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Finding Friendly Neighborhood: Optimal D2D Relaying in mmWave IoT Networks

IEEE Access 2025年 13卷 89193-89207页

作者： Krishna, J. Vamshi Vijay Mahendran, V. Badarla, Venkataramana Indian Institute of Technology at Tirupati Department of Computer Science and Engineering Andhra Pradesh Tirupati517619 India

Device-to-Device (D2D) relaying helps improve the coverage range and throughput of the millimeter Wave (mmWave) networks. This work focuses on improving the uplink throughput in a single-cell mmWave-based Internet-of-Things (IoT) network. The key factors involve the placement of relays and neighbor selection (NS) (i.e., the process of selecting source IoTs within the relay node's coverage range). The problem is formulated using model-based bandits, and the associated structural results are analyzed. Considering the resource-constrained nature of IoTs, we have developed a simple rule-based NS as an alternative to computationally intensive learning-based neighbor-selection processes from state of the art. By utilizing the rule-based NS, we show that there exists a conducive coverage range (called a friendly neighborhood) for the relay node to choose the neighbors. Accordingly, a geometry-based relay placement mechanism is proposed to maximize the uplink throughput. The optimality of the proposed solutions is shown analytically, and their performance concerning the state of the art is evaluated using extensive simulation. © 2013 IEEE.

关键词： Beamforming

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Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Panigrahi, Bhawani Sankar Kanna, R. Kishore Das, Pragyan Paramita Sahoo, Susanta Kumar Dutta, Tanusree Department of Computer Science & Engineering GITAM Institute of Technology GITAM University Vishakhapatnam India Chennai India Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar India Odisha Sarang India Hyderabad India

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centers, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies. © (2024) Panigrahi, B.S. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Machine learning

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Web information extraction and fake news detection in twitter using optimized hybrid bi-gated deep learning network

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Multimedia Tools and Applications 2025年第11期84卷 9471-9504页

作者： Jadhav, Dhanaraj Singh, Jaibir Department of Computer Science and Engineering Om Parkash Jogender Singh University Rajasthan Churu331303 India

In today's scenario, extracting information from websites is a challenging problem because of the increasing amount of information shared on the Internet. Recently, there has been an increase in the popularity of using social media platforms like Twitter, Facebook, Instagram, etc., where everyone can share information very easily. However, malicious users were also increasing on social media, so they shared fake news to mislead the users. Most recently, fake news has spread widely on Twitter. Thus, to solve this problem, this paper proposes an effective deep learning mechanism based on an optimized deep attention (EDLM-ODA) network for detecting fake news on Twitter. First, the input data is converted into a CSV file in JSON format and then pre-processed using tokenization, special character removal, punctuation removal, lowercase, stemming, and spelling correction. The term frequency-inverse average document frequency (TF-IADF), n-gram level vectorizer, character level vectorizer, and hashing vectorizer are feature extractors to extract substantial features. Further, the extracted features are fed into the dual attention based convolutional bi-gated reptile optimal network (DA_CBiG_RepNet) model to classify real and fake news. In the DA_CBiG_RepNet model, the extended reptile optimization algorithm (EROA) is applied to minimize the loss function. The proposed EDLM-ODA network is implemented using the Python platform, and the performance is evaluated in terms of various evaluation metrics using the PHEME, Liar, and FakeNewsNet datasets. Further, the performance of the EDLM-ODA network is compared with prevailing methods. As a result, the implemented outcomes proved that the EDLM-ODA network had attained a better accuracy of 99%, 99.12%, and 99.2% than the prevailing methods using PHEME, Liar, and FakeNewsNet datasets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Inverse problems

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Well Bot—A Reinforcement Learning and Sentiment Analysis-Based Chatbot Implementation for Mental Well-Being 6th

Well Bot—A Reinforcement Learning and Sentiment Analysis-Ba...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Sohara Banu, A.R. Nagaveni, V. Gowda, Naveen Chandra Yerriswamy, T. Gururaj, M. Varshitha, N. School of Computer Science and Engineering REVA University Bengaluru India Department of Computer Science and Engineering AMC College of Engineering Bengaluru India

ISBN: (纸本)9789819986279

As the world becomes more and more competitive, the number of people experiencing stress, anxiety, and other mental health problems is rapidly rising. In today’s society, stress and pressure are affecting everyone, including school-going children and elderly people. We believe that an individual’s mental health is equally important as their physical health. But unfortunately, there are not enough resources for mental health care or adequate facilities in Indian society;thus, individuals struggle on their own, without assistance or support. Therefore, we suggest a solution for designing a chatbot that will serve as a communication channel for users. The users can let out their feelings and thus get relieved of the stress and/or anxiety that is clogged up. The chatbot application will send pop-up notifications to the user based on inputs of the user’s mood from Google Health installed on the mobile. The sentiment analysis module will generate appropriate notifications and converse with the user once they begin chatting with the chatbot. The chatbot uses Google’s Dialogflow services, including natural language processing and reinforcement learning, to generate responses. This will benefit people who are depressed and are afraid to express their feelings out loud or fear being judged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Thoracic-net: Explainable artificial intelligence (XAI) based few shots learning feature fusion technique for multi-classifying thoracic diseases using medical imaging

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Multimedia Tools and Applications 2025年第9期84卷 5397-5433页

