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International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Alex F. Mendelson Maria A. Zuluaga Brian F. Hutton Sebastien Ourselin Translational Imaging Group University College London London UK Institute of Nuclear Medicine University College London London UK Centre for Medical Radiation Physics University of Wollongong NSW Australia Dementia Research Centre University College London UK

Machine learning research in image-based computer aided diagnosis is a field characterised by rich models and relatively small datasets. In this regime, conventional statistical tests for cross validation results may no longer be optimal due to variability in training set quality. We present a principle by which existing statistical tests can be conservatively extended to make use of arbitrary numbers of repeated experiments. We apply this to the problems of interval estimation and pair wise comparison for the accuracy of classification algorithms, and test the resulting procedures on real and synthetic classification tasks. The interval coverages in the synthetic task are notably improved, and the comparison has both increased power and reduced type I error. Experiments in the ADNI dataset show that the low replicability of split-half based tests can be dramatically improved.

关键词： Accuracy Imaging Training Estimation Testing Support vector machines prediction algorithms

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Integrated Digital Twins System for Oil Temperature prediction of Power Transformer Based on Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 13746-13756页

作者： Lyu, Zhihan Wan, Zhibo Bian, Zengxu Liu, Yuqi Zhao, Wei Xidian Univ Sch Comp Sci & Technol Xian 710126 Shaanxi Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China

Oil temperature is an important index to reflect the state of transformer and predict the fault and remaining useful life of transformer. However, the oil temperature of the transformer is affected by various factors, such as weather conditions, load changes, holidays, seasons, and so on. In order to predict oil temperature more accurately, this study proposes a VCEEMDAN data decomposition algorithm to extract the characteristics of oil temperature data. And we proposed a hybrid long-term series forecasting model of oil temperature based on VCEEMDAN, particle swarm optimization (PSO) and Informer. At the same time, the digital twins system of the manipulator is configured to assist personnel in maintenance when the oil temperature is abnormal. And the digital twins system of an oil-immersed power transformer to monitoring of transformer status. Finally, the above research is integrated into a digital twins system, including transformer condition monitoring, oil temperature prediction, and intelligent maintenance based on Internet of Things (IOT). The proposed prediction method was compared with the six existing models on three datasets. From the experimental results, the best index improvement is 77.97% higher than the second-best model. And the simulation system can provide a good reference for the practical application of the factory. This article presents a hybrid framework that achieves a accurate transformer oil temperature prediction.

关键词： Oils Power transformers Oil insulation Predictive models Temperature distribution Internet of Things Data models Accuracy prediction algorithms Measurement Animation avatar digital twins industrial application Internet of Things (IOT) long-term series forecasting system modeling and simulation

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Reconfigurable Intelligent Surfaces: How to Enhance Multiuser Channel prediction?

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第2期13卷 446-450页

作者： Shehzad, M. Karam Assaad, Mohamad Rose, Luca Nokia Stand Radio Interface & Access Grp RIAG F-91300 Massy France Cent Supelec Lab Signals & Syst F-91192 Gif Sur Yvette France

Reconfigurable intelligent surface (RIS) is envisioned as the key enabler of a smart wireless environment, offering a nearly energy-neutral solution to control the dynamic propagation of electromagnetic signals. On the other hand, channel prediction can bring promising benefits in various aspects of wireless systems, e.g., reducing channel state information feedback overhead. prediction of a highly dynamic channel is cumbersome. This letter focuses on enhancing the performance of multiuser channel prediction by introducing time correlation in the composite channel while exploiting quasi-RIS. We show that the intelligently configured RIS improves channel prediction performance while saving sufficient RIS elements for other applications and simplifies machine learning architecture.

关键词： Optimization Biological cells Wireless communication Partitioning algorithms Sociology prediction algorithms Correlation CSI prediction IRS machine learning reconfigurable intelligent surface time correlation

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Research on Iron Core Loss prediction Based on Data-Driven and Machine Learning algorithms

Research on Iron Core Loss Prediction Based on Data-Driven a...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Hao Jin Lei Dong China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper focuses on the performance optimization of magnetic components, and discusses the key issues such as the construction of a data-driven core loss prediction model. A data-driven core loss prediction model is constructed, which integrates a variety of machine learning algorithms, such as random forest, gradient lift tree, K-nearest neighbor regression, decision tree regression and artificial neural network, and improves the stability and accuracy of prediction through weighted prediction model. Finally, with the core loss prediction model as the objective function, particle swarm optimization algorithm was used to determine the optimization conditions of the magnetic components, including the optimal material, temperature, frequency, waveform and magnetic flux density peak, and the overall performance optimization of the magnetic components was realized.

