A Lightweight YOLO-Based Architecture for Apple Detection on Embedded Systems by Juan Carlos Olguín-Rojas, Juan Irving Vasquez, Gilberto de Jesús López-Canteñs, Juan Carlos Herrera-Lozada, Canek Mota-Delfin

“…In Mexico, the manual detection of damaged apples has led to inconsistencies in product quality, a problem that can be addressed by integrating vision systems with machine learning algorithms. The YOLO (You Only Look Once) neural network has significantly improved fruit detection through image processing and has automated several related tasks. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…The dataset was preprocessed using the Standard Scaler to standardize it, ensuring each feature has a mean of zero and a standard deviation of one, followed by outlier detection with Cook’s distance. Hyperparameter optimization made using the Differential Evolution (DE) method improved the performance of models. …”
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Field Study of UAV Variable-Rate Spraying Method for Orchards Based on Canopy Volume by Pengchao Chen, Haoran Ma, Zongyin Cui, Zhihong Li, Jiapei Wu, Jianhong Liao, Hanbing Liu, Ying Wang, Yubin Lan

“…A DJI M300 drone equipped with LiDAR was used to capture high-precision 3D point cloud data of tree canopies. An improved progressive TIN densification (IPTD) filtering algorithm and a region-growing algorithm were applied to segment the point cloud of fruit trees, construct a canopy volume-based classification model, and generate a differentiated prescription map for spraying. …”
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Physical Information Neural Network-Based Seepage Behavior Analysis of Earth and Rock Dams by XUE binghan, HUANG zhenhua, LEI Jianwei, FANG Hongyuan

“…The distribution patterns align with established literature, confirming the algorithm's capability to effectively handle material heterogeneity and accurately capture permeability anisotropy effects on seepage field distribution.ConclusionsThe results demonstrate that the proposed algorithm achieves significantly enhanced precision in locating the seepage escape point. …”
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How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression by Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith M. Burnfield

“…Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg–Marquardt backpropagation algorithm and compared with MLR models. …”
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Text Analysis of Digital Commentary on Ice and Snow Tourism Based on Artificial Intelligence and Long Short-Term Memory Neural Network by Qi Zhuang, Zhengjie Chu, Jun Li

“…To address these challenges, this study proposes an intelligent sentiment analysis algorithm based on a multi-model fusion approach: the Improved Dynamic Convolutional and Attention-based Bidirectional Long Short-Term Memory Model (IDCAN-BiLSTM). …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF by Hui Ouyang, Weibo Li, Feng Gao, Kangzheng Huang, Peng Xiao

“…For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. …”
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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

“…Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm‐extreme learning machine (PSO‐ELM). …”
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YOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning by Yangyang Liu, Liangping Tu, Jianxi Li, Jiawei Miao, Gengqi Lin, Chenying Zhao

“…In this paper, we construct a data set for strong gravitational lensing searches that consist of known lenses and lens candidates from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys (the DESI Legacy Imaging Surveys) and the Dark Energy Survey (DES) and non-lenses from the Legacy Surveys Data Release 9 (DR9). We apply the YOLOX algorithm as the basic framework and improve it by selecting the optimal optimizer, activation function, attention mechanism, and loss function. …”
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Resource Allocation for Edge-enhanced Distributed Power Wireless Sensor Network by Gang WU, Jinhui ZHOU, Hui LI

“…Aiming at the problems of high access latency, high energy consumption, and deteriorating user service quality in traditional wireless power sensor networks, the mobile edge computing (MEC) is introduced to establish a joint optimization model of access latency and network energy consumption, with objective to minimize the joint function of latency and energy consumption. …”
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Performance evaluation of enhanced deep learning classifiers for person identification and gender classification by Vasu Krishna Suravarapu, Hemprasad Yashwant Patil

“…Further, an adaptive coati optimization algorithm is used to adjust the hyperparameters of the classification models. …”
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Application of artificial intelligence in the diagnosis and treatment of lacrimal disorders: challenges and opportunities by PENG Xintong, LI Guangyu

“…However, several challenges persist, such as the complexity of multimodal data integration, limitations in model generalization capabilities, and the need for real-time prediction and dynamic adjustments, all of which necessitate continuous technological innovations, algorithm optimization, and interdisciplinary collaborations. …”
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Multi-Underwater Target Interception Strategy Based on Deep Reinforcement Learning by Wenhao GAN, Yunfei PENG, Lei QIAO

“…Next, the multi-agent proximal policy optimization algorithm was used to construct a scalable state and action space and design a compound reward function, enhancing interception efficiency and cooperation of AUVs. …”
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Bidirectional Q-learning for recycling path planning of used appliances under strong and weak constraints by Yang Qi, Jinxin Cao, Baijing Wu

“…To validate the efficacy of the proposed algorithm, gridbased modeling of real-world environments is utilized. …”
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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. …”
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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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Research on Laser Radar Inspection Station Planning of Vehicle Body-In-White (BIW) with Complex Constraints by Lijuan Li, Siyi Wang, Jichao Ma, Xiaobing Gao

“…Focusing on the spatially constrained industrial environments and complex measurement specifications, the work reformulates Laser Radar inspection planning as a multi-constrained optimization problem challenge. Firstly, a parametric geometric modeling approach is developed to define measurement spaces for individual features, accompanied by an innovative maximal complete subgraph mining algorithm to intelligently identify shared feasible measurement regions among multiple features. …”
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Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review by Pierpaolo Dini, Davide Paolini

“…The effectiveness of ML applications in this domain, however, is highly dependent on the selection of quality datasets, relevant features, and suitable algorithms. Advanced techniques such as active learning are being explored to enhance ANN model performance by improving the models’ responsiveness to diverse and nuanced battery behavior. …”
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Industrial-Grade 3D Detection for Laser Cleaning of Metallic Components Based on Neural Radiance Fields and Temporal-Spatial Learning by Dandan Qi

“…Improve the collaborative modeling ability between multi-temporal images through cross-scale attention fusion; 4. …”
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