IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety

Numerous elderly folks reside alone in their homes. Seniors may find it difficult to ask for assistance if they fall. As the elderly population keeps growing, elderly fall incidents are becoming a critical public health concern. Creating a fall detection system for the elderly using IoT and blockchain is the aim of this study. Data collection, pre-processing, feature extraction, feature selection, fall detection, and emergency response and assistance are the six fundamental aspects of the proposed model. The sensor data is collected from wearable devices using elderly such as accelerometers and gyroscopes. The collected data is pre-processed using missing value removal, null value handling. The features are extracted after pre-processed data using statistical features, autocorrelation, and Principal Component Analysis The proposed approach utilizes a novel hybrid HSSTL combines Teaching-Learning-Based Optimization and Spring Search Algorithm to select the optimal features. The proposed approach employs TriNet, including Long Short-Term Memory, optimized Convolutional Neural Network (CNN), and Recurrent Neural Network for accurate fall detection. To enhance fall detection accuracy, use the optimized Convolutional Neural Network obtained through the hybrid optimization model HSSTL. Securely store fall detection information in the Blockchain network when a fall occurs. Alert neighbours, family members, or those providing immediate assistance about the fall occurrence using Blockchain network. The proposed model is implemented in Python. The effectiveness of the suggested model is evaluated using metrics for accuracy, precision, recall, sensitivity, specificity, f-measure, NPV, FPR, FNR, and MCC. The proposed model outperformed with the maximum accuracy of 0.974015 at an 80% learning rate, whereas the suggested model had the best accuracy score of 0.955679 at a 70% learning rate

User-centric secured smart virtual assistants framework for disables

Research on intelligent secured virtual assistant (ISVA) systems for disabled people is essential in order to meet the special requirements and overcome the difficulties they confront. The delicate nature of user interactions makes security and privacy considerations paramount in virtual assistant platforms. The gaps and weaknesses in existing systems can be identified by researching the context of current practice concerning their features, usability, limits in security procedures, and privacy restrictions. Therefore, we present a framework that combines blockchain-based security with federated learning (FL) to address the current shortcomings of virtual assistant technology. The examination focuses on two primary facets of cutting-edge virtual assistants. Firstly, it evaluates existing IoT-based virtual personal assistant systems designed for persons with disabilities, examining their features, usability, and limitations. The aim is to identify the specific needs and requirements of individuals with disabilities, considering their unique challenges and preferences in utilizing virtual assistant technologies. Second, considering the sensitivity of the information sent between users and virtual assistants, it explores the issues of security and privacy that arise while using such systems. The investigation covers authentication, data encryption, access control, and data privacy rules to provide a snapshot of the prevailing state protecting virtual assistants. Besides this, the framework strengthens the privacy and security of virtual assistants using blockchain technology. Through several empirical trials, it is found that the framework maintains better performance and usability, along with the provision of robust security mechanisms to safeguard user data and guarantee privacy

Impact of Digital Transformation toward Sustainable Development

The rapid advancements in digital technologies have prompted organizations to embrace digital transformations (DTs) in order to enhance efficiency, gain a competitive advantage, and achieve long-term sustainability objectives. However, the successful adoption of innovative digital technologies necessitates the careful consideration of various factors, such as stakeholder engagement, resource allocation, risk mitigation, and the availability of resources and implementation support. This study examines the sustainable adoption of innovative digital technologies (DTs) within digital transformations. The data for this study were collected from 760 stakeholders through a questionnaire survey and analyzed using SPSS software (Version 27). This study’s results underscore the significance of considering the efficiency of the transformation process and the long-term sustainability outcomes for organizations. The findings of the analysis clarify that integrating sustainability principles and DT has a positive impact on the effectiveness of the transformation, as indicated by environmental, social, and economic performance indicators. This study’s novelty lies in its focus on incorporating sustainability principles into the digital transformation process. The results of this study demonstrate that organizations’ long-term sustainability outcomes are enhanced when their digital transformation goals align with the Sustainable Development Goals (SDGs). The purpose of this study emphasizes the importance of arranging digital transformations with sustainable objectives to ensure the overall success and longevity of transformation efforts

IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety

Numerous elderly folks reside alone in their homes. Seniors may find it difficult to ask for assistance if they fall. As the elderly population keeps growing, elderly fall incidents are becoming a critical public health concern. Creating a fall detection system for the elderly using IoT and blockchain is the aim of this study. Data collection, pre-processing, feature extraction, feature selection, fall detection, and emergency response and assistance are the six fundamental aspects of the proposed model. The sensor data is collected from wearable devices using elderly such as accelerometers and gyroscopes. The collected data is pre-processed using missing value removal, null value handling. The features are extracted after pre-processed data using statistical features, autocorrelation, and Principal Component Analysis The proposed approach utilizes a novel hybrid HSSTL combines Teaching-Learning-Based Optimization and Spring Search Algorithm to select the optimal features. The proposed approach employs TriNet, including Long Short-Term Memory, optimized Convolutional Neural Network (CNN), and Recurrent Neural Network for accurate fall detection. To enhance fall detection accuracy, use the optimized Convolutional Neural Network obtained through the hybrid optimization model HSSTL. Securely store fall detection information in the Blockchain network when a fall occurs. Alert neighbours, family members, or those providing immediate assistance about the fall occurrence using Blockchain network. The proposed model is implemented in Python. The effectiveness of the suggested model is evaluated using metrics for accuracy, precision, recall, sensitivity, specificity, f-measure, NPV, FPR, FNR, and MCC. The proposed model outperformed with the maximum accuracy of 0.974015 at an 80% learning rate, whereas the suggested model had the best accuracy score of 0.955679 at a 70% learning rate

Early dementia detection with speech analysis and machine learning techniques

This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection

An Informed Decision Support Framework from a Strategic Perspective in the Health Sector

This paper introduces an informed decision support framework (IDSF) from a strategic perspective in the health sector, focusing on Saudi Arabia. The study addresses the existing challenges and gaps in decision-making processes within Saudi organizations, highlighting the need for proper systems and identifying the loopholes that hinder informed decision making. The research aims to answer two key research questions: (1) how do decision makers ensure the accuracy of their decisions? and (2) what is the proper process to govern and control decision outcomes? To achieve these objectives, the research adopts a qualitative research approach, including an intensive literature review and interviews with decision makers in the Saudi health sector. The proposed IDSF fills the gap in the existing literature by providing a comprehensive and adaptable framework for decision making in Saudi organizations. The framework encompasses structured, semi-structured, and unstructured decisions, ensuring a thorough approach to informed decision making. It emphasizes the importance of integrating non-digital sources of information into the decision-making process, as well as considering factors that impact decision quality and accuracy. The study’s methodology involves data collection through interviews with decision makers, as well as the use of visualization tools to present and evaluate the results. The analysis of the collected data highlights the deficiencies in current decision-making practices and supports the development of the IDSF. The research findings demonstrate that the proposed framework outperforms existing approaches, offering improved accuracy and efficiency in decision making. Overall, this research paper contributes to the state of the art by introducing a novel IDSF specifically designed for the Saudi health sector

Critical Success Factors and Challenges in Adopting Digital Transformation in the Saudi Ministry of Education

Many countries are using digital transformation to increase their productivity and organizational performance. In Saudi Arabia, digital transformation is a crucial part of their Saudi Vision 2030 plan, but it is still in its early stages. To understand the factors that affect the adoption of digital transformation. The study used a qualitative interview to identify the critical success factors and challenges in adopting digital transformation at the Ministry of Education of Saudi Arabia. The main results of the study show, first, the seven main success factors include technology, employee engagement, vendor partnerships, budget, top management support, culture, and strategy. Second, the main seven challenges include organizational and strategic stakes, resistance to change, governance, data, cost, and IT infrastructure. The study developed a framework that shows the main success factors and challenges that affect adopting digital transformation in the Ministry of Education. These findings can benefit many individuals and groups, such as academics, business people, and the public, and can apply this research in other contexts. This research aimed to determine the primary factors contributing to the success of digital transformation in the Ministry of Education and the challenges that arise when implementing it, specifically within the Saudi Arabian Ministry of Education

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively

Managing Uncertainties in Supply Chains for Enhanced E-Commerce Engagement: A Generation Z Perspective on Retail Shopping through Facebook

This research investigates the uncertainties in supply chains using symmetrical and asymmetrical modeling tools, focusing on the attitudes of millennials towards Facebook retail shopping. By exploring antecedents such as pleasure, credibility, and peer interaction, this study delves into the extent of E-commerce via Facebook among Generation Z in the Middle East. Built on an exhaustive literature review, a conceptual framework is designed targeting solely Generation Z members. Employing partial least squares structural equation modeling for data analysis, the findings indicate a strong correlation between attitude and the propensity of Generation Z to make Facebook retail purchases (R2 = 0.540), affecting enjoyment, credibility, and peer communication (R2 = 0.589). This study offers strategies for supply chain improvements and validates the potential of E-commerce on Facebook among Generation Z

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems