MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Engineering and Technology

World University root node

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH \& TECHNOLOGY (IJERT)

A Study on Cassandra: Data Model

Stress reduction is important for maintaining overall human health. There are different methodologies for detecting stress including clinical tests, traditional methods and various sensors and systems developed using either a smartphone, wearable devices or sensors connected to the human body. In this paper, a novel methodology is proposed by creating a personalized model from the generalized model because stress differs from person to person for the same work profile. A generalized model for stress detection is developed from smartphone and E4 device data of all the available individuals. A generalized model is used to build a personalized model that is a person-specific model and will be build up over a time of time when enough amount of person-specific data gets collected. This proposed methodology intends to give more accuracy as two devices are used with the novel approach of model building. Various machine learning algorithms such as ANN, xgboost, and SVM are implemented with the E4 device dataset while the LASSO regression model is used for smartphone data. ANN worked best than xgboost and SVM with 93.71% accuracy. In LASSO, 0.6556 RMSE is achieved. © Springer Nature Switzerland AG 2020.

Lecture Notes on Data Engineering and Communications Technologies

Novel approach for stress detection using smartphone and E4 device

Dialogue Systems are increasingly popular with the recent advances in neural approaches and NLP applied to conversational AI. Alexa, Siri, Cortana, Google Mini are handily used by many users to do small tasks and control their home appliances in hands free style. Enterprises are also deploying 24 × 7 dialogue agent in place of traditional customer support to increase user engagement and improve their processes. Dialogue Systems are also augmented with Robots to improve human-robot dialogues.Conversational Agents are classified into two main types: Social bots/Chitchat bots and Task Oriented Dialogue Agents. Social bots aim to engage user with unstructured human conversations. These dialogue agents don't have fixed aim to complete and focus more on carrying out open domain conversations. For example ALIZA, Microsoft XiaoIce etcOn the other hand, Task oriented dialogue agents help user to accomplish certain tasks in specific domains like Restaurant booking, Flight reservation, customer support etc. These are popularly used in controlling home appliances and carrying out simple tasks by users in day to day life. Siri, Alexa, Google Mini, Cortana are task oriented dialogue agents. There is increasing interest in building task completion dialogue agents that span over multiple sub-domains to accomplish a complex user goal.With the increasing acceptance of Dialogue Agents, there is need of high-quality, large-scale dialogue datasets for better performance of task oriented dialogue agent in changing environment. Neural approaches are applied to design intelligent dialogue agents frequently which require very large datasets. However, there are following challenges while building intelligent task completion dialogue systems. Firstly, there are a lot of datasets available for chit-chat bots but they are not directly relevant to task oriented systems. Secondly, to scale out the system to new domains with limited in-domain data.In this paper, we studied different data collection methods, important characteristics of dialogue datasets and their potential uses. This paper presents a survey of publicly available datasets and their applicability for designing modern task-oriented dialogue agents. © 2019 IEEE.

International Conference on Mechatronics, Remote Sensing, Information Systems, and Industrial Information Technologies, ICMRSISIIT 2019

Survey of Available Datasets for Designing Task Oriented Dialogue Agents

Due to the significant increase in the size of the internet and the number of users on this platform there has been a tremendous increase in load on various websites and web-based applications. This load is from the user end which causes unforeseen conditions which leads to unacceptable consequences such as crash or a data loss scenario at the webserver end. Therefore, there is a need to reduce the load on the server as well as the chances of network attacks that increase with the increased user base. The undue consequences such as data loss and server crash are caused due to two main reasons: The first one being an overload of users and the second due to an increased number of automatic programs or robots. A technique can be utilized to overcome this scenario by introducing a delay in the operation speed on the user end through the use of a CAPTCHA mechanism. Most of the classical approaches use a single method for the generation of the CAPTCHA, to overcome this proposed model uses the versatile image CAPTCHA generation mechanism. We have introduced a system that utilizes manualbased, face detection-based, colour based and random object insertion technique to generate 4 different random types of CAPTCHA. The proposed methodology implements a region of interest and convolutional neural networks to achieve the generation of the CAPTCHA effectively. © 2020 IEEE.

