Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…The study focuses on three bacterial strains including Pseudomonas pseudoalcaligenes CECT 5344, Pseudomonas putida KT2440, and Escherichia coli ATCC 25,922 cultured in Luria Bertani (LB) and M63 media, across varying initial pH levels, time intervals, and bacterial cell concentrations (OD600). Key input variables for the models included bacterial type, culture medium type, initial pH, time (hours), and bacterial cell concentration, all critical to pH dynamics. …”
Get full text

Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer by Mateusz Bielecki, Khadijeh Saednia, Fang-I Lu, Shely Kagan, Danny Vesprini, Katarzyna J. Jerzak, Roberto Salgado, Raffi Karshafian, William T. Tran

“…Background: Patients with advanced breast cancer (BC) may be treated with stereotactic ablative radiotherapy (SABR) for tumor control. Variable treatment responses are a clinical challenge and there is a need to predict tumor radiosensitivity a priori. …”
Get full text

Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients by Yinan Wang, Yinan Wang, Lizhou Gong, Yang Zhao, Yewei Yu, Hanxu Liu, Xiao Yang

“…However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. …”
Get full text

Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. …”
Get full text

Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Due to long-term exposure to complex and variable environmental conditions, concrete dam's structural safety faces numerous challenges. …”
Get full text

Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…But since cardiomyocytes are objects with a high level of complexity and have significant morphological variability, automatic classification is complicated by the lack of implemented methods. …”
Get full text

Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest by Gulfem Ozlu Ucan, Omar Abboosh Hussein Gwassi, Burak Kerem Apaydin, Bahadir Ucan

“…However, its effectiveness is challenged by methodological variability and biological differences between individuals. …”
Get full text

Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The model introduces a dynamic causal weight adjustment mechanism to adaptively optimize the causal relationship intensity between variables and adopts a hierarchical multilevel feature extraction strategy to effectively capture complex spatiotemporal dependencies, thereby enhancing prediction accuracy and model interpretability. …”
Get full text

MentalAId: an improved DenseNet model to assist scalable psychosis assessment by Muxi Li, Farong Liu, Fei Du, Guolin Hong, Qing Hu, Zhi-Liang Ji, Pan You

“…MentalAId learned subtle variations in 49 routine clinical hematological tests and two demographic variables (sex and age) across 28,746 individuals spanning four distinct cohorts: psychotic inpatients (n = 9,271), non-psychotic inpatients with various diseases (n = 14,508), healthy controls (n = 1,826), and drug-naïve first-episode psychosis (FEP) patients (n = 3,141). …”
Get full text

Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The workflow involves identifying optimal predictors from land surface model (LSM) outputs, meteorological variables and climatic indices using a novel Bayesian network (BN) technique for raster-based TWSA simulations. …”
Get full text

Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…Literature analysis reveals that, early studies primarily used demographic and clinical variables, while recent efforts integrate genetic, lifestyle, and environmental predictors. …”
Get full text

Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations by Alireza Hayati, Mohammad Reza Abdol Homayuni, Reza Sadeghi, Hassan Asadigandomani, Mohammad Dashtkoohi, Sajad Eslami, Mohammad Soleimani

“…<b>Methods</b>: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. …”
Get full text

Role of Biological Age in the Determination of Long‐Term Cause‐Specific Death Following Percutaneous Coronary Interventions by Mandeep Singh, Paul A. Friedman, Rajiv Gulati, Abdallah El Sabbagh, Bradley R. Lewis, Amrit Kanwar, Claire E. Raphael, Mohammed A. Al‐Hijji, Zachi I. Attia, Atta Behfar, James L. Kirkland

“…A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model. …”
Get full text

Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…However, the inherent variability in the composition of chickpea flour, influenced by genetic diversity, environmental conditions, and processing techniques, poses significant challenges to standardisation and quality control. …”
Get full text

DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery by Jie Li, Chengyong Zhu, Chenbo Yang, Quan Zheng, Binhui Wang, Jingmin Tu, Qian Zhang, Sheng Liu, Xinfa Wang, Jiangwei Qiao

“…However, the irregular and variable morphology of rapeseed inflorescences presents significant challenges in segmentation. …”
Get full text

Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model by Changan Yang, Muhammad Awais Ashraf, Mudassar Riaz, Pascal Umwanzavugaye, Kavimbi Chipusu, Kavimbi Chipusu, Hongyuan Huang, Yueqin Xu

“…The incorporation of residual and multiscale convolutional structures, along with the use of long skip connections, effectively addressed issues of edge blurring and nodule size variability. …”
Get full text

Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island&#x2019;s Renewable Energy System by Muhammad Ali Iqbal, Joon-Min Gil, Soo Kyun Kim

“…The rapid integration of renewable energy sources into power grids has created an urgent need for accurate energy demand and supply forecasting models capable of managing the inherent variability of renewable energy generation. The combination of fluctuating consumer demand patterns and high variability across different energy sources presents significant challenges in maintaining a reliable balance between supply and demand. …”
Get full text

Predicting mucosal healing in Crohn’s disease: development of a deep-learning model based on intestinal ultrasound images by Li Ma, Yuepeng Chen, Xiangling Fu, Jing Qin, Yanwen Luo, Yuanjing Gao, Wenbo Li, Mengsu Xiao, Zheng Cao, Jialin Shi, Qingli Zhu, Chenyi Guo, Ji Wu

“…Key Points Response to medication treatment is highly variable among patients with CD. High-resolution IUS images of the intestinal wall may hide significant characteristics for treatment response. …”
Get full text

Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model&#x2019;s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adversarial conditions. …”
Get full text

Deep learning approach for automated ‘Kent’ mango maturity grading in compliance with Peruvian standards by Orlando Salazar-Campos, Javier Moran Ruiz, José Luis Peralta, Mirian Rubio Cieza, Breysi Salazar Medina, Johonathan Salazar-Campos

“…However, accurate classification of Mangifera indica L. remains challenging due to high variability in external appearance and the subjectivity of visual maturity assessment. …”
Get full text