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

Engineering and Technology

As the importance of hose reels is known in applications such as fire and safety systems, agricultural equipment, underwater diving equipment and oil industries, it is also worth observing the need of hose reels for applications involving the suction (negative) pressures. Some of the applications of negative pressures or vacuum include sewage and drainage cleaning systems, coal industries, cement industries and many more. So, it becomes of utmost importance to develop equipment that increase the convenience using vacuum for various applications. One such equipment for increasing the convenience for applications involving vacuum would be a hose reel. It is proposed to develop a hose reel that is acclimated to high vacuum pressures. The effect of different parameters associated with the seal materials and their effect on sealing performance using FEA tool will also be studied. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

World University root node

School of Mechanical Engineering

Journal of Pharmaceutical Negative Results

Development of a Hose Reel (Swivel Joint) Suitable for High Suction Pressures

In the past two years 2020 and 2021, the COVID-19 outburst has had a serious effect on human life. The effects and side effects of COVID-19 are being stroked in almost every discipline relevant to survival and development. The healthcare system was in a problematic situation during this tough time in pandemic situation all over the world. One of the many precautions and protections used to break the chain of spreading of this virus is wearing a mask and keeping safe distance. In a network of smart cities where entirely public spaces are monitored by Closed-Circuit Television (CCTV) cameras, we are offering a strategy in this study that restricts the spread of COVID-19 by identifying people not wearing mask. The network alerts the proper authority whenever a person without a mask is discovered. It is believed that our research will help many countries throughout the world stop the spread of this infectious disease. We've examined this on 200 real people in the present, with a 100% success rate. It is also observed that when more than one person in front of CCTV success rate reduced exponentially © 2022 IEEE.

School of Computer Engineering & Technology

2022 IEEE Pune Section International Conference, PuneCon 2022

Artificial Intelligence and Deep Learning based Face Mask Detection System using Generic Camera

Hepatitis C is a liver disease whose infection is often silent and can lead to fibrosis or cirrhosis if it becomes chronic and goes undetected. It is generally spread through blood-to-blood contact. Hence, it is important to accurately classify blood donors as healthy blood donor or a person having Hepatitis C infection before blood transfusion happens. Nowadays, machine learning has been used in various domains including health care for accurate and fast results. This paper proposes a framework for accurate classification of blood donors using five machine learning algorithms, namely logistic regression, support vector machine, k-nearest neighbours, decision tree, and neural networks. The backward elimination technique is implemented for feature selection to improve the classification accuracy. The experimental results show that k-nearest neighbours perform better with the testing accuracy of 94.3% than other classifiers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

A Comparison of Machine Learning Techniques for Categorization of Blood Donors Having Chronic Hepatitis C Infection

This paper presents a novel technique for planning and execution of Wireline Formation Testing (WFT) jobs using recent applications of machine learning. WFT measurements provide a link between the static petrophysical measurements and dynamic rock-fluid properties for enhanced formation evaluation. However, despite the availability of newer generation tools for these services, there are still obstacles related to formation test job design and real-time optimization. Skilled geoscientists traditionally use quicklook analysis and theoretical models in designing WFT jobs to acquire quality dataset. In practice, operators have access to a detailed data repository. However, the learnings from historical jobs may be overlooked during dynamic real time operations and engineers may typically regress to simpler or experienced based models. Some of the methods are self-made to reduce time and energy in data acquisition. Digitization and automation of data provide an opportunity for application of data analytics to support these real-time measurements for an objective evaluation of the dynamic data. In this paper we propose use of unsupervised machine learning models, by which routinely recorded real-time measurements can be used to optimize the test design and real-time operations. Basic principles of interpretation of WFT are used to develop quality parameters and classification based unsupervised machine learning models (Self organized maps, K-means and Hierarchical Clustering). These machine based recommendations can aid in designing and optimizing the real-time job while ensuring quality of the results. In this way, highest quality testing results can be acquired to improve the integration of the dynamic data into the petrophysical analysis. This also will enable standards to be established for real-time data acquisition that can save testing time while improving data and quality. Results show that machine learning models can be a powerful tool to develop a machine based recommendation systems for classification of WFT responses that are dependent on petrophysical parameters. © 2022, Offshore Technology Conference. All rights reserved.

