SMARTPHONE-BASED HUMAN ACTIVITY RECOGNITION USING ARTIFICIAL INTELLIGENCE METHODS AND ORIENTATION INVARIANT FEATURES

ABSTRACT  The implementation of digital health for monitoring human health and physical activities has become one of the integral aspects of maintaining a healthy lifestyle and reducing the onset of diseases. The process of developing a physical activity monitoring system using sensors embedded in smartphones has extensive applications in elderly care, ambient assisted living, sports

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On the Emergence of Zero Trust Architecture in Enterprise Networks-A Survey on Implementation Methods, Strengths and Open Problems

ABSTRACT Perimeter security measures are used to protect corporate networks. The perimeter security approaches are based on the principle that everything inside the network is protected and trusted by default. However, with the security threats in cloud computing platforms, Internet of Things and others, these castle-and-moat security measures are found to be deficient. Thus, a

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Artificial Neural Network Algorithm in Nutritional Assessment: Implication for Machine Learning Prediction in Nutritional Assessments in Strict Veganism

ABSTRACT A considerable number of published research has indicated that evaluating the success of weight-loss therapy involves proper dietary examination. On the other hand, the bulk of dietary evaluation methods currently in use have favored manual memory recall. In the current study, we used an artificial neural network (ANN) machine learning algorithm to construct an

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EFFECT OF INTELLIGENT TUTORING SYSTEM ON EDUCATION

ABSTRACT Intelligent Tutoring Systems (ITSs) are an area of artificial intelligence in education that supports personalized teaching and learning. ITS allows teachers/lecturers to provide customized educational materials to the students while they learn a particular domain of knowledge at their own desired time without human intervention. This research investigated the effect of ITS on the

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Advancing Cybersecurity: A Gru-ATT-SVM Approach for Phishing Url Detection

ABSTRACT  The rise of technology has transformed the digital realm into a multifaceted space where people engage in various activities such as banking, shopping, education, and entertainment. However, this shift has also exposed users and businesses to constant threats from cybercriminals, particularly phishers, who jeopardize the safety of online interactions and pose significant risks to

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TRANSFER LEARNING FOR TOMATO LEAF DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS ON MOBILE PLATFORMS

ABSTRACT In this paper, with the use of transfer learning on mobile platforms, a new approach for Tomato Leaf Disease Detection (TLDD) was accomplished utilising convolutional neural networks (CNNs). Our main goal was to create a model that would enable real-time mobile diagnostics by quickly and correctly identifying diseases of tomato leaves. The base CNN

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ADVANCED DATA POISONING ATTACK DETECTION IN DEEP LEARNING MODELS USING INTEGRATED APPROACH

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

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