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

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

Engineering and 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

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

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 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.

School of Electronics & Communication Engineering

Fulltext

Procedia Computer Science

Comparative Analysis of Machine Learning Algorithms for Analyzing NASA Kepler Mission Data

Emotions play an essential role in human life for planning and decision making. Emotion identification and recognition is a widely explored field in the area of artificial intelligence and affective computing as a means of empathizing with humans and thereby improving human machine interaction. Though audio visual cues are vital for recognizing human emotions, they are sometimes insufficient in identifying emotions of people who are good at hiding emotions or people suffering from Alexithymia. Considering other dimensions like Electroencephalogram (EEG) or text, along with audio visual cues can aid in improving the results in such situations. Taking advantage of the complementarity of multiple modalities normally helps capture emotions more accurately compared to single modality. However, to achieve precise and accurate results, correct fusion of these multimodal signals is solicited. This study provides a detailed review of different multimodal fusion techniques that can be used for emotion recognition. This paper proposes in-depth study of feature-level fusion, decision-level fusion and hybrid fusion techniques for identifying human emotions based on multimodal inputs and compare the results. The study concentrates on three different modalities i.e., facial images, audio and text for experimentation; at least one of which differs in temporal characteristics. The result suggests that hybrid fusion works best in combining multiple modalities which differ in time synchronicity.

International Journal of Advanced Computer Science and Applications

Review on Multimodal Fusion Techniques for Human Emotion Recognition

The soil is the most fundamental component for the survival of any living thing that can be found on this planet. A little less than 41 percent of Indians are employed in agriculture, which accounts for approximately 19 percent of the country's gross domestic product. As is the case in every other industry, researchers and scientists in this one are exerting a lot of effort to enhance agricultural practices by utilising cutting-edge methods such as machine learning, artificial intelligence, big data, and so on. The findings of the study described in this paper are predicated on the assumption that the method of machine learning results in an improvement in the accuracy of the prediction of soil chemical characteristics. The correlations that were discovered as a result of this research are essential for comprehending the comprehensive approach to predicting the soil attributes using ML/DL models. A number of findings from previous study have been reported and analysed. A state of the art machine learning algorithm, including Logistic Regression, KNN, Support Vector Machine and Random Forest are implemented and compared. Additionally, the innovative Deep Learning Hybrid CNN-RF and VGG-RNN Model for Categorization of Soil Properties is also implemented along with CNN. An investigation into the significance of the selected category for nutritional categorization revealed that a multi-component technique provided the most accurate predictions. Both the CNN-RF and VGG-RNN models that were proposed were successful in classifying the soil with average accuracies of 95.8% and 97.9%, respectively, in the test procedures. A study was carried out in which the CNN-RF model, the VGG-RNN model, and five other machine learning and deep learning models were compared. The suggested VGG-RNN model achieved superior accuracy of classification and real-time durability, respectively. © 2022 The authors.

International Journal on Recent and Innovation Trends in Computing and Communication

A Novel Hybrid AI Federated ML/DL Models for Classification of Soil Components

Data mining algorithms has a wide application in banking domain. Classification algorithms are the one of the popularly used algorithms in the banking sector. One of the major applications of classification algorithms in banking sector is predicting the credit worthiness of the borrower. In this research the dataset is taken from prowessIQ and the dataset is highly imbalanced. The researchers have used data sampling techniques to balance the data. Our approach is to use the ensemble classifiers which are performing pretty well even on unbalanced datasets. Three ensemble techniques are studied in this paper namely Random Forest (RF), XGB and LGBM classifiers. Hyper parameter tuning is done to improve the accuracy of the model. With best possible hyperparameters RF has given an accuracy of 90.2%, XGB has given an accuracy of 92.7% and LGBM has given an accuracy of 92.9%. LGBM has better classified borrowers as worthy and non-worthy even when the dataset is unbalanced. © 2022 IEEE.

2022 IEEE 3rd Global Conference for Advancement in Technology, GCAT 2022

Ensemble Approach with Hyperparameter Tuning for Credit Worthiness Prediction

In today's world, agriculture is the most important source of growth. It is an essential component of both economic and social life. Plant disease is becoming an important field in India, in the literal sense of the word. In the past, disease detection systems were designed for monocot or dicot plant families. Gradually, as scientific and special to some science or trade progress, more safe, good, ready, and working well methods are offered and have undergone growth for early detection of plant disease through the smallest wide space for turning time. We have instrumented a careful way to show monocot and dicot disease in this paper. Our methodical approach aids in the treatment of the disease by reducing the number of errors. To detect plant leaf disease, we took three steps. It primarily takes three forms: first, breaking down the leaf into parts, then removing the points, and finally, ordering. It has come to be part of the acted-on part of plant leaf using the k-means quince into group's expert way in the breaking down into parts process. The material's feel, appearance, and color are revealed after it is broken down into primarily form parts. Finally, the information gleaned from the features is applied to the order using the Support Guide Machine to detect plant disease. © 2022 IEEE.