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;

&nbsp;

MIT World Peace University

Engineering and Technology

Elections are an integral part of democracy. With democracy being the most popular government type in today's world, the need for the use of free and fair elections to elect a presidential candidate or a party has increased. Elections allow everyone in the country above a certain age group (Age limit as per a country or state's norms) irrespective of their gender, caste, color, religion, etc., to take part in election and vote for the candidate or party of their choice. The most popular methods of conducting elections are using ballot paper and electronic voting machines (EVM's). These methods come with their own set of challenges such as lack of security, inefficiency, bogus voting, increased expenditure and ease of manipulation of the props used for voting. In order to tackle these challenges, we are proposing an implementation of elections by making use of permissioned blockchain technology. We aim to make a decentralized voting system using Hyperledger Fabric framework to establish a permissioned blockchain network which would negate the disadvantages of previously used voting mechanisms out there and restrengthen the true meaning of free, fair and sovereign election. In this paper we have described several use cases explaining the various security measures that could be added in order to improve security of the voting systems. Also, we have made use of Hyperledger Caliper to benchmark our blockchain based voting system and test out the system performance with respect to different chaincodes. © 2022 IEEE.

World University root node

School of Computer Engineering & Technology

2022 International Conference on IoT and Blockchain Technology, ICIBT 2022

Permissioned Blockchain Voting System using Hyperledger Fabric

The Internet of Things is helping humans to automate life by providing different tools and making it more easier. It can also be viewed as things connected in a network. This network can provide several smart services. On the other hand, face recognition is something which provides us a way to make our system intelligent enough to recognize people, which makes it easier to find the potential suspects from a security point of view. Nowadays security cameras are used to build safety, where a person is responsible to detect any problems/ suspects and report the same to concerned authorities. This paper presents an idea to automate processes using IoT and face recognition methods. © 2022 IEEE.

2022 IEEE 7th International conference for Convergence in Technology, I2CT 2022

Smart Surveillance System Using Face Detection for Residentials

The performance of the central solar receiver should be properly examined for an effective design of a solar power or solar heating system. The performance of solar receiver is affected by three parameters. These are the amount of solar radiations, the state of the heating medium and the temperature of the surrounding air. Also, while evaluating the performance of a solar receiver, the ambient weather conditions should be taken into account. As a result, when a large amount of direct light strikes a solar receiver, the power generated increases as well. In contrast, as the amount of direct light falling on the solar receiver diminishes, so does the amount of electricity generated. As a result, it is generally understood that on sunny days, the performance of the central solar receiver will be higher, while on overcast days, the performance will be reduced. The solar receiver collects heat from diffused or reflecting light (when the sun is obscured by clouds) and absorbs reflective light from glossy or light-colored surfaces. The experimental results reveal that solar radiation collection performance is dependent on climatic circumstances; thus, when installing a solar receiver, it should be suitable for the specific region depending on weather conditions. © 2022 Published under licence by IOP Publishing Ltd.

School of Mechanical Engineering

IOP Conference Series: Earth and Environmental Science

An experimental study of solar receiver under cloudy weather conditions

Sentiment analysis is an important application of Natural Language Processing. It is a technique used to find out polarity of the text written in the feedback, comment or review. There are mainly three types of polarity i.e. negative, positive and neutral. Polarities are mainly used in opinion mining, decision making, and extraction of feelings and emotions of the user. Sentiment analysis is mainly used in Internet era where number of applications is available where users can give their remark easily and frankly. Sentiment Analysis works as a dominant tool for users and helps in finding out the needful information and also used to combine the collective sentiments of the reviews. Numbers of methods are available to perform the sentiment analysis. This paper contributes an impression about different methods invented to perform sentiment analysis and gives brief idea about scope of research. © Grenze Scientific Society, 2022.

13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

Blueprint of Emerging Techniques used in Sentiment Analysis

The world has witnessed tremendous increase in the use of social media platforms in the last decade. Posting and updating on such platforms have become a way for people to express their opinions, experiences, feelings, views etc. Along with all these, they tend to post and share their personal Information. This Information can be used in advantageous way to understand their needs and requirements The approach, known as personality prediction, entails sepΑarating digital input into attributes and mapping it according to a personality model. In this paper, we are interested to predict the personality of such users. Researchers have shown based on the social behavior towards friends, number of friends, groups joined etc. can help in predicting the personality trait from Facebook and Twitter. The text posts for the user are converted to be suitable representation using GloVe algorithm. And then feeding it to Convolution Neural Net, a popular Deep Learning architecture on the text posts that was implemented for the traits present in the Big Five trait theory. This method helped to achieve comparable outputs with other recent models © 2022 School of Science, IHU. All rights reserved

