Chronic liver disease detection using ranking and projection-based feature optimization with deep learning by Sumaiya Noor, Salman A. AlQahtani, Salman Khan

“…Our findings show that the proposed model can potentially improve diagnostic accuracy and support timely medical intervention, ultimately enhancing patient outcomes.…”
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The Detection Optimization of Low-Quality Fake Face Images: Feature Enhancement and Noise Suppression Strategies by Ge Wang, Yue Han, Fangqian Xu, Yuteng Gao, Wenjie Sang

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A twin CNN-based framework for optimized rice leaf disease classification with feature fusion by Prameetha Pai, S. Amutha, Mustafa Basthikodi, B. M. Ahamed Shafeeq, K. M. Chaitra, Ananth Prabhu Gurpur

“…The framework integrates an optimized feature fusion algorithm using pre-trained CNN models to improve disease detection accuracy. …”
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Rolling optimization method of virtual power plant demand response based on Bayesian Stackelberg game by Binxi Huang

“…This is iteratively solved using the whale algorithm to determine the optimal power generation distribution scheme for each unit on both the supply side and demand sides. …”
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Optimal Allocation Method of Integrated Energy System Considering Joint Operation of Multiple Flexible Resources by DENG Qianwen, LI Qi, QIU Yibin, LI Doumeng, HUO Shasha, CHEN Weirong

“…Then, aimed at the uncertainty of renewable energy output, the optimal clustering number is determined by Elbow method, and typical wind speed scenarios are obtained by K-means clustering algorithm. …”
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Deep Learning-Based Real-Time 6D Pose Estimation and Multi-Mode Tracking Algorithms for Citrus-Harvesting Robots by Hyun-Jung Hwang, Jae-Hoon Cho, Yong-Tae Kim

“…Additionally, we present methods for training an EfficientPose-based model for 6D pose estimation and ripeness classification, and an algorithm for determining the optimal harvest sequence among multiple fruits. …”
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Application of GA in Furniture Modeling Style Design by Heng Yu, Chunjing Liu

“…In order to improve the production efficiency of furniture, it is necessary to optimize it. …”
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Mechanical performance prediction of basalt fiber reinforced concrete based on random forest and hyperparameter optimization by Dalian Bai, Junwen Chen, Jialiang Wang, Cunpeng Liu

“…This study applies the Random Forest (RF) algorithm combined with hyperparameter optimization methods including Grid Search (GS), Random Search (RS), Bayesian Optimization (BO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Optuna Optimization (OO) to build a predictive model. …”
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Research on optimization of C4 repair operation of Harmony electric locomotive based on preventive maintenance. by Yihan Liu, Jieping Wu, Qiaoyu Wu

“…In this paper, for the C4 repair operation of the harmonious electric locomotive, using the preventive maintenance method, we constructed a multi-component maintenance plan optimization model with the aim of maximizing the availability, and solved it by using a heuristic genetic algorithm. …”
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Incremental GA-Based 3D Trajectory Optimization for Powered Parachute Aerial Delivery Systems by Hanafy M. Omar, Ayman Hamdy Kassem

“…A six-degrees-of-freedom (6-DOF) dynamic model of the PPC is developed, complemented by a novel optimization technique called Incremented Genetic Algorithms (IGA). …”
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. …”
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A joint optimization method of multi backhaul link selection and power allocation in 6G by Qingyang LI, Xueting LI, Xiaorong ZHU

“…Aiming at the problem of limited single backhaul link capability of base stations in hotspot areas of the 6G system, a joint optimization method was proposed for multi-backhaul link selection and power allocation in an elastic coverage system, so that the data packets can select the appropriate backhaul link and transmission power according to their service characteristics and link conditions.Firstly, the transmission delay of data packets on the small cell sub-queue was analyzed by using queuing theory.Then, the optimization objective was modeled with the maximum delay tolerance elasticity value.Finally, the optimization problem were solved by the Hungarian algorithm and the Lagrangian duality and gradient descent method.The simulation results show that, compared with the traditional algorithm, the algorithm proposed reduces the average delay of URLLC (ultra-reliable and low-latency communication) service data packets and eMBB (enhanced mobile broadband) service data packets by 17% and 14% respectively, and effectively improves the network transmission rate.…”
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UV-Vis spectroscopy coupled with firefly algorithm-enhanced artificial neural networks for the determination of propranolol, rosuvastatin, and valsartan in ternary mixtures by Ahmed Serag, Maram H. Abduljabbar, Yusuf S. Althobaiti, Farooq M. Almutairi, Shaker T. Alsharif, Rami M. Alzhrani, Marwa F. Ahmed, Atiah H. Almalki

