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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Fake News Detection on Social Media Using a Stacking Model 5th

Fake News Detection on Social Media Using a Stacking Model

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Ramesh, G.S. Supriya, K.H.S. Akash, P. Rukmananda Reddy, A. Tejaswini, V. Dharmireddi, Srinivasarao Department of Computer Science and Engineering VNR VJIET Telangana Hyderabad India Department of Cyber Security MasterCard St. LouisMO63304 United States

ISBN: (纸本)9789819997060

Fake news has existed since long before the Internet was invented. It can be defined as "fictitious articles deliberately fabricated to deceive readers". While some news may have a grain of truth, it frequently lacks any contextual information. They may not contain any sources which can be verified. Some of the reports may contain straightforward factual statements, but they are intentionally worded in an inflammatory sense, often omitting crucial information, or just give one point of view. We tried to understand the nuances in fake news in this research paper by its language characteristics. Initially the news articles content was pre-processed using techniques like Lemmatization and TF-IDF. The data was then classified using a Stacking Classifier, using Naïve Bayes, SVM, and Decision Tree is used as the base models and Logistic Regression is used as Metamodel. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Coarse registration of color point clouds by combining color feature and geometric feature 12

Coarse registration of color point clouds by combining color...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Sun, Peng Jiang, Shuhang Luan, Haiyang Wang, Chen Wang, Wanqi Liu, Yu School of Opto-Electronic Engineering Changchun University of Science and Technology Jilin Changchun130022 China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu Sichuan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Coarse registration is the initial step of aligning a point clouds with other clouds, aiming to put two point clouds in the correct position. There are many coarse registration methods, and among them, the SAC-IA (Sample Consensus Initial Alignment) is widely used. It selects corresponding point pairs by matching the geometric features of point clouds. It has high registration accuracy and fast registration speed. However, when the geometric features of the point clouds are relatively simple or not distinctive, its registration performance may not be very effective. With the development of color image sensors, acquiring color point clouds has become increasingly important. The RGB information of point clouds can effectively compensate for the shortcomings of the SAC-IA algorithm. The color characteristics of the interested points are extracted by fusing the color information of feature points and their neighboring points, including the first-order moment of point clouds color and the CFH of the point clouds, based on the traditional SAC-IA algorithm. Experiments have shown that the improved SAC-IA algorithm has better accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Color

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Auto-scalable and fault-tolerant load balancing mechanism for cloud computing based on the proof-of-work election

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science China(Information sciences) 2022年第1期65卷 131-146页

作者： Xiaoqin FENG Jianfeng MA Shaobin LIU Yinbin MIAO Ximeng LIU School of Cyber Engineering Xidian University State Key Laboratory of Cryptology Shaanxi Key Laboratory of Network and System Security Xidian University College of Mathematics and Computer Science Fuzhou University

Load balancing mechanism in technologies such as cloud computing has provided a huge opportunity for the development of large-scale projects. Although the conventional view is to build mechanisms that adopt a dynamic load balancing strategy, existing strategies cannot automatically scale platform(network) servers(nodes) to adapt for dynamic requests, but only guarantee load balancing for the pre-deployed nodes, thereby increasing resource consumption and decreasing networks' efficiency. We contend that existing load balancing mechanisms are inadequate for deploying dynamic applications. In this regard, we first adopt both load balancing and cloud computing virtualization technologies to modularly design a load balancing mechanism that provides a dynamically auto-scalable solution for large-scale and dynamic computing scenarios. Furthermore, we adopt the proof-of-work consensus, for a novel use during the lifecycle of master nodes in case of system failure caused by a failed master node, to demonstrate a fault-tolerant load balancing mechanism. We theoretically evaluate the security requirement of our mechanism and analyze its performance. Experimental results show that the mechanism supports auto-scalability and has a better performance compared to existing mechanisms such as the ordinary cluster.

关键词： cloud computing load balancing auto-scalable proof-of-work fault-tolerant

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HCL-Net: A Single Image Rain Removal Algorithm Based on Channel Attention and Multi-scale Feature Fusion 6

HCL-Net: A Single Image Rain Removal Algorithm Based on Chan...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Huang, Jin He, Pei Guangzhou University School of Cyberspace Security Guangzhou China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China

ISBN: (纸本)9798350325485

The investigation of rain removal from pictures has always been a pivotal matter in the domain of computer vision. Nevertheless, the majority of existing algorithms employ encoder-decoder structures that have been trained on artificially synthesized datasets, without taking into account the harm that heavy rain can cause to actual images. Batch normalization is unsuitable for low-level computer vision duties, and skip connections in encoder-decoder architectures are not always appropriate for rain removal. This has resulted in setbacks in relevant domains. In this article, we propose a groundbreaking single-image rain removal algorithm that is founded on channel attention and multi-scale feature fusion. Initially, we build a contextualized dilated restoration block to restore the rain-Affected image during the feature extraction phase. Secondly, we create a half-instance normalization residual block to conserve more scale information and to augment the modeling capacity of the model, without sacrificing performance while hastening model convergence. Finally, in response to the view that simple skip connections may not always be advantageous, we have constructed a LeakyReLU channel attention block to aggregate information and enhance the effectiveness of skip connections. Our comprehensive experiments on authentic rain datasets and images provide evidence that our model can more proficiently remove rain streaks and accumulations from pictures, in contrast to the existing methods. © 2023 IEEE.

关键词： Image reconstruction

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Multi-Layer Perceptrons and Convolutional Neural Networks Based Side-Channel Attacks on AES Encryption

Multi-Layer Perceptrons and Convolutional Neural Networks Ba...

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2023 International Conference on engineering Technology and Technopreneurship, ICE2T 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Faculty of Information Science and Technology Bangi 43600 Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350304107

Side - channel attacks (SCA) exploit vulnerabilities introduced by insecure implementations in order to get information about the data that is being processed or the data itself via device leakages. The majority of modern attack techniques use AES systems, and several studies have exploited side-channel leakages. Exploiting leakages for the first and final rounds, often known as outer rounds, is frequently the foundation of the strategy. There is a possibility that outer round leakages cannot be attacked due to inadequate countermeasures or device design. The attacker has to focus their attention on the inner rounds. Deep learning side-channel attacks, whether they include profiling or not, have the potential to recover secret keys stored in cryptographic devices that use masked AES. Mask value profiling is being used in more recent techniques for the extraction of secret keys. The objective of our research is to defeat masked AES by deep learning methods like as multi-layer perceptrons and convolutional neural networks are used. In an environment when profiling is not taking place, we extract sensitive information using DDLA. The HW (Hamming Weight) model is used by our approach in order to carry out DDLA by means of an innovative binary labeling strategy. Using the ChipWhisperer power traces and the ASCAD dataset, we show profiling and non-profiling attacks. The accuracy of key recovery is at its maximum when the AES implementation is either masked or unmasked. © 2023 IEEE.

关键词： Convolutional neural networks

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Intermittent Fault Diagnosis of Split-Star Networks and its Applications

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IEEE Transactions on Parallel and Distributed Systems 2023年第4期34卷 1253-1264页

作者： Song, Jiankang Lin, Limei Huang, Yanze Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou Fujian350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou Fujian350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability t I PMC(S n 2) tIPMC(Sn2) of n-dimensional split-star network Sn2 under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the n-dimensional split-star network Sn2 under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN. © 1990-2012 IEEE.

关键词： Fault detection

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