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Higher Education Institutions (HEIs) consider resource optimization as an essential concern. Cloud computing (CC) in the fourth industrial revolution became the de-facto standard for delivering IT resources and services. CC is now a mainstream technology, andHEIs across the globe are rapidly transitioning to this model; hence, maintaining the retention of the customers of such technologies is challenging for cloud service providers. Current research concerning CC focused on adoption and acceptance. However, there is still a scarcity of research concerning such technology’s continued use in an organizational setting. Drawing on the prior literature in organizational-level continuance, this paper established a positivist quantitative-empirical study to bridge the research gap and assess the precursors for a continuance of cloud technology in HEIs. Subsequently, this study developed a conceptual framework by integrating the IS success model and the IS discontinuance model through the lens of the TOE framework. The data were collected from the decision-makers of Malaysian HEIs that have adopted CC services, and analyzed using Structural equation Modelling (SEM) based on Partial Least Squares (PLS). The results indicate that the continuance intention can be predicted by technology, organizational, environmental, and other contextualized factors, explaining 85.2% of the dependent variables’ variance. The paper closes with a discussion of the research limitations, contribution, and future directions.
Yousef Qasem; Rusli Abdullah; Yusmadi Jusoh; Rodziah Atan; Shahla Asadi. Analyzing Continuance of Cloud Computing in Higher Education Institutions: Should We Stay, or Should We Go? Sustainability 2021, 13, 4664 .
AMA StyleYousef Qasem, Rusli Abdullah, Yusmadi Jusoh, Rodziah Atan, Shahla Asadi. Analyzing Continuance of Cloud Computing in Higher Education Institutions: Should We Stay, or Should We Go? Sustainability. 2021; 13 (9):4664.
Chicago/Turabian StyleYousef Qasem; Rusli Abdullah; Yusmadi Jusoh; Rodziah Atan; Shahla Asadi. 2021. "Analyzing Continuance of Cloud Computing in Higher Education Institutions: Should We Stay, or Should We Go?" Sustainability 13, no. 9: 4664.
This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.
Mehrbakhsh Nilashi; Shahla Asadi; Rabab Abumalloh; Sarminah Samad; Fahad Ghabban; Eko Supriyanto; Reem Osman. Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART). Sustainability 2021, 13, 3870 .
AMA StyleMehrbakhsh Nilashi, Shahla Asadi, Rabab Abumalloh, Sarminah Samad, Fahad Ghabban, Eko Supriyanto, Reem Osman. Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART). Sustainability. 2021; 13 (7):3870.
Chicago/Turabian StyleMehrbakhsh Nilashi; Shahla Asadi; Rabab Abumalloh; Sarminah Samad; Fahad Ghabban; Eko Supriyanto; Reem Osman. 2021. "Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)." Sustainability 13, no. 7: 3870.
Green information technology (IT) adoption has helped enhance the overall organization’s environmental sustainability. Developing the strategies for effective adoption of Green IT is one of the essential goals of decision-makers. This study purposes to investigate the factors that influence decision-makers’ intention to use Green IT and the proposed green IT adoption model in Malaysian manufacturing firms. The 183 valid data were obtained using survey questionnaires from Malaysia’s manufacturing industries’ industrial managers and examine collect data through two analytical techniques. Two-staged structural equation modeling and artificial neural network applied for hypotheses evaluation and finding the significance level of every factor in the model. The outcomes of hypotheses evaluation through structural equation modeling revealed that managerial interpretation and ascription of responsibility have a significant role in predicting the adoption of green information technology in manufacturing companies. Besides, the Artificial Neural Network (ANN) results showed that the managerial interpretation and ascription of responsibility are considered as the most significant factors of green information technology adoption. This study will help the decision-makers and policymakers develop policies and programs for the effective employment of green information technology in manufacturing industries.
Shahla Asadi; Mehrbakhsh Nilashi; Sarminah Samad; Parveen Fatemeh Rupani; Hesam Kamyab; Rusli Abdullah. A proposed adoption model for green IT in manufacturing industries. Journal of Cleaner Production 2021, 297, 126629 .
AMA StyleShahla Asadi, Mehrbakhsh Nilashi, Sarminah Samad, Parveen Fatemeh Rupani, Hesam Kamyab, Rusli Abdullah. A proposed adoption model for green IT in manufacturing industries. Journal of Cleaner Production. 2021; 297 ():126629.
Chicago/Turabian StyleShahla Asadi; Mehrbakhsh Nilashi; Sarminah Samad; Parveen Fatemeh Rupani; Hesam Kamyab; Rusli Abdullah. 2021. "A proposed adoption model for green IT in manufacturing industries." Journal of Cleaner Production 297, no. : 126629.
The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers’ decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers’ decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers’ decision making. We conclude with the implications of our study for future research and practice.
Mehrbakhsh Nilashi; Shahla Asadi; Behrouz Minaei-Bidgoli; Rabab Ali Abumalloh; Sarminah Samad; Fahad Ghabban; Ali Ahani. Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics 2021, 61, 101597 -101597.
AMA StyleMehrbakhsh Nilashi, Shahla Asadi, Behrouz Minaei-Bidgoli, Rabab Ali Abumalloh, Sarminah Samad, Fahad Ghabban, Ali Ahani. Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics. 2021; 61 ():101597-101597.
Chicago/Turabian StyleMehrbakhsh Nilashi; Shahla Asadi; Behrouz Minaei-Bidgoli; Rabab Ali Abumalloh; Sarminah Samad; Fahad Ghabban; Ali Ahani. 2021. "Recommendation agents and information sharing through social media for coronavirus outbreak." Telematics and Informatics 61, no. : 101597-101597.
Since consumers, governments, and society in general are increasingly concerned about the loss of natural resources, along with pollution of the environment, there is currently a significant tendency to recognize the value of green innovation toward the achievement of sustainable development. Hotels are considered responsible for a considerable proportion of the environmental pollution caused by the tourism industry. Yet, few studies have considered the effects that green innovation may have on sustainable performance in the hotel industry. Consequently, the present study aimed to investigate the factors influencing the adoption of green innovation, and its potential effects on the performance of the hotel industry. Data collection was performed through inspection of 183 hotels in Malaysia. Data analysis was carried out employing the partial least squares method. The two factors of environmental and economic performance were determined to have the strongest influence, affecting the green innovation procedures positively and significantly. The results of the present study have major implications for hospitality research, since they demonstrate the importance and potential of green innovation in promoting sustainable performance in the hotel industry. The proposed model and the identified influencing factors of green innovation can assist policy makers and hotel managers in understanding the drivers leading to the adoption of these practices in the hotel industry.
Shahla Asadi; Seyedeh OmSalameh Pourhashemi; Mehrbakhsh Nilashi; Rusli Abdullah; Sarminah Samad; Elaheh Yadegaridehkordi; Nahla Aljojo; Nor Shahidayah Razali. Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry. Journal of Cleaner Production 2020, 258, 120860 .
AMA StyleShahla Asadi, Seyedeh OmSalameh Pourhashemi, Mehrbakhsh Nilashi, Rusli Abdullah, Sarminah Samad, Elaheh Yadegaridehkordi, Nahla Aljojo, Nor Shahidayah Razali. Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry. Journal of Cleaner Production. 2020; 258 ():120860.
Chicago/Turabian StyleShahla Asadi; Seyedeh OmSalameh Pourhashemi; Mehrbakhsh Nilashi; Rusli Abdullah; Sarminah Samad; Elaheh Yadegaridehkordi; Nahla Aljojo; Nor Shahidayah Razali. 2020. "Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry." Journal of Cleaner Production 258, no. : 120860.
A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.
Mahmood Safaei; Shahla Asadi; Maha Driss; Wadii Boulila; Abdullah Alsaeedi; Hassan Chizari; Rusli Abdullah; Mitra Safaei. A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks. Symmetry 2020, 12, 328 .
AMA StyleMahmood Safaei, Shahla Asadi, Maha Driss, Wadii Boulila, Abdullah Alsaeedi, Hassan Chizari, Rusli Abdullah, Mitra Safaei. A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks. Symmetry. 2020; 12 (3):328.
Chicago/Turabian StyleMahmood Safaei; Shahla Asadi; Maha Driss; Wadii Boulila; Abdullah Alsaeedi; Hassan Chizari; Rusli Abdullah; Mitra Safaei. 2020. "A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks." Symmetry 12, no. 3: 328.
Adoption of green information technology (Green IT) as an initiative of pro-environmental behavior is quite sparse among Malaysian students. Due to the need to integrate personality traits in environmental behavioral studies, the current study deriving from the planned behavior theory attempts to investigate the influence of attitudinal factors on students’ pro-ecological behavioral intention to practice Green IT. Extremely few studies explored the moderating role of personality traits in the context of Green IT adoption and thus this study attempts to fill the current research gap. The personality traits of openness, agreeableness and conscientiousness were included in the research model as moderating variables. A number of 262 pieces of data were collected from students. Based on the partial least squares approach and bootstrapping method, the results revealed that except social norms, other variables greatly influenced the intention to practice Green IT. Moreover, the moderating effects analysis showed that the personality trait of conscientiousness was the only trait that significantly moderated the relationships in the proposed model.
Mohammad Dalvi-Esfahani; Zohre Alaedini; Mehrbakhsh Nilashi; Sarminah Samad; Shahla Asadi; Majid Mohammadi. Students’ green information technology behavior: Beliefs and personality traits. Journal of Cleaner Production 2020, 257, 120406 .
AMA StyleMohammad Dalvi-Esfahani, Zohre Alaedini, Mehrbakhsh Nilashi, Sarminah Samad, Shahla Asadi, Majid Mohammadi. Students’ green information technology behavior: Beliefs and personality traits. Journal of Cleaner Production. 2020; 257 ():120406.
Chicago/Turabian StyleMohammad Dalvi-Esfahani; Zohre Alaedini; Mehrbakhsh Nilashi; Sarminah Samad; Shahla Asadi; Majid Mohammadi. 2020. "Students’ green information technology behavior: Beliefs and personality traits." Journal of Cleaner Production 257, no. : 120406.
Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time‐series‐based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well‐known algorithms using benchmark real‐life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.
Mahmood Safaei; Abul Samad Ismail; Hassan Chizari; Maha Driss; Wadii Boulila; Shahla Asadi; Mitra Safaei. Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach. Software: Practice and Experience 2019, 50, 428 -446.
AMA StyleMahmood Safaei, Abul Samad Ismail, Hassan Chizari, Maha Driss, Wadii Boulila, Shahla Asadi, Mitra Safaei. Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach. Software: Practice and Experience. 2019; 50 (4):428-446.
Chicago/Turabian StyleMahmood Safaei; Abul Samad Ismail; Hassan Chizari; Maha Driss; Wadii Boulila; Shahla Asadi; Mitra Safaei. 2019. "Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach." Software: Practice and Experience 50, no. 4: 428-446.
Big data has increasingly appeared as a frontier of opportunity in enhancing firm performance. However, it still is in early stages of introduction and many enterprises are still un-decisive in its adoption. The aim of this study is to propose a theoretical model based on integration of Human-Organization-Technology fit and Technology-Organization-Environment frameworks to identify the key factors affecting big data adoption and its consequent impact on the firm performance. The significant factors are gained from the literature and the research model is developed. Data was collected from top managers and/or owners of SMEs hotels in Malaysia using online survey questionnaire. Structural Equation Modelling (SEM) is used to assess the developed model and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) technique is used to prioritize adoption factors based on their importance levels. The results showed that relative advantage, management support, IT expertise, and external pressure are the most important factors in the technological, organizational, human, and environmental dimensions. The results further revealed that technology is the most important influential dimension. The outcomes of this study can assist the policy makers, businesses and governments to make well-informed decisions in adopting big data.
Elaheh Yadegaridehkordi; Mehrbakhsh Nilashi; Liyana Shuib; Mohd Hairul Nizam Bin Md Nasir; Shahla Asadi; Sarminah Samad; Nor Fatimah Awang. The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications 2019, 40, 100921 .
AMA StyleElaheh Yadegaridehkordi, Mehrbakhsh Nilashi, Liyana Shuib, Mohd Hairul Nizam Bin Md Nasir, Shahla Asadi, Sarminah Samad, Nor Fatimah Awang. The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications. 2019; 40 ():100921.
Chicago/Turabian StyleElaheh Yadegaridehkordi; Mehrbakhsh Nilashi; Liyana Shuib; Mohd Hairul Nizam Bin Md Nasir; Shahla Asadi; Sarminah Samad; Nor Fatimah Awang. 2019. "The impact of big data on firm performance in hotel industry." Electronic Commerce Research and Applications 40, no. : 100921.
This study is the first attempt that aims to develop a comprehensive decision-making model for Software as a Service (SaaS) adoption in the educational environment. Accordingly, a new hybrid Multi-Criteria Decision Making (MCDM) approach of Grey Relational Analysis (GRA), Classification and Regression Trees (CART), and Fuzzy Rule-Based (FRB) techniques is developed to reveal the importance level of significant factors, model adoption status in the form of “IF-THEN” rules, and predict the level of adoption based on the significant adoption factors and their relationships. This study is the first-hand experience that takes complementary advantages of GRA, CART, and FRB techniques for technology adoption decision-making. The findings can be used as a guide by the administrator of universities, ministry of education, and services providers to successfully proceed for SaaS-based applications adoption in the educational environments.
Elaheh Yadegaridehkordi; Mehrbakhsh Nilashi; Liyana Shuib; Shahla Asadi; Othman Ibrahim. Development of a