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Journal articles on the topic 'K-shot classification'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Khaled, Alzamel, and Alajmi Manayer. "Few-shot Learning Approach for Arabic Scholarly Paper Classification using SetFit Framework." WSEAS TRANSACTIONS ON COMMUNICATIONS 23 (December 27, 2024): 89–95. https://doi.org/10.37394/23204.2024.23.12.

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Focus on the few-shot approach has increased recently for TC as it is competitive with fine-tuning models that need a large dataset [14]. In NLP, the process of using PTMs to classify new data is preferable to the expensive process of training a model from scratch. This can be considered a kind of TL, i.e., it focuses on reusing knowledge of PTMs to solve different problems, as long as the pre-training data is appropriately comparable. Transferring knowledge allows the model to circumvent the lack of data and enable FSL as a low-cost solution. To clarify, the term shot refers to a single examp

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Minhas, Rabia A., Ali Javed, Aun Irtaza, Muhammad Tariq Mahmood, and Young Bok Joo. "Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network." Applied Sciences 9, no. 3 (2019): 483. http://dx.doi.org/10.3390/app9030483.

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Broadcasters produce enormous numbers of sport videos in cyberspace due to massive viewership and commercial benefits. Manual processing of such content for selecting the important game segments is a laborious activity; therefore, automatic video content analysis techniques are required to effectively handle the huge sports video repositories. The sports video content analysis techniques consider the shot classification as a fundamental step to enhance the probability of achieving better accuracy for various important tasks, i.e., video summarization, key-events selection, and to suppress the

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Thomas, Hugo, Guillaume Gravier, and Pascale Sébillot. "One-shot relation retrieval in news archives: adapting N-way K-shot relation Classification for efficient knowledge extraction." Procedia Computer Science 246 (2024): 1060–69. http://dx.doi.org/10.1016/j.procs.2024.09.525.

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Rizinski, Maryan, Andrej Jankov, Vignesh Sankaradas, Eugene Pinsky, Igor Mishkovski, and Dimitar Trajanov. "Comparative Analysis of NLP-Based Models for Company Classification." Information 15, no. 2 (2024): 77. http://dx.doi.org/10.3390/info15020077.

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The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow, costly, and vendor-specific assignments. Therefore, we investigate recent natural language processing (NLP) advancements to automate the company classification process. In particular, we employ and evaluate various NLP-based models, including zero-shot learning, One-vs-Rest classification, multi-class classifiers, and ChatGPT-aided classifica

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Tian, Pinzhuo, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, and Yang Gao. "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12087–94. http://dx.doi.org/10.1609/aaai.v34i07.6887.

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To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm. However, most existing methods only focus on the traditional 1-way segmentation setting (i.e., one image only contains a single object). This is far away from practical semantic segmentation tasks where the K-way setting (K > 1) is usually required by performing the accurate multi-object segmentation. To deal with this issue, we formulate the few-shot semantic segmentation task as a learning-based pixel classific

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Yanas Rajindran and Hanza Parayil Salim. "A Comparative Analysis of Clustering Methods on the 20 Newsgroups Dataset for Analytics." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3075–78. https://doi.org/10.32628/cseit25112788.

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This paper presents a comparative analysis of two different approaches for clustering textual data from the 20 Newsgroups dataset. The first approach leverages a Large Language Model (LLM) to classify each text into predefined categories using zero-shot classification. The second approach applies to the traditional K-Means clustering algorithm on text embeddings. We evaluate both methods by comparing their predicted clusters against true labels for assessment. For K-Means, we also explore a semi-supervised variant with centroid initialization based on true labels.

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Wang, Miaorui. "Few-shot Image Information Mining and Its Application in UAV Early Warning." Applied and Computational Engineering 150, no. 1 (2025): 155–65. https://doi.org/10.54254/2755-2721/2025.22704.

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Artificial intelligence applications in vertical fields face the problem of a small and unbalanced number of annotated samples. This article attempts to solve the small sample problem from three perspectives: data, model, and algorithm, by selecting UAV early warning. For data, conventional image enhancement, Mixup enhancement, random masking, and Partial convolution techniques are used to enhance the paradigm of Few-shot samples. Transfer learning techniques are applied to algorithms. Firstly, an autoencoder is trained using many unlabeled images combined with a masking strategy. Secondly, a

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Seo, Minjo, and Hyunsoo Kim. "Irregular Openings Identification at Construction Sites Based on Few-Shot Learning." Buildings 15, no. 11 (2025): 1834. https://doi.org/10.3390/buildings15111834.

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The construction industry frequently encounters safety hazards, with falls related to undetected openings being a major cause of fatalities. Identifying unstructured openings using computer vision is challenging due to their unpredictable nature and the difficulty of acquiring large labeled datasets in dynamic construction environments. Conventional deep learning methods require substantial data, limiting their applicability. Few-shot learning (FSL) offers a promising alternative by enabling models to learn from limited examples. This study investigates the effectiveness of an FSL approach, sp

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Wang, Aili, Chengyang Liu, Dong Xue, Haibin Wu, Yuxiao Zhang, and Meihong Liu. "Hyperspectral Image Classification Based on Cross-Scene Adaptive Learning." Symmetry 13, no. 10 (2021): 1878. http://dx.doi.org/10.3390/sym13101878.

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Aiming at few-shot classification in the field of hyperspectral remote sensing images, this paper proposes a classification method based on cross-scene adaptive learning. First, based on the unsupervised domain adaptive technology, cross-scene knowledge transfer learning is carried out to reduce the differences between source scene and target scene. At the same time, depthwise over-parameterized convolution is used in the deep embedding model to improve the convergence speed and feature extraction ability. Second, two symmetrical subnetworks are designed in the model to further reduce the diff

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Li, Liangwei, Lin Liu, Xiaohui Du, et al. "CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification." Sensors 22, no. 24 (2022): 9980. http://dx.doi.org/10.3390/s22249980.

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Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. H

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Xu, Tuo, Ying Wang, Jie Li, and Yuefan Du. "Generative Adversarial Network and Mutual-Point Learning Algorithm for Few-Shot Open-Set Classification of Hyperspectral Images." Remote Sensing 16, no. 7 (2024): 1285. http://dx.doi.org/10.3390/rs16071285.

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Existing approaches addressing the few-shot open-set recognition (FSOSR) challenge in hyperspectral images (HSIs) often encounter limitations stemming from sparse labels, restricted category numbers, and low openness. These limitations compromise stability and adaptability. In response, an open-set HSI classification algorithm based on data wandering (DW) is introduced in this research. Firstly, a K-class classifier suitable for a closed set is trained, and its internal encoder is leveraged to extract features and estimate the distribution of known categories. Subsequently, the classifier is f

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Amin, Anang Hudaya Muhamad, and Asad I. Khan. "One-shot Classification of 2-D Leaf Shapes Using Distributed Hierarchical Graph Neuron (DHGN) Scheme with k-NN Classifier." Procedia Computer Science 24 (2013): 84–96. http://dx.doi.org/10.1016/j.procs.2013.10.030.

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Hsiao, Chao-Hsiang, Huan-Che Su, Yin-Tien Wang, Min-Jie Hsu, and Chen-Chien Hsu. "ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification." Sensors 25, no. 13 (2025): 4233. https://doi.org/10.3390/s25134233.

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Defect detection in mass production lines often involves small and imbalanced datasets, necessitating the use of few-shot learning methods. Traditional deep learning-based approaches typically rely on large datasets, limiting their applicability in real-world scenarios. This study explores few-shot learning models for detecting product defects using limited data, enhancing model generalization and stability. Unlike previous deep learning models that require extensive datasets, our approach effectively performs defect detection with minimal data. We propose a Siamese network that integrates Res

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Lu, Zikui, Zixi Chang, Mingshu He, and Luona Song. "Zero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing." Sensors 25, no. 2 (2025): 545. https://doi.org/10.3390/s25020545.

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With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification. AG-ZSL primarily learns two mapping functions: one that captures tra

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Pałczyński, Krzysztof, Sandra Śmigiel, Damian Ledziński, and Sławomir Bujnowski. "Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset." Sensors 22, no. 3 (2022): 904. http://dx.doi.org/10.3390/s22030904.

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The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I–XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was

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Wang, Pan, Jianzhong Liu, Yinbao Zhang, Zhiyang Zhi, Zhijian Cai, and Nannan Song. "A Novel Cargo Ship Detection and Directional Discrimination Method for Remote Sensing Image Based on Lightweight Network." Journal of Marine Science and Engineering 9, no. 9 (2021): 932. http://dx.doi.org/10.3390/jmse9090932.

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Recently, cargo ship detection in remote sensing images based on deep learning is of great significance for cargo ship monitoring. However, the existing detection network is not only unable to realize autonomous operation on spaceborne platforms due to the limitation of computing and storage, but the detection result also lacks the directional information of the cargo ship. In order to address the above problems, we propose a novel cargo ship detection and directional discrimination method for remote sensing images based on a lightweight network. Specifically, we design an efficient and lightw

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Shahrabadi, Somayeh, Telmo Adão, Emanuel Peres, Raul Morais, Luís G. Magalhães, and Victor Alves. "Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting." Algorithms 17, no. 3 (2024): 106. http://dx.doi.org/10.3390/a17030106.

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The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computational training that allows providing such support is frequently hindered by various challenges related to datasets, including the scarcity of examples and imbalanced class distributions, which have detrimental effects on the production of accurate models. For a proper approach to these challenges, strategies smarter than the traditional brute for

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Pradhan, Biswajeet, Husam A. H. Al-Najjar, Maher Ibrahim Sameen, Ivor Tsang, and Abdullah M. Alamri. "Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks." Remote Sensing 12, no. 10 (2020): 1676. http://dx.doi.org/10.3390/rs12101676.

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Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discrimina

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Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library an

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Lee, Yoonho, Wonjae Kim, Wonpyo Park, and Seungjin Choi. "Discrete Infomax Codes for Supervised Representation Learning." Entropy 24, no. 4 (2022): 501. http://dx.doi.org/10.3390/e24040501.

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For high-dimensional data such as images, learning an encoder that can output a compact yet informative representation is a key task on its own, in addition to facilitating subsequent processing of data. We present a model that produces discrete infomax codes (DIMCO); we train a probabilistic encoder that yields k-way d-dimensional codes associated with input data. Our model maximizes the mutual information between codes and ground-truth class labels, with a regularization which encourages entries of a codeword to be statistically independent. In this context, we show that the infomax principl

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Zhou, Yu, Ronggang Cao, Anqi Zhang, and Ping Li. "Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals." Sensors 24, no. 15 (2024): 4804. http://dx.doi.org/10.3390/s24154804.

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The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include

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Fei, Yunqiao, Jingchao Fan, and Guomin Zhou. "Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering." Applied Sciences 15, no. 2 (2025): 628. https://doi.org/10.3390/app15020628.

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In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills fro

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Rodríguez-Cantelar, Mario, Marcos Estecha-Garitagoitia, Luis Fernando D’Haro, Fernando Matía, and Ricardo Córdoba. "Automatic Detection of Inconsistencies and Hierarchical Topic Classification for Open-Domain Chatbots." Applied Sciences 13, no. 16 (2023): 9055. http://dx.doi.org/10.3390/app13169055.

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Current State-of-the-Art (SotA) chatbots are able to produce high-quality sentences, handling different conversation topics and larger interaction times. Unfortunately, the generated responses depend greatly on the data on which they have been trained, the specific dialogue history and current turn used for guiding the response, the internal decoding mechanisms, and ranking strategies, among others. Therefore, it may happen that for semantically similar questions asked by users, the chatbot may provide a different answer, which can be considered as a form of hallucination or producing confusio

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Ma, Tianle, and Aidong Zhang. "AffinityNet: Semi-Supervised Few-Shot Learning for Disease Type Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1069–76. http://dx.doi.org/10.1609/aaai.v33i01.33011069.

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While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the “big p, small N” problem (i.e., a relatively small number of samples with highdimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the Affinity

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Qi, Jiahao, Jundong Zhang, Qingyan Meng, Jiaming Ju, and Haotian Jiang. "Detection of Auxiliary Equipment in Engine Room Based on Improved SSD." Journal of Physics: Conference Series 2173, no. 1 (2022): 012060. http://dx.doi.org/10.1088/1742-6596/2173/1/012060.

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Abstract To improve the intelligent operation and maintenance of ships, the research on the perception of engine room auxiliary equipment (A/E) will realize AUTO-0 and replace the engineers. Due to compact layout of the cabin and the different shapes of equipment, the Single Shot MultiBox Detector (SSD) that uses the traditional convolution method for feature extraction cannot adapt to the complex environment. Therefore, an improved A/E detection algorithm based on SSD is proposed. Firstly, we collected the images of actual ships, annotated them manually, and used the K-Means clustering algori

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AL-TAHRAWI, MAYY M., and RAED ABU ZITAR. "POLYNOMIAL NETWORKS VERSUS OTHER TECHNIQUES IN TEXT CATEGORIZATION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 02 (2008): 295–322. http://dx.doi.org/10.1142/s0218001408006247.

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Many techniques and algorithms for automatic text categorization had been devised and proposed in the literature. However, there is still much space for researchers in this area to improve existing algorithms or come up with new techniques for text categorization (TC). Polynomial Networks (PNs) were never used before in TC. This can be attributed to the huge datasets used in TC, as well as the technique itself which has high computational demands. In this paper, we investigate and propose using PNs in TC. The proposed PN classifier has achieved a competitive classification performance in our e

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Farhad, Moomal, Mohammad Mehedy Masud, Azam Beg, Amir Ahmad, and Luai Ahmed. "A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques." Applied Sciences 13, no. 11 (2023): 6582. http://dx.doi.org/10.3390/app13116582.

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The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy, provides significant benefits in clinical practice by improving the efficiency and accuracy of diagnosis. Deep learning techniques show remarkable success in analyzing these videos by automating tasks such as classification, detection, and segmentation. In this paper, we review the application of deep learning techniques for analyzing medical diagnostic videos, with a focus on ultrasound and endoscopy. The methodology for selecting the papers consists of two major steps. First, we selected around 350 papers

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Alotaibi, Nouf Saeed, Hassan Ibrahim Ahmed, and Samah Osama M. Kamel. "Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City." Sensors 23, no. 16 (2023): 7135. http://dx.doi.org/10.3390/s23167135.

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The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two pha

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Lehner, Johannes, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer, Lukas Miklautz, and Sepp Hochreiter. "Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 2965–73. http://dx.doi.org/10.1609/aaai.v38i4.28078.

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Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencode

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Chajia, Meryem, and El Habib Nfaoui. "Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression." Future Internet 16, no. 12 (2024): 453. https://doi.org/10.3390/fi16120453.

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Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embe

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Farmanesh, Amir, Raúl G. Sanchis, and Joaquín Ordieres-Meré. "Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited." Sensors 25, no. 10 (2025): 3048. https://doi.org/10.3390/s25103048.

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AI-oriented quality inspection in manufacturing often faces highly imbalanced data, as defective products are rare, and there are limited possibilities for data augmentation. This paper presents a systematic comparison between Deep Transfer Learning (DTL) and Contrastive Learning (CL) under such challenging conditions, addressing a critical gap in the industrial machine learning literature. We focus on a galvanized steel coil quality classification task with acceptable vs. defective classes, where the vast majority of samples (>95%) are acceptable. We implement a DTL approach using strategi

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Farhat, Farshid, Mohammad Mahdi Kamani, and James Z. Wang. "CAPTAIN: Comprehensive Composition Assistance for Photo Taking." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (2022): 1–24. http://dx.doi.org/10.1145/3462762.

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Many people are interested in taking astonishing photos and sharing them with others. Emerging high-tech hardware and software facilitate the ubiquitousness and functionality of digital photography. Because composition matters in photography, researchers have leveraged some common composition techniques, such as the rule of thirds and the perspective-related techniques, in providing photo-taking assistance. However, composition techniques developed by professionals are far more diverse than well-documented techniques can cover. We present a new approach to leverage the underexplored photograph

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Gurioli, Andrea, Maurizio Gabbrielli, and Stefano Zacchiroli. "Stylometry for real-world expert coders: a zero-shot approach." PeerJ Computer Science 10 (November 20, 2024): e2429. http://dx.doi.org/10.7717/peerj-cs.2429.

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Code stylometry is the application of stylometry techniques to determine the authorship of software source code snippets. It is used in the industry to address use cases like plagiarism detection, code audits, and code review assignments. Most works in the code stylometry literature use machine learning techniques and (1) rely on datasets coming from in vitro coding competition for training, and (2) only attempt to recognize authors present in the training dataset (in-distribution authors). In this work we give a fresh look at code stylometry and challenge both these assumptions: (1) we recogn

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Rodrigues, João Antunes, Alexandre Martins, Mateus Mendes, José Torres Farinha, Ricardo J. G. Mateus, and Antonio J. Marques Cardoso. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning." Energies 15, no. 24 (2022): 9387. http://dx.doi.org/10.3390/en15249387.

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Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the El

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Jayanti, Nikmah, and Charos George Selan. "ANALISIS NILAI AKURASI PENGOLAHAN CITRA PENDETEKSIAN RINTANGAN KERJA TRAKTOR MENGGUNAKAN K-MEANS CLUSTERING." Jurnal Informatika Progres 14, no. 1 (2022): 25–32. http://dx.doi.org/10.56708/progres.v14i1.318.

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The use of agricultural tractors as mechanical aids for tillage using agricultural tractors can make work lighter, faster, more efficient and do big jobs in a relatively short time. Along with the development of technology, many innovations have been developed by humans, including in the field of digital image processing. Segmentation is one of the methods in digital image processing to distinguish objects in an input image. One of the algorithms that can be used for image segmentation is K-Means. Many algorithms are used in image classification. Algorithms that can be used to complete the sup

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KIM, EUNG-KYEU, JIAN-TONG WU, SHINICHI TAMURA, et al. "COMPARISON OF NEURAL NETWORK AND K-NN CLASSIFICATION METHODS IN VOWEL AND PATELLAR SUBLUXATION IMAGE RECOGNITIONS." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 04 (1993): 775–82. http://dx.doi.org/10.1142/s0218001493000388.

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We make a comparision of classification ability between BPN (BackPropagation Neural Network) and k-NN (k-Nearest Neighbor) classification methods. Voice data and patellar subluxation images are used. The result was that the average recognition rate of BPN was 9.2 percent higher than that of the k-NN classification method. Although k-NN classification is simple in theory, classification time was fairly long. Therefore, it seems that real time recognition is difficult. On the other hand, the BPN method has a long learning time but a very short recognition time. Especially if the number of dimens

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Feyzioglu, Ahmet, and Yavuz Selim Taspinar. "Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types." Sensors 23, no. 4 (2023): 2222. http://dx.doi.org/10.3390/s23042222.

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Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately

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Ramezan, Christopher A., Timothy A. Warner, Aaron E. Maxwell, and Bradley S. Price. "Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data." Remote Sensing 13, no. 3 (2021): 368. http://dx.doi.org/10.3390/rs13030368.

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The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data with

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B. R. Sathish, B. R. Sathish, and Radha Senthilkumar B. R. Sathish. "A Hybrid Algorithm for Feature Selection and Classification." 網際網路技術學刊 24, no. 3 (2023): 593–602. http://dx.doi.org/10.53106/160792642023052403004.

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<p>With a recent spread of intelligent information systems, massive data collections with a lot of repeated and unintentional, unwanted interference oriented data are gathered and a huge feature set are being operated. Higher dimensional inputs, on the other hand, contain more correlated variables, which might have a negative impact on model performance. In our model a Hybrid method of selecting feature was developed by combining Binary Gravitational Search Particle Swarm Optimization (HBGSPSO) method with an Enhanced Convolution Neural Network Bidirectional Long Short Term Memory (ECNN-

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Park, Youngki, and Youhyun Shin. "Applying Object Detection and Embedding Techniques to One-Shot Class-Incremental Multi-Label Image Classification." Applied Sciences 13, no. 18 (2023): 10468. http://dx.doi.org/10.3390/app131810468.

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In this paper, we introduce an efficient approach to multi-label image classification that is particularly suited for scenarios requiring rapid adaptation to new classes with minimal training data. Unlike conventional methods that rely solely on neural networks trained on known classes, our model integrates object detection and embedding techniques to allow for the fast and accurate classification of novel classes based on as few as one sample image. During training, we use either Convolutional Neural Network (CNN)- or Vision Transformer-based algorithms to convert the provided sample images o

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Riza Adrianti Supono and Muhammad Azis Suprayogi. "Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 5 (2021): 911–18. http://dx.doi.org/10.29207/resti.v5i5.3403.

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Distribution of tickets to the destination unit is a very important function in the helpdesk application, but the process of distributing tickets manually by admin officers has drawbacks, namely ticket distribution errors can occur and increase ticket completion time if the number of tickets is large. Helpdesk text classification becomes important to automatically distribute tickets to the appropriate destination units in a short time. This study was conducted to compare the performance of helpdesk text classification at the Directorate General of State Assets of the Ministry of Finance using

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Gil, Artur, Qian Yu, Mohamed Abadi, and Helena Calado. "Using aster multispectral imagery for mapping woody invasive species in pico da vara natural reserve (Azores Islands, Portugal)." Revista Árvore 38, no. 3 (2014): 391–401. http://dx.doi.org/10.1590/s0100-67622014000300001.

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This paper aims to assess the effectiveness of ASTER imagery to support the mapping of Pittosporum undulatum, an invasive woody species, in Pico da Vara Natural Reserve (S. Miguel Island, Archipelago of the Azores, Portugal). This assessment was done by applying K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Maximum Likelihood (MLC) pixel-based supervised classifications to 4 different geographic and remote sensing datasets constituted by the Visible, Near-Infrared (VNIR) and Short Wave Infrared (SWIR) of the ASTER sensor and by digital cartography associated to orography (altitude

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Qiu, Zefeng, Binbin Wu, Qi Chu, Xianpeng Xie, Ruhao Sun, and Shuhui Jia. "Spatiotemporal Classification of Short-Duration Heavy Rainfall in Beijing Using K-Shape Clustering." Water 17, no. 7 (2025): 968. https://doi.org/10.3390/w17070968.

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Understanding the spatiotemporal dynamics of short-duration heavy rainfall (SDHR) is critical for urban flood management. This study applies the K-shape clustering algorithm to classify 105 SDHR events in Beijing (2009–2021) using hourly rainfall data. Compared to K-means and DTW, K-shape prioritizes temporal shape alignment, crucial for capturing phase-shifted rainfall patterns. Three clusters emerged: (1) localized moderate-intensity events (13.3% of events) peaking at noon (11:00–14:00 LST) in western/southeastern regions, with weak burstiness (44.3% stations peak within 0–1 h) and moderate

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Choi, Wonjun, Gunwoo Park, Yejun Park, Jeongwoo Yun, and Tae-Won Park. "A Study on Cloud Classification Methods: Manual and Machine Learning Approaches." Korean Science Education Society for the Gifted 16, no. 1 (2024): 83–93. http://dx.doi.org/10.29306/jseg.2024.16.1.83.

