Search Results - (( pattern detection bayes algorithm ) OR ( java application mining 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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Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

“…The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. …”
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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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Android Mobile Malware Classification Based on System Call and Permission Using Tokenization by Intan Nurfarahin Binti Ahmad

An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

“…However, these algorithms often fall short in consistently detecting and classifying network intrusions, particularly when distinctions between classes are subtle or when facing evolving attack patterns. …”
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Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms by Liaghat, Shohreh, Ehsani, Reza, Mansor, Shattri, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja, Azam, Siti H. M. N.

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Forgery detection algorithm based on texture features by Ahmed I.T., Hammad B.T., Jamil N.

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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Investigating optimal smartphone placement for identifying stairs movement using machine learning by Muhammad Ruhul Amin, Shourov, Husman, Muhammad Afif, Toha, Siti Fauziah, Jasni, Farahiyah

“…The data was trained against 6 machine learning algorithms namely Decision Tree, Logistic Regression, Naive Bayes, Random Forest, Neural Networks and KNN. …”
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Suspicious activities detection for anti-money laundering using machine learning techniques by Lim, Aun Chir

“…XGBoost is selected as the core detection engine due to its superior performance among five supervised machine learning algorithms tested: Random Forest, Naïve Bayes, Support Vector Machine and Artificial Neural Network. …”
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iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems by Azad, Saiful, Mahmud, Mufti, Kamal Zuhairi, Zamli, Kaiser, M. Shamim, Jahan, Sobhana, Razzaque, Md Abdur

“…These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. …”
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Advancing machine learning for identifying cardiovascular disease via granular computing by Ku Muhammad Naim, Ku Khalif, Noryanti, Muhammad, Mohd Khairul Bazli, Mohd Aziz, Mohammad Isa, Irawan, Mohammad Iqbal, ., Muhammad Nanda, Setiawan

“…Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. …”
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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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Machine Learning-Based Stress Level Detection from EEG Signals by Nirabi, Ali, Abd Rhman, Faridah, Habaebi, Mohamed Hadi, Sidek, Khairul Azami, Yusoff, Siti Hajar

“…Stress is associated with the brain activities of human beings that can be scanned by electroencephalogram (EEG) signals which is very complex and often challenging to understand the signal's pattern. This paper presented a system to detect the stress level from the EEG signals using machine learning algorithms. …”
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An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach by Mohamed Yassin, Warusia

“…Therefore, Statisticalbased Packet Header Anomaly Detection (SPHAD) and a hybridized Naive Bayes and Random Forest classifier (NB+RF) are considered for the ADS, and Signature-based Packet Header Intrusion Detection (SPHID) is proposed as the SDS. …”
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Assessment of near-infrared and mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm plantation by Liaghat, Shohreh

“…Reflectance spectra were pre-processed and principal component analysis (PCA) was performed to obtain PC scores as input features used in different pattern recognition algorithms in order to select the best learning model of Ganoderma discrimination. …”
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