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Multi-Objective Optimization of 3D Cell Deployment in Sustainable B5G/6G Networks: Balancing Performance and Sustainability

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IEEE Transactions on Network and Service Management 2025年

作者： Chien, Wei-Che Jeon, Gwanggil Cho, Hsin-Hung National Dong Hwa University Department of Computer Science and Information Engineering Hualien Taiwan Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of National Ilan University Department of Computer Science and Information Engineering YiLan26047 Taiwan

In recent years, the exponential increase in mobile and Internet of Things (IoT) data traffic has placed substantial demands on infrastructure for Internet Service Providers (ISPs). To meet these demands sustainably, it is critical to enhance energy efficiency, resource utilization, and cost-effectiveness while reducing the carbon footprint. Simply adding hardware is not a viable solution. This study introduces an innovative approach to 3D cellular deployment in sustainable B5G/6G networks, designed to optimize Quality of Service (QoS) for users and IoT devices. Although 5G/B5G utilizes millimeter waves for high data rate transmission, their limited coverage and susceptibility to interference from buildings pose unique deployment challenges. To address these, we formulate the 3D cellular deployment problem as a Multi-Objective Optimization (MOO) problem and propose an advanced deployment strategy using VA-NSGA-II, a metaheuristic-based algorithm. By factoring in building interference, Received Signal Strength Indicator (RSSI), coverage, deployment cost, and a balance between performance and sustainability, VA-NSGA-II provides an optimal deployment solution. Simulation results demonstrate that VA-NSGA-II achieves effective deployment performance across various building materials, highlighting its adaptability and effectiveness in different environmental scenarios. © 2004-2012 IEEE.

关键词： Internet of things

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Application Interface for GPenSIM 1st

Application Interface for GPenSIM

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1st International Symposium on systems Modelling and Simulation, SMS 2024

作者： Feng, Yuming Samolej, Slawomir Davidrajuh, Reggie School of Computer Science and Engineering Chongqing Three Gorges University Wenzhou China Department of Computer and Control Engineering Rzeszow University of Technology Rzeszow Poland Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

ISBN: (纸本)9789819646128

The General-purpose Petri Net Simulator (GPenSIM) is a tool used by researchers to model, simulate, and analyze the performance of discrete event systems. Its popularity stems from its simple interface, extensibility, and compatibility with the MATLAB platform. This paper focuses on the crucial role of GPenSIM in Petri Net model-based software-to-software and software-to-hardware access and control, highlighting unique features and capabilities in its application interface. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： MATLAB

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S3-GHOSTNET: skin disease detection via Sand Cat Swarm Optimization deep GHOSTNET with facial images

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Australian Journal of Electrical and Electronics engineering 2025年

作者： Rathinam, Vinoth A, Ramathilagam K, Valarmathi S, Erana Veerappa Dinesh Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi India Department of Computer Science and Business Systems Ramco Institute of Technology Raiapalayam India

Skin is the largest organ in the human body and covers the entire body. Face skin diseases are a wide range of disorders that can affect the skin on the face. In this paper, a novel S3-GHOSTNET has been proposed to identify the face skin diseases. Initially, the input images are gathered from the Derm Net dataset and pre-processed using the Contrast stretching Adaptive Gaussian star Filter for enhancing the quality of the image. The enhanced images are fed into Modified Deep V3+ for segmenting the diseases affected region in the images. Then the segmented images are fed into deep learning-based Shuffle Net for extracting the features of the images. Finally, the required features are selected using Sand Cat Swap Optimization and using Ghost Net the Penta type of face skin diseases are classified. The proposed method achieves an accuracy rate of 99.34% in the normal classes and 98.94% in the abnormal classes. Additionally, the proposed method archives high-level Precision, Recall and F1 Scores of 98.05%, 97.73%, and 97.66% in the normal and abnormal classes. The proposed S3-GHOSTNET achieve the overall accuracy rate of 0.49%, 1.49%, 0.36% and 0.79% comparing the existing methods such as GLCN, Leaky RELU, CNN and Eff2Net, respectively. © 2025 Engineers Australia.

