tang, “Collaboration Between Unmanned Aerial and Ground Vehicles” Available:

file:///C:/Users/Helly%20Patel/Downloads/UAV-UGV.pdf 18. S.GrimeH.F.Durrant-Whyte, “Data fusion in decentralized sensor networks”, Available: https://doi.org/10.1016/0967-0661(94)90349-

2

19. 20. Alexei Makarenko, Alex Brooks, Stefan Williams, Hugh DurrantWhyte, “A Decentralized Architecture for Active Sensor Networks”, ARC Centre of Excellence in Autonomous Systems (CAS),

Available:https://www.researchgate.net/publication/4077169_A_dece ntralized_architecture_for_Active_Sensor_Networks

12.

Authors: Yashasvi Swapnesh Kumar Parikh, Narsingani Amisha Darshakbhai, Hetal Gaudani

Paper Title: Text Summary Generation Techniques

Abstract: Pattern Recognition is pertinent field in autonomous text summarization for extraction of features

from relative and non relative text documents. Here we provide empirical evidence that the method of Deep

learning using RNN outperforms various techniques in terms of speed as well as metrics in abstractive

summarization of multi-modal documents. We performed observational analysis on over 8 different techniques

documented.

Keywords: Automatic Summarization, Natural Language Processing, Extractive Summary.

References: 1. J.N.Madhuri and Ganesh Kumar.R , “Extractive Text Summarization Using Sentence Ranking”

2. C. Lakshmi Devasena and M. Hemalatha (2012) “Automatic Text Categorization and Summarization using Rule Reduction “, IEEEInternational Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012

3. Eliseo Reategui, Miriam Klemann and Mateus David Finco, (2012) “Using a Text Mining Tool to Support Text Summarization”,

12th IEEE International Conference on Advanced Learning Technologies. 4. Jingquiang Chen and HaiZhuge (2018) “Extractive Text-Image Summarization Using Multi-Modal RNN”, 14th International

Conference on Semantics, Knowledge and Grids (SKG)

5. JiHyungLee,,Johana, Kyungmin Kim, Kim Nuri, Jaedong Lee(2014), “Summary generation apparatus and method reflecting document feature”

6. Julian M. Kupiec,Jan O. Pedersen,Francine R. Chen,Daniel C. Brotsky,Steven B. Putz(1995), “Automatic method of extracting

summarization using feature probabilities” 7. InderjeetMani,EugenioCiurana,NicholasD'Aloisio-Montilla,Bart K. Swanson(2012) “Method and apparatus for automatically

summarizing the contents of electronic documents”, Proceedings of Workshop on Text Summarization. 8. Julian M. Kupiec,HinrichSchuetze (1999) “System for genre-specific summarization of documents”

50-54

9. Leonid Batchilo, Valery Tsourikov, Igor Sovpel(2007) "Computer Based Summarization Of Natural Language Documents” 10. Goldstein et al(1999)., “Summarizing Text Documents: Sentence Selection and Evaluation Metrics”, Proceedings of the 22nd annual

international ACM SIGIR conference on Research and development in information retrieval.

11. Kenton M. Lyons, Barbara Rosario, Trevor Pering, Roy Want(2012), “Methods and systems to summarize a source text as a function of contextual information”

12. Wei JiaCai, Xiao XiaoLian, Shixia Liu, Shimei Pan, Wei Hong Qian, Yang Qiu Song, Qiang Zhang. Michelle Xue Zhou(2009),

“Producing a visual summarization of text documents” 13. Amjad Abu-Jbara and DragomirRadev. (2011), “ Coherent citationbased summarization of scientific papers.”, In Proceedings of the

49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for

Computational Linguistics, 500–509. 14. Harold P Edmundson(1969). “New methods in automatic extracting”, Journal of the ACM (JACM) 16, 2 (1969), 264–285.

15. Kessler et al.(1997), “ Automatic Detection of Text Genre” , Proceedings of ACL 35 and EACL 8, Morgan Kaufmann Publishers,

San Francisco, California, pp. 32–38. 16. Abuobieda, A., Salim, N., Albaham, A. T., Osman, A. H., & Kumar, Y. J. (2012), “Text summarization features selection method

using pseudo genetic-based model. In International conference on information retrieval knowledge management (pp.193–197).

17. G Erkan and Dragomir R. Radev(2004), “LexRank: Graph-based Centrality as Salience in Text International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7S, May 2020 54 Retrieval Number:

G10160597S20/2020©BEIESP DOI: 10.35940/ijitee.G1016.0597S20 Published By: Blue Eyes Intelligence Engineering & Sciences

Publication Summarization”, Journal of Artificial Intelligence Research, Research, Vol. 22, pp. 457-479. 18. Udo Hahn and Martin Romacker (2001), “The SYNDIKATE text Knowledge base generator”, Proceedings of the first International

conference on Human language technology research, Association for Computational Linguistics , ACM, Morristown, NJ, USA.

19. Balahur, Alexandra.,Lloret, Elena., Boldrini, Ester., Montoyo, Andrés., Palomar, Manuel., &Martı´nez-Barco, Patricio (2009),“ Summarizing threads in blogs using opinion polarity.”, Proceedings of the workshop on events in emerging text types (pp. 23–31).

20. Gong, Y.H., Liu, X(2002), “Generic text summarization using relevance measures and latent semantic analysis.”, Proceedings of the

24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19–25. ACM, New York

21. D. Ai, Y. Zheng, and D. Zhang(2010), “Automatic text summarization based on latent semantic indexing”, Journal of Artificial Life

and Robotics, Springer, vol. 15, no. 1 22. Deerwester S, Dumais S, Furnas G, et al (1990), ”Indexing by latent semantic analysis”, J Am Soc Inform Sci 41(6):391–407

23. Christian S. Perone(2011), “Short introduction to Vector Space Model (VSM)”, blogs-christianperone, Machine Learning :: Text

feature extraction (tf-idf) – Part I 24. Saranyamol C S and Sindhu L(2014), “A Survey on Automatic Text Summarization”, International Journal of Computer Science and

Information Technologies, Vol. 5(6)

25. Chengqing Z (2008),“Statistical natural language processing (in Chinese)”, Tsinghua University Press, Beijing 26. Hearst, MA (1997), “TextTiling: segmenting text into multi-paragraph subtopic passages”, Comput Linguistics 32(1)

27. PadmaPriya, G. and K. Duraiswamy(2014), “An approach for text summarization Using deep learning algorithm”, ISSN: 1549-3636.

28. G. Salton and C. Buckley(1998), “Term-weighting approaches in automatic text retrieval,'' Information Processing & Management, vol. 25, no. 5, pp. 513-523

29. 29. Ronald A. Fein, et al(1999), “Document summarizer for word processors”

13.

Authors: Meshwa Mistry, Vidhi Suthar, KaPatel Shruti, Priyang Bhatt

Paper Title: Recommender System for Engineering College

Abstract: In this paper, we present research on developing a recommender system that helps students who want

to take admission in engineering colleges. There are various engineering colleges in Gujarat. After completion of

12th Science, if students want to seek their admission in engineering, there are so many choices. Students generally

face problems in choosing the better college as per their merit number. Colleges may have facilities such as campus

facilities, university grants, the infrastructure of institutes, hostel facility, NBA and NAAC grading, placement

record, tie-up with industries, faculties or educational history too. Students and parents do not have exact

information about these all. Moreover, there is no such website or an application which gives the suggestion or

recommend institutions where students can get admission. After studying these issues facing by parents and

students, we are going to develop a recommender system for engineering institutes which can recommend to

students as per their merit number and user reviews.