Journal of Engineering Science and Technology Review

Convolution Neural Network Regression Model to Predict Personality Scores

A considerable percentage (over 70%) of dwellings across the globe involve masonry elements. Masonry structures are well known to be weak in resisting seismic actions. This paper presents the effectiveness of (near surface mounted) containment reinforcement as earthquake resistant feature for masonry walls. Analytical prediction of the response of unreinforced (UR) and reinforced (R) masonry structures to earthquakes are hindered by the complexity and cost of micro and macro models. Consequently, researchers have come up with simplified models of masonry walls to capture seismic responses. The Equivalent Frame Model (EFM) is one such simplified approach. The present work evaluates the performance of two-storey UR and R masonry walls subjected to In-plane seismic action through Incremental Dynamic Analysis (IDA) and Fragility Analysis (FA). EFM has been employed in commercial FEM software SAP2000.v21 to evaluate the seismic response of these walls to the ensemble of near-field and far-field excitations. Finally, the work demonstrates the superiority of Containment Reinforced (CR) masonry against seismic action. © 2022

School of Civil Engineering

Materials Today: Proceedings

Fragility analysis of containment reinforced masonry wall using equivalent frame model

This article proposes active learning strategies for delivering engineering courses to the students in order to make learning more engaging, interesting, accomplishing, and joyful. The education system was impacted by the unprecedented situations generated by the Coronavirus pandemic from the last two academic years. The student community is also going through difficult situations on many levels. The primary challenge for teachers was to engage the students passionately in learning while maintaining a stress-free, healthy and fruitful environment in the online classes. For a stress-free teaching-learning process, adopting a variability in course content delivery methods would be a smart choice for teachers. This paper discusses the most effective active learning strategies in higher education. The work also highlights examples of implementation and an analysis of the effectiveness of active learning methodologies in terms of learners understanding, competency, satisfaction and knowledge gain. The findings indicate that the active learning strategy has assisted students in being more comfortable and engaged in effective learning in the online mode. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

School of Electronics & Communication Engineering

Journal of Engineering Education Transformations

Active Learning Techniques for Effective Online Teaching and Learning in Higher Education

Indian economy is contributed heavily by agriculture. Most of the Indian farmers rely on their instincts to decide the crop to be sown at a particular time of year. They do not realize that the crop output is circumstantial, and depended heavily on the present-day weather and soil conditions. A single uninformed decision by the farmer can have undesirable consequences on the economic conditions of the region and also mental and financial impacts on the farmer himself. Applying systematic Machine Learning models will effectively help to alleviate this issue. The dataset used in the paper is built by adding up the datasets of India’s rainfall, climate, and fertilizer. Machine learning models will be used on the dataset to get the highest accuracy model to recommend a crop for the farm's location. This recommendation will help the farmers in India to make learned decisions about the crops. The recommendation will take into account the parameters like the farm's location, sowing season, soil properties, and climate. © 2022 American Institute of Physics Inc.. All rights reserved.

AIP Conference Proceedings

Digital Agriculture: Crop Recommendation in India Using Machine Learning Models

The researchers face a difficult problem in detecting and identifying underwater fish species. Marine researchers and ecologists must evaluate the comparative profusion of fish species in their environments on a regular basis and track population trends. Researchers have presented a number of underwater computer vision, machine learning-based automatic systems for fish detection and classification. However, due of the changing undersea environment, it is extremely challenging to find the ideal system for detecting and classifying fish. Because light has such a strong influence in the aqueous medium, conducting research in this environment is difficult. The MobileNet model is utilised to detect and recognise the fish breed in the proposed work. The dataset is preprocessed before the model is implemented in order to obtain appropriate performance metrics. The work is based on the Kaggle dataset, which has nine different fish breeds in total. With a 99.74 percent accuracy, the model can detect and recognise nine different breeds. In comparison to other state of art methods, the model exhibits promising results. © 2022 IEEE.

2022 International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

Underwater Fish Detection and Classification using Deep Learning

The quest for learning more about the minute intricacies of the universe has fascinated mankind since ages. From stars to planets and asteroids to exoplanets, each research endeavor in astronomy has supplemented our knowledge about the cosmos. NASA's Kepler Mission is one monumental step in this direction wherein, telescopes conduct a survey of the Milky Way galaxy and try to identify thousands of earth-size and other smaller planets in or near the habitable zone, so as to determine the thousands or even millions of stars in our galaxy that might have such orbiting planets. Exoplanet is any new planet outside the solar system which orbits a star. Identifying new exoplanets gives us a chance to precisely understand the planet formation processes. Earlier, it was a laborious endeavour to mine the mission data using traditional algorithms and churn out the possible exoplanets. Ever since the advent of various machine learning algorithms, this process has become quite seamless. However, not all algorithms give equal and promising results when it comes to analysing different types of data. A comparative study of algorithms helps in this regard, thereby identifying the pros and cons of different algorithms for analysing certain forms of data. In this paper, we initially focus on feature set reduction using principal component analysis and thereafter make a detailed comparative analysis of ML algorithms for the identification of exoplanets, in the NASA Kepler mission data. © 2022 Elsevier B.V.. All rights reserved.