Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models by Xiaotian Ye, Weifeng Zhou

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Confidence-Aware Ship Classification Using Contour Features in SAR Images by Al Adil Al Hinai, Raffaella Guida

“…In this paper, a novel set of 13 handcrafted features derived from the contours of ships in synthetic aperture radar (SAR) images is introduced for ship classification. …”
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Crime Prediction Using Decision Tree (J48) Classification Algorithm. by Ivan, Niyonzima, Emmanuel Ahishakiye, Elisha Opiyo Omulo, Danison Taremwa

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Rare classification of a lower eyelid lesion as tubular apocrine adenoma by Nishita T. Sheth, Christopher Stamey, Christopher R. Dermarkarian

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A real time approach to user interest classification using DPI by Kang CHEN, Huazheng FU, Chong CHEN, Yuyi ZHANG, Yu WEI

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CLASSIFICATION OF PROFESSIONAL EDUCATIONAL PROGRAMS IN THE RUSSIAN FEDERAITION: PROBLEMS AND POSSIBLE DECISIONS by B. A. Sazonov

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A multigene phylogeny toward a new phylogenetic classification of Leotiomycetes by Peter R. Johnston, Luis Quijada, Christopher A. Smith, Hans-Otto Baral, Tsuyoshi Hosoya, Christiane Baschien, Kadri Pärtel, Wen-Ying Zhuang, Danny Haelewaters, Duckchul Park, Steffen Carl, Francesc López-Giráldez, Zheng Wang, Jeffrey P. Townsend

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Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder by Muhammad Faqih Dzulqarnain, Abdul Fadlil, Imam Riadi

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Semi-supervised Gaussian process classification algorithm addressing the class imbalance by Zhan-guo XIA, Shi-xiong XIA, Shi-yu CAI, Ling WAN

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Implementation of Haar Cascade and Adaboost Algorithms in Photo Classification on Social Networks by Rezty Amalia Aras, Hutami Endang

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Exploring Urban Fear of Crime using Unsupervised Classification - The Case of Athens by Dimitra Stamou

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Enhanced Brain Tumor Classification through Gamma Correction in Deep Learning by Muhammad Naufal, Harun Al Azies, Rivaldo Mersis Brilianto

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A Classification of Basic Helix-Loop-Helix Transcription Factors of Soybean by Karen A. Hudson, Matthew E. Hudson

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Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine by Bayu Yanuargi, Ema Utami, Kusrini, Arli Aditya Parikesit

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The Effect of Resampling Techniques on Model Performance Classification of Maternal Health Risks by Nia Mauliza, Aisha Shakila Iedwan, Yoga Pristyanto, Anggit Dwi Hartanto, Arif Nur Rohman

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DRCCT: Enhancing Diabetic Retinopathy Classification with a Compact Convolutional Transformer by Mohamed Touati, Rabeb Touati, Laurent Nana, Faouzi Benzarti, Sadok Ben Yahia

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Classification and Recognition of Fluvial Geomorphic Units Based on Process and Form Relationship by YU Zicheng, DENG Rui, ZHANG Jing, ZHAO Jinyong, DING Yang, LI Xuan

“…Clarifying the types of fluvial geomorphic units and their spatial distribution can better recognize and understand rivers.In the face of problems such as the imperfection and difficult implementation of the existing classification and recognition methods of geomorphic units in China,the mature classification systems of Fryirs and Brierley geomorphic units in other countries were summarized,and the process and form relationship of different geomorphic unit types was sorted out.The Daning River,a first-level tributary of the Yangtze River,was taken as a practical case.The classification and recognition process of fluvial geomorphic units based on the process and form relationship was formed,and the spatial distribution of geomorphic units was defined by combining with the field survey data of air,sky,and ground.The classification system of Fryirs and Brierley geomorphic units was divided into four categories:alluvial and erosional bedrock and boulder units,mid-channel sedimentary units,riparian sedimentary units,and alluvial and erosional fine-grained units,including 42 types of terrace falls,deep pools,and point beaches.The types of geomorphic units included in the confluence section of the Lianghekou area of the Daning River and Houxi River were tandem plunge,rapid beach,slippery water,deep pool,shallow beach,slow flow area,side beach,and heart beach,accounting for 16.43%,21.51%,7.21%,20.45%,16.90%,13.59%,and 3.91% of the studied river section,respectively.Quantitative analysis of the spatial distribution of geomorphic units can provide theoretical support for the restoration of damaged habitats and habitat assessment in the river section.…”
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Multiple Hierarchical Cross-Scale Transformer for Remote Sensing Scene Classification by Dan Zhang, Wenping Ma, Licheng Jiao, Xu Liu, Yuting Yang, Fang Liu

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Etiological classification of high sedimentation rate and relation with other inflammatory markers by Sultan Gozde Temiz, Baris Sagcan, Helin Tantekin, Kadem Arslan

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A Classification Model Based on Interval Rule Inference Network with Interpretability by Yunxia Zhang, Yiming Zhong, Xiaochang Wu, Jing Bai

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