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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

共 195356 条记录，以下是301-310 订阅

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Quantitative Assessment in Dynamic PET/CT for Hepatocellular Carcinoma through Bayesian Prior information-based Actor-Critic algorithm 24

Quantitative Assessment in Dynamic PET/CT for Hepatocellular...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Li, Siming Huang, Jingchun He, Jianfeng Wang, Shaobo Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming Yunnan China PET/CT Center Hospital of Kunming University of Science and Technology First People's Hospital of Yunnan Kunming Yunnan China

ISBN: (纸本)9798400717482

Dynamic positron emission tomography/computed tomography (PET/CT) is a promising medical imaging technique that enables the visualization of biological processes via tracer activity. Analyzing the time-activity curves (TACs) of PET using pharmacokinetic models allows for the extraction of quantifiable physiological information relevant to hepatocellular carcinoma (HCC). Estimation of the pharmacokinetic model parameters requires advanced algorithms. The challenge lies in achieving accurate parameter estimation and obtaining parameter distributions that align with physiological characteristics. This paper introduces a reinforcement learning-based Actor-Critic (AC) algorithm and proposes a Bayesian prior information-based Actor-Critic (BPAC) algorithm. A dynamic parameter estimation environment for agent interaction was established, incorporating Bayesian optimization. The preceding information obtained through Bayesian optimization was used to design the reward and penalty signals in the interaction environment, and sampled as the initial value for the agent to estimate the parameters of 24 real HCC cases. Receiver operating characteristic (ROC) analyses were performed, using P-values and areas under the curve (AUC) to evaluate diagnostic performance between normal and tumor groups. Compared to the AC, the BPAC demonstrates statistically significant differences in K1, k2, k3, fa, and vb (all P © 2024 Copyright held by the owner/author(s).

关键词： Pharmacokinetics

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Smart Hackathon Management System with Machine Learning Integration 3

Smart Hackathon Management System with Machine Learning Inte...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Konkepudi, Bhagya Rekha Peddarapu, Rama Krishna Singam, Vamshi Krishna Katanguri, Sai Shashidhar Reddy Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331515911

In the rapidly growing landscape of hackathons and competitive coding events, managing large-scale participation and evaluating diverse submissions have become increasingly challenging. Traditional methods of handling hackathons are often inefficient, particularly in assessing and summarizing large volumes of participant data, presentations, and project documentation. This paper introduces a Smart Hackathon Management System that integrates advanced machine learning techniques to address these challenges, focusing on text summarization using seq2seq models and image-based text extraction. The proposed system automates the summarization of textual content, such as project proposals and idea presentations, allowing juries to make quick, informed decisions. Additionally, it incorporates a robust image text extraction module, designed to accurately retrieve and analyze text from images, further enhancing the system's capability to handle various forms of data. This integration of machine learning models within a hackathon management platform not only streamlines the evaluation process but also improves accuracy and reduces the manual workload involved in event management. The effectiveness of the proposed system is validated through extensive experiments, highlighting its potential for broader applications in event management and other domains requiring efficient handling of large datasets. © 2025 IEEE.

关键词： Optical character recognition

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An improved underwater target detection algorithm combined with programmable gradient information 24

An improved underwater target detection algorithm combined w...

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Hu, Gangzhi Chen, Chaoyang Yi, Zunhui He, Lei Hunan University of Science and Technology Hunan China

ISBN: (纸本)9798400710681

Underwater target detection technology has garnered extensive attention as a crucial auxiliary aquaculture technique in the domain of intelligent aquaculture. Nevertheless, owing to the degradation of underwater optical images resulting from the complexity of the underwater environment, the conventional target detection algorithm encounters challenges regarding detection accuracy, real-time performance, and computing resources in underwater applications. To address these issues, an enhanced YOLOv8 optimization approach tailored for underwater target detection is proposed. In this methodology, the Programmable Gradient information method is employed for auxiliary supervision to acquire reliable gradient information, and skip step links are eliminated during the neural network’s forward propagation to accommodate the circumstances of object-background overlap and feature information disappearance in underwater images. The experimental results demonstrate that the strengthened network model exhibits superior performance in underwater target detection, with the accuracy of mAP50 reaching 87.4%. Its performance surpasses that of YOLOv3, YOLOv5, YOLOv6, and YOLOv8 networks of the same scale, and its real-time performance is also suitable for deployment on underwater vehicles. © 2024 Copyright held by the owner/author(s).

