Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…Skin cancer if detected early and treated in time can be controlled and its deadly impacts arrested completely. Algorithms applied in convolutional neural network (CNN) could lead to an enhanced speed of identifying and distinguishing a disease, which in turn leads to early detection and treatment. …”
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Precision Medicine in Hematologic Malignancies: Evolving Concepts and Clinical Applications by Rita Khoury, Chris Raffoul, Christina Khater, Colette Hanna

“…Despite significant progress, challenges remain, including high costs, disparities in access, a lack of standardization, and integration barriers in clinical practice. However, advances in single-cell sequencing, spatial transcriptomics, drug repurposing, immunotherapies, pan-cancer trials, precision prevention, and AI-guided algorithms offer promising avenues to refine treatment and improve outcomes. …”
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OPT-IQA: Automated camera parameters tuning framework with IQA-guided optimization by Jan-Henner Roberg, Vladyslav Mosiichuk, Ricardo Silva, Luís Rosado

“…It also facilitates the seamless integration of supplementary IQA metrics and optimization algorithms to support additional use cases. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…These models closely mirrored actual prescribing patterns, suggesting feasibility for clinical integration. Conclusions ML models can accurately predict physician-prescribing behavior for anemia management in HD patients, indicating a promising role for artificial intelligence (AI) in improving treatment quality and operational efficiency.…”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…Emerging solutions like federated learning (35% accuracy improvement in pan-African E. coli prediction) and explainable AI (SHAP-guided nitrate models) address these barriers. …”
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Tribological Performance Enhancement in FDM and SLA Additive Manufacturing: Materials, Mechanisms, Surface Engineering, and Hybrid Strategies—A Holistic Review by Raja Subramani, Ronit Rosario Leon, Rajeswari Nageswaren, Maher Ali Rusho, Karthik Venkitaraman Shankar

“…Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. …”
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ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions by Pavan Kumar S, Nirav Pravinbhai Bhatt

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Theoretical Model, Model Innovation, and Important Implications of DeepSeek Empowering Library Knowledge Services by ZHANG Xingwang, LI Jie, LI Sifan, WANG Xiaopei

“…The large-scale modeling technologies represented by DeepSeek, ZhipuAI, ChatGPT, etc. are reshaping the boundaries and forms of knowledge services through the deep integration of new-generation artificial intelligence technologies and knowledge service systems, providing important theoretical and technical support for the development of library intelligence strategies during the 15th Five-Year Plan period. …”
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Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ter... by Wang Peng BS, Wanyin Qi BS, Yunhua Li BS, Sanhong Zhang BS, Juan Long BS

“…Therefore, this study aimed to develop ternary classification models to classify AIS, MIA, and IAC by leveraging insights from 15 machine-learning algorithms and integrating ITH scores with clinical data. …”
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Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions by Marios Spanakis, Eleftheria Tzamali, Georgios Tzedakis, Chryssalenia Koumpouzi, Matthew Pediaditis, Aristides Tsatsakis, Vangelis Sakkalis

“…AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. …”
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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

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Role of artificial intelligence in earlier diagnosis of bronchopulmonary dysplasia by Lam Q. Bui, Sandhya J. Kadam

“…Artificial intelligence (AI) is becoming an integral part of healthcare. …”
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Digital remote monitoring of people with multiple sclerosis by Michelangelo Dini, Michelangelo Dini, Giancarlo Comi, Letizia Leocani, Letizia Leocani, Letizia Leocani

“…We focus on tools and techniques applied to data from wearable sensors, smartphones, and other connected devices, as well as AI-based methods for the analysis of big data.ResultsWearable sensors and machine learning algorithms show significant promise in monitoring motor symptoms, such as fall risk and gait disturbances. …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…Research question: What is the current landscape of AI applications in chordoma management, and what are the key limitations and future directions for integrating AI into clinical practice for this rare malignancy? …”
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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China by Ao Zhang, Xin-wen Zhao, Xing-yuezi Zhao, Xiao-zhan Zheng, Min Zeng, Xuan Huang, Pan Wu, Tuo Jiang, Shi-chang Wang, Jun He, Yi-yong Li

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Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease by Yashbir Singh, ME, PhD, Quincy A. Hathaway, MD, PhD, Karthik Dinakar, PhD, Leslee J. Shaw, PhD, Bradley Erickson, MD, PhD, Francisco Lopez-Jimenez, MD, MBA, MSc, Deepak L. Bhatt, MD, MPH, MBA

“…The article discusses the challenges of integrating AI with topological considerations and the need for specialized methodologies beyond traditional performance metrics. …”
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Multi-objective optimization of EMS facilities using multi-source data: A case study in Dangtu, China by Jinze Li, Xiao Wang, Qiyan Zhang, Peng Tang

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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

“…Future advancements may include real-time monitoring, integrating AI by IoT expedients for dynamic decision-making, and enhancing sustainability by minimizing water usage, fertilizers, and environmental impact in agriculture.…”
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Study on Energy Efficiency and Maintenance Optimization of Run-Out Table in Hot Rolling Mills Using Long Short-Term Memory-Autoencoders by Ju-Woong Yun, So-Won Choi, Eul-Bum Lee

“…Furthermore, Long Short-Term Memory (LSTM) networks were chosen to address the time-series nature of the data. By integrating the technical advantages of these two algorithms, a predictive maintenance model based on the LSTM-AE algorithm, named the Run-Out Table Predictive Maintenance Model (ROT-PMM), was developed. …”
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A comparative study of ultra-massive MIMO intelligent receivers with adversarial robustness and energy efficiency for 6G applications by Pushkar Nidagundi, Advesh Darvekar, Malik Amber, Ramesh R

“…This paper proposes a novel Taguchi-based optimization framework that systematically fine-tunes power allocation, modulation schemes, and space-time coding parameters, reducing computational complexity by up to 85 % compared to exhaustive search. The approach integrates advanced technologies including UM-MIMO, cognitive radio, and Artificial Intelligence (AI)-based detection to enhance robustness against adversarial attacks. …”
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