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Optimizing Parkinson’s Disease Detection: Hybrid S-transform-EEG Feature Reduction Through Trajectory Analysis

SN computer science 2025年第2期6卷 1-14页

作者： Afonso, Melina Maria Edla, Damodar Reddy Reddy, R. Ravinder Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim 403703 India Department of Computer Engineering Goa College of Engineering Farmagudi Goa Ponda 403401 India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad 500075 India

With Parkinson’s disease (PD) becoming more common, there is an increasing demand for accurate technology-driven PD detection. This research attempts to address this requirement. Because of its accessibility, use, and non-invasiveness, electroencephalography (EEG) has shown to be a potential technique for detection. Extraction of a wide range of features from the intricate EEG signals of Parkinson’s disease patients has been made possible by utilizing deep learning algorithms and transformation methods, enabling comprehensive analysis. But this data is enormous and presents computational difficulties, particularly when developing accessible and detection systems. To address this, the research proposes a feature reduction model that uses a hybrid approach to cut down the computational complexity. This approach combines four different feature scoring methods-ANOVA F-value, Mutual Information, Chi-square, and linear regression F-value-to generate a unified ranking score for each feature extracted from S-transformed EEG images. This score is thus generated leveraging the strengths of the diverse mechanisms and used to sort the features. The model decreases the feature set depending on these scores and utilizes a deep neural network to detect PD. This methodically lowers the feature count while monitoring the impact on the accuracy of the neural network. Finding the ideal feature combination is the goal, but maintaining the capacity to detect between people who have and don’t have Parkinson’s disease is a must. By balancing diagnostic accuracy and computational efficiency, the suggested technique ultimately yields accuracy rates of 95.94% and 97.31% on two different datasets with the reduced features, which outperforms other state-of-the art optimization algorithms compared in this research. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brain computer interface Deep learning Electroencephalography Feature reduction Parkinson’s disease Stockwell-transform

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Optimized Resource Allocation in Vehicle Edge Computing through Platoon Collaboration

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IEEE Internet of Things Journal 2025年第11期12卷 16129-16141页

作者： Zhao, Liang Feng, Yuhang Hawbani, Ammar Xu, Lexi Liu, Zhi Bi, Yuanguo Shenyang Aerospace University School of Computer Science Shenyang110136 China China Unicom China United Network Communications Corporation Research Institute Beijing100048 China University of Electro-Communications Department of Computer and Network Engineering Japan Northeastern University School of Computer Science and Engineering Shenyang110819 China

In modern vehicular networks, the absence of infrastructure support such as Roadside Units (RSUs) presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the Convoy Formation Algorithm considering Future Positions (CFA-FP) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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A transfer learning approach for continuous speech recognition system in Indian language sadri

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International Journal of Information Technology (Singapore) 2025年 1-10页

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur 713209 India

Sadri is the most widely used language of the Chotanagpur Plateau region of India. This is primarily a spoken language and developing an automatic speech recognition (ASR) system in Sadri is extremely important. When we searched the literature, we found no open ASR system or relevant resources in Sadri. So, we worked on developing an ASR system in Sadri. We created a Sadri speech corpus of around 20 h, employing 53 native speakers. Then, we implemented the baseline model using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). As the size of the training data was not sufficient, we investigated a transfer learning technique to improve performance. We found that ASR data are openly available in other related languages such as Hindi and Bengali. We used Bengali and Hindi data of 100 h each in a transfer learning framework along with the Sadri data. In our experiments, we found that the transfer learning-based model outperforms the baseline model. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： ASR Deep learning Indian language Sadri Speech recognition

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Two-Timescale Hierarchical Contract for Joint Computation Offloading and Energy Management in Edge Computing System

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1745-1760页

作者： Yan, Min Wang, Li Xu, Lianming Hou, Luyang Han, Zhu Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

To mitigate the rising energy costs in edge computing, edge servers (ESs) can receive revenues from reducing their energy usage by contracting with virtual power plant (VPP). ESs also respond to user equipment (UE) by providing computation offloading services. However, such two-layer coordinated trading of computation offloading and energy management in a VPP-ES-UE architecture should address the information asymmetry issue and uncertain task arrivals, posing challenges to maximizing the stakeholders' utility. In this paper, we formulate the two-layer coordinated trading as a hierarchical contracting problem, which addresses the information asymmetry using two contract models. We design a VPP-ES energy contract on a large timescale and an ES-UE computation contract on a small timescale, where ESs need to coordinate the future task arrivals under the energy reduction target assigned by VPP. We construct a two-timescale virtual queue to achieve the energy reduction goal by the long-term queue stability constraint and employ Lyapunov optimization to transform the original problem into an online optimization problem without requiring future information. An online two-timescale hierarchical contract optimization algorithm is proposed to solve the transformed problem. The simulation results demonstrate that our method achieves higher social welfare compared to other benchmarks. © 2013 IEEE.

关键词： Computation offloading

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A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment

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computers, Materials & Continua 2025年第4期83卷 13-64页

作者： Muhammad Arif Muhammad Rashid Department of Computer Science and Artificial Intelligence Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia Department of Computer and Network Engineering Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia

Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s ***,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models ***,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various *** factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these ***,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using *** identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software *** the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning *** the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major *** a result,this article provides valuable insights for researchers,practitioners,and *** assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.

