A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance by Mst Alema Khatun, Mohammad Abu Yousuf, Taskin Noor Turna, AKM Azad, Salem A. Alyami, Mohammad Ali Moni

“…<b>Methods:</b> This paper proposes a novel strategy using a three-stage feature ensemble combining deep learning (DL) and machine learning (ML) for accurate and automatic classification of activity recognition. …”
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Improved Splitting-Integrating Methods for Image Geometric Transformations: Error Analysis and Applications by Hung-Tsai Huang, Zi-Cai Li, Yimin Wei, Ching Yee Suen

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Identification and validation of hub genes related to neutrophil extracellular traps-mediated cell damage and immune recruitment during abdominal aortic aneurysm by Chuanlong Lu, Heng Wang, Maolin Qiao, Runze Chang, Jinshan Chen, Lizheng Li, Keyi Fan, Sheng Yan, Ruijing Zhang, Honglin Dong

“…By using weighted gene co-expression network analysis and machine learning algorithms, we ultimately identified 9 hub genes (MMP9, CXCR4, CYBB, TNFAIP3, PIK3CD, LTB, CXCL13, SELL, STAT4). …”
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Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasíli... by Guilherme Ferreira da Silva, Marcos Vinícius Ferreira, Lucy Takehara Chemale, Igor Vasconcelos Santana, Nilson Francisquini Botelho

“…Our findings underscore the value of machine learning techniques, particularly SOM, in geoscientific data analysis. …”
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A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives by Xuan Li, Bingxue Zhu, Sijia Li, Lushi Liu, Kaishan Song, Jiping Liu

“…Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. …”
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Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis by Serena Dattola, Augusto Ielo, Giuseppe Varone, Giuseppe Varone, Alberto Cacciola, Angelo Quartarone, Lilla Bonanno

“…Early and accurate differential diagnosis between FTD, its subtypes, and other dementias, such as Alzheimer's disease (AD), is crucial for appropriate treatment planning and patient care. Machine learning (ML) techniques have shown promise in enhancing diagnostic accuracy by identifying complex patterns in clinical and neuroimaging data that are not easily discernible through conventional analysis.MethodsThis systematic review, following PRISMA guidelines and registered in PROSPERO, aimed to assess the strengths and limitations of current ML models used in differentiating FTD from other neurological disorders. …”
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RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study by Mingcan Tang, William Haese-Hill, Fraser Morton, Carl Goodyear, Duncan Porter, Stefan Siebert, Thomas D. Otto

“…To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. …”
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Secure Biometric Identification Using Orca Predators Algorithm With Deep Learning: Retinal Iris Image Analysis by Louai A. Maghrabi, Mohammed Altwijri, Sami Saeed Binyamin, Fouad Shoie Alallah, Diaa Hamed, Mahmoud Ragab

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An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty by Mohsen Pourmohammad Shahvar, Davide Valenti, Alfonso Collura, Salvatore Micciche, Vittorio Farina, Giovanni Marsella

“…Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies.…”
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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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Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study by Ningkun Xiao, Ningkun Xiao, Xinlin Huang, Yujun Wu, Baoheng Li, Wanli Zang, Khyber Shinwari, Khyber Shinwari, Irina A. Tuzankina, Valery A. Chereshnev, Valery A. Chereshnev, Guojun Liu

“…AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. …”
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Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care by Panteleimon Pantelidis, Polychronis Dilaveris, Samuel Ruipérez-Campillo, Athina Goliopoulou, Alexios Giannakodimos, Panagiotis Theofilis, Raffaele De Lucia, Ourania Katsarou, Konstantinos Zisimos, Konstantinos Kalogeras, Evangelos Oikonomou, Gerasimos Siasos

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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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Histological Grade, Tumor Breadth, and Hypertension Predict Early Recurrence in Pediatric Sarcoma: A LASSO-Regularized Micro-Cohort Study by Alexander Fiedler, Mehran Dadras, Marius Drysch, Sonja Verena Schmidt, Flemming Puscz, Felix Reinkemeier, Marcus Lehnhardt, Christoph Wallner

“…This exploratory study aimed to identify clinical features associated with first tumor recurrence using a machine learning approach tailored to low-event settings. …”
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Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems by Malik Stephanie, Daßler David, Patel Dharm, Klute Carola, Klengel Robert, Dietrich Andreas, Kaufmann Kai, Hennig Carsten, Wehnert Danny, Ebert Matthias

“…A root cause analysis identified the failure pattern through material diagnostics of several power modules from inverters previously installed in the field. …”
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Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

“…However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. …”
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Rapid detection and quantification of Nile Red-stained microplastic particles in sediment samples by Masashi Tsuchiya, Tomo Kitahashi, Yosuke Taira, Hitoshi Saito, Kazumasa Oguri, Ryota Nakajima, Dhugal J. Lindsay, Katsunori Fujikura

“…To estimate the contamination levels and distribution patterns, and develop countermeasures, the amount of MPs must be understood. …”
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Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China by Lingzhi Luo, Liang Wang, You Li, Hongying Cao, Yanling Guo, Xiaoyong Liao

“…This is the first study to combine high-resolution machine learning mapping, source apportionment, and multi-scale risk assessment in an urban–rural context. …”
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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

“…The use of artificial intelligence to identify morphological patterns in erythrocytes would improve our diagnostic capability. …”
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