Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study by Zhi-Jun Lyu, Qi Lu, YiMing Song, Qian Xiang, Guanghui Yang

“…The data-driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. …”
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A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting by Xuecheng He, Jujie Wang

“…Through experimental comparison, the model shows high prediction accuracy and generalization ability.…”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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Comparative Analysis of Artificial Neural Networks and Evolutionary Algorithms in DEA-<i>β</i>-MSV Portfolio Optimization by Abdelouahed Hamdi, Arezou Karimi, Farshid Mehrdoust, Samir Brahim Belhaouari

“…This paper proposes a hybrid methodology for portfolio optimization by integrating the data envelopment analysis (DEA) model with the mean semivariance (MSV) framework. …”
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A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…This aids with 1D-CNN weight training. GA methodically optimizes the model’s GRU parameters. The data processed were finally used by the hybrid 1D-CNN-Optimized GRU network to predict cardiovascular illness. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…Eventually, the whale optimization algorithm (WOA) is used to optimally choose the hyperparameters of the CNN‐BiGRU model. …”
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Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest by Vivek Kumar Pandey, Shiv Prakash, Tarun Kumar Gupta, Priyanshu Sinha, Tiansheng Yang, Rajkumar Singh Rathore, Lu Wang, Sabeen Tahir, Sheikh Tahir Bakhsh

“…To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters. …”
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Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data by Amirhossein D Naghdi, Grzegorz Kaszuba, Stefanos Papanikolaou, Andrzej Jaszkiewicz, Piotr Sankowski

“…As part of the experiment, we utilize local search coupled with classical interatomic potentials to explore the local structure space and show that utilization of this procedure greatly improves optimization capability of the neural model. We also expand the model with an extra submodule, which attains 42% improvement on modeling the crystalline phase of the structures. …”
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Optimization of Mechanical Properties and Manufacturing Time through Experimental and Statistical Analysis of Process Parameters in Selective Laser Sintering by Adrian Korycki, Christian Garnier, Valerie Nassiet, Charles Tarek Sultan, France Chabert

“…These statistical models were used to define an optimal set of parameters. …”
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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. …”
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A near-optimal resource allocation strategy for minimizing the worse-case impact of malicious attacks on cloud networks by Yu-Fang Chen, Frank Yeong-Sung Lin, Kuang-Yen Tai, Chiu-Han Hsiao, Wei-Hsin Wang, Ming-Chi Tsai, Tzu-Lung Sun

“…The proposed model integrates Virtual Machine (VM) initiation decisions and employs the Contest Success Function (CSF) within a two-player max–min game framework to dynamically allocate resources. …”
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Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method by Changlong Li, Zengye Su, Yudan Nie, Zhiyi Ye, Jinyi Li, Jing Wang, Zicong Yang, Xuxin Li, Weijian Zeng, Yanjian Chen

“…This research establishes an integrated “monitoring-modelling-decision” paradigm, driven by multi-source data and machine learning, offering a practical and robust tool that provides valuable guidance for enhancing resource allocation efficiency and promoting sustainable precision agriculture in complex topographical regions, thereby holding significant reference value for optimising agricultural production nationwide.…”
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Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services by Nuzaiha Mohamed, Reem Lafi Almutairi, Sayda Abdelrahim, Randa Alharbi, Fahad M. Alhomayani, Amer Alsulami, Salem Alkhalaf

“…Eventually, the presented DCFNN-SOCVDC approach employs a stork optimization algorithm method for the hyperparameter tuning method involved in the DCFNN model. …”
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Optimization of Nitrogen Fertilization Strategies for Drip Irrigation of Cotton in Large Fields by DSSAT Combined with a Genetic Algorithm by Zhuo Yu, Weiguo Fu

“…Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approaches in simulating nitrogen–crop interactions (calibrated with multi-year phenological datasets), we develop a genetic algorithm-embedded decision system that simultaneously optimizes nitrogen use efficiency (NUE) and economic returns. …”
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Optimizing Infectious Disease Diagnostics through AI–Driven Hybrid Decision Making Structures based on Image Analysis by Ahsan Muhammad, Damaševičius Robertas, Shahzad Sarmad

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Robust & Optimal Predictive Current Control for Bi-Directional DC-DC Converter in Distributed Energy Storage Systems by Haris Sheh Zad, Abasin Ulasyar, Adil Zohaib, Muhammad Irfan, Zeeshan Yaqoob, Samid Ali Haider

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BASED ON NEURAL NETWORK RELIABILITY STUDY OF SHEARER’S CUTTING PART by ZHAO LiJuan, FAN JiaYi

“…Roller is an important task of the coal winning machine cutting coal institutions,its structure and motion parameters will directly affect the working efficiency and working reliability of coal winning machine.Based on virtual prototype technology coal winning machine the coupled model is established,through dynamic simulation of coal winning machine equivalent stress values of key parts;Simulation different drum rotating speed,drawing speed,cylinder helix Angle,and the cutting line spacing they cut the shell and the equivalent stress value of planet carrier,the roller structure and motion parameters on reliability of key parts of coal winning machine cutting part influence trend;Combined with neural network technology,with different roller structure and motion parameters of the equivalent stress of key parts of coal winning machine values as the neural network training sample,the helix Angle of optimization design,stress value of key parts in the hour of cylinder helix Angle.The research for the drum more accurate theoretical foundation for the selection of structure and motion parameters,has certain engineering application value.…”
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Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu by Yu Cheng, Xu Chen, Xiaohua Ding, Linting Zeng

“…This study aims to use different data source with statistical models and machine learning algorithm to help car-sharing operator to choose the optimal location of new stations and adjust the location of existing stations. …”
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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…In this study, advanced compositional models were developed using a broad range of machine learning (ML) techniques to predict Rs efficiently and reliably. …”
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State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms by Romel Carrera, Leonidas Quiroz, Cesar Guevara, Patricia Acosta-Vargas

“…The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. …”
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