MSFCN: A Multiscale Feature Correlation Network for Remote Sensing Image Scene Change Detection by Feng Xie, Zhongping Liao, Jianbo Tan, Zhiguo Hao, Shining Lv, Zegang Lu, Yunfei Zhang

“…In addition, cosine embedding loss is used to constrain the scene binary change detection task and construct a multitask loss for model optimization. On the Hanyang and WH-MAVS datasets, MSFCN achieved average scene classification accuracies of 93.33% and 94.86%, scene-level binary change detection accuracies of 95.71% and 98.13%, and scene-level semantic change detection accuracies of 90.00% and 93.95%, respectively, significantly better than the comparison methods.…”
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Coverage Hole Recovery in Hybrid Sensor Networks Based on Key Perceptual Intersections for Emergency Communications by He Li, Shixian Sun, Chuang Dong, Qinglei Qi, Cong Zhao, Zufeng Fu, Peng Yu, Jiajia Liu

“…Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. …”
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Resource Allocation in User-Centric Cell-Free Massive MIMO URLLC Systems With Network Slicing by Han Lu, Weiwei Xia, Weiwei Miao, Mingxuan Zhang, Feng Yan, Lianfeng Shen

“…For the resource allocation problem, a successive rate lower bound maximization algorithm is employed to obtain the convex lower bound of the data rate under short packet transmission, and the successive convex approximation (SCA) method is used to determine the optimal bandwidth and frequency allocation. Simulation results show that the proposed scheme improves the sum data rate by at least 10.24% and reduces the average transmission delay by at least 33.40% compared to the existing algorithms.…”
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Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method. by Sirikorn Sangchocanonta, Pakinee Pooprasert, Nichapa Lerthirunvibul, Kanyarak Patchimnan, Phongphan Phienphanich, Adirek Munthuli, Sujittra Puangarom, Rath Itthipanichpong, Kitiya Ratanawongphaibul, Sunee Chansangpetch, Anita Manassakorn, Visanee Tantisevi, Prin Rojanapongpun, Charturong Tantibundhit

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A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model by Xiaoliang Jia, Guoyan Zhang, Zhiqiang Wang, Huacong Li, Jing Hu, Songlin Zhu, Caiwei Liu

“…During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. …”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Especially for the external testing dataset (hospital 2/hospital 3), the specificity is much higher than comparison methods (0.6250–0.6731 vs. 0.2569–0.2788). The graph neural network‐based deep learning method holds the potential to assist clinicians, aiding in treatment planning, patient management, follow‐up strategies, resource optimization, and overall healthcare decision‐making.…”
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Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters by Hauwau Abdulrahman Aliyu, Ibrahim Olawale Muritala, Habeeb Bello-Salau, Salisu Mohammed, Adeiza James Onumanyi, Ore-Ofe Ajayi

“…Leveraging Particle Swarm Optimization (PSO) algorithm for diabetes data balancing and a genetic algorithm to select the optimal architecture for various machine learning classifiers. …”
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Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning by Wenjun XU, Silei WU, Fengyu WANG, Lan LIN, Guojun LI, Zhi ZHANG

“…In order to quickly restore emergency communication services for large-scale post-disaster users, a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed, which could address the significant differences and dynamics of communication services caused by a large number of access users, and the difficulty of expansion caused by centralized algorithms.Specifically, a distributed k-sums clustering algorithm considering service differences of users was designed in the network characterization layer, which could make each unmanned aerial vehicle base station (UAV-BS) adjust the local networking natively and simply, and obtain states of cluster center for multi-agent RL.In the trajectory control layer, multi-agent soft actor critic (MASAC) with distributed-training-distributed-execution structure was designed for UAV-BS to control trajectory as intelligent nodes.Furthermore, ensemble learning and curriculum learning were integrated to improve the stability and convergence speed of training process.The simulation results show that the proposed distributed k-sums algorithm is superior to the k-means in terms of average load efficiency and clustering balance, and MASAC based trajectory control algorithm can effectively reduce communication interruptions and improve the spectrum efficiency, which outperforms the existing RL algorithms.…”
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Enhancing education quality with hybrid clustering and evolutionary neural networks in a multi phase framework by Saleem Malik, Chandrakanta Mahanty, Jnanaranjan Mohanty, Krunal Vaghela, T. Narmadha, R. Sivaranjani, Javed Khan Bhutto, Saiful Islam, Anwar Khan, Amanuel Zewdie

