ABSTRACT Deep learning models are capable of handling large amounts of data, with a high ability to predict based on features and patterns embedded in the data. Deep learning models are severely challenged by data poisoning attacks resulting in inaccurate predictions and model misclassification. In literature, several works have been identified to mitigate it. An
ABSTRACT This study focused on the development of a classification model needed for the detection of heart diseases based on information about relevant features using a deep neural network (DNN) architecture. Data was collected from a public data repository provided by Kaggle following which the data was preprocessed and feature selection was used to select
ABSTRACT News is information that is presented through print, broadcast, Internet, or from mouth to mouth. For the ease of news, we classify news based on different category to help users to find relevant news rapidly. This classification results in the use of classifier engine to split any news into the respective category. This research
ABSTRACT The future Internet of Things (IoT) system connects the physical world into cyberspace via billions of intelligent sensors and devices. The physical network’s service-oriented architecture (SOA) establishes interoperability among different IoT devices. This connection between various devices has resulted in the heterogeneous nature of IoT networks. Thus, the IoT system imperatively calls for human-to-machine
ABSTRACT Resource allocation in a 5G heterogeneous network involves efficiently managing and distributing network resources to different types of devices and services to ensure optimal performance and quality of service. Some of the key aspects considered for allocation in a 5G heterogeneous network include frequency spectrum allocation, cell association, handover, load balancing, interference management, network
ABSTRACT Climate change is a pressing concern in Africa. In this paper, we employ sentiment analysis, event spike detection, and topic modelling techniques to analyse social media posts about climate change in Africa. In contrast to prior findings, we find that discourse on climate change in Africa covers a wide variety of topics including politics,
ABSTRACT Segmentation of individual cattle in the feedlot has been a herculean task for computer vision algorithms with great implications for animal farming. In this paper, segmentation of individual cattle in the feedlot was conducted by employing the algorithm of Mask R-CNN. Intersection over union (IOU) threshold of 0.5, average precision (AP) and mean average
ABSTRACT We explore recent advancements in embedded computer vision, particularly in object detection, recognition, and tracking. This study was motivated by the need to comprehend and optimize embedded vision technology; it meticulously examines various artificial intelligence methods and algorithms tailored for this field. By scrutinizing their strengths and weaknesses, it aims to address existing challenges
ABSTRACT The paper presents a proposed data-driven path loss prediction over conventional models for wireless communication design, especially in the setting of dynamic signal variations in advanced Fifth Generation (5G) technologies. The research analyzes key parameters to train a multi-layered neural network using a robust data-driven model based on neural network architecture. Significant correlations show
ABSTRACT This research endeavours to develop a COVID-19 Skepticism Logistic Regression model (CSLRM) aimed at mitigating the spread of COVID-19 pandemic. The primary objective of this research is to assess comprehensively the multifaceted influences on the spread of COVID-19, particularly in the context of socio-economic factors, skepticism, and awareness levels, utilizing data mining techniques. The