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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.
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Sciences Publication Retrieval Number: G10070597S20/2020©BEIESP DOI: 10.35940/ijitee.G1007.0597S20
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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
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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.
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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
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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
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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
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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
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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
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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
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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 .
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10. E. Kochkina , M. Liakata , A. Zubiaga , All-in-one: multi-task learning for rumour verification, in: Proceedings of the Twenty
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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.
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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
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