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A Comprehensive Study of Resource Provisioning and Optimization in Edge Computing

computers, Materials & Continua 2025年第6期83卷 5037-5070页

作者： Sreebha Bhaskaran Supriya Muthuraman Department of Computer Science and Engineering Amrita School of ComputingBengaluru560035Amrita Vishwa VidyapeethamIndia

Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge *** its importance,integrating Software Defined Networks(SDN)for enhancing resource orchestration,task scheduling,and traffic management remains a relatively underexplored area with significant innovation *** paper provides a comprehensive review of existing mechanisms,categorizing resource provisioning approaches into static,dynamic,and user-centric models,while examining applications across domains such as IoT,healthcare,and autonomous *** survey highlights challenges such as scalability,interoperability,and security in managing dynamic and heterogeneous *** exclusive research evaluates how SDN enables adaptive policy-based handling of distributed resources through advanced orchestration ***,proposes future directions,including AI-driven optimization techniques and hybrid *** addressing these emerging opportunities,thiswork serves as a foundational reference for advancing resource management strategies in next-generation cloud,fog,and edge computing *** survey concludes that SDN-enabled computing environments find essential guidance in addressing upcoming management opportunities.

关键词： Cloud computing edge computing fog computing resource provisioning resource allocation computation offloading optimization techniques software defined network

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Streamlining Financial Predictions: A Web Application for Stock, Cryptocurrency, and Market Sentiment Analysis 4

Streamlining Financial Predictions: A Web Application for St...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vivekanand, Chettiyar Vani Kumar, P. Amizhthini, A. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. This study will propose a web application that uses advanced time series models to try and overcome such challenges. The system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models;Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis;Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting;Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets;and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. This solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. The goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model. © 2025 IEEE.

关键词： Ethereum

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CAAM- CNN With Autoencoder Attention Mechanism for Recommendation System to Improve Trust in E-commerce Industry 2

CAAM- CNN With Autoencoder Attention Mechanism for Recommend...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sethi, Vikas Kumar, Rajneesh Mehla, Stuti Department of Computer Science and Engineering Ambala Mullana India Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798350383287

COVID-19 crisis expedited the spread of e-commerce, moving beyond luxury goods and services and towards new businesses, product categories and basic necessities that are important to many people. Customers can have access to a wide range of products from the comfort and security of their homes but still have trusting issues while purchasing online. Online feedback either comments or ratings given by customers, has the power to affect how consumers make purchasing decisions. This meteoric expansion of digitized information and number of users over internet brought the use of recommender systems as a significant solution by offering more proactive and individualized information services and enable users to quickly narrow their choices and make wise ***, the majority of existing recommendation models have poor accuracy issues as a result of data scarcity and cold start issues because recommender systems only have a limited amount of explicit data and this also lead to trust issue where users are fear of being taken advantage due to misleading information on the e-commerce websites. To solve this problem and provide quality recommendations, a new trust-based approach CNN with Autoencoder Attention Mechanism (CAAM) is proposed. CAAM uses static and dynamic trust combined with the user-item preferences and pass through the auto-encoder to reduce noise and make encoded features visible. After that convolution is used to combine encoded features with user and item features in latent space to provide nonlinear mapping and increase the reliability of features and to improve the recommendations which ultimately lead to improve the trust of people over *** Epinions dataset is used to evaluate the concept in a number of measures. © 2024 IEEE.

