Search Results - (( java implementation clustering algorithm ) OR ( basic learning tree algorithm ))

Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

“…A simulator for desktop grid environment has been developed using Java as the implementation language due to its wide popularity. …”
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Enhancing Performance of Batik SMEs through Cleaner Production Adoption: Insights from Clustering Algorithm Analysis by Achmad, Fandi, Zulkarnain, Ibnu, Rumanti, Augustina Asih, Wiratmadja, Iwan Inrawan, Shuhidan, Shuhaida Mohamed

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A web-based implementation of k-means algorithms by Lee, Quan

“…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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Application-Programming Interface (API) for Song Recognition Systems by Murtadha Arif Sahbudin, Chakib Chaouch, Salvatore Serrano

Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

“…Results of studying color segmentation using machine-learning algorithm and color space analysis is presented in this thesis. …”
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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Estimation of electric vehicle turning radius through machine learning for roundabout cornering by Ashaa, Supramaniam, Muhammad Aizzat, Zakaria, Kunjunni, Baarath, Mohamad Heerwan, Peeie, Ahmad Fakhri, Ab. Nasir, Muhammad Izhar, Ishak

“…This paper presents an alternative approach for estimating the turning radius using machine learning technique. While on-board sensors are unable to offer adequate information on vehicle states to the algorithm, vehicle states other than those directly detected by on-board sensors can be inferred using machine learning (ML) approaches based on the collected data. …”
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Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

“…One of the prominent methods to improve machine learning accuracy is by using ensemble method which basically employs multiple base models. …”

Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system by Beg, Abul Hashem

“…The PLSR architecture model, workflow and algorithms are described. The PLSR has been developed using Java Programming language. …”
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A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

“…Any resampling algorithm is not a necessity in the case of this proposed algorithm. …”
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques by Jifroudi, Hamidreza Maskani

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Empirical study on intelligent android malware detection based on supervised machine learning by Abdullah, Talal A.A., Ali, Waleed, Abdulghafor, Rawad Abdulkhaleq Abdulmolla

“…More significantly, this paper empirically discusses and compares the performances of six supervised machine learning algorithms, known as K-Nearest Neighbors (K-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), which are commonly used in the literature for detecting malware apps.…”
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A deep learning approach: The impact of sentiment analysis of Bangladeshi workers over the world by Tomal, Md Raihanul Islam, Kader, Tanveer, Kohbalan, Moorthy, Mazlina, Abdul Majid

“…TF-IDF vectorization was used for feature extraction, followed by basic machine learning algorithms such as Decision Tree, Support Vector Machine, and Naive Bayes. …”
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Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

“…Therefore, four supervised machine learning classification algorithms such as Multinomial Naïve Bayes, Support Vector Machine, Decision Trees, and kNearest Neighbors were investigated. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza by Ghulam , Murtaza

“…The extracted features are evaluated through six machine learning (ML) classifiers namely softmax, k-nearest neighbor (kNN), support vector machine, linear discriminant analysis, decision tree, and naive Bayes. …”
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Predictive modeling and feature attribution of CO₂ adsorption on LDH-derived materials using machine learning approach by Pinto, Mavin Jason, Sharath, S. S., Sudhakar, K., Priya, S. Shanmuga, Thirunavukkarasu, I.

“…We report the application of six machine learning models, namely Random Forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), Light gradient boosting machine (LightGBM), categorical boosting (CatBoost), and Adaptive boosting (AdaBoost) to predict CO2 adsorption capacity on LDH-derived materials. …”
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Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features by Wen Yee Wong, Ayman Khallel Ibrahim Al-Ani, Khairunnisa Hasikin, Anis Salwa Mohd Khairuddin, Sarah Abdul Razak, Hanee Farzana Hizaddin, Mohd Istajib Mokhtar, Muhammad Mokhzaini Azizan