Machine Learning-Based Multi-Objective Optimization for Enhancing the Performance of Block Support Structures for Electron Beam Additive Manufacturing by Mustafa M. Nasr, Wadea Ameen, Abdulmajeed Dabwan, Abdulrahman Al-Ahmari

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A guideline study for optimal machine learning approaches to predict the off-design performance of sCO2-PCHEs by Xin Sui, Wenqi Wang, Chunyang Liu, Peixin Dong

“…As research increasingly focuses on predicting the off-design performance of PCHEs using machine learning (ML) approaches, there is a clear need to identify the most cost-effective method that offers optimal accuracy and generalization across a wide range of operating conditions in sCO2 power cycles. …”
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Development of a deep learning predictive model for estimating higher heating value in municipal solid waste management by Nasreen Banu Mohamed Ishaque, S. Metilda Florence

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Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…This study aims to analyse the performance of MAPbI3-based perovskite solar cells (PSCs) by integrating machine learning (ML) models with the SCAPS-1D simulator. …”
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Six Sigma-Based Mathematical Optimization Framework for Flux-Switching Machines: A Roadmap for Quality, Performance, and Manufacturing Tolerances by Chiweta E. Abunike, Ogbonnaya I. Okoro, Sumeet S. Aphale

“…This study presents a Design for Six Sigma (DFSS) optimization framework that integrates sensitivity analysis, response surface modeling (RSM), and multi-objective genetic algorithms to address these challenges. …”
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Enhancing Route Optimization in Road Transport Systems Through Machine Learning: A Case Study of the Dakhla-Paris Corridor by Najib El Karkouri, Lahcen Hassine, Younes Ledmaoui, Hasna Chaibi, Rachid Saadane, Nourddine Enneya, Mohamed El Aroussi

“…The study relies on applying advanced mathematical modeling techniques and analyzing several datasets to train various machine learning algorithms. …”
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A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems by Ripal Ranpara, Osamah Alsalman, Om Prakash Kumar, Shobhit K. Patel

“…In the proposed research paper study, by integrating advanced machine learning techniques such as random forest classifier and support vector machines classifier with knowledge distillation and adaptive energy-aware optimization, GreenMU achieves a balanced trade-off between computational efficiency and cybersecurity accuracy. …”
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Systematic Review of Hierarchical and Multi-Agent Optimization Strategies for P2P Energy Management and Electric Machines in Microgrids by Paul Arévalo, Danny Ochoa-Correa, Edisson Villa-Ávila, Vinicio Iñiguez-Morán, Patricio Astudillo-Salinas

“…This review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. …”
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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

“…However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. …”
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Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms by Yi Liu, Bingxian Bian, Shiyu Chen, Bingqian Zhou, Peng Zhang, Lisong Shen, Hui Chen

“…The results of RT‐PCR and bioinformatics analysis revealed four promising biomarkers with optimal diagnostic and prognostic potential. ROC analysis and Kaplan–Meier curves highlighted CHI3L1, FCGBP, VSIG2, and TFF2 as promising biomarkers for GC, offering superior modeling accuracy. …”
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Leader election of dynamic wireless intelligent control machine in sensor network distributed processing by Eko Sediyono, Ferry Wahyu Wibowo, Hindriyanto Dwi Purnomo

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AutoML: A systematic review on automated machine learning with neural architecture search by Imrus Salehin, Md. Shamiul Islam, Pritom Saha, S.M. Noman, Azra Tuni, Md. Mehedi Hasan, Md. Abu Baten

“…AutoML (Automated Machine Learning) is an emerging field that aims to automate the process of building machine learning models. …”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
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Method of Dynamic Modeling and Robust Optimization for Chain Transmission Mechanism with Time-Varying Load Uncertainty by Taisu Liu, Yuan Liu, Peitong Liu, Xiaofei Du

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Method on intrusion detection for industrial internet based on light gradient boosting machine by Xiangdong HU, Lingling TANG

“…Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.…”
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Enhanced and predictive modelling of direct shoot regeneration of Evolvulus alsinoides (L.) using ANN machine learning model and genetic stability studies by Collince Omondi Awere, Kasinathan Rakkammal, Andaç Batur Çolak, Mustafa Bayrak, Ogolla Fredrick, Valentine Chikaodili Anadebe, Manikandan Ramesh

“…Based on our results, the MLP was able to optimize the variables accurately. The results indicated good performance in modelling and optimization of in vitro de novo direct regeneration. …”
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Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study by Shailesh Appukuttan, Shailesh Appukuttan, Aude-Marie Grapperon, Aude-Marie Grapperon, Mounir Mohamed El Mendili, Hugo Dary, Maxime Guye, Annie Verschueren, Jean-Philippe Ranjeva, Shahram Attarian, Wafaa Zaaraoui, Matthieu Gilson

“…Efforts are therefore commonly directed toward improving the classification models, in an effort to optimize outcomes given the limited data. …”
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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…These algorithms were chosen for their strong global search capabilities and adaptability to complex datasets, ensuring optimal parameter selection for improved model performance. …”
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Assessing smart cities' effectiveness: machine learning approaches by Oleh Berezsky, Olha Kovalchuk, Kateryna Berezka, Roman Ivanytskyy

“…The research compares various models, ultimately selecting the optimal Fast Large Margin model. …”
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A comprehensive review of flood-prone area zonation using ensemble and hybrid machine learning models with a framework proposal for modelling by Gen Long, Sarintip Tantanee, Korakod Nusit, Pitikhate Sooraksa

“…A key contribution is a proposed framework for practising ensemble or hybrid modeling. The framework comprises data preparation, checking for multicollinearity, factor selection and weighting, optional factor optimization, k-fold cross validation where appropriate, ensemble or hybrid modeling, and model evaluation. …”
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