Machine learning-driven nanoparticle toxicity by Zied Hosni, Sofiene Achour, Fatma Saadi, Yangfan Chen, Mohammed Al Qaraghuli

“…By using a robust dataset, we deployed Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms to identify key NP features that significantly influence cellular toxicity. …”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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Does metaverse improve recommendations quality and customer trust? A user-centric evaluation framework based on the cognitive-affective-behavioural theory by Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Osama Halabi, Raian Ali

“…They can analyse large amounts of data using machine learning algorithms and predictive analytics capabilities to provide highly relevant recommendations to users. …”
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Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study by Izabela Rojek, Dariusz Mikołajewski, Adam Mroziński, Marek Macko, Tomasz Bednarek, Krzysztof Tyburek

“…By using IoT sensors and smart meters, buildings can collect real-time data on energy usage patterns, occupancy, temperature, and lighting conditions.AI algorithms then analyze this data to identify inefficiencies, predict energy demand, and suggest or automate adjustments to optimize energy use. …”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…Multidimensional bioinformatic algorithms were utilized to establish a risk score model for ARGs. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. …”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…It is used to conduct predictive tasks without programming instructions by creating algorithms. …”
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Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study by Mariz Kasoha, Panagiotis Sklavounos, Istvan Molnar, Meletios P. Nigdelis, Bashar Haj Hamoud, Erich-Franz Solomayer, Gilbert Georg Klamminger

“…Abstract Even though non-invasive prediction of endometriosis may seem technically feasible using sophisticated machine learning algorithms, a standard clinical use case for non-surgical diagnosis of endometriosis has not yet been established. …”
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Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review by Natalie S Blencowe, Neil J Smart, George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway

“…Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. …”
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Sensor‐Guided Smart Irrigation for Tomato Production: Comparing Low and Optimum Soil Moisture in Greenhouse Environments by Ibrahim Dirlik, Ferhat Uğurlar, Cengiz Kaya

“…Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. …”
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Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition by Jiachen Ge, Jianping Cai, Gaolei Zhang, Deyu Li, Lianyuan Tao

“…Prognostic genes were identified to construct predictive models through various machine learning approaches. …”
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Early detection of Cercospora beticola and powdery mildew diseases in sugar beet using uncrewed aerial vehicle-based remote sensing and machine learning by Koç Mehmet Tuğrul, Rıza Kaya, Kemal Özkan, Merve Ceyhan, Uğur Gürel, Fatih Yavuz Fidantemiz

“…Additionally, the study demonstrated that early detection of diseases is possible using K-nearest neighbors and logistic regression algorithms, exhibiting high discrimination and predictive accuracy.…”
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Oil Development Engineering Company, Tehran, Iran by Seyed Ali Mohammad Tajalli, Mazda Moattari, Vahid Naghavi, Mohammad Reza Salehizadeh

“…Leveraging a hybrid approach that combines the Extended Kalman Filter (EKF), Real-Time Transient Modeling (RTTM), and machine learning algorithms, the framework offers accurate insights into the pipeline’s operational status. …”
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Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods by Sisi Li, Sheng Hu, Lin Wang, Fanyu Zhang, Ninglian Wang, Songbai Wu, Xingang Wang, Zongda Jiang

“…The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. …”
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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…The positive correlation between judge evaluations and sensor data indicates judges’ ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). …”
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Hybrid AI and semiconductor approaches for power quality improvement by Ravikumar Chinthaginjala, Asadi Srinivasulu, Anupam Agrawal, Tae Hoon Kim, Sivarama Prasad Tera, Shafiq Ahmad

“…The research concludes that although deep learning models offer superior accuracy and predictive power for complex power quality scenarios, practical deployment requires careful balancing of computational demands and addressing class distribution challenges.…”
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). …”
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Mapping forest types along ecological gradient in Pakistan by Naveed Ahmad, Syed Ghias Ali

“…This study suggests that mapping of forest types through machine learning algorithms may be improved by incorporating other explanatory variables such as microclimate, soil types, nutrients, anthropogenic, demographic factors and spectral indices.…”
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Advancing multidisciplinary management of pediatric hyperinflammatory disorders by Francesco La Torre, Giovanni Meliota, Adele Civino, Angelo Campanozzi, Valerio Cecinati, Enrico Rosati, Emanuela Sacco, Nicola Santoro, Ugo Vairo, Fabio Cardinale

“…The review advocates for a multidisciplinary approach, integrating standardized diagnostic algorithms and disease-specific expertise to optimize patient care. …”
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