Classification of progressive banking products in optimal ranges in order to create of banking product-transformer for increasing efficiency of banking activity by E. V. Boganova

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AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification by Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang

“…However, the issues of feature extraction effectiveness, model complexity, and model design uncertainty constrain the further development of time series classification. …”
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Classification of power quality disturbances in microgrids using a multi-level global convolutional neural network and SDTransformer approach. by Junzhuo Jiang, Hao Wu, Changhua Zhong, Hong Song

“…This approach exhibits strong resistance to noise and enhanced generalization skills, markedly improving the detection accuracy of power quality issues within microgrids.…”
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A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification by 𝐒𝐢𝐫𝐚𝐣𝐮𝐬 𝐒𝐚𝐥𝐞𝐡𝐢𝐧, Shakila Rahman, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐍𝐮𝐫, 𝐀𝐡𝐦𝐚𝐝 𝐀𝐬𝐢𝐟, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐁𝐢𝐧 𝐇𝐚𝐫𝐮𝐧, JIA UDDIN

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Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification by Weichao Yang

“…To address the issues of accuracy and generalization in action recognition within complex tennis training scenarios, this study proposes an Adaptive Semantic-Enhanced Convolutional Neural Network (ASE-CNN) model. …”
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HierLabelNet: A Two-Stage LLMs Framework with Data Augmentation and Label Selection for Geographic Text Classification by Zugang Chen, Le Zhao

“…However, the effectiveness of traditional classification approaches is hindered by several issues, including data sparsity, class imbalance, semantic ambiguity, and the prevalence of domain-specific terminology. …”
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A Comparative Analysis of Convolutional Neural Network (CNN): MobileNetV2 and Xception for Butterfly Species Classification by Mehta Pradnyatama, Christy Atika Sari, Eko Hari Rachmawanto, Hussain Md Mehedul Islam

“…Transfer learning with pre-trained ImageNet weights was implemented, and both models were enhanced with custom classification layers. Data augmentation and class weighting mitigated dataset imbalance issues. …”
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CLASSIFICATION OF MICROCONTROLLER INTEGRATED CIRCUIT ON THE POCKET OF JEDEC TRAY USING CONVOLUTIONAL NEURAL NETWORK IN EMBEDDED MACHINE LEARNING SYSTEM by Mark M. Pallones, King Harold A. Recto

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RE-BPFT: An Improved PBFT Consensus Algorithm for Consortium Blockchain Based on Node Credibility and ID3-Based Classification by Junwen Ding, Xu Wu, Jie Tian, Yuanpeng Li

“…To address issues related to centralization risk in leader selection, we introduce a weighted random primary node election mechanism. …”
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GAPNet: Single and multiplant leaf disease classification method based on simplified SqueezeNet for grape, apple and potato plants by Özge Nur Özaras, Asuman Günay Yılmaz

“…For this reason, many studies have been carried out on plant leaf disease classification. In this study, a simple and effective leaf disease classification method was developed. …”
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Fusion of non-iterative deep neural network feature extraction with kernel extreme learning machine for plant disease classification by Kirti Kirti, Navin Rajpal, Virendra P. Vishwakarma, Pramod Kumar Soni

“…In this work, a novel hybrid approach is proposed using a ResNet-50 based deep neural network integrated with a Kernel Extreme Learning Machine (KELM) classifier for efficient and accurate plant disease classification. Unlike conventional deep learning methods that rely on iterative training and heavy resources, our approach uses a non-iterative, single-pass KELM classifier to significantly reduce computational complexity while maintaining high classification performance. …”
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Hyperspectral Image Few-Shot Classification Based on Spatial–Spectral Information Complementation and Multilatent Domain Generalization by Qianhao Yu, Yong Wang

“…Hyperspectral image (HSI) few-shot classification aims to classify HSI samples of novel categories with limited training HSI samples of base categories. …”
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Identification and classification of risk factors for mental health problems in healthcare workers using a systemic framework: an umbrella review by Payam Khanlari, AhmadAli NoorbalaTafti, Fakhradin Ghasemi, Shahrzad Ghiyasvandian, Kamal Azam, Seyed Abolfazl Zakerian

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FldtMatch: Improving Unbalanced Data Classification via Deep Semi-Supervised Learning with Self-Adaptive Dynamic Threshold by Xin Wu, Jingjing Xu, Kuan Li, Jianping Yin, Jian Xiong

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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

“…Currently, convolutional neural network (CNN) and transformer-based hyperspectral image (HSI) classification methods have attracted significant attention owing to their effective feature representation capabilities. …”
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TongueNet: a multi-modal fusion and multi-label classification model for traditional Chinese Medicine tongue diagnosis by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Xinliang Lü

“…Experimental results demonstrate that TongueNet outperforms existing models in both disease nature and disease location classification tasks. Specifically, in the disease nature classification task, it achieves 89.12% accuracy and an AUC of 83%; in the disease location classification task, it achieves 86.47% accuracy and an AUC of 81%. …”
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Comparative analysis of data-driven models on detection and classification of electrical faults in transmission systems: Explainability, applicability and industrial implications by Chibueze D. Ukwuoma, Dongsheng Cai, Chiagoziem C. Ukwuoma, Chinedu I. Otuka, Qi Huang

“…Using a publicly available simulated dataset generated through MATLAB Simulink to represent various fault types, the QDA-P model achieved binary classification scores of 0.988, 0.980, 0.982, 0.995, and 0.987 while recording a multi-class classification score of 0.982, 0.979, 0.982, 0.982, and 0.980 for accuracy, precision, specificity, recall, and F1-score, respectively. …”
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Synthesis of an Algorithm for Classifying Underwater Objects on Based on the Analysis of their Noise Spectra by Evgeniy Storozhok, Vladimir Kristalinskii

“…In the presented classification algorithm, harmonic components of the noise spectrum of an underwater object are used as classification features. …”
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Employees’ Satisfaction and Sentiment Analysis toward BERSATU Application by Ayub Prasetyo, Hotniar Siringoringo, Firda Amalia

“…Based on the evaluated five algorithms classification, SVM and Naïve Bayes were superior with accuracy above 97% with better F1-Score. …”
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Cardiovascular-kidney-metabolic syndrome — a new concept of the association of risk factors and metabolic disorders within the cardiovascular and renal continuum. View on the issue... by G. G. Arabidze, M. N. Mamedov

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