GMTBLC: a deep learning-based bi-modal network traffic classification method by WEI Debin, JIANG Qinlong, WEN Jinglong, WANG Xinrui

“…To address the issues of traditional traffic classification models, such as insufficient feature extraction and low classification accuracy, a dual-modal network traffic classification method based on group mix attention (GMA) with a transformer and a bi-directional long short-term memory (Bi-LSTM) network, named group mix transformer and Bi-LSTM for traffic classification (GMTBLC), was proposed. …”
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Enhancing malaria detection and classification using convolutional neural networks-vision transformer architecture by Emmanuel Ahishakiye, Fredrick Kanobe, Danison Taremwa, Bartha Alexandra Nantongo, Leonard Nkalubo, Shallon Ahimbisibwe

“…More so, existing machine learning models used in malaria detection and classification have low performance and overfitting issues. …”
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Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification by Yasin Kaya, Ezgisu Akat, Serdar Yıldırım

“…However, the manual classification of brain tumors is a laborious and complex task. …”
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Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) by Nabila Henisaniyya, Citra Pertiwi, Anita Desiani, Ali Amran, Muhammad Arhami

“…One commonly applied method for early detection involves classification using a data mining approach. Among the algorithms frequently used for classification are the ID3 algorithm and Artificial Neural Networks (ANN). …”
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Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2. by Praveen Talari, Bharathiraja N, Gaganpreet Kaur, Hani Alshahrani, Mana Saleh Al Reshan, Adel Sulaiman, Asadullah Shaikh

“…Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. …”
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Breaking barriers in ICD classification with a robust graph neural network for hierarchical coding by Suyang Xi, Jiesen Shi, Jiachen Yan, MingJing Lin, Xinyi Zhou, Yuan Cheng, Hong Ding, Chia Chao Kang

“…Abstract The accurate classification of International Classification of Diseases (ICD) codes is a complex and critical multi-label task in clinical documentation, involving the assignment of diagnostic codes to medical discharge summaries. …”
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Stanislav Voronin’s Universal Classification of Onomatopoeic Words: a Critical Approach (Part 1) by M. A. Flaksman

“…Introduction. The universal classification of onomatopoeic words was first introduced in 1969 by Stanislav V. …”
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A dual-branch model combining convolution and vision transformer for crop disease classification. by Qingduan Meng, Jiadong Guo, Hui Zhang, Yaoqi Zhou, Xiaoling Zhang

“…To address these issues, this paper proposes a dual-branch model for crop disease classification, which combines Convolutional Neural Network (CNN) with Vision Transformer (ViT). …”
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Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors by Vatsala Anand, Ajay Khajuria, Rupendra Kumar Pachauri, Vinay Gupta

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Hotspots Identification and Classification of Dockless Bicycle Sharing Service under Electric Fence Circumstances by Ying Hui, Yingkun Xie, Qing Yu, Xiaolei Liu, Xinyuan Wang

“…In this paper, a novel methodology of bicycle hotspots identification and classification is proposed to support parking management. …”
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Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods by Arizadayana Zahalan, Samila Mat Zali, Ernie Che Mid, Noor Fazliana Fadzail

“…This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. …”
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MCDGMatch: Multilevel Consistency Based on Data-Augmented Generalization for Remote Sensing Image Classification by Pingping Liu, Xiaofeng Liu, Zetong Liu, Haodong Li, Qiuzhan Zhou

“…The exponential growth of remote sensing image data and the high cost of manual annotation have led to insufficient labeled data, limiting classification performance. Semi-supervised methods can address this issue, but most existing approaches lack multilevel consistency constraints in both embedding space and prediction probabilities, resulting in weak feature expressiveness. …”
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LED array-based multi-angle light scattering for aspirating smoke detection and classification by Soocheol Kim, Hoesung Yang, Kwangsoo Cho, Kyuwon Han, Yusun Ahn, Jin Hwa Ryu, Kangbok Lee

“…The feasibility of smoke detection and classification was verified by evaluating the classification performance of 10 types of fire and non-fire aerosols using general supervised learning algorithms. …”
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Efficient Attention Transformer Network With Self-Similarity Feature Enhancement for Hyperspectral Image Classification by Yuyang Wang, Zhenqiu Shu, Zhengtao Yu

“…Moreover, self-attention operations in transformer-based HSIC methods may introduce irrelevant spectral–spatial information, and thus may consequently impact the classification performance. To mitigate these issues, in this article, we introduce an efficient deep network, named efficient attention transformer network (EATN), for practice HSIC tasks. …”
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Orga-Dete: An Improved Lightweight Deep Learning Model for Lung Organoid Detection and Classification by Xuan Huang, Qin Gao, Hanwen Zhang, Fuhong Min, Dong Li, Gangyin Luo

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The Effects of the COVID-19 Pandemic on Abnormal Uterine Bleeding According to the PALM-COEIN Classification by Onur Bektas, Kivilcim Bektas, Sirin Aydin, Gulsen Dogan Durdag, Husnu Celik

“… OBJECTIVE: To investigate the impact of the COVID-19 pandemic on abnormal uterine bleeding (AUB) according to the PALM-COEIN classification. STUDY DESIGN: Patients who underwent surgical intervention due to AUB were categorized according to the PALM-COEIN classification, and the pandemic period was compared with the pre-pandemic period. …”
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Breast Cancer Classification With Enhanced Interpretability: DALAResNet50 and DT Grad-CAM by Suxing Liu, Galib Muhammad Shahriar Himel, Jiahao Wang

“…Automatic classification of breast cancer in histopathology images is crucial for accurate diagnosis and effective treatment planning. …”
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The Effect of SMOTE and Optuna Hyperparameter Optimization on TabNet Performance for Heart Disease Classification by Danang Wijayanto, Robert Marco, Acihmah Sidauruk, Mulia Sulistiyono

“…While numerous studies have explored various approaches for heart disease classification, challenges related to data imbalance and improper parameter settings remain persistent issues that affect model performance. …”
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Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks by Suzhao Bi, Rongjian Lu, Qiang Xu, Peiwen Zhang

“…To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). …”
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Segmentation and Classification of Skin Cancer Diseases Based on Deep Learning: Challenges and Future Directions by Abdulrahman Dira Khalaf, Hazlina Hamdan, Alfian Abdul Halin, Noridayu Manshor

“…This study also examines the critical challenges of deploying DL models in clinical practice, covering issues including dataset diversity, model interpretability, and real-world implementation feasibility. …”
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