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International Symposium on Telecommunications, IST

作者： Narges Movahedkor Reza Shahbazian Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnique) Tehran Iran Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Rende Italy

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating energy consumption in dense 5G networks, this paper proposes a decentralized federated learning (DFL)-enhanced Deep Reinforcement Learning (DRL) framework for intelligent BS switching. Our approach leverages the distributed nature of 5G networks to collaboratively learn optimal BS on/off policies without centralized control. By combining DFL with DRL, we enable efficient knowledge sharing among BSs while maintaining privacy and reducing communication overhead. The cost function designed in this paper aims to balance energy savings as well as Quality of Service ($\mathbf{Q o S}$) requirements. Moreover, to enhance exploration and accelerate convergence, we incorporate an exploration network into the DRL agent and adopt a novel approach of model training. Performed simulations demonstrate the effectiveness of our proposed framework in achieving significant energy reduction of over 23% and total cost of 19%, while maintaining satisfactory QoS performance compared to the existing methods.

关键词： Training Base stations Energy consumption Privacy 5G mobile communication Federated learning Switches Quality of service Deep reinforcement learning Telecommunications

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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IEEE International Conference on systems, Man and Cybernetics

作者： Guotong Wu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC.

关键词： Training Accuracy Machine learning Approximation algorithms Graph neural networks Approximation methods Cybernetics Testing

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Optimal Strategies Trajectory with Multi-Local-Worlds Graph

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computers, Materials & Continua 2023年第4期75卷 2079-2099页

作者： Xiang Yu Chonghua Wang Xiaojing Zheng Chaoyu Zeng Brij BGupta School of Electronics and Information Engineering Taizhou UniversityTaizhouZhejiang318000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China School of Economics and Management Weifang University of Science and TechnologyShouguangShandong262700China National Computer System Engineering Research Institute of China Beijing102699China Department of Computer Science and Information Engineering Asia UniversityTaiwanTaichung413China Research and Innovation Department Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates Staffordshire University Stoke-on-TrentST42DEUK

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

关键词： Multi-Local-world graph stochastic differential game optimal strategy attractor

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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International Electrical engineering Congress (iEECON)

作者： Waris Rattanimit Thanyarat Nomponkrang Electrical Technology and Automation System Faculty of Engineering and Technology Siam Technology College Bangkok Thailand Department of Computer Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

In the digital era, learning programming has become an essential skill, and assessing students' understanding in this area plays a crucial role in enhancing educational quality. However, evaluating subjective programming exam responses, especially those involving conceptual explanations or procedural descriptions, remains a challenging task due to the diversity and complexity of answers. Manual grading can lead to delays and inconsistencies in scoring. This research aims to develop an automated system for evaluating Thai subjective programming exams using Natural Language Processing (NLP) techniques to assess the correctness of responses. The system is implemented in Python, utilizing the ThaiNLP library for word tokenization, alongside Machine Learning algorithms such as Naive Bayes, Decision Tree, and Support Vector Machine (SVM) for classification and prediction of answers. The experimental dataset includes responses from 120 students who answered subjective questions about Python commands. Expert testing indicated high user satisfaction with an average score of 4.05. The findings show that SVM achieved the highest accuracy at 85.33%. This system can enhance efficiency and consistency in exam evaluation, reducing grading time and contributing to standardized scoring.

关键词： Support vector machines Electrical engineering Machine learning algorithms Manuals Machine learning Tokenization Libraries Programming profession Python Testing

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

A Framework for Deepfake Detection using Convolutional Neura...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Soundarya, B.C. Gururaj, H.L. Naveen Kumar, C.M. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Convolutional neural networks

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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

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Journal of Advanced Research 2023年 48卷 191-211页

作者： Ukwuoma, Chiagoziem C. Qin, Zhiguang Belal Bin Heyat, Md Akhtar, Faijan Bamisile, Olusola Muaad, Abdullah Y. Addo, Daniel Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China IoT Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen 518060 China Centre for VLSI and Embedded System Technologies International Institute of Information Technology Telangana Hyderabad 500032 India Department of Science and Engineering Novel Global Community Educational Foundation Hebersham 2770 NSW Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center Chengdu University of Technology Chengdu China Department of Studies in Computer Science University of Mysore Manasagangothri Mysore India Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul 05006 South Korea

Introduction: Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. Objectives: A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. Methods: The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. Results: The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. Conclusion: The proposed hybrid deep learning framewo

关键词： Chest X-ray imaging Explainable artificial intelligence (XAI) Pneumonia identification Self-attention network Transfer ensemble learning Transformer encoder (TE)

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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13th International Electrical engineering Congress, iEECON 2025

作者： Rattanimit, Waris Nomponkrang, Thanyarat Siam Technology College Electrical Technology and Automation System Faculty of Engineering and Technology Bangkok Thailand King Mongkut's University of Technology North Bangkok Department of Computer Education Bangkok Thailand

ISBN: (纸本)9798331543952

关键词： Support vector machines

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Mahfuzur Rahman Shuvo Ashifur Rahman Vishwanath Akuthota Tanay Paul Mahfujul Islam Md Sadi Ashraf Prosenjit Roy Md Tanzim Reza Department of Management Information System International American University United States Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh TechOptima Hyderabad India Department of Computer Science and Engineering BRAC University Bangladesh Department of Computer Science Prairie View A & M University Texas United States

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

The automobile service industry’s explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of $\mathbf{8 5 \%}$, recall of $\mathbf{7 0 . 8 \%}$, and an F1-score of $\mathbf{7 7 . 2 \%}$, demonstrating the efficacy of integrating classical ML with generative AI. The system’s average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system’s Pro

关键词： Radio frequency Measurement Generative AI Navigation Scalability Hybrid power systems User experience Reliability Random forests Multi-agent systems

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining 23

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Parallel Acceleration of Real-time Feature Extraction Based on SURF Algorithm 15

Parallel Acceleration of Real-time Feature Extraction Based ...

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15th International Conference on computer Research and Development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

Visual feature extraction is the key to target registration calculation. Compared with SIFT, SURF feature extraction algorithm has less computational complexity and better applicability when image signal-to-noise ratio is low. In this paper, an FPGA-based SURF algorithm for real-time feature extraction and parallel acceleration is designed for large-field scene registration applications of space targets. Firstly, the application scenario of space target is analyzed, and the grayscale features of the data containing the target used for registration are counted. Then, the five main parts of SURF algorithm were analyzed, and the algorithm was simplified based on the gray feature of the image. Finally, according to the simplified results, the parallel pipeline architecture of each module is designed based on FPGA. SURF computing modules are also given in this paper the design of the architecture and data flow of information, in XC7K325 completed the whole design of experiment, the results show that the design for 1024 × 1024 pixel image, single frame image processing time need only 51 us, resource usage is not more than 18.6%, power consumption is not more than 7.5 W, The computational efficiency is 87% higher than the previous design. Low signal-to-noise ratio (SNR) was used to simulate space target scene data for simulation test calculation, and the SURF descriptor error was less than 3%, ensuring the system's calculation error while effectively utilizing resources. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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