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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Uma Maheswari S Myasar Mundher adnan Lathakumari K R Pradeep V Hirald Dwaraka Praveena Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science The Islamic university Najaf Iraq Dept of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Science and Engineering Alva's Institute of Engineering and Technology Mangalore India Department of ECE School of Engineering Mohan Babu University Erstwhile Sree Vidya nikethan Engineering College Andhra Pradesh India

The lung cancer generally presented a pulmonary nodule on images of diagnostic and correct estimation in malignant pulmonary nodules is difficult for diagnosis and protecting of lung cancer. However, the existing methods has less classification accuracy because of irrelevant features on classification of lung cancer. In this research, proposed a Monarch Butterfly Optimization based Multi-Convolution Neural Network (MBO based M-CNN) method for lung cancer classification. dataset used for lung cancer classification is LIDC-IDRI dataset and it is pre-processed by Histogram Equalization (HE). Then, the features are extracted by Gray Level Co-occurrence Matrix (GLCM) and extracted features are selected by Monarch Butterfly Optimization (MBO) algorithm. Then, the lung cancer classification is performed by Multi-Convolution Neural Network (M-CNN). The performance of proposed algorithm is tested in terms of accuracy, precision, sensitivity and specificity. Developed technique obtained accuracy 99.71%, precision 95.39%, sensitivity 99.46%, f1-score 97.45% that is better than previous method like Adaptive Boosting Self-Normalized Multiview Convolution Neural Network (AdaBoost-SNMV-CNN).

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Waveform Boundary Detection for Partially Spoofed Audio

Waveform Boundary Detection for Partially Spoofed Audio

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zexin Cai Weiqing Wang Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Data Science Research Center Duke Kunshan University Kunshan PR China

The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of the utterance is replaced, either with synthetic or natural audio clips, has recently been reported as one scenario of audio deepfakes. As deepfakes can be a threat to social security, the detection of such spoofing audio is essential. Accordingly, we propose to address the problem with a deep learning-based frame-level detection system that can detect partially spoofed audio and locate the manipulated pieces. Our proposed method is trained and evaluated on data provided by the ADD2022 Challenge. We evaluate our detection model concerning various acoustic features and network configurations. As a result, our detection system achieves an equal error rate (EER) of 6.58% on the ADD2022 challenge test set, which is the best performance in partially spoofed audio detection systems that can locate manipulated clips.

关键词： Deepfakes Adaptation models Error analysis Signal processing Feature extraction Acoustics Security

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A lightweight Federated Learning-based Intrusion detection model for Internet of Things

A lightweight Federated Learning-based Intrusion detection m...

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IEEE International Conference on Multi Topic

作者： Noshina Tariq Mamoona Humayun Ghadah Naif Alwakid Bushra Almas NZ Jhanjhi Muhammad Zakwan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Arts Humanities and Social Sciences University of Roehampton London United Kingdom Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Institute of Information Technology Quaid-i-Azam University Islamabad Pakistan School of Computer Science Taylor’s University Selangor Malaysia Department of Avionics Engineering Air University Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The advancement of sophistication in botnet intrusions on Internet of Things (IoT) systems and the resources required by traditional IDS make strong security solutions necessary, which reduce resource usage while enhancing detection capability. Therefore, this paper tests the performance and performance comparison of classification models based on lightweight Federated Learning (FL) for IoT botnet intrusion detection using Random Forest (RF), XGBoost, and LightGBM classifiers. Utilizing a large-scale dataset from IoT traffic, the three models were tested in metrics such as confusion matrices, ROC curves, and Precision-Recall curves. The results show that the RF and XGBoost classifiers achieve high AUCs of 1.00, thereby, there is no trade-off between sensitivity and specificity. On the other hand, LightGBM scored a low of 0.58 AUC. The results show that robust ensemble methods can be applied to handle complex and imbalanced datasets commonly presented in IoT traffic in FL environments.

关键词： Radio frequency Measurement Federated learning Botnet Intrusion detection Sensitivity and specificity Internet of Things Ensemble learning Security Random forests

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Expression of Concern for: Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Expression of Concern for: Medical Image Fusion Solid Works ...