Keywords: Recommendation System, SVD, Pivot table, Similarity Measure

References: 1. A User-Friendly College Recommending System Using User-Profiling and Matrix Factorization Technique by Sheetal Grease, Varsha

Powar and Debajyoti Mukhopadhyay Published at May 2017 IEEE https://www.researchgate.net/publication/322006324. 2. Friendbook: A Semantic-Based Friend Recommendation System for Social Networks by Zhibo Wang, Jilongliao, Quimg Cao, Hairong Qi,

and Zhi Wang Published at 2017 IEEE. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-

3075, Volume-9 Issue-7S, May 2020 59 Retrieval Number: G10180597S20/2020©BEIESP DOI: 10.35940/ijitee.G1018.0597S20 Published

By: Blue Eyes Intelligence Engineering & Sciences Publication

3. Graduate School Recommendation System: Assisting Admission Seekers to Apply for Graduate Studies in Appropriate Graduate Schools

by M. Hasan, S. Ahmed, D.M. Abdullah, and M.S. Rahman, 2016 IEEE. 4. Collage Recommendation System for Admission by Deokatemonali, Gholave Dhanashri, Jarad Dipali, Khomane Tejaswini at 2018.

55-59

14.

Authors: Mohit Panjabi, Karan Patel

Paper Title: Digital Dustbin - Smart Bins for Smart Cities

Abstract: This paper researches around the area of the solution of the garbage disposal and

waste management with the help of technology. It gives a detailed model of how we can achieve

the goal of ‘Clean India’ together with the use of sensors, cameras, servers, and even human

psychology. With the revolutions taking place all over the world on the subject of the climate

crisis and global warmings, it becomes a duty of every citizen to contribute to the future

lifestyle. Satisfying all the parameters, at first, past researches have been compared, objectives

60-65

have been defined and then a working model is thus presented. It has always been difficult to

change the mind-set of a whole lot of people, and thus in this research paper, addressing this

problem, the solution of such product development is given which satisfies the need of the

citizens and also contributes effectively to the waste management system. The model is proved

with a prototype, and all the facts& figures are provided, which are necessary.

Keywords: Arduino, digital dustbin, digitalization, garbage disposal, ultrasonic sensor, waste

management.

References: 1. S. Zavare, R. Parashare, S. Patil, P. Rathod, and P. V. Babanne, ―Smart City Waste Management System Using GSM,‖ Int. J.

Comput. Sci. Trends Technol., vol. 5, no. 3, pp. 74– 78, 2017.DQQ. 2. Trushali S. Vasagade, Shabanam S. Tamboli, Archana D. Shinde, "Dynamic solid waste collection and management system based

on sensors elevator and GSM", Inventive Communication and Computational Technologies (ICICCT) 2017 International Conference

on, pp. 263-267, 2017. 3. Rishabh Kumar Singhvi, Roshan Lal Lohar, Ashok Kumar, Ranjeet Sharma, Lakhan Dev Sharma, Ritesh Kumar Saraswat, "IoT

BasedSmart Waste Management System: India prospective", Internet of Things: Smart Innovation and Usages 2019 4th International

Conference on, pp. 1-6, 2019. 4. Narayan Sharma, Nirman Singha, Tanmoy Dutta, ―Smart Bin Implementation for Smart Cities‖, International Journal of Scientific

& Engineering Research, Volume 6, Issue 9, September-2015 ISSN 2229-55

5. Ahmed Imteaj, Mahfuzulhoq Chowdhury and Md. ArafinMahamud, ―Dissipation of Waste using Dynamic Perception and Alarming System: A Smart City Application‖, 2nd Int'l Conf on Electrical Engineering and Information & Communication Technology

(ICEEICT) 2015 Jahangirnagar University, Dhaka-1342, Bangladesh, 21-23 May 2015.

6. NorfadzliaMohd Yusof, AimanZakwanJidin, Muhammad Izzat Rahim, ―Smart Garbage Monitoring System for Waste Management‖, MATEC Web of Conferences 97, 01098 (2017), https://doi.org/10.1051/matecconf/20179701098

7. Joke O. Adeyemo ; Oludayo O. Olugbara ; Emmanuel Adetiba, ―Smart city technology based architecture for refuse disposal

management‖, https://ieeexplore.ieee.org/document/7530704 8. N. Sathish Kumar ; B. Vuayalakshmi ; R. Jenifer Prarthana ; A. Shankar, ―IOT based smart garbage alert system using Arduino

UNO‖, https://ieeexplore.ieee.org/document/7848162

9. Folianto, Fachmin et al. ―Smartbin: Smart wastemanagement system.‖ 2015 IEEE Tenth InternationalConference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (2015): 1-2.

10. 10. R. Rajkamal, V. Anitha, P. G. Nayaki, K. Ramya and E. Kayalvizhi, "A novel approach for waste segregation at source level for

effective generation of electricity — GREENBIN," 2014 International Conference on Science Engineering and Management Research (ICSEMR), Chennai, 2014, pp. 1-4.

15.

Authors: Priyanka Patel, DippalIsrani, Mrugendrasinh Rahevar

Paper Title: Snow Cover Area Detection using NDSI and Band Ratio Method

Abstract: Glaciers are a main source of water during summer in Himalayan areas.

Corresponding to the historical studies, glacier is directly affected by climate change. It is

important to identify change in snow cover area (Glacier area) to identify change in glacier.

Remote sensing and GIS technology are used to monitor Snow covered area. This paper focuses

on Sentinel-2B data of trisul glacier which is a part of Indian Himalayas to identify glacier.

These multispectral images were extracted from USGS Earth Explorer. The sentinel-2B data

are processed using Semi automated Classification Plugin (SCP) of QGIS tool. Snow covered

area is identified by using two automated methods: Normalized Difference Snow Index (NDSI)

and Band Ratio. For NDSI reflectance of visible, shortwave band is used. For Band Ratio

reflectance of near infrared, shortwave infrared band is used. It is challenging to detect snow

covered area from the satellite as snow covered area and cloud area have same white colure i.e.

same reflectance. In this paper, represents experiments on two methods for snow area extraction

on satelliteimages.

Keywords: Glacier, Band Ratio, Geospatial, NDSI, QGIS.

References: 1. Kanevski, M., A. Pozdnukhov, and V. Timonin.,"Machine learning algorithms for geospatial data. Applications and software tools",

Internationa Congress on Environmental Modelling and Software,(2008), Vol. 4, pp. 320-327,2008.

2. Kumar, Uttam,"Algorithms For Geospatial Analysis Using MultiResolution Remote Sensing Data.",PhD diss., G25135,2014. 3. Nuth,Christopher,JackKohler,MaxKönig,AngelavonDeschwanden, Jon Ove Methlie Hagen et al., "Decadal changes from a

multitemporal glacier inventory of Svalbard.", The Cryosphere ,Vol. 7, No. 5, pp. 1603-1621,2013.

3. Bajracharya, SamjwalRatna, Sudan Bikash Maharjan, and FinuShrestha,"The status and decadal change of glaciers inBhutan from the 1980s to 2010 based on satellite data.", Annals of Glaciology, Vol. 55, No. 66, pp. 159-166, 2014.

4. Shangguan, Donghui, Shiyin Liu, Yongjian Ding, Lizong Wu, Wei

Dengetal.,"GlacierchangesintheKoshiRiverbasin,centralHimalaya, from 1976 to 2009, derived from remote-sensing imagery.", Annals of Glaciology, Vol. 55, No. 66, pp. 61-68,2014.

