Detection and Classification of Sporadic E Using Convolutional Neural Networks by J. A. Ellis, D. J. Emmons, M. B. Cohen

“…The foEs CNN binary classification model achieved an accuracy of 74% and F1‐score of 0.70. …”
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An Explainable Hybrid CNN–Transformer Architecture for Visual Malware Classification by Mohammed Alshomrani, Aiiad Albeshri, Abdulaziz A. Alsulami, Badraddin Alturki

“…The proposed model is evaluated using three benchmark datasets—Malimg, MaleVis, VirusMNIST—encompassing 61 malware classes. …”
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Model of the malicious traffic classification based on hypergraph neural network by Wenbo ZHAO, Zitong MA, Zhe YANG

“…As the use and reliance on networks continue to grow, the prevalence of malicious network traffic poses a significant challenge in the field of network security.Cyber attackers constantly seek new ways to infiltrate systems, steal data, and disrupt network services.To address this ongoing threat, it is crucial to develop more effective intrusion detection systems that can promptly detect and counteract malicious network traffic, thereby minimizing the resulting losses.However, current methods for classifying malicious traffic have limitations, particularly in terms of excessive reliance on data feature selection.To improve the accuracy of malicious traffic classification, a novel malicious traffic classification model based on Hypergraph Neural Networks (HGNN) was proposed.The traffic data was represented as hypergraph structures and HGNN was utilized to capture the spatial features of the traffic.By considering the interrelations among traffic data, HGNN provided a more accurate representation of the characteristics of malicious traffic.Additionally, to handle the temporal features of traffic data, Recurrent Neural Networks (RNN) was introduced to further enhance the model’s classification performance.The extracted spatiotemporal features were then used for the classification of malicious traffic, aiding in the detection of potential threats within the network.Through a series of ablative experiments, the effectiveness of the HGNN+RNN method was verified.These experiments demonstrate the model’s ability to efficiently extract spatiotemporal features from traffic, resulting in improved classification performance for malicious traffic.The model achieved outstanding classification accuracy across three widely-used open-source datasets: NSL-KDD (94% accuracy), UNSW-NB15 (95.6% accuracy), and CIC-IDS-2017 (99.08% accuracy).These results underscore the potential significance of the malicious traffic classification model based on hypergraph neural networks in enhancing network security and its capacity to better address the evolving landscape of network threats within the domain of network security.…”
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Classification of Lactate Level Using Resting-State EEG Measurements by Saad Abdulazeez Shaban, Osman Nuri Ucan, Adil Deniz Duru

“…The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. …”
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An Enhanced Approach Using AGS Network for Skin Cancer Classification by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim, Youjin Shin

“…The AGS network integrates three key modules: Augmentation (A), GAN (G), and Segmentation (S). …”
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Empowering Healthcare: TinyML for Precise Lung Disease Classification by Youssef Abadade, Nabil Benamar, Miloud Bagaa, Habiba Chaoui

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Exploration and Practice of Classification Indexing Combined with Large Language Models by JIANG Peng, REN Yan, ZHU Beiling

“…The overall classification accuracy of the model has improved by 2.16%, and the non-rejection accuracy has increased by 3.77%. …”
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Transformer attention fusion for fine grained medical image classification by Danyal Badar, Junaid Abbas, Raed Alsini, Tahir Abbas, Wang ChengLiang, Ali Daud

“…Abstract Fine-grained visual classification is fundamental for medical image applications because it detects minor lesions. …”
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Encrypted traffic classification encoder based on lightweight graph representation by ZhenWei Chen, XiaoXu Wei, YongSheng Wang

“…For end-to-end training, an improved Transformer-based model is employed with relative position encoding of time series to generate final classification results for downstream tasks. To evaluate the reliability of the method, the proposed approach is tested on three application classification datasets: WWT, ISCX-2012, and ISCX-Tor, for classifying network encrypted traffic and conducting ablation experiments for comparison. …”
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Ensemble Machine Learning Model for Classification of Spam Product Reviews by Muhammad Fayaz, Atif Khan, Javid Ur Rahman, Abdullah Alharbi, M. Irfan Uddin, Bader Alouffi