“…An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. …”
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Kinematics Analysis and Performance Optimization of Low Coupling PRPaR/2CRR Parallel Mechanism by Zhang Li, Zhu Lei, Xu Dong

“…What's more, a multi-objective optimization design model was established based on the comprehensive performance of the workspace volume, the global dexterity, and the global operability, and the optimal structural parameter size solution was calculated using the NSGA-Ⅱ algorithm. …”
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Control and Stability Analysis of Double Time-Delay Active Suspension Based on Particle Swarm Optimization by Kaiwei Wu, Chuanbo Ren

“…So, it is indicating that the active suspension with double time-delay feedback control has a better control effect in improving the ride comfort of the car, and it has important reference value for further research on suspension performance optimization.…”
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Multi-Objective Parameter Optimization of Electro-Hydraulic Energy-Regenerative Suspension Systems for Urban Buses by Zhilin Jin, Xinyu Li, Shilong Cao

“…To streamline multi-objective optimization processes, a particle swarm optimization–back propagation (PSO-BP) neural network surrogate model was developed to approximate the complex co-simulation system. …”
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Inter-Satellite Handover Method Based Multi-Objective Optimization in Satellite-Terrestrial Integrated Network by Renpeng LIU, Bo HU, Hequn LI

“…The high-speed motion of low-earth orbit communication satellites results in a highly dynamic network topology, and the spatio-temporal distribution of resources in the satellite-terrestrial integrated network is non-uniform.When multiple users and services switch between satellites, a large number of handover requests are triggered, leading to intensified network resource competition.As a result, the limited satellite resources cannot meet all the handover requests, leading to a significant decrease in handover success rate.In view of the above problem, the multi-objective optimization based satellite handover method was proposed.It introduced the satellite coverage spatio-temporal graph and transforms the dynamic continuous topology into static discrete snapshots, accurately depicted the connections between satellite nodes and users at different times and locations.The multi-objective optimization model was established for satellite handover decisions, and anadaptive accelerated multi-objective evolutionary algorithm(AAMOEA) was proposed to optimized user data rate and network load simultaneously, ensured handover success rate and enhanced network service capability.It built a STIN communication simulation environment and tested the multi user handover performance in a multi satellite overlapping coverage scenario.The results demonstrated that the multi-objective optimization-based satellite handover method achieved an average handover success rate improvement of over 20% compared to benchmark algorithms.…”
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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…The model achieved 96.60% accuracy, 95.79% precision, 96.65% recall, and 95.92% F1-score, outperforming baseline RL algorithms such as Deep Q-Learning (92.21% accuracy), Advantage Actor-Critic (94.34% accuracy), and Trust Region Policy Optimization (95.12% accuracy). …”
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Advancing Smart Energy: A Review for Algorithms Enhancing Power Grid Reliability and Efficiency Through Advanced Quality of Energy Services by José M. Liceaga-Ortiz-De-La-Peña, Jorge A. Ruiz-Vanoye, Juan M. Xicoténcatl-Pérez, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Ricardo A. Barrera-Cámara, Daniel Robles-Camarillo, Marco A. Márquez-Vera, Francisco R. Trejo-Macotela, Luis A. Ortiz-Suárez

“…By concentrating on key aspects—reliability, availability and operational efficiency—the study reviews how various algorithmic approaches, from machine learning models to classical optimisation techniques, can significantly improve power grid management. …”
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Study on the peak shaving operation of cascade hydropower stations based on the plant-wide optimal curve by Fengshuo Liu, Kui Huang, Xuanyu Shi, Longqing Zhao, Yangxin Yu, Xueshan Ai, Xiang Fu

“…This study proposes a novel method to enhance RES and hydropower utilization through: 1) Establishing a plant-wide optimal output-water level-outflow relationship curve based on the output-head-flow relationship and the flow-head loss and outflow-tailwater level relationship curves for each unit; 2) Developing a short-term peak shaving model for cascaded hydropower stations that incorporates wind and PV power which defines minimum coefficient of variation of residual load as objective functions, and improving discrete differential dynamic programming successive approximation (DDDPSA) method to solve it; 3) Analyzing the peak shaving capacity of the cascade hydropower station by varying water consumption for power generation. …”
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