Search Results - (( java application mining algorithm ) OR ( source estimation learning algorithm ))

Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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Hybrid FFT-ADALINE algorithm with fast estimation of harmonics in power system by Goh, Zai Peng, Mohd Radzi, Mohd Amran, Thien, Yee Von, Hizam, Hashim, Abdul Wahab, Noor Izzri

“…Hybrid fast Fourier transform Adaptive LINear Element (FFT-ADALINE) algorithm for fast and accurate estimation of harmonics is proposed in this study. …”
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Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms by Shaharum, Nur Shafira Nisa, Mohd Shafri, Helmi Zulhaidi, Wan Ab. Karim Ghani, Wan Azlina, Samsatli, Sheila, Yusuf, Badronnisa

“…Hence, the mapping of oil palm distributions via machine learning algorithm was better than that via non-machine learning algorithm.…”
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Effective source number enumeration approach under small snapshot numbers by Ge, Shengguo

“…This study also makes a significant contribution to data science by providing a comprehensive method for estimating the number of signal sources, which is integrated with a machine learning model. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting by Md Salleh N.S., Suliman A., Jorgensen B.N.

Linear Discriminate Analysis And K-Nearest Neighbor Based Diagnostic Analytic Of Harmonic Source Identification by Jopri, Mohd Hatta, Abdullah, Abdul Rahim, Manap, Mustafa, Nor Shah, Mohd Badril, Sutikno, Tole, Too, Jing Wei

“…This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. …”
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Visual Crowd Counting System Using Deep Learning by Mohd Wafi Nazrul Adam, Mohd Ridhwan Oxley Adam

“…The back-end will be using a neural network model based on the Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (Zhang, et al., 2016) and is developed through the PyTorch framework, an open-source machine learning library. The model will be trained using the Mall Dataset and the Adam optimization algorithm. …”
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An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models by Jalil-Masir H., Fattahi R., Ghanbari-Adivi E., Asadi Aghbolaghi M., Ehteram M., Ahmed A.N., El-Shafie A.

State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model by Dickson Neoh Tze How, Dr.

“…To overcome these limitations, this study proposes a novel DL architecture for SOC estimation using the Transformer model with the self-supervised learning (SSL) framework. …”

Estimating Forest Aboveground Biomass Density Using Remote Sensing and Machine Learning : A RSME Approach by Yaniza Shaira, Zakaria, Mohd Fadzil, Akhir, Aidy, M Muslim, Nur Afiqah, Ariffin, Azizul, Ahmad

“…An accurate estimation of aboveground biomass (AGB) density is essential for effective forest management, carbon stock monitoring, and informed land management decisions. …”
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Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins by Sidek L.M., Basri H., Marufuzzaman M., Deros A.M., Osman S., Hassan F.A.

“…To forecast unexpected flood occurrences, faster flood prediction necessitates computational prediction models such as Machine Learning (ML) algorithms, which are extensively utilized around the world. …”

Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

“…The hybrid Wavelet Multiresolution Analysis and Machine learning algorithm (WMRA-ML) is used to extracts the useful hidden knowledge from decomposed one-cycle fault transient signals (voltage & current) from four Matlab/Simulink CIGRE models. …”
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Artificial neural network (ANN) as post-processing stage for chemically selective field effect transistor (CHEMFET) sensor selectivity based-on ion concentration / Nurhakimah Abd A... by Abd Aziz, Nurhakimah

“…Other than developing supervised learning, this study also was focusing on exploration of unsupervised learning mainly in blind source separation (BSS) algorithm to separate the interface signal. …”
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A detailed description on unsupervised heterogeneous anomaly based intrusion detection framework by Udzir, Nur Izura, Hajamydeen, Asif Iqbal

“…More effort has been taken in utilizing the data mining and machine learning algorithms to construct anomaly based intrusion detection systems, but the dependency on the learned models that were built based on earlier network behaviour still exists, which restricts those methods in detecting new or unknown intrusions. …”
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