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Text Categorization Using Naive Bayes Algorithm
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Text categorization using naive bayes algorithm
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A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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Mobile banking Trojan detection using Naive Bayes / Anis Athirah Masmuhallim
Published 2024“…The objectives of this project are to study the requirement of the Naive Bayes algorithm in Mobile Banking Trojan detection, to develop a webbased detection system for Mobile Banking Trojan using Naive Bayes, and to evaluate the performance and accuracy of the Naive Bayes algorithm in the Mobile Banking Trojan detection. …”
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Applying learning to filter text
Published 2005“…Text filtering has been a successful application especially in e-mail filtering. The use of probabilistic approaches such as naïve Bayes algorithm is the effective algorithms currently known for learning to filter or classify text document.Naïve Bayes algorithm is one of the algorithms in Machine Learning that manipulates probability estimation or reasoning about the observed data.The growing of bulk e-mail or known as spam e-mail becomes a threat to users’ privacy and network load and in the case of e -mail filtering,naïve Bayes classifier can be trained to automatically detect spam messages.Similar to the e-mail, forum application may be misused by the user to send bad messages and in some extent may offence other readers.Forum filtering may be less important compared to e-mail spam filtering; however there is a possibility of using naïve Bayes to learn the messages and automatically detect bad messages.Most of the forum application found in the web is applying keyword based text filtering which scan the words and change the detected words into certain representation.Instead of defining a set of keywords to filter the forum messages, this paper will explains the experiment in applying a learning to filter text especially in the educational and anonymous forum message, where there is no user registration required to submit messages.…”
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Divorce prediction using Naive Bayes / Alia Hannani Ahmad Bakri
Published 2024“…The study focuses on developing a divorce prediction system using the Naive Bayes algorithm, a widely used classifier. The system achieved 98% accuracy in predicting divorce based on a comprehensive dataset. …”
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Anomaly-based intrusion detection through K-means clustering and naives Bayes classification
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Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification
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An algorithm for Elliott Waves pattern detection
Published 2018“…Based on this knowledge, an algorithm for detection of these patterns is designed, developed and tested. …”
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Article -
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An algorithm for Elliott Waves pattern detection
Published 2018“…Based on this knowledge, an algorithm for detection of these patterns is designed, developed and tested. …”
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Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
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Final Year Project -
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Design and implementation of cordic algorithm with sinusoidal pulse width modulation switching strategy
Published 2017“…Usually, one sinusoidal wave is used for one inverter. In this design, SPWM is used for multilevel inverter application in photovoltaic (PV) system. …”
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Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks
Published 2014“…Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.…”
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