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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是211-220 订阅

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PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

引用

3rd international conference on algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Boosting algorithms Empowering Heart Disease Prediction for Enhanced Medical Accuracy 3

Boosting Algorithms Empowering Heart Disease Prediction for ...

引用

3rd international conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Sharma, Urvashi Saxena, Kanak Samrat Ashok Technological Institute Computer Science & Engineering Department Vidisha India Samrat Ashok Technological Institute Head Computer Science & Engineering Department Vidisha India

ISBN: (纸本)9798331523893

Heart disease remains a leading cause of mortality worldwide, necessitating accurate and reliable predictive models to aid early diagnosis and treatment. Traditional machine learning methods like LR and DT Classifiers offer reasonable recall rates but suffer from lower precision and accuracy. To address this challenge, we employed advanced boosting algorithms, specifically GradientBoostingClassifier and XGBClassifier, to enhance predictive accuracy in heart disease detection. Our study shows that these algorithms achieve accuracy rates above 93%, with precision exceeding 52%, and a significantly low False Positive Rate below 0.5%. However, the trade-off involves lower recall rates, suggesting some true cases remain undetected. Boosting algorithms thus provide a robust approach for heart disease prediction, though attention to false negatives is essential for comprehensive application. © 2025 IEEE.

关键词： Boosting algorithms Gradient Boosting Classifier Heart Disease Prediction Machine Learning Medical Accuracy XGBClassifier

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Industrial IoT Smart Manufacturing systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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I2C Bridge on FPGA to Facilitate Vast Connection of Heterogeneous IoT Devices in Agriculture Applications 24

I2C Bridge on FPGA to Facilitate Vast Connection of Heteroge...

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8th international conference on algorithms, Computing and systems, ICACS 2024

作者： Patsianotakis, Charalampos Xenakis, Apostolos Chaikalis, Costas Kosmanos, Dimitrios Stamoulis, George Kakarontzas, George Electrical and Computer Engineering University of Thessaly Volos Greece Digital Systems University of Thessaly Larissa Greece

ISBN: (纸本)9798400718304

The integration of sensor devices into an IoT network is experiencing significant growth. Along with the rise of several application demands, it is necessary to continuously develop new designs to accommodate these changes. To facilitate the integration of new features into the IoT system, the electronics of end - node devices are comprised of the parts related to the Main board (MCU unit), various Radio - Frequency (RF) modules and other essential components. These include the peripheral units, designed specifically to meet the user's application requirements. The goal is to maintain a single Main Board for all devices, thereby reducing logistics management effort and costs. To this end, this paper presents a solution, tailored at consolidating the production parts diversity, within the Agriculture IoT applications domain, thereby streamlining manufacturing processes and enhancing logistics personnel productivity. The proposed approach revolves around the initialization of a communication protocol translator, between the Integrated Circuits (ICs) of Peripheral - Boards and the Main - Board, effectively simplifying the interface, across all productions. Finally, we design and test a software drive, to facilitate the integration at firmware level. Our energy efficient experiments prove the applicability of our proposed solution. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Firmware

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Machine Learning algorithms for Dynamic Financial Management and Fraud Detection: Implementing Anomaly Detection, and Risk Assessment Techniques 1

Machine Learning Algorithms for Dynamic Financial Management...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Haripriya, T. Chandramouli, S. Kavitha, M. Parne, Malathi Devi Srinivas, Desini Bala, B. Kiran Vignana Jyothi Institute of Management Department of Finance Bachupally Hyderabad India Reah School of Business Management Aicte Osmania University Hyderabad India Siddhartha Institute of Computer Sciences Ibrahimpatnam India Hyderabad India ManPower Development College Hyderabad India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798331529246

Ensuring security and identifying fraudulent activity have become critical concerns in an era where financial transactions are increasingly conducted digitally. There is a need to enhance the efficacy of the existing fraud detection systems, which requires the application of mainly the higher order machine learning algorithms. This study explores a hybrid approach that blends autoencoder-based anomaly detection with XGBoost classification. By computing reconstruction losses, the autoencoder is used to detect departures from typical transaction patterns. The proposed model is implemented using python software. The XGBoost model further refines this approach by classifying transactions based on features derived from the autoencoder and additional transaction attributes. The hybrid model achieves an accuracy of 99%. These outcomes demonstrate how well and quickly the hybrid model can identify fraudulent transactions while reducing errors. By improving overall system performance and adjusting to changing fraud tactics, this method provides a notable breakthrough in financial fraud detection. © 2025 IEEE.

