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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Nemade, Rohan Jha, Suraj Anand, Harsh Chandrasekaran, Saravanan Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural Networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. That is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. This attempt to build and train a CNN model using galaxy images as our domain. The Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. The Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. The Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. The Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. The Enhanced CNN outperforms the various existing state-of-the models. © 2024 IEEE.

关键词： Convolutional neural networks

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Cloud-Driven Machine Learning with AWS: A Comprehensive Review of Services 2

Cloud-Driven Machine Learning with AWS: A Comprehensive Revi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Ravindranathan, Manya K. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350306415

With the rise of cloud computing, people now have 24/7 online access to IT resources, which is a transformational force. Amazon Web Services (AWS) is a market leader in the field of cloud service providers, providing a broad range of platform, software, and infrastructure services. In this review article, we explore the domain of cloud-driven Machine Learning (ML) with an emphasis on the extensive resources and support provided by AWS. The ability of the AWS ecosystem to provide scalable and effective ML applications is highlighted in this study. It looks at the range of AWS services created to make building, training, and deploying cloud-based ML applications easier. Our analysis includes an AWS Machine Learning as a Service (MLaaS) product categorization, illuminating the range of resources and options available to ML practitioners. We also take into account findings from comparable studies that have used AWS services for a range of purposes. In conclusion, this article offers a thorough introduction for individuals wishing to explore the world of cloud- driven ML using AWS. It provides insightful information on AWS services, their uses, and how they are influencing ML in the cloud. © 2024 IEEE.

关键词： AWS AWS Free Tier Services Cloud Computing Distributed Computing Machine Learning as a Service

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A PSO based Computation Offloading Model in Edge Computing 2

A PSO based Computation Offloading Model in Edge Computing

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Rajwar, Dipankar Kumar, Dinesh National Institute of Technology Jamshedpur Department of Computer Science and Engineering Jamshedpur India

ISBN: (纸本)9798350383652

Limitations of traditional cloud computing have fueled the rise of edge computing. This technique addresses the latency and real-time processing issues that come with using a typical cloud, and it operates at the edge of the network. While the applications of edge computing have grown, it's requiring a more sophisticated offloading strategy. Considerations needed to be made for elements including network circumstances, computing complexity, and energy efficiency. In this work, a Particle Swarm Optimization (PSO) based offloading model is proposed to optimize computation offloading in edge computing. PSO dynamically distributes the tasks for optimal efficiency and highlights its effectiveness in minimizing system costs, reducing task execution time and energy consumption, that improve overall performance. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal Fusion Framework for Precise Multimedia Authentication

LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal F...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Samesh Enathe, V.P. Chandra Mouli, S. Dheepthi, R. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing ubiquity of deepfake technology presents a serious risk to the legitimacy and reliability of multimedia material across a number of sectors, from identity verification to news distribution. Current deepfake detection methods frequently have trouble identifying minor manipulations and are unable to keep up with the latest generation techniques, which creates a serious vulnerability in the defence against the improper use of synthetic media. Using Dense Convolutional Neural Networks (Dense CNN) and Multi-Modal Fusion, this study presents a novel method for deepfake detection. Our Dense CNN architecture which draws inspiration from Dense Net improves sensitivity to complex manipulations while optimizing feature reuse through dense connectivity patterns, thereby mitigating the shortcomings of existing systems. Our suggested approach dynamically combines temporal and visual modalities, enhanced by Multi-Modal Fusion, to offer a comprehensive contextual knowledge that enhances detection accuracy. After thorough tests on several datasets, our method performs exceptionally well and is particularly good at identifying advanced deepfake variations. Our suggested methodology improves the state-of-the-art in deepfake identification by addressing the shortcomings of current systems and providing a reliable response to the urgent problems brought on by the malicious usage of synthetic media in practical applications. Adding Large Language Models (LLMs) is essential to improving the accuracy of the system. To add another level of scrutiny, LLMs are deliberately used to characterize and analyze portions of multimedia information that are vulnerable to manipulation. © 2024 IEEE.

关键词： Convolutional neural networks

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A Review on Sentiment Analysis and Opinion Mining 8th

A Review on Sentiment Analysis and Opinion Mining

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Verma, Umang Singh, Pardeep Department of Computer Science and Engineering National Institute of Technology Hamirpur Hamirpur India

ISBN: (纸本)9789819766741

Sentiment analysis (SA) is the process of computationally recognizing and classifying the opinions conveyed within a piece of text or some other forms of information. This paper underscores the significance of sentiment analysis (SA) that serves as a vital tool for businesses, aiding in customer understanding and facilitating informed decision-making. The study includes a review of the literature on many machine learning methods in addition to deep learning models. Furthermore, the paper delves into the utilization of nature-inspired algorithms for feature selection in the context of sentiment analysis (SA), and examples include Particle Swarm Optimization, Firefly Algorithms and Genetic Algorithms among others. These nature-inspired algorithms are chosen to iteratively search for optimal feature subsets, aiding in enhancing the overall efficacy of sentiment analysis methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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Real Time Medicinal Plant Identification & Classification Using Random Forest Algorithm 10

Real Time Medicinal Plant Identification & Classification Us...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Nidhya, R. Imambee, K. Geethanjali, M. Anusha, M. Lakshmi Vyshnavi, I. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Angallu India

ISBN: (纸本)9798350353068

In recognition of their therapeutic qualities, medicinal plants have been used for millennia, greatly influencing traditional medical systems all over the world. But classifying and identifying these plant species by hand can be laborious and prone to mistakes. In this study, we suggest a unique method for identifying and detecting species of medicinal plants using machine learning techniques specifically, Random Forest. As part of the method we used, we gathered a broad dataset with pictures of eighty distinct kinds of medicinal plants. Preprocessing methods are used to standardize characteristics and improve image quality. Next, the Random Forest method is used for classification, taking advantage of its capacity to manage intricate datasets and yield comprehensible outcomes. The outcomes of the experiments show how well the suggested method works for correctly identifying and identifying medicinal plant species. The model demonstrates its promise for practical applications in domains including pharmacology, botany, and traditional medicine by achieving a high level of classification accuracy of 98%. Additionally, because of the Random Forest algorithm's interpretability, researchers may get insights into the factors that influence categorization judgments, which helps them better understand the traits that set distinct plant species apart. © 2024 IEEE.

