SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification by Bin Wang, Gongchao Chen, Juan Wen, Linfang Li, Songlin Jin, Yan Li, Ling Zhou, Weidong Zhang

“…However, due to the large volume and complex features of hyperspectral corn image data, existing methods often fall short in feature extraction and utilization, leading to low classification accuracy. To address these issues, this paper proposes a spectral-spatial attention transformer network (SSATNet) for hyperspectral corn image classification. …”
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Analysis of breast cancer classification and segmentation techniques: A comprehensive review by Malaya Kumar Nath, Kohilavani Sundararajan, Shanmathi Mathivanan, Bhagyashree Thandapani

“…This study identifies ongoing issues essential for driving future advancements in BC detection and segmentation to improve clinical outcomes.…”
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A classification and summarization method for analysis of research activities in an academic faculty by Eduardo Rocha Loures, Yongxin Liao, Osiris Canciglieri Junior

“…The investigation of the existing issues in the current research activities is usually considered as the primary and challenging step. …”
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Radio frequency based distributed system for noncooperative UAV classification and positioning by Chaozheng Xue, Tao Li, Yongzhao Li

“…With the increasing popularity of civilian unmanned aerial vehicles (UAVs), safety issues arising from unsafe operations and terrorist activities have received growing attention. …”
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MSA K-BERT: A Method for Medical Text Intent Classification by Yujia Yuan, Guan Xi

“…Improving medical text intent classification accuracy can assist the medical field in achieving more precise diagnoses. …”
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Multiple Classifier System for Handling Imbalanced and Overlapping Datasets on Multiclass Classification by Dessy Siahaan, Anwar Fitrianto, Khairil Anwar Notodiputro

“…The performance of classification models suffer when the dataset contains imbalanced and overlapping data. …”
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Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging by Anh T. Tran, Tal Zeevi, Seyedmehdi Payabvash

“…The technical strategies summarized in this review can enhance the robustness and generalizability of deep learning models for segmentation and classification to improve their reliability for real-world clinical practice.…”
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A comprehensive dataset of mandarin leaf images for classificationMendeley Data by Mushfiqur Rahman, Imtiaz Ahmed, Mahin Ahmed

“…The research is devoted to mandarin leaf classification using a deep learning approach. Citrus cultivation has a big market in Bangladesh. …”
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Increasing the Number of Sciences: Logical Foundations of the Classification of the Sciences and the Separation of the Sciences by Eşref Altaş

“…The apodictic background of the distinctions in the form of whole–partial sciences, primary-secondary sciences, universal-particular sciences, which all emerged depending on the generality-specificity relationship between the subjects and issues of sciences, is revealed. Problems related to the classification and hierarchy of science, such as how sciences differ from each other in terms of their subjects, whether it is possible to separate sciences from their methods, and whether it is possible to have two different sciences dealing with the same subject, are discussed. …”
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XAI-FruitNet: An explainable deep model for accurate fruit classification by Shirin Sultana, Md All Moon Tasir, S.M. Nuruzzaman Nobel, Md Mohsin Kabir, M.F. Mridha

“…In agricultural technology, precise fruit classification is essential yet challenging due to inherent interclass similarities and intra-class variabilities among fruit species. …”
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Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges by Tomer Raitsis, Yossi Elgazari, Guy E. Toibin, Yotam Lurie, Shlomo Mark, Oded Margalit

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Taxonomic Robot Identifiers: Toward General Classification and Oversight for Autonomous Systems by Satoru Isaka

“…It first examines in depth the current industry standards and academic literature, identifying major issues in building an effective taxonomy. To resolve these problems, the paper establishes new fundamental principles to classify broad classes of machines and develops a coding system for consistent naming, identification, classification, and organization. …”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

“…The emergence of many new malware variants has resulted in relatively poor detection accuracy using traditional methods. To address issues such as poor accuracy and the inability to effectively handle malware obfuscation, this paper proposes a malware classification method based on improved ResNet50 and improved VGG16 feature fusion (MC-KDFF). …”
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Central Pixel-Based Dual-Branch Network for Hyperspectral Image Classification by Dandan Ma, Shijie Xu, Zhiyu Jiang, Yuan Yuan

“…Hyperspectral image classification faces significant challenges in effectively extracting and integrating spectral-spatial features from high-dimensional data. …”
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A Systematic Review of AI-Based Techniques for Automated Waste Classification by Farnaz Fotovvatikhah, Ismail Ahmedy, Rafidah Md Noor, Muhammad Umair Munir

“…We further present an in-depth review of over fifteen publicly available waste classification datasets, highlighting key limitations such as dataset imbalance, real-world variability, and standardization issues. …”
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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

“…In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). …”
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Robust multi-label surgical tool classification in noisy endoscopic videos by Adnan Qayyum, Hassan Ali, Massimo Caputo, Hunaid Vohra, Taofeek Akinosho, Sofiat Abioye, Ilhem Berrou, Paweł Capik, Junaid Qadir, Muhammad Bilal

“…In this paper, we propose a systematic methodology for developing robust models for surgical tool classification using noisy endoscopic videos. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts through collective intelligence; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. …”
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Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods by Kalina Kitova, Ivan Ivanov, Vincent Hooper

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Automatic Detection of Laparoscopic Videos Distortion Using Machine Learning Classification by Mohamed Belmokeddem, Kamila Khemis, Salim Loudjedi

“…Real-time laparoscopic video frequently encounters issues such as blur and smoke, often stemming from lens contamination. …”
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Adaptive Spectral Correlation Learning Neural Network for Hyperspectral Image Classification by Wei-Ye Wang, Yang-Jun Deng, Yuan-Ping Xu, Ben-Jun Guo, Chao-Long Zhang, Heng-Chao Li

“…To address these issues, a new adaptive spectral-correlation learning neural network (ASLNN) is proposed for HSI classification. …”
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