5. Berthier, Etienne, H. Vadon, David Baratoux, Yves Arnaud, C. Vincent

etal.,"Surfacemotionofmountainglaciersderivedfromsatelliteoptical imagery.", Remote Sensing of Environment, Vol. 95, No. 1, pp. 14- 28, 2005.

6. Kääb, A,"Combination of SRTM3 and repeat ASTER data for deriving alpineglacierflowvelocitiesintheBhutanHimalaya.",RemoteSensing of Environment, Vol. 94, No. 4, pp. 463-474,2005.

7. Luckman, Adrian, Duncan J. Quincey, and D. Benn, "Quantification of Everest region glacier velocities between 1992 and 2002,

using satellite radar interferometry and feature tracking.", Journal of Glaciology, Vol. 55, No. 192, pp. 596-606,2009.

66-70

8. Berthier,Etienne,EricSchiefer,GarryKCClarke,BrianMenounos,and FrédériqueRémy,"Contribution of Alaskan glaciers to sea-level rise derived from satellite imagery.", Nature Geoscience, Vol. 3, No. 2, pp. 92,2010.

9. Gardelle, Julie, Etienne Berthier, Yves Arnaud, and A. Kaab,"Regionwide glacier mass balances over the Pamir-Karakoram-

Himalaya during 1999-2011 (vol 7, pg 1263, 2013).", The Cryosphere, Vol. 7, No. 6,pp. 1885-1886,2013. 10. Rees,H.Gwyn,andDavidN.Collins.,"Regionaldifferencesinresponse of flow in glacier‐fed Himalayan rivers to climatic warming.",

Hydrological Processes: An International Journal, Vol. 20, No. 10, pp. 2157-2169,2006.

11. Racoviteanu, Adina E., Richard Armstrong, and Mark W. Williams., "Evaluation of an ice ablation model to estimate the contribution of melting glacier ice to annual discharge in the Nepal Himalaya.", Water Resources Research, Vol. 49, No. 9, pp.

5117-5133,2013. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-

9 Issue-7S, May 2020 70 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: G10220597S20/2020©BEIESP DOI: 10.35940/ijitee.G1022.0597S20

12. Kulkarni, A. V., J. Srinivasulu, S. S. Manjul, and P. Mathur.,―Field ased spectral reflectance to develop NDSI method for the

snow cover monitoring.‖,JournaloftheIndianSocietyofRemoteSensing,Vol.30, No. 1- 2, pp. 73–80,2002. 13. Brown, Ross D., and Barry E. Goodison.,"Interannual variability in reconstructed Canadian snow cover, 1915–1992.", Journal of

Climate, Vol.9, No. 6, pp. 1299-1318,1996.

14. Frei, Allan, Marco Tedesco, ShihyanLee, James Foster, Dorothy 15. K. Hall et al.,"A review of global satellite-derived snow products." , Advances in Space Research, Vol. 50, No. 8, pp. 1007—

1029,2012.

16. Haq, M. Anul, Kamal Jain, and K. P. R. Menon.,―Change Monitoring Of Gangotri Glacier Using Satellite Imagery.‖, 12th ESRI India User Conference, pp. 1-8,2011.

17. Atif, Iqra, Muhammad Ahsan Mahboob, and JavedIqbal,"Snow cover area change assessment in 2003 and 2013 using MODIS data

of the Upper Indus Basin, Pakistan." Journal of Himalayan Earth Sciences,Vol. 48, No. 2, pp. 117,2015 18. Hall, D. K., J. P. Ormsby, R. A. Bindschadler, and HonnappaSiddalingaia.,―Characterization of snow and ice reflectance zones

on glaciers using Landsat Thematic Mapper data.‖, Annals of Glaciology, Vol. 9, pp. 1–5,1987.

19. 20. Haq,M.Anul,KamalJain,andK.P.R.Menon,―DevelopmentofNew ThermalRatioIndexforSnow/IceIdentification.‖,InternationalJournal of Soft Computing and Engineering (IJSCE), Vol. 1, No. 6, pp.

2231- 2307,2012.

16.

Authors: G. G. Shah, H. N. Patel

Paper Title: Regression Model Method for Analyze the Association Rules using Major Parameters

Abstract: Using the data mining user can extract the information. Frequent itemsets is one of

the popular task in data mining. Association Rule Analysis is the task of discovering association

rules that occur frequently in a given large data set.The task is to find certain relationships

among a set of itemsets in the database. There are two fundamental parameter(measurement) is

Support and Confidence.Traditional association rule mining techniques employ predefined

support and confidence values. But, it’s observed that specifying minimum support value of the

minded rules in advance often leads to either too many or too few rules, which negatively

impacts the performance of the overall system.This paper proposes a non-linear regression

model using support, confidence and association rules. To predict the number of rules under

the given explanatory variables say parameters. Use the R language for the Rules generations

and also uses significance test to verify regression coefficients. Using the coefficient test and

F-test verify the model.

Keywords: Association Rules, Regression, Regression Coefficients, Multiple Correlation, F-

test

References: 1. B. Ramageri, “DATA MINING TECHNIQUE AND APPLICATIONS,” Indian Journal of Computer Science and Engineering, 2010. 2. D. T. Le, F. Ren, and M. Zhang, “A Regression-Based Approach for Improving the Association Rule Mining through Predicting the

Number of Rules on General Datasets,” Lecture Notes in Computer Science PRICAI 2012: Trends in Artificial Intelligence, pp. 229–

240, 2012. 3. 3. Agrawal, R., Imieliński, T. and Swami, A. (1993). Mining association rules between sets of items in large databases. ACM

SIGMOD Record, 22(2), pp.207-216.

71-74

17.

Authors: Bhatt Meet, Joshi Hari, Vaghasiya Denil, Rohit R Parmar, Pradeep M Shah

Paper Title: Gesture-Based Robot Control with Kinect Sensor

Abstract: With the recent improvement in technology, controlling a robot wirelessly has become possible.

Controlling a robot wirelessly increases its mobility. It also opens up the possibility of different kinds of robot

designs. However, controlling a robot wirelessly using human computer interface bring more challenges to the

researchers. This paper implements a human gesture-based robot control system. It uses a Kinect sensor which

consists of a depth sensor, RGB camera. The Kinect sensor obtains the skeletal data from the subject. Using

mathematical calculation, it calculates the angle deviation between the different parts of the arm based on its

movement. The angle deviation data measured by the Kinect sensor will be transmitted by using a Wi-Fi module.

The use of the Wi-Fi module will increase mobility. On the receiver, we use a microcontroller which will convert

the received angle deviations values into equivalent PWM which will be used to control the servo motors. The

combination of the different servo motors will result in the robotic movement

Keywords: Kinect, Skeleton tracking, Raspberry Pi 3B, Servo motors, hardware, Human Computer Interface

References:

75-78

1. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, "Real-time human pose recognition in parts from single depth images," International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

2. I. Mikic, M. M. Trivedi, E. Hunter, and P. Cosman, "Human body model acquisition and tracking using voxel data," Int. J. Comput.

Vis., vol. 53, no. 3, pp. 199-223, 2003. 3. F. Caillette, A. Galata, and T. Howard, "Real-time 3- D human body tracking using learned models of behavior," Comput. Vis. Image

Understand., vol. 109, pp. 112-125, 2008.

