Explainable artificial intelligence for sustainable urban water systems engineering by Shofia Saghya Infant, Sundaram Vickram, A Saravanan, C M Mathan Muthu, Devarajan Yuarajan

“…Presenting cutting-edge approaches in XAI (such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual analysis), this review defines the evolution of explainability approaches specifically for hydrological modelling, demand prediction, and leak detection. As an example, the SHAP values have quantified the impact of meteorological variables on urban runoff models, resulting in a 15 % increase in the prediction accuracy. …”
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Identification of atrial fibrillation using heart rate variability: a meta-analysis by Ziwei Yin, Changxin Liu, Chenggong Xie, Zixing Nie, Jiaming Wei, Wen Zhang, Hao Liang

“…Recently, artificial intelligence (AI) algorithms have leveraged heart rate variability (HRV) patterns to enhance the accuracy of AF identification.MethodsWe conducted a systematic review of the literature by searching four major biomedical databases—PubMed, Web of Science, Embase, and Cochrane Library—spanning from their inception to December 13, 2024, following the PRISMA guidelines. …”
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PWM offline variable application based on UAV remote sensing 3D prescription map by Leng Han, Zhichong Wang, Miao He, Yajia Liu, Xiongkui He

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An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo by Maria Teresa Verde, Mattia Fonisto, Flora Amato, Annalisa Liccardo, Roberta Matera, Gianluca Neglia, Francesco Bonavolontà

“…This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use.…”
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Body weight and BMI variability linked to dementia risk: A meta-analysis by Sitian Fang, Lewei Guan, Huimin Jian, Xijian- Dai, Lianggeng Gong

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An intelligent prediction method for rock core integrity based on deep learning by Zhaoxia Hu, Hua Mei, Lei Yu

“…Abstract To address the issue of serious inefficiency in the traditional manual evaluation methods of rock core integrity, a deep learning-based algorithm named IDA-RCF (Intelligent detection algorithm for Rock Core Fissure) is proposed in this paper, which realizes the automatic evaluation of rock core integrity in accordance with the fissure identification results. …”
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An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques by William Hoyos, Kenia Hoyos, Rander Ruiz, Jose Aguilar

“…Abstract Background Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. …”
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Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems by Carl Lee Tolbert

“…The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. …”
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Deep Learning for Cardiovascular Disease Detection by Shivan H. Hussein, Najdavan A. Kako

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Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses by İsmail Baydili, Burak Tasci, Gülay Tasci

“…ECG-based approaches have provided insights into emotional regulation and stress-related conditions using heart rate variability. Speech analysis frameworks, leveraging large language models (LLMs), have improved the detection of cognitive impairments and psychiatric symptoms through nuanced linguistic feature extraction. …”
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A Machine Vision Approach for Grain Quality Control During Separation by Serhii Stepanenko, Alvian Kuzmych, Serhii Kharchenko, Boris Andrey, Viktor Dnes, Daryna Volyk, Roman Kalinichenko

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Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components by Romaine Byfield, Ahmed Shabaka, Milton Molina Vargas, Ibrahim Tansel

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Enhancing Fetal Anomaly Scans with Artificial Intelligence: Advances, Applications, and Challenges by Dominic Gabriel ILIESCU

“… The fetal anomaly scan is a critical imaging evaluation during pregnancy, essential for detecting structural abnormalities. However, its accuracy is often compromised by factors such as high fetal mobility, maternal obesity, inter-observer variability, and protocol limitations. …”
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Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence by Liliana Ibeth Barbosa-Santillán, Bertha Patricia Guzman-Velazquez, Ma. Teresa Orozco-Aguilera, Leticia Flores-Pulido

“…By integrating artificial intelligence techniques with image analysis, our model detects visual patterns associated with violent threats, creating safer digital environments. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…The ViT-ResNet152 model’s convolutional and transformer processing elements worked together to improve the accuracy of the diagnosis to 91.33% and make it better at finding different cases of autism spectrum disorder (ASD).The research outcomes demonstrate that AI tools show promise for delivering highly precise and standardized methods to detect ASD at an early stage. Future research needs to include multiple data types as well as extend dataset variability while optimizing hybrid architecture systems to elevate diagnostic forecasting. …”
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Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

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Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine by Guo-Liang Hou, Bao-Qiang Dong, Ben-Xing Yu, Jian-Yu Dai, Xing-Xing Lin, Ze-Zhong Cheng

“…The integration of artificial intelligence (AI) into acupuncture research is accelerating the transformation of this traditional, experience-based practice into a data-driven, precision discipline. …”
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Implications of artificial intelligence in periodontal treatment maintenance: a scoping review by Raafat Musief Sarakbi, Sudhir Rama Varma, Sudhir Rama Varma, Lovely Muthiah Annamma, Vinay Sivaswamy, Vinay Sivaswamy

“…Traditional diagnostic methods in periodontology often rely on subjective clinical assessments, which can lead to variability and inconsistencies in care. Imbibing artificial intelligence (AI) facilitates a significant solution by enhancing precision metrics, treatment planning, and personalized care. …”
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Artificial intelligence and radiomics applications in adrenal lesions: a systematic review by Matteo Ferro, Octavian Sabin Tataru, Giuseppe Carrieri, Gian Maria Busetto, Ugo Giovanni Falagario, Martina Maggi, Felice Crocetto, Biagio Barone, Francesco Del Giudice, Michele Marchioni, Daniela Terracciano, Giuseppe Lucarelli, Pasquale Ditonno, Raul Gherasim, Ciprian Todea-Moga, Giuseppe Fallara, Marco Tozzi, Antonio Cioffi, Roberto Bianchi, Alessio Digiacomo, Alessandro Veccia, Alessandro Antonelli, Maria Chiara Sighinolfi, Luigi Schips, Bernardo Rocco

“…Background: Adrenal lesions, often incidentally detected, present diagnostic challenges in distinguishing benign from malignant or hormonally active lesions. …”
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Performance Analysis of Artificial Intelligence Models for Classification of Transmission Line Losses by Abraham O. Amole, Oluwagbemiga E. Ajiboye, Stephen Oladipo, Ignatius K. Okakwu, Ibrahim A. Giwa, Olamide O. Olusanya

“…However, achieving optimal power loss minimization requires a more data-driven and intelligent approach that transcends traditional methods. …”
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