Enhancing land cover object classification in hyperspectral imagery through an efficient spectral-spatial feature learning approach. by Masud Ibn Afjal, Md Nazrul Islam Mondal, Md Al Mamun

“…While 3D CNNs can capture joint spectral-spatial information, they often encounter issues related to network depth and complexity. To address these issues, we propose an innovative land cover object classification approach in HSIs that integrates segmented principal component analysis (Seg-PCA) with hybrid 3D-2D CNNs. …”
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Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN by Burak Duran, Saeed Eftekhar Azam, Masoud Sanayei

“…The model showed a reduction in accuracy percentage when adapting from a Single-Source Domain to Multiple-Target Domains, revealing potential issues with non-homogeneous data distribution and catastrophic forgetting. …”
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A Rule Based Feature Selection Approach for Target Classification in Wireless Sensor Networks with Sensitive Data Applications by Zhiyong Hao, Bin Liu

“…One of the important issues faced in the domain of target classification in wireless sensor networks is the restricted lifetime of individual sensors, caused by limited battery capacity. …”
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Ageing of Society — Local Disparities. Classification of the Pomorskie Voivodship Communes According to the Pace and Progress of Local Communities’ Ageing by Anita Richert-Kaźmierska

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Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost by Nabila Ayunda Sovia, Ni Wayan Surya Wardhani, Eni Sumarminingsih, Elvo Ramadhan Shofa

“…Classifying imbalanced datasets presents significant challenges, often leading to biased model performance, particularly in multiclass classification. This study addresses these issues by integrating Convolutional Neural Networks (CNN) and XGBoost, leveraging CNN’s exceptional feature extraction capabilities and XGBoost's robust handling of imbalanced data. …”
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Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model by Yang He, Shah Nazir, Baisheng Nie, Sulaiman Khan, Jianhui Zhang

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CoGraphNet for enhanced text classification using word-sentence heterogeneous graph representations and improved interpretability by Pengyi Li, Xueying Fu, Juntao Chen, Junyi Hu

“…In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues. To overcome information loss, we construct separate heterogeneous graphs for words and sentences, capturing multi-tiered contextual information. …”
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A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. by Yuping Yin, Haodong Zhu, Lin Wei

“…To address these issues, this paper proposes a lightweight multi-layer feature fusion classification method for hyperspectral images based on spatial and channel reconstruction (SCNet). …”
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Classification of possible solutions regarding business engineering problems by using complex Pythagorean fuzzy rough WASPAS approach by Tahir Mahmood, Walid Emam, Jabbar Ahmmad, Muhammad Iftikhar, Ubaid ur Rehman, Dragan Pamucar

“…Moreover, we have provided an illustrative example for the classification of the solutions regarding the problems in business engineering. …”
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Enhancing pathological feature discrimination in diabetic retinopathy multi-classification with self-paced progressive multi-scale training by Qiuji Zhou, Yongde Guo, Wenjian Liu, Yifeng Liu, Yanzhen Lin

“…Additionally, ensemble learning with Kullback–Leibler (KL) divergence-based collaborative regularization improves classification consistency. The method’s effectiveness is demonstrated through experiments on the integrated APTOS and MESSIDOR-Kaggle dataset, achieving an AUC of 0.9907 in 4-class classification, marking a 2.2% improvement compared to the ResNet-50 baseline. …”
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Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network by Yuanhua Fu, Zhiming He

“…Hence, it is vital to develop an effective method of identifying drones to address the above issues. Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. …”
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A Principal Component Analysis-Based Feature Optimization Network for Few-Shot Fine-Grained Image Classification by Meijia Wang, Boyuan Zheng, Guochao Wang, Junpo Yang, Jin Lu, Weichuan Zhang

“…Extensive experiments validate the effectiveness of MFSM, revealing substantial improvements in classification accuracy for few-shot fine-grained image classification (FSFGIC) tasks.…”
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A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis by V. Balachandar, K. Venkatesh

“…It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. …”
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Classification of Precipitation Types and Investigation of Their Physical Characteristics Using Three-Dimensional S-Band Dual-Polarization Radar Data by Choeng-Lyong Lee, Wonbae Bang, Chia-Lun Tsai, GyuWon Lee

“…A novel classification algorithm for precipitation types (CP) was developed to address frequent misclassification issues between shallow convection and intense stratiform precipitation using existing methods and to enhance an understanding of their physical characteristics. …”
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Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. …”
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SADASNet: A Selective and Adaptive Deep Architecture Search Network with Hyperparameter Optimization for Robust Skin Cancer Classification by Günay İlker, İnik Özkan

“…<b>Conclusions:</b> With these outcomes, this method aims to enhance the classification of skin cancer and contribute to the advancement of deep learning.…”
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MWMOTE-FRIS-INFFC: An Improved Majority Weighted Minority Oversampling Technique for Solving Noisy and Imbalanced Classification Datasets by Dong Zhang, Xiang Huang, Gen Li, Shengjie Kong, Liang Dong

“…In addition, the integration of classification fusion iterative filters (INFFC) helps mitigate synthetic noise issues, both raw data and synthetic data noise. …”
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Literatura w grach wideo. Rozeznanie wstępne by Mikołaj Ołownia

“… This article is the first attempt to describe the issue of using literary texts in role-playing video games. …”
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CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning by Yun Chu, Qiuhao Wang, Enze Zhou, Ling Fu, Qian Liu, Gang Zheng

“…Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. …”
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Summarizing Recent Developments on Autism Spectrum Disorder Detection and Classification Through Machine Learning and Deep Learning Techniques by Masroor Ahmed, Sadam Hussain, Farman Ali, Anna Karen Gárate-Escamilla, Ivan Amaya, Gilberto Ochoa-Ruiz, José Carlos Ortiz-Bayliss

“…Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. …”
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