The ethical legitimacy of autonomous Weapons systems: reconfiguring war accountability in the age of artificial Intelligence by Jie Guo

“…Proponents justify AWS on grounds of (1) enhanced military efficiency and reduced soldier casualties, (2) improved compliance with international humanitarian law (IHL) through algorithmic precision, and (3) operational necessity in high-threat environments. …”
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Spatio-temporal variability of San Francisco Bay Plume from space by Piero L. F. Mazzini, Cassia Pianca, Cassia Pianca, L. Fernando Pareja-Roman, Kelly L. Cole, Ryan K. Walter, Renato M. Castelao, Elias J. Hunter, Robert J. Chant

“…West Coast, they form the San Francisco Bay Plume (SFBP), which spreads offshore and influences the Gulf of the Farallones (GoF), an ecologically significant region in the California Current System that is also home to three National Marine Sanctuaries. This paper provides the first observationally based investigation of the spatio-temporal variability of the SFBP, using a plume tracking algorithm applied to more than two decades (2002-2023) of ocean color data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard satellites Aqua and Terra. …”
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Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages by Süreyya Betül Rufaioğlu, Ali Volkan Bilgili, Erdinç Savaşlı, İrfan Özberk, Salih Aydemir, Amjad Mohamed Ismael, Yunus Kaya, João P. Matos-Carvalho

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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models by Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh

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Next-Gen UAV-Satellite Communications: AI Innovations and Future Prospects by Sherief Hashima, Ahmad Gendia, Kohei Hatano, Osamu Muta, Mostafa S. Nada, Ehab Mahmoud Mohamed

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Neural Networks Method in modeling of the financial company’s performance by I. P. Kurochkina, I. I. Kalinin, L. A. Mamatova, E. B. Shuvalova

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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The framework integrated diverse ML algorithms, including Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Balanced Bagging (BG), Gradient Boosting (GB), and Convolutional Neural Networks (CNNs). …”
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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…In addition, the SHAP values indicated that surgical treatment was the most important prognostic risk factor for OS at 6-months, 1-year, 2-years, and 3-years. After further balancing the baseline characteristics between the surgical and non-surgical groups using PSM, we observed that patients with BM who underwent surgical intervention showed significantly better survival outcomes across all subgroups compared to non-surgical patients, though unmeasured confounders may contribute to this association.ConclusionWe developed a novel nomogram for predicting prognostic factors in RCC patients with BM, offering a valuable tool to support accurate clinical decision-making. …”
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Real-world evidence market: key players and key segments by A. R. Kasimova, A. S. Kolbin

“…They may be necessary for administrators (to create and improve algorithms for treating patients with various nosologies), physicians (to make a better clinical decision in favor of the patient) and directly to patients and their relatives (to better understand the treatment process). …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

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Intelligent routing for human activity recognition in wireless body area networks by Enas Selem Elmosallamy, Mohammed F. Soliman

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Defining Heritage Science: A Consilience Pathway to Treasuring the Complexity of Inheritable Human Experiences through Historical Method, AI, and ML by Andrea Nanetti

“…Artificial intelligence and machine learning algorithms can assist the next generation of historians, heritage stakeholders, and decision-makers in (1) decoding unstructured knowledge and wisdom embedded in selected cultural artefacts and social rituals, (2) encoding data in machine-readable systems, (3) aggregating information according to the user’s needs in real time, and (4) simulating the consequences of either erasing, neglecting, putting in latency, or preserving and sharing specific human experiences. …”
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An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery by Hao Zhu, Yiyan Zhou, Danyang Shen, Kejia Wu, Xiaojie Gan, Xiaofeng Xue, Weigang Zhang, Xiaohua Yang, Junyi Qiu, Ding Sun

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Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions by Yu Zhou, Jing Li, Xiaomei Hou, Zhen Li, Yanan Xu, Yan Wang, Mingze Sun, Fumin Zheng, Enhui Guo, Jun Zhou

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Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning by Jianbo Li, Qin Shi, Yi Yang, Jikui Xie, Qiang Xie, Ming Ni, Xuemei Wang, Xuemei Wang

“…After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. …”
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Decentralized multi-agent federated and reinforcement learning for smart water management and disaster response by H. Mancy, Naglaa E. Ghannam, Amr Abozeid, Ahmed I. Taloba

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A machine learning-based depression risk prediction model for healthy middle-aged and older adult people based on data from the China health and aging tracking study by Fang Xia, Jie Ren, Linlin Liu, Yanyin Cui, Yufang He

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Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening by Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

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Study protocol for a pragmatic randomised controlled trial in Belgian primary care and hospital settings on the effectiveness of an eHealth self-management support programme consis... by Josefien van Olmen, An De Groef, Nele Devoogdt, Lore Dams, Bart Morlion, Mira Meeus, G Lorimer Moseley, Louise K Wiles, Peter Hibbert, Patrick Neven, Ines Nevelsteen, Steffen Fieuws, Geert Crombez, Michel Mertens, Ceren Gursen, Wiebren Tjalma, Lander Willem, Lauren C Heathcote, Mark Catley, Anna Vogelzang, Marthe Van Overbeke, Emma Tack, Sophie Van Dijck, Annick L De Paepe, Rani Vanhoudt, Davina Wildemeersch, Femke De Backere

“…Utilising a chatbot format, the eHealth programme delivers pain science education and promotes physical activity (PA), personalised through decision-tree-based algorithms to support pain self-management. …”
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