“…Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. …”
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Simple energy detector for two-stage classification for antidrone systems by Zurovac Snežana, Petrović Nikola, Joksimović Vasilija, Pokrajac Ivan, Mikanović Darko, Sazdić-Jotić Boban

“…This is a specialized approach to drone signal detection based on two-stage classification with two key components: a method based on spectrogram energy detection and deep learning classification. …”
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Classifications of haemodialysis vascular access stenosis: a scoping review by Petr Waldauf, Michael Corr, Stephen O’neill, Katerina Lawrie, Jan Malik, Libor Janousek, Stepan Maly, Jaroslav Chlupac

“…Three classifications were dedicated to VA stenosis, all based on the anatomical location of lesions. …”
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A novel hybrid convolutional and transformer network for lymphoma classification by Mohamed Yacin Sikkandar, Sankar Ganesh Sundaram, Muteb Nasser Almeshari, S. Sabarunisha Begum, E. Siva Sankari, Yousef A. Alduraywish, Waeal J. Obidallah, Fahad Mansour Alotaibi

“…This study proposes a hybrid deep learning framework—Hybrid Convolutional and Transformer Network for Lymphoma Classification (HCTN-LC)—designed to enhance the precision and interpretability of lymphoma subtype classification. …”
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Low-Latency Neural Network for Efficient Hyperspectral Image Classification by Chunchao Li, Jun Li, Mingrui Peng, Behnood Rasti, Puhong Duan, Xuebin Tang, Xiaoguang Ma

“…The networks created by implementing our strategies are both compact in structure and hardware-friendly. After testing on three different datasets, the proposed networks achieve significantly better inference speed and energy-saving ability over advanced classification networks and lightweight models, while maintaining an equivalent or even better classification performance.…”
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Research on Mount Wilson Magnetic Classification Based on Deep Learning by Yuanbo He, Yunfei Yang, Xianyong Bai, Song Feng, Bo Liang, Wei Dai

“…In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. It includes three stages, generating object locations, detecting objects, and merging detections. …”
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Features and classification of solid solution behavior of ternary Mg alloys by Tao Chen, Yuan Yuan, Jun Wang, Jiajia Wu, Bin Wang, Xianhua Chen, Nele Moelans, Jingfeng Wang, Fusheng Pan

“…The solid solution behavior of a set of two different alloying elements in Mg alloy systems are suggested to be classified into three categories: inclusivity, exclusivity and proportionality. …”
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Assessment and classification of different ashes from waste incinerators in Thailand by Raji Muthuraja, Chatpong Na Pombhejara, Sunantha Ganesan, Chodchanok Attaphong, Nattaya Morawan, Juckrit Vicheanteab, Dao Janjaroen

“…A comparison of the chemical and physical properties of five sources of ashes from the waste incinerators in three regions in Thailand, namely MFA1, MFA2, IFA, and fly ash that used in a ready-mixed concrete plant, CFA1 and CFA2 was conducted. …”
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Neural Classification of Argument Elements and Styles in Arabic Competitive Debates by Al-Zawqari Ali, Mohamed Ahmed, Abdul Gabbar Al-Sharafi, Mohammad M. Khader, Ali Safa, Gerd Vandersteen

“…Using this dataset, we form two tasks: 1) three-way rhetorical-style classification, and 2) full 13-label element detection. …”
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The Smart Product Backlog: A Classification Model of User Stories by Mauricio Gaona-Cuevas, Victor Bucheli Guerrero, Fredy H. Vera-Rivera

“…Additionally, it had employed three binary classification techniques: logistic regression, k-nearest neighbors (k-NN), and support vector machines (SVM). …”
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A new classification for dislocated and displaced proximal humeral fractures by Sadaki Mitsuzawa, Hisataka Takeuchi, Kenta Ijiri, Yuya Furusho, Shinnosuke Yamashita, Yoshihiro Tsukamoto, Satoshi Ota, Eijiro Onishi, Tadashi Yasuda

“…We developed an entirely new classification, the Mitsuzawa classification, for dislocated and displaced proximal humeral fractures and tested all three classifications for their intra- and interobserver reliability. …”
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