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Enhancing Personalized Treatment in Diabetes Using Genomic Data and Deep Learning Models: A Systematic Review
Abstract: Diabetes remains a significant global health challenge, necessitating innovative approaches for early detection and personalized treatment. Recent advancements in deep learning and genomic research have revolutionized diabetes prediction and management by enabling more accurate and individualized interventions. This systematic review explores the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid
Clustering In Wireless Sensor Networks Using Agglomerative Algorithm: An Energy-Efficient and Flexible Approach
Abstract: In this work, Hierarchical Agglomerative Clustering (HAC) Algorithm is utilised to enhance cluster formation in Wireless Sensor Networks (WSNs), addressing the energy efficiency challenge. WSNs consist of multiple sensor nodes with limited energy resources, which necessitates effective methods for data aggregation and transmission to extend network lifespan. The HAC algorithm begins with each sensor
A Robust Semantic Web Framework for Actionable Knowledge Discovery (AKD)
Abstract: The growing demand for extracting meaningful and actionable insights from data mining and knowledge discovery systems has led to a paradigm shift from the Knowledge Discovery Process (KDP) to the Actionable Knowledge Discovery (AKD) process. However, significant challenges remain in actionable knowledge discovery. First, the lack of integration between human domain expertise and mining
A Reformed Dropping Function Based Active Queue Management Mechanism for Network Routers
Abstract: In light of the fact that the rate of traffic generation on the Internet is ever increasing, the need for an effective algorithm that takes a firm stand against the adverse impact of network traffic congestion remains germane. Since its dissemination in the early 1990s, the Random Early Detection in short RED has gained
Design and Development of Automatic Speech Recognition (ASR) System for Low-resource Language Using Convolutional Neural Network Model
Abstract: The advancement of Automatic Speech Recognition (ASR) systems for low-resource languages is a formidable challenge due to restricted linguistic data and computational resources. The Yorùbá language is among the oldest languages in Africa, characterized by a rich literary and grammatical heritage and it is among Low-resource Languages. In this study, convolutional neural network (CNN)
Comparative Analysis of Machine Learning Techniques for Churn Prediction
Abstract: Getting customers to come back has become an essential strategy across industry as old customers tend to be cheaper to keep, compared to attracting new customers. Thus a comparative analysis of machine learning techniques for customer churn prediction assume a great importance in order to enhance the customer relationship management and guide the companies
M-Learning Adoption Among Open and Distance Learners in Nigeria
Abstract: The perennial challenge of infrastructural deficits has continued to haunt higher educational development in developing economies of the world. Citizens have resulted to the ‘jakpa phenomenon’ in search for quality and accessible education. Coincidentally, as smartphones continue to swiftly penetrate all continents of the world, a sound understanding of users’ adoption intentions remain critical
Enhancing Classification in Imbalanced Symbol Engineering Drawings using Affine 2D Geometric Transformation
Abstract In the design of smart cities, the digitisation of engineering symbols ensures that accurate intelligent systems can be deployed. Errors, resulting from the inability of humans to accurately read and analyse manual engineering symbols often lead to catastrophic consequences. However, the digitised engineering symbols come hampered with the class imbalance problem. Some recent publications
Explorative Analysis on Email Spam Filtering Sampling a Deep learning and non-deep learning algorithm
ABSTRACT Email spam includes unwanted emails with tendency of causing harm to the receiver. Addressing this gave rise to spam detection and filtering. While several deep learning and non-deep learning algorithms have been applied for email spam filtering with different data for each of the research, this paper is geared towards combining most of these
