Machine Learning in Acute Stroke Neuroimaging. A Systematic Literature Review by D. Matuliauskas, I. Stražnickaitė, A. Samuilis, D. Jatužis

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Prestress Assisted Machining: Achieving high surface integrity in thin wall milling by Álvaro Sáinz de la Maza García, Luis Norberto López de Lacalle Marcaide, Gonzalo Martínez de Pissón Caruncho

“…As fatigue is promoted by tensile surface residual stresses, which tend to arise in machining operations, it is common to perform non-conventional post-processing operations to introduce compressive surface residual stresses; this step is costly and sometimes inefficient.This article proposes a novel machining technology to ensure compressive residual stresses near the machined surface, increasing at the same time component rigidity during milling and controlling the tendency to vibrate, which leads to lower surface roughness. …”
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Machine Learning and data mining tools applied for databases of low number of records by Hubert Anysz

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Feature engineering for fault detection and diagnosis in Power Transmission Lines using a tree-based approach by Hassan N. Noura, Zaid Allal, Ola Salman, Khaled Chahine

“…Subsequently, a preprocessing phase involved the introduction of new features as part of feature engineering. Six machine learning classifiers were employed in a bi-phased system: the primary objective was to detect faulty samples within the data, then diagnose these faults and distinguish their types. …”
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Enhancing Software Sustainability: Leveraging Large Language Models to Evaluate Security Requirements Fulfillment in Requirements Engineering by Ahmad F. Subahi

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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

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MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…This included removing null values, interpolating missing data and downsampling to weekly intervals engineering to improve model performance. Time series decomposition and the creation of lag features were also utilized to capture temporal dependencies effectively. …”
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Soft Computing Solutions for Reducing the Carbon Footprint of Fly Ash Based Concrete. Advances in Civil Engineering by Awoyera, Paul O., Adetola, Joshua, Nayeemuddin, Mohammed, Mewada, Hiren, George Fadugba, Olaolu

“…The construction industry significantly contributes to environmental degradation,with many structures exhibiting high carbon footprints throughout their construction processes and lifespans.Activities such as cement hydration and other commoncon-struction practices substantially influence environmental conditions overtime,necessitating a critical evaluation of material and design choices.This study reported the environmental impact of fly ash(FA),which is largely used to enhance concrete strength.A prediction of two end point indicators,that is,global warming potential(GWP)and CO2 emission using soft computing methods are presented,which are particularly effective for handling complex,non linear relationships in environmental data.To achieve this, two machine learning approaches,the random forest(RF)and decision tree(DT)models,are employed to assess the environ- mental impact of structural materials and designs.Two data sets were obtained from reputable databases,including ResearchGate, Science Direct, Semantic Scholar,and Mendeley Data.The models are trained to explore the potential for optimizing structural designs and material selection stominimize environmental impacts.Feature importance is analyzed using Shapley values,providing insights into the most influential factors affecting GWP and CO2 emission Model performance is evaluated using R2 and root mean square error(RMSE) metrics. …”
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Comparison of various machine learning regression models based on Human age prediction by Dr.Manaf K Hussein

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Fake News Detection System using Machine Learning with Recurrent Neural Networks by Kuditat O. Jimoh, Lawrence O. Omotosho, Ibrahim K. Ogundoyin, Olajide Y. Adebayo

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A Spatially Informed Machine Learning Method for Predicting Sound Field Uncertainty by Xiangmei Chen, Chao Li, Haibin Wang, Yupeng Tai, Jun Wang, Cyrille Migniot

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Metric-based defect prediction from class diagram by Batnyam Battulga, Lkhamrolom Tsoodol, Enkhzol Dovdon, Naranchimeg Bold, Oyun-Erdene Namsrai

“…In the literature, various approaches such as machine learning (ML) and deep learning (DL), have been proposed and proven effective in detecting defects in source code during the implementation or testing phases of the software development life cycle (SDLC). …”
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Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines by Chi-Man Vong, Weng-Fai Ip, Pak-kin Wong, Jing-yi Yang

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Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability by Cheng-Yu Tsai, Xiu-Rong Huang, Po-Tsun Kuo, Tzu-Tao Chen, Yun-Kai Yeh, Kuan-Yuan Chen, Arnab Majumdar, Chien-Hua Tseng

“…This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying feature engineering informed by medical knowledge to physiological waveforms, enabling the prediction of shock one hour in advance without relying on blood tests. …”
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Early Prediction of Stroke Risk Using Machine Learning Approaches and Imbalanced Data by Hassan Qassim

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Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning by Nama'a Hamed, Fadwa Al Azzo

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Challenges and control strategies for disrupting passive oxide layer formation in electrochemical machining by Ritesh Kumar Upadhyay

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Predictive Technology Assessment by Means of a Structure-Based Method of Machine Learning by Manja Mai-Ly PFAFF, Uwe FRIEß, Andreas OTTO, Matthias PUTZ

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Noise-Adaptive SOGI–HOSM Observer for Sensorless Speed Control of Induction Machines by Kobena Badu Enyam, Desmond Hammond, Francis Boafo Effah, Lily Akosua Kyei-Baffour, Benecentia Mafo-Kwofie

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Investigation of notch wear mechanisms in the machining of nickel-based Inconel 718 alloy by Wit Grzesik

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