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

Multi-label learning based on positive label correlations using predictive apriori by Al Azaidah, Raed Hasan Saleh

“…Multi-label Learning (MLL) is a general task in data mining that consists of three main tasks: classification, label ranking, and multi-label ranking. …”
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Multi label ranking based on positive pairwise correlations among labels by Alazaidah, Raed, Ahmad, Farzana Kabir, Mohsin, Mohamad

“…The first objective is to propose a new multi-label ranking algorithm based on the positive pairwise correlations among labels, while the second objective aims to propose new simple PTMs that are based on labels correlations, and not based on labels frequency as in conventional PTMs. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

“…Here, there is a collection of classes with labels and the problem is to label a new observation or data point belonging to one or more classes of data. …”
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Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

“…For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. …”
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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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Multi-label incremental kernel extreme earning machine for food recognition / Chen Sai by Chen , Sai

“…Then used ARCIKELM-ML for multi-label classification. In the framework, the hidden and output neurons corresponding to new labels are added and the classifier progressively remodels its structure like the new labels are introduced from the beginning of the training process. …”
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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. However, the labelling of data is costly and limited in some fields. …”
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A hierarchical deep convolutional neural network for asphalt pavement crack detection and classification / Nor Aizam Muhamed Yusof by Muhamed Yusof, Nor Aizam

“…To ease these processes, this study introduces a new app, CrackLabel, that can automatically label patches in the crack images into two groups, crack and non-crack. …”
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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

“…Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. …”
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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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Visual codebook analysis in image understanding / Hoo Wai Lam by Hoo, Wai Lam

“…This thesis aims to investigate the limitations of current visual codebook algorithms and propose new solutions to deal with the identified problems. …”
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Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Dynamic android malware category classification using semi-supervised deep learning by Mahdavifar, Samaneh, Kadir, Andi Fitriah Abdul, Fatemi, Rasool, Alhadidi, Dima, Ghorbani, Ali A

“…We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. …”
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Route Optimization System by Zulkifli, Abdul Hayy

“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS by FUDDOLY, AINI RACHMANIA KUSUMAAGAMA

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Training data selection for record linkage classification by Zaturrawiah Ali Omar, Zamira Hasanah Zamzuri, Noratiqah Mohd Ariff, Mohd Aftar Abu Bakar

“…The top and imbalanced construction was found to be the most effective in producing training data with 100% correct labels. Random forest and support vector machine classification algorithms were compared, and random forest with the top and imbalanced construction produced an F1 -score comparable to probabilistic record linkage using the expectation maximisation algorithm and EpiLink. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

“…For contextual text classification, the pre-trained LLM is further train on classificationspecific labeled data in a process called fine-tuning. …”
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BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.