A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). …”
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Optimization of emergency logistics for urban flooding with consideration of rainfall effects by Peiwen Zhang, Chenxing Zhang, Pan Zhang, Xuxian Yan, Huawei Yang

“…This study proposes a two-stage optimization framework for emergency logistics. First, a supply distribution model is developed by integrating resource scarcity indices and disaster severity indices, optimized through a simulated annealing algorithm. …”
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OPTIMIZATION OF STOCK KEEPING AND SPACE UTILIZATION POLICY FOR A WAREHOUSE by Basel Zbeda, Mazen Sımı, Husam Jehad Hasan Omar, Mhd Hazem Alhammamı, Faris Albakar, Zehra Düzgit

“…Furthermore, all three algorithms significantly reduce storage space usage and improve space utilization compared to the warehouse's current practices.…”
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Novel Design and Optimization of Porous Titanium Structure for Mandibular Reconstruction by Renshun Liu, Yuxiong Su, Weifa Yang, Xiaobing Dang, Chunyu Zhang, Ruxu Du, Yong Zhong

“…The design and optimization technique of the porous structure presented in this paper can be used to control peak stress, improve porosity, and fabricate a lightweight scaffold, which provides a potential solution for mandibular reconstruction.…”
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Dynamic optimization of intersatellite link assignment based on reinforcement learning by Weiwu Ren, Jialin Zhu, Hui Qi, Ligang Cong, Xiaoqiang Di

“…Different from the swarm intelligence method in principle, this algorithm models the combinatorial optimization problem of links as the optimal sequence decision problem of a series of link selection actions. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…The final phase involves classifying the data using the Shallow hybrid quantum-classical convolutional neural network (SHQCNN) model. To improve the effectiveness of the classification approach, the hyperparameters present in the SHQCNN model are fine-tuned using the shuffled shepherd optimization algorithm (SSOA). …”
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Multi-objective performance optimization of turbofan engine for test run by WEI Bofei, WANG Yuting, GUO Zexuan, LIU Feng, XI Feng, SI Shubin, CAI Zhiqiang

“…Then, the multi-objective performance optimization model based on tree augmented naive bayes is established and compared and verified with the current mainstream algorithm for verification. …”
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USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception by Hongqiang Deng, Shuzhe Zhang, Xiaodong Wang, Tianxin Han, Yun Ye

“…Additionally, on the Flow-Img and DOTA-v1.0 datasets, USD-YOLO improves mAP50-95 by 2.5% and 2.5%, respectively.…”
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Optimization of treatment for patients with combined unstable pelvic and thoracic injuries by О.А. Бур’янов, В.П. Кваша, В.М. Дьомін, Д.В. Мясніков, В.М. Лянскорунський

“…Its solution requires a multidisciplinary approach, and further study will allow to refine treatment strategies and improve outcomes. The purpose was to improve treatment outcomes in patients with combined injuries of the pelvis and chest by optimizing treatment algorithm. …”
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Evaluation of Traditional and Data-Driven Algorithms for Energy Disaggregation Under Sampling and Filtering Conditions by Carlos Rodriguez-Navarro, Francisco Portillo, Isabel Robalo, Alfredo Alcayde

“…Non-intrusive load monitoring (NILM) enables the disaggregation of appliance-level energy consumption from aggregate electrical signals, offering a scalable solution for improving efficiency. This study compared the performance of traditional NILM algorithms (Mean, CO, Hart85, FHMM) and deep neural network-based approaches (DAE, RNN, Seq2Point, Seq2Seq, WindowGRU) under various experimental conditions. …”
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Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway by Wenliang Zhou, Junli Tian, Jin Qin, Lianbo Deng, TangJian Wei

“…And its heuristic algorithm is designed to optimize the multiperiod mixed train schedule beginning with generating an initial solution by scheduling all types of train type by type and then repeatedly improving their periodic schedules until the objective value cannot be reduced or the iteration number reaches its maximum. …”
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TransUNet Image 3D Reconstruction with Hyperparameter Optimization by Xiaofang Wang, Zhihao Luo, Mingrui Gou, Kerui Mao, Liang Zhou

“…The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization. …”
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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Finally, the EIOU loss function is introduced to measure the overlap between the predicted box and the real box more accurately and improve the detection accuracy. The test results of KITTI and CCTSDB2021 public traffic datasets show that compared with the original YOLOv5 model, the improved algorithm reduces the number of parameters by 20%, the calculation amount by 21%, and mAP@0.5 by 1.0%. …”
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Quality of Experience Optimization for AR Service in an MEC Federation System by Huong Mai do, Tuan Phong Tran, Myungsik Yoo

“…To address this shortcoming, we studied AR subtask offloading and resource allocation in a multi-hop, multi-access edge computing federation. Our approach improves the quality of experience (QoE) by optimizing video quality and reducing delay while ensuring fairness, which is modeled as the ratio between provided and required quality. …”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…The developed sigmoid (SG) activation function-based ELM (SG-ELM) shows performance improvement over sine (SI) function-based ELM (SI-ELM) model and PS-SVR model with an improvement of 79.25 % and 89.4 % using root mean square error (RMSE) performance measuring parameter. …”
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Generative Design-Driven Optimization for Effective Concrete Structural Systems by Hossam Wefki, Mona Salah, Emad Elbeltagi, Majed Alinizzi

“…The process of designing reinforced concrete (RC) buildings has traditionally relied on manually evaluating a limited number of layout alternatives—a time-intensive process that may not always yield the most functionally efficient solution. This research introduces a parametric algorithmic model for the automated optimization of RC buildings with solid slab systems. …”
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An optimal federated learning-based intrusion detection for IoT environment by A. Karunamurthy, K. Vijayan, Pravin R. Kshirsagar, Kuan Tak Tan

“…The Chimp optimization algorithm is used in the proposed work to select optimal features. …”
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To the analysis of methods and mechanisms of predictive modeling of onboard equipment reliability when solving problems of aircraft maintenance workload planning by B. I. Ogunvoul, V. D. Budaev, D. O. Sizikov, N. V. Gorbakon, A. V. Vlasova

“…Finally, an efficient maintenance plan that takes into account the predicted failures has been developed using an optimization algorithm. Validation of the model’s predictive capabilities and optimization of the maintenance strategy are performed by comparing with archived data on previously performed work. …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing by Shazia Habib, Waseem, Zeeshan Khan, Salah Boulaaras, Mati ur Rahman, Saeed Islam, Rafik Guefaifia

“…This innovative approach offers a sophisticated solution for complex thermal models, improved prediction accuracy for nonlinear heat transfer, parameter-driven optimization in porous media heat transfer, and increased model efficiency for real-time thermal management.…”
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