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

Cosmology is the study of the universe comprising stars and galaxies. Advancement in the telescope has made it possible to capture high -resolution images, which can be analysed using machine learning (ML) algorithms. This paper classifies the star galaxy dataset into two classes: star and galaxy using ML algorithms and compares their classification performance. It is observed that random forest provides better accuracy of 78\% as compared to other ML classifiers. Further to improve the classification accuracy, we proposed a CNN (Convolution Neural Network) model and achieved an accuracy of 92.44\%. Since the CNN model itself extracts the characteristics, it exhibits superior classification accuracy.

School of Computer Engineering & Technology

Engineering and Technology

World University root node

INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY

STAR-GALAXY CLASSIFICATION USING MACHINE LEARNING ALGORITHMS AND DEEP LEARNING

With lockdowns and overburdened medical facilities during the Covid-19 pandemic, technology and computing paradigms play a vital role in providing remote healthcare solutions. We assess as how the existing computing paradigms could be deployed to prevent the spread of the disease, expedite the diagnosis, and facilitate remote monitoring of patients to reduce the burden on the overstretched medical facilities. The chapter will include a literature survey based on the articles published in but not limited to Science Direct, Google Scholar, Research Gate, and PubMed. This study weighs the pros and cons of using different paradigms in diverse scenarios and provides recommendations for efficient healthcare solutions. The chapter also focuses on the issues related to edge computing, such as resource provisioning, energy preservation, etc. In this era of technology, edge computing can be used to enhance the efficacy of healthcare solutions without burdening healthcare professionals and facilities. In this chapter, experimentation will focus on deploying intelligent techniques in the edge computing paradigm. © 2023 Elsevier Inc. All rights reserved.

Intelligent Edge Computing for Cyber Physical Applications

Applicability of edge computing paradigm for Covid-19 mitigation

Corona viruses are a genus of viruses that infect vertebrate and birds, causing a variety of diseases. They induce a variety of respiratory problems in people. This study investigates COVID-19 infection rates and estimates the pandemic’s scope, recovery rate, and death rate. We used Support Vector Machine (SVM), Random Forest, Decision Tree, K-nearest neighbor, and other well-known machine learning and mathematical modeling approaches. For disease diagnosis, study used three unique disease data sets (Asthma, Diabetes, and AIDS) provided there in UCI machine learning repository. After getting positive results, we applied these algorithms on COVID-19 data set. We used several categorization algorithms, each with its own set of benefits. The study’s findings support the use of machine learning in disease early detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

Applying ML on COVID-19 Data to Understand Significant Patterns

The research of brain tumor classification by hand is a time-consuming operation with the potential for human mistakes. An accurate and automatic brain tumor classification technique is provided in this approach. To segment the brain part first, a thresholding approach is performed, followed by a morphological operation. Because the brain Magnetic Resonance Imaging(MRI) dataset is limited, the Convolutional Neural Network's (CNN) 'VGG19' transfer learning network is used. With little weights and photos of benign, glioma, and meningioma, VGG19 classification layer is substituted by the softmax layer. After the model is created, it is saved and using Flask, a WebApp is created that will help us to detect the brain tumor using Hybrid Multi-Cloud and Machine Learning Operations(MLOps) tools. Identity and Access Management (IAM) is used to keep the data secure. This end-to-end system can be used for object detection applications in hospitals and local medical clinics. © 2022 IEEE.

2022 IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

Brain Tumor Detection using MLops and Hybrid Multi-Cloud

Rough set theory provides simple and lightweight technique to classify mobile nodes in mobile grids. Mobile devices are resource constrained and hence rough set theory can be used for classification. The purpose of our work is to devise classification approach using rough set theory to classify nodes in a mobile grid. Here decision table contains node features which are used to classify nodes into Positive, Negative and Boundary sets. Proposed approach can be deployed on Android devices with anytime-anywhere computing paradigm. The novelty of our approach is that we applied non-computationally intensive rough set theory for efficient classification of nodes.

NODE CLASSIFICATION MODEL FOR ON-THE-FLY COMPUTING BASED MOBILE GRIDS USING ROUGH SET THEORY

Enterprise optimization has become a major target in the pharmaceutical industry because of the increasing pressure for staying relevant in the worldwide commercial center. The ever-increasing competition, changing regulatory requirements, new emerging technologies along with a lot of other factors are pushing pharmaceutical manufacturers towards Enterprise optimization for the purpose of fast-Tracking innovation to boost their productivity and product quality. The objectives of Enterprise optimization include profit maximization, asset utilization, management of inventory levels, and coordinated decision making. These business exercises are moderately troublesome because of the association of various risk factors. In this paper, applications that have relevance to Enterprise optimization have been reviewed, with special attention to innovative drug development life cycles which are prone to substantial risks. This paper proposes a decision-making methodology with the identification of uncertainties and risks based on the Fuzzy AHP approach. © 2021 IEEE.

2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

Enterprise Optimization in Pharmaceutical Industry

A mobile grid (MG) consists of interconnected mobile devices which are used for high performance computing. Fault tolerance is an important property of mobile computational grid systems for achieving superior arrangement reliability and faster recovery from failures. Since the failure of the resources affects task execution fatally, fault tolerance service is essential to achieve QoS requirement in MG. The faults which occur in MG are link failure, node failure, task failure, limited bandwidth etc. Detecting these failures can help in better utilisation of the resources and timely notification to the user in a MG environment. These failures result in loss of computational results and data. Many algorithms or techniques were proposed for failure handling in traditional grids. The authors propose a checkpointing based failure handling technique which will improve arrangement reliability and failure recovery time for the MG network. Experimentation was conducted by creating a grid of ubiquitously available Android-based mobile phones.

International Journal of Grid and High Performance Computing

Application Checkpointing Technique for Self-healing from Failures in Mobile Grid Computing

Mobile grid (MG) is emergi ng as a new computing paradigm due to the ubiquitous availability of mobile devices. With the advancement in the capability of these devices, computationally intensive tasks can be executed using a peer-to-peer grid of such devices. MG can provide an edifice to execute parallel computationally intensive tasks. Key challenges that crop up while computing on a MG are resource constrained environment, inefficient resource allocation, high failure probability, etc. As a result, selection of appropriate nodes for task execution becomes critical for successful execution of the application. In this paper, we propose an efficient resource allocation model (ERAM) which provides resource allocation with failure handling. We created a MG comprising of Wi-Fi Direct connected Android smartphones. Different scenarios are considered for the purpose of experimentation. Our approach performs well with respect to application completion time, % battery consumption and recovery time from failure in comparison with existing techniques.