An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…Optimising heat transfer in biomedical systems, especially in blood-mediated liquids, is essential for precise medication administration and thermal ablation treatments. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…A machine learning study developed a parallel Convolutional Neural Network algorithm with 96.4% recall and 99.1% precision for study screening. …”
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Optimized Quality of Service for Real-Time Wireless Sensor Networks Using a Partitioning Multipath Routing Approach by Mohammed Zaki Hasan, Tat-Chee Wan

“…The proposed approach significantly improves the network lifetime, while reducing energy consumption and decreasing average end-to-end delays within the sensor network via optimised resource sharing in intermediate nodes compared with existing routing algorithms.…”
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Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures by Duban A. Paternina-Verona, Oscar E. Coronado-Hernández, Vicente S. Fuertes-Miquel, Manuel Saba, Helena M. Ramos

“…Among the 31 algorithms tested, decision trees, efficient linear models, and ensemble classifiers achieved 100% accuracy for filling processes, while k-Nearest Neighbours (KNN) provided 97.2% accuracy for emptying processes. …”
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Investigating the feasibility of utilising bagasse ash and reclaimed asphalt pavement dust in geopolymer mortar by Sajan Sharma, Shailja Bawa

“…This study provides valuable insights into optimising mixtures for targeted properties while facilitating large-scale landfill waste recovery, reducing environmental hazards, and replacing cement-based mortar, thereby contributing to sustainable construction practices.…”
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Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features by Huanyin Su, Shanglin Mo, Huizi Dai, Jincong Shen

“…Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. …”
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Accelerating machine learning at the edge with approximate computing on FPGAs by Luis Gerardo León-Vega, Eduardo Salazar-Villalobos, Jorge Castro-Godínez

“… Performing inference of complex machine learning (ML) algorithms at the edge is becoming important to unlink the system functionality from the cloud. …”
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Tofacitinib monotherapy as maintenance treatment in juvenile dermatomyositis: a case report by Enrique Calvo-Aranda, Delia Argüelles Balas, Estefanía Barral Mena, M Ángeles Martín Díaz

“…We present the case of a child with JDM who achieved sustained remission with tofacitinib monotherapy following the failure of previous therapies. While its use is currently restricted to refractory cases, these findings suggest its potential for earlier integration into treatment algorithms. …”
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A machine learning-powered energy consumption prediction system with API by Toyeeb Adekunle Abd’Azeez, Lanre Olatomiwa

“…The lower MAPE and the higher R2 score indicate the superiority of the ExtraTreeRegressor over other algorithms. While energy consumption is characterised by high variance, our optimised model effectively interprets interactions between input features and predicts the equivalent energy consumed with a lower RMSE of 11.75. …”
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Using artificial intelligence for sustainable crop production – a comprehensive review with a focus on potato production by Magdalena Piekutowska

“…By utilizing big data analysis, precision monitoring, and automation, AI can significantly improve agricultural outcomes. For instance, AI algorithms can optimise the use of natural resources and chemical inputs, leading to improved yield forecasting and more effective management of diseases and pests that affect crops. …”
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Mathematical modeling of fastening conditions in piggyback transport: ensuring safety and stability across different transport modes by Ziyoda Mukhamedova, Diyor Boboyev, Shukhrat Saidivaliev, Rustam Abdullaev, Jamshid Kobulov, Sardorbek Akhmedov, Sherzod Fayzibayev

“…This study has employed Newtonian mechanics and the D'Alembert principle to analyse how different forces, such as gravity, inertia, aerodynamic resistance, and centrifugal forces, affect the stability of semi-trailers while in motion. Systematic algorithms and simulation tools, such as MATLAB, were utilised to determine how speed and external factors affected the forces acting on the fasteners and the resulting movement of the cargo. …”
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Interpretable Identification of Dynamic Adaptive Streaming over HTTP (DASH) Flows Based on Feature Engineering by Arkadiusz Biernacki

“…We employed the following three algorithms that represent different clustering methodologies: partition-based, density-based, and probabilistic approaches. …”
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Lightweight network for insulator fault detection based on improved YOLOv5 by Dehua Weng, Zhiliang Zhu, Zhengbing Yan, Moran Wu, Ziang Jiang, Nan Ye

“…Current fault diagnosis methods heavily depend on manual visual inspection, leading to inefficiency and inaccuracies. While computer vision algorithms have been developed, their high requirements for running environments limit their applicability on edge devices. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. …”
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A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories by Hannelore Sebestyen, Daniela Elena Popescu, Rodica Doina Zmaranda

“…From leveraging machine learning and blockchain for anomaly detection and real-time threat response to optimising lightweight algorithms for resource-limited devices, researchers propose innovative and adaptive solutions to address emerging threats. …”
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A hybrid theoretical–numerical–experimental framework for robust health monitoring of thin-walled hollow composite members using guided waves by Akshay Prakash Kalgutkar, Shirsendu Sikdar, Sauvik Banerjee, Karl Walton, Rakesh Mishra

“…These findings contribute to the development of robust damage identification algorithms for structural health monitoring, providing valuable insights for optimising the maintenance and performance of composite structures in critical engineering environments, ensuring safety and operational efficiency.…”
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Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning. by Liam Brierley, Anna Fowler

“…However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. …”
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Identifying the optimal number of topics in text mining: a case study on reindeer pastoralism literature by Barbara Contiero, Øystein Holand, Giulio Cozzi

“…Text mining and topic analysis algorithms which group textual contents in the most efficient way, are becoming increasingly useful to summarise the main information contained in large data corpus of complex scientific fields. …”
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Wind energy potential assessment using the Weibull distribution method for future energy self-sufficiency by Badr El Kihel, Nacer Eddine El Kadri Elyamani, Abdelhakim Chillali

“…This research contributes significantly to provincial energy planning by demonstrating the effectiveness of statistical methods in assessing wind potential while offering strategic recommendations for integrating optimised wind turbines to strengthen local energy self-sufficiency.…”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…In terms of accuracy, logistic regression, XGBoost and random forest performed similarly at approximately 85%. While most models demonstrated high specificity (>97%), the SVM stood out for having the highest sensitivity (58.57%), although with a lower accuracy (62.44%). …”
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