Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset by Ahmed E. Mansour Atwa, El-Sayed Atlam, Ali Ahmed, Mohamed Ahmed Atwa, Elsaid Md. Abdelrahim, Ali I. Siam

“…Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. …”
Get full text

Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation by Milad Shoryabi, Ahmad Hajipour, Afshin Shoeibi, Ali Foroutannia

Get full text

Classification of chest radiographs into healthy/pneumonia using Harris-Hawks Algorithm optimized deep-features by K. Vijayakumar, Mohammad Nazmul Hasan Maziz, Swaetha Ramadasan, Seifedine Kadry, S. Arunmozhi

“…The experimental outcome authenticates that this PDL-tool helps to offer improved accuracy with the HHA-optimized features. This work provided an accuracy of 99.3750% during healthy/pneumonia detection with FFV and Support Vector Machine (SVM), and detection accuracy of 88.5417% during viral/bacterial pneumonia detection with FFV and SVM.…”
Get full text

Aerial Biological Target Classification Based on Time–Frequency Multi-Scale Feature Fusion Network by Lianjun Wang, Rui Wang, Weidong Li, Jiangtao Wang, Yujia Yan, Cheng Hu

“…The analysis revealed that radar cross section (RCS) features are insufficient to support insect and bird classification tasks, as aerial biological targets may be detected in radar sidelobes, leading to uncertainty in RCS values. …”
Get full text

Quasiperiodic Oscillations and Reflection Feature Evolution in 4U 1630-47 Observed with Insight-HXMT by Jiashi Chen, Wei Wang

“…The main science aims to study the reflection features and evolution of this accreting black hole using the observations of detecting quasiperiodic oscillations (QPOs) and quasi-regular modulations (QRMs). …”
Get full text

Parametric and nonparametric two-sample tests for feature screening in class comparison: a simulation study by Elena Landoni, Federico Ambrogi, Luigi Mariani, Rosalba Miceli

“…The identification of a location-, scale- and shape-sensitive test to detect differentially expressed features between two comparison groups represents a key point in high dimensional studies. …”
Get full text

Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights by Priya Metri, Swetta Kukreja

“…Most studies relied on survey-based data (68 %) and employed PHQ-9 or GAD-7 scales for input features. This review highlights existing gaps in cross-cultural generalization and calls for the development of interpretable and hybrid models for improved risk prediction.This review aims to conduct a comprehensive examination of the etiological factors contributing to the development of suicidal thoughts in students, with the goal of enabling early detection through the application of AI and machine learning techniques.This paper aims to review the current state-of-the-art, highlight the limitations, and emphasizes the need to shift toward hybrid and ensemble deep learning models, which have shown early promise but lack extensive analysis in current literature.…”
Get full text

LRU-Net: lightweight and multiscale feature extraction for localization of ACL tears region in MRI images by Xiaojun Si, Liang Yan, Cui Shi, Yang Xu

“…Furthermore, it employs a dynamic feature extraction module for adaptive multiscale feature extraction. …”
Get full text

Research on the Motion Features Model for Underwater Targets with Multiple Highlights and Multiple Micro-Motion Forms by Tong-jing SUN, Zihan ZHOU, Dongliang PENG

“…Finally, a time-frequency analysis method is employed to extract motion features and estimate target parameters, thereby validating the accuracy and effectiveness of the proposed model. …”
Get full text

XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

“…Model performance improved with the inclusion of cognitive assessments, with the most comprehensive model (Model 5) achieving the highest accuracy (86%) and area under the curve (AUC = 0.8359). Feature importance analysis revealed that variables such as memory tests, depressive symptoms, and age were significant predictors of MCI conversion. …”
Get full text

Research of the clinical features, risk factors, and surgical diagnosis of intramural stones in patients with gallbladder stones by Xiaobing Luo, Hongying Cai, Xiaofeng Wang, Ruihong Ma, Gang Wang, Sangui Wang, Tie Qiao

“…Abstract Crystals or stones within the gallbladder wall in patients with gallbladder stones (GBS) have been occasionally reported, but their clinical features and aetiology remain unclear. This retrospective study analysed 323 consecutive patients with GBS who underwent rigid choledochoscopic gallbladder-preserving cholecystolithotomy to determine the detection rate, clinical features, and potential risk factors of gallbladder intramural stones (IS). …”
Get full text

ANALYTICAL FEATURES OF SYNTHETIC MDMB(N)-073F CANNABIMIMETICS AND ITS MARKERS IN BIOLOGICAL MATERIAL by S. S. Kataev, O. N. Dvorskaya, M. A. Gofenberg, A. V. Labutin, A. B. Melentyev

“…The aim of the research is to study both analytical features of synthetic MDMB(N)-073F cannabimimetics of indazole carboxamides group by gas chromatography methods combined with tandem mass spectrometry (GC-MS) and high performance liquid chromatography with high-resolution mass spectrometry (HPLC-HRMS) as well as characteristics of the major MDMB(N)-073F metabolite, its glucuronide and derivatives, using gas chromatography with mass-spectrometric (GC-MS) detection and high-performance liquid chromatography (HPLC) with MS/MS mass spectrometry (HPLC-MS/MS) in urine samples to be applied in expert practice, chemical-toxicological and forensic and chemical analyses.Materials and methods. …”
Get full text

EEG alpha/beta features as a biomarker for quantifying pain in patients with lumbar disk herniation by Rumei Li, Rumei Li, Wanqi Shao, Shumei Zhao, Lingli Wang, Chao Yu, Chao Yu, Lanying Liu, Kuiying Yin

Get full text

Advanced Human Pose Estimation and Event Classification Using Context-Aware Features and XGBoost Classifier by Wasim Wahid, Aisha Ahmed AlArfaj, Ebtisam Abdullah Alabdulqader, Touseef Sadiq, Hameedur Rahman, Ahmad Jalal

“…Keypoint detection is achieved through pose estimation, and full-body feature extraction includes using OpenPose for movable body parts, the Lucas-Kanade method for a 3D Cartesian view, and Texton Map techniques. …”
Get full text

Status and serological features of occult hepatitis B virus infection among blood donors in Xuzhou by LIU Jiahui, WANG Bei, ZHOU Liling

Get full text

Clinical Epidemiology Features and Risk Factors for Acute Diarrhea Caused by Rotavirus A in Vietnamese Children by Dang Van Chuc, Dang Phuong Linh, Dang Viet Linh, Pham Van Linh

“…Among the 321 children included in our analysis, 221 (68.8%) children were positive for RVA. …”
Get full text

Features of the epidemic situation on tuberculosis in the conditions of climate change in the Far North of the Republic of Sakha (Yakutia) by Svetlana Zorina, Egor Prokopyev, Maria Vinokurova, Olga Kondrateva, Mr Ruslan Khachetlov, Irina Lizinfeld, Liubov Parolina

Get full text

Clinical and psychopathological features of course and correction of depressive disorders in patients with brain tumors, prevention and rehabilitation by V.V. Ogorenko, A.G. Kyrychenko, O.M. Hnenna

Get full text

Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

“…This method offers a practical tool for clinicians and researchers to support early diagnosis and personalized risk assessment of AD, thus aiding in timely and informed clinical decision-making.Accurate Prediction: Gradient Boosting model achieved 93.9 % accuracy for early Alzheimer’s detection.Explainability: SHAP values provided interpretable insights into key clinical features.Clinical Tool: A Streamlit-based web app enabled real-time, explainable predictions for users.…”
Get full text

Clinical features of rhinovirus infection in hospitalized adult patients during the epidemic season 2017–2018 by M. A. Bichurina, L. V. Voloshchuk, A. Go, M. M. Pisareva, D. A. Guzhov

Get full text