This study examines the effects of trustworthiness, expertize, perceived usefulness (PU), and perceived ease of use (PEOU) on usage intention toward user-generated content (UGC) and online reviews ...

Age and gender differences in online travel reviews and user-generated-content (UGC) adoption: extending the technology acceptance model (TAM) with credibility theory

The Determinants of Digital Payment Systems’ Acceptance under Cultural Orientation Differences: The Case of Uncertainty Avoidance

With the wide application of Internet-of-Medical-Things (IoMTs) or sensor nodes which equipped with sensors. These networked sensors are used to gather enormous data from different smart healthcare applications, and this collected data process for making appropriate decisions. Edge computing is an efficient platform that provides computational resources to collect sensor data. In the meantime, intelligent and accurate resource management by Artificial Intelligence (AI) has become the center of attention, especially in healthcare systems. With the help of AI, IoMT based healthcare devices will remarkably enhance the computational speed and range. But the challenging issue in these energy-hungry, short battery life, and delay intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Thus, this paper proposes Computation Offloading using Reinforcement Learning (CORL) scheme to minimize latency and energy consumption. We first formulate the problem as a combined latency and energy cost minimization problem, satisfying the lack of limited battery capacity and service latency deadline constraints. Moreover, proposed algorithm search optimal available resources node to offload task towards the trade-off between energy and latency. The experimental results show the benefits of the proposed scheme in terms of saving energy, minimizing latency, and maximum utilization of node resources in edge-enabled sensor networks. We are using an iFogSim simulator to validate the proposed scheme under realistic assumptions.

Smart Healthcare: RL-Based Task Offloading Scheme for Edge-Enable Sensor Networks

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.

Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique. 

Machine Learning and Deep Learning in Real-Time Applications

A Wireless Sensor Network (WSN) is of paramount importance in facilitating smart Agricultural Internet of Things (Agri-IoT). It connects numerous sensor nodes or devices to develop a robust framework for efficient and seamless communication with improved throughput for intelligent networking. Such enhancement has to be facilitated by an adequate and smart machine learning-based resource allocation approach. With the ensuing surge in the volume of devices being deployed from the smart Agri-IoT, applications such as intelligent irrigation, smart crop monitoring and smart fishery would be largely benefited. However, the existing resource allocation techniques would be inefficient for such anticipated energy-efficient networking. To this end, we develop a distributed artificial intelligence approach that applies efficient multi-agent learning over the WSN scenario for intelligent resource allocation. The approach is based on dynamic clustering which coupled tightly with the Back-Propagation Neural Network and empowered by the Particle Swarm Optimization (BPNN-PSO). We implement the overall framework using a Bayesian Neural Network, where the outputs from BPNN-PSO are supplied as weights to the underlying neuron layer. We observe that the cost function and energy consumption demonstrate a substantial improvement in terms of cooperative networking and efficient resource allocation. The approach is validated with simulations under realistic assumptions.

An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

This research paper describes an integrated framework based on both the ‘Technology–Organization–Environment (TOE) framework’ and ‘Diffusion of Innovation (DoI) theory’. The study explores the noteworthy factors and sub-factors which are pertinent for the adoption of cloud computing in the Ethiopian Higher Education (EHE) sector. The technology literature herein was based on technology adoption frameworks and theories which were studied in order to identify a set of factors and sub-factors relevant to cloud computing adoption. It resulted in conceptualizing an integrated TOE–DoI framework for cloud computing adoption in higher education at the university in Ethiopia and in developing its reliable measures. Accordingly, a quantitative study was done with a questionnaire survey comprising 500 respondents in connection with 4 factors (technological, organizational, environmental and socio-cultural). Consequently, the cloud computing adoption in Ethiopia was established using factors and concepts adopted from the study. It confirmed that the TOE–DoI approach to higher education in Ethiopia is authenticated. Thus, the four factors’ reliability statistics validated with a Cronbach’s alpha a = 0.739, 0.712, 0.761, 0.841, and Cronbach’s alpha ‘a’ based on standard items a = 0.740, 0.713, 0.762 and 0.842 for technology, organizational, environmental, and socio-cultural factors. This indicates that scaling the four aspects therein suggests profound evidence to determine a cloud computing adoption in EHE with TOE–DoI integration.

An integrated TOE–DoI framework for cloud computing adoption in the higher education sector: case study of Sub-Saharan Africa, Ethiopia

The sole intention of this study was to examine the moderating effects of age and gender on electronic payment based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theoretical model. Using a sample of 1098 respondents, we collected and analysed the data. The results confirm that “performance expectancy”, effort expectancy for male respondents were higher than the mean for female respondents while the mean score of the variable for social influence for female is higher than the male respondents. On the other hand, the findings statistically concluded that the moderating effect of age was significant.

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Examining the Intervening Role of Age and Gender on mobile payment Acceptance in Ghana: UTAUT Model

Today, humans live in the era of rapid growth in electronic devices that are based on artificial intelligence, including the significant growth in the manufacture of machines that perform intelligent human tasks to solve complex situations. Artificial intelligence will significantly influence the development of many domains, especially the medical domain, which relies heavily on artificial intelligence techniques in diagnosing disease data and manufacturing drugs and vaccines. Artificial intelligence has unexpectedly advanced in helping physicians and healthcare workers save many lives, especially during the spread of the COVID-19 virus. This article reviews some literature that have applied deep learning techniques to detect COVID-19 based on chest x-rays and CT-scans images. This article concluded that deep learning techniques have a fundamental and significant role in diagnosing a big dataset of images and assisting specialists in determining whether a person is infected (positive cases).
 

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Kamal Kant Hiran

Papers

Machine Learning and Deep Learning in Real-Time Applications

An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

An integrated TOE–DoI framework for cloud computing adoption in the higher education sector: case study of Sub-Saharan Africa, Ethiopia

Examining the Intervening Role of Age and Gender on mobile payment Acceptance in Ghana: UTAUT Model

Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective