Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes by Shoupeng Ding, Chunxiao Huang, Jinghua Gao, Chun Bi, Yuyang Zhou, Zihan Cai

“…We identified T-cell-associated metabolic differentially expressed genes (TCM–DEGs) through integrated differential expression analysis and machine learning algorithms (XGBoost, SVM–RFE, and Boruta). …”
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MEMS and IoT in HAR: Effective Monitoring for the Health of Older People by Luigi Bibbò, Giovanni Angiulli, Filippo Laganà, Danilo Pratticò, Francesco Cotroneo, Fabio La Foresta, Mario Versaci

“…The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. …”
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Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System by Zhonghe Guo, Yanzhang Li, Yuchen Wang, Haoxuan Liu, Rui Guo, Jingzhong Ma, Xiaoming Wu, Dong Jiang, Tianling Ren

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Computer Viewing Model for Classification of Erythrocytes Infected with <i>Plasmodium</i> spp. Applied to Malaria Diagnosis Using Optical Microscope by Eduardo Rojas, Irene Cartas-Espinel, Priscila Álvarez, Matías Moris, Manuel Salazar, Rodrigo Boguen, Pablo Letelier, Lucia San Martín, Valeria San Martín, Camilo Morales, Neftalí Guzmán

“…<i>Materials and Methods:</i> A total of 27,558 images of human blood sample extensions were obtained from a public data bank for analysis; half were of parasite-infected red cells (<i>n</i> = 13,779), and the other half were of uninfected erythrocytes (<i>n</i> = 13,779). Six models (five machine learning algorithms and one pre-trained for a convolutional neural network) were assessed, and the performance of each was measured using metrics like accuracy (A), precision (P), recall, F1 score, and area under the curve (AUC). …”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…Furthermore, anomaly detection systems powered by advanced algorithms and machine learning enable real-time database activities analysis, addressing challenges like preprocessing, model training and scalability. …”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…Furthermore, anomaly detection systems powered by advanced algorithms and machine learning enable real-time database activities analysis, addressing challenges like preprocessing, model training and scalability. …”
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Comparison of Various Feature Extractors and Classifiers in Wood Defect Detection by Kenan Kiliç, Kazım Kiliç, İbrahim Alper Doğru, Uğur Özcan

“…The findings show that the most effective features in detecting defective wood are extracted by the Local Binary Pattern (LBP) method and the most effective classifier is the Random Forest Algorithm. …”
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A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based Optimization and Attention Mechanism for Enhanced Dam Deformation Prediction by Lei Wang, Jiajun Wang, Dawei Tong, Xiaoling Wang

“…To address these issues, this study aimed to improve the predictive accuracy and interpretability in dam deformation modeling by proposing a novel LSTM seq2seq model that integrates a chaos-based arithmetic optimization algorithm (AOA) and an attention mechanism. The AOA optimizes the model’s learnable parameters by utilizing the distribution patterns of four mathematical operators, further enhanced by logistic and cubic mappings, to avoid local optima. …”
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Technology acceptance model for online education: identifying interdisciplinary topics and their evolution based on BERTopic model by Songyu Jiang, Hao Li, Du Gan

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Altered gray matter morphometry in psychogenic erectile dysfunction patients: A Surface-based morphometry study by Zilei Tian, Ziyang Ma, Beihong Dou, Xiaopeng Huang, Guangsen Li, Degui Chang, Tao Yin, Peihai Zhang

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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

“…MethodsIn this study, a dynamic model based on machine learning algorithms is proposed. The model's output, which combines the number and timing of bodily injury accidents, plays a crucial role in calculating reserves for non-life insurance products. …”
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Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection by Margaryta Prazdnikova

“…By leveraging a depersonalized database from SSO CITHC SAA, containing medical records collected during a decade of operating, this study seeks to reveal how the identification of critical patterns and factors can improve prediction accuracy. …”
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Identification of Exosome-Associated Biomarkers in Diabetic Foot Ulcers: A Bioinformatics Analysis and Experimental Validation by Tianbo Li, Lei Gao, Jiangning Wang

“…This was followed by GO/KEGG analyses and a PPI network construction. Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). …”
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A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García, Domingo Ortiz-Boyer

“…By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use categories, such as home, work, and forest. …”
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A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards by Arthur Gonsales da Silva, Arthur Gonsales da Silva, Roger Pinho de Oliveira, Caio de Oliveira Bastos, Caio de Oliveira Bastos, Elena Almeida de Carvalho, Bruno Duarte Gomes

“…Additionally, we evaluated five classical ML models for classifying the extracted patterns: (a) Support Vector Machine (SVM); (b) GaussianNB (GNB); (c) AdaBoost (ADB); (d) K-Nearest Neighbors (KNN); and (e) Random Forest (RF). …”
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Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit by Nadea Putri Nur Fauzi, Siti Khomsah, Aditya Dwi Putra Wicaksono

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Precision neuropsychology in the area of AI by Astri J. Lundervold

“…Key opportunities include enhanced pattern recognition in traditional assessments (e.g., digital clock drawing), continuous monitoring of symptom fluctuations (e.g., Attention Deficit Disorder), and personalized assessment and treatment procedures based on individual needs (e.g., learning disorders). …”
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A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md Mehedi Hasan Shawon

“…Therefore, a sleep study that includes sleep patterns and disorders is crucial to enhancing our knowledge about individual health status. …”
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Applications of Artificial Intelligence in Drug Repurposing by Zhaoman Wan, Xinran Sun, Yi Li, Tianyao Chu, Xueyu Hao, Yang Cao, Peng Zhang

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Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine by Karishma Sahoo, Prakash Lingasamy, Masuma Khatun, Sajitha Lulu Sudhakaran, Andres Salumets, Vino Sundararajan, Vijayachitra Modhukur

“…Aberrant methylation patterns enable early cancer detection and therapeutic stratification; however, their complex patterns necessitates advanced analytical tools. …”
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