4. Brogardh, T.: Present and future robot control development - An industrial perspective. Annual Reviews in Control 31, 69–79 (2007) 5. Chen, L., Wei, H., Ferryman, J.: A survey of human motion analysis using depth imagery. Pattern Recognition Letters 34, 1995–

2006 (2013)

6. Dutta, T.: Evaluation of the Kinect sensor for 3-D kinematic measurement in the workplace. Applied Ergonomics 43, 645–649 (2012) 7. 7... Pan, Z., Polden, J., Larkin, N., VanDuin, S., Norrish, J.: Recent progress on programming methods for industrial robots. Robotics

and Computer-Integrated Manufacturing 28, 87–94 (2012)

8. Madakam, S., Ramaswamy, R. and Tripathi, S. (2015) Internet of Things (IoT): A Literature Review. Journal of Computer and Communications, 3, 164-173

9. M. Van den Bergh, D. Carton, R. de Nijs, N. Mitsou, C. Landsiedel, K. Kuehnlenz, D. Wollherr, L. Van Gool, and M. Buss. Real-

time 3D hand gesture interaction with a robot for understanding directions from humans. In Proc. of 20th IEEE international symposium on robot and human interactive communication, pages 357–362, 2011.

10. Biao Ma, Wensheng Xu, and Songlin Wang. A robot control system based on gesture recognition using Kinect. TELKOMNIKA

Indonesian Journal of Electrical Engineering, 11(5):2605–2611, May 2013. 11. Ali, Ahmed &Elmisery, Fathy& Mostafa, Ramadan & Hussein, Mohammed. (2014). Motion Control of Robot by using Kinect

Sensor. Research Journal of Applied Sciences, Engineering and Technology. 8. 1384-1388. 10.19026/rjaset.8.1111.

12. Kun Qian, JieNiu, and Hong Yang. Developing a gesture based remote human-robot interaction system using kinect. International Journal of Smart Home, 7(4), July 2013.

13. 13. K. Lai, J. Konrad, and P. Ishwar. A gesture-driven computer interface using kinect. In IEEE Southwest Symposium on Image

Analysis and Interpretation (SSIAI), pages 185–188, 2012.

18.

Authors: Henil Chopada, Maitri Patel, Rushikesh Desai, Divya Ebenezer Nathaniel

Paper Title: Fake Information Detection Techniques

Abstract: Fake or unverified information spreads same as actual facts on the internet, hence maybe going viral

and influencing the general public opinion and their choices. fake news represents the maximum present-day forms

of fake and unverified facts, respectively, and need to be detected as soon as possible for averting their results.

The interest in efficient detection techniques has been therefore growing very rapid in the remaining years. In this

paper we present survey on the specific techniques to computerized detection of fake news proposed in the latest

literature. Particularly, this paper focus on five main aspects. First, we record and discuss the various definitions

of fake news that have been considered in various literatures. Second, we highlight how the collection of applicable

data for simulation of fake information detection is tough and we present the numerous approaches, which have

been adopted to accumulate this information, additionally the publicly available datasets. Third, we describe the

features that have been used in various fake news detection techniques. Fourth, we provide an evaluation of various

techniques used for detection. In the end, we discuss future directions that could be considered for this problem.

Keywords: data mining, text mining, machine learning, deep learning, natural language processing,

classification, fake news

References: 1. O. Ajao , D. Bhowmik , S. Zargari , Fake news identification on twitter with hybrid CNN and RNN models, in: Proceedings of the

Ninth International Conference on Social Media and Society, ACM, New York, NY, USA, 2018, pp. 226–230 .

2. C. Castillo , M. Mendoza , B. Poblete , Information credibility on twitter, in: Proceedings of the Twentieth International Conference on World Wide Web, ACM, Hyderabad, India, 2011, pp. 675–684 .

3. Y.-C. Chen , Z.-Y. Liu , H.-Y. Kao , Ikm at semeval-2017 task 8: convolutional neural networks for stance detection and rumor

verification, in: Proceedings of the Eleventh International Workshop on Semantic Evaluation (SemEval-2017), 2017, pp. 465–469 4. G.L. Ciampaglia , P. Shiralkar , L.M. Rocha , J. Bollen , F. Menczer , A. Flammini , Computational fact checking from knowledge

networks, PloS One 10 (6) (2015) .

5. L. De Alfaro , V. Polychronopoulos , M. Shavlovsky , Reliable aggregation of boolean crowdsourced tasks, in: Proceedings of the Third AAAIConference on Human Computation and Crowdsourcing, 2015, pp. 42–51 .

6. L. Derczynski , K. Bontcheva , Pheme: veracity in digital social networks, in: Proceedings of the Tenth Joint ACL ISO Workshop

on Interoperable Se- mantic Annotation (ISA), Reykjavik, Iceland, 2014, pp. 19–22 . 7. N. Di Fonzo , P. Bordia , Rumor, gossip and urban legends, Diogenes 54 (1) (2007) 19–35 . 8. W. Ferreira , A. Vlachos , Emergent:

a novel data-set for stance classification, in: Proceedings of the Conference of the North American Chapter of the Association for

Computational Linguistics: Human Language Technologies, 2016, pp. 1163–1168 . 8. G. Giasemidis , C. Singleton , I. Agrafiotis , J.R. Nurse , A. Pilgrim , C. Willis , D.V. Greetham , Determining the veracity of

rumours on twitter, in: Proceedings of the International Conference on Social Informatics, Springer, 2016, pp. 185–205 .

9. B.D. Horne, S. Adali, This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news, 2017 [Online Available: https://arxiv.org/abs/1703.09398 ].

10. E. Kochkina , M. Liakata , A. Zubiaga , All-in-one: multi-task learning for rumour verification, in: Proceedings of the Twenty

Seventh International Conference on Computational Linguistics, 2018, pp. 3402–3413 . 11. K.P.K. Kumar , G. Geethakumari , Detecting misinformation in online social networks using cognitive psychology, Human-centric

Comput. Inf. Sci. 4 (1) (2014) 14 .

12. J. Ma , W. Gao , P. Mitra , S. Kwon , B.J. Jansen , K.-F. Wong , M. Cha , Detecting rumors from microblogs with recurrent neural networks, in: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI, address, 2016, pp.

3818–3824 .

13. T. Mikolov , I. Sutskever , K. Chen , G. Corrado , J. Dean , Distributed representations of words and phrases and their compositionality, in: Proceedings of the Twenty Sixth International Conference on Neural Information Processing Systems -

Volume 2, in: NIPS, Curran Associates Inc., USA, 2013, pp. 3111–3119 .

14. T. Mitra , E. Gilbert , CREDBANK: a large-scale social media corpus with associated credibility annotations, in: Proceedings of the Ninth International AAAI Conference on Web and Social Media, 2015, pp. 258–267 .

15. L. Page , S. Brin , R. Motwani , T. Winograd , The PageRank citation ranking: bringing order to the web, Technical Report, Stanford InfoLab, 1999 .

16. M. Potthast , S. Köpsel , B. Stein , M. Hagen , Clickbait detection, in: Proceedings of the European Conference on Information

Retrieval, Springer, 2016, pp. 810–817 .

79-86

17. Y. Qin, D. Wurzer, V. Lavrenko, C. Tang, Spotting rumors via novelty detection, CoRR (2016) . [Onilne. Available: https://arxiv.org/abs/1611.06322 ]. [77] J.R. Quinlan , C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc.,

San Francisco, CA, USA, 1993 .

