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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Qin, Zheyi Paffenroth, Randy Jayasumana, Anura P. Colorado State University Electrical and Computer Engineering Fort Collins United States Worcester Polytechnic Institute Department of Data Science Program WorcesterMA United States

ISBN: (纸本)9798350324457

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

关键词： graph analytics graph coordinate graph embedding green AI network embedding

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Few-Shot Partial Multi-View Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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Utilizing Machine Learning Techniques for Forecasting Agricultural Commodity Prices 2

Utilizing Machine Learning Techniques for Forecasting Agricu...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Benjamin, Minu Inba Shanthini Watson Gowtham, D. Reddy, S. Anand Kumar Krishna, D. Durga Radha Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linear regression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informed decisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting. © 2024 IEEE.

关键词： Bayesian regression Decision tree Ensemble methods exploratory data analysis hybrid models K-Nearest Neighbors linear regression Machine learning Random Forest XGBoost

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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Performance analysis of Jute/Kenaf fiber polyester hybrid composites for non-structural applications

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Interactions 2024年第1期245卷 1-16页

作者： Sahare, Shilpa B. Jadhav, Varsha D. Satishkumar, P. Vijayan, D.S. Saminathan, Rajasekaran Department of Mechanical Engineering Yeshwantrao chavan college of engineering Maharashtra Wanadongri India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Chennai India Department of Mechanical Engineering College of Engineering and computer science Jazan University Jazan Saudi Arabia

High-performance mechanical properties and environmental benefits are demonstrated by natural fiber (NF)-reinforced polymer composites. The study created Jute fiber (JF) and Kenaf fiber (KF) reinforced polyester hybrid composites using hot compression molding. The composites were analyzed and studied for their physical, mechanical, and tribological properties and water absorption rate. The study aimed to identify a significant hybrid of NF reinforced polymer composites suitable for commercial applications in engineering. Analyzed several mechanical properties including tensile strength, flexural strength, compression strength, impact strength and hardness. The fractured areas of the tensile sample were examined with a scanning electron microscope (SEM). The water absorption properties were assessed by submerging the composite samples in distilled water and determining the percentage of water penetration. The tribological behavior was assessed using a pin-on-disc (POD) tribometer to estimate the coefficient of friction (COF) and Specific wear rate (SWR). The study concluded that hybrid composites outperformed single-fiber composites in all forms. The hybrid composite consisting of 25% Jute/5% Kenaf/70% VE shown substantial improvements in mechanical properties as compared to the pure resin sample: a 106.2% rise in TS, 111.8% in FS, 27.5% in compression strength, 59.3% in impact strength, and 115.4% in hardness. The results show that combining Jute and Kenaf fibers with a polyester matrix improves mechanical properties, making it suitable for non-structural electrical and electronics insulating board and component applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Water absorption

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Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

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YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition 1

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms fo...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Wajdi, Achmad Zahir Wibowo, Prasetyo Sasaki, Shiori Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Faculty of Data Science Musashino University Tokyo Japan

ISBN: (纸本)9798350365191

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%. © 2024 IEEE.

关键词： Marine Waste Neural Net-work Object Detection TACO dataset YOLO

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Server Cloud Based Security Surveillance Systems: a Design science Research 11

Server Cloud Based Security Surveillance Systems: a Design S...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nurenie, Alexander Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area. © 2023 IEEE.

关键词： Cost effectiveness

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