Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning by Alireza Bagheri Rajeoni, Breanna Pederson, Susan M. Lessner, Homayoun Valafar

“…<b>Conclusions:</b> This technique has the potential to facilitate large-scale aneurysm analysis and improve clinical decision-making by reducing dependence on annotated datasets.…”
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AI-driven FMEA: integration of large language models for faster and more accurate risk analysis by Ibtissam El Hassani, Tawfik Masrour, Nouhan Kourouma, Jože Tavčar

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Expanding and Interpreting Financial Statement Fraud Detection Using Supply Chain Knowledge Graphs by Shanshan Zhu, Tengyun Ma, Haotian Wu, Jifan Ren, Daojing He, Yubin Li, Rui Ge

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Blockchain-Based Supply Chain Management Ensuring Transparency and Traceability in Logistics Operations by Chinthamu Narender, A Swathi, Banka Venkata Ramana, S Jagadheeswaran, Yoganandh Sampathirao, Jebaseeli S Kanmani

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Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Ramiro Saltos, Ignacio Carvajal, Fernando Crespo, Richard Weber

“…The experiments showcase how these indices can effectively validate dynamic clustering solutions and guide parameter tuning for optimal performance, and support practical applications such as dynamic community detection in social networks and informed decision-making in dynamic environments. The results highlight the significant potential of these new validation indices and the I-DFCM algorithm in advancing the field of dynamic clustering.…”
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An induction machine‐based drive system and supervisory control for application to smart water‐pumped storage and networks by O. S. Ebrahim, M. A. Badr

“…Under the wide umbrella of supervisory control, the following objectives are achieved: (1) Recurrent neural network (RNN) and RNN‐aided Kalman filter are proposed for wired and wireless sensor fault‐tolerant control of the plant based on Wald's likelihood ratio test. (2) Cheap fuzzy logic‐based availability assessment scheme is presented and deployed on cloud to arrange for preventive maintenance of the starter. (3) Robust energy saving mode decision‐making algorithm is developed for best energetic cost reduction. (4) Remote synchronisation method of multi‐motor starting with a mechanically switched capacitor is introduced to minimise voltage dip. …”
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Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. …”
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Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models by Rasool Esmaeilyfard, Mohsen Bayati

“…Robustness tests introduced noise and missing data, while computational efficiency was assessed in terms of training time, inference speed, and memory usage. …”
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Regression-based predictive modelling of software size of fintech projects using technical specifications by Iqra Kanwal, Ali Afzal Malik

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Diagnostic Tools Before Thyroidectomy: A Surgical Perspective by Flaviu Mureşan, Ovidiu Vasile Fabian, Emilia Maria Pătruţ, Olga Hilda Orăşan, Ana Valea, Radu Bogdan Popescu

“…This article discusses the essential preoperative investigations required for thyroidectomy and their impact on surgical decision-making.…”
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Public discourses and personal narratives of learning from disaster by Mario A. Soriano, Patrick Jaojoco, Braeden Carroll, Reed Maxwell, Allison Carruth

“…We address this gap by leveraging a computational approach to analyze the thematic content in >15,000 newspaper articles and 300 academic abstracts along with conducting field interviews and producing a public-facing podcast series to assess lessons learned from Haiyan by institutions and individuals. …”
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Improving network security using keyboard dynamics: A comparative study by Ugwunna, C.O., Chukwuogo, O.E., Alabi, O.A., Kareem, M.K., Belonwu, T.S., Oloyede, S.O.

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Rationale and design of the THIRST Alert feasibility study: a pragmatic, single-centre, parallel-group randomised controlled trial of an interruptive alert for oral fluid restricti... by Yang Chen, Anoop Shah, Folkert W Asselbergs, R Thomas Lumbers, Daniel Higgins, Yogini Jani, Nausheen Saleem, Kris Chafer, Matthew Robert Sydes

“…A restriction in oral fluid intake is commonly imposed on patients as an adjunct to pharmacological therapy with loop diuretics, but there is a lack of evidence from traditional randomised controlled trials (RCTs) to support the safety and effectiveness of this intervention in the acute setting.This study aims to explore the feasibility of using computer alerts within the electronic health record (EHR) system to invite clinical care teams to enrol patients into a pragmatic RCT at the time of clinical decision-making. …”
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Entrepreneurship skills application among ICT Graduates of Muni University, Uganda by Ritah, Nafuna, Nicholas, Nkamwesiga, Phelix, Businge Mbabazi

“…The results show that the graduates are competent in: computer literacy and information technology (ICT applications); creativity, innovation and opportunity identification; life skills (problem solving, time management, decision making, ability to learn and change management); literacy and numeracy among others. …”
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Multivariate analysis of water quality of Sacramento-San Joaquin Delta by Rahul Kumar, Prachi Pandey, Aditya Pandey, Ujjwal Kumar, Vikrant Singh, Patricia Gilbert, Lydia Kenison, Jeffrey Caudill, Vijay P. Singh, Pramod K. Pandey

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BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers by Cheng Lv, Xu-Jun Shu, Xu-Jun Shu, Quan Liang, Jun Qiu, Zi-Cheng Xiong, Jing bo Ye, Shang bo Li, Cheng Qing Liu, Jing Zhen Niu, Sheng-Bo Chen, Hong Rao

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Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models by Y.-C. Huang, J. Padarian, B. Minasny, A. B. McBratney

“…<p>Uncertainty quantification is a crucial step in the practical application of soil spectral models, particularly in supporting real-world decision making and risk assessment. While machine learning has made remarkable strides in predicting various physiochemical properties of soils using spectroscopy, its practical utility in decision making remains limited without quantified uncertainty. …”
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Advancing patient care with AI: a unified framework for medical image segmentation using transfer learning and hybrid feature extraction by Nazife Çevik, Taner Çevik, Onur Osman, Shtwai Alsubai, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed

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Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

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AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change by Hasib Khan, Wafa F. Alfwzan, Rabia Latif, Jehad Alzabut, Rajermani Thinakaran

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