Neuroscience informatics最新文献_第8页

Deep learning-based edge detection for random natural images 基于深度学习的随机自然图像边缘检测

Neuroscience informatics Pub Date : 2025-03-01 Epub Date: 2024-11-28 DOI: 10.1016/j.neuri.2024.100183

Kanija Muntarina , Rafid Mostafiz , Sumaita Binte Shorif , Mohammad Shorif Uddin

{"title":"Deep learning-based edge detection for random natural images","authors":"Kanija Muntarina , Rafid Mostafiz , Sumaita Binte Shorif , Mohammad Shorif Uddin","doi":"10.1016/j.neuri.2024.100183","DOIUrl":"10.1016/j.neuri.2024.100183","url":null,"abstract":"<div><div>Edge detection plays a critical role in computer vision, particularly in the analysis of random natural images. It serves as a fundamental step in tasks such as image segmentation, shape extraction, pattern recognition, auto-navigation, and motion analysis, with applications spanning various domains including radar and sonar image processing. The edge detection model attempts to identify points in digital images where significant intensity changes occur, known as edges or region boundaries. Traditionally, edge detection relied on gradient-based operators, which often produced jagged edges and were susceptible to image noise. In recent years, the emergence of deep learning technology has revolutionized this field by utilizing its ability to automatically learn complex features from natural images. Deep learning approaches offer significant advantages in capturing high-level representations, thereby improving the accuracy and robustness of edge detection algorithms. Moreover, the effectiveness of edge detection techniques varies depending on the content and classification of images, such as natural scenes, medical images, or underwater environments. This study aims to evaluate and compare the performance of five widely used deep learning-based edge detection methods to identify the most effective approach specifically tailored for natural images. Through comprehensive experimentation and analysis, this research contributes to advancing the state-of-the-art in edge detection for random natural images, providing insights into the optimal application of deep learning techniques in this domain.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding risk factors of post-stroke mortality 了解中风后死亡的危险因素

Neuroscience informatics Pub Date : 2025-03-01 Epub Date: 2024-11-29 DOI: 10.1016/j.neuri.2024.100181

David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo

{"title":"Understanding risk factors of post-stroke mortality","authors":"David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo","doi":"10.1016/j.neuri.2024.100181","DOIUrl":"10.1016/j.neuri.2024.100181","url":null,"abstract":"<div><div>Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in neurosurgical procedures: Quantum computing and its applications in MRI-guided laser interstitial thermal therapy 神经外科手术的进展：量子计算及其在核磁共振引导激光间质热治疗中的应用

Neuroscience informatics Pub Date : 2025-03-01 Epub Date: 2025-01-03 DOI: 10.1016/j.neuri.2024.100185

Afzal Hussain , Ashfaq Hussain , Mohammad Rashid

引用次数: 0

Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in neurodegenerative diseases 综合分析神经退行性疾病中的 lncRNA-miRNA-mRNA ceRNA 网络

Neuroscience informatics Pub Date : 2024-12-01 Epub Date: 2024-09-30 DOI: 10.1016/j.neuri.2024.100176

Mehran Asadi Peighan , Negar Sadat Soleimani Zakeri , Seyed Mehdi Jazayeri , Sajjad Nematzadeh , Habib MotieGhader

{"title":"Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in neurodegenerative diseases","authors":"Mehran Asadi Peighan , Negar Sadat Soleimani Zakeri , Seyed Mehdi Jazayeri , Sajjad Nematzadeh , Habib MotieGhader","doi":"10.1016/j.neuri.2024.100176","DOIUrl":"10.1016/j.neuri.2024.100176","url":null,"abstract":"<div><h3>Background</h3><div>Neurodegenerative diseases are one of the main causes of physical or behavioral complications which is considered as one of the main health concerns of the elderly population. However, treatment options for neurological diseases are still limited. Recent advances in bioinformatics studies provide an opportunity to understand the mechanisms of these diseases to identify therapeutic targets. In this research, the mRNAs involved in Alzheimer's, Multiple sclerosis, Parkinson's, and Huntington's neurological diseases, which are regulated by upstream factors, have been investigated. Only 2% of all transcripts of a gene are translated into protein and the rest are converted into miRNAs, lncRNAs or circRNAs. miRNAs have crucial role in regulating mRNAs and in a similar sequence lncRNAs or circRNAs are crucial in regulating miRNAs, which disrupts gene expression.</div></div><div><h3>Results</h3><div>To discover above relations in neurodegenerative disease, miRNA-mRNA and lncRNA-miRNA bipartite networks were constructed and then were integrated to construct lncRNA-miRNA-mRNA tripartite networks. Constructing these networks leads to understand deeply about the structure of this mechanism and introducing new biomarkers for the studied diseases. In the next step, enrichment analysis was performed to recognize the genes involved in crucial pathways. Finally, the obtained biomarkers were investigated over the previous studies to prove the accuracy of proposed method.</div></div><div><h3>Conclusions</h3><div>In conclusion, for all four diseases, several numbers of mRNAs, miRNAs and lncRNAs were identified, which are introduced as biomarkers extracted by this study.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 4","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topic modeling of neuropsychiatric diseases related to gut microbiota and gut brain axis using artificial intelligence based BERTopic model on PubMed abstracts 利用基于人工智能的 BERTopic 模型对 PubMed 摘要中与肠道微生物群和肠道脑轴相关的神经精神疾病进行主题建模