18. V. Rubin , N. Conroy , Y. Chen , S. Cornwell , Fake news or truth? Using satirical cues to detect potentially misleading news, in: Proceedings of the NAACL-CADD Second Workshop on Computational Approaches to Deception Detection, San Diego,

California, USA, 2016, pp. 7–17 .

19. V.L. Rubin , Y. Chen , N.J. Conroy , Deception detection for news: three types of fakes 52 (1) (2015) 1–4 . 20. K. Shu , A. Sliva , S. Wang , J. Tang , H. Liu , Fake news detection on social media: a data mining perspective, ACM SIGKDD

Explor. Newslett. 19 (1) (2017) 22–36 .

21. C. Song, C. Tu, C. Yang, Z. Liu, M. Sun, CED: credible early detection of social media rumors, 2018 . [Online. Available: https://arxiv.org/abs/1811.04175 ]. [88] E. Tacchini, G. Ballarin, M.L. Della Vedova, S. Moret, L. de Alfaro, Some like it hoax:

automated fake news detection in social networks, CoRR (2017). [Online. Available: https://arxiv.org/abs/1704.07506 ].

22. W.Y. Wang , “Liar, liar pants on fire”: a new benchmark dataset for fake news detection, in: Proceedings of the Fifty Fifth Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics,

2017, pp. 422–426 .

23. K. Wu , S. Yang , K.Q. Zhu , False rumors detection on Sina Weibo by propagation structures, in: Proceedings of the IEEE Thirty First International Conference on Data Engineering (ICDE), IEEE, 2015, pp. 651–662 .

24. Y. Wu , P.K. Agarwal , C. Li , J. Yang , C. Yu , Toward computational fact-checking, Proc. VLDB Endow. (PVLDB) 7 (7) (2014)

589–600 . 25. H. Zhang , Z. Fan , J.-h. Zheng , Q. Liu , An improving deception detection method in computer-mediated communication, J. Netw.

7 (11) (2012) 1811–1816 .

26. A . Zubiaga , A . Aker , K. Bontcheva , M. Liakata , R. Procter , Detection and resolution of rumours in social media: a survey, ACM Comput. Surv. 51 (2) (2018) 32:1–32:36 .

27. A. Zubiaga , M. Liakata , R. Procter , K. Bontcheva , P. Tolmie , Towards detecting rumours in social media, in: Proceedings of

the AAAI Workshop: AI for Cities, 2015, pp. 35–41 . 28. 29. A. Zubiaga , M. Liakata , R. Procter , G. Wong Sak Hoi , P. Tolmie , Analysing how people orient to and spread rumours in

social media by looking at conversational threads, PLOS ONE 11 (3) (2016) 1–29 .

19.

Authors: Nikita Mesvaniya, Meghna Dhruva, Ishita Khared, Krishna Adhia

Paper Title: Real-Time Lane Detection and Object Recognition in Self-Driving Car using YOLO neural network

and Computer Vision

Abstract: The Darwinism of Artificial Intelligence and robotics has grown up incredibly. Recently, there are a

lot of progress have been undertaken in the context of Autonomous vehicle. Robo-car or self driving car consist

many module like localization and mapping, scene understanding, movement planning, and driver state. In

movement planning lane perception and recognition of the object plays vital role. This proposed state-of-art

recognizes the road track in the video‘s frame and perform lane detection using canny edge detector and Hough

transform algorithm. In this paper, Object recognition is possible with help of YOLO (you only look once) which

is one of the real time CNN methods aims to detect object inside the image as part of road track. The result

manifests the road lane detection guidance and object recognition along with prediction probability and bounding

box.

Keywords: Convolution neural network, Hough Transform , Lane Detection, Object Recognition , YOLO.

References: 1. Aziz, M., Prihatmanto, A., & Hindersah, H. (2017). Implementation of Lane Detection Algorithmfor Self-Driving Car on Toll Road

Cipularang using Python Language. 4th International Conference on Electric Vehicular Technology (ICEVT), 144-148.

doi:10.1109/ICEVT.2017.8323550

2. Ćorović, A., Ilić, V., Đurić, S., Marijan, M., & Pavković, B. (2018). The Real-Time Detection of Traffic Participants Using YOLO Algorithm. 26th Telecommunications Forum (TELFOR), 1-5. doi:10.1109/TELFOR.2018.8611986

3. Kim, J.-G., Yoo, J.-H., & Koo, J.-C. (2018). Road and Lane Detection using Stereo Camera. International Conference on Big Data and Smart Computing (BigComp), 649-652. doi:10.1109/BigComp.2018.00117

4. Liu, C., Tao, Y., Liang, J., Li, K., & Chen, Y. (2018). Object Detection Based on YOLO Network. 4th Information Technology and

Mechatronics Engineering Conference (ITOEC 2018), 799-803. doi:10.1109/ITOEC.2018.8740604 5. Nugraha, A., Su, S.-F., & Fahmizal. (2017). Convolutional, Towards Self-driving Car Using Convolutional Neural Network and Road

Lane Detector. 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-¬Mechanical System, and

Information Technology (ICACOMIT), 65-69. doi:10.1109/ICACOMIT.2017.8253388 6. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Conference

on Computer Vision and Pattern Recognition (CVPR), 1-10. doi:10.1109/CVPR.2016.91

7. Shanmugamani, R. (2018). Deep Learning for Computer Vision. PACKT. 8. Tran, L.-A., & Le, M.-H. (2019). Robust U-Net-based Road Lane Markings Detection for Autonomous Driving. International

Conference on System Science and Engineering (ICSSE), 62-66. doi:10.1109/ICSSE.2019.8823532

9. 9. Zhang, R., Yang, Y., Wang, W., Zeng, L., Chen, J., & McGrath, S. (2018). An Algorithm for Obstacle Detection based on YOLO and Light Filed Camera. Twelfth International Conference on Sensing Technology (ICST), 223-226.

doi:10.1109/ICSensT.2018.8603600 10. Zhao, X., Liu, P., Zhang, M., & Zhao, X. (2010). A Novel Line Detection Algorithm in

Images Based on Improved Hough Transform and Wavelet Lifting Transform. International Conference on Information Theory and Information Security, 767-771. doi:10.1109/ICITIS.2010.568968.

87-92

20.

Authors: Ruchitesh Malukani, Nihaal Subhash, Chhaya Zala

Paper Title: Automatic Image Captioning Methods

Abstract: A language known to humans is a natural language. In computer science it is the most challenging

task to make the computers understand the natural languages and generating caption automatically from the given

image. While a lot of work has been done, the total solution to this problem has been demonstrated daunting so

far. Image captioning is a crucial job involving linguistic image understanding and the ability to generate

interpretation of sentences with proper and accurate structure. It requires expertise in Image processing and natural

language processing. The publishers suggest in this practice a system using the multilayer Convolutional Neural

Network (CNN) to generate language describing the images and Long Short Term Memory (LSTM) to concisely

frame relevant phrases using the driven keywords. We aim in this article to provide a brief overview of current

93-97

methods and algorithms of image captioning using deep learning. We also address datasets and measurement

criteria widely used for the same

Keywords: Image captioning, Deep learning, Computer Vision, Natural language processing, CNN, RNN,

LSTM.

References: 1. Kulkarni, G., Premraj, V., Ordonez, V., Dhar, S., Li, S., Choi, Y., Berg, A.C. and Berg, T.L., 2013. Babytalk: Understanding and

generating simple image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12), pp.2891-2903.

