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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

International Journal of Fuzzy Systems 2024年第1期26卷 368-389页

作者： Zavieh, Hadi Javadpour, Amir Ja’fari, Forough Sangaiah, Arun Kumar Slowik, Adam School of Economics and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900–347 Portugal Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan. © The Author(s) 2023.

关键词： Markov processes

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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Intrusion Detection Framework in IoT Networks

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SN computer Science 2023年第4期4卷 1-17页

作者： Bajpai, Soumya Sharma, Kapil Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity University Madhya Pradesh Maharajpura Dang Gwalior India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

The Internet of Things (IoT) deals with the internet and physical objects such as smart home automation, industrial applications, smart cities, health and fitness, environmental monitoring, etc. The IoT network is interconnected over the internet for data analysis and sharing. It has made it possible for people and organizations to accomplish more with less, both in terms of time and resources. However, this accomplishment of the IoT has also contributed to a worrying increase in IoT network threats. Botnet invasions are likely the most worrisome of these assaults. Attackers are also becoming more inventive as time and technology develop. To recognize these assaults and detect these incursions before they cause the system to become unresponsive, it is crucial to deploy stronger and more efficient machine learning technologies. By employing supervised learning and evaluating its overall performance, this work aims to design an intrusion detection framework in IoT networks using machine learning (IDFML). The proposed IDFML will examine the various performance measures for each of the different classifiers. Results show that the proposed IDFML has a 98.68% attack detection accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Internet of Things (IoT) Intrusion detection system (IDS) IoT20IDS Machine learning (ML)

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Multi-Lingual Semantic-Based Sentiment Analysis Using Generative AI

Multi-Lingual Semantic-Based Sentiment Analysis Using Genera...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Biswas, Praloy Arockiam, Daniel Neogi, Subhrendu Guha Sengupta, Somenath Saraswat, Sanjeev Chanda, Rishit Amity University Madhya Pradesh Department of Computer Science and Engineering Madhya Pradesh Gwalior India The Neotia University Department of Computer Science and Engineering West Bengal Sarisha India Heritage Institute of Technology Department of Computer Science and Engineering West Bengal Kolkata India Institute of Business Management GLA University Mathura India SRM Institute of Science and Technology Faculty of Engineering and Technology Tamilnadu Chennai India

ISBN: (纸本)9798350388534

Generative AI models have extensive applications in Natural Language Processing (NLP) and large language models (LLMs) across various domains. They demonstrate high performance in numerous NLP tasks such as summarization, language understanding, reasoning, language generation, question-answering, sentiment categorization, and translation. Newer LLMs are projected to analyse and generate text in multiple languages because to their training on multilingual datasets, like ChatGPT, BLOOMZ, and other similar versions. Considering how frequently LLMs are used, it is critical to assess their effectiveness in multilingual environments. A major issue to be noted is that in a zero-shot context, the present generative models are not very good at producing text in Indian languages. Several scholarly articles studied generative LLMs in the English language. In contrast, LLM for Indic languages is not meant to be used in a zero-shot manner in downstream applications due to poor generating performance. Generative LLMs evaluate models on standard NLP benchmarks covering NLP datasets in diverse languages. We compared the performance of generative LLMs with non-autoregressive models on these tasks to evaluate the generative AI model's performance compared to the previous generation of LLMs. A framework for evaluating generative LLMs in the multilingual setting has been evaluated to provide future directions. © 2024 IEEE.

关键词： Generative adversarial networks

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Advanced Fake Review Detection via Aspect Extraction and TPA-BiLSTM-CSSA 2

Advanced Fake Review Detection via Aspect Extraction and TPA...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, Deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. Dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BiLSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BiLSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

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LearnAlytics: The Smart Way to Analyze, Adapt, and Achieve in E-Learning

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SN computer Science 2025年第1期6卷 1-30页

作者： Sengupta, Subhabrata Bardhan, Sayan Das, Rupayan Chakrabarti, Satyajit Department of Information Technology Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata India Department of Computer Science and Engineering Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata India