关键词： Underwater imaging

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A Design Thinking Approach to the Development of Digitalized Hotel Valet Services

A Design Thinking Approach to the Development of Digitalized...

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2025 international conference on Advancement in Data Science, E-learning and information System, ICADEIS 2025

作者： Makky, Muhammad Al Raharjo, Tito Pandu Rahman, Sheina Fathur Yasirandi, Rahmat School of Computing Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia School of Information Technology King's Mongkut Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798331513320

Technological advancements in Indonesia have brought numerous benefits, including to hospitality industries like valet parking. However, traditional valet services still rely on manual processes, leading to inefficiencies such as long wait times, miscommunication, and security concerns. This study focuses on developing a digital valet system using the Design Thinking Approach to address these challenges. The proposed prototype demonstrates strong acceptance, addressing user expectations and improving overall service efficiency. During the testing phase, the System Usability Scale (SUS) was employed with 50 participants who had experience in valet services, resulting in a score of 77.7. This score places the prototype in the 'Good' grade and 'Acceptable' category, indicating that the proposed system successfully addresses the functionality and requirements of its intended users. By modernizing valet operations, this study highlights the potential for broader adoption of digital solutions in the hospitality sector, ultimately improving guest experiences and operational effectiveness. © 2025 IEEE.

关键词： Efficiency

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14th international conference on Frontier computing, FC 2024

14th International Conference on Frontier Computing, FC 2024

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14th international conference on Frontier computing, FC 2024

ISBN: (纸本)9789819627936

The proceedings contain 104 papers. The special focus in this conference is on Frontier computing. The topics include: Multimedia Digital AI Painting System Based on Machine Vision Algorithm;intelligent Decision Optimization of Big Data in the Digital Economy Under Deep Reinforcement Learning;development of Virtual Maintenance Training Platform for Electric Motors;design of Automatic Driving and Parking System for New Energy Vehicles Based on Artificial Intelligence;application of Machine Learning Algorithms to Mental Health Data Processing for College Students;on the Translation Strategy of "Intangible Cultural Heritage" Based on Visual Multimodality;research on the Improvement of Human-Machine Collaborative Machine Translation Model Based on Internet;research on Subway Station Space Design from the Perspective of Regional Culture;application of Intelligent Algorithm in Image Recognition Under the Vision of Artificial Intelligence;risk Assessment Model of Electricity Customer Data Based on Dynamic Grid Generation technology;an Evaluation Model of Equestrian Athletes’ Sense of Speed Based on Inertial Sensor Network;emotion Analysis of Low Resource Domain Based on Transfer Learning and Meta-Learning Methods;digital technology Integrated with Artificial Intelligence Digital Product Research and Development;research on Artificial Neural Network Prediction of the Strength of Metal-Based Carbon Fiber Reinforced Polymer Layer;Intelligent Recommendation Algorithm for New Media Content Based on AI technology;design of Daily Life Ability Training System Based on Intelligent Evaluation Algorithm;business Model for Developing Power Load Resources;practice of Artificial Intelligence technology in Enterprise Human Resource Management;medical Service Management Based on Modern information technology;tetris Game System Based on Java.

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601462

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble Learning Technique;the Impact of information Security Policies and Innovations in Digital technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU Data for Data-Driven Analysis of Grid Frequency Events;machine Learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced Datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;Teaching Computer Science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of Data Encryption Standard (DES) and Advanced Encryption Standard (AES) in Security Issues of Cloud computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601424

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Deep Joint Source Channel coding via Attention for Wireless Image Transmission 14th

Deep Joint Source Channel Coding via Attention for Wireles...