关键词： Recommender systems

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Revolutionising facility layout: a case study of dynamic facility layout in cable production

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International Journal of Web engineering and Technology 2024年第3期19卷 267-309页

作者： Vineetha, G.R. Rasheedali, Shiyas Chekkot Division of Computer Science and Engineering Department of Mechanical Engineering Cochin University College of Engineering CUSAT Kuttanadu Kerala Alappuzha India

In the competitive landscape of globalised markets, businesses must prioritise cost reduction for sustained competitiveness. This study delves into the dynamic facility layout problem (DFLP) within a cable production company in Kerala, emphasising adaptability to changing production demands. Addressing material handling costs and rearrangement expenses, the research evaluates the efficacy of the current static layout and explores the benefits of transitioning to a dynamic layout. The case study reveals potential cost savings through the strategic restructuring of machine arrangements. The innovative machine learning-based genetic algorithm (ML-GA) integrates machine learning algorithms, genetic algorithms, and a local search method, offering a cutting-edge solution to dynamic facility layout challenges. By considering demand variability and relocation costs, the study provides insights for informed decision-making, emphasising the significance of material flow patterns. This research contributes to enhancing efficiency and profitability, providing practical implications for businesses navigating the complexities of modern manufacturing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Plant layout

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An Efficient Crypto Based Ransomware Prevention Method in Android Platform 3

An Efficient Crypto Based Ransomware Prevention Method in An...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Kulshreshtha, Vyom Motwani, Deepak Sharma, Pankaj Amity University Computer Science & Engineering Madhya Pradesh Gwalior India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350384598

A relatively new kind of intrusion attack called ransomware aims to demand a ransom from its target. Ransomware attacks are carried out by cybercriminals to financially affect victims by damaging their gadgets. Due to Android's widespread use worldwide, assaults on smartphones using this operating system are growing exponentially. While there are many different kinds of ransomware attacks, this article mainly concentrates on crypto-ransomware as, once the victim's files are encrypted, it renders data irretrievable. We provided a thorough examination based on machine learning crypto-ransomware detection techniques in this study. This detection must be successful to stop the attack from achieving its objective. In this work, we provide a classification scheme for the security level of crypto-ransomware for detection as well as prevention. This framework includes data collection, preprocessing, feature extraction and selection, or classification, and the application of multiple machine learning techniques. We assessed the accuracy, recall, F1-score, and precision scores using a single Android ransomware dataset. The comparison findings show that the Support Vector Machines (SVM) classifier performs better than competing approaches across all performance metrics, with a 99.5% accuracy rate in detecting crypto Android ransomware. Unlike most previous studies that provide an analysis of crypto-ransomware, this is a thorough assessment that focuses on detection techniques. © 2024 IEEE.

关键词： Android malware

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Digitalization of Objects using Augmented Reality and Machine learning - A Secure and Seamless Approach

Digitalization of Objects using Augmented Reality and Machin...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Vashishtha, Srishti Jain, Sarthak Sujal Singh, Sunny Gupta, Vasu Arora, Shaveta Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India Computer Science and Engineering The NorthCap University Gurgaon India

ISBN: (纸本)9798350375237

With the ascent of digitalization and artificial intelligence, there is an increasing demand for intelligent systems across industries. Machine Learning (ML) drives this evolution by enabling computers to learn autonomously without explicit programming, thereby amplifying the capabilities of digital systems. Moreover, ML enhances cybersecurity by aiding anomaly detection, threat identification, and pattern recognition, bolstering digital security measures. Augmented Reality (AR) engines, powered by ML advancements, transform human-computer interaction. AR's capabilities extend beyond projecting digital imagery into the physical world;it now accurately identifies individuals and objects, removes backgrounds seamlessly, and transmits results securely. Our innovative app revolutionizes AR by seamlessly transposing physical objects into digital environments by using BASNet for Scale Invariant Feature Transformation (SIFT) and detecting objects using YOLO v7. By eliminating the need for third-party services and facilitating direct scanning of physical objects into digital environments, the app ensures optimal pixel quality. Moreover, it prioritizes cybersecurity by employing secure transmission protocols and encryption standards to safeguard data during transfer. This seamless fusion of AR and ML not only enhances user experiences but also fortifies digital security, marking a significant step forward in technological advancement. © 2024 IEEE.