Artificial intelligence (AI) is a powerful technology with the potential to revolutionize various fields by incorporating human-like intelligence into systems and robots. Different types of AI, such as visual, textual, and analytical, can enhance applications and solve real-world problems. However, the dynamic and diverse nature of real-world data poses challenges in developing effective AI models. This research explores the application of machine learning (ML) and deep learning (DL) for information retrieval within a smart E-Learning system. It introduces an AI model called LearnAlytics, designed to assist decision-makers in implementing ML and DL techniques for information retrieval. LearnAlytics offers valuable insights applicable to various real-world scenarios and application domains. Extensive simulation results demonstrate that LearnAlytics outperforms traditional models, showcasing its superior effectiveness. The key contributions of this research include the development of a novel AI model for smart E-Learning, significantly enhanced information retrieval capabilities, data-driven decision-making support, generalizability and scalability of the model, and empirical validation of its superior effectiveness compared to traditional models. This research represents a significant advancement in AI-powered information retrieval and provides a practical framework for leveraging ML and DL to enhance learning experiences in smart E-Learning systems and beyond. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Deep learning Machine learning Predictive analytics Smart education

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Customer Spending Prediction on E-Commerce Website 15

Customer Spending Prediction on E-Commerce Website

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Salake, Sudha V. Patil, Pankaja Kunchur, Pavan Bangarashetti, Sadhana Managuli, Manjunath Patil, Sheetal B. Department of Computer Science and Engg KLS Gogte Institute of Technology Belagavi India Department of Information Science and Engg Basaweshwar Engineering College Bagalkot India Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi India Department of Computer Science and Engg SJB Institute of Technology Belagavi India

ISBN: (纸本)9798331300579

In the ever-evolving world of e-commerce, understanding customer behavior and predicting their spending patterns is crucial for businesses to thrive. This work presents an innovative application designed to predict customer spending on an e-commerce website. By leveraging advanced machine learning techniques and utilizing comprehensive customer data, the application offers valuable insights that can be used to optimize sales strategies and enhance customer *** application employs a robust predictive model trained on historical customer data, including purchase history, browsing patterns, demographic information, and transactional *** application incorporates real-time data updates to ensure continuous improvement and adaptability to changing customer *** proposed application offers a powerful tool for e-commerce businesses to predict customer spending, optimize sales strategies, and deliver personalized experiences. By harnessing the power of machine learning and leveraging comprehensive customer data, businesses can stay competitive in the dynamic e-commerce landscape and foster long-term customer loyalty. © Grenze Scientific Society, 2024.

关键词： Marketplaces

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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Health Pulse Kiosk: An AI-Driven IoT Solution for Comprehensive Health Diagnostics

Health Pulse Kiosk: An AI-Driven IoT Solution for Comprehens...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Amrutesh, Aniverthy Darshan Kumar, T. Bharadwaj, Shreelakshmi Asha Rani, K. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798331517953

Health monitoring and diagnostics play a crucial role in the early detection and prevention of diseases, especially in underserved areas where access to healthcare facilities is limited. The Health Pulse Kiosk addresses this challenge by providing a self-service, all-in-one solution for real-time health monitoring. Equipped with non-invasive sensors for measuring vital signs such as heart rate, blood pressure, oxygen saturation, and body temperature, the Kiosk enables individuals to monitor their health independently. This system integrates machine learning (ML) and deep learning (DL) models, including Naive Bayes, Random Forest, and MobileNetV2, to analyze and interpret the collected data. The Kiosk's design emphasizes user accessibility and ease of interaction, presenting real-time health metrics through a web interface. Performance evaluations demonstrate that MobileNetV2 achieves the highest accuracy of 99.56379%, and rapid response times averaging 1.2 seconds. This approach not only enhances healthcare accessibility but also empowers individuals to take proactive control of their health. © 2024 IEEE.

关键词： Blood pressure

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Assessing the Impact of Migration from SOA to Microservices Architecture

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SN computer Science 2023年第5期4卷 1-7页

作者： Raj, Vinay Bhukya, Hanumanthu Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering (Networks) Kakatiya Institute of Technology and Science Telangana Warangal 506015 India

Microservices has become a buzzword in IT and large enterprise firms such as Netflix, Twitter, Spotify and others have started to design their applications by adopting this new architectural style. A few organizations have started migrating their traditional monolithic and SOA-based applications to microservices in order to benefit from the features of new style. Software architects, on the other hand, are in chaos whether to adopt this new style or not as they are unaware of the pros and cons of microservices architecture. Also, the impact of the migration on SOA-based applications in terms of performance and complexity is unknown, leading to dilemma on the migration process. In this paper, a study of the migration impact on the existing SOA based applications to microservices is presented. For this study, change propagation probability and architectural stability metrics are used to examine the effect of migrating a SOA-based application to microservices architecture. The proposed approach is illustrated on a case study application designed using SOA and then migrated to microservices. It is observed from the results that though the impact of migration is high, migrating to microservices has significant benefits and it is best suitable for large enterprise applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Change propagation Microservices Migration Service oriented architecture Stability

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