Search Results - (( pattern detection packet algorithm ) OR ( java application tree algorithm ))
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An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach
Published 2015“…As ADS can process massive volumes of packets, the amount of processing time needed to discover the pattern of the packets is also increased accordingly and resulting in late detection of the attack packets. …”
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Thesis -
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Network intrusion detection and alert system
Published 2024“…Signature-based detection compares network traffic packets with a real-time updated database of known attack patterns, while anomaly-based detection algorithms learn normal behavior patterns and identify deviations. …”
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Final Year Project / Dissertation / Thesis -
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Non-invasive pathological voice classifications using linear and non-linear classifiers
Published 2010“…In the second experiment, the detection of the specific type of voice disorders has been carried out through twoclass pattern classification problems. …”
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A lightweight graph-based pattern recognition scheme in mobile ad hoc networks.
Published 2012“…Both algorithms show comparable detection results. Thus, the lightweight, low computation DHGN based detection scheme offers an effective security solution in MANETs.…”
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Book Section -
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Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain
Published 2019“…Second, the MFO algorithm was utilised to select the optimal discriminative features. …”
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Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest
Published 2026“…The framework analyzes extracted traffic features, including packet length and IP address patterns, to detect deviations from normal behaviour without requiring labelled data. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Q-Learning-based detection of IPv6 intrusions: a behavioral and performance study
Published 2025“…To address this limitation, the present study proposes a self-learning model using reinforcement learning techniques, specifically the Q-Learning algorithm, to classify network intrusions based on learned behavioural patterns autonomously. …”
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Proceeding Paper -
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
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Conference or Workshop Item -
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AI powered asthma prediction towards treatment formulation: an android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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Error concealment technique using wavelet neural network for wireless transmitted digital images
Published 2012“…In the case, where the missing regions of pixels are containing textures, edges, and other image features that are not easily handled by concealment algorithms. It therefore, necessitated to use denoising rather than EC algorithms. …”
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Thesis -
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AI powered asthma prediction towards treatment formulation : An android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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