Analysis of injured-skin SS-OCT images based on combined attention UNet. by Xiyu Zheng, Jingyuan Wu, Qiong Ma, Diantao Luo, Qingyu Cai, Haiyang Sun, Hongxing Kang

“…These models were used to segment the damaged skin regions, followed by a 3D reconstruction method to quantitatively evaluate the volume of skin damage while analyzing changes about laser dose and recovery time.The BM3D algorithm effectively suppressed high-noise components, significantly improving image clarity. …”
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Pavement pothole detection system based on deep learning and binocular vision by Tian Guan, Jianyuan Cai, Yu Wang, Wei Yang, Xiaobo Chang, Yi Han

“…Finally, vehicle experiments were conducted to verify the effectiveness of the algorithm in meeting detection requirements while considering long-range perception accuracy. …”
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Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage by Yongqiang Zhang, Haijie Pang, Jinlong Ma, Guilei Ma, Xiaoming Zhang, Menghua Man

“…On the MNIST dataset, the accuracy of the spiking neural network decreased by 3.91% at a sparsity of 30%, while the artificial neural network decreased by 10.13%. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“… The experimental results showed that the logistic regression model performed better than the nearest neighbor algorithm with a precision of 96%, recall of 98%, and F1- score of 97% in the thalassemia intermedia category, while it had a precision of 97%, recall of 95%, and F1- score of 96% in the thalassemia major category, indicating that logistic regression performed well in distinguishing between these two categories. it has been shown that logistic regression is more effective than the K-nearest neighbor algorithm in classifying thalassemia patients, especially those with thalassemia major. …”
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User preference–based QoS-aware service function placement in IoT-Edge cloud by Briytone Mutichiro, Younghan Kim

“…The proposed solutions are evaluated based on different metrics and the obtained results show the gap between the heuristic user preference placement algorithm and the optimal solution to be minimal.…”
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MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency.…”
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Efficient scheduling of multiple software projects for work continuity and identical completion time by Abdulrahman Aldhubaiban, Ali AlMatouq

“…Our continuous variable model was solved in the cloud for optimality for large instances of upto 40 different software projects and 100 employees in less than 21 min using nonlinear programming algorithms. • A continuous variable nonlinear model is developed to efficiently schedule large-scale software projects. • The model enables scheduling for multiple projects with identical completion times while ensuring work continuity. • A cloud-based program architecture is designed to facilitate the testing of multiple solvers online.…”
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Simulation analysis of path planning for workpiece clamping robots based on digital twin technology by Xin PAN, Min LIANG, Yanchao YIN, Zhong CHEN

“…Targeting feasible regions through elliptical constraints accelerates convergence toward optimal paths, while techniques such as redundant node elimination and initial path smoothing further improve path quality, resulting in shorter, smoother routes. …”
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Order picking dataset from a warehouse of a footwear manufacturing companyMendeley Data by Rodrigo Furlan de Assis, William de Paula Ferreira, Mustapha Ouhimmou

“…This dataset is highly versatile and suitable for developing optimization algorithms for picker routing, order batching, wave generation, and intralogistics, as well as for advancing automation and robotics research through navigation-specific data for autonomous guided vehicles (AGVs) and robotic systems. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Abstract We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. …”
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Generation of Genuine Multipartite Entangled States via Indistinguishability of Identical Particles by Kobra Mahdavipour, Farzam Nosrati, Stefania Sciara, Roberto Morandotti, Rosario Lo Franco

“…The presented theoretical approach, while already implementable with current linear optics architectures, has the potential to bring clear advantages over existing technologies, such as in quantum computing search algorithms and in the design of new experiments in quantum optics or other platforms.…”
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The benefits and trade-offs of multi-variable calibration of the WaterGAP global hydrological model (WGHM) in the Ganges and Brahmaputra basins by H. M. M. Hasan, P. Döll, P. Döll, S.-M. Hosseini-Moghari, F. Papa, A. Güntner, A. Güntner

“…<p>While global hydrological models (GHMs) are affected by large uncertainties regarding model structure, forcing and calibration data, and parameters, observations of model output variables are rarely used to calibrate the model. …”
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Efficient Resources Provisioning Based on Load Forecasting in Cloud by Rongdong Hu, Jingfei Jiang, Guangming Liu, Lixin Wang

“…Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. …”
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Resource trading strategies with risk selection in collaborative training market. by Quyuan Wang, Xinyu Ni, Yuping Tu, Ying Wang, Jiadi Liu

“…Simulation results demonstrate that the proposed strategies and algorithms offer acceptable performance in terms of algorithmic efficiency and strategic effectiveness, while also preserving fundamental economic principles and supporting stable market dynamics.…”
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Marine fish species recognition based on improved YOLOv5s by ZHANG Haifeng, LU Xinchun, FENG Bo, YANG Jin

“…Improved some C3 modules of the backbone network, and integrated CA coordination attention mechanism into the C3 module, which improved the recognition accuracy and detection speed of the model while reducing the number of model parameters. Finally, optimized the path aggregation network of the model to enhance the feature fusion ability of the network.ResultsThe experimental results showed that the improved Our⁃YOLOv5s model had a mAP of 98.4% and a detection speed of 64 s-1 in the dataset, which was 2.4% and 6 s-1 higher than the original model, respectively.ConclusionThe model can meet the real⁃time detection requirements of marine fish.…”
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Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li, Kejing Cheng

“…The optimal period for TN inversion was determined. The SVM-RFE algorithm outperformed the models built without feature selection in terms of accuracy. …”
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Can Knowledge Distillation Enable Seismic Interpolation, Super-Resolution, and Denoising Simultaneously? by Benwen Zhang, Linrong Wang

“…Consequently, SNet inherits TNet&#x2019;s capabilities and further refines them during optimization. For comparison, we combine three interpolation algorithms with three super-resolution algorithms and select the one with the best performance. …”
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Prediction of permeability and effective porosity values using ANN in Maleh field by Mohammed Essa Nassani, Ali Alaji

“…The ANN was tested on independent data and demonstrated exceptional performance, achieving 96% accuracy for effective porosity and 98% for permeability predictions in sandstone formations. This efficient algorithm eliminates the need for core sample analysis, reducing costs and time while improving prediction reliability, making it a valuable tool for subsurface characterization and resource exploration.…”
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LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 by Haoran Feng, Xiqu Chen, Zhaoyan Duan

“…A Focal-EIoU loss function is also integrated to optimize the model’s training process. Experimental results show that compared to YOLOv8, the LCDDN-YOLO model reduces the number of parameters by 12.9% and the floating-point operations (FLOPs) by 9.9%, while precision, mAP@50, and recall improve by 4.6%, 6.5%, and 7.8%, respectively, reaching 89.5%, 85.4%, and 80.2%. …”
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Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram by XIONG Qunfang, LIN Jun, YUAN Xiwen, XU Yanghan, YUE Wei, LI Yuanzhengyu

“…These abnormal image data were saved and utilized for further training and optimizing the model. Experimental results reveals that the proposed approach effectively detected and recognized ART traffic lights, achieving an average detection precision of 84.76% on designated roads during the daytime while exhibiting good real-time performance.…”
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