关键词： Adaptive filters

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A deep learning approach to analyse stress by using voice and body posture

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Soft Computing 2025年第3期29卷 1719-1745页

作者： Gupta, Sumita Gambhir, Sapna Gambhir, Mohit Majumdar, Rana Shrivastava, Avinash K. Pham, Hoang Amity School of Engineering & Technology Amity University Noida India Department of Computer Science George Mason University WashingtonVA United States Verispire Consulting Corporation San FranciscoCA United States Sister Nivedita University Kolkata West Bengal Calcutta India IMI Kolkata West Bengal Kolkata India Department of Industrial and Systems Engineering Rutgers University PiscatawayNJ United States

In the current scenario, where we can see young people struggling for their careers, they are even fighting a battle with their stress and tension. None of their work is done without stress to complete their task and compete with others. To overcome stress, one should have good emotional intelligence to cope with emotions and any upcoming stress. But at some point, due to lack of guidance, some people don’t know how to analyze the situations and how to handle them without taking the stress and end up with anxiety, depression, disappointment, suicide, heart attack, stroke etc. Due to the advancement of Human–computer Interaction (HCI), medical science has leveled up to another peak. Machine Learning and Deep Learning played a major role in such interactions and predictions. Many applications have been developed in past years based on machine learning and deep learning. One of those applications is related to psychology and is still in research. These applications can be used for emotion and stress analysis among people, especially youngsters. Research in this field is being conducted using various verbal and non-verbal parameters. This paper addresses the research problem of improving emotion recognition accuracy and robustness to better analyze and manage stress. The primary objective is to develop an advanced Emotion Recognition System (ERS) that leverages deep learning algorithms to analyses both verbal and non-verbal cues—specifically, speech and body posture, including facial expressions. We have further integrated it with the Flask web framework to make an Emotion Recognition System that takes input in the form of video and audio to analyze Emotions and Stress. We have also compared our proposed ERS with existing ones and found that our ERS gives better results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Enhancing Fairness and Performance: Scalable Hybrid Solutions for Class Imbalance in Big Data Analytics

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Procedia computer science 2025年 258卷 1050-1061页

作者： Puneetha B H Manoj Kumar M V Prashanth B S Ajay Kumara M.A. Department of computer science and Business Systems Bapuji Institute of Engineering and Technology Davanagere Karnataka Department of Information Science and Engg. Nitte Meenakshi Institute of Technology Bangalore-560064 Karnataka Department of Computer Science University of North Carolina Wilmington USA

Recent advancements in handling class imbalance include hybrid approaches that combine data-level and algorithmic-level techniques. However, challenges such as computational efficiency and adaptability to dynamic datasets remain to be prominently addredssed. This research paper introduces novel hybrid techniques that combine both data-level and algorithmic-level approaches to address class imbalance in large datasets. These techniques are designed to improve class distribution and processing efficiency while enhancing prediction accuracy, particularly for minority classes. The proposed model applies K-means SMOTE for over-sampling the minority class and centroid-based undersampling for the majority class, followed by Classification using XGBoost. Evaluations on multiple datasets demonstrate significant improvements in precision, recall, AUC-ROC, and F1-score compared to traditional methods. In addition to enhancing robustness and fairness, the approach effectively addresses challenges related to scalability, data diversity, and dynamic data sources in big data analytics. The hybrid technique achieved a 10-20% improvement in key performance metrics, including precision, recall, F1-score, and AUC-ROC, across various UCI datasets such as Diabetes, Hepatitis, and German Credit, confirming the model’s superiority in handling class imbalance and improving overall Classification performance.

关键词： Class imbalance Undersampling Oversampling XGBoost

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Credit Card Fraud Detection Using Random Forest and K-Nearest Neighbors (KNN) Algorithms 5th

Credit Card Fraud Detection Using Random Forest and K-Neares...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Alhabib, Abdulaziz Abdulrhman Alasiri, Abdulaziz Fae Alharbi, Mazen Bunayan Ahmad, Sultan Eljialy, A.E.M. Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O.Box. 151 Alkharj11942 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O.Box. 151 Alkharj11942 Saudi Arabia Chandigarh University Punjab Mohali Gharuan140413 India

ISBN: (纸本)9789819773701

In this paper, we explored the application of both the Random Forest and K-Nearest Neighbors (KNN) algorithms for fraud detection. Fraudulent activities pose significant threats to various industries, making their detection crucial. We reviewed related works on fraud detection, highlighting the effectiveness of both the Random Forest and KNN methods. We proposed a system utilizing a combination of Random Forest and KNN models for credit card fraud detection, detailing its architecture and methodology. The system preprocesses and splits data, trains the models, and evaluates their performance on a testing set. We analyzed the results, demonstrating the accuracy, robustness, and scalability of the proposed system. Additionally, we presented data on the most common hours for fraudulent transactions and the top jobs associated with them. Overall, this study emphasizes the efficacy of utilizing both Random Forest and KNN algorithms in mitigating the risks associated with fraud and offers a valuable tool for safeguarding against financial malfeasance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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Interpretable Arabic Hate Speech Detection using Large Language Model

