Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez, Ricardo Chaparro-Sánchez

“…This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably.…”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. …”
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The Emerging Role of Computed Tomography Coronary Angiography in the Left Main Stem Percutaneous Coronary Intervention by Asad Shabbir, Abdelrahman Attia, Nikkole Marie Weber, Mustafa Alhassan, Monika Radike, Conor M Lane, Apurva Bhavana Challa, Malgorzata Wamil

“…Recent technological advancements, including detailed multiplanar and three-dimensional (3D) reconstructions, CT-derived fractional flow reserve (CTFFR), and the integration of artificial intelligence (AI) algorithms, have expanded the capabilities of CTCA. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…The system captures glucose-related optical signals, which are analyzed using various machine learning algorithms, including a novel Convolutional Neural Network–Attention Hybrid Model (CNN-AHM). …”
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Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinomaResearch in context by Bolin Song, Amaury Leroy, Kailin Yang, Tanmoy Dam, Xiangxue Wang, Himanshu Maurya, Tilak Pathak, Jonathan Lee, Sarah Stock, Xiao T. Li, Pingfu Fu, Cheng Lu, Paula Toro, Deborah J. Chute, Shlomo Koyfman, Nabil F. Saba, Mihir R. Patel, Anant Madabhushi

“…Pathology and radiology focused AI-based prognostic models have been independently developed for OPSCC, but their integration incorporating both primary tumour (PT) and metastatic cervical lymph node (LN) remains unexamined. …”
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Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT by Meysam Dadgar, Amaryllis Verstraete, Jens Maebe, Yves D’Asseler, Stefaan Vandenberghe

“…Abstract Background This study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals. …”
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SMART DELAY PREDICTION: SUPERVISED MACHINE LEARNING SOLUTIONS FOR CONSTRUCTION PROJECTS by Pramodini Sahu, Dillip Kumar Bera, Pravat Kumar Parhi, Meenakshi Kandpal

“…Future studies ought to develop hybrid ML models that fit in explainable AI techniques and real-time data on construction applications to assure predictive accuracy and usability in practice. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…Basophil count, while ranked highest by SHAP, showed low sensitivity, highlighting the difference between algorithmic weight and bedside utility. Conclusion: These findings support the integration of routine, readily available laboratory data into an explainable AI framework to accurately predict culture positivity. …”
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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application by Roman Rodriguez-Aguilar, Jose-Antonio Marmolejo-Saucedo, Utku Köse

“…It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. …”
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Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool by Hatem Ali, Arun Shroff, Tibor Fülöp, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. …”
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Migración mediante apilado por difracción en medios con gradiente constante de velocidad by Castillo López Luis Antonio

“…Esto se logra al aplicar a los datos de entrada un peso al operador de apilado por difracción, cuyo desarrollo teórico es basado en la integral tipo Kirchhoff. AI escoger el propio peso para apilado de datos, el resultado del proceso de la migración es una sección sísmica, donde la amplitud es proporcional al coeficiente de reflexión, conocido como migración con amplitudes verdaderas. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…Against this backdrop, the research aims to explore the path of AI-aided design for customized landscape architecture scenarios and, by combining network big data and fine-tuning model technology of artificial intelligence generated content (AIGC), bridge the gap between big data analysis research and scenario generation in design practice, and construct a lightweight aided landscape design approach in response to the trend of segmentation, specialization, and customization in landscape design practice.MethodsThe research adopts the Research for Design (RfD) methodology to build an approach framework for integrating research and design practice. …”
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Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives by Nouman Ali, Nimra Hanif, Hassan Abbas Khan, Muhammad Abdullah Waseem, Afshan Saeed, Sadia Zakir, Abeeha Khan, Mejerrah Aamir, Adeeba Ali, Aamir Ali, Amna Saleem

“…Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. …”
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Smart UAV-assisted rose growth monitoring with improved YOLOv10 and Mamba restoration techniques by Fan Zhao, Zhiyan Ren, Jiaqi Wang, Qingyang Wu, Dianhan Xi, Xinlei Shao, Yongying Liu, Yijia Chen, Katsunori Mizuno

“…This research lays the groundwork for broader applications of UAV and AI technologies in sustainable agriculture. The findings pave the way for advanced, data-driven precision agriculture, integrating deep learning with remote sensing methodologies to improve floriculture management.…”
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Balcerzak, Michal and Julia Kapelańska-Pręgowska, eds. 2024. Artificial Intelligence and International Human Rights Law. Developing Standards for a Changing World by Itziar Artíñano Ortiz

“…Recent initiatives that aim to establish ethical and legal standards to address the risks associated with AI are highlighted. Mass surveillance, algorithmic bias, and privacy violations stand out among these aspects. …”
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Dark Side of the Web: Dark Web Classification Based on TextCNN and Topic Modeling Weight by Gun-Yoon Shin, Younghoan Jang, Dong-Wook Kim, Sungjin Park, A-Ran Park, Younghwan Kim, Myung-Mook Han

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Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimizatio... by Hanxuan Wang, Raman Kumar, Raman Kumar, Ashutosh Pattanaik, Rajender Kumar, Rajender Kumar, Ali Saeed Owayez Khawaf Aljaberi, Mayada Ahmed Abass

“…The PRISMA framework was used to ensure the structured literature search, eligibility assessment, and documentation of the selection process. 185 peer-reviewed articles (2015–2025) were analyzed and organized into seven refined thematic clusters: ‘mechanical behavior modeling using neural networks’, ‘AI-driven alloy design and compositional optimization’, ‘atomic-scale modeling and physics-guided learning’, ‘AI applications in welding and thermomechanical processing’, ‘biomaterials and microstructural optimization’, ‘corrosion modeling and degradation prediction’, ‘data-driven design and integrated optimization frameworks’. …”
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Information-guided adaptive learning approach for active surveillance of infectious diseases by Qi Tan, Chenyang Zhang, Jiwen Xia, Ruiqi Wang, Lian Zhou, Zhanwei Du, Benyun Shi

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Predicting Patients’ Revisit Intention Based on Satisfaction Scores: Combination of Penalized Regression and Neural Networks by Farshid Abdi, Shaghayegh Abolmakarem, Amir Karbassi Yazdi, Paul Leger, Yong Tan, Giuliani Coluccio

“…The analysis of the results indicates that while the Neural Network model shows superior prediction accuracy, the Lasso Regression method is efficient in identifying relevant features. By integrating AI approaches and thoroughly examining satisfaction ratings in the Iranian healthcare industry, this research makes a significant contribution. …”
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Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning by Zhibin Jiang, Zhibin Jiang, Keli Hu, Keli Hu, Jia Qu, Zekang Bian, Zekang Bian, Donghua Yu, Donghua Yu, Jie Zhou, Jie Zhou

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