Neuroscience informatics Pub Date : 2024-12-01 Epub Date: 2024-09-10 DOI: 10.1016/j.neuri.2024.100175

Ashok Kumar , Avi Karamchandani , Sourabh Singh

{"title":"Topic modeling of neuropsychiatric diseases related to gut microbiota and gut brain axis using artificial intelligence based BERTopic model on PubMed abstracts","authors":"Ashok Kumar , Avi Karamchandani , Sourabh Singh","doi":"10.1016/j.neuri.2024.100175","DOIUrl":"10.1016/j.neuri.2024.100175","url":null,"abstract":"<div><p>Gut microbiota play a crucial role in complex interactions of the gut brain axis between the gastrointestinal system and the central nervous system. The intricate network of bidirectional communication between the gut and brain, mediated through neural, hormonal, and immunological pathways, known as the gut-brain axis, has been implicated in the pathophysiology of several mental, neurological and behavioral disorders. Alterations in the gut microbiota composition, or dysbiosis, have been associated with disorders like Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Autism Spectrum Disorder, Ischemic Stroke, Eating Disorders, depression, anxiety, stress and addiction. In this study, a Python package BERTopic, based on Artificial Intelligence based Natural Language Processing using Transformer model BERT, specializing in topic modeling, was applied to abstracts of 3,482 PubMed articles published from year 2014 until May 2024, to explore the mental, neurological, and behavioral diseases influenced by the gut microbiota. There were some variations in individual runs of BERTopic due to stochastic nature of one of its components, but overall the discovered topics corresponded to major neuropsychiatric diseases. To understand the impact of the variability in outcomes ten repeated runs of BERTopic were performed with keeping identical parameters. The major topics that were found consistently in all the ten repeated runs of BERTopic were Depression, Alzheimer Disease, Autism Spectrum Disorder, Parkinson's Disease, Multiple Sclerosis, Ischemic Stroke, Anorexia Nervosa and Schizophrenia.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 4","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528624000207/pdfft?md5=6ac1f58830303096fa9a0b1ce22216a5&pid=1-s2.0-S2772528624000207-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain network analysis in Parkinson's disease patients based on graph theory 基于图论的帕金森病患者大脑网络分析

Neuroscience informatics Pub Date : 2024-12-01 Epub Date: 2024-08-30 DOI: 10.1016/j.neuri.2024.100173

Shirin Akbari , Mohammad Reza Deevband , Amin Asgharzadeh Alvar , Emadodin Fatemi Zadeh , Hashem Rafie Tabar , Patrick Kelley , Meysam Tavakoli

{"title":"Brain network analysis in Parkinson's disease patients based on graph theory","authors":"Shirin Akbari , Mohammad Reza Deevband , Amin Asgharzadeh Alvar , Emadodin Fatemi Zadeh , Hashem Rafie Tabar , Patrick Kelley , Meysam Tavakoli","doi":"10.1016/j.neuri.2024.100173","DOIUrl":"10.1016/j.neuri.2024.100173","url":null,"abstract":"<div><p>Development of Parkinson's disease causes functional impairment in the brain network of Parkinson's patients. The aim of this study is to analyze brain networks of people with Parkinson's disease based on higher resolution parcellations and newer graphical features. The topological features of brain networks were investigated in Parkinson's patients (19 individuals) compared to healthy individuals (17 individuals) using graph theory. In addition, four different methods were used in graph formation to detect linear and nonlinear relationships between functional magnetic resonance imaging (fMRI) signals. The functional connectivity between the left precuneus and the left amygdala, as well as between the vermis 1-2 and the left temporal lobe was evaluated for the healthy and the patient groups. The difference between the healthy and patient groups was evaluated by parametric t-test and nonparametric U-test. Based on the results, Parkinson's patients exhibited a noteworthy reduction in centrality criterion compared to healthy subjects. Moreover, alterations in the regional features of the brain network were evident. Applying centrality criteria and correlation coefficients revealed significant distinctions between healthy subjects and Parkinson's patients across various brain areas. The results obtained for topological features indicate changes in the functional brain network of Parkinson's patients. Finally, similar areas obtained by all three methods of graph formation in the evaluation of connectivity between paired regions in the brain network of Parkinson's patients increased the reliability of the results.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 4","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528624000189/pdfft?md5=497cd4dae00c848fa7f89e59ba820beb&pid=1-s2.0-S2772528624000189-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring age-related functional brain changes during audio-visual integration tasks in early to mid-adulthood 探索早中期视听整合任务中与年龄相关的大脑功能变化

Neuroscience informatics Pub Date : 2024-12-01 Epub Date: 2024-08-30 DOI: 10.1016/j.neuri.2024.100172

Prerna Singh , Ayush Tripathi , Tapan Kumar Gandhi , Lalan Kumar

{"title":"Exploring age-related functional brain changes during audio-visual integration tasks in early to mid-adulthood","authors":"Prerna Singh , Ayush Tripathi , Tapan Kumar Gandhi , Lalan Kumar","doi":"10.1016/j.neuri.2024.100172","DOIUrl":"10.1016/j.neuri.2024.100172","url":null,"abstract":"<div><p>The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into age-related AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audio-visual integration task with a specific focus on assessing the brain maturation effects in various age groups, particularly in early-mid adulthood. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18–25 years), Transition (26–33 years), and Middle (34–50 years) age cohort healthy participants. We utilized source-based features for the classification of these age groups. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings unveiled diminished levels of brain activation among middle-aged individuals, which escalate when exposed to AVI stimuli. Lower frequency bands showed substantial changes with increasing age during AVI. Our results demonstrated that implementing the k-means elbow method during the AVI task successfully categorized brain regions into five distinct brain networks. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 4","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528624000177/pdfft?md5=dc193c32edd7c262e7a74f98e3ce48b2&pid=1-s2.0-S2772528624000177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Portability rules detection by Epilepsy Tracking META-Set Analysis 通过癫痫追踪 META 集分析检测可移植性规则

Neuroscience informatics Pub Date : 2024-09-01 Epub Date: 2024-07-10 DOI: 10.1016/j.neuri.2024.100168

Christian Riccio , Roberta Siciliano , Michele Staiano , Giuseppe Longo , Luigi Pavone , Gaetano Zazzaro

{"title":"Portability rules detection by Epilepsy Tracking META-Set Analysis","authors":"Christian Riccio , Roberta Siciliano , Michele Staiano , Giuseppe Longo , Luigi Pavone , Gaetano Zazzaro","doi":"10.1016/j.neuri.2024.100168","DOIUrl":"10.1016/j.neuri.2024.100168","url":null,"abstract":"<div><p>Epilepsy is a severe and common neurological disease that causes sudden and irregular seizures, necessitating patient-specific detection models for effective management. The proposed methodology, Epilepsy Tracking META-Set Analysis, establishes portability rules that identify similar patients, enabling the transfer of these detection models from one patient to another. Main issue is to identify clusters of patients analyzing a set of meta-features of each patient in terms of clinical descriptors, performance metrics of a machine learning model for seizure detection, and data complexity measures. The investigation of complexity measures represents a novelty in such a medical field, allowing to compare patients and to support automated seizure detection methods. The proposed methodology is validated using the well-known Epileptic Seizure EEG Database from the Epilepsy Center of the University Hospital of Freiburg and demonstrates promising results in transferring detection models to new cases.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 3","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277252862400013X/pdfft?md5=bddb7aa1afbb35278f232c5c831c5841&pid=1-s2.0-S277252862400013X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ERP and functional connectivity reveal hemispheric asymmetry in perceptual grouping ERP和功能连接揭示感知分组的半球不对称性

