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检索条件"任意字段=2016 IEEE International Conference on Progress in Informatics and Computing"

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Proceedings of the 2010 ieee international conference on progress in informatics and computing, PIC 2010: Preface

Proceedings of the 2010 IEEE International Conference on Pro...

引用

Proceedings of the 2010 ieee international conference on progress in informatics and computing, PIC 2010 2010年 1卷

作者： Zhou, Mengqi Wang, Yinglin Inoue, Katsuro IEEE Beijing Section China Shanghai Jiao Tong University China Osaka University Japan

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Proceedings of the 2010 ieee international conference on progress in informatics and computing, PIC 2010: Preface

Proceedings of the 2010 IEEE International Conference on Pro...

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Proceedings of the 2010 ieee international conference on progress in informatics and computing, PIC 2010 2010年 2卷

作者： Zhou, Mengqi Wang, Yinglin Inoue, Katsuro IEEE Beijing Section China Shanghai Jiao Tong University China Osaka University Japan

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Modified Metaheuristic Optimization Algorithm for Hyperparameter Tuning in Mental Health Applications 6

Modified Metaheuristic Optimization Algorithm for Hyperparam...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Kozakijevic, Stanislava Jovanovic, Luka Tedic, Smiljana Jankovic, Ninoslava Zivkovic, Miodrag Bacanin, Nebojsa Faculty of Media and Communication Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9798331522667

The World health organization considers a mentally healthy person to be able to manage life’s stresses, work productively, and contribute to their communities. However, mental disorders, affecting 970 million people in 2019, often reduce quality of life, necessitating accurate diagnosis and therapy. Affective disorders, encompassing depressive and manic episodes, vary in severity and pattern, with conditions like bipolar disorder and recurrent mood disorders identified through ICD-10 classifications. Diagnosis, typically made by expert teams, remains challenging due to the complex and variable nature of mental disorders, leading to frequent misdiagnoses that hinder treatment progress and erode patient trust. Artificial intelligence (AI) holds promise in supporting mental health diagnosis by identifying subtle patterns in symptoms and validating expert conclusions. While AI’s specialized nature poses limitations, integrating it into clinical practice can improve diagnostic accuracy and reduce errors. High quality models can assist doctors by ruling out less likely conditions and confirming diagnoses. Optimizing AI models, however, requires effective hyperparameter selection, a complex task classified as NP-hard. Metaheuristic algorithms offer a solution, enabling efficient parameter tuning and improving AI model performance. Given the critical implications of accurate mental health diagnoses, leveraging AI and optimization techniques is essential to enhance outcomes and improve lives. This work proposed a modified metahetusirc aimed at optimizing classification accuracy. When evaluated on a genuine dataset the best performing models attain an accuracy of 0.972222 suggesting feasibility in real world situations. © 2025 ieee.

关键词： Patient treatment

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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ieee international conference on Consumer Electronics (ICCE)

作者： Linh Nguyen Cheol-Hong Min Hanho Lee Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA Department of Electrical. and Computer Engineering University of St. Thomas USA Department of Electrical and Computer Engineering Inha University Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10– 6 to 10– 9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous tradeoff between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reli-ability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

关键词： 6G mobile communication Quantum computing Computer network reliability System performance Bit error rate Parity check codes Error correction codes Decoding Reliability Channel coding

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), ieee international conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Proceedings of 2016 ieee 15th international conference on Cognitive informatics and Cognitive computing, ICCI∗CC 2016

Proceedings of 2016 IEEE 15th International Conference on Co...

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15th ieee international conference on Cognitive informatics and Cognitive computing, ICCI*CC 2016

ISBN: (纸本)9781509038466

The proceedings contain 76 papers. The topics discussed include: deep learning and deep thinking new application framework by CICT;identifying users and activities with cognitive signal processing from a wearable headband;image-to-image face recognition using dual linear regression based classification and electoral college voting;design and implementation of user-oriented video streaming service based on machine learning;disaster-aware smart routing scheme based on symbiotic computing for highly-available information storage systems;zero-crossing analysis of LeÁvy walks for real-time feature extraction: composite signal analysis for strengthening the IoT against DDOS attacks;sentiment user profile analysis based on forgetting curve in mobile environments;an information theoretic criterion for adaptive multiobjective memetic optimization;a sparse temporal mesh model for brain decoding;a geometrical and logical unification of mind, light and matter;qualitative analysis of pre-performance routines in throwing using simple brain-wave sensor;simplification and visualization of brain network extracted from FMRI data using cerebra;an action guided constraint satisfaction technique for planning problem;and an efficient reduction algorithm based on natural neighbor and nearest enemy.

