Search Results - (( java implication based algorithm ) OR ( global classification learning algorithm ))

The forecasting of poverty using the ensemble learning classification methods by Zamzuri, Muhammad Haziq Adli, Nadilah, Sofian, Hassan, Raini

“…This research was conducted to forecast poverty using classification methods. Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. …”
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

“…Backpropagation (BP) learning algorithm is the well-known learning technique that trained ANN. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…To overcome this problem, in recent years, researchers obtained some achievements with combination of invariant local features such as Scale Invariant Feature Transform (SIFT) with global feature of leaf images. Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media by Sutranggono, Abi Nizar, Sarno, Riyanarto, Ghozali, Imam

“…The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. …”
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Global and local clustering soft assignment for intrusion detection system: a comparative study by Mohd Rizal Kadis, Azizi Abdullah

“…The ability of IDS to detect new sophisticated attacks compared to traditional method such as firewall is important to secure the network. Machine Learning algorithm such as unsupervised learning and supervised learning is capable to solve the problem of classification in IDS. …”
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Fusion of moment invariant method and deep learning algorithm for COVID-19 classification by Ervin Gubin Moung, Chong, Joon Hou, Maisarah Mohd Sufian, Mohd Hanafi Ahmad Hijazi, Jamal Ahmad Dargham, Sigeru Omatu

“…This paper proposes a fusion of a moment invariant (MI) method and a DL algorithm for feature extraction to address the instabilities in the existing COVID-19 classification models. …”
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Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection by Olufemi, Osaji Emmanuel, Othman, Mohammad Lutfi, Hizam, Hashim, Othman, Muhammad Murtadha, Ammar, Aker Elhadi Emhemed Alhaaj, Okeke, Chidiebere Akachukwu, Onuabuchi, Nwagbara Samuel

“…The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. …”
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Deep learning framework for hierarchical-based object identification and description by Alamro, Loai C. A.

“…Hence, DL approaches lack of global generalization, hierarchical learning, and correlation learning. …”
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Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network by Gao, Yuan, Mohd Kasihmuddin, Mohd Shareduwan, Chen, Ju, Zheng, Chengfeng, Romli, Nurul Atiqah, Mansor, Mohd. Asyraf, Zamri, Nur Ezlin

“…It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

“…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data by Qaedi, Kasyful, Abdullah, Mardina, Yusof, Khairul Adib, Hayakawa, Masashi, Zulhamidi, Nur Fatin Irdina

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An improvement of back propagation algorithm using halley third order optimisation method for classification problems by Abdul Hamid, Norhamreeza

“…Back Propagation (BP) has proven to be a robust algorithm for different connectionist learning problems which commonly available for any functional induction that provides a computationally efficient method. …”
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Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

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Extremal region selection for MSER detection in food recognition by Razali, Mohd Norhisham, Manshor, Noridayu, Abdul Halin, Alfian, Mustapha, Norwati, Yaakob, Razali

“…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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Multi-label learning based on positive label correlations using predictive apriori by Al Azaidah, Raed Hasan Saleh

“…Along with that, two algorithms have been constructed by capturing the positive correlations where the first algorithm (MLR-PC) captures the positive global correlations and the second algorithm (MLCBA) proposes an adaption of AC algorithm to handle MLC based on the positive local correlations. …”
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Artificial fish swarm optimization for multilayer network learning in classification problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam

“…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

“…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. …”
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An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification by Wan Azman,, Wan Nur Fadhlina Syamimi, Ku Azir, Ku Nurul Fazira, Mohd Khairuddin, Adam

“…This work proposes using machine learning algorithms and electronic nose technology to recognise and forecast the contamination in leftover cooked food. …”
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Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population by Abdullah, Siti Fairuz

“…A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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