关键词： anomaly detection autoencoder fraud detection machine learning xgboost

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： Data reliability

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Advancements in Isogeny-Based Cryptography: A Mathematical Approach to Post-Quantum Security

Advancements in Isogeny-Based Cryptography: A Mathematical A...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Soni, Abhay Singh, Sunidhi Chaturvedi, Atul Allenhouse Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331529833

In this paper, we have discussed isogeny-based cryptography as a rather appealing option for post-quantum security. We provide analysis of current protocols like SIDH (Supersingular Isogeny Diffie-Hellman) and SIKE (Supersingular Isogeny Key Encapsulation) setting up fundamental issues with their security and performance. In this work we introduce a novel isogeny pathfinding algorithm that employs graph theory as well as probabilistic models in order to lower its computational complexity. The results show significant improvement in both efficiency and security, strengthening the case for the use of isogeny based systems in a quantum-resilient cryptographic space. © 2025 IEEE.

关键词： Encryption algorithms

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A Study of Predicting Social Networking Sites(SNS) Media Disorder Survey Data Using a Machine Learning Approach

A Study of Predicting Social Networking Sites(SNS) Media Dis...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Munia, Jerin Akther Tahmiduzzaman, K.B.M. Saad, Izazul Haque Islam, Md Mynul Akash, Abdul Hady Zumma, Md. Thoufiq Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331509828

This study uses survey data and machine learning algorithms to forecast social media disorder in people. A total of 600 individuals answered questions on their social media usage patterns, internet habits, demographics, and signs of social media disorder. This dataset was used to train a number of machine learning models, such as Gaussian Naive Bayes (GaussianNB), Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM). According to the findings, the GaussianNB model had the greatest accuracy of 97%, followed by SVM (94%), Random Forest (91%), Decision Tree (92%), and KNN (90%). These results demonstrate how well machine learning can identify those who may be at risk for social media disorder. In order to improve prediction performance, future research will concentrate on increasing feature size, diversifying datasets, and refining models. Furthermore, incorporating these models into user-friendly apps and intervention programs may offer useful resources for the early diagnosis and treatment of social media illness. © 2025 IEEE.

关键词： Adversarial machine learning

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GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large systems of Nonlinear Equations 4th

GPU Acceleration of the Enhanced Jaya Optimization Algorit...

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4th international conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Silva, Bruno Guerreiro Lopes, Luiz Doctoral Program in Informatics Engineering University of Madeira Madeira Island Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Island Funchal9020-105 Portugal

ISBN: (纸本)9783031812408

This paper presents a GPU-based parallel implementation of the Enhanced Jaya (EJAYA) algorithm for solving large-scale systems of nonlinear equations. EJAYA is a gradient-free metaheuristic optimization scheme based on the population-based parameter-less Jaya algorithm that has recently been shown to be capable of solving nonlinear equation systems, a class of challenging problems that are difficult to solve by traditional approaches, particularly as systems become larger. The proposed parallel algorithm was implemented using the Julia programming language on a high-performance GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB GDDR6X VRAM and was tested using a set of difficult scalable nonlinear equation system problems. The obtained results, with average speedups up to 122.25x, evidenced the capability of the proposed GPU-based parallel algorithm to meet the computational demands of the important class of problems under consideration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Performance Optimization in SDN-VANET Using Particle Swarm Optimization and Differential Evolution algorithms 3rd

Performance Optimization in SDN-VANET Using Particle Swarm O...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Phong, Lam Thien Ninh, Bui Trung Mai, Dinh Thi Thai Department of Telecommunications Systems Faculty of Electronics and Telecommunications University of Engineering and Technology - VietNam National University HaNoi Viet Nam

ISBN: (纸本)9783031809422

Software-Defined Vehicular Ad Hoc Network (SDN-VANET), merging the Software-Defined Network (SDN) with VANET, offers many applications, from environmental monitoring to intelligent transportation systems. However, ensuring optimal performance of the networks amidst dynamic vehicular environments remains a formidable challenge. This paper proposes Particle Swarm Optimization Algorithm (PSO) and Differential Evolution Algorithm (DE) as optimal techniques in SDN-VANET, aiming to optimize TCP Window size and UDP Buffer size. After running simulations and evaluations, the results showed that these optimal algorithms effectively improved network performance components, such as increasing throughput, increasing bandwidth, reducing the number of TCP packet retransmissions, and reducing packet dropping rate (PDR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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