Sperm swarm optimization for many objective power flow problems with enhanced performance evaluation in power systems by Wulfran Fendzi Mbasso, Ambe Harrison, Pradeep Jangir, Idriss Dagal, Hossam Kotb, Njimboh Henry Alombah, Raman Kumar, Aseel Smerat, Emmanuel Fendzi Donfack, Saad F. Al-Gahtani, Z. M. S. Elbarbary

“…Abstract This research presents an advanced optimization framework motivated from biological sources using the Sperm Swarm Optimization (SSO) algorithm to specifically deal with the Many-Objective Optimal Power Flow (MaO-OPF) problem in power systems. …”
Get full text

Deep Reinforcement Learning-Based Two-Phase Hybrid Optimization for Scheduling Agile Earth Observation Satellites by Guanghui Zhou, Zhicheng Jin, Dongning Liu

“…The experimental results demonstrate that the TPHO framework with MRC rules achieves superior performance, yielding a total reward improvement exceeding 16% compared with the A-ALNS algorithm in the most complex scenario involving 1200 tasks, yet requiring less than 3% of the computational duration of A-ALNS.…”
Get full text

Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

Get full text

HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

“…The HiGMA algorithm plays a crucial role in identifying and extracting the most relevant regions of affected skin through optimal segmentation. …”
Get full text

A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms by Yuan Zhang, Zhekui Fan, Wenjia Yan, Chentian Ge, Huasheng Sun

“…With the optimal model, three red-edge bands of Landsat OLI were subsequently obtained in alignment with their derived vegetation indices. …”
Get full text

Design of intelligent optimization of sports strategy and training decision support system based on deep reinforcement learning by Hua Xu, Bing Lin, Long Liu

“…The data is preprocessed by a sliding window average filter algorithm to eliminate noise and outliers. The system adopts the DQN (Deep Q-Network) architecture and applies dual DQN technology to improve model stability. …”
Get full text

Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.…”
Get full text

Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework by Auref Rostamian, Amir Davari Malekabadi, Marx Vladimir De Souda Miranda, Vinicius Edurado Botechia, Denis José Schiozer

“…The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. …”
Get full text

Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning by Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Roa’a Khaled, Andrea Buccoliero, Syed Baqir Hussain Shah, Angelo Di Terlizzi, Giacomo Di Benedetto, Marco Agostino Deriu

“…To address these limitations, this study proposes a comprehensive pipeline combining transfer learning, feature selection, and machine-learning algorithms to improve detection accuracy. Multiple pretrained CNN models were evaluated, with Xception emerging as the optimal choice for its balance of computational efficiency and performance. …”
Get full text

Priority Load Management for Improving Supply Reliability of Critical Loads in Healthcare Facilities Under Highly Unreliable Grids by Ndukwe Henry Ibiam, Fadi Kahwash, Jubaer Ahmed

“…It was found that the priority-based algorithm improved the reliability served to the most critical loads at the expense of the least critical. …”
Get full text

Novel concept for the healthy population influencing factors by Yuhao Shen, Jichao Wang, Lihua Ma, Huizhe Yan

“…Community analysis divides the floating population into different health records and promotes targeted intervention measures. The random forest model improves the accuracy and universality of predictions. …”
Get full text

An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA-DSA) is developed for the detection of SIP-based distributed reflection denial-of-service (DRDoS) attacks in SIP network. …”
Get full text

SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…The SABO algorithm optimizes the weights and bias thresholds of the ELM model, which effectively reduces the risk of local optima and improves its predictive performance and stability. …”
Get full text

Process parameter optimization of laser beam machining for AISI -P20 mold steel using ANFIS method by Abdullah Eaysin, Sarower Kabir, Ebru Gunister, Nur Jahan, Amir Hamza, Muhammad Ali Zinnah, Adib Bin Rashid

“…The Brute Force algorithm identified the minimum combination for an optimal parameter set. …”
Get full text

A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks by Nan Li, Jiabin Chen, Yan Yuan, Xiaochun Tian, Yongqiang Han, Mingzhe Xia

“…In this paper, we propose an indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN). …”
Get full text

SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

Get full text

Bayesian Network-Based Landslide Susceptibility Safe Route Assessment in the Face of Uncertain Knowledge and Various Information by Xinyu Gao, Bo Wang, Wen Dai, Yuanmin Liu

“…The BN model effectively integrates multi-source data and uncertainty knowledge to generate an accurate LSM, while the improved A* algorithm combines safety and efficiency considerations to optimize routes. …”
Get full text

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. …”
Get full text

YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images by Prabu Selvam, P. Shanmuga Sundari, M. Tamilselvi, T. Suresh, M. Murugappan, Muhammad E. H. Chowdhury

“…We evaluated YOLO-SAIL against two public benchmark datasets, the SAR Ship Detection Dataset (SSDD) and the High Resolution SAR Images Dataset (HRSID). The proposed model, YOLO-SAIL, achieves an F1-score of 98.22% in SSDD datasets and 94.68% in HRSID datasets, outperforming most existing models.…”
Get full text

Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN by Zhenbing ZHAO, Shuai ZHANG, Wei JIANG, Peng WU

“…Then, the FPN model is improved: based on the prior knowledge of bolts, the convolutional network is used to achieve bottom-up feature extraction, the bilinear interpolation method is used to transfer the high-level semantic information of the features to each level from top to bottom, the convolution filtering method is used to laterally strengthen the information fusion of high-level semantic features and high-resolution features, and a more optimized feature expression of bolts with missing pins is obtained. …”
Get full text