关键词： Machine learning algorithms Magnetic cores Core loss Predictive models prediction algorithms Stability analysis Particle swarm optimization Optimization Thermal stability Regression tree analysis

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Perception-Based prediction for Efficient Kinesthetic Coding

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2530-2534页

作者： Xu, Yiwen Huang, Qingfeng Zheng, Quanfei Fang, Ying Zhao, Tiesong Fuzhou Univ Fujian Key Lab Intelligent Proc & Wireless Transmi Fuzhou 350025 Peoples R China Fuzhou Univ Coll Zhicheng Fuzhou 350025 Peoples R China

Integrating haptic feedback with audio and video not only expands the perceptual dimensions of multimedia applications but also enhances user engagement and experience. However, higher signal sampling rates and multi-degree-freedom in haptic interaction increase data significantly. For low-latency and reliable transmission of haptic signal (i.e. tactile and kinesthetic signals), efficient haptic coding is crucial. Existing algorithms overlook haptic signal characteristics, leaving room for improvement. We analyze the statistical characteristics of kinesthetic signals in-depth. Based on the local linear characteristics of position and velocity signals, and the sparse distribution of force signal, we propose an improved kinesthetic coding algorithm by combining dead-zone coding with segmented linear prediction. Extensive experiments on the standard datasets of the IEEE P1918.1.1 Haptic Codecs Task Group demonstrate the superior performance compared to state-of-the-art methods, achieving a more than halved reduction in data transmission rates with high signal-to-noise ratios and structural similarity.

关键词： Encoding Haptic interfaces prediction algorithms Force Signal processing algorithms Mathematical models Linear regression Data compression haptics kinesthetic coding regression algorithm

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Machine Learning-Based Cardiovascular Disease prediction: A Comparative Analysis of Classification algorithms for Enhanced Diagnostic Accuracy

Machine Learning-Based Cardiovascular Disease Prediction: A ...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Harsheeta Singh Vanshaj Satija Priyadharshini K Department of Computing Technologies SRM Institute of Science and Technology Kattankulathar Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Cardiovascular diseases (CVDs) were one of the major causes of death in all the continent, to such an extent that early detection and accurate diagnosis of CVDs is crucial to the early intervention. The aim of this research is to predict the likely hood of a heart related condition based on ML techniques in order to increase diagnostic precision. To develop predictive models, a dataset of 383 patient records along with 13 key attributes such as age, cholesterol levels, blood pressure, heart rate was used. The implementation of various ML algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bayes, and Logistic Regression in Python was done with the help of Jupyter Notebook environment. And the study focuses on data preprocessing, feature selection, and algorithm performance evaluation by means of accuracy, precision, recall, and F1-score. SVM was found to be the model with the highest accuracy of 86% on the tested models, which shows its ability to cope with the complexity of clinical data. In consequence, it also emphasizes the promise of ML driven predictive systems for timely, data driven decision support in the clinical setting, and in so doing, improves patient outcomes.

关键词： Support vector machines Logistic regression Accuracy Nearest neighbor methods prediction algorithms Data models Bayes methods Decision trees Cardiovascular diseases Random forests

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Applications of Equilibrium Decision Theory in Low-Carbon Economic Operation of Wind-Solar-Hydrogen Integrated Energy System Considering prediction Uncertainties

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Zhu, Qiannan Chang, Pengxia Li, Shiqi Xiao, Yulong Wu, Ting Li, Lihao Saeed, Adnan Li, Chaoshun Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

With the increasing integration of renewable energy into power grids, integrated energy systems (IES) have gained global attention. However, the stochastic and uncertain characteristics of wind and solar challenge the stable grid operation significantly. To address these issues, this article proposes an optimal scheduling model for wind-solar-hydrogen IES, incorporating prediction uncertainty and demand response (DR). The equilibrium decision theory framework, combined with frequency enhanced decomposed transformer, interval prediction and decision, is proposed to quantify the uncertainties and determine the action plans. Then, an optimal scheduling model is designed to maximize economic revenue and minimize pollution emissions (PE), which is solved using multiobjective elephant clan optimization algorithm. Furthermore, DR strategy is integrated to enhance system flexibility and stability. Empirical results demonstrate that increased uncertainty leads to more conservative scheduling, improving system robustness. Additionally, DR boosts ER by over 3.50% and reduces PE by more than 3.20% across various scenarios.

关键词： Uncertainty Load modeling Electricity Predictive models Job shop scheduling Probabilistic logic prediction algorithms Transformers Supply and demand Demand response (DR) equilibrium decision theory integrated energy system (IES) optimal scheduling prediction uncertainty wind-solar-hydrogen wind-solar-hydrogen

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prediction Of Mental Health in Working People Using Machine Learning algorithms

Prediction Of Mental Health in Working People Using Machine ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Kumar P Senthil Pandi S Jain Bai B Mithun Dineshkumar Department of CSE Rajalakshmi Engineering College Chennai India UST Global Trivandrum India Khoury College of Computer Science Northeastern University Boston United States

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The professional and personal quality of life are both improved by early diagnosis and treatment of mental health issues. Depression is a major factor in disability all over the world. Professionals can treat internal health conditions earlier and improve their patients' quality of life by examining these issues. Depression is the leading cause of mental health problems worldwide. Professionals in the workforce must address early internal health issues to avoid more complex issues. A comprehensive overview of ML and its use in healthcare operations is provided in this paper. In addition, it provides a summary of the author's own research on the application of ML to internal health, highlighting the limitations that are currently in place and the areas that require additional observation. algorithms based on ML are now extremely useful for diagnosing issues and analyzing medical data. Joining information from many sources and AI approaches assisted us with achieving the best exactness of 94.061 with the irregular woodland classifier.

关键词： Accuracy Machine learning algorithms Mental health Medical services Predictive models Depression prediction algorithms Mobile applications Random forests Medical diagnostic imaging

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Musculoskeletal Modeling Based on Muscle Synergy for prediction of Hand and Wrist Movements

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2025年第1期7卷 337-346页

作者： Pan, Lizhi Li, Qiyang Li, Jianmin Tianjin Univ Sch Mech Engn Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300350 Peoples R China Tianjin Univ Inst Med Robot & Intelligent Syst Tianjin 300072 Peoples R China

Decoding human movements using electromyography (EMG) signals is important for the development of EMG-based human-machine interfaces (HMIs). This study proposed a novel muscle synergy-based musculoskeletal model (MM) for prediction of hand and wrist movements, including wrist flexion/extension, wrist adduction/abduction, wrist pronation/supination, and metacarpophalangeal (MCP) flexion/extension. Ten limb-intact subjects were recruited for the offline experiment, and 15-channel EMG signals from the subject's forearm were recorded. Using the non-negative matrix factorization (NMF) algorithm, four pairs of excitation signals were extracted from the multi-channel EMG signals. Then the MM driven by the extracted muscle excitations was adopted to predict hand and wrist movements. The proposed method was compared with the NMF algorithm and artificial neural network (ANN), and the prediction performance of the three was evaluated with Pearson's correlation coefficient (r) and normalized root mean square error (NRMSE). The total average r of the proposed MM was 0.8475 across all subjects and all movement types, approximately 0.123 higher than NMF algorithm and 0.106 higher than ANN. In addition, the total average NRMSE of the proposed MM was 0.16125 across all subjects and all movement types, approximately 0.074 lower than NMF algorithm and 0.037 lower than ANN. In brief, the proposed MM showed significantly improved prediction accuracy over the NMF algorithm and ANN. This study provides a promising approach for the control of robotic arms and prostheses in EMG-based HMIs.

关键词： Electromyography Muscles Wrist prediction algorithms Sensors Kinematics Low-pass filters Artificial neural networks Solid modeling Predictive models Muscle synergy electromyography musculoskeletal model human-machine interface

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Comparison of Machine Learning algorithms for Propagation Path Loss prediction Using Ray Tracing Data

Comparison of Machine Learning Algorithms for Propagation Pa...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Chaewon Yoon Junseok Kim Hyuk-Je Kim Chung Sup Kim Sung Woong Choi Il-Kyoo Lee Dept of electrical electronics& Control Engineering Kongju National University Republic of Korea Radio Research Division ETRI Daejeon Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In this paper, we compared several regression algorithms through machine learning approach to predict path loss in an outdoor environment using only the location information of Tx and Rx. We constructed an outdoor environment with intersections, buildings and collected path loss data through Ray-tracing. After training by adjusting hyperparameters, two algorithms with low Mean Squared Error (MSE) values were compared in detail by statistically comparing the values of Mean Absolute Error (MAE), MSE, Root Mean Square Error (RMSE), and $\mathbf{R}^{\mathbfprediction}$ using K-fold cross validation. The results showed that the K-Nearest Neighbor (K-NN) algorithm had the best prediction performance for path loss. The prediction model also showed faster prediction time compared to the estimation time of Ray tracing.

关键词： Training Machine learning algorithms Machine learning Ray tracing Predictive models Nearest neighbor methods prediction algorithms Propagation losses Information and communication technology Root mean square

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