2. Xu, Kelvin, et al. "Show, attend and tell: Neural image caption generation with visual attention." International conference on

machine learning. 2015. 3. Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Effective approaches to attention-based neural machine

translation." arXiv preprint arXiv:1508.04025 (2015). 4. Papineni, Kishore, et al. "BLEU: a method for automatic evaluation of machine translation." Proceedings of the 40th annual meeting

on association for computational linguistics. Association for Computational Linguistics, 2002.

5. Ali Farhadi, Mohsen Hejrati, Mohammad Amin Sadeghi, Peter Young, Cyrus Rashtchian, Julia Hockenmaier, and David Forsyth. 2010. Every picture tells a story: Generating sentences from images. In European conference on computer vision. Springer, 15–29.

6. Siming Li, Girish Kulkarni, Tamara L Berg, Alexander C Berg, and Yejin Choi. 2011. Composing simple image descriptions using

web-scale n-grams. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning. Association for Computational Linguistics, 220–228.

7. Mason, Rebecca, and Eugene Charniak. "Nonparametric method for data-driven image captioning." Proceedings of the 52nd

Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2014. 8. V. Ordonez, X. Han, P. Kuznetsova, G. Kulkarni, M. Mitchell, K. Yamaguchi, K. Stratos, A. Goyal, J. Dodge, A. Mensch, et al.

Large scale retrieval and generation of image descriptions. International Journal of Computer Vision (IJCV), 2015.

9. Genevieve Patterson, Chen Xu, Hang Su, and James Hays. 2014. The sun attribute database: Beyond categories for deeper scene

understanding. International Journal of Computer Vision.

10. Ryan Kiros, Ruslan Salakhutdinov, and Rich Zemel. 2014. Multimodal neural language models. In Proceedings of the 31st

International Conference on Machine Learning (ICML-14). 595–603. 11. Yuan, Aihong, Xuelong Li, and Xiaoqiang Lu. "3G structure for image caption generation." Neurocomputing 330 (2019): 17-28.

12. Aneja, Jyoti, Aditya Deshpande, and Alexander G. Schwing. "Convolutional image captioning." Proceedings of the IEEE

Conference on Computer Vision and Pattern Recognition. 2018. 13. Yan, Shiyang, et al. "Image captioning using adversarial networks and reinforcement learning." 2018 24th International Conference

on Pattern Recognition (ICPR). IEEE, 2018. 14. Li, Jiayun, et al. "Image captioning with weakly-supervised attention penalty."

arXiv preprint arXiv:1903.02507 (2019). 14. Anne Hendricks, Lisa, et al. "Deep compositional captioning: Describing novel object categories without paired training data."

Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

15. Yao, Ting, et al. "Incorporating copying mechanism in image captioning for learning novel objects." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.

16. Gao, Lianli, et al. "Hierarchical LSTMs with adaptive attention for visual captioning." IEEE transactions on pattern analysis and

machine intelligence (2019). 17. You, Quanzeng, et al. "Image captioning with semantic attention." Proceedings of the IEEE conference on computer vision and

pattern recognition. 2016.

18. Banerjee, Satanjeev, and Alon Lavie. "METEOR: An automatic metric for MT evaluation with improved correlation with human judgments." Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or

summarization. 2005.

19. 20. Jiang, Wenhao, et al. "Learning to guide decoding for image captioning." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.

21.

Authors: Deep Kaneria, Brijesh Patel

Paper Title: Sentiment Analysis for Twitter Data

Abstract: With the advancements in web technology and its growth, there's an incredible volume of information

present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet

emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking

sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on

various topics, and can discussion with different groups and communities, or post messages across the world. In

the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for

sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly

unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and

comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive

Bayes & Support Vector Machine, we provide research on twitter data.

Keywords: Machine Learning (ML), Naïve Bayes (NB), Twitter, Support Vector Machine (SVM), Sentiment

Analysis (SA).

References: 1. Mitali Desai, Mayuri Mehta, "Techniques of Sentiment Analysis for Twitter Data-A Comprehensive Survey", IEEE on Computing,

Communication and Automation, pp.149154, April 2016. 2. Jatinder Kaur, "A Review paper on Twitter Sentiment Analysis,Techniques", International Journal for Research in Applied Science

& Engineering Technology, vol.4, pp.137-141, October-2016.

3. C. Romero and S. Ventura, "Educational Data Mining: A Review of the State-of-the-Art," in Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, 2010, vol. 40, no.6, pp. 601-618

4. J.M. DiMicco and D.R. Millen, “Identity Management: Multiple Presentations of Self in Facebook,” Proc. the Int‟l ACM Conf.

Supporting Group Work, pp. 383-386 5. M. Vorvoreanu and Q. Clark, “Managing Identity Across Social Networks,” Proc. Poster Session at the ACM Conf. Computer

Supported Cooperative Work

6. M. Vorvoreanu, Q.M. Clark, and G.A. Boisvenue, “Online Identity Management Literacy for Engineering and Technology Students,” J. Online Eng. Education, vol. 3, article 1

98-101

7. M. Ito, H. Horst, M. Bittanti, D. boyd, B. Herr-Stephenson, P.G. Lange, S. Baumer, R. Cody, D. Mahendran, K. Martinez, D. Perkel, C. Sims, and L. Tripp, Living and Learning with New Media: Summary of Findings from the Digital Youth Project. The John D. and

Catherine T. MacAuthur Foundation

8. S. Bahrainian and A. Dangel, “Sentiment Analysis using Sentiment Features”, in Int. joint Conf. of Web Intelligence and Intelligent Agent Technologies, 2013, pp. 26-29.

9. G. Gautam and D. Yadav, “Sentiment analysis of twitter data using machine learning approaches and semantic analysis”, in 7th Int.

Conf. on Contemporary Computing, 2014, pp. 437-442. 10. A.C.E.S Lima. and L.N. de Castro, “Automatic sentiment analysis of Twitter messages”, in 4th Int. Conf. on Computational Aspects

of Social Networks (CASoN), 2012

11. “Three Cool and Inexpensive Tools to Track Twitter Hashtags”, June 11, 2019. [Online]. Available Sentiment Analysis for Twitter Data 101 Retrieval Number: G10190597S20/2020©BEIESP DOI: 10.35940/ijitee.G1019.0597S20 Published By: Blue Eyes

Intelligence Engineering & Sciences Publication http://dannybrown.me/2013/06/11/threecool-toolstwitterhashtags/ [Accessed: 3-

Dec-2019]. 12. Pang, B. and Lee, L. “A sentimental education: Sentiment analysis using subjectivity,summarization based on minimum cuts”. 42nd Meeting of the Association for Computational Linguistics[C] (ACL-04), pp. 271-278, 2004,

12. Kharde, Vishal, and Prof Sonawane. "Sentiment analysis of twitter data: a survey of techniques." arXiv preprint arXiv:1601.06971

(2016). 13. Ray, Sunil, “6 Easy-Steps to Learn Naive Bayes Algorithm (with Code in Python).” Analytics Vidhya, 11 Apr. 2018,

www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/. 15. Cortes, Corinna; Vapnik, Vladimir N. (1995). "Support-vector

networks", Machine Learning, 20 (3): pp. 273–297. 14. 16. N. Kasture and P. Bhilare, “An Approach for Sentiment analysis on a social networking site”, Computing Communication Control

and Automation (ICCUBEA), pp. 390-395, 2015. 17. X. Chen, M. Vorvoreanu and K. Madhavan, “Mining Social Media Data to

Understand Students‟ Learning Experiences”, IEEE Transaction, vol. 7, no. 3, pp. 246-259, 2014. 15. 18. V. Singh and S. K. Dubey, “Opinion mining and analysis: A literature review”, in 5th Int. Conf. on Confluence The Next

Generation Information,Technology Summit (Confluence), pp. 232-239, 2014

22.