“…Particle Swarm Optimization (PSO) optimizes neural network weight assignments, dynamically fine-tuning network topologies depending on the complex student dataset. …”
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Multi-objective optimization prediction model for building parameters of photovoltaic windows based on NSGA II-BP by Jiran Zhang, Lingling Zhang, Panpan Ren, Wengang Hao, Ao Xu

“…Moreover, the resulting energy saving rate, annual average power generation growth rate, and UDI growth rate are compared with the initial values to evaluate the effectiveness of the optimal solution. …”
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Hybrid FFT-ARMA-Burg Modeling and LSTM-Enhanced BBO Optimization for Fault Diagnosis in Induction Motors by Khadidja Boudraa, Mohammed Assam Ouali, Mohamed Ladjal

“…Further, this paper proposes an LSTM neural network that refines BBO-optimized parameters to improve fault frequency sensitivity. …”
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Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning by Wenjun XU, Silei WU, Fengyu WANG, Lan LIN, Guojun LI, Zhi ZHANG

“…In order to quickly restore emergency communication services for large-scale post-disaster users, a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed, which could address the significant differences and dynamics of communication services caused by a large number of access users, and the difficulty of expansion caused by centralized algorithms.Specifically, a distributed k-sums clustering algorithm considering service differences of users was designed in the network characterization layer, which could make each unmanned aerial vehicle base station (UAV-BS) adjust the local networking natively and simply, and obtain states of cluster center for multi-agent RL.In the trajectory control layer, multi-agent soft actor critic (MASAC) with distributed-training-distributed-execution structure was designed for UAV-BS to control trajectory as intelligent nodes.Furthermore, ensemble learning and curriculum learning were integrated to improve the stability and convergence speed of training process.The simulation results show that the proposed distributed k-sums algorithm is superior to the k-means in terms of average load efficiency and clustering balance, and MASAC based trajectory control algorithm can effectively reduce communication interruptions and improve the spectrum efficiency, which outperforms the existing RL algorithms.…”
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Geospatial cost and emission assessment of universal fiber-to-the-neighborhood (FTTnb) broadband infrastructure strategies for Sub-Saharan Africa by Ogutu B Osoro, Edward J Oughton, Fabion Kauker

“…Yet, pushing out fiber broadband to local areas is essential, even if the final access network is still wireless. Here, we design least-cost fiber-to-the-neighborhood (FTTnb) architectures using two spatial optimization Steiner tree algorithms to jointly determine investment costs, environmental emissions, and social carbon costs. …”
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Intelligent optimization method for hazardous materials transportation routing with multi-mode and multi-criterion collaborative constraints by Fan Chen, Qing Zhu

“…In response to this, a method for multi-mode transportation network and multi-criterion route optimization is proposed. …”
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Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles by Bouzi Wissam, Bentaieb Samia, Ouamri Abdelaziz

“…This research proposes a new single-agent deep reinforcement-learning model using a deep Q-Network (DQN) to optimize traffic lights, aiming to reduce waiting times and increase vehicle speed, with particular emphasis on emergency vehicles. …”
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Multi-Objective Optimization of the Rotary Turning of Hardened Mold Steel for Energy Saving and Surface Roughness Improvements by Tat Khoa Doan, Trung Thanh Nguyen, An-Le Van

“…The Bayesian regularized feed-forward neural network was applied to develop the SCE and Ra models. …”
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Digital Transformation for Sustainable Transportation: Leveraging Industry 4.0 Technologies to Optimize Efficiency and Reduce Emissions by Hajar Fatorachian, Hadi Kazemi, Kulwant Pawar

“…Using a qualitative research approach, semi-structured interviews and focus groups were conducted with industry experts, and the data were analyzed using thematic analysis and qualitative network mapping in NVivo software. The findings reveal that IoT enhances real-time monitoring, AI enables dynamic route optimization, and predictive analytics supports proactive maintenance, collectively achieving an average emission reductions of 30%. …”
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Enhancing microgrid performance: Optimal proactive reactive power dispatch using photovoltaic active power forecasts by Alexander Casilimas‐Peña, Karol Rosen, César Angeles‐Camacho

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Level Set Based Coverage Holes Detection and Holes Healing Scheme in Hybrid Sensor Network by Xiaosheng Yu, Chengdong Wu, Dongyue Chen, Nan Hu

“…This algorithm could leverage mobility to optimize the average coverage rate and the average movement distance of the mobile nodes. …”
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