Interpretable Arabic Hate Speech Detection using Large Langu...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Majed Alwateer Ibrahim Gad Mahmoud Elmarhomy Ghada Elmarhomy Hasan Hashim Malik Almaliki El-Sayed Atlam Department of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Computer Science and Information College of Applied Taibah University Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content.

关键词： Technological innovation Accuracy Social networking (online) Smart cities Large language models Hate speech Text categorization Writing Reliability Stakeholders

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Robust Mean Field Social control: A Unified Reinforcement Learning Framework

arXiv

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arXiv 2025年

作者： Xu, Zhenhui Chen, Jiayu Wang, Bing-Chang Wu, Yuhu Shen, Tielong Department of Systems and Control Engineering The Institute of Science Tokyo Tokyo152-8552 Japan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

This paper studies linear quadratic Gaussian robust mean field social control problems in the presence of multiplicative noise. We aim to compute asymptotic decentralized strategies without requiring full prior knowledge of agents’ dynamics. The primary challenges lie in solving an indefinite stochastic algebraic Riccati equation for feedback gains, and an indefinite algebraic Riccati equation for feedforward gains. To overcome these challenges, we first propose a unified dual-loop iterative framework that simultaneously handles both indefinite Riccati-type equations, and provide rigorous convergence proofs for both outer-loop and inner-loop iterations. Second, recognizing that biases may arise in iterative processes due to estimation and modeling errors, we analyze the robustness of the proposed algorithm by using the small-disturbance input-to-state stability technique. This ensures convergence to a neighborhood of the optimal solution, even in the existence of disturbances. Finally, to address the limitation of requiring precise knowledge of agents’ dynamics, we employ the integral reinforcement learning technique to develop a data-driven method within the dual-loop iterative framework. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm. © 2025, CC BY-NC-ND.

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Comprehensive Analysis of Pose Estimation and Machine Learning Classifiers for Precise Yoga Pose Detection and Classification

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Procedia computer science 2025年 258卷 3345-3356页

作者： Meghana J.H. Chethan H.K. K.S. Santhosh Kumar S.P. Shiva Prakash Department of Studies in Computer Science University of Mysore Karnataka Mysuru-570006 India Department of Computer Science and Engineering Maharaja Institute of Technology Thandavapura Mysuru-571302 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Mysuru-570006 Karnataka India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru-570006 Karnataka India

Yoga contributes to mental and physical well-being by improving flexibility, strength, balance, and emotional stability when integrated into daily routines. This ancient practice can become more accessible and adaptable to a wider audience when combined with modern artificial intelligence (AI). This study introduces a comprehensive system for detecting and classifying yoga poses using computer vision and machine learning techniques. Central to this work is the application of posture estimation algorithms, such as MediaPipe, PoseNet, and OpenPose, to identify key points on the human body within a single image or video frame. These key points are analyzed in both two-dimensional (2D) and three-dimensional (3D) spaces to construct a skeletal representation of the body, enabling accurate classification of yoga poses. The study focuses on five distinct yoga poses: Downdog, Goddess, Plank, Tree, and Warrior II. To categorize these poses, machine learning classifiers including Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes utilize the key points extracted from the pose estimation models. This research is distinctive in its thorough evaluation of various conventional classifiers across multiple yoga positions. A comprehensive comparative analysis is essential for identifying the most effective classifiers for posture detection and classification. The dataset used in this study has been carefully curated to encompass a wide array of yoga poses and is divided into training and testing sets at various ratios (90:10, 80:20, 70:30, and 60:40) to ensure robust validation. Results indicate the system’s effectiveness, with SVM and KNN consistently achieving high values for Accuracy PE , Precision PE , Recall PE , and F1-S core PE across all yoga poses. Notably, Random Forest attains up to 100% Accuracy PE in detecting and classifying certain poses, demonstrating its robustness and reliability. This research highlights the potential of integ

关键词： Yoga Pose Estimation Machine Learning Deep Learning

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