Encrypted traffic classification encoder based on lightweight graph representation by ZhenWei Chen, XiaoXu Wei, YongSheng Wang

“…Abstract In recent years, traffic encryption technology has been widely adopted for user information protection, leading to a substantial increase in encrypted traffic in communication networks. To address issues such as unclear local key features and low classification accuracy in traditional malicious traffic detection and normal application classification, this paper introduces an encrypted traffic classification encoder based on lightweight graph representation. …”
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BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…However, binary quantization in transformers faces challenges such as degradation of feature representation capability after binarizing self-attention mechanisms and decline in fusion efficiency of multiscale spectral–spatial information, leading to relatively lagging progress in this field. To address these issues, this study proposes a novel binary vision transformer architecture tailored for hyperspectral image classification. …”
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Classification of Nanomaterial Drug Delivery Systems for Inflammatory Bowel Disease by Wang H, Zhou F, Shen M, Ma R, Yu Q

“…This review summarizes the classification of NDDS for IBD, and concludes that, despite ongoing challenges, NDDS may represent an effective treatment approach for IBD. …”
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Construction of a Multimodal Dataset for Emergency Event Identification and Classification by Yifan ZHANG, Zuqin CHEN, Jike GE, Mingkun HE, Jie TAN

“…It not only expands the category range, but also provides more detailed classification in the natural disaster category, which is crucial for developing robust and accurate multimodal classification models. …”
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FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification by Peixian Zhuang, Xiaochen Zhang, Hao Wang, Tianxiang Zhang, Leiming Liu, Jiangyun Li

“…To address these issues, we present a novel frequency-aware hierarchical Mamba (FAHM) for HSI classification. …”
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IoT Based Meat Freshness Classification Using Deep Learning by Zarif Wasif Bhuiyan, Syed Ali Redwanul Haider, Adiba Haque, Mohammad Rejwan Uddin, Mahady Hasan

“…This paper introduces a novel Internet of Things (IoT) system that integrates gas sensors and advanced machine learning models, particularly deep learning, to address these issues effectively. The system combines image-based classification using a custom Convolutional Neural Network (CNN) with gas sensor data to provide a comprehensive, real-time solution for the classification of both beef and mutton in terms of species and freshness. …”
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Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds by Muhammad Hamza Tariq, Haseeb Sultan, Rehan Akram, Seung Gu Kim, Jung Soo Kim, Muhammad Usman, Hafiz Ali Hamza Gondal, Juwon Seo, Yong Ho Lee, Kang Ryoung Park

“…To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. …”
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The Development of Entertainment Programs on Mongolian National Radio and Television and Issues of Literary Skill by . Suriga

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A review on classification of imbalanced data for wireless sensor networks by Harshita Patel, Dharmendra Singh Rajput, G Thippa Reddy, Celestine Iwendi, Ali Kashif Bashir, Ohyun Jo

“…Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. …”
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Classification criteria of paediatric Behcet's disease: An evolving area of study by Sathish Kumar

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Personalised Affective Classification Through Enhanced EEG Signal Analysis by Joseph Barrowclough, Nonso Nnamoko, Ioannis Korkontzelos

“…Background and Objectives Declining mental health is a prominent and concerning issue. Affective classification, which employs machine learning on brain signals captured from electroencephalogram (EEG), is a prevalent approach to address this issue. …”
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Histiocytic neoplasms: modern aspects of pathogenesis, classification, diagnosis and treatment by O. D. Gurieva, T. T. Valiev, A. I. Pavlovskaya

“…Considering MAPK-signaling pathway mutations role (including BRAFV600E) in the pathogenesis of histiocytic cell tumors the importance of genetic studies in the diagnosis and choice of therapeutic strategy in these diseases increases. In the current issue modern classifications of histiocytic cell tumors, morphological and immunophenotypic features of histiocytic and dendritic cells, and the role of recurrent somatic mutations of the MAPK signaling pathway in the pathological histiocytic proliferation are presented. …”
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Image Classification of Indonesian Snacks using Convolutional Neural Network by Kunti Eliyen, Abidatul Izzah, Fikha Rizky Aullia

“…One way to address this issue is through image classification of Indonesian traditional snacks using Convolutional Neural Networks (CNN). …”
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Safe and secure classification protocol in two-tiered sensor networks by Rui LI, Jin-guo LI, Hao CHEN

“…A safe and security classification protocol named SSC was proposed for two-tiered sensor networks,which enable storage nodes to process classification correctly without knowing both the value of classifying rules and the data which will be classified.To protect privacy,an oblivious comparison technique was presented.MHash,which enable storage nodes to compare data items from sink and sensors without knowing their values.Based on MHash and prefix membership verification technique,classification target was achieved in protecting the privacy of both sensor collected data and sink issued classification rules.To verify the correctness of classification results,a crossed neighborhood technique was proposed which organize sensors and data items in one sensor in sequences,to allow the sink checking the correctness of sampling classification results.Analysis and experimental results validate the efficacy and efficiency of SSC protocol.…”
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Research on APT groups malware classification based on TCN-GAN. by Daowei Chen, Hongsheng Yan

“…This paper addresses the issue of tracing and attributing APT malware groups. …”
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Spectral-Spatial Convolutional Hybrid Transformer for Hyperspectral Image Classification by Haixin Sun, Jingwen Xu, Fanlei Meng, Mengdi Cheng, Qiuguang Cao

“…However, how to integrate the spectral and spatial features effectively after dimensionality reduction while retaining key information remains to be optimized further. To address this issue, the Spectral-spatial convolutional hybrid Transformer (SSCHFormer) hyperspectral classification model is proposed in this article. …”
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Dynamic graph structure evolution for node classification with missing attributes by Xiaomeng Song, Bin Zhou, Yanjiang Wang, Weifeng Liu

“…Graph completion learning (GCL) methods have been developed to address this issue, aiming to reconstruct missing node attributes based on existing structural relationships. …”
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Advancing ECG Signal Classification With a Fuzzy Classifier Approach by Jan Rabcan, Vitaly Levashenko, Elena Zaitseva, Miroslav Kvassay

“…However, preprocessing can also lead to the loss of small but potentially important information, which may affect the performance of classification algorithms. In this paper, we present an approach for ECG signal classification that addresses the issue of information loss during preprocessing. …”
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Semantic Patent Classification Using Stack Generalization of Deep Models by Shahla Nemati

“…The process of manually annotating is both tedious and time-consuming. To solve this issue, we have introduced a deep ensemble model for semantic paragraph-level pattern classification based on the semantic content of patents. …”
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Loan classification using a feed-forward neural network by U. I. Behunkou

“…The purpose of the study is to construct and study the use of a feed-forward neural network to solve the problem of loan classification, as well as to conduct a comparative analysis of the neural networkbased approach with the existing approach based on logistic regression.Methods. …”
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