New Secured E-Government Efficiency Model for Sustainable Services Provision

E-government projects in some developing countries face many challenges to provide sustainable services for e-efficiency. Literature shows that most governments suffer from lack of technology and restrictions associated with budgets and human resources. These factors constitute the main obstacles impeding the effective implementation of sustainable and secured e-government services. In addition to these obstacles, the e-government efficiency models adopted by some developing countries do not deliver an appropriate strategic plan for disseminating all sustainable and secured e-government services. Therefore, this paper proposes a new secured model for e-government efficiency to provide sustainable and e-efficiency services. This goal can be achieved using five determinants: detailed process, streamlined services, quick accessibility, use of latest techniques, and trust and awareness, which are discussed in this study. The proposed model has been validated by using a pilot study conducted through case study and method of application and implementation. The findings indicate that both service providers such as governments and users of e-government services took advantage of the proposed model. Accordingly, sustainable e-government services may increase

Vision graph neural network-based neonatal identification to avoid swapping and abduction

Infant abductions from medical facilities such as neonatal switching, in which babies are given to the incorrect mother while in the hospital, are extremely uncommon. A prominent question is what we can do to safeguard newborns. A brand-new vision graph neural network (ViG) architecture was specifically created to handle this problem. Images were divided into several patches, which were then linked to create a graph by connecting their nearest neighbours to create a ViG model, which converts and communicates information between all nodes based on the graph representation of the newborn's photos taken at delivery. ViG successfully captures both local and global spatial relationships by utilizing the isotropic and pyramid structures within a vision graph neural network, providing both precise and effective identification of neonates. The ViG architecture implementation has the ability to improve the security and safety of healthcare facilities and the well-being of newborns. We compared the accuracy, recall, and precision, F1-Score, Specificity with CNN, GNN and Vision GNN of the network. In that comparison, the network has a Vision GNN accuracy of 92.65%, precision of 92.80%, F1 score of 92.27%, recall value of 92.25%, and specificity of 98.59%. The effectiveness of the ViG architecture was demonstrated using computer vision and deep learning algorithms to identify the neonatal and to avoid baby swapping and abduction

Reinforcement learning-based AI assistant and VR play therapy game for children with Down syndrome bound to wheelchairs

Some of the most significant computational ideas in neuroscience for learning behavior in response to reward and penalty are reinforcement learning algorithms. This technique can be used to train an artificial intelligent (AI) agent to serve as a virtual assistant and a helper. The goal of this study is to determine whether combining a reinforcement learning-based Virtual AI assistant with play therapy. It can benefit wheelchair-bound youngsters with Down syndrome. This study aims to employ play therapy methods and Reinforcement Learning (RL) agents to aid children with Down syndrome and help them enhance their abilities like physical and mental skills by playing games with them. This Agent is designed to be smart enough to analyze each patient's lack of ability and provide a specific set of challenges in the game to improve that ability. Increasing the game's difficulty can help players develop these skills. The agent should be able to assess each player's skill gap and tailor the game to them accordingly. The agent's job is not to make the patient victorious but to boost their morale and skill sets in areas like physical activities, intelligence, and social interaction. The primary objective is to improve the player's physical activities such as muscle reflexes, motor controls and hand-eye coordination. Here, the study concentrates on the employment of several distinct techniques for training various models. This research focuses on comparing the reinforcement learning algorithms like the Deep Q-Learning Network, QR-DQN, A3C and PPO-Actor Critic. This study demonstrates that when compared to other reinforcement algorithms, the performance of the AI helper agent is at its highest when it is trained with PPO-Actor Critic and A3C. The goal is to see if children with Down syndrome who are wheelchair-bound can benefit by combining reinforcement learning with play therapy to increase their mobility

Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

A wide variety of applications like patient monitoring, rehabilitation sensing, sports and senior surveillance require a considerable amount of knowledge in recognizing physical activities of a person captured using sensors. The goal of human activity recognition is to identify human activities from a collection of observations based on the behavior of subjects and the surrounding circumstances. Movement is examined in psychology, biomechanics, artificial intelligence and neuroscience. To be specific, the availability of pervasive devices and the low cost to record movements with machine learning (ML) techniques for the automatic and quantitative analysis of movement have resulted in the growth of systems for rehabilitation monitoring, user authentication and medical diagnosis. The self-regulated detection of human activities from time-series smartphone sensor datasets is a growing study area in intelligent and smart healthcare. Deep learning (DL) techniques have shown enhancements compared to conventional ML methods in many fields, which include human activity recognition (HAR). This paper presents an improved wolf swarm optimization with deep learning based movement analysis and self-regulated human activity recognition (IWSODL-MAHAR) technique. The IWSODL-MAHAR method aimed to recognize various kinds of human activities. Since high dimensionality poses a major issue in HAR, the IWSO algorithm is applied as a dimensionality reduction technique. In addition, the IWSODL-MAHAR technique uses a hybrid DL model for activity recognition. To further improve the recognition performance, a Nadam optimizer is applied as a hyperparameter tuning technique. The experimental evaluation of the IWSODL-MAHAR approach is assessed on benchmark activity recognition data. The experimental outcomes outlined the supremacy of the IWSODL-MAHAR algorithm compared to recent models