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This study is based on the mentorship course in CNU Science Education Institute for the Gifted. Clouds are classified into ten types proposed by Luke Howard and documented in the Cloud Atlas. In this study, we performed the cloud classification using manual approach and machine learning techniques. A total of 365 cloud images were directly taken for the mentorship course. Initially, we visually assessed cloud features in each image and manually classified cloud images into 11 types considering cloud features. The 11 types were named according to their distinguishing features. Subsequently, clo

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Xu, Jing, Han Zhang, Jinfang Zheng, Philippe Dovoedo, and Yanbin Yin. "eCAMI: simultaneous classification and motif identification for enzyme annotation." Bioinformatics 36, no. 7 (2019): 2068–75. http://dx.doi.org/10.1093/bioinformatics/btz908.

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Abstract Motivation Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to a

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Jiang, Xiaoli, Jing Zhou, Xinyue Qiao, Chang Peng, and Shiwen Su. "A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data." Computational Intelligence and Neuroscience 2022 (September 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/9891971.

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In this paper, a novel distance-based multilabel classification algorithm is proposed. The proposed algorithm combines k-nearest neighbors (kNN) with neighborhood classifier (NC) to impose double constraints on the quantity and distance of the neighbors. In short, the radius constraint is introduced in the kNN model to improve the classification accuracy, and the quantity constraint k is added in the NC model to speed up computing. From the neighbors with the double constraints, the probabilities for each label are estimated by the Bayesian rule, and the classification judgment is made accordi

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Ma, Yu, Yu Ling Gao, and Shao Yun Song. "The Spatial Classification Algorithm of K-Nearest Neighbor Based on Spatial Predicate." Advanced Materials Research 706-708 (June 2013): 1928–31. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1928.

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Traditional k-Nearest Neighbor Algorithm (short for KNN) is usually used in the spatial classification; however, the problem of low-speed searching exists in this method. In order to avoid this kind of disadvantage, this paper puts forward a new spatial classification algorithm of K-nearest neighbor based on spatial predicate. This method searches the object set which is similar to the test object in spatial concept and uses spatial predicate to help search the object set, which narrows the searching range and reduces the operating time of KNN algorithm.

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Muta'alimah, Muta'alimah, Cindy Kirana Zarry, Atha Kurniawan, Hauriya Hasysya, Muhammad Farhan Firas, and Nurin Nadhirah. "Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms." Public Research Journal of Engineering, Data Technology and Computer Science 2, no. 1 (2024): 18–25. http://dx.doi.org/10.57152/predatecs.v2i1.1099.

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Advancements in technology have made online shopping popular among many. However, the use of offline marketing models is still considered a profitable and important way of business development. This can be seen in the 2022 Association of Retail Entrepreneurs of Indonesia (APRINDO), which states that 60% of Indonesians shop offline, and in 2023, more than 75% of continental European consumers will prefer to shop offline. This is because many benefits can be achieved through offline marketing that cannot be obtained from online marketing. Therefore, classification of patterns and trends is perfo

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Gou, Jiaolong, Xudong Niu, Xi Chen, Shuxin Dong, and Jing Xin. "Identification of Abnormal Electricity Consumption Behavior of Low-Voltage Users in New Power Systems Based on a Combined Method." Energies 18, no. 10 (2025): 2528. https://doi.org/10.3390/en18102528.

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With the rapid growth in low-voltage electricity demand, abnormal electricity consumption behavior is becoming more and more frequent, which not only threatens the safe and stable operation of power systems, but also causes huge economic losses. In order to effectively meet this challenge, it is of great practical significance to carry out monitoring and analysis of abnormal power consumption of low-voltage users. In this paper, a new detection model of abnormal power consumption behavior of low-voltage power users in power system based on the hybrid model, namely the K-GBDT model, is proposed

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Ansar, Hira, Amel Ksibi, Ahmad Jalal, et al. "Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier." Applied Sciences 12, no. 13 (2022): 6481. http://dx.doi.org/10.3390/app12136481.

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In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convo

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