Search Results - (( level classification clustering algorithm ) OR ( java application based algorithm ))

Combining cluster quality index and supervised learning to predict students’ academic performance by Suhaila Zainudin, Rapi’ah Ibrahim, Hafiz Mohd Sarim

“…The best cluster is further analysed using classification to predict students’ academic performance. …”
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Text spam messages classification using Artificial Immune System (AIS) algorithms by Nurul Fadhilah Sulaiman

Efficient classifying and indexing for large iris database based on enhanced clustering method by Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy, Khalaf, Ahmad Taha

“…The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. …”
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ChoCD : Usable and secure graphical password authentication scheme by Radhi Rafiee Bin Afandi

Effective gene selection techniques for classification of gene expression data by Yeo, Lee Chin

“…The selected subset of genes is then be used to train the classifiers for constructing rules for future tissue classification problem. Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Nowadays, the use of SVM is very perspective for the big data classification. SVM provides a global solution for data classification but SVM is highly sensitive to noise data and may not be effective when the level of noise data is high. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Nowadays, the use of SVM is very perspective for the big data classification. SVM provides a global solution for data classification but SVM is highly sensitive to noise data and may not be effective when the level of noise data is high. …”
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Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features by Othman, E.S., Faye, I., Hussaan, A.M.

“…The formed clusters are then used as ground truth for classification purposes. …”
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Design and analysis of management platform based on financial big data by Chen, Yuhua, Mustafa, Hasri, Zhang, Xuandong, Liu, Jing

“…In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. …”
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An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title by Suhaimi, Azrina

“…The index terms are then clustered using Fuzzy clustering algorithms based on similarity. …”
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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

“…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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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

“…Classic bag of visual words algorithm is based on k-means clustering and every SIFT features belongs to one cluster and it leads to decreasing classification results. …”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification by Mohammed, Alhassan Afnan

“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

“…For this purpose, an algorithm which hybridized the Intensity Based Analysis (IBA), Grey Level Co-occurrence Matrices (GLCM), Adaptive Network-based Fuzzy Inference System (ANFIS) and £article ~warm Optimization (PSO) Clustering Algorithm (CAPSOCA) is proposed. …”
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques by ASHRAF, AMNA, MOHD NAWI, NAZRI, MUHAMMAD AAMIR, MUHAMMAD AAMIR

“…In the first phase, the manifold learning approach is used to improve the ‘feature selection by clustering’. Clustering algorithms such as K-means, spectral clustering, and the Gaussian Mixer Model have been tested with manifold learning approaches for adaptive feature selection. …”
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Segmenting nodules of lung tomography image with level set algorithm and neural network by Chong, Soon Yee, Tan, Min Keng, Yeo, Kiam Beng, Mohd Yusof Ibrahim, Hao, Xiaoxi, Teo, Kenneth Tze Kin

“…It is diagnosed generally by analysing a tissue cluster formation called 'nodule' inside the lung. …”
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Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi by Muhammad, Faisal

“…The classification technique using the CSLI-Cluster, DVI, and HDI criteria showed that as many as 22 villages had the status of Less Development level, and 8 villages were declared Developed. …”
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Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning by Suhaila, Bahrom, Anuar, Ab Rani, Aisyah Amalina, Mohd Noor

“…Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels and generate actionable recommendations. …”
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