A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory

Clustering is a popular data analysis and data mining problem. Symmetry can be considered as a pre-attentive feature, which can improve shapes and objects, as well as reconstruction and recognition. The symmetry-based clustering methods search for clusters that are symmetric with respect to their centers. Furthermore, the K-means (K-M) algorithm can be considered as one of the most common clustering methods. It can be operated more quickly in most conditions, as it is easily implemented. However, it is sensitively initialized and it can be easily trapped in local targets. The Tabu Search (TS) algorithm is a stochastic global optimization technique, while Adaptive Search Memory (ASM) is an important component of TS. ASM is a combination of different memory structures that save statistics about search space and gives TS needed heuristic data to explore search space economically. Thus, a new meta-heuristics algorithm called (MHTSASM) is proposed in this paper for data clustering, which is based on TS and K-M. It uses TS to make economic exploration for data with the help of ASM. It starts with a random initial solution. It obtains neighbors of the current solution called trial solutions and updates memory elements for each iteration. The intensification and diversification strategies are used to enhance the search process. The proposed MHTSASM algorithm performance is compared with multiple clustering techniques based on both optimization and meta-heuristics. The experimental results indicate the superiority of the MHTSASM algorithm compared with other multiple clustering algorithms

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

The current state of Association Rule Mining (ARM) technology is heading towards a critical yet profitable direction. The ARM process uncovers numerous association rules, determining correlations between itemsets, forming building blocks that have led to revolutionary scientific discoveries. However, a high level of privacy is vital for protecting sensitive rules, raising privacy concerns. Researchers have recently highlighted challenges in the Privacy-Preserving Association Rule Mining (PPARM) field. Many studies have proposed workarounds for the PPARM dilemma by using metaheuristics. This paper conducts a systematic literature review on metaheuristic-based algorithms addressing PPARM challenges. It explores existing studies, providing insights into diverse metaheuristic approaches tackling PPARM problems. A detailed taxonomy is presented, offering a structured classification of metaheuristic-based algorithms specific to PPARM. This classification facilitates a nuanced understanding of the field by categorizing these algorithms into metaphor-based and non-metaphor-based groups, with a discussion of the nature of the representation schemes for each category identified in the survey. The review extends its analysis to encompass the latest applied approaches, highlighting the diversification of existing metaheuristic algorithms in the PPARM context. Moreover, common datasets and evaluation metrics identified from selected studies are documented to provide a deeper understanding of the methodological choices made by researchers in this domain. Finally, a discussion of existing challenges and potential future directions is presented. This review serves as a helpful guide that outlines previous research and presents potential future opportunities for metaheuristic-based algorithms in the context of PPARM

Intelligent Adaptive Optimisation Method for Enhancement of Information Security in IoT-Enabled Environments

The usage of the Internet increased dramatically during the start of the twenty-first century, entangling the system with a variety of services, including social media and e-commerce. These systems begin producing a large volume of data that has to be secured and safeguarded from unauthorised users and devices. In order to safeguard the information of the cyber world, this research suggests an expanded form of differential evolution (DE) employing an intelligent mutation operator with an optimisation-based design. It combines a novel mutation technique with DE to increase the diversity of potential solutions. The new intelligent mutation operator improves the security, privacy, integrity, and authenticity of the information system by identifying harmful requests and responses and helping to defend the system against assault. When implemented on an e-commerce application, the performance of the suggested technique is assessed in terms of confidentiality, integrity, authentication, and availability. The experimental findings show that the suggested strategy outperforms the most recent evolutionary algorithm (EA)

Green Requirement Engineering: Towards Sustainable Mobile Application Development and Internet of Things

Mobile usage statistics show the one thing that cannot be overlooked, which is the overwhelming usage of smartphones. According to the statistics, there are approximately 6.4 billion users of smartphones. Considering the world population, this rate of smart phone usage is more than 80%. Mobile development is the fastest prominent trend, although web development cannot be denied. However, the fact is that mobile platforms are considered cumbersome and complex when it comes to accomplishing requirement engineering processes, especially when mobile applications are combined with the Internet of Things (IoT). These complexities result in barriers to sustainable mobile development. The difficulty and differences occur due to various limitations, either that of mobile devices or others. Some of those from mobile devices include processor, battery, and touch screens, user experience in terms of touch screens, user context, and interactive behaviors. Other limitations include the difference in the software development lifecycle and the difference in the software development process due to inconsistency in user requirements with the aforementioned limited device capabilities. The target objective of this research is to investigate and identify all possible challenges related to mobile applications and connected mobile devices (IoT) while executing the requirement engineering process. This study can further the existing state of knowledge by contributing to the list of challenges faced in the requirement gathering process of mobile application development. Furthermore, it can also help practitioners, specifically those involved in the requirement gathering process, to carefully consider these challenges before executing the requirement engineering process