Football sports video tracking and detection technology based on YOLOv5 and DeepSORT by Bin Wang

“…The outcomes indicated that the average accuracy value of the improved YOLOv5 model for target detection was more than 90%, which effectively reduced the number of computational parameters. The detection performance under target overlap and uneven lighting and shadows exceeded 90%, and the difference between the algorithm and other algorithms was at least greater than 2%. …”
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The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis by Zeyu Chen, Zheng Lin, Zihan Lin, Qi Zhang, Haoyun Zhang, Haiwen Li, Qing Chang, Jianqi Sun, Feng Li

“…Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. …”
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Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attribu... by Seid Mohammad Alavi-Siney, Jalal Saba, Alireza Fotuhi Siahpirani, Jaber Nasiri

“…Based on the results of Leave-One-Out Cross-Validation (LOOCV), various prediction values were computed for all 10 classifiers of LDA, QDA, FDA, MDA, RDA, Naive Bayes, Decision Tree, Linear SVM, Radial SVM, and Random Forest in terms of accuracy, sensitivity and specificity parameters. …”
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Advancing Skin Disease Diagnosis: A Multimodal Approach Utilizing Telegram Api Token Chatbot for Text and Image Analysis in Skin Disease Classification by Modigari Narendra, T. S. Harshini, L. Jani Anbarasi

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Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome by Satya P. Singh, Sukrit Gupta, Jagath C. Rajapakse

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YOLOv5 garbage classification method with GhostNet by Li Yao, Hu Junguo, Le Yang

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Optimizing Monkeypox Lesions Detection With a Lightweight Hybrid Model by Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Abduljabbar S. Ba Mahel, Ahmed Aziz, Mashael M. Khayyat, Ammar Muthanna

“…Existing methods often rely on large architectures that demand high computational resources and lack interpretability. To address these issues, we propose a lightweight, interpretable architecture that truncates MobileNetV2 for efficient feature extraction, incorporates Ghost Modules to reduce redundancy, and integrates a SA-MobileViT block to capture both local and global dependencies in lesion images. …”
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BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection by Long Guo, Lubin Wang, Qiang Yu, Xiaolan Xie

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Using Wearable Sensors for Sex Classification and Age Estimation from Walking Patterns by Rizvan Jawad Ruhan, Tahsin Wahid, Ashikur Rahman, Abderrahmane Leshob, Raqeebir Rab

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Numerical and experimental investigation of piezoelectric-pneumatic material jet printing method for high-viscosity ink by Xinyi Hu, Xiaoran Dong, Zhanda Li, Junhui Long, Yuan Jin, Hui Li

“…In this work, a two-dimensional computational fluid dynamics model is presented to elucidate the multiphase aerodynamic phenomenon and deposition morphology of jet printing features. …”
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Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning by Amin Abdollahi Dehkordi, Mehdi Neshat, Alireza Khosravian, Menasha Thilakaratne, Ali Safaa Sadiq, Seyedali Mirjalili

“…Abstract Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. …”
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A One-Dimensional Depthwise Separable Convolutional Neural Network for Bearing Fault Diagnosis Implemented on FPGA by Yu-Pei Liang, Hao Chen, Ching-Che Chung

“…The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load. …”
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Image Classification Model Based on Contrastive Learning With Dynamic Adaptive Loss by Quandeng Gou, Jingxuan Zhou, Zi Li, Fangrui Zhang, Yuheng Ren

“…Notably, the model achieves high classification accuracy while maintaining a relatively low parameter size (23.9MB) and computational complexity (5.7Mflops). …”
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Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information by Liang Xu, Emily Zakem, JL Weissman

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The quartic anharmonic oscillator – an oscillator-basis expansion approach. II. Study of the wave functions and acceleration of the expansions convergence by V. A. Babenko, A. V. Nesterov

“…The method we propose for studying the model, based on expanding the system's wave function in a complete set of harmonic oscillator eigenfunctions, facilitates a thorough analysis and evaluation of all parameters and features of the corresponding quantum systems. …”
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URT-YOLOv11: A Large Receptive Field Algorithm for Detecting Tomato Ripening Under Different Field Conditions by Di Mu, Yuping Guou, Wei Wang, Ran Peng, Chunjie Guo, Francesco Marinello, Yingjie Xie, Qiang Huang

“…These factors often hinder accurate feature extraction, leading to recognition errors and reduced computational efficiency. …”
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Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation by Zeguo Zhang, Zeguo Zhang, Zeguo Zhang, Liang Cao, Liang Cao, Liang Cao, Jianchuan Yin, Jianchuan Yin, Jianchuan Yin

“…Key components include: (1) IPCA preprocessing to reduce dimensionality and noise in 2D wind field data; (2) depthwise-separable convolution (DSC) blocks to minimize parameters and computational costs; (3) multi-head attention (MHA) and residual mechanisms to improve spatial-temporal feature extraction and prediction accuracy. …”
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FL-DBENet: Double-branch encoder network based on segment anything model for farmland segmentation of large very-high-resolution optical remote sensing images by W. Feng, F. Guan, C. Sun, W. Xu, W. Xu

“…To further streamline the model, we integrate a Low-Rank Adaptation (LoRA) module into SAM’s image encoder, reducing training parameters and computational demands. Additionally, a prompt mixer module is developed to integrate diverse features effectively. …”
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Rotten strawberry classification based on EfficientNet V2 algorithm fused with GCN and CA-Transformer by WANG Wei, YANG Shizhong, GONG Yucheng, GAO Sheng, DENG Zhaopeng

“…Finally, learning parameters were introduced on the basis of the traditional residual structure to achieve dynamic feature fusion.ResultsThe GC-EfficientNet V2 model improved the accuracy by 1.86% and the recall by 1.49% compared to the baseline model. …”
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A Hybrid Strategy for Forward Kinematics of the Stewart Platform Based on Dual Quaternion Neural Network and ARMA Time Series Prediction by Jie Tao, Huicheng Zhou, Wei Fan

“…The DQ-BPNN is partitioned into real and dual parts, composed of parameters such as driving-rod lengths, maximum and minimum lengths, to extract more features. …”
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