Search Results - (( developing feature clustering algorithm ) OR ( java application sensor algorithm ))

Development Of Fall Risk Clustering Algorithm In Older People by Wong, Kam Kang

“…The proposed algorithm consists of several stages, includes data pre-processing, feature selection, feature extraction, clustering and characteristic interpretation. …”
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Extracting feature from images by using K-Means clustering algorithm / Abdul Hakim Zainal Abidin by Zainal Abidin, Abdul Hakim

“…For this research, the meaningful information that will be extracting is eye feature. Methodology of this research consists of Planning and Analysis, Data Collection, Algorithm Design and Development and Testing. …”
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Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

“…The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. …”
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Development of an effective clustering algorithm for older fallers by Goh, Choon Hian, Wong, Kam Kang, Tan, Maw Pin *, Ng, Siew Cheok, Chuah, Yea Dat, Kwan, Ban Hoe

“…The proposed fall risk clustering algorithm grouped the subjects according to features. …”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

“…Thus in this study we intend to overcome these problems by determining a feature subset and the number of the cluster problems after developing an algorithm which simultaneously solved these issues. …”
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Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method by Al-Mohair, Hani Kaid Saif

“…Based on these feature extraction results, a system based on a combination of an MLP ANN and k-means clustering which employs the YIQ color space and the statistical features of human skin as inputs is developed for human skin detection. …”
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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction by Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.

“…Lately, a fully online buffer-based clustering algorithm for handling evolving data streams (BOCEDS) was developed. …”

Centre based evolving clustering framework with extended mobility features for vehicular ad-hoc networks by Talib, Mohammed Saad

“…This framework uses an evolving data clustering algorithm by adopting the concept of grid granularity to capture the features of a cluster more efficiently. …”
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K-means Clustering Analysis for EEG Features of Situational Interest Detection in Classroom Learning by Othman, E.S., Faye, I., Babiker, A., Hussaan, A.M.

“…This paper proposes a method to detect situational interest in classroom learning using k-means algorithms. The developed algorithm in this paper had been tested on features from ten students who experienced mathematics learning in a classroom. …”
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A maximal-clique-based clustering approach for multi-observer multi-view data by using k-nearest neighbor with S-pseudo-ultrametric induced by a fuzzy similarity by Khameneh, Azadeh Zahedi, Ghaznavi, Mehrdad, Kilicman, Adem, Mahad, Zahari, Mardani, Abbas

“…Under this framework, different algorithms have been developed where the output relies on an exact distance calculated based on the objects’ features. …”
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An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents by Al-Dyani, Wafa Zubair Abdullah

“…To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. …”
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Unsupervised place recognition for assistive mobile robots based on local feature descriptions. by Tang, Sai Hong, Ramli, Abdul Rahman, Samsudin, Khairulmizam

“…This method fuses several image features using speed up robust features (SURF) by agglomerating them into a union form of features inside each place cluster. …”
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Topological mapping and qualitative localization based on K-adjacent union clustering algorithm by Karasfi, Babak

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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…Euclidean Distance, Pearson Correlation and Matching Matrix were used to measure the performance of the feature extraction and clustering methods. Recognition software achieved 87.14%, EPD algorithm achieved 73.57% and HMT algorithm achieved 74.30%) prediction accuracy with OTs. …”
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Pengesanan nombor plat kenderaan menggunakan alkhwarizmi gugusan dan kelancaran jarak larian(GKJL) by Siti Norul Huda Sheikh Abdullah, Marzuki Khalid, Khairuddin Omar, Rubiyah Yusof

“…A new algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) approach was applied to locate the license plate at the right position. …”
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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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k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data by Alfred, Rayner, Shin, Kung Ke, Sainin, Mohd Shamrie, On, Chin Kim, Pandiyan, Paulraj Murugesa, Ag Ibrahim, Ag Asri

“…However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset.A feature selection process can be introduced to overcome this problem.These selected features can be further optimized to achieve a good classification result.Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. …”
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Unsupervised and online place recognition for mobile robot based on local features description by Tang, Sai Hong, Hamami, Ghazali, Karasfi, Babak, Nakhaeinia, Danial

“…This method combines several image features using Speedup Robust Features (SURF) by accumulating them into a union form of features inside each place cluster. …”
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An intelligent categorization tool for malay research articles by Mohd Norhisham Razali, Rayner Alfred, Chin, Kim On

“…Hence, by increasing the mapping percentage for the bilingual clusters, a more robust clustering algorithm can be developed for clustering bilingual documents. …”
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