Authors: Vaishali Patelia, Maulika S. Patel

Paper Title: A Comparative Study of Classification Techniques for P300 Speller

Abstract: P300 speller in Brain Computer Interface (BCI) allows locked-in or completely paralyzed patients to

communicate with humans. To achieve the performance of characterization and increase accuracy, machine

learning techniques are used. The study is about an event related potential (ERP) P300 signal detection and

classification using various machine learning algorithms. Linear Discriminant Analysis (LDA) and Support Vector

Machine (SVM) are used to classify P300 and Non-P300 signal from Electroencephalography (EEG) signal. The

performance of the system is evaluated based on f1-score using BCI competition III dataset II. In our system, we

used LDA and SVM classification algorithms. Both the classifiers gave 91.0% classification accuracy.

Keywords: Brain Computer Interface, P300 Speller, Event Related Potential, Linear Discriminant Analysis,

Support Vector Machine.

References: 1. Abdulkader, Sarah N., AymanAtia, and Mostafa-Sami M. Mostafa. "Brain computer interfacing: Applications and challenges."

Egyptian Informatics Journal 16.2 (2015): 213-230.

2. Guy, Violaine, et al. "Brain computer interface with the P300 speller: Usability for disabled people with amyotrophic lateral

sclerosis." Annals of physical and rehabilitation medicine 61.1 (2018): 5-11.

3. Krigolson, Olave E., et al. "Choosing MUSE: Validation of a lowcost, portable EEG system for ERP research." Frontiers in

neuroscience 11 (2017): 109. 4. Renard, Yann, et al. "Openvibe: An open-source software platform to design, test, and use brain–computer interfaces in real and

virtual environments." Presence: teleoperators and virtual environments 19.1 (2010): 35-53.

5. Blankertz, B. "Documentation second wadsworth BCI dataset (P300 evoked potentials) data acquired using BCI2000 P300 Speller Paradigm." BCI Classification Contest November (2002).

6. Schalk, Gerwin, et al. "BCI2000: a general-purpose brain-computer interface (BCI) system." IEEE Transactions on biomedical

engineering 51.6 (2004): 1034-1043. 7. Venthur, Bastian, and Benjamin Blankertz. "Mushu, a free-and open source BCI signal acquisition, written in python." 2012 Annual

International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012.

8. Venthur, Bastian, et al. "Wyrm: A brain-computer interface toolbox in python." Neuroinformatics 13.4 (2015): 471-486. 9. Venthur, Bastian, et al. "Pyff---A Pythonic Framework for Feedback Applications and Stimulus Presentation in Neuroscience."

Frontiers in neuroscience 4 (2010): 179.

10. Hongchang Shan, Yu Liu, TodorStefanov “A Simple Convolutional Neural Network for Accurate P300 Detection and Character Spelling in Brain Computer Interface” Artificial Intelligence (IJCAI-18).

11. Qi, Hongzhi, et al. "A Speedy Calibration Method Using Riemannian Geometry Measurement and Other-Subject Samples on A

P300 Speller." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26.3 (2018): 602-608. 12. Elsawy, Amr S., et al. "A principal component analysis ensemble classifier for P300 speller applications." Image and Signal

Processing and Analysis (ISPA), 2013 8th International Symposium on. IEEE, 2013.

13. Hoffmann, Ulrich, Jean-Marc Vesin, and TouradjEbrahimi. "Recent advances in brain-computer interfaces." IEEE International Workshop on Multimedia Signal Processing (MMSP07). No. LTS-CONF-2007- 063. 2007.

14. Kaper, Matthias, et al. "BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm." IEEE

Transactions on Biomedical Engineering 51.6 (2004): 1073-1076. 15. Rakotomamonjy, Alain, and Vincent Guigue. "BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller." IEEE

transactions on biomedical engineering 55.3 (2008): 1147-1154.

16. Xu, Tao, et al. "Learning Emotions EEG-based Recognition and Brain Activity: A Survey Study on BCI for Intelligent Tutoring System." Procedia Computer Science 130 (2018): 376-382.

17. Li, Qi, et al. "Training set extension for SVM ensemble in P300- speller with familiar face paradigm." Technology and Health Care

Preprint (2018): 1-14. 18. Fabien Lotte. “A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces.”

Eduardo Reck Miranda; Julien Castet. Guide to Brain-Computer Music Interfacing, Springer, 2014

19. Qu, Jun, et al. "A Novel Three-Dimensional P300 Speller Based on Stereo Visual Stimuli." IEEE Transactions on Human-Machine Systems (2018).

20. L. A. Farwell e E. Donchin, “Talking off the top of your head: toward a mental prosthesis utilizing eventrelated brain potentials”, Electroencephalogr. Clin. Neurophysiol., vol. 70, n. 6, pagg. 510–523, 1988.

21. Lee, Yu-Ri, and Hyoung-Nam Kim. "A data partitioning method for increasing ensemble diversity of an eSVM-based P300 speller."

Biomedical Signal Processing and Control 39 (2018): 53-63.

102-106

22. Liu, Mingfei, et al. "Deep learning based on Batch Normalization for P300 signal detection." Neurocomputing 275 (2018): 288-297.

23. 23. Zhimin Lin1, Chi Zhang 1, Ying Zeng2,1, Li Tong1 & Bin Yan1 “A novel P300 BCI speller based on the Triple RSVP

paradigm” Scientific reports 8.1 (2018)

23.

Authors: Neel Pradip Shah, Sheetal Jeshwani, Pavni Bhatt

Paper Title: An Introduction on Interpretable Machine Learning

Abstract: As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical

practices and fair uses arise, which has motivated the research community to look inside and develop methods to

interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help

with the ethical questions but also can provide various insights into the working of these machine learning models,

which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in

many machine learning applications, the feature of interpretability is the primary value that they offer. However,

in practice, many developers select models based on the accuracy score and disregarding the level of

interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily

explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable

Machine learning, and the methods used for interpretation and explanations.

Keywords: Machine Learning, Interpretability, Black Box Models, Explainable Artificial Intelligence

References: 1. Monlar Christoph, “Interpretable Machine Learning - A Guide for Making Black Box Models Explainable” Available:

https://christophm.github.io/interpretable-ml-book/, 2019. 2. Kleinbaum David G., et al, Logistic regression. New York: Springer-Verlag, 2002.

3. Quinlan J. Ross, Learning decision tree classifiers. ACM Computing Surveys (CSUR) 28(1), pp.71-72, 1996.

4. Gunning David, Explainable Artificial Intelligence (XAI). Defence Advanced Research Projects Agency (DARPA)”, Available: https://www.darpa.mil/attachments/XAIProgramUpdate.pdf, 2017.

5. Ribeiro Marco T., Singh Sameer, Guestrin Carlos, Why should i trust you? Explaining the predictions of any classifier, In: 22nd

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp 1135-1144. 6. Lundberg Scott M., and Su-In Lee, A Unified Approach to Interpreting Model Predictions, arXiv preprint arXiv: 1705.07874, 2017.

7. 7. Vojnović Milan, “Contest Theory - Incentive Mechanisms and Ranking Methods,” Cambridge University Press, 2016.

107-111

24.

