Multi-Modal Emotion Detection and Sentiment Analysis by Shoaib Sikunder Malik, Muhammad Ilyas, Yasin Ul Haq, Rabia Sana, Muhamamd Saad Razzaq, Fahad Maqbool, Muhammad Salman Pathan

“…Afterwards, we implement model ensembling across the three modalities. This multi-modal integration proves essential in providing a clearer and more comprehensive understanding of the sentiments conveyed and achieved more than 80% accuracy. …”
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Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems by Payman Goodarzi, Julian Schauer, Andreas Schütze

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Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning by Alka Jalan, Deepti Mishra, Marisha, Manjari Gupta

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Comprehensive assessment of risk factors and development of novel predictive tools for perioperative hidden blood loss in intertrochanteric femoral fractures: a multivariate retros... by Linbing Lou, Lei Xu, Xiaofei Wang, Cunyi Xia, Jihang Dai, Le Hu

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Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis by Jianhua Sun, Ye Cao, Ying Zhou, Baoqiao Qi

“…This study focuses on the development and evaluation of deep learning models for the classification of knee joint injuries using Magnetic Resonance Imaging (MRI) data. …”
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EfficientNetB0-Based End-to-End Diagnostic System for Diabetic Retinopathy Grading and Macular Edema Detection by Long X, Gan F, Fan H, Qin W, Li X, Ma R, Wang L, Hu R, Xie Y, Chen L, Cao J, Shao Y, Liu K, You Z

“…Additionally, Grad-CAM visualization highlighted key image regions influencing the model’s decision, enhancing its interpretability. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…Methods: A total of 1,972 adult patients who underwent complete blood count, C-reactive protein, procalcitonin (PCT), and blood culture testing at a tertiary hospital were retrospectively included. Three models-random forest, H2O automated ML, and an ensemble model-were developed and evaluated using standard classification metrics [area under the curve (AUC)-receiver operating characteristic (ROC), sensitivity, specificity, F1 score]. …”
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Spot the bot: the inverse problems of NLP by Vasilii A. Gromov, Quynh Nhu Dang, Alexandra S. Kogan, Assel Yerbolova

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Predicting mother and newborn skin-to-skin contact using a machine learning approach by Sanaz Safarzadeh, Nastaran Safavi Ardabili, Mohammadsadegh Vahidi Farashah, Nasibeh Roozbeh, Fatemeh Darsareh

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Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules—a systematic review and meta-analysis by Karishma Jassal, Karishma Jassal, Melissa Edwards, Afsaneh Koohestani, Afsaneh Koohestani, Wendy Brown, Jonathan W. Serpell, Jonathan W. Serpell, James C. Lee, James C. Lee

“…The pooled meta-analysis incorporated 16 area under the curve (AUC) results derived from 15 models across three studies yielding a combined estimate of 0.82 (95% CI: 0.81–0.84) indicating moderate-to-good classification performance across machine learning (ML) and deep learning (DL) architectures. …”
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Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

“…Before making classification decisions, the integration ability of high-weight features was enhanced to further improve the model's detection precision. …”
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Moving Toward Automated Construction Management: An Automated Construction Worker Efficiency Evaluation System by Chaojun Zhang, Chao Mao, Huan Liu, Yunlong Liao, Jiayi Zhou

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Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma by Hyerim Park, Jooyeon Lee, Seungeun Lee, Joon-Yong Jung

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Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features by Lin Zhang, Zhihan Xu, Yan Feng, Zhijie Pan, Qinyao Li, Ai Wang, Yanfei Hu, Xueqian Xie

“…Multi-feature selections and decision tree models were performed on radiomics features and semantic features separately to select the most important features for Masaoka–Koga staging and WHO classification. …”
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Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning by Junseok Lee, Jumi Park, Seongju Lee, Seong-Yong Moon, Kyoobin Lee

“…This study proposes an automatic diagnosis method based on state-of-the-art semantic segmentation and classification models to predict the extraction difficulty of maxillary and mandibular M3s and possible complications (sinus perforation and inferior alveolar nerve (IAN) injury). …”
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Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning by Shupei Zhang, Huapeng Shi, Wei Zhang, Ying Pang, Pengju Sun

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Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java by Silfiana Lis Setyowati, Asyifah Qalbi, Rafika Aristawidya, Bagus Sartono, Aulia Rizki Firdawanti

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Enhancing public transportation through user feedback: the case of Braamfontein, Johannesburg by Retsepile Kalaoane, Trynos Gumbo

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Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting by Cheng Ji, Dong Luo, Xiaole Shen, Leilei Xu, Hongwei Pan, Yuwei Liu

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Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…Feature extraction utilizes ResNet50, InceptionV3, and visual geometry group (VGG)-19 and combines their outputs for classification. …”
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