Predicting Theta/Alpha Neurofeedback Success through Psychological and Personality Profiles: A Hybrid Approach Using Multilayer Perceptron and Elastic Net Models [0.03%]
基于心理和人格特征的theta/alpha神经反馈成功的预测：使用多层感知器和弹性网络模型的混合方法
Siminsadat Hasheminia,Nasrin Sho&#x;ouri,Maryam Tayefeh Mahmoudi
Siminsadat Hasheminia
Background: The present study aimed to identify and analyze the psychological, cognitive, and neurophysiological factors influencing success in theta/alpha neurofeedback training. The research focused on how personality d...
Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data [0.03%]
基于稀疏表示的分类高维多类微阵列数据的有效技术研究
Maliheh Miri,Mohammad Taghi Sadeghi,Vahid Abootalebi
Maliheh Miri
Background: Sparse representation (SR) has shown strong performance in classification tasks, particularly for high-dimensional data such as microarray gene expression profiles. These datasets present significant challenge...
Supervised Volumetric Segmentation of White and Gray Matter from Brain Positron Emission Tomography Images Using Magnetic Resonance Labels [0.03%]
基于磁共振图像标签的脑 positron emission tomographym图的白质和灰质体素分割方法
Kiarash Danesh,Alireza Karimian,Peyman Sharifian et al.
Kiarash Danesh et al.
Background: This study evaluates the performance of five deep convolutional neural networks (DCNNs) for supervised segmentation of white matter (WM) and gray matter (GM) in brain positron emission tomography (PET) images ...
Effective Connectivity-based Unsupervised Channel Selection Method for Electroencephalography [0.03%]
基于有效连接的脑电通道无监督选择方法
Neda Abdollahpour,Nabi Sertac Artan,Ian Daly et al.
Neda Abdollahpour et al.
Background: Analyzing neural data such as electroencephalography (EEG) data often involves dealing with high-dimensional datasets, where not all channels provide equally meaningful information. Selecting the most relevant...
Enhancement of Digital Mammography Images Using Neutrosophic Divergence Score Based on Intuitionistic Fuzzy Entropy [0.03%]
基于直觉模糊熵的中性分歧得分的数字乳腺X线摄影图像增强技术
Leila Pourreza,Nasser Aghazadeh,Mahdi Hashemzadeh
Leila Pourreza
Background: Uncertainty in medical images-especially mammograms-caused by low contrast and insufficient brightness creates difficulties in detecting masses and microcalcifications. These limitations often lead to diagnost...
Monitoring the Adverse Implications of Coronavirus 2019-induced Pulmonary Complications on Patients' Respiratory Capacity and Physical Abilities with Moderate and Severe Signs during Interval Follow-up [0.03%]
监测2019冠状病毒病肺部并发症对中重度患者间歇期呼吸能力和身体机能不良影响的作用
Farzaneh Aali,Zahra Habibi,Faranak Aali et al.
Farzaneh Aali et al.
Background: The respiratory system of individuals with coronavirus 2019 (COVID-19) experiences significant strain due to the body's immunological response and inflammation, leading to organ failure. Over time, patients ha...
SynthECG: Python Framework and ECG Image Datasets for Digitization, Lead Detection, and Waveform Segmentation [0.03%]
基于Python的SynthECG心电图图像框架及数据库及其端到端数字化、导联定位与波形分割方法
Masoud Rahimi,Reza Karbasi,Abdol-Hossein Vahabie
Masoud Rahimi
Background: Digitizing electrocardiogram (ECG) images into structured time-series data is critical for clinical analysis, but it remains challenging due to the lack of standardized datasets, especially under realistic sce...
Effects of Electrical Stimulation and Lidocaine Injection of Lateral Habenula Nucleus in the Addicted Rats with Morphine Self-administration [0.03%]
电针刺激及利多卡因注射对莫达非尼自陷大鼠腹侧被盖区成瘾效应的影响
Farahnaz Ataei,Saeed Khatamsaz,HojjatAllah Alaei et al.
Farahnaz Ataei et al.
Background: Addiction is one of the critical problems in public health. The lateral habenula (LHb) is a brain structure that plays an important role in sleep, reward-based decision-making, punishment avoidance, and stress...
Improving Skin Lesion Diagnosis: A Hybrid Approach Using Orthogonal Combination of Local Binary Pattern Features and Ensemble Learning for Diagnostic Accuracy [0.03%]
一种混合方法：利用局部二值模式特征正交组合集成学习提高皮肤病变诊断的准确性
Nasrin Rahmani,Hossein Ebrahimnezhad
Nasrin Rahmani
Background: As the world becomes wealthier and people expect higher standards of care, the demand for healthcare services is growing rapidly. This puts significant pressure on existing medical resources and systems, makin...
A Dimensionality Reduction Approach for Motor Imagery Brain-Computer Interface Using Functional Clustering and Graph Signal Processing [0.03%]
基于功能聚类和图信号处理的运动想象脑机接口降维方法研究
Mohammad Davood Khalili,Vahid Abootalebi,Hamid Saeedi-Sourck
Mohammad Davood Khalili
Background: This paper introduces an approach for dimensionality reduction and classification of electroencephalogram signals in motor imagery brain-computer interface (MI-BCI) systems. ...