Authors: Palak Chatwani, Shivani Shah, Purvi Ramanuj

Paper Title: Different Ray-Casting Algorithm Implementations for Volume Rendering

Abstract: Volume Rendering is the way to achieve 3D visualization. Volume Rendering is used for visualization

of 2D projections of 3D data. In volume rendering techniques, direct volume rendering techniques (DVR) can be

divided into image order and object order. Image order technique can be achieved by ray-casting algorithm. Ray-

casting algorithm is used for raysurface interaction tests to solve problems in computer graphics like collision

detection and hidden surface removal. In DVR, the ray is pushed through the object and 3D scalar field of interest

is sampled along the ray inside the object. Over the years, different approaches towards this algorithm took place.

This paper represents the review and analysis of different approaches of raycasting algorithm.

Keywords: - Volume Rendering, Ray-casting, 3D visualization

References: 1. Dubey, Rashmi, Sarika Jain, and R. S. Jadon. "Volume Rendering: A Compelling Approach to Enhance the Rendering

Methodology." 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT).

IEEE, 2016.

2. Callahan, Steven P., et al. "Direct volume rendering: A 3D plotting technique for scientific data." Computing in Science & Engineering 10.1 (2008): 88-92.

3. Binyahib, Roba, et al. "A scalable hybrid scheme for ray-casting of unstructured volume data." IEEE transactions on visualization

and computer graphics 25.7 (2018): 2349-2361. 4. Sans, Francisco, and Rhadamés Carmona. "Volume ray casting using different GPU based parallel APIs." 2016 XLII Latin

American Computing Conference (CLEI). IEEE, 2016.

5. Lee, Ahyun, and Insung Jang. "Mouse Picking with Ray Casting for 3D Spatial Information Open-platform." 2018 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2018.

6. Mehaboobathunnisa, R., AA Haseena Thasneem, and M. Mohamed Sathik. "Ray grouping based ray casting for visualization of

medical data." 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16). IEEE, 2016.

7. Y. Zhang, P. Gao and X. Li, "A novel parallel ray-casting algorithm," 2016 13th International Computer Conference on Wavelet

Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2016, pp. 59-61. 8. Zhang, Kui, Lingchen Zhu, and Lin OuYang. "Application of 3Dvisualization of carbonate rock pore facies based on ray casting

algorithm." 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2017.

9. Liang, Jianming, et al. "Visualizing 3D atmospheric data with spherical volume texture on virtual globes." Computers & Geosciences 68 (2014): 81-91.

10. Shicai, Yu, Wu Qianjun, and Lu Rong. "An high efficient and speed algorithm of Ray Casting in volume rendering." 2011

International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2011. 11. Luo, Jianxin, et al. "An efficient Ray Casting method for terrain visualization." 2011 International Conference on Multimedia

Technology. IEEE, 2011.

12. Tan, Jianhao, et al. "Design of 3D visualization system based on vtk utilizing marching cubes and ray casting algorithm." 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Vol. 2. IEEE, 2016.

13. Wenli, Yang, Zeng Zhiyuan, and Yang Li. "Three-Dimensional Reconstruction of Coarse-Grained Soil Fabric Based on Improved

Ray. Casting Volume Rendering Algorithm." 2009 International Forum on Information Technology and Applications. Vol. 2. IEEE, 2009

112-117

14. Kim, Taeho, and Jinah Park. "Analyzing 3D cell data of optical diffraction tomography through volume rendering." 2018 International Workshop on Advanced Image Technology (IWAIT). IEEE,2018.

15. 15. Luo, Jianxin, et al. "An efficient Ray Casting method for terrain visualization." 2011 International Conference on Multimedia

Technology. IEEE, 2011.

25.

Authors: Mansi Gunsai, Shreya Patel, Kinjal V. Joshi

Paper Title: Heart Disease Prediction Methods

Abstract: In recent times, heart diseases are considered one of the deadliest causes of mortality and morbidity

among the population of the world. Predicting the probability of the occurrence of cardiovascular diseases has

become one of the most important objectives of the medical analysis system. The conventional methods have

proved to be inefficient in prior prediction of heart diseases because of several contributing risk factors like

diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and others. Due to such limitations, medical

practitioners rely on various modern Machine Learning and Data Mining approaches such as ANN, Naïve Bayes,

SVM, etc. Such models have proved to be effective in providing accurate predictions from the huge amount of

medical data available. The main aim of this paper is to analyse various machine learning approaches adopted in

different research works and to deduce which techniques are most beneficial and precise.

Keywords: Heart Disease Prediction, Machine Learning, Artificial Neural Network, Naïve Bayes, Decision Tree,

Genetic Algorithm

References: 1. 1.Ankita Dewan and Meghna Sharma, “Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification

Published in: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

2. M. Akhil and Dr. PritiChandrab, “Heart Disease Prediction System using Associative Classification and Genetic Algorithm

“International Conference on Emerging Trends in Electrical, Electronics and Communication Technologies-ICECIT, 2012. 3. Majid Feshki&OmidSojoodi, “Improving the Heart Disease Diagnosis by Evolutionary Algorithm of PSO and Feed Forward Neural

Network “Published in: 2016 Artificial Intelligence and Robotics (IRANOPEN).

4. SadafRoostaee& Hamid Reza Ghaffary, “Diagnosis of Heart Disease Based on Meta Heuristic Algorithms and Clustering Methods”, Journal of Electrical and Computer Engineering Innovations JECEI, Vol. 4, No. 2, 2016.

5. SellappanPalaniappan, RafiahAwang. “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, Published in:

2008 IEEE/ACS International Conference on Computer Systems and Applications. 6. NouraAjam,"Heart Diseases Diagnosis using Artificial Neural Network “Network and Complex Systems ISSN 2224-610X ISSN

2225-0603 Vol.5, No.4, 2015.

7. TülayKarayilan and Özkan, “Prediction of Heart Disease Using Neural Network”, Published in: 2017 International Conference on Computer Science and Engineering (UBMK). 8. Reddy Prasad, Pidaparthi Anjali, S. Adil, “Heart Disease Prediction using Logistic

Regression Algorithm using Machine Learning”, International Journal of Engineering and Advanced Technology (IJEAT)ISSN:

2249 – 8958, Volume-8, Issue-3S, February 2019. 8. LathaParthiban and R. Subramanian, “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm”,

International Journal of Biological and Life Sciences 3:3 2007.

9. Syed Umar Amin, Kavita Agarwal, “Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors”,Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013).

10. R. Chitra and Dr.V. Seenivasagam, “Heart Disease Prediction System Using Supervised Learning Classifier”, Bonfring

International Journal of Software Engineering and Soft Computing, Vol. 3, No. 1, March 2013. 11. Khemphila&Boonjing, “Heart disease Classification using Neural Network and Feature Selection"2011 21st International

Conference on Systems Engineering (ICSEng).

12. AH Chen, SY Huang, PS Hong,"HDPS: Heart Disease Prediction System”, Published in: 2011 Computing in Cardiology. 13. J. Thomas, “Human Heart Disease Prediction System using Data Mining Techniques",2016 International Conference on Circuit,

Power and Computing Technologies [ICCPCT].

14. 15. D. Cooke, C. Ordonez, E. V. Garcia, E. Omiecinski, E. Krawczynska,R. Folks, C. Santana, L. de Braal, and N. Ezquerra, “Data mining of large myocardial perfusion SPECT (MPS) databases to improve diagnostic decision making,” J. Nucl. Med., vol. 40, no.

5, 1999. 16. Carlos Ordonez,” Association Rule Discovery with the Train and Test Approach for Heart Disease Prediction”, IEEE transactions on information technology in biomedicine, vol. 10, no. 2, april 2006

118-121