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14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： Chang, Haoze Ma, Lin Wang, Xuedong School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9783031861956

In digital communication, efficiently transmitting image and video data through constrained channels remains challenging nowadays. Traditional methods using separate source and channel coding often fail in dynamic environments. In this paper, we introduce a novel deep learning based (DL) attention joint source channel coding (AttenJSCC) approach, which enhances robustness and efficiency in wireless image transmissions. By integrating source and channel coding into a unified framework and incorporating our Enhanced Attention Feature (EAF) modules and the ECA attention mechanism, our method outperforms some of the existing JSCC techniques, especially in low SNR conditions. Our framework not only overcomes the limitations of current technologies but also reduces the storage and computational needs on edge devices, facilitating more efficient real time communication. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Channel coding

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Detection of Malicious Interpreted Code using ML to Optimize Parameters for Minimizing Prediction Errors

Detection of Malicious Interpreted Code using ML to Optimize...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dhawan, Manoj Kuril, Devendra Panadiwal, Himanshu Rawat, Hitesh Rawat, Anjali Rawat, Romil School of Engineering Avantika University Ujjain India Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Information Technology Indore India University of Extremadura Department of Business Management and Economics Badajoz Spain University of Extremadura Department of Computer and Communication Technology Badajoz Spain LabGeoInf-Research LABoratory in GEOmatics and INFormation systems Rome Italy

ISBN: (纸本)9798331538538

Mobile devices face growing cybersecurity threats, including malware, data breaches, malicious applications, and social engineering attacks. Cybercriminals exploit these vulnerabilities to gain unauthorized access, disrupt communication, and compromise sensitive information. Conventional malware detection methods, such as signature-based and heuristic techniques, often struggle to identify evolving threats due to their reliance on predefined patterns. This study presents an advanced malware detection framework that integrates a Reservoir Network with a Deep Learning Approach (DLA) to enhance detection accuracy and minimize prediction errors. The proposed method utilizes two static features-API call permissions and alert signals- to improve detection efficiency. Experimental evaluation, conducted using the Drebin malware dataset, demonstrates that the proposed model achieves an accuracy of 99.7 %, reduces the false positive rate to below 1 %, and ensures optimal processing speed with a batch size of 64 and a learning rate of 0.001. Compared to traditional approaches, this framework exhibits superior adaptability to obfuscated malware while enabling real-time threat detection. The findings emphasize the effectiveness of machine learning (ML)-driven security mechanisms in addressing emerging cybersecurity threats. © 2025 IEEE.

关键词： Computer viruses

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Joint Optimization of Transmission and Computation for Multi-source MEC System Based on Deep Reinforcement Learning 20th

Joint Optimization of Transmission and Computation for Mu...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Zhang, Qi Zhu, Jianhang Gong, Jie School of Computer Science and Engineering Sun Yat-sen University Guangzhou51006 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Guangdong Key Laboratory of Information Security and Technology Sun Yat-sen University Guangzhou51006 China

ISBN: (纸本)9789819628636

Due to the limitation of computing resources in end nodes, computation tasks in Internet of Things (IoT) can be offloaded to mobile edge servers. To measure the effectiveness and timeliness of data, Age of information (AoI) is proposed as a timeliness metric. In this paper, we consider the AoI minimization problem for computation-intensive status update. Our former work has designed optimal policy for Peak AoI (PAoI) optimization in single source case. This paper further studies the average AoI optimization in multi-source case, which is challenging. We focus on a multi-source Mobile Edge computing (MEC) system and study when to generate a new packet. The system operates in two modes: preemptive and non-preemptive computing server. Firstly, we formulate this problem as a Markov Decision Process (MDP). Then, we adopt Deep Reinforcement Learning (DRL) algorithm to optimize average AoI and PAoI respectively, considering both random transmission and computation time. Simulation results demonstrate that preemptive server gets better performance than non-preemptive server. We further extend the PAoI-optimal policy in single source case to the multi-source case, which is shown to be optimal for minimizing average PAoI with the DRL algorithm approaching its performance. When minimizing average AoI, the DRL algorithm achieves better performance than the PAoI-optimal policy. © IFIP international Federation for information Processing 2025.

关键词： Deep reinforcement learning

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