Remote Sensing Image Scene Classification Based on Mutual Learning With Complementary Multi-Features by Anzhi Chen, Mengyang Xu

“…A novel neural network model based on mutual learning, with complementary multi-features (MLCMFNet), is proposed for scene classification, addressing common issues with insufficient extraction to more effectively learn target features from remote sensing images. …”
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ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

“…Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. …”
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Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis by D. Dhinakaran, L. Srinivasan, S. Edwin Raja, K. Valarmathi, M. Gomathy Nayagam

“…Since the medical datasets are large and complex, they give rise to certain problematic issues such as computational complexity, limited memory space, and a lesser number of correct classifications. …”
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Enhancing agricultural research with an Attention-Based Hybrid Model for precise classification of rice varieties by Nuzhat Noor Islam Prova

“…As a staple food feeding over half of the world’s population, rice needs well defined classification techniques to improve agricultural yields, the supply chain, and food safety. …”
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Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds by Abdul Rachman Manga', Herawati Herawati, Purnawansyah Purnawansyah

“…The YOLOv9 model was trained across 90 epochs, aiming to achieve high accuracy in breed detection and classification. The evaluation results demonstrate the model's strong performance, achieving a precision of approximately 0.9 and a recall of 0.8. …”
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ECGNet: High-Precision ECG Classification Using Deep Learning and Advanced Activation Functions by Muhammad Abubakar, Ahsan Raza, Arfan Ali Nagra, Anas Bilal, Ahmad Alshammari, Monir Abdullah, Sahraoui Dhelim

“…Additionally, the Modified SparseMax classifier is employed to enhance the classification by mapping logits to sparse probability distributions. …”
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Margin weighted robust discriminant score for feature selection in imbalanced gene expression classification. by Sheema Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan

“…High-dimensional gene expression data poses significant challenges for binary classification, particularly in the context of feature selection methods. …”
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Waveshift Augmentation: A Physics-Driven Strategy in Fine-Grained Plant Disease Classification by Gent Imeraj, Hitoshi Iyatomi

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An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks by Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana, Tarequl Islam, Mohammad Motiur Rahman, Hani Alshahrani, Adel Sulaiman, Mana Saleh Al Reshan

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The Classification of Metastatic Spine Cancer and Spinal Compression Fractures by Using CNN and SVM Techniques by Woosik Jeong, Chang-Heon Baek, Dong-Yeong Lee, Sang-Youn Song, Jae-Boem Na, Mohamad Soleh Hidayat, Geonwoo Kim, Dong-Hee Kim

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Image-Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches by Lama A. Aldakhil, Haifa F. Alhasson, Shuaa S. Alharbi, Rehan Ullah Khan, Ali Mustafa Qamar

“…This survey article explores the complexities inherent in multiclass classification for breast cancer diagnosis, aiming to improve patient care, efficiency, and timeliness. …”
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Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking by Nafa Nur Adifia Nanda, Yuniar Farida, Wika Dianita Utami

“…Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. …”
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Neural Architecture Search for Hyperspectral Image Classification: A Comprehensive Review and Future Perspectives by Aili Wang, Xinyu Liu, Kang Zhang, Haoran Lv, Haibin Wu, Xing Chen, Manman Yao

“…Although deep learning significantly improves classification performance through automatic feature extraction, manually designed network architectures suffer from issues such as dependence on expert experience and lack of flexibility. …”
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MFN: Multi-Scale Frequency Feature Fusion Network for Multi-Classification Image Segmentation by Ji Xiao, Li Jianfang, Zhao Peng, Li Xiaochen, Zhang Chengchun, Zheng Junyi, Pang Yonghui, Huang Xiangsheng

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XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones by Qian Sun, Jie Zeng, Lulu Dai, Yangliu Hu, Lin Tian

“…The internal capture processes and matching results of DL models are useful for addressing these issues. The key challenges involve obtaining this internal information from DL-based anomaly detection methods, using this internal information to establish new classifications for uncovered protocol attacks and tracing the input back to the anomalous protocol sequences. …”
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An innovative approach to advanced voice classification of sacred Quranic recitations through multimodal fusion by Esraa Hassan, Abeer Saber, Omar Alqahtani, Nora El-Rashidy, Samar Elbedwehy

“…Compared with the traditional voice classification strategies, VSCF aims at solving issues regarding limitations of the adopted datasets and variations among different reciters. …”
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Preprocessing-Free Convolutional Neural Network Model for Arrhythmia Classification Using ECG Images by Chotirose Prathom, Ryuhi Fukuda, Yuto Yokoyanagi, Yoshifumi Okada

“…Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, which effectively capture the patterns associated with different abnormalities and achieve high classification performance. However, these models face challenges in terms of input coverage and robustness against data imbalance issues. …”
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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

“…To address these issues, this paper proposes an Iterative Pixel-Based Dimensional Voting model (Pixel-DMV), which enhances classification accuracy by iteratively analyzing pixel similarities within 3 x 3 neighborhood pixels. …”
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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…The model demonstrated a log loss of 0.18 and outstanding performance on new metrics, including Weighted Error Impact Score (WEIS), Dynamic Class Stability Index (DCSI), and Harmonized Classification Risk (HCR), signifying its effectiveness. …”
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Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference by Haibin Zhu, Yaxin Mu, Wupeng Xie, Kang Xing, Bin Tan, Yashi Zhou, Zhongde Yu, Zhiying Cui, Chuang Zhang, Xin Liu, Zhenghuan Xia

“…To address the aforementioned issues, a novel classification framework for sidelobe-affected SAR imagery is proposed. …”
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