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检索条件"任意字段=Proceedings of the 5th International Conference on Algorithms, Computing and Systems"

共 7631 条记录，以下是81-90 订阅

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5th international conference on Neural computing for Advanced Applications, NCAA 2024

5th International Conference on Neural Computing for Advance...

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5th international conference on Neural computing for Advanced Applications, NCAA 2024

ISBN: (纸本)9789819770038

the proceedings contain 89 papers. the special focus in this conference is on Neural computing for Advanced Applications. the topics include: Online Car-Hailing Order Matching Method Based on Demand Clustering and Reinforcement Learning;Multi-objective Optimization of Antenna Based on Improved WOA-BP Neural Network;A Multi-mechanism Collaborative Seagull Optimization Algorithm for Optimizing BP Neural Network Classification Model;an Analysis on Balance Model of Exploration and Exploitation Under Decoupled-Learning Pattern for Large-Scale Particle Swarm Optimizers;marine Ship Detection Under Fog Conditions Based on an Improved Deep-Learning Approach;skeleton-Based Point Cloud Sampling and Its Facilitation to Classification;multi-agent Reinforcement Learning for Taxi-Fleet Cruising Strategy in Ride-Hailing Services;a New Indoor Occupancy Detection Model by Integrating the Efficient Multi-scale Attention Mechanism into the EfficientDet Model;a Novel Automatic Generation Method for Neural Network by Using Iterative Function System;a Predictive Maintenance Platform for a Conveyor Motor Sensor System Using Recurrent Neural Networks;A Deep Learning-Based Method Facilitates scRNA-seq Cell Type Identification;adaptive Hierarchical Clustering Based Student Group Exercise Recommendation via Multi-objective Evolutionary Method;Quantile Regression and GCN Ensembled Hybrid Interval Forecasting Model for Wind Power Generation;efficient Path Planning for Large-Scale Vehicular Networks via Multi-agent Mean Field Reinforcement Learning;a Fast and Accurate Reconstruction Method for Boiler Temperature Field Based on Inverse Distance Weight and Long Short-Term Memory;RoBERTa-wwm-CBA: A Mental Disease Identification Model Based on RoBERTa-wwm and Hybrid Neural Networks;3D Pose Estimation of Markerless Fish on Deep Learning;safety Helmet-Wearing Detection Method Fusing Pose Estimation;Clothes Image Retrieval via Learnable FashionCLIP;mannequin2Real+: A Two-Stage Framework for Generating P

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5th international conference on Neural computing for Advanced Applications, NCAA 2024

5th International Conference on Neural Computing for Advance...

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5th international conference on Neural computing for Advanced Applications, NCAA 2024

ISBN: (纸本)9789819770069

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5th international conference on Neural computing for Advanced Applications, NCAA 2024

5th International Conference on Neural Computing for Advance...

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5th international conference on Neural computing for Advanced Applications, NCAA 2024

ISBN: (纸本)9789819770007

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14th international conference on Applications and Techniques in Information Security, ATIS 2024

14th International Conference on Applications and Techniques...

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14th international conference on Applications and Techniques in Information Security, ATIS 2024

ISBN: (纸本)9789819797424

the proceedings contain 24 papers. the special focus in this conference is on Applications and Techniques in Information Security. the topics include: Optical Neural Networks – A Strategy for Secure Quantum computing;guarding Against Quantum threats: A Survey of Post-Quantum Cryptography Standardization, Techniques, and Current Implementations;cryptographic Distinguishers through Deep Learning for Lightweight Block Ciphers;detection and Mitigation of Email Phishing;securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques;Design of Image Encryption Technique Using MSE Approach;Low Latency Binary Edward Curve Crypto Processor for FPGA Platforms;Augmenting Security in Edge Devices: FPGA-Based Enhanced LEA Algorithm with S-Box and Chaotic Functions;machine Learning Approach for Malware Detection Using Malware Memory Analysis Data;DDOS Attack Detection in Virtual Machine Using Machine Learning algorithms;an Unsupervised Method for Intrusion Detection Using Novel Percentage Split Clustering;HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control systems Using MLPNN and Attention Mechanisms;silent threats: Monitoring Insider Risks in Healthcare Sector;Enhanced Deep Learning for IIoT threat Intelligence: Revealing Advanced Persistent threat Attack Patterns;Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water Utilities;Enhancing FGSM Attacks with Genetic algorithms for Robust Adversarial Examples in Remote Sensing Image Classification systems;GAN-Enhanced Multiclass Malware Classification with Deep Convolutional Networks;IOT Based Locker Access System with MFA & Remote Authentication;a Secure Authentication Scheme between Edge Devices Using Hypergraph Hashing Technique in Iot Environment;enhancing Access Control and Information Sharing in Cloud IoT with an Effective Blockchain-Based Authority System.

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WCES 2021 - proceedings of the Workshop on Control and Embedded systems, Co-located with EASTON 100 - 5th international conference on computing Sciences, ICCS 2021

WCES 2021 - Proceedings of the Workshop on Control and Embed...

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2021 Workshop on Control and Embedded systems, WCES 2021

the proceedings contain 12 papers. the topics discussed include: performance evaluation of distributed networks for Internet of things;connectivity pattern analysis for virtual simulation design, based on high-performance game analysis;computational study for improvement of aerodynamic performance of airfoil by changing various aerodynamic properties;an experimental design approach to IoT enabled smart parallel irrigation system using embedded microcontrollers;to develop, test and record a 3 lead EMG electrode and flex sensor on a 3D prosthetic limb with different gait patterns using Arduino microcontroller;and low-cost system for the management of hospital services, applied to hospitalized patients through the use of IoT technology.

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

ISBN: (纸本)9783031859328

the proceedings contain 28 papers. the special focus in this conference is on Image Processing, Computer Vision, and Pattern Recognition. the topics include: Image-Based Seal Recognition: Approaches and Challenges in Current Automated systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Quantum Task Allocation and Scheduling with Security Enhancements via Quantum Key Distribution 5

Quantum Task Allocation and Scheduling with Security Enhance...

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5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Singh, Animesh Mohan, Ankita Nayak, Satyakam Sharma, Neeraj Chakraborty, Sanjukta Bandyopadhyay, Anjan Kiit University School of Computer Engineering Bhubaneswar751024 India Lovely Professional University School of Computer Engineering Jalandhar144411 India Techno International New Town Kolkata700156 India

ISBN: (纸本)9798331522100

As quantum computing progresses, the need for optimized task allocation and scheduling becomes paramount to efficiently harness quantum resources. Simultaneously, ensuring the security of communications in quantum systems is critical, given their vulnerability to quantum-specific attacks. this paper introduces a framework for quantum task allocation and scheduling, augmented with security enhancements using Quantum Key Distribution (QKD). By incorporating novel quantum scheduling algorithms and cryptographic protections, this mechanism offers both high efficiency and strong security. Analytical formulations and simulations confirm the robustness of our approach, demonstrating improvements in both scheduling efficiency and secure communication. © 2025 IEEE.

关键词： Qubits

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Intelligent Legal and Technical Strategies to Mitigate Cybersquatting: Bridging Global Frameworks and Indian Contexts 5

Intelligent Legal and Technical Strategies to Mitigate Cyber...

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5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Mondal, Avijit Paikaray, Jharasri Chatterjee, Pinaki Sankar Asbm University School of Information Systems Bhubaneswar India Asbm University School of Law Bhubaneswar India Kiit University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

Cybersquatting, the unauthorized registration of domain names mimicking established trademarks, threatens online identity management and intellectual property rights. this study examines the legal and regulatory frameworks addressing cybersquatting, focusing on India's reliance on the Trade Marks Act, 1999, and the IT Act, 2000, in contrast with international policies like UDRP and ACPA. through case studies and statistical insights, it identifies legislative gaps in India and proposes actionable reforms to enhance protections, promote ethical digital practices, and foster a secure online ecosystem. © 2025 IEEE.

关键词： international cooperation

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Achieving 5G Network Energy Efficiency by Hybrid Machine Learning Models for Channel and Power Allocation 5

Achieving 5G Network Energy Efficiency by Hybrid Machine Lea...

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5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Ramya, H. Jaya, T. Rajendran, V. Department of Electronic and Communication Engineering Tamil Nadu Chennai India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522100

High data speeds, low latency, and huge connection are becoming increasingly important in 5G networks, making energy efficiency a significant concern. In order to achieve the twin objectives of lowering energy consumption and keeping network performance intact, our research centers on creating hybrid machine learning models to improve channel and power allocation. 5G networks are complex and dynamic, with varying user requirements, fluctuating traffic loads, and different environmental circumstances. Traditional resource allocation algorithms have a hard time keeping up. the authors of this work offer a hybrid strategy that is both adaptable and scalable by integrating supervised and unsupervised learning with reinforcement learning (RL). To begin, the hybrid model uses supervised learning to allocate channels and power using past network data. Next, reinforcement learning takes into account network circumstances, user mobility, and traffic variations to make real-time adjustments to the allocation. To improve the distribution of resources in dense networks, unsupervised learning is used to find clusters and patterns in the data. the system is able to optimize energy consumption and anticipate and respond to changes in the network environment thanks to the integration of these models. With throughput and quality of service (QoS) unaffected, the simulation results show that the hybrid machine learning model is 30% more energy efficient than traditional techniques. In light of increasing data demands and strict energy requirements, the suggested technology provides a viable option for 5G networks that are both environmentally friendly and highly efficient. © 2025 IEEE.

关键词： Energy utilization

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Maternal risk prediction by early detection of Pre-Eclampsia and high-risk pregnancies using machine learning 5

Maternal risk prediction by early detection of Pre-Eclampsia...

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5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Kaur, Manpreet Girija, R. Singh, Maneesha Gupta, Tanvi Marwaha, Shreya Manav Rachna University Haryana Faridabad India Manav Rachna International Institute of Research and Studies Centre for Health Innovations Haryana Faridabad India

ISBN: (纸本)9798331522100

After 20 weeks of pregnancy, pre-eclampsia, a severe pregnancy syndrome, usually manifests as increased blood pressure and injury to other organs. In order to prevent adverse consequences for both the mother and the fetus, early detection and forecasting are essential for prompt intervention and therapy. Machine learning techniques are highly effective tools for predictive analytics in the healthcare industry, with the ability to identify high-risk pregnancies and enhance clinical decision-making. In this study, it has been suggested that machine learning be used to forecast pre-eclampsia by utilizing clinical and perinatal demographic information. We use a dataset containing information on maternal age, blood pressure readings, and other relevant parameters to train and evaluate predictive models. Artificial neural networks were found to be the most effective approach, achieving an accuracy of 75%, demonstrating their potential in accurately predicting pre-eclampsia and supporting timely medical interventions. © 2025 IEEE.

关键词： Prediction models

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