Search Results - (( developing interactive classification algorithm ) OR ( java evaluation learning algorithm ))

Development of interactive application for classification of Artocarpus Species by Abdul Ghapar, Nadia

“…The aim of this research is to identify the classification of Artocarpus species by using interactive application and the effectiveness of the interactive application for classification of Artocarpus species. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

“…The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Identify texture of MRI human brain using Adaptive Fuzzy C-Means (AFCM) Algorithm / Faridatul Akma Mohd Noor by Faridatul Akma, Mohd Noor

“…The result of this research can be uses for segmentation and classification process. To access its viability, a prototype with interactive graphical user interface (GUI) was developed and tested for its reliability. …”
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Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm by Addie Irawan, Hashim, Marni Azira, Markom, Abdul Hamid, Adom, Mohd Muslim Tan, E. S.

“…The mobile robot and the developed algorithm are tested in static environment. …”
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Mobile phone customers churn prediction by using Elman and Jordan recurrent neural network with reinforcement learning algorithm / Muhammad Syahir Mohd Ribuan by Mohd Ribuan, Muhammad Syahir

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Classification of hand gestures from EMG signals / Diaa Albitar by Albitar, Diaa

“…This study is to develop classification model to classify six hand gestures using Artificial Intelligent algorithm. …”
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VHDL modeling of EMG signal classification using artificial neural network by Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran, Ullah, Mohammad Habib

“…The acquired and processed EMG signal requires classification before utilizing it in the development of interfacing which is the most difficult part of the development process. …”
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Improved building roof type classification using correlation-based feature selection and gain ratio algorithms by Norman, M., Mohd Shafri, Helmi Zulhaidi, Pradhan, Biswajeet, Yusuf, B.

“…Accurate feature selection is a necessary step to improve the accuracy of classification. This process depends on the number of feature attributes available for interactive synthesis of common characteristics that discriminate different features. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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EMG signal classification for human computer interaction: a review by Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran

“…This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into computer command.…”
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Pattern classification of human interactions from videos / Muhsin Abdul Mohammed by Muhsin , Abdul Mohammed

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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

“…This is taking advantage on the discriminative feature provided by both methods, statistical and CSP filter, which is expected to increase the accuracy of the eye state classification algorithm. The process of developing the EEG eye state classification algorithm, includes data extraction, pre-processing, data normalization, feature extraction, feature selection and classification are detailed out in this paper. …”
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

“…This is taking advantage on the discriminative feature provided by both methods, statistical and CSP filter, which is expected to increase the accuracy of the eye state classification algorithm. The process of developing the EEG eye state classification algorithm, includes data extraction, pre-processing, data normalization, feature extraction, feature selection and classification are detailed out in this paper. …”
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

“…In order to overcome this problem, a classification model for intent recognition is developed. …”
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

“…Enhancements in cognitive neuroscience and brain imaging technologies such as Human-Computer Interaction (HCI) have started to provide human with the ability to interact directly with the brain. …”
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EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN by WANG, CHIA WOON

“…Besides, common spatial pattern (CSP) is the well-known method for classification algorithm in the BCI field. However, application of CSP in EEG eye state classification considered uncommon as compared to motor imagery classification. …”
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Development Of Algorithms For Vehicle Classification And Speed Estimation From Dynamic Scenes By On-Board Camera Using Image Processing Techniques by Aminuddin, Nur Shazwani

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Vision-based egg grade classifier by Zalhan M.Z., Sera Syarmila S., Mohd Nazri I., Mohd Taha I.

Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

“…In this paper, the stacking ensemble method is used to increase the accuracy of the machine learning model for LSM where the base (first-level) learners use five ML algorithms namely decision tree (DT), k-nearest neighbor (KNN), AdaBoost, extreme gradient boosting (XGB) and random forest (RF). …”