Search Results - (( web application learning algorithm ) OR ( _ application learning algorithms ))*
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Web Usage Mining for UUM Learning Care Using Association Rules
Published 2004“…E-Learning is one of the Web based application where it will facing with large amount of data. …”
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Web usage mining for UUM learning care using association rules
Published 2004“…In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. …”
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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
Published 2000“…This system is achieved using the Genetic Algorithm which is capable of performing information retrieval and learning algorithm. …”
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Cyberbullying detection: a machine learning approach
Published 2022“…Machine learning is a hot topic and it is widely implemented in software, web application and more. …”
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Final Year Project / Dissertation / Thesis -
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Intelligent cooperative web caching policies for media objects based on decision tree supervised machine learning algorithm
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Car dealership web application
Published 2022“…In this project, a web application and a web service were proposed and implemented. …”
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Final Year Project / Dissertation / Thesis -
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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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Comparative study of machine learning algorithms in data classification
Published 2025“…The results will help with real-world data mining applications of machine learning and be a useful guide for further study and practical applications.…”
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Final Year Project / Dissertation / Thesis -
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Bot detection using machine learning algorithms on social media platforms
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Proceeding Paper -
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Target heart rate zone detector during exercise based on real-time facial expression using single shot detection algorithm / Muhammad Azziq Shamsudin, Raihah Aminuddin and Ummu Mar...
Published 2022“…The object detection machine learning model used in this project is Single Shot Detector (SSD) algorithm. …”
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Book Section -
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Defect green coffee bean detection using image recognition and supervised learning
Published 2022“…The model was integrated as web application with Flask where user can upload the image and the system will return result with precision and prediction. …”
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Context-Aware Recommender System based on machine learning in tourist mobile application / Nor Liza Saad … [et al.]
Published 2022“…Furthermore, data were collected simulated based on the mobile application prototype to be used for finding the suitable machine learning algorithms in the recommendation system module. …”
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Adaptive security architecture for protecting RESTful web services in enterprise computing environment
Published 2018“…The proposed security architecture is constructed as an adaptive way-forward Internet-of-Things (IoT) friendly security solution that is comprised of three cyclic parts: learn, predict and prevent. A novel security component named â��intelligent security engineâ�� is introduced which learns the possible occurrences of security threats on SOA using artificial neural networks learning algorithms, then it predicts the potential attacks on SOA based on obtained results by the developed theoretical security model, and the written algorithms as part of security solution prevent the SOA attacks. …”
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Adaptive security architecture for protecting RESTful web services in enterprise computing environment
Published 2018“…The proposed security architecture is constructed as an adaptive way-forward Internet-of-Things (IoT) friendly security solution that is comprised of three cyclic parts: learn, predict and prevent. A novel security component named â��intelligent security engineâ�� is introduced which learns the possible occurrences of security threats on SOA using artificial neural networks learning algorithms, then it predicts the potential attacks on SOA based on obtained results by the developed theoretical security model, and the written algorithms as part of security solution prevent the SOA attacks. …”
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Review on ubiquitous education system with multi-agent synchronization on mobile learning application environment
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Conference or Workshop Item -
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Brain tumor image segmentation using deep learning approach
Published 2022“…Deep learning algorithm is able to provide good tumor segmentation results compared to other conventional segmentation algorithms as it learns from the labeled brain MRIs to predict the location of tumor region and consequently segment the tumor. …”
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Final Year Project / Dissertation / Thesis -
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A New Mobile Botnet Classification based on Permission and API Calls
Published 2024“…As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. …”
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