Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records by Mamadou Dia, Ghazaleh Khodabandelou, Syed Muhammad Anwar, Alice Othmani

“…These results underscore the potential of our proposed architecture in delivering accurate and reliable mental disorder detection through EEG analysis, paving the way for advancements in early diagnosis and treatment strategies.…”
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Comparison of detection and manifestations of metastatic hepatocellular carcinoma by ultrasound at different frequencies by Hong QIN, Yuli ZHU, Qiannan ZHAO, Feihang WANG, Hansheng XIA, Wentao KONG, Wenping WANG

“…ResultsHigh-frequency grayscale ultrasound had a higher detection rate (71.1% vs. 36.8%, P＜0.001). Subgroup analysis showed higher detection rates with chemotherapy history (88.9% vs. 33.3%, P＝0.002), fatty liver (71.9% vs 31.3%, P＜0.001) or superficial lesion (within 20 mm, 76.5% vs 41.2%, P＝0.031). …”
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Objective Assessment of Cancer Biomarkers Using Semi-Rare Event Detection by Jeroen A. W. M. van der Laak, Albertus G. Siebers, Sabine A. A. P. Aalders, Johanna M. M. Grefte, Peter C. M. de Wilde, Johan Bulten

“…Objective and reproducible assessment of cancer biomarkers may be performed using rare event detection systems. Because many biomarkers are not true ‘rare events’, in this study a semi-rare event detection system was developed. …”
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Advanced machine learning techniques for social support detection on social media by Olga Kolesnikova, Moein Shahiki Tash, Zahra Ahani, Ameeta Agrawal, Raúl Monroy, Grigori Sidorov

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Multimodal Deep Learning in Early Autism Detection—Recent Advances and Challenges by Sheril Sophia Dcouto, Jawahar Pradeepkandhasamy

“…The analysis revealed that integrating multiple modalities, including neuroimaging, genetics, and behavioral data, is key to achieving higher accuracy in early ASD detection. …”
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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection by Vasily Lubashevskiy, Hamza Ejjbiri, Ihor Lubashevsky

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PREVALENCE AND EFFECTIVENESS OF DETECTING GASTROINTESTINAL DISORDERS ASSOCIATED WITH CHRONIC FATIGUE IN SEAMEN by Olexandr M. Ignatiev, Oleksij I. Paniuta, Tetiana L. Prutiian

“…Underdiagnosis is associated with both symptom relief during the period between trips and the non-disclosure of seamen's complaints. The organizational features of the work of the medical commission exclude a possibility of primary detection of peptic ulcer and irritable bowel syndrome in seamen.…”
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DCE-YOLOv8: Lightweight and Accurate Object Detection for Drone Vision by Jinsu An, Dong Hee Lee, Muhamad Dwisnanto Putro, Byeong Woo Kim

“…The DCE module focuses on extracting features pertinent to small objects. Subsequently, the rate of missed detections is mitigated by comprehensively merging the shallow and deep features extracted from the neck part. …”
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Improved Hypernymy Detection Algorithm Based on Heterogeneous Graph Neural Network by Li Ren, Jing Huang, Hai-Tao Jia, Shu-Bao Sun, Kai-Shi Wang, Yi-Le Wu

“…To address the problem of overfitting in traditional graph attention networks, the calculation order of adjacency node aggregation is changed in the heterogeneous graph to capture dynamic attention features. In addition, a pipeline for hierarchical system construction is designed and implemented, combining the divide-and-conquer approach with loop detection algorithms. …”
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Breast Cancer Detection Using Mammography: Image Processing to Deep Learning by Shahzad Ahmad Qureshi, Aziz-Ul-Rehman, Lal Hussain, Touseef Sadiq, Syed Taimoor Hussain Shah, Adil Aslam Mir, Muhammad Amin Nadim, Darnell K. Adrian Williams, Tim Q. Duong, Qurat-Ul-Ain Chaudhary, Natasha Habib, Asrar Ahmad, Syed Adil Hussain Shah

“…Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. …”
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Automatic detection and visualization of temporomandibular joint effusion with deep neural network by Yeon-Hee Lee, Seonggwang Jeon, Jong-Hyun Won, Q.-Schick Auh, Yung-Kyun Noh

“…The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. …”
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Visualization of large-scale user association feature data based on a nonlinear dimensionality reduction method by Nong Linlin

“…The prosperity of data science and the booming growth of the internet industry have made the analysis and processing of large-scale user-related feature data a particularly important issue in modern society. …”
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Feature comparison from laser speckle imaging as a novel tool for identifying infections in tomato leaves by H. Félix-Quintero, J.C. Avila-Gaxiola, J.R. Millan-Almaraz, C.M. Yee-Rendón

“…In the present work we propose the use of laser speckle imaging as non-invasive and marker-free qualitative technique that highlights significant differences between healthy and diseased leaves through image analysis. We observed distinct visual variations, indicating the detectable impact of viral infection on leaf structure. …”
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A hyperspectral stealth material design method based on the composition and mixing spectral feature of desert soil by Xiaodong Ma, Biao Wei, Xiaolong Qing, Yaqin Wang, Lun Qi, Xueyu Wu, Le Yuan, Xiaolong Weng

“…Firstly, the correlation between the composition and typical spectral detected characteristics of the desert soil was systematically analyzed. …”
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Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. We applied two feature selection methods, Sequential Backward Feature Selection (SBFS) and Analysis of Variance (ANOVA) to extract significant proteins from a dataset of 146  proteins. …”
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Diagnostic model for COPD patients with nocardia infection: a study based on clinical features and risk factors by Kai Zhang, Kangli Yang, Hongmin Wang

“…Conclusion: This validated nomogram provides a clinically actionable tool for early Nocardia detection in COPD patients, addressing a critical diagnostic gap. …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). <b>Methods:</b> Our study was based on cross-sectional study to analysis voice impairment. …”
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Genomic insights into host-associated variants and transmission features of a ToBRFV isolate from Mexico by Erika Janet Zamora-Macorra, Daniel Leobardo Ochoa-Martínez, Claudia Yaritza Chavarín-Camacho, Rosemarie W. Hammond, Katia Aviña-Padilla

“…Understanding its genomic features and transmission mechanisms is critical for effective disease management. …”
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Clinical features and prognostic factors of pediatric Langerhans cell histiocytosis: a single-center retrospective study by Yunfeng Lu, Liying Liu, Qi Wang, Bingju Liu, Ping Zhao, Guotao Guan, Yunpeng Dai

“…An analysis was conducted on 82 recently identified LCH cases to retrospectively evaluate the initial symptoms, therapeutic alternatives, and extended results. …”
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A Unified Framework for Fault and Performance Prediction Using Spatio-Temporal Geometric Features Based on STSFE by Dong-Hyun Kang, A-Youn Yang, Jong-Min Lee, Jong-Gu Lee

“…This paper proposes a unified deep-learning framework for fault and performance prediction in communication equipment by utilizing spatiotemporal geometric features. The core methodology, Spatio-Temporal Slope Feature Extraction (STSFE), transforms irregular time-series data into slope-, area-, and volume-based representations, capturing both temporal dynamics and spatial correlations. …”
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