关键词： Machine learning

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Improved dynamic threshold based fuzzy logic to detect and classify diabetic retinopathy

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Multimedia Tools and Applications 2025年第6期84卷 3117-3135页

作者： Bhimavarapu, Usharani Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India

Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness globally, with its severity classified into non-proliferative and proliferative stages. Effective detection and segmentation of multiple lesions are crucial for accurate diagnosis and management. This study presents a novel fuzzy logic-based model for the segmentation of multi-lesion diabetic retinopathy, which addresses the complexities associated with varying severity levels. The proposed multi-phase approach employs a comprehensive fuzzy inference system to enhance the detection and classification of diverse lesions related to diabetic retinopathy. Evaluated using three benchmark datasets—Messidor-2, Eyepacs, and IDRiD—proposed model achieved an accuracy of 99.57% on the Messidor-2 dataset and consistently high precision, recall, and F1-scores exceeding 99% across all datasets. Comparative analyses reveal that the proposed model significantly outperforms existing approaches, with an accuracy increase of 2.45% in multi-lesion segmentation metrics. Additionally, statistical validation demonstrates the robustness of our findings, with p-values less than 0.001. This study emphasizes the potential of fuzzy logic in enhancing diagnostic capabilities for diabetic retinopathy through precise lesion segmentation, ultimately aiding in improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fuzzy inference

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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Goyal, Diya Guru Prasad, M.S. Garg, Ishika Tewari, Shashank Pandu N, K. Asha, P.N. Dept. of Computer Science and Engineering Dehradun India Srinivas University Institute of Engineering & Technology Dept. of Computer Science and Engineering Surathkal India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371314

Technology is very volatile in nature, in a way that it keeps on changing daily. Restaurants are no different in the effects of this changing scenario. The primary objective of this research paper is to explore the historical trajectory of restaurants, analyze the current trends shaping the industry, and delve into the potential of chatbot integration to overcome existing challenges. Chatbots can play a pivotal role in enhancing the performance of businesses as per the research done. An integrated Dialog Flow application has been proposed, incorporating Rule-based grammar matching and M.L matching algorithms. This proposed work is trying to make NLP-based chatbots which may expedite the endeavor of businesses to produce and become profitable. © 2024 IEEE.

关键词： Python

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DisCovHAR: Contrastive Attention for Human Activity Recognition under Distribution Shifts

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IEEE Internet of Things Journal 2025年第12期12卷 21973-21983页

作者： Chen, Luke Odema, Mohanad Faruque, Mohammad Abdullah Al University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Advances in Internet of Things (IoT) wearable sensors and edge-artificial intelligence (Edge-AI) have enabled practical realizations of machine learning (ML)-enabled mobile sensing applications like human activity recognition (HAR). The effective deployment of these data-driven models necessitates learning robust representations capable of handling prevalent distribution shifts (DS), including new users, device positions, rotations, and more. In that respect, contrastive learning (CL) has shown promise in learning transformation-invariant features, outperforming traditional HAR methods. However, recent findings reveal that the contrastive loss induces shrinkage and expansion of the feature space which may limit the generalization capacity of the model. To address this, we propose DisCovHAR, a contrastive attention method to selectively apply the contrastive loss to a subset of the feature space through the transformer encoder attention mechanism. Extensive experiments on three HAR datasets (DSADS, PAMAP2, and USCHAD) demonstrate its superiority over state-of-the-art methods. Specifically, our approach yields up to 4.47% and 7.82% average accuracy improvements in subject-wise and position-wise generalization settings. Furthermore, DisCovHAR demonstrates up to 5.07% increased robustness compared to prior methods under multivariate distribution shift scenarios. © 2025 IEEE.

关键词： Contrastive Learning

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Selected Area of Wireless Communications and Networking

Proceedings of the ACM Symposium on Applied Computing

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Proceedings of the ACM Symposium on Applied Computing 2024年 1733-1734页

作者： Kim, Dongkyun Wang, Wei School of Computer Science and Engineering Kyungpook National University Korea Republic of Department of Computer Science San Diego State University United States

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