Automatic Classification of Online Learner Reviews Via Fine-Tuned BERTs by Xieling Chen, Di Zou, Haoran Xie, Gary Cheng, Zongxi Li, Fu Lee Wang

“…This study investigated the effectiveness of fine-tuned BERT models for the automated classification of topics in online course reviews and explored the variations of these topics across different disciplines and course rating groups. …”
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Supervised methods of machine learning for email classification: a literature survey by Muath AlShaikh, Yasser Alrajeh, Sultan Alamri, Suhib Melhem, Ahmed Abu-Khadrah

“…This review delves into issues concerning spam filtering and email classification through supervised machine learning techniques, offering a comprehensive evaluation of strategies, methods, performance indicators, and the benefits and drawbacks of different research. …”
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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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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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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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SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs by Faima Abbasi, Muhammad Muzammal, Qiang Qu, Farhan Riaz, Jawad Ashraf

“…The study of attributed networks is challenging due to noise, sparsity, and class imbalance issues. In this work, we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks. …”
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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 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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Enhanced remote sensing image feature classification using STFF-PSPNet by Haiying Li, Jiaqi Gao, Yang Liu, Chenxi Huang, Lijun Li, Liqiang Zhang

“…Abstract Semantic segmentation of remotely sensed images is crucial for urban planning and change detection, yet faces issues like sample imbalance and low data quality. …”
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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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Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification by Wenchen Chen, Yanmei Zhang, Jianping Chu, Xingbo Wang

“…Although existing FSL methods improve classification performance by enhancing domain invariance through domain adaptation, they often overlook the critical issue of high inter-class similarity and large intra-class variability. …”
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TN5000: An Ultrasound Image Dataset for Thyroid Nodule Detection and Classification by Huan Zhang, Qianglin Liu, Xiaolin Han, Lijuan Niu, Weidong Sun

“…With the emergence of artificial intelligence, deep learning based methods can provide assistance to radiologists, whose performance heavily depends on the quantity and quality of training data, but current ultrasound image datasets for thyroid nodule either directly utilize the TI-RADS assessments as labels or are not publicly available. Faced with these issues, an open-access ultrasound image dataset for thyroid nodule detection and classification is proposed, i.e. the TN5000, which comprises 5,000 B-mode ultrasound images of thyroid nodule, as well as complete annotations and biopsy confirmations by expert radiologists. …”
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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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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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Cross-Modal Compositional Learning for Multilabel Remote Sensing Image Classification by Jie Guo, Shuchang Jiao, Hao Sun, Bin Song, Yuhao Chi

“…However, most existing methods struggle to effectively integrate visual features of images and high-level semantic information of labels, which limits their ability to obtain fine-grained image features with rich semantic information for classification. To address these issues, we propose a novel cross-modal compositional learning (CMCL) model for MLRSIC, which fully utilizes label information to improve classification performance. …”
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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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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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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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A Combined CNN-LSTM Network for Ship Classification on SAR Images by Abdelmalek Toumi, Jean-Christophe Cexus, Ali Khenchaf, Mahdi Abid

“…This study underscores the potential of customized architectures to address SAR-specific challenges and enhance the efficiency of target classification.…”
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