Fast Multimodal Trajectory Prediction for Vehicles Based on Multimodal Information Fusion by Likun Ge, Shuting Wang, Guangqi Wang

“…Finally, we propose a multi-stage decoder that generates more accurate and reasonable predicted trajectories by predicting trajectory reference points and performing spatial and posture optimization on the predicted trajectories. Comparative experiments with existing advanced algorithms demonstrate that our method improves the minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR) by 10.3%, 10.3%, and 14.5%, respectively, compared to the average performance. …”
Get full text

MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency by Dechuan Kong, Dechuan Kong, Yandi Zhang, Xiaohu Zhao, Yanqiang Wang, Lei Cai

“…A Multi-Scale Joint Loss framework facilitates dynamic network optimization.ResultsExperimental results demonstrate that MUFFNet outperforms existing state-of-the-art models while consuming fewer computational resources and aligning enhanced images more closely with human visual perception.DiscussionThe enhanced images generated by MUFFNet exhibit better alignment with human visual perception, making it a promising solution for improving underwater robotic vision systems.…”
Get full text

Enhancement Ear-based Biometric System Using a Modified AdaBoost Method by Abdulkareem Merhej Radhi, Subhi Aswad Mohammed

“…The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. …”
Get full text

Explainable AI-Based Ensemble Clustering for Load Profiling and Demand Response by Elissaios Sarmas, Afroditi Fragkiadaki, Vangelis Marinakis

“…Notably, while ensemble clustering often ranked among the top performers, it did not consistently surpass all individual algorithms, indicating its potential for further optimization. …”
Get full text

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. …”
Get full text

Designing and implementing a Web-based real time routing service for crisis management (a case study for district 11 of Tehran) by javad sadidi, seyed hassan hosseini sajedi

“…In this service ANP model was used to evaluate increasing interaction between the factors, and since the purpose of this study was to find the best possible routs between two nodes by non-negative weight according to the main distance factor, Dijkstra's algorithm has been chosen as a proper routing algorithm. …”
Get full text

Detecting Botrytis Cinerea Control Efficacy via Deep Learning by Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov, Xiangping Cheng

“…Experimental results show that the validation loss of this method reaches 0.007, with a mean absolute error of 0.0148, outperforming other comparative models. This study enriches the theory of gray mold control and provides information technology for optimizing and selecting its inhibitors.…”
Get full text

Default Risk Prediction of Enterprises Based on Convolutional Neural Network in the Age of Big Data: Analysis from the Viewpoint of Different Balance Ratios by Zhe Li, Zhenhao Jiang, Xianyou Pan

“…Second, we propose a comprehensive metric model based on multimachine learning algorithms (CMM-MLA) to select the best-derived dataset with the optimal balance ratio and feature combination. …”
Get full text

A Self-Supervised Adversarial Deblurring Face Recognition Network for Edge Devices by Hanwen Zhang, Myun Kim, Baitong Li, Yanping Lu

“…The model employs a generative adversarial network (GAN) as the core algorithm, optimizing its generation and recognition modules by decomposing the global loss function and incorporating a feature pyramid, thereby solving the balance challenge in GAN training. …”
Get full text

Blind super-resolution network based on local fuzzy discriminative loss for fabric data augmentation by Ning Dai, Xiaohan Hu, Kaixin Xu, Xudong Hu, Yanhong Yuan, Bo Cao, Luhong Shi

“…To address these challenges, this paper proposes a blind super-resolution algorithm for fabric defect data augmentation. The model is based on Real-ESRGAN and has been optimized specifically for the resolution degradation module to better adapt to the resolution degradation process in fabric images. …”
Get full text

A Registration Method for Historical Maps Based on Self-Supervised Feature Matching by Zikang Qin, Yumin Feng, Gang Wu, Qing Dong, Tianxin Han

“…Experimental results indicate that our solution achieves superior performance compared to existing models, with RMSE reduced by up to 20%, ROCC improved by up to 10%, and processing time shortened by at least 15%.…”
Get full text

Role,application and challenges of IoT in smart EV charging management:a review by Tripti Kunj, Kirti Pal

“…Additionally, the paper emphasizes the importance of adaptive algorithms and machine learning models for predictive maintenance and efficient resource allocation. …”
Get full text

A Non-invasive Load Recognition Approach Incorporating SENet Attention Mechanism and GA-CNN by Xin SHEN, Gang WANG, Yitao ZHAO, Zhao LUO, Zhao LI, Xiaohua YANG

“…Secondly, the U-I trajectory map of the residential load is extracted and weighted pixelated to obtain the WVI (Weighted pixelated VI) feature matrix through computation, which is applied as the feature coefficient to train the SENet-CNN model. Finally, by virtue of the genetic algorithm, the SENet-CNN model is trained and the hyperparameters of the CNN-SENet model are optimized to improve the model load recognition performance and computational efficiency. …”
Get full text

Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction by Ahya Radiatul Kamila, Johanes Fernandes Andry, Francka Sakti Lee, Felliks F. Tampinongkol

“…It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. …”
Get full text

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. …”
Get full text

Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk by Changxiao Zhao, Wei Zhang, Fangzheng Dong, Jun Dai, Lei Dong

“…The resource allocation method is evaluated according to the fault propagation risk model, and a heuristic algorithm is applied to optimize the resource allocation method of the IMA system. …”
Get full text

A Lightweight Method for Road Defect Detection in UAV Remote Sensing Images with Complex Backgrounds and Cross-Scale Fusion by Wenya Zhang, Xiang Li, Lina Wang, Danfei Zhang, Pengfei Lu, Lei Wang, Chuanxiang Cheng

“…Experimental findings indicate that the CSGEH-YOLO algorithm surpasses the baseline YOLOv8s, achieving a 3.1% improvement in mAP. …”
Get full text

Advanced Human Pose Estimation and Event Classification Using Context-Aware Features and XGBoost Classifier by Wasim Wahid, Aisha Ahmed AlArfaj, Ebtisam Abdullah Alabdulqader, Touseef Sadiq, Hameedur Rahman, Ahmad Jalal

“…The system begins with preprocessing steps, including converting videos into image sequences, applying sliding window techniques, and converting images to grayscale, then extracting human silhouettes using binary masks. We use the GrabCut algorithm for human detection and perform skeletonization with Hough transform algorithm. …”
Get full text

Design and Prototype Verification of a 3-meter Aperture Wrap-rib Reflector by ZHANG Han, YAN Zhongxi, XIANG Ping, WU Minger

“…The shape of the lenticular tube wrap-rib was optimized by combining the form-finding analysis of the flexible reflector with the genetic algorithm. …”
Get full text

Editorial by Christian Gütl

“…Besma Hezili and Hichem Talbi from Algeria address the collaborative auto-diversified optimization scheme (CADOS) for solving continuous and combinatorial optimization problems by exploring the synergy of various optimization algorithms and enhance their effectiveness and efficiency, particularly for higher-dimensional problems. …”
Get full text