A Knowledge-Driven Framework for AI-Augmented Business Process Management Systems: Bridging Explainability and Agile Knowledge Sharing by Danilo Martino, Cosimo Perlangeli, Barbara Grottoli, Luisa La Rosa, Massimo Pacella

“…Methods: This study presents a novel framework that synergistically integrates the Socialization, Externalization, Combination, and Internalization knowledge model (SECI), Agile methods (specifically Scrum), and cutting-edge AI technologies, including explainable AI (XAI), process mining, and Robotic Process Automation (RPA). …”
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Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review by Sijing Wang, Ruoyu Zhou, Yijia Ren, Meiyuan Jiao, Honglai Liu, Cheng Lian

“…As artificial intelligence (AI) technology evolves, data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life (RUL). …”
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Development of GUI-Driven AI Deep Learning Platform for Predicting Warpage Behavior of Fan-Out Wafer-Level Packaging by Ching-Feng Yu, Jr-Wei Peng, Chih-Cheng Hsiao, Chin-Hung Wang, Wei-Chung Lo

“…This study presents an artificial intelligence (AI) prediction platform driven by deep learning technologies, designed specifically to address the challenges associated with predicting warpage behavior in fan-out wafer-level packaging (FOWLP). …”
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Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images by Kotaro Waki, Katsuya Nagaoka, Keishi Okubo, Masato Kiyama, Ryosuke Gushima, Kento Ohno, Munenori Honda, Akira Yamasaki, Kenshi Matsuno, Yoki Furuta, Hideaki Miyamoto, Hideaki Naoe, Motoki Amagasaki, Yasuhito Tanaka

“…In addition, when tested with a small number of images containing soft palate data, the model showed high precision: the best AUC model had 0.91 (sensitivity 0.86, specificity 0.79). This study presents a significant advance in the development of an AI-based non-invasive screening tool for the identification of high-risk ESCC patients. …”
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Digital traceability in horticulture: a systematic review of edge-cloud-blockchain-terminal (ECBT) integration with IoT and AI technologies by Yan Huang, Xin Li, Luyi Xu, Yongqiang Ma

“…Technology coverage analysis revealed fragmented adoption: IoT dominates (45%, n = 18), followed by blockchain (32%, n = 13) and AI/ML (23%, n = 9), with only 3% achieving full ECBT integration despite demonstrated benefits. …”
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AI-MedLeafX: a large-scale computer vision dataset for medicinal plant diagnosisMendeley Data by Md. Fahim Ferdous, Faysal Bin Khaled Nissan, Nur Muhammad Nibir, Md. Hasan Imam Bijoy

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First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network by Andrew Gascoyne, Wendy Lomas

“…A growing issue within conservation bioacoustics is the laborious task of analysing the vast amount of data generated from the use of passive acoustic monitoring devices. In this paper, we present an alternative AI model which has the potential to help alleviate this problem. …”
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Visual Discomfort in the Built Environment: Leveraging Generative AI and Computational Analysis to Evaluate Predicted Visual Stress in Architectural Façades by Cleo Valentine, Arnold J. Wilkins, Heather Mitcheltree, Olivier Penacchio, Bruce Beckles, Ian Hosking

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A Semi-Automated, Hybrid GIS-AI Approach to Seabed Boulder Detection Using High Resolution Multibeam Echosounder by Eoin Downing, Luke O’Reilly, Jan Majcher, Evan O’Mahony, Jared Peters

“…Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. …”
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AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks by Chaima Chabira, Ibraheem Shayea, Gulsaya Nurzhaubayeva, Laura Aldasheva, Didar Yedilkhan, Saule Amanzholova

“…This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. …”
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Effectiveness of AI-generated orthodontic treatment plans compared to expert orthodontist recommendations: a cross-sectional pilot study by Orlando Motohiro TANAKA, Gil Guilherme GASPARELLO, Sergio Luiz MOTA-JÚNIOR, Mohamad Jamal BARK, Jacqueline de Almeida Antunes ROZYSCKI, Rafael Bordin WOLANSKI

“…ABSTRACT Introduction: Artificial intelligence (AI) has become a prominent focus in orthodontics. …”
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AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis. by Michael Alexander Ramirez Sierra, Thomas R Sokolowski

“…Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. …”
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Pilot study of ASSORT (AI-based Symptom Stratification in Ophthalmology for Rapid Triage): a triage tool for ophthalmic emergencies by Claudio Xompero, Lorenzo Rossi, Francesca Amoroso, Antonio Bechara Ghobril, Diana Elena Ionita, Eric H. Souied, Carl-Joe Mehanna

“…Introduction: ASSORT (AI-based Symptom Stratification in Ophthalmology for Rapid Triage) is a GPT-4-based triage tool designed to assess ophthalmic emergencies using a three-tier color-coded system. …”
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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

“…To address this challenge, this study presents a next-generation AI-based predictive framework utilizing three hybrid machine learning techniques: adaptive neuro-fuzzy inference system with genetic algorithm (ANFIS-GA), convolutional neural networks with long short-term memory (CNN-LSTM), and multi-objective optimization (MOO). …”
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AI4Food Project: Application of Personalised Nutrition Integrating Artificial Intelligence in Nutritional Interventions Focused on Weight Loss by Isabel Espinosa-Salinas, Gala Freixer, Sergio Romero-Tapiador, Blanca Lacruz Pleguezuelos, Ruben Tolosana, Javier Ortega-Garcia, Enrique Carrillo-De Santa Pau

“…The objective of this work is to present the AI4Food project. Massive data capture has been carried out using traditional and digital methods throughout a nutritional intervention for healthy weight loss in order to compare both data collection methodologies and integrate all the information of the variables obtained with artificial intelligence. …”
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Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis by Akella S. Narasimha Raju, K. Venkatesh, M. Rajababu, Ranjith Kumar Gatla, Shaik Jakeer Hussain, G. Satya Mohan Chowdary, T. Ganga Bhavani, Mohammed Kareemullah, Sameer Algburi, Ali Majdi, Ahmed M. Abdulhadi, Wahaj Ahmad Khan

“…The proposed ViT–DeepLabV3 + system establishes a basis for advanced AI support to colorectal diagnosis by utilizing self-attention strategies and learning with different scales of context. …”
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Initiation of China Alliance of Research in High Myopia (CHARM): protocol for an AI-based multimodal high myopia research biobank by Tien-Yin Wong, Zi-Bing Jin, Hai-Long He, Yi-Xin Liu, Hao Song, Tian-Ze Xu

“…This initiative stands out not only for its extensive scope but also for its innovative application of artificial intelligence (AI) to assist in diagnosis and treatment decisions. …”
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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

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CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability by Gregory W. Kyro, Matthew T. Martin, Eric D. Watt, Victor S. Batista

“…It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. …”
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AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol by Fergus Gleeson, Alex Novak, Indrajeet Das, Ruchir Shah, Edwin van Beek, Sarim Ather, Howell Fu, Nabeeha Salik, Alan Campbell, Farhaan Khan, Abdala Trinidad Espinosa Morgado, Marusa Kotnik, Louise Wing, John Murchison, Jong Seok Ahn, Sang Hyup Lee, Ambika Seth

“…Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. …”
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