关键词： Image classification

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An Empirical Study of Nature-Inspired Algorithms for Feature Selection in Medical Applications

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Annals of Data Science 2024年 1-46页

作者： Arora, Varun Agarwal, Parul Department of Computer Science and Information Technology Jaypee Institute of Information Technology Sector-62 Noida201301 India

Nature-inspired algorithms (NIA) are proven to be the potential tool for solving intricate optimization problems and aid in the development of better computational techniques. In recent years, these algorithms have raised considerable interest to optimize feature selection problems. In literature, NIA is found to select relevant features among available features in the diagnosis of many chronic diseases. In this paper, a comprehensive review of existing nature-inspired feature selection techniques is presented. Along with this, the fundamental definitions of feature selection and the usage of NIA to optimize feature selection are shown. We have given a review showcasing the NIA application for selecting feature subsets from the available features in the domain of medical applications. The paper reviews and analyzes numerous relevant papers from 2008 to 2022 on feature selection through NIA on biomedical applications. Moreover, to find the best optimization algorithm for feature selection, we have conducted experiments among four well-known nature-inspired algorithms on ten benchmark datasets of the biomedical domain for classification. We have reported results on various state-of-the-art evaluation measures and presented the convergence graphs for analysis. Based on the average rank of fitness values, Particle Swarm Optimization is found to be better than Harris Hawk Optimization, Grey Wolf Optimization, and Whale Optimization. In this paper, we have also presented some open challenges of this research area to guide researchers as well as experts of computational intelligence for future work. The paper will help future researchers understand the use and implementation of nature-inspired algorithms for feature selection in the medical domain. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature Selection

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Fog-Driven Heart Attack Prediction from Wearable Edge Devices

Fog-Driven Heart Attack Prediction from Wearable Edge Device...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Ravindranathan, Manya K. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350364088

Cardiovascular diseases, such as heart attacks, are a significant global health concern, responsible for a great deal of annual mortality. The introduction of wearable edge devices with advanced sensors has enabled continuous realtime monitoring of physiological data. This research aims to use fog computing, a decentralized computing approach, to introduce a system for predicting heart attacks based on data from these wearable devices. The system features a custom Deep Learning (DL) model at the fog layer, allowing realtime analysis of health data without relying on centralized cloud processing. The model obtained an accuracy of 90% which is a significant improvement than the traditional models. This work contributes to the field of cloud computing in healthcare, showcasing the potential of fog-based solutions for timely and context-aware heart attack prediction. Successful outcomes could lead to scalable and decentralized healthcare systems, supporting proactive cardiac care and im-nrovina overall patient well-being. © 2024 IEEE.

关键词： Fog

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Virtual dressing room using Deep learning 2

Virtual dressing room using Deep learning

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Kurtakoti, Sangareddy B. Kavana, V. Manasa, T.Y. Mayuk Gowda, A. Shashank, C.B. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (纸本)9798350383867

Clients Fashion in the twenty-first century has become intertwined with daily life, varying based on factors like physical stature, gender, and social context. Despite the rise of online shopping, the traditional in-store experience remains dominant, but challenges like limited fitting roomsand privacy concerns persist. To address this, our aim is to develop a highly realistic virtual dressing system, leveraging deep learning techniques, particularly Generative Adversarial Networks (GAN). This system predicts the semantic layout of areference image, determines whether to generate or preserve image content accordingly, resulting in lifelike try-on experiences. Through virtual reality, customers can explore a wide array of clothing items without physically wearing them, saving time and effort. Moreover, retailers stand to benefit by streamlining inventory management and conserving space. ©2024 IEEE.

关键词： Generative adversarial networks

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Exploring The Trade-off between Efficient Service Placement Time and Optimized Fog Colonies Utilizing Advanced Genetic Algorithm with Diverse Network Topologies 24

Exploring The Trade-off between Efficient Service Placement ...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Verma, Nilesh Kumar Khetavath, Jairam Naik Department Of Computer Science And Engineering National Institute Of Technology Raipur Raipur India

ISBN: (纸本)9798400708329

Optimized Fog colony is one of the aid of fog computing, consisting of fog devices, enable efficient management of large fog domains. Well-designed fog colonies can operate independently, resulting in effectively resource utilization and improved system performance. However, there is a lack of optimization approaches for organizing fog devices into colonies and determining which fog colony should be used to stipulated and execute the services required by an IoT application is its primary challenge known as the "efficient service placement time"(ESPT). Contrary to the prevalent practice of treating ESPT as a single objective optimization problem, which often proves insufficient for accommodating the escalating complexities of engineering practice, our study takes a different approach. We present a modeling framework that considers the ESPT in fog computing as a constrained multi-objective optimization problem. Our secondary objective is to minimize the response time of services within the fog colonies. We employ the advanced elitist non-dominated sorting genetic algorithm (MS-NSGA) to optimize the fog colonies for constrained multi-objective service placement problem. We compare our experiment for three different network topologies with various configuration. The experimental result demonstrate the best trade-off between service placement time and proposed scheme for Barabasi-Albert network topology. Additionally, the results also indicate a clear trend towards reduced response time. © 2024 ACM.

关键词： Fog

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