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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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Recent Trends and Challenges in Assistive Applications for Sinhala-Speaking Adults with Dyslexia: A Decade in Review 5

Recent Trends and Challenges in Assistive Applications for S...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Perera, Peshala Sumanathilaka, Deshan Informatics Institute of Technology School of Computing Colombo006 Sri Lanka Swansea University School of Mathematics and Computer Science United Kingdom

ISBN: (纸本)9798331530983

This paper discusses assistive technologies developed for Sinhala-speaking dyslexics, focusing on the challenges they face and how existing tools attempt to address them. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzed research publications from 2015 to 2024, highlighting topics such as phonetic processing, text normalization, and error detection. Of the 85 initially reviewed, 32 were included in the meta-analysis to represent major challenges posed by the lack of annotated datasets and Natural Language Processing (NLP) resources tailored to the cognitive and linguistic needs of the adult patient population. This review points out the need for the extension of low-resource language toolkits and proper integration of assistive NLP applications. The study concludes that while significant progress has been made, a collaborative, interdisciplinary approach is crucial to improving literacy, independence, and social participation for Sinhala-speaking adults with dyslexia. © 2025 ieee.

关键词： Sentiment analysis

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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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Proceedings of 2014 ieee 13th international conference on Cognitive informatics and Cognitive computing, ICCI∗CC 2014

Proceedings of 2014 IEEE 13th International Conference on Co...

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13th ieee international conference on Cognitive informatics and Cognitive computing, ICCI*CC 2014

ISBN: (纸本)9781479960811

The proceedings contain 80 papers. The topics discussed include: from information revolution to intelligence revolution: big data science vs. intelligence science;polyscale analysis and fractional operators for cognitive systems;towards a differential diagnostic of PTSD using cognitive computing methods;big data and societal challenges;overcoming the fear of semantics;non-Kolmogorov probability and its use for constructing a model of human perception process;position update mechanisms for enhanced particle swarm classification;cognitive reasoning and inferences through psychologically based personalised modelling of emotions using associative classifiers;functional networks of anatomic brain regions;cognitive identification of systems using nonlinear dynamics;a bio-inspired architecture of the vestibular system for body balance of a humanoid robot;rank level fusion of multimodal cancelable biometrics;and a cognitive approach for texture analysis using neighbors-based binary patterns.

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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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Speech Emotion Recognition Using ML Models and Audio Features

Speech Emotion Recognition Using ML Models and Audio Feature...

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Annual ieee Systems conference

作者： Tytiana James T. Maheswara Reddy Yenumula Tanner Mergist Rafeeq Muhammad Ali Harimi Kasem Khalil Ashok Kumar School of Computing and Informatics University of Louisiana at Lafayette Lafayette USA Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The ability to recognize and respond to human emotions has become a crucial component in enhancing humancomputer interaction, with the development of speech emotion recognition technologies playing a pivotal role in this advancement. While deep learning models have driven significant progress in the field of speech emotion recognition, traditional machine learning algorithms offer a practical alternative, balancing performance with lower computational requirements. This study presents a comprehensive approach to speech emotion recognition using the Berlin Emotional Speech Database, classifying seven emotional states: anger, sadness, anxiety or fear, disgust, boredom, happiness, and neutrality. The study employs a range of acoustic features, including pitch, RMS energy, MFCCs, and formants, which are combined with 14 statistical descriptors and extracted using tools like OpenSMILE. Preprocessing steps, such as normalization, noise reduction, and silence removal, are applied to enhance the quality and reliability of the data. The performance of traditional machine learning models, including Support Vector Machine, Random Forest, and $k$ -Nearest Neighbors, is evaluated on the processed dataset. The results demonstrate the effectiveness of these traditional models, with Support Vector Machine achieving the highest classification accuracy of 90.65 %, followed by Random Forest and $k$ -Nearest Neighbors. The results of this study highlight the capacity of traditional machine learning techniques to effectively capture the complexities of emotional expression, while circumventing the computational burden associated with deep learning models. The practical relevance of this research extends to real-time applications across various domains, including healthcare, virtual assistants, and customer service, where the demand for efficient and reliable emotion recognition systems is paramount.

关键词： Support vector machines Deep learning Emotion recognition Accuracy Computational modeling Virtual assistants Anxiety disorders Speech recognition Feature extraction Random forests

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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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PerCom WiP'14: 2014 ieee international conference on pervasive computing and communications work in progress - Welcome and committees

2014 IEEE International Conference on Pervasive Computing an...

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2014 ieee international conference on Pervasive computing and Communication Workshops, PERCOM WORKSHOPS 2014 2014年

作者： Martin, Thomas L. Schiele, Gregor Virginia Tech United States Insight Galway National University of Ireland Galway Ireland

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