Neuroscience informatics Pub Date : 2024-09-01 Epub Date: 2024-06-11 DOI: 10.1016/j.neuri.2024.100167

Shefali Gupta, Tapan Kumar Gandhi

{"title":"ERP and functional connectivity reveal hemispheric asymmetry in perceptual grouping","authors":"Shefali Gupta, Tapan Kumar Gandhi","doi":"10.1016/j.neuri.2024.100167","DOIUrl":"10.1016/j.neuri.2024.100167","url":null,"abstract":"<div><p>The human visual system can effortlessly group small components into entities to form an object, but the role of the hemispheres in this processing is still unknown. Understanding the hemispherical processing of perceptual grouping is crucial for unraveling the complexities of visual perception. We have attempted to examine the processing of perceptual grouping in both hemispheres of the human brain. The neural data was collected for 15 healthy subjects while they viewed displays featuring either ‘structure’ (line segments composed of dots) or ‘non-structure’ (random dots). ERPs were recorded and assessed in both frontal and occipital regions of the left and right hemispheres for structure and non-structure stimuli. Our results revealed higher activation for structure compared to non-structure in both brain hemispheres, with notably amplified activity observed in the right hemisphere. Moreover, a decrease in task-related alpha power and an increase in PLI functional connectivity were observed during the perceptual grouping of structures. A novel finding that the Granger causality exhibits a higher value for perceptual grouping when information flows from the right to the left hemisphere, in contrast to communication from left to right, is obtained. Thus, the right hemisphere demonstrated distinct dominance in activation amplitude, task-related alpha power, functional connectivity, and directional functional connectivity related to perceptual grouping. Furthermore, our findings suggest that perceptual grouping involves communication between the frontal and occipital brain regions. By elucidating the hemispherical mechanisms underlying perceptual grouping, this research not only advances our understanding of basic cognitive processes but also offers practical implications for fields such as neurorehabilitation and artificial intelligence.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 3","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528624000128/pdfft?md5=4f146121bf72087fb432e590c83bb6f2&pid=1-s2.0-S2772528624000128-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141416170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble machine learning-based approach to predict cervical cancer using hybrid feature selection 使用混合特征选择预测宫颈癌的基于机器学习的集合方法

Neuroscience informatics Pub Date : 2024-09-01 Epub Date: 2024-08-10 DOI: 10.1016/j.neuri.2024.100169

Khandaker Mohammad Mohi Uddin , Abdullah Al Mamun , Anamika Chakrabarti , Rafid Mostafiz , Samrat Kumar Dey

{"title":"An ensemble machine learning-based approach to predict cervical cancer using hybrid feature selection","authors":"Khandaker Mohammad Mohi Uddin , Abdullah Al Mamun , Anamika Chakrabarti , Rafid Mostafiz , Samrat Kumar Dey","doi":"10.1016/j.neuri.2024.100169","DOIUrl":"10.1016/j.neuri.2024.100169","url":null,"abstract":"<div><p>Cervical cancer has recently emerged as the leading cause of premature death among women. Around 85% of cervical cancer cases occur in underdeveloped countries. There are several risk factors associated with cervical cancer. This study describes a novel predictive model that uses early screening and risk trends from individual health records to forecast cervical cancer patients' prognoses. This study uses machine learning classification techniques to investigate the risk factors for cervical cancer. Additionally, use the voting method to evaluate all models and select the most appropriate model. The dataset used in this study contains missing values and shows a significant imbalance. Thus, the Random Oversampling technique was used as a sampling method. We used Principal Component Analysis (PCA) and XGBoost feature selection techniques to determine the most important features. To predict the accuracy, we used several machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), k-nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), AdaBoost (AdB), Gradient Boosting (GB), Multilayer Perceptron (MLP), and Nearest Centroid Classifier (NCC). To demonstrate the efficacy of the suggested model, a comparison of its accuracy, sensitivity, and specificity was performed. We used the Random Oversampling approach along with the Ensemble ML method, hard voting on RF and MLP, and achieved 99.19% accuracy. It is demonstrated that the ensemble ML classifier (hard voting) performs better at handling classification problems when features are decreased and the high-class imbalance problem is handled.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"4 3","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528624000141/pdfft?md5=d40278a84049b884c55e84f0cfc57216&pid=1-s2.0-S2772528624000141-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0