关键词： Edge machine learning tiny machine learning model compression inference learning algorithms

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Developed a Machine Learning and Deep Learning Model for 5G MIMO Data Based Beam Selection and Intelligent Network Analytics

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SN computer science 2025年第3期6卷 1-21页

作者： Ashok, P. Suganya, R. Lekha, P. Pandian, Poornima Subashini, M. Nithya, M. Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Tamilnadu India Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam Engineering College Tamilnadu India Department of Computer Science and Engineering Sri Sairam Engineering College Tamilnadu India

Introduces an innovative application of 5G MIMO data in singular value decomposition (SVD) focusing on deep learning beam-selection and intelligent network analytics. This research explores the potential of leveraging 5G MIMO data to enhance network performance and efficiency through advanced ML techniques. Deep learning algorithms are employed for beam-selection, optimizing the transmission of data in 5G networks to improve throughput and reliability. Intelligent network analytics are applied to extract valuable insights from the vast amount of data generated by 5G MIMO systems, enabling proactive network management and optimization. By harnessing the capabilities of deep learning and intelligent analytics, this research aims to address key challenges in 5G network optimization and management. The integration of SVD techniques with 5G MIMO data offers a novel approach to enhancing network performance, enabling more efficient utilization of resources and improved quality of service for users. The insights gained from intelligent network analytics facilitate informed decision-making and proactive maintenance leading to greater reliability and resilience in 5G networks. This research contributes to the advancement of 5G technology by leveraging ML to unlock the full potential of MIMO data for optimizing network performance and enabling intelligent network management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Beam-selection Deep learning Intelligent management MIMO Network analytics Network performance Optimization Proactive maintenance Singular value decomposition

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Absorbing Time Synchronization Errors Using Shadow Queues in Time-Sensitive Networking

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IEEE Internet of Things Journal 2025年第9期12卷 12912-12926页

作者： Yang, Hao Zhang, Tong Feng, Xiaoqin Ma, Yanying Zhang, Liwei Ren, Fengyuan Lanzhou University School of Information Science and Engineering Lanzhou730000 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Time-sensitive networking (TSN) is widely used in industrial automation and automotive applications due to its ability to provide deterministic transmission. To meet the stringent deterministic requirements of time-sensitive traffic, the mainstream traffic management mechanism time-aware shaper (TAS) relies on time synchronization among network devices. TAS schedules periodic traffic transmission using preallocated time windows. However, in real networks, time-sensitive traffic may not be precisely forwarded as scheduled, thus failing to achieve the expected performance. A fundamental reason is that the statically planned transmission schemes cannot accommodate dynamic time synchronization errors. To address this problem, we propose a novel TSN traffic scheduling strategy called robust TAS (RTAS), which utilizes a dual-queue gating structure to define a new scheduling rule for absorbing time synchronization errors to guarantee the real-time performance of scheduled traffic (ST). We conducted extensive simulations on OMNeT++ to study the performance of RTAS in industrial automation scenarios. The results show that RTAS enables deterministic transmission with imperfectly synchronized clocks and minimizes the impact of time synchronization errors on ST. © 2014 IEEE.

关键词： Queueing theory

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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Enhancing Human Age Detection: The Impact of Data Augmentation and Balancing on CNN Performance 1st

Enhancing Human Age Detection: The Impact of Data Augmentati...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Alrubaye, Maryam Hameed, Alaa Ali Jamil, Akhtar Department of Artificial Intelligence Engineering Istinye University Istanbul Turkey Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9783031823763

Accurate age detection is a crucial aspect in various sectors including security, marketing, and demographic analysis, where understanding the age of individuals can lead to better service delivery and enhanced user experience. This study explores the enhancement of age detection algorithms using Convolutional Neural Networks (CNNs). Leveraging a unique combination of the UTKFace and Facial-age datasets, our approach introduces an advanced data augmentation and balancing technique. This methodological innovation significantly contributes to optimizing CNN performance in age detection tasks. The proposed technique not only overcomes the common challenges in age estimation but also achieves an accuracy of 90.51%, and a noteworthy F1 score of 89.82%, which demonstrates the model’s accuracy and robustness. The findings of this research hold substantial implications for improving age detection processes, offering a promising direction for future applications in various fields that require reliable age estimation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery

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IEEE Transactions on Biomedical engineering 2025年第6期72卷 1886-1896页

作者： Xu, Jialang Wang, Jiacheng Yu, Lequan Stoyanov, Danail Jin, Yueming Mazomenos, Evangelos B. University College London UCL Hawkes Institute The Department of Medical Physics and Biomedical Engineering London United Kingdom Xiamen University Department of Computer Science School of Informatics Xiamen China The University of Hong Kong Department of Statistics and Actuarial Science Hong Kong National University of Singapore Department of Biomedical Engineering The Department of Electrical and Computer Engineering Singapore

Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait priors for SIS, incorporating global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE), to boost SIS performance in each site. GPD represents the first attempt at head- wise assignment for multi-headed self-attention personalization. To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer- wise aggregation solutions via hypernetworks for each site's personalized parameters. The mutual shape information of instruments is maintained and shared via SGE, which enhances the cross-style shape consistency on the image level and computes the shape-similarity contribution of each site on the prediction level for updating the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51% Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. © 1964-2012 IEEE.

关键词： Robotic surgery

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