Compilation and functional classification of telomere length-associated genes in humans and other animal species by E. V. Ignatieva, N. S. Yudin, D. M. Larkin

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Frequency and Texture Aware Multi-Domain Feature Fusion for Remote Sensing Scene Classification by Russo Ashraf, Kang-Hyun Jo

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Fuzzy Lattice Reasoning for Pattern Classification Using a New Positive Valuation Function by Yazdan Jamshidi Khezeli, Hossein Nezamabadi-pour

“…This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. …”
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Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) by Emmanuel Gbenga Dada, David Opeoluwa Oyewola, Stephen Bassi Joseph, Onyeka Emebo, Olugbenga Oluseun Oluwagbemi

“…This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. …”
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Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems by Ming Yang, Hao Ma, Bomin Chen, Guangtao Dong

“…Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine (SVM) is used for path classification to enhance the confidence of classification. …”
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ON THE WAYS OF AUTOMATED PROCESSING OF SPATIAL GEOMETRY OF THE SYSTEM “GATE-CASTING” FOR SOLVING OF THE CLASSIFICATION PROBLEMS by A. N. Chichko, T. V. Matushinets, L. V. Markov, V. F. Sobolev

“…The algorithm of taxonomy, which can be used for solving of problems of castings classification in the systems of computeraided design of foundry technologies, is described. …”
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Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study by Eman H. Alkhammash

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NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification by Shuaiqi Liu, Beibei Liang, Siqi Wang, Bing Li, Lidong Pan, Shui-Hua Wang

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Investigation on Texture Changes and Classification between Cold-Fresh and Freeze-Thawed Tan Mutton by Dongdong Li, Yaling Peng, Haihong Zhang

“…By combining these feature wavelengths with classified variables, the SNV + MSC−CARS−PLS-DA (partial least squares-discriminate analysis, PLS-DA) and SNV + MSC−SPA−PLS-DA models for classification of C-F and F-T Tan mutton were established. …”
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Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information by Hyontai Sug

“…Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. …”
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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

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Fungicide Resistance Action Committee's (FRAC) Classification Scheme of Fungicides According to Mode of Action by Frederick M. Fishel

“… This guide addresses resistance to pesticides and describes the Fungicide Resistance Action Committee's (FRAC) classification of fungicides and bactericides registered for use in Florida by their modes of action. …”
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Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach by Shokufeh Yaraghi, Toktam Khatibi

“…The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. …”
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A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning by Zhen Yuan, Jinfeng Liu

“…With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. …”
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Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments by Zhihong Cui, Xiangwei Zheng, Xuexiao Shao, Lizhen Cui

“…Sleep stage classification plays an important role in the diagnosis of sleep-related diseases. …”
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Solar photovoltaic panel cells defects classification using deep learning ensemble methods by H. Tella, A. Hussein, S. Rehman, B. Liu, A. Balghonaim, M. Mohandes

“…Significantly, the ResNet18 model achieves an accuracy of 73.02 % in a straightforward binary classification task, highlighting that individual models can surpass ensembles in particular circumstances. …”
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PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification by Kaikai Zhao, Youjiao Si, Liangchao Sun, Xiangjiao Meng

“…Conclusion: This study significantly reduces the parameter count through an innovative model structure while maintaining high accuracy and stability, demonstrating outstanding performance in lung cancer cell classification tasks. The model holds the potential to become an efficient, accurate, and cost-effective auxiliary diagnostic tool for pathological classification of lung cancer in the future.…”
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Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies by Xian Wu, Huan Liu

“…First, resampling technology is used to preprocess the unbalanced dataset to obtain a relatively balanced training set. Then, use the classic backpropagation neural network, classic k-nearest neighbor, and classic Naive Bayes three algorithms as the base classifier and use the Bagging strategy to get the ensemble learning model. …”
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A systematic review of deep learning techniques for apple leaf diseases classification and detection by Assad Souleyman Doutoum, Bulent Tugrul

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Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model by Cihan Akyel, Bünyamin Ciylan

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