Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

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Deep learning-based semantic segmentation with novel navigation line extraction for autonomous agricultural robots by Chijioke Leonard Nkwocha, Ning Wang

“…Then, we proposed a novel navigation line extraction algorithm based on the Douglas–Peucker algorithm which uses the output mask from the semantic segmentation to extract the centre navigation line for robot guidance. …”
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Dynamic-budget superpixel active learning for semantic segmentation by Yuemin Wang, Ian Stavness

“…A static budget could result in over- or under-labeling images as the number of high-impact regions in each image can vary.MethodsIn this paper, we present a novel dynamic-budget superpixel querying strategy that can query the optimal numbers of high-uncertainty superpixels in an image to improve the querying efficiency of regional active learning algorithms designed for semantic segmentation.ResultsFor two distinct datasets, we show that by allowing a dynamic budget for each image, the active learning algorithm is more effective compared to static-budget querying at the same low total labeling budget. …”
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Multi-Source Heterogeneous Data-Driven Digital Delivery System for Oil and Gas Surface Engineering by Wei Zhang, Zhixiang Dai, Taiwu Xia, Gangping Chen, Yihua Zhang, Jun Zhou, Cui Liu

“…To address the challenges of data fragmentation, inconsistent standards, and weak interactivity in oil and gas field surface engineering, this study proposes an intelligent delivery system integrated with three-dimensional dynamic modeling. Utilizing a layered collaborative framework, the system combines optimization algorithms and anomaly detection methods during data processing to enhance the relevance and reliability of high-dimensional data. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. …”
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Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district by Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao

“…After the water conservation retrofit, non-saline soil increased by 326.5 km2, indicating improved soil conditions. The overall accuracy of the Gradient Boosting Tree classification model is 95.00 % and the Kappa coefficient is 0.93, which is the optimal model for land use type classification. …”
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Know your competitor! Analyzing and predicting the location of competing stores: The case study of Valora at Swiss railway stations by Thomas Wieland

“…Machine learning algorithms are used to check and optimize the predictive ability of the models. …”
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases by Veerayuth Kittichai, Morakot Kaewthamasorn, Apinya Arnuphaprasert, Rangsan Jomtarak, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

“…The model’s performance was also improved by a fine-tuned procedure between k-nearest neighbor and its normalized distance of each data point, including precision of 0.833 ± 0.134, specificity of 0.930 ± 0.054, recall of 0.838 ± 0.118, and accuracy of 0.915 ± 0.025, respectively. …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…In addition, the improved Channel Attention Concatenation (CAC) strategy adaptively learns channel-wise importance weights during feature concatenation, further optimizing multi-scale semantic feature fusion and enhancing the model’s sensitivity to critical crater features. …”
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Cattle identification based on multiple feature decision layer fusion by Dongxu Li, Baoshan Li, Qi Li, Yueming Wang, Mei Yang, Mingshuo Han

“…The corresponding cattle IDs of these features are then processed using One-Hot encoding to serve as the final input for the decision layer, and various ensemble strategies are used to optimize the model. The results show that using the multimodal decision fusion method makes the recognition accuracy reach 95.74%, 1.4% higher than the traditional optimal unimodal recognition accuracy. …”
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3D Human Motion Capture Method Based on Computer Vision by A. D. Obukhov, D. L. Dedov, E. O. Surkova, I. L. Korobova

“…Based on the skeletal points obtained from the neural network, a three-dimensional reconstruction of the human body model was carried out using various triangulation algorithms.Results. …”
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Artificial neural networks in predicting impaired bone metabolism in diabetes mellitus by S. S. Safarova

“…Further, the obtained data were processed using the MATLAB software to develop an ANN with a training (80%) and test (20%) set. The ANN model was trained by optimizing the relationship between a set of input data (a number of clinical and laboratory parameters: gender, age, body mass index, duration of diabetes mellitus, etc.) and a set of corresponding output data (variables reflecting the state of bone metabolism: bone mineral density, markers of bone remodeling).Results. …”
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Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest by Hindayati Mustafidah, Suwarsito Suwarsito, Rahmat Setiawan, Abdul Karim

“…Feature extraction was applied to capture color characteristics (HSV), texture patterns (GLCM), and morphological features (regionprops). The model was trained on 70% of the dataset and tested on the remaining 30%. …”
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Crowd Evacuation in Stadiums Using Fire Alarm Prediction by Afnan A. Alazbah, Osama Rabie, Abdullah Al-Barakati

“…This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. …”
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Bidirectional Relationship Between Hypoalbuminemia and Postoperative Pneumonia in Elderly Hip Fracture Patients: A Retrospective Cohort Study by Wang J, Yu H, Xu X, Guo J

“…After predefined participants selection inclusion and exclusion criteria, 1661 surgically treated HF patients were included and analyzed utilizing multiple statistical models, including univariate logistic regression, Lasso regression, and Boruta algorithm for variable selection, while multivariate logistic regression and propensity score matching (PSM) for assess the bidirectional relationship between hypoalbuminemia and postoperative pneumonia. …”
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Deep learning assisted analysis of biomarker changes in refractory neovascular AMD after switch to faricimab by Michael Hafner, Franziska Eckardt, Jakob Siedlecki, Benedikt Schworm, Tina R. Herold, Ben Asani, Siegfried G. Priglinger, Johannes B. Schiefelbein

“…Future research should focus on refining AI models to improve predictive accuracy and assessing long-term outcomes to further optimize disease management. …”
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Fourier Analysis of CMFD Method in Cylindrical Geometry by WEN Yuchen, HAO Chen, WANG Yizhen

“…The coarse mesh finite difference (CMFD) method has been widely adopted as an acceleration algorithm for neutron transport calculations, effectively reducing computational costs and improving convergence. …”

Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis by Iyad Almadani, Aaron L. Robinson, Mohammed Abuhussein

“…Many researchers focus on improving reproductive health in sows and ensuring successful breeding by accurately identifying the optimal time of ovulation through estrus detection. …”
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Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…This framework holds transformative potential for pediatric endocrinology and orthopedics by standardizing BAA, improving diagnostic equity, and optimizing resource use. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. …”
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