作者： Bhosale, Yogesh H. Patnaik, K. Sridhar Zanwar, S.R. Singh, Saket Kr. Singh, Vandana Shinde, U.B. Department of Computer Science & Engineering Birla Institute of Technology Ranchi Mesra India India India India

Background & Need: The early detection of thoracic diseases and COVID-19 (coronavirus disease) significantly limits propagation and increases therapeutic outcomes. This article focuses on swiftly distinguishing COVID-19 patients with 10 chronic thoracic illnesses from healthy examples. The death rates of COVID-19-confirmed patients are rising due to chronic thoracic illnesses. Method: To identify thoracic illnesses (Consolidation, Tuberculosis, Edema, Fibrosis, Hernia, Mass, Nodule, Plural-thickening, Pneumonia, Healthy) from X-ray images with COVID-19, we provide an ensemble-feature-fusion (FFT) deep learning (DL) model. 14,400 chest X-ray images (CXRI) of COVID-19 and other thoracic illnesses were obtained from five public sources and applied UNet-based data augmentation. High-quality images were intended to be provided under the CXR standard. To provide model parameters and feature extractors, four deep convolutional neural networks (CNNs) with a proprietary CapsNet as the backbone were employed. To generate the ensemble-fusion classifiers, we suggested five additional USweA (Unified Stacking weighted Averaging)-based comparative ensemble models as an alternative to depending solely on the findings of the single base model. Additionally, USweA enhanced the models' performance and reduced the base error-rate. USweA models were knowledgeable of the principles of multiple DL evaluations on distinct labels. Results: The results demonstrated that the feature-fusion strategy performed better than the standalone DL models in terms of overall classification effectiveness. According to study results, Thoracic-Net significantly improves COVID-19 context recognition for thoracic infections. It achieves superior results to existing CNNs, with a 99.75% accuracy, 97.89% precision, 98.69% recall, 98.27% F1-score, shallow 28 CXR zero-one loss, 99.27% ROC-AUC-score, 1.45% error rate, 0.9838 MCC, (0.98001, 0.99076) 95% CI, and 5.708 s to test individual CXR. This suggested USweA m

关键词： COVID-19

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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality Through Explainable AI and Large Language Models

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IEEE Access 2025年 13卷 84689-84712页

作者： Kumar Kundu, Ripan Anuarul Hoque, Khaza University of Missouri-Columbia Department of Electrical Engineering and Computer Science ColumbiaMO65211 United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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An efficient approach for congestion detection and control in VANETs

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International Journal of Ad Hoc and Ubiquitous Computing 2025年第2期49卷 105-118页

作者： Chaurasia, Brijesh Kumar Rishiwal, Vinay Shiblee, Mohammad Shukla, Man Mohan Yadav, Mano Department of Computer Science and Engineering Pranveer Singh Institute of Technology UP Kanpur India Department of Computer Science and Information Technology Faculty of Engineering and Technology MJP Rohilkhand University UP Bareilly India Industrial Engineering Department Taif University Taif21944 Saudi Arabia Pranveer Singh Institute of Technology UP Kanpur India Department of Computer Science Bareilly College UP Bareilly India

Vehicular ad hoc network (VANET) is a promising technology that uses a variety of messages to deliver convenience and safety features. VANET has unique characteristics regarding the high speed and variable density of vehicles. The short time for vehicular communication, especially at high speeds, presents difficulties. Channel utilisation, especially for safety messages, is a big issue due to its unique characteristics, which can lead to congestion, higher delays, and energy waste. This problem affects the performance of the VANET as a whole and the efficiency of VANET applications, such as the emergency notification system. This work aims to design a novel mechanism for managing and detecting congestion in VANET to mitigate these issues. The proposed mechanism uses priority-based channel assignment to handle congestion control in VANET. The extensive simulation results demonstrate that the proposed approach effectively addresses congestion, ensuring a more reliable environment for VANET operations. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Signaling

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Morphological operators that considerably improve the generalised hough transform

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Multimedia Tools and Applications 2025年第6期84卷 3223-3237页

作者： Baykal, Ibrahim Cem Computer Engineering Adana A. T. Science & Technology University Adana Sarıçam Turkey

Hough Transform has difficulty detecting short lines. One way to improve the results is to oversample images in order to make those short lines longer. Unfortunately, this method is computationally expensive. In this article, three new binary morphological operators are introduced that can stretch binary edge images. The first operator thins the output of the edge detector so that the second one can stretch it. The Stretch operator does not alter the coordinates of the end points of a line (other than multiply by 3) nor make it thicker. The third one, the smooth operator, is designed for smoothing the jaggedness of the lines stretched by the stretch operator. These operators are a computationally efficient way of improving the results of the Hough Transform for lines, circles and other shapes. All of the operators are implemented using the OpenCV library and the source code is publicly shared on GitHub. © The Author(s) 2025.

关键词： Hough transforms

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Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain

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Annals of Data science 2024年第5期11卷 1563-1580页

作者： Kanapala, Ambedkar Pal, Sukomal Dara, Suresh Jannu, Srikanth Department of Computer Science and Engineering Geethanjali College of Engineering and Technology TS Hyderabad501301 India Varanasi India School of Technology Woxsen University TS Hyderabad502345 India Department of Computer Science and Engineering Vaagdevi Engineering College TS Warangal506005 India

Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Information retrieval

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