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Welcome from the general chairs

PIC 2016 - Proceedings of the 2016 IEEE International Confer...

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PIC 2016 - Proceedings of the 2016 ieee international conference on progress in informatics and computing 2016年 7-8页

作者： Zhang, Kang Zhou, Mengqi Wang, Yinglin University of Texas Dallas United States IEEE Beijing Section China Shanghai University of Finance and Economics China

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Refinement of a Newton Reciprocal Algorithm for Arbitrary Precision Numbers 4

Refinement of a Newton Reciprocal Algorithm for Arbitrary Pr...

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4th ieee international conference on progress in informatics and computing (ieee PIC)

作者： Cheng, Yiping Liu, Ze Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

ISBN: (纸本)9781509034840

For arbitrary precision numbers, reciprocal computing algorithms based on Newton iteration is asymptotically the fastest. In this work we provide a refined algorithm based on the Newton reciprocal algorithm by Brent and Zimmermann in their MCA book. The key techniques used in the refinement are D1 balancing, clear specification, remainder operation, and economical multiplication. The refined algorithm is more general, and gives exact and unique result. Numerical results show that these improvements are made without the cost of time efficiency. There is still the potential to further improve the efficiency by utilizing a short multiplication algorithm.

关键词： Newton iteration numerical algorithms computer algebra multiprecision computing

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An Optimized Service Selection Model Based on Dynamic Trust 4

An Optimized Service Selection Model Based on Dynamic Trust

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4th ieee international conference on progress in informatics and computing (ieee PIC)

作者： Wang, Zongjiang Wang, Yingshu Zhongyuan Univ Technol Sch Comp Sci Zhengzhou Peoples R China Zhengzhou Inst Technol Zhengzhou Peoples R China

ISBN: (纸本)9781509034840

A cloud service selection model of the cloud service management system was proposed based on dynamic trustworthiness in order to select a trusted service which satisfies user's request from a lot of services with similar or same functions. A service selection algorithm was proposed in order to select the closest classification for the user's requester. A trust evaluation mechanism was introduced, combined with direct trust and domain recommended trust. Then a service resource was selected among the requester's classification, which is trusted, When the transaction was completed, the service satisfaction was evaluated and the trust degree was updated. Simulation results show that the model can improve the service requesters' satisfaction and has certain resilience to intentional noncooperation.

关键词： cloud computing dynamic trust cloud service selection SLA

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CRUPA: A Container Resource Utilization Prediction Algorithm for Auto-Scaling Based on Time Series Analysis 4

CRUPA: A Container Resource Utilization Prediction Algorithm...

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4th ieee international conference on progress in informatics and computing (ieee PIC)

作者： Meng, Yang Rao, Ruonan Zhang, Xin Hong, Pei Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China China Natl Aeronaut Radio Elect Res Inst Software Dept Shanghai Peoples R China

ISBN: (纸本)9781509034840

computing resource utilization of applications may vary over time and inappropriate static resource provision would cause resource wastage or performance loss. Auto scaling based on instant demand is a simple solution but it also introduces latency of scaling. An accurate resource demand prediction algorithm is able to eliminate the side effects of scaling. To address this issue, we present CRUPA, a resource utilization prediction algorithm based on a time series analysis model (ARIMA) combined with Docker container technique. We also design a comparison experiment to evaluate the proposed algorithm and its average forecast error is only 6.5% in the short term, which is much lower than the most common model based on threshold (16.9%) on the same dataset. The result shows that CRUPA not only has high prediction accuracy but also scales the resource well.

关键词： cloud computing auto scaling docker container

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