School of Mechanical Engineering

2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020

Versatile CAPTCHA Generation Using Machine Learning and Image Processing

To track student attendance, colleges and schools use traditional methods involving manual marking on sheets. These methods are timeconsuming and tedious for teachers. The proposed system automatically records the student's attendance during lecture hours using facial recognition technology with image processing instead of the traditional manual methods. The proposed system works in a controlled environment, in which students, face images are captured and then upon recognition, their attendance is automatically marked in an Excel sheet. Student's face is detected using the Viola-Jones technique, whereas Linear Discriminate Analysis (LDA) along with KNN and SVM is used for face recognition. Experimentation shows that it provides better accuracy than the existing PCA and other techniques.. © 2020 IEEE.

Proceedings - International Conference on Smart Electronics and Communication, ICOSEC 2020

Automatic Students Attendance Marking System Using Image Processing and Machine Learning

S arcasm is a subtle type of irony, which can be widely used in social networks. It is usually used to transmit hidden information to criticize and ridicule a person and to recognize. The sarcastic reorganization system is very helpful for the improvement of automatic sentiment analysis collected from different social networks and microblogging sites. S entiment analysis refers to internet users of a particular community, expresse d attitudes and opinions of identification and aggregation. In this paper, to detect sarcasm, a pattern-based approach is proposed using Twitter data. Four sets of features that include a lot of specific sarcasm is proposed and classify tweets as sarcastic and non-sarcastic. The proposed feature sets are studied and evaluate its additional cost classifications. © 2020 IEEE.

Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020

Machine learning based sarcasm detection on twitter data

Cardiovascular diseases are one of the most vital causes offatality. Cardiovascular disease prediction is a critical challenge in the area of clinical data analysis. Machine learning and Neural Networks are more promising in assisting decide and predict from the massive data produced by healthcare. We have noted different features had used in recent developments of the machine learning model. In this paper, we proposed machine learning techniques to predict cardiovascular disease using features. BMI is one of the highlighting features we used for prediction. BMI is important in predicting cardiovascular disease. The main focus of the article isthe effect ofBMI onthe prediction of cardiovascular disease. The model has proposed with different features as well as regression and classification techniques. We conclude that BMI is a significant factor while predicting cardiovascular disease. © 2020 IEEE.

2020 IEEE Pune Section International Conference, PuneCon 2020

Cardiovascular Disease Prediction Using Machine Learning Models

eLearning has completely transformed the way in which learning is imparted to students. Relevance of the topic being taught is often found lacking in learning material. Students can access lot of contents or video for learning different topics but keeping track of its relevance for him is a tedious job. Recommendation system plays an important role in such situations. The system which provides recommendation is a reference system that would recommend an education work to a student based on the works already done. This paper reviews the main paradigms of recommendation systems using explicit and implicit feedback and the various methodologies that have been implemented to design recommender systems to enhance learning. The concepts of eLearning and recommendation systems are summarized. eLearning recommendation systems will be useful to enhance learning.

School of Polytechnic and Skill Development

Proceeding of International Conference on Computational Science and {\ldots}

Recommender System in eLearning: A Survey

Increasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree-based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV-based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics-based technique of Local Path Reestablishment (LPR) and greedy-based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments.

International Journal of Communication Systems

Scheduling data aggregation trees to extend network lifetime in sensor networks

This paper surveys tools and techniques used in the Big-data and Big-data computing. The principles upon which Big-data and dataintensive computations work have been explored with some outlines provided by various researchers in the field. The paper starts with the traditionalcluster computing and further explores about the Hadoop system as a tool to solve Big-data issues. It also covers the significant growth in cloudcomputing towards hosting MapReduce to use for Data-intensive computations as one of the services available through clouds. It has brief coverageabout Microsoft Azure and Amazon clouds for MapReduce services to be provided through Internet.