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

Machine learning has revolutionized medical technology through tiny machine learning (TinyML). With TinyML, machine learning models can be deployed on devices with minimal resources. Some examples are microcontrollers, wearables, and Internet of Things devices. TinyML creates unparalleled prospects for personalized, real-time healthcare applications by utilizing the power of edge computing and AI. This chapter aims to examine TinyML's potential to revolutionize medical monitoring and diagnosis by exploring its potential use in healthcare. We emphasized TinyML's tremendous influence on healthcare through many critical sectors, such as wearable devices, remote patient monitoring, drug development, diagnosis, and therapy. This chapter considers the challenges and opportunities associated with implementing TinyML in healthcare. The study continues with a look at the future of TinyML in healthcare. The work focuses on several TinyML use cases in the healthcare sector, emphasizing the necessity of researchers, practitioners, and policymakers working together to develop effective security solutions. In addition to highlighting the problems and issues in the field of TinyML in healthcare, the proposed study will also contribute significantly to future research and development.

Engineering and Technology

TinyML for Edge Intelligence in IoT and LPWAN Networks 2024

<a href="https://www.sciencedirect.com/science/article/pii/B9780443222023000191">TinyML applications and use cases for healthcare</a>

Web services are a simple but effective method of connecting to network resources. To offer end-users data from their virtualized resources, all modern organizations provide web service interfaces. With the increased usage of multi-channel communication modes (mobile phones, tablets, and laptops), web services are excellent for guaranteeing seamless data flow across all platforms. While Web services address many difficulties, there are a few issues that consumers or requesters have when they locate a web service that satisfies their requirements. With the increased use of online services, there is a greater demand for an effective and almost flawless web service recommendation system. Cold start, sparsity, and overspecialization are some of the problems with recommender systems. To address this issue, in this paper, collaboration filtering and demographic filtering based on models are proposed as a new approach. Using this technique, recommendations can be generated based on the demographic characteristics of users, such as age, gender, occupation, etc. We propose using web services based on the results of both tests. This paper attempts to propose a solution to the cold start problem by using a hybrid filtering method.

Recent Advances in Material, Manufacturing, and Machine Learning

Web service recommendation based on user's demographic attributes

Mutation testing is devised for measuring test suite adequacy by identifying the artificially induced faults in software. This paper presents a novel approach by considering multiobjectives-based optimization. Here, the optimal test suite generation is performed using the proposed water cycle water wave optimization (WCWWO). The best test suites are generated by satisfying the multiobjective factors, such as time of execution, test suite size, mutant score, and mutant reduction rate. The WCWWO is devised by a combination of the water cycle algorithm (WCA) and water wave optimization (WWO). The hierarchical attention network (HAN) is used for classifying the equivalent mutants by utilizing the MutPy tool. Furthermore, the performance of the developed WCWWO+HAN is evaluated in terms of three metrics—mutant score (MS), mutant reduction rate (MRR), and fitness— with the maximal MS of 0.585, higher MRR of 0.397, and maximum fitness of 0.652.

Fulltext

International Journal of Swarm Intelligence Research

Multi-Objective Optimization Model and Hierarchical Attention Networks for Mutation Testing

World University root node

Smart Innovation, Systems and Technologies

Agricultural App Development Using Machine Learning and Deep Learning: A Review

School of Mechanical Engineering

Lecture Notes in Networks and Systems

Slider-Crank Four-Bar Mechanism-Based Ornithopter: Design and Simulation

Detecting Soil Fertility for Agriculture Land Using IOT and Prediction of Crops Using Machine Learning Algorithm

In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images. © 2023 Baqiyatallah University of Medical Sciences. All rights reserved.

International Journal on Recent and Innovation Trends in Computing and Communication

Lightweight MobileNet Model for Image Tempering Detection

Image Security is playing a significant role in the field of communication system and Internet. Cryptography is initially developed to protect the interaction between two parties known as sender and receiver in the presence of malicious third parties called as attackers by using the process known as encryption. The given algorithm transforms the plain image into cipher image if the same key is used. The encryption is done by using various algorithm, transformation and many more techniques to secure the image. This article presents a novel image encryption technique. This AES algorithm is incredibly reliable and helps to maintain images safe from malicious attack. The sender has an image, and this image is split into several segments. After the encryption and decryption, the receiver merges those images. In the proposed system, keys will generate manually. This research paper presents an effective method for encrypting & decrypting data on the cloud. Through this method, users can store data securely in the cloud without being damaged or corrupted. This system has an idea about authentication. Receivers' digital signature is stored on the cloud to decrypt the image by generating digital signature that match with the stored one. If both signatures match, then it can be user verified and authenticated. © 2023 IEEE.

International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023 - Proceedings

Image Security Enhancement on Cloud Storage using AES Algorithm

World is surrounded with key buzz emerging from technological use by common man. Technology lends a hand to achieve the fitness by providing assistance. Walking is predominantly and usually preferred exercise by mass. Walking pattern analysis or gait analysis expands the probable zone for researcher to contribute in gaining and maintaining fitness for common people. There are number of parameters influencing walking pattern of the person. The motive of this study is to verify the relation among various spatiotemporal parameters used in gait analysis. Paper tests and comments on correlation among selected spatiotemporal parameters on the basis of analysis performed on 50 healthy participants from Pune, India. Contributing participants are in the age group of 20–75 years combining male and female members. GAITRite Walkway is used to perform recording of the gait pattern. Pearson’s correlation coefficient is used for the testing the correlation within two selected parameters. The correlation coefficient varies within range of −1 to +1, indicating positive, negative, or no correlation. Correlation results are verified through application of them in classification of subjects in two age groups. Support vector machine, logistic regression, and K-nearest neighbor machine learning approaches are tested. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Perceiving Correlation Among Spatiotemporal Gait Parameters and Verifying Its Relation Using Machine Learning Classification Technique Pilot Study for Indian Population

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.