International journal of neural systems最新文献

{"title":"Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning.","authors":"Francisco J Martinez-Murcia, Juan Eloy Arco, Carmen Jimenez-Mesa, Fermin Segovia, Ignacio A Illan, Javier Ramirez, Juan Manuel Gorriz","doi":"10.1142/S0129065724500436","DOIUrl":"10.1142/S0129065724500436","url":null,"abstract":"<p><p>Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with <i>R2</i> up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450043"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning.","authors":"Sriram Kumar P, Jac Fredo Agastinose Ronickom","doi":"10.1142/S0129065724500278","DOIUrl":"10.1142/S0129065724500278","url":null,"abstract":"<p><p>In clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and Wearable stress and affect detection (WESAD) for 4-class dimensional and three-class categorical emotional classification, respectively. These signals were pre-processed, and decomposed into phasic signals using the 'convex optimization to EDA' method. Further, the phasic signals were segmented into two equal parts, each subsequently segmented into five nonoverlapping windows. Spectrograms were then generated using short-time Fourier transform and Mel-frequency cepstrum for each window, from which we extracted 85 features. We built four machine learning models for the first part, second part, and whole phasic signals to investigate their performance in emotion recognition. In the CASE dataset, we achieved the highest multi-class accuracy of 62.54% using the whole phasic and 61.75% with the second part phasic signals. Conversely, the WESAD dataset demonstrated superior performance in three-class emotions classification, attaining an accuracy of 96.44% for both whole phasic and second part phasic segments. As a result, the second part of EDA is strongly recommended for optimal outcomes.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450027"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140178366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge Computing Transformers for Fall Detection in Older Adults.","authors":"Jesús Fernandez-Bermejo, Jesús Martinez-Del-Rincon, Javier Dorado, Xavier Del Toro, María J Santofimia, Juan C Lopez","doi":"10.1142/S0129065724500266","DOIUrl":"10.1142/S0129065724500266","url":null,"abstract":"<p><p>The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450026"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140137568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Objective Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification.","authors":"Chenyi Zhang, Yu Xue, Ferrante Neri, Xu Cai, Adam Slowik","doi":"10.1142/S012906572450014X","DOIUrl":"10.1142/S012906572450014X","url":null,"abstract":"<p><p>Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it. However, the solution space expands exponentially with the dataset's dimensionality. Simultaneously, the extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated and redundant features [H. Adeli and H. S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, <i>AI Mag.</i> <b>17</b> (1996) 87-93]. Consequently, existing MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems (LSMOFSPs). Different LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely on a single candidate solution generation strategy (CSGS), which may be less efficient for diverse LSMOFSPs [H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, <i>J. Struct. Eng. ASCE</i> <b>123</b> (1997) 880-888; M. Aldwaik and H. Adeli, Advances in optimization of highrise building structures, <i>Struct. Multidiscip. Optim.</i> <b>50</b> (2014) 899-919; E. G. González, J. R. Villar, Q. Tan, J. Sedano and C. Chira, An efficient multi-robot path planning solution using a* and coevolutionary algorithms, <i>Integr. Comput. Aided Eng.</i> <b>30</b> (2022) 41-52]. Moreover, selecting an appropriate MOEA and determining its corresponding parameter values for a specified LSMOFSP is time-consuming. To address these challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed, combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along with five modified efficient CSGSs, to generate new solutions. Experiments were conducted on ten datasets, and the results demonstrate that the number of features is effectively reduced by MOSaPSO while lowering the classification error rate. Furthermore, superior performance is observed in comparison to its counterparts on both the training and test sets, with advantages becoming increasingly evident as the dimensionality increases.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450014"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction.","authors":"Francesco Carlo Morabito","doi":"10.1142/S0129065724020015","DOIUrl":"10.1142/S0129065724020015","url":null,"abstract":"","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2402001"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138815298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the Effectiveness of Eigentrust in Computing the Reputation of Social Agents in Presence of Collusion.","authors":"Mariantonia Cotronei, Sofia Giuffrè, Attilio Marcianò, Domenico Rosaci, Giuseppe M L Sarnè","doi":"10.1142/S0129065723500636","DOIUrl":"10.1142/S0129065723500636","url":null,"abstract":"<p><p>The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be <i>a priori</i> considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2350063"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Federated Learning for Heterogeneous Model and Data","authors":"Hussain Ahmad Madni, Rao Muhammad Umer, G. Foresti","doi":"10.1142/s0129065724500199","DOIUrl":"https://doi.org/10.1142/s0129065724500199","url":null,"abstract":"","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"3 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139525091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics","authors":"Lin Chen, Lu Leng, Ziyuan Yang, Andrew Beng Jin Teoh","doi":"10.1142/s0129065724500205","DOIUrl":"https://doi.org/10.1142/s0129065724500205","url":null,"abstract":"","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"4 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Striatum- and cerebellum-modulated epileptic networks varying across states with and without interictal epileptic discharges","authors":"Sisi Jiang, Haonan Pei, Junxia Chen, Hechun Li, Zetao Liu, Yuehan Wang, Jinnan Gong, Sheng Wang, Qifu Li, M. Duan, V. Calhoun, Dezhong Yao, Cheng Luo","doi":"10.1142/s0129065724500175","DOIUrl":"https://doi.org/10.1142/s0129065724500175","url":null,"abstract":"","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"8 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139525556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal covariance network reflects individual cognitive flexibility","authors":"Lin Jiang, S. Eickhoff, S. Genon, Guangying Wang, Chanlin Yi, Runyang He, Xunan Huang, Dezhong Yao, Debo Dong, Fali Li, Peng Xu","doi":"10.1142/s0129065724500187","DOIUrl":"https://doi.org/10.1142/s0129065724500187","url":null,"abstract":"","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"5 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139525355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}