Search Results - (( java application optimization algorithm ) OR ( label classification approach algorithm ))

Comparative analysis of text classification algorithms for automated labelling of quranic verses by Adeleke, Abdullah, Samsudin, Noor Azah, Mustapha, Aida, Mohd Nawi, Nazri

“…In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. …”
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An improve unsupervised discretization using optimization algorithms for classification problems by Mohamed, Rozlini, Samsudin, Noor Azah

“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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An improve unsupervised discretization using optimization algorithms for classification problems by Mohamed, Rozlini, Samsudin, Noor Azah

“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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Multi label ranking based on positive pairwise correlations among labels by Alazaidah, Raed, Ahmad, Farzana Kabir, Mohsin, Mohamad

“…Multi-Label Classification (MLC) is a general type of classification that has attracted many researchers in the last few years. …”
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Nearest neighbour group-based classification by Samsudin, Noor A., Bradley, Andrew P.

“…In addition, it is shown that algorithms that pool information from the whole test set perform better than two-stage approaches that undertake a vote based on the class labels of individual test samples.…”
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Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm by Dhewa, Oktaf Agni, Utama, Safitri Yuliana, Nasuha, Aris, Gunawan, Teddy Surya, Pratama, Gilang Nugraha Putu

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Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie by Mat Saffie, Nur Amira

“…Furthermore, most classification algorithms, using either fuzzy or non-fuzzy approaches, produce results in the form of crisp or categorical classification outcomes. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

“…Samples in the same cluster have the same label. The aim of data classification is to set up rules for the classification of some observations that the classes of data are supposed to be known. …”
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Contrastive Self-Supervised Learning for Image Classification by Tan, Yong Le

“…In computer vision, most of the existing state-of-the-art results are dominated by models trained in supervised learning approach, where abundant of labelled data is used for training. …”
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Visual codebook analysis in image understanding / Hoo Wai Lam by Hoo, Wai Lam

“…Therefore, a zero-shot learning approach is needed to classify those images that have not been seen by the classification model before. …”
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Modelling semantic context for novelty detection in wildlife scenes by Yong, SP, Deng, JD, Purvis, MP

“…An advantage of our approach is that it can be used for scene classification and novelty detection at the same time. …”
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EMOTION RECOGNITION USING GALVANIC SKIN RESPONSE (GSR) SIGNAL by RAMOS UKAR, YAKOBUS

“…The classified affective GSR signals with labels were obtained from the arousal seven-point emotional scale approach using machine learning algorithms. …”
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Knowledge base processing method based on text classification algorithm by Baisheng Zhong, Mohd Shamrie Sainin, Tan Soo Fun

“…The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. …”
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Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…This model combines a rule-based approach of sentiment analysis and a supervised machine learning algorithm to classify the text. …”
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Hyper-heuristic framework for sequential semi-supervised classification based on core clustering by Adnan, Ahmed, Muhammed, Abdullah, Abd Ghani, Abdul Azim, Abdullah, Azizol, Huyop @ Ayop, Fahrul Hakim

“…Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. …”
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Semi-supervised learning for sentiment classification with ensemble multi-classifier approach by Aribowo, Agus Sasmito, Basiron, Halizah, Abd Yusof, Noor Fazilla

“…Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…Seven machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DTT), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Deep Neural Network (DNN)—were used for multi-label classification of the complications. The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images by Ibrahim, I., Ibrahim, Z., Khalil, K., Mokji, M.M., Abu Bakar, S.A.R.S., Mokhtar, N., Ahmad, W.K.W.

“…The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

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A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms by Yu, Chiung Chang, A Hamid, Isredza Rahmi, Abdullah, Zubaile, Kipli, Kuryati, Amnur, Hidra

“…Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. …”
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