Search Results - (( java implication based algorithm ) OR ( knowledge based streaming algorithm ))

A study of density-grid based clustering algorithms on data streams by Amini, A., Saybani, M.R., Sahaf Yazdi, S.R.A.

Get full text
Get full text

On density-based data streams clustering algorithms: A survey by Teh, Y.W.

“…Recently, a lot of density-based clustering algorithms are extended for data streams. …”
Get full text
Get full text
Get full text

An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning by Islam, Md Kamrul

“…Data stream clustering plays an important role in data stream mining for knowledge extraction. …”
Get full text
Get full text

A buffer-based online clustering for evolving data stream by Islam, Md. Kamrul, Ahmed, Md. Manjur, Kamal Z., Zamli

“…In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). …”
Get full text
Get full text
Get full text

An adaptive density-based method for clustering evolving data streams / Amineh Amini by Amini, Amineh

“…This study proposes a density-based algorithm for clustering evolving data streams. …”
Get full text
Get full text
Get full text

An Evolutionary Stream Clustering Technique for Outlier Detection by Supardi, N.A., Abdulkadir, S.J., Aziz, N.

“…Nearly all traditional density-based clustering algorithms can be extended to the latest ones for data streams study purposes. …”
Get full text
Get full text

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube by Al?amri R., Murugesan R.K., Almutairi M., Munir K., Alkawsi G., Baashar Y.

“…TSHC when added to Buffer?based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. …”

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. …”

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

“…Thus, using neural network-based semi-supervised stream data learning is inadequate due to capture the changes in the distribution and characteristics of various classes of data while avoiding the effect of the outdated stored knowledge in neural networks (NN). …”
Get full text
Get full text

The Security Level Of Two Xored-based A5 Crypto System by Nur Hafiza Zakaria, Kamaruzzaman Seman, Ismail Abdullah

“…Five (5) statistical tests were used to test the strength of the algorithm. Both of the proposed modified XORed-based A5/1 algorithms successfully passed the five (5) statistical tests.…”

Hazard identification on fractionation column using rule based expert system by Mohd Yunus, Mohd Yusri, Ali, Mohamad Wijayanuddin

“…The process-general knowledge which consists of rule-based expert system is developed from simulation result obtained from HYSIS process simulator. …”
Get full text
Get full text

Data stream clustering by divide and conquer approach based on vector model by Khalilian, Madjid, Mustapha, Norwati, Sulaiman, Nasir

Get full text
Get full text

Hyper-heuristic framework for sequential semi-supervised classification based on core clustering by Adnan, Ahmed, Muhammed, Abdullah, Abd Ghani, Abdul Azim, Abdullah, Azizol, Huyop @ Ayop, Fahrul Hakim

“…However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). …”
Get full text
Get full text
Get full text

A review on big data stream processing applications: contributions, benefits, and limitations by Ahmed Alwaisi, Shaimaa Safaa, Abbood, Maan Nawaf, Jalil, Luma Fayeq, Kasim, Shahreen, Mohd Fudzee, Mohd Farhan, Hadi, Ronal, Ismail, M. A.

“…Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. …”
Get full text
Get full text
Get full text

Clustering for binary data sets by using genetic algorithm-incremental K-means by Saharan, S., Baragona, R., Nor, M. E., Salleh, R. M., Asrah, N. M.

“…For the purpose of this research, GA was combined with the Incremental Kmeans (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. …”
Get full text
Get full text

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

Get full text
Get full text
Get full text

Real-time anomaly detection using clustering in big data technologies / Riyaz Ahamed Ariyaluran Habeeb by Riyaz Ahamed , Ariyaluran Habeeb

“…Based on the outcome of the analysis, this research proposed a novel framework namely real-time anomaly detection based on big data technologies (RTADBDT), along with supporting implementation algorithms. …”
Get full text
Get full text
Get full text

IoT flood monitoring system by Vajathan, Vimalan Pillai, Dhinagaran, Sachinn, Thivianathan, Timothy Nathan, Tamilamuthan, Shaarveen, Govindasamy, Sanjay

Get full text
Get full text
Get full text

Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows by Zhang, Shasha, Dong, Qiming, Yasin, Megat Al Imran, Fang, Ng Chwee

“…This research demonstrates a new algorithmic method of moderating emotional content within Chinese dating reality shows based on cross-media analysis, combining text, audio, video, and social media feedback. …”
Get full text
Get full text