School of Petroleum Engineering

Proceedings of the Annual Offshore Technology Conference

Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods

Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Indonesian Journal of Electrical Engineering and Computer Science

Comparison of feed forward and cascade forward neural networks for human action recognition

Past decade has shown an overwhelming use of social media. Inspite of multi-fold benefits of using social media platforms, it is being misused due to increasing prevalence of inappropriate content in form of hate speech and abusive language shared on these platforms. Hate speech content is one such form of content that has shown dramatic increase in recent years. Automated techniques like AI and NLP have reported significant performance in detection of hate speech content. In order to extract the textual properties from online content that help in the detection of hate speech, feature extraction and representation using NLP are essential. There is an increasing trend in academia and industry to the use of pretrained neural network language models for hate speech and offensive content detection. This paper presents the performance of important feature extraction techniques: TD-IDF, GloVe, FastText and BERT for binary classification and multiclass classification of hate speech with Convolutional Neural Network (CNN) architecture as the base network for all the techniques. © 2022 IEEE.

School of Electronics & Communication Engineering

2022 International Conference on Signal and Information Processing, IConSIP 2022

Feature Representation Techniques for Hate Speech Detection on Social Media: A Comparative Study

With the advent of driverless car systems, automatic traffic sign recognition is must. The proposed deep learning model performs well in recognising forty-three traffic signs. The model also can be ported on Jetson-Nano device resulting into a portable standby device. © Grenze Scientific Society, 2022.

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

Deep Learning Approach for Traffic Sign Recognition

The inescapable utilization of Internet and social media has opened doors of social event continuous data across the globe in an exceptionally limited capacity to focus time. Social media networks are becoming a focal point for disseminating news that take the form of fake news and rumors. A rumor refers to a statement or a post that is false and is circulated fast on social media without verifying its truthfulness. Rumors have an adverse effect not only on an individual, but also it extends to the entire society. While manual detection and screening of rumors are becoming a dire need, automatic detection of rumors is an emerging research with various machine learning algorithms. The paper summarizes the utility of various machine learning algorithms for automatic rumor detection. Various steps like feature engineering and classification are also thoroughly studied and detailed in this paper. This paper coins to further research directions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Machine Learning Approaches for Rumor Detection on Social Media Platforms: A Comprehensive Survey

Kinship verification based on facial images is a recent and challenging problem in computer vision and machine learning. It has many real-world potential applications including tracing of missing children cases, social media image analysis, and image annotation. The existing methods do not focus on probability distribution differences between kinship facial images; that is why the performance of these methods is poor. The deep learning methods required a large amount of data. Practically, parents and their children share common features. To take into account this transfer learning based framework is proposed. The extracted hand-crafted features are given to a transfer learning framework that transfers the knowledge acquired from facial images of parents to children and vice versa. We evaluated the proposed methods on standard kinship datasets and experimental results showed that the proposed method outperforms in terms of accuracy and computational efficiency as compared to state of art methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Innovation, Systems and Technologies

Feature Based Transfer Learning for Kinship Verification

Social Media has become a vital component of every individual's life in society opening a preferred spectrum of virtual communication which provides an individual with a freedom to express their views and thoughts. While virtual communication through social media platforms is highly desirable and has become an inevitable component, the dark side of social media is observed in form of detrimental/objectionable content. The reported detrimental contents are fake news, rumors, hate speech, aggressive, and cyberbullying which raise up as a major concern in the society. Such detrimental content is affecting person’s mental health and also resulted in loss which cannot be always recovered. So, detecting and moderating such content is a prime need of time. All social media platforms including Facebook, Twitter, and YouTube have made huge investments and also framed policies to detect and moderate such detrimental content. It is of paramount importance in the first place to detect such content. After successful detection, it should be moderated. With an overflowing increase in detrimental content on social media platforms, the current manual method to identify such content will never be enough. Manual and semi-automated moderation methods have reported limited success. A fully automated detection and moderation is a need of time to come up with the alarming detrimental content on social media. Artificial Intelligence (AI) has reached across all sectors and provided solutions to almost all problems, social media content detection and moderation is not an exception. So, AI-based methods like Natural Language Processing (NLP) with Machine Learning (ML) algorithms and Deep Neural Networks is rigorously deployed for detection and moderation of detrimental content on social media platforms. While detection of such content has been receiving good attention in the research community, moderation has received less attention. This research study spans into three parts wherein the first part emphasizes on the methods to detect the detrimental components using NLP. The second section describes about methods to moderate such content. The third part summarizes all observations to provide identified research gaps, unreported problems and provide research directions. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.