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Sustainable Computing and data Communication Systems (ICSCDS), International Conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9781665491990

ISBN: (纸本)9781665492003

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System

Development of Fast Prediction System of User Action for a F...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： N.R. Rajalakshmi L. Sharmila S. Prabu T. Veeramakali G. Arulkumaran Pranshu Chourasia Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India School of Computing and Information Technology REVA UNIVERSITY Bangalore Karnataka India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Management accounting Heuristic algorithms Electricity Prediction algorithms Dynamic programming Batteries Temperature control Particle swarm optimization Energy management systems Optimization

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk Electrical and Computer Engineering Department The Ohio State University United States BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025, CC BY.

关键词： Deep reinforcement learning

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ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction

arXiv

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arXiv 2024年

作者： Manjunath, Yoga Suhas Kuruba Szymanowski, Mathew Wissborn, Austin Li, Mushu Zhao, Lian Zhang, Xiao-Ping Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Computer Science and Engineering Lehigh University BethlehemPA United States Shenzhen Key Laboratory of Ubiquitous Data Enabling Tsinghua Shenzhen International Graduate School Tsinghua University China

Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse. © 2024, CC BY.

关键词： Quality of service

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ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains

arXiv

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arXiv 2025年

作者： Zhao, Chonghe Zhou, Yipeng Zhang, Shengli Sheng, Quan Z. Zhang, Yang Wen, Shiting College of Electronic and Information Engineering Shenzhen University Shenzhen China School of Computing Macquarie University Sydney Australia Anuradha and Vikas Sinha Department of Data Science University of North Texas DentonTX United States School of Computer and Data Engineering NingboTech University Ningbo China

Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the most popular implementation of PoA due to its low communication overhead and energy consumption. However, our study unveils that the speed to process transactions by Clique is severely restricted by 1) the long communication delay of full blocks (each containing a certain number of transactions) between consensus nodes;and 2) occurrences of no-turn blocks, generated by no-turn nodes if an in-turn block generation fails. Consequently, Clique struggles to support distributed applications requiring a high transaction processing speed, e.g., online gaming. To overcome this deficiency, we propose an Express Clique (ExClique) algorithm by improving Clique from two perspectives: compacting blocks for broadcasting to shorten communication delay and prohibiting the occurrences of no-turn blocks. For performance evaluation, we implement ExClique by modifying Geth of Ethereum, the software implementing Clique, and deploy a permissioned blockchain network by using container technology. The experimental results show that ExClique achieves a substantial enhancement in transactions per second (TPS). Specifically, it boosts TPS by 2.25× in a typical network with 21 consensus nodes and an impressive 7.01× in a large-scale network with 101 consensus nodes when compared to Clique. © 2025, CC BY.

关键词： Blockchain

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A Deep Learning Framework for Prediction of Heart Diseases from Clinical Observations

A Deep Learning Framework for Prediction of Heart Diseases f...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Daze Thomas Mohan M V. Narasimharaj Yamuna S Artificial Intelligence & Data Science SNS College of Engineering Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Information Technology Bannari Amman Institute of Technology Erode India

Among today's youth, heart disease has emerged as a major health concern. Many forms of cardiac disease can be traced back to bad habits and clinical characteristics. Many other models have been proposed by researchers to aid in the diagnosis of various conditions, however the vast majority either has poor accuracy performance or are overly complex to implement in real-time clinical settings. In addition, these models tend to function correctly for smaller class sets, but their performance degrades with an increase in class count. To facilitate answer these issues, this study suggests creating a novel, effective, and extensible strategy for disease prediction. This employs a deep learning technique, which boosts multiple sets of clinical data using a combination of StackingCV classifier-bagging and feature-boosting. Feature variance is increased by augmenting these vectors with a boosting of classifiers at the outset. AdaBoost strategy (ABA), Support-Vecto-Classifier (SVC), and Extreme-Gradient-Boosting (XGBoost) are then applied to these characteristics to help improve accuracy performance at the cost of minuscule increases in computational delays. Single illness classifier accuracy was 93.44% and multiple disease classifier accuracy was 60.5% when the model was tested using datasets from the University of California, Cleveland. When contrasted to various state-of-the-art methods and classifiers, such as a Deep-Neural-Network (DNN), Random-Forest (RF), XGBoost, &SVC, the suggested model was shown to be at least 3.9% more accurate. Thus, it is appropriate for use in live medical settings.

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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