A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT)

BACKGROUND: In almost all lower and lower middle-income countries, the healthcare system is structured in the customary model of in-person or face to face model of care. With the current global COVID-19 pandemics, the usual health care service has been significantly altered in many aspects. Given the fragile health system and high number of immunocompromised populations in lower and lower-middle income countries, the economic impacts of COVID-19 are anticipated to be worse. In such scenarios, technological solutions like, Telemedicine which is defined as the delivery of healthcare service remotely using telecommunication technologies for exchange of medical information, diagnosis, consultation and treatment is critical. The aim of this study was to assess healthcare providers' acceptance and preferred modality of telemedicine and factors thereof among health professionals working in Ethiopia. METHODS: A multi-centric online survey was conducted via social media platforms such as telegram channels, Facebook groups/pages and email during Jul 1- Sep 21, 2020. The questionnaire was adopted from previously validated model in low income setting. Internal consistency of items was assessed using Cronbach alpha (α), composite reliability (CR) and average variance extracted (AVE) to evaluate both discriminant and convergent validity of constructs. The extent of relationship among variables were evaluated by Structural equation modeling (SEM) using SPSS Amos version 23. RESULTS: From the expected 423 responses, 319 (75.4%) participants responded to the survey questionnaire during the data collection period. The majority of participants were male (78.1%), age <30 (76.8%) and had less than five years of work experience (78.1%). The structural model result confirmed the hypothesis "self-efficacy has a significant positive effect on effort expectancy" with a standardized coefficient estimate (s) of 0.76 and p-value <0.001. The result also indicated that self-efficacy, effort expectancy, performance expectancy, facilitating conditions and social influence have a significant direct effect on user's attitude toward using telemedicine. User's behavioral intention to use telemedicine was also influenced by effort expectancy and attitude. The model also ruled out that performance expectancy, facilitating conditions and social influence does not directly influence user's intention to use telemedicine. The squared multiple correlations (r2) value indicated that 57.1% of the variance in attitude toward using telemedicine and 63.6% of the variance in behavioral intention to use telemedicine is explained by the current structural model. CONCLUSION: This study found that effort expectancy and attitude were significantly predictors of healthcare professionals' acceptance of telemedicine. Attitude toward using telemedicine systems was also highly influenced by performance expectancy, self-efficacy and facilitating conditions. effort expectancy and attitude were also significant mediators in predicting users' acceptance of telemedicine. In addition, mHealth approach was the most preferred modality of telemedicine and this opens an opportunity to integrate telemedicine systems in the health system during and post pandemic health services in low-income countries.

/pdf/healthcare-providers-acceptance-of-telemedicine-and-x012w0py1q.pdf

Healthcare providers' acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model.

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

https://www.mdpi.com/2071-1050/13/15/8379/pdf

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees

The research aimed to analyse the factors influencing citizens’ acceptance of electronic government in Kurdistan region of Iraq. The researcher developed six research hypotheses. A quantitative method used in order to analyse this study in Kurdistan region of Iraq. The present study was confined to 256 units from Kurdish citizens were selected. A focus method sampling used to analyse this research by focusing on students’ perceptions. The researcher used single regression analysis to investigate the technology acceptance model and its relationship with e-government in Kurdistan region of Iraq. It was found that. This thesis is expected to be interesting to the information systems consultants, experts and specialists who provide and study the factors influencing consumers’ acceptance of electronic government services. Also, it could be interesting to academics and researchers who research on the factors that influence citizens’ in implementing electronic government.

Factors Influencing the Citizens’ Acceptance of Electronic Government

This paper proposes an innovative image cryptosystem algorithm using the properties of the block encryption, 4D logistic map and DNA systems. Multiple key sequences are generated and pixel substitution is performed by using nonlinear 4D logistic map, then encryption is performed by using DNA rules to ensure that the different blocks are encrypted securely. The results of the experiment indicate that the proposed Non Linear 4D Logistic Map and DNA (NL4DLM_DNA) sequence based algorithm gives better performance, which is analyzed on the basis of security, quality, attack resilience, diffusion and running time as compared to some previous works.

Fast and Secure Medical Image Encryption Based on Non Linear 4D Logistic Map and DNA Sequences (NL4DLM_DNA)

The purpose of this paper, acceptance of Electronic Health Records (EHRs) system in the Jordan eHealth sector, is to contribute to develop the quality of hospitals, reduce medical error issues and ...

Healthcare professionals’ acceptance Electronic Health Records system: Critical literature review (Jordan case study)

Communication methods in the world are constantly evolving, this means that the amounts of data sent through open networks are in populated quantities, so there must be means to protect that data sent, and this could be done by information protection systems by encryption and coding of the data in order to protect the content of that data sent. The main aim of encrypting and encoding data is to protect the integrity of the data, and also to ensure the confidentiality of the data source, this is done through the use of algorithms for encryption and encoding the data, with the aim of changing its content using those algorithms and returning its original content with the same algorithms used to encrypt it, through a symmetric secret key for encryption and decryption. In this paper, the researcher highlights the weakness of the basic RC4 algorithm, which lies in the stability of the key used in scheduling the key, as a prelude to generating the stream keys used in serial byte encryption, so the RC4-Pr algorithm was modified by adding an additional permutation function by performing a key permutation operation to produce a different key in each 16-bytes (128-bits) cipher round.

Intelligent security in the era of AI: The key vulnerability of RC4 algorithm

Recently, the Vehicular Ad Hoc Network, or VANET, has emerged as the most essential topic for researchers and the automobile industry to discuss in order to enhance the level of safety enjoyed by road users. Users of VANET need to be able to access both safety-related and non-safety-related apps. In this paper, we offer sixteen different kinds of attacks, as well as potential defenses against them.

Vulnerabilities in Vehicular Ad Hoc Networks and Possible Countermeasures

One of Jordan's most significant agricultural crops is the date palm tree. The high level of interest in date palm farming is a result of the crop's superior economic viability when compared to other agricultural crops; Jordan's annual investments in this sector are expected to be more than $500 million. Recently, the Jordanian ministry of agriculture reported that many trees are vulnerable to damage because of several diseases related to date palms. In this study, the convolutional neural network (CNN) and support vector machine (SVM) algorithms are used to detect and classify date palm diseases. Four common diseases are considered in this paper: bacterial blight, brown spots, leaf smut, and white scales. The palm farms in the northern Jordan Valley, Kaggle, the National Center for Agricultural Research, and other sources provided the dataset used in this study. The experimental results show that CNN is effective mechanism for detecting and classifying Date Palm disease especially when large dataset is used in training the algorithm.

Classifying Date Palm Tree Diseases Using Machine Learning

Nowadays, cyber crimes are increasing and have affected large organizations with highly sensitive information. Consequently, the affected organizations spent more resources analyzing the cyber crimes rather than detecting and preventing these crimes. Network forensics plays an important role in investigating cyber crimes; it helps organizations resolve cyber crimes as soon as possible without incurring a significant loss. This paper proposes a new approach to analyze cyber crime evidence. The proposed approach aims to use cyber crime evidence to reconstruct useful attack evidence. Moreover, it helps investigators to resolve cyber crime efficiently. The results of the comparison of the proposed approach prove that it is more efficient in terms of time and cost compared with the generic and the modern process approach for network forensics. Index Terms—Cyber crime, network forensics, proactive approach, evidence investigation component.

/pdf/pnfea-a-proposal-approach-for-proactive-network-forensics-2sx4jqnq4w.pdf

PNFEA: A Proposal Approach for Proactive Network Forensics Evidence Analysis to Resolve Cyber Crimes

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