Research Progress on Process Optimization of Metal Materials in Wire Electrical Discharge Machining by Xinfeng Zhao, Binghui Dong, Shengwen Dong, Wuyi Ming

“…The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. …”
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Machine Learning in Public Governance: A Systematic Review of Applications, Trends and Challenges by Y. Nuruly, G. N. Sansyzbayeva, L. Z. Ashirbekova, S. K. Tazhiyeva

“…The prospects for further research are related to the empirical validation of decisions, the development of ethical audit methods, and institutional training for responsible, sustainable, and contextually adaptive use of algorithmic tools in the public administration system.…”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“… The study is dedicated to the problem of classification of structural and functional development stage of cardiomyocytes derived from the induced pluripotent stem cells with application of the digital image processing methods and machine learning algorithms, in particular, neural networks. …”
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Enhanced bearing health indicator extraction using slope adaptive signal decomposition for predictive maintenance by Dev Bhanushali, Pooja Kamat, Harsh Dhiman

“…This study introduces the Slope Adaptive Signal Decomposition (SASD) algorithm, a novel method for extracting enhanced intrinsic machine health indicators from vibration data. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…This study presents a novel integrated framework for the structural analysis and optimization of multi-floor buildings by combining validated theoretical models with machine learning and evolutionary algorithms. The proposed Process–Action–Response System (PARS-Solution) accurately computes key structural responses—such as deformations, shear forces, and bending moments—based on eleven critical design parameters (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mn>1</mn></msub></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mn>11</mn></msub></semantics></math></inline-formula>). …”
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An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives by Wei Wei, Liming Yan, Shun Tian, Xisheng Xu, Keke Sun

“…The experimental results verify the effectiveness of the proposed algorithm.…”
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Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control by Jibran Saleem, Umar Raza, Mohammad Hammoudeh, William Holderbaum

“…This research presents the SmartIoT Hybrid Machine Learning (ML) Model, a novel integration of Attribute-Based Authentication and a lightweight machine learning algorithm designed to enhance security while minimising computational overhead. …”
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Assessing cyber risks in construction projects: A machine learning-centric approach by Dongchi Yao, Borja García de Soto

“…A two-phase model development strategy is proposed to select the optimal model for each risk. (2) For risk factor analysis, ML feature analysis methods are adapted to identify risk factors that contribute significantly to risks of specific projects. (3) For the risk reduction strategy, a greedy optimization algorithm is proposed to efficiently address high-contributing risk factors. …”
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Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model by Haiqing Yao, Heng Cao, Jin Li

“…Recently, with high classification precision and affordable complexity, one-class quarter-sphere support vector machine (QSSVM) has been introduced to deal with the online and adaptive outlier detection in WSN. …”
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Quantifying training response in cycling based on cardiovascular drift using machine learning by Artur Barsumyan, Artur Barsumyan, Raman Shyla, Anton Saukkonen, Christian Soost, Jan Adriaan Graw, Rene Burchard, Rene Burchard, Rene Burchard

“…Based on aerobic decoupling (power-to-heart rate ratio) and cardiovascular drift of each test ride, a prediction model was created using ML (Logistic regression, Variational Gaussian Process models and k-nearest neighbors algorithm) that indicated whether or not an athlete was responding to the training. …”
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Risk assessment of oral leukoplakia by DNA content enhanced by machine learning models by Matheus Henrique Lopes Dominguete, Vera Cavalcanti de Araújo, Fernanda Viviane Mariano, Carmen Silvia Passos Lima, João Figueira Scarini, Paulo de Camargo Moraes, Victor Angelo Martins Montalli, Larissa Agatti, Zuraiza Mohamad Zaini, Luíza Hellmeister, Marcelo Sperandio

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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

“…Driven by the urgent necessity for accurate environmental data in urban settings, this research leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based approach to refine SPS30 low-cost sensor data influenced by hygroscopicity in Turin, Italy. …”
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Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction by Lei Li, Xue Gao

“…Then, based on stochastic queueing theory and the proposed prediction method, a maximum cloud service profit resource search algorithm based on the network workload prediction algorithm is proposed to identify a suitable number of virtual machines (VMs) in order to avoid delays in resource adjustment and increase the service profit. …”
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A Camera-Embedded Self-Adaptable Finger With Multi-Modal Sensing Capabilities for Robotic Manipulation by Muhammad Usman Khalid, Hafiz Muizz Ahmed Sethi, Nirmal Kumar Ravikumar, Halar Haleem, Alessandro Seitone, Mattia Frascio, Perla Maiolino, Matteo Zoppi

“…This work introduces a novel vision-based sensing approach for self-adaptable soft fingers using an embedded camera, demonstrating proprioceptive and exteroceptive capabilities. …”
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Heart Disease Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective by Enoch Sakyi-Yeboah, Edmund Fosu Agyemang, Vincent Agbenyeavu, Akua Osei-Nkwantabisa, Priscilla Kissi-Appiah, Lateef Moshood, Lawrence Agbota, Ezekiel N. N. Nortey

“…Four ensemble tree-based algorithms were used in this study: adaptive boosting, extreme gradient boosting, random forest, and extremely randomized trees, investigating their ability to predict heart disease. …”
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Interpretable Identification of Dynamic Adaptive Streaming over HTTP (DASH) Flows Based on Feature Engineering by Arkadiusz Biernacki

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Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses by da Pan

“…Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). …”
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Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy by Lorenzo Villani, Martina Casciola, Davide Astiaso Garcia

“…This study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. …”
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Enhancing Engineering and Architectural Design Through Virtual Reality and Machine Learning Integration by Ali Shehadeh, Odey Alshboul

“…This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. …”
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Early Warning Systems for Plant Diseases in delta regions: Machine Learning Approaches by Biswas Debarghya, Sharma Priti

“…Some patterns and anomalies can indicate the onset of plant diseases, and the algorithms are trained to recognize them. Models that need to adapt to rapidly changing environmental conditions and those that rely on real-time data processing become very interesting for Delta regions.…”
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