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1
Security alert framework using dynamic tweet-based features for phishing detection on twitter
Published 2019“…This model is then embedded into the detection algorithm together with the inclusion of dynamic tweet-based features which are not as part of the features used to train a classification model for phishing tweet detection. …”
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2
Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…Criminal risk is predicted using classification models for a particular time interval and place. …”
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Conference or Workshop Item -
3
A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…A vector quantization model that incorporate rough sets attribute reduction and rules generation with a modified version of the K-means clustering algorithm was developed, implemented and tested as a part of a speech recognition framework, in which the Learning Vector Quantization (LVQ) neural network model was used in the pattern matching stage. …”
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4
Personality prediction using Random Forest algorithm / Wan Abdul Qayyum Abdul Wahab
Published 2023“…The research objectives included developing and executing a data gathering strategy, analyzing the data, and assessing the model's performance. The study methodology included a systematic approach to problem solving, such as developing data collecting tools, selecting research approaches, and implementing data analysis procedures. …”
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5
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…Based on this result, the neural network's platform for CA classification is successfully developed using the proposed framework. …”
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6
A novel framework for potato leaf disease detection using an efficient deep learning model
Published 2022“…Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticilliumwilt (PVw) and Potato Healthy (PH) class. …”
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A novel framework for potato leaf disease detection using an efficient deep learning model
Published 2022“…Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticilliumwilt (PVw) and Potato Healthy (PH) class. …”
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Poverty risk prediction based on socioeconomic factors using machine learning approach
Published 2025“…This study seeks to develop a predictive model of measuring poverty risk using socioeconomic factors based on a machine learning framework. …”
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Student Project -
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
Published 2020“…In this paper, efficient and fastidious classification is performed by analysing the frequency bands of the input EEG signal via discrete wavelet transform, which is relying on the deep convolutional neural network based classification. …”
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Conference or Workshop Item -
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Context enrichment framework for sentiment analysis in handling word ambiguity resolution
Published 2024“…Machine learning algorithms are deployed to perform sentiment classification. …”
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Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification
Published 2023“…Several benchmark data sets have been used to empirically evaluate the performance of the proposed model in pattern classification. …”
Conference Paper -
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Using genetic algorithms to optimise land use suitability
Published 2012“…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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14
Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…It is organized into three phases: preliminary investigation, implementation and analysis, and validation. The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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15
A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
Published 2022“…The introduced framework in this study has great potential for practical implementation on a home-based sleep staging device.…”
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Detection and analysis of fake reviews on online service portal
Published 2022“…This strategy provides incorrect information to new customers who are looking to purchase such things or services, and as a result, a system that can identify and eliminate misleading reviews are required to solve the problem. In this paper, a framework of a Machine Learning based fake review detection model has been proposed to identify which classification algorithm is the most effective with the proposed framework.…”
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Final Year Project / Dissertation / Thesis -
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An Artificial Intelligence-Based Knowledge Management System for Outcome-Based Education Implementing in Higher Education Institutions
Published 2025“…Recommendation system on learning analysis was implemented in a hybrid algorithm combines Rule-based and Content-based filtering algorithms. …”
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From Employees to Entrepreneurs: A Qualitative Exploration of Career Transitions in Ghana
Published 2025“…Recommendation system on learning analysis was implemented in a hybrid algorithm combines Rule-based and Content-based filtering algorithms. …”
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A new fuzzy criterion-referenced assessment with a fuzzy rule selection technique and a monotonicity-preserving similarity reasoning scheme
Published 2013“…Nevertheless, there are several limitations in combining FIS models and CRA, as follows. (i) It is difficult to maintain the monotonicity property of the FIS-based CRA model; (ii) it is difficult and impractical to form a complete fuzzy rule base when the number of required rules is large, and (iii) reducing fuzzy rules may cause the “tomato classification” problem. …”
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