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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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2022 Global Conference on Robotics, Artificial Intelligence and Information Technology, GCRAIT 2022

作者： Li, Yu Tong, Xiangrong School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

In view of the insufficient researches on the dynamic change of user trust in existing trust-based recommendation algorithms, a DDPG-TRA algorithm based on deep reinforcement learning and attention mechanism is proposed, which can catch the user interests and dynamically update user trust. The algorithm firstly uses the state module combined with the attention mechanism to model the user's state, and then recommends item. When the user adopts the item, it integrates social trust relationships and similarity relationships to predict item score, and computes the error between the predicted score and the actual score. Finally, the algorithm gets the recommendation reward and updates the trust with the score difference. We conduct experiments on three datasets and compared with other algorithms, and the proposed algorithm can get more accurate results. © 2022 IEEE.

关键词： Reinforcement learning

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A Recommendation Model with Attention-Aware Graph Matching 5

A Recommendation Model with Attention-Aware Graph Matching

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Ma, Xinxin Cui, Zhendong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9781665499132

Under the background of information overload, recommendation system can provide decision-making tools for information producers to promote information and information consumers to obtain personalized preference information. Use user-item attribute information for interaction can effectively improve the performance of the recommendation model. Existing work models and aggregates the attribute interaction of users and items in the same way, without effectively distinguishing them. However, this can't express the feature interaction between attributes well. In this work, we propose Attention-aware Graph Matching Recommendation model (AGMRec), which convert the recommendation process into a graph matching problem. In particular, we design internal-node message passing and external-node matching to model different orders feature interactions between attributes. Then the proposed model uses an attention approach to fuse different aspects of the nodes. Experimental results on two real-world datasets show its superiority over the state-of-the-arts. © 2022 IEEE.

关键词： Recommender systems

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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An Evolutionary Model for Earthquake Prediction Considering Time-Series Evolution and Feature Extraction and Its Application

An Evolutionary Model for Earthquake Prediction Considering ...

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2022 International Conference on computer Modelling and Big Data Analytics, CMBDA 2022

作者： Du, Depeng Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai China

Seismic activity presents characteristics such as relatively concentrated distribution, high destructiveness, huge secondary hazards, complex change patterns, and unpredictable future activity conditions. It is of great theoretical significance and application and promotion value to research magnitude prediction for earthquake-prone areas. This study attempts to develop an earthquake prediction model based on seismic data from 1970 to 2021 and to predict earthquakes of magnitude (4.5-6). Firstly, the data are statistically analyzed using data statistics to analyze trends, identify patterns and characteristics of change, and make decisions conducive to development. Secondly, feature extraction and selection are carried out by convolutional neural networks to select the optimal features. The CNN_BiLSTM model was constructed and compared with other prediction models (long and short-term memory neural network, back propagation neural network, and support vector regression) to obtain prediction results for sensitivity, specificity, true prediction values, false prediction values, and accuracy values were compared and analyzed. The earthquake magnitude predictions for Japan and surrounding areas using the above techniques show essential and encouraging results, thus taking an essential step towards an eventual robust prediction mechanism. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Ring-Overlap: A Storage Scaling Mechanism For Consortium Blockchain

Ring-Overlap: A Storage Scaling Mechanism For Consortium Blo...

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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Liu, Wenxuan Zhang, Donghong Zhao, Jindong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

With the rapid development of blockchain applications, the data that need to be stored increases dramatically, and the blockchain is about to face the problem of storage limitations. To deal with this problem, this paper proposes a storage scaling mechanism for Hyperledger Fabric, which relieves the storage pressure by dividing the peer nodes into clusters, and each node only stores partial data instead of the whole ledger. First, all accounting nodes are divided into clusters that include several nodes respectively, and part of the whole block data is stored in a single cluster;then, the block data are stored overlappingly on some nodes in the cluster, and each block is guaranteed to have some copies in a cluster. By arranging the copies on selected nodes according to our proposed mechanism, all the blocks are overlapped in a cluster. Theoretical analysis and simulation show that the storage volume occupied by nodes is decreased significantly in blockchain applications with frequent transactions, and in the case that the number of node failures in a single cluster does not exceed the threshold, the mechanism can still guarantee data integrity. Moreover, for applications with frequent transactions, storage space consumption can be significantly reduced without increasing excessive query time overhead. © 2022 IEEE.

关键词： Blockchain

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Explicit and Implicit Feature Interaction Based on Attention Networks for Click-Through Rate Prediction 5

Explicit and Implicit Feature Interaction Based on Attention...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Li, Shiqi Cui, Zhendong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9781665499132

Click-through rate (CTR) prediction, which aims to estimate the probability of a user to click on a given item, plays a critical role in both recommender systems and online advertising. Recently, a number of deep neural network-based approaches have been developed in an implicit and bit-wise interaction, which the order of interaction is inconclusive. In this work, a novel model called eXtreme Deep Attention Interaction Network (xAtInt) is proposed to capture meaningful combination features explicitly. On the one hand, xAtInt learn meaningful feature interaction via attention mechanism;on the other hand, it is capable of finding arbitrary degree interaction at a vector-wise. Furthermore, to improve performance, we integrate DNN with xAtInt learning explicit and implicit feature interactions. Extensive experiments are conducted on real-world dataset. The results show that our model present good performance compared existing state-of-the-art approaches. © 2022 IEEE.

关键词： Deep neural networks

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Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

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Fluid Dynamics & Materials Processing 2023年第6期19卷 1369-1383页

作者： Xing Deng Feipeng Da Haijian Shao School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.

关键词： Blood cell analysis deep learning models classification-detection

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Data-Driven Group Formation control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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Research on multimodal relation extraction method based on contrast learning 3

Research on multimodal relation extraction method based on c...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhao, Jingsheng Ni, Qihui Yan, Shuai Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong266000 China School of Computer Science and Technology Suzhou University Suzhou Jiangsu215006 China

ISBN: (纸本)9781510680449

Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of heterogeneity between multi-modal data, a multi-modal relation extraction model MRECL based on contrastive learning is constructed. Firstly, the feature representation of image and text is obtained through ViT and BERT models;secondly, contrastive learning is introduced to align image-text features to reduce modality heterogeneity in advance;then image is used as prefix guidance information, and multi-modal interaction fusion is carried out with text information through attention mechanism;finally, the probability of all relation classification is calculated through Softmax layer and the prediction relation is output. Experimental results show that the precision, recall, and F1 values of MRECL on the MNRE dataset reach 83.23%, 82.19%, and 82.70%, respectively, achieving better performance than the current SOTA model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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