Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…In order to reveal the intrinsic mechanism of prediction by such architectures, we adopted a coupled CNN-LSTM model based on the explainability technique SHapley Additive exPlanations (SHAP) to predict the rainfall-runoff process and identify key input feature factors, and took the Beijiang River Basin in China as an example, so as to improve the explainability and credibility of this black-box model. …”
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Research on manufacturing quality improvement based on product gene evaluation method and a meta-heuristic algorithm with hybrid encoding scheme by Wenxiang Xu, Chao Wang, Shimin Xu, Junyong Liang, Dezheng Liu, Baigang Du

“…To address the model, an improved genetic algorithm (GA) and artificial bee colony algorithm (ABC) with hybrid encoding scheme (H-IGA-IABC) is designed by considering the different types of gene elements. …”
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Optimizing Photovoltaic Grid‐Connected Power Systems Through Artificial Intelligence and Robust Nonlinear Control by Collins Ngong Sang, Harrison Ambe, Arnaud flanclair Tchouani Njomo, Zeric Tabekoueng Njitacke, Wulfran Fendzi Mbasso, Pradeep Jangir, Aseel Smerat

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Multi-Target Firefighting Task Planning Strategy for Multiple UAVs Under Dynamic Forest Fire Environment by Pei Zhu, Shize Jiang, Jiangao Zhang, Ziheng Xu, Zhi Sun, Quan Shao

“…In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. …”
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Classification and Recognition Method for Bearing Fault based on IFOA-SVM by Wei Zhang, Zhihua Ma

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Performance evaluation of STATCOM placement in real-world power system through DOA-NVSP algorithm for congestion management and stability improvement by Tayo Uthman Badrudeen, Funso K. Ariyo, Nnamdi Nwulu

“…The study concluded that the proposed model was able to improve the stability and congestion management of the NPN.…”
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A Study on Improving of Adaptive Sensorless Control Performance via Funnel Control by Jun-Seo Han, Gyung-Jae Cho, Hyeon-Ji Jin, Geun-Ho Lee

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering by Laith Abualigah, Saleh Ali Alomari, Mohammad H. Almomani, Raed Abu Zitar, Hazem Migdady, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu

“…The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. …”
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Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario by Sifeng ZHU, Yu WANG, Hao CHEN, Hai ZHU, Zhengyi CHAI, Chengrui YANG

“…In the context of the Internet of vehicles, the placement and deployment number of edge servers directly affect the efficiency of edge computing.Due to the high cost of deploying a large edge server on a macro base station and a base station, it can be complemented by deploying a small edge server on a micro base station, and the cost reduction needs to be optimized by optimizing the placement of large edge servers.In order to minimize the deployment cost and service delay of the edge server, and maximize the operator’s revenue and server load balance, the edge server placement problem combined with the vehicle networking user application service was modeled as a multi-objective optimization problem and a placement scheme based on improved NSGA-Ⅱ algorithm was proposed.The experimental results show that the proposed scheme can reduce the deployment cost of edge servers by about 44%, the latency by about 14.2%, and improve the revenue of operators by 24.2%, which has good application value.…”
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Predicting Student Performance through Machine Learning Methods: Naive Bayesian Classifier by Chenyang Li, Qianzhi Zhou, Luming Du, Sihan Zhang

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An Interpretable Data-Driven Dynamic Operating Envelope Calculation Method Based on an Improved Deep Learning Model by Yun Li, Tunan Chen, Jianzhao Liu, Zhaohua Hu, Yuchen Qi, Ye Guo

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Research on an Apple Recognition and Yield Estimation Model Based on the Fusion of Improved YOLOv11 and DeepSORT by Zhanglei Yan, Yuwei Wu, Wenbo Zhao, Shao Zhang, Xu Li

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Target detection technology of coal mine wheeled robot combining improved CNN and self attention mechanism by Junfei TANG, Hailong XING, Su LI, Taotao ZHANG, Heng LIU, Shiyu YAO

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FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 by ZHANG TianYuan, SUN HuEr, ZHU JiYang, ZHAO Yang

“…Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly,the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model,which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally,this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults,using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%,using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.…”
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Improved Modulated Model Predictive Control: Reducing Total Harmonic Distortion, Computational Burden, and Parameter Sensitivity by Seyed Rasul Eftekhari, Ali Mosallanejad, Hamidreza Pairo, Jose Rodriguez

“…This paper presents an improved modulated model predictive control (M2PC) method, which is implemented on synchronous reluctance motor (SynRM) drives, significantly enhancing performance by streamlining the control algorithm. …”
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Flood change detection model based on an improved U-net network and multi-head attention mechanism by Fajing Wang, Xu Feng

“…Specifically, the accuracy of the model algorithm in this work reaches 95.52%, marking a 3.46% improvement over the baseline U-Net network. …”
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Deformation analysis by an improved similarity transformation by Vahid Mahboub

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An Improved Low-Bit-Rate Image Compression Framework Based on Semantic-Aware Model and Neighborhood Attention by Chengbin Zeng, Liang Zhang

“…In future work, we plan to improve the model’s robustness under low-light conditions and enhance compression efficiency through lightweight optimization techniques, enabling broader real-world deployment.…”
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A Target Detection Model Based on Improved Tiny-Yolov3 Under the Environment of Mining Truck by Dong Xiao, Feng Shan, Ze Li, Ba Tuan Le, Xiwen Liu, Xuerao Li

“…Therefore, this paper proposes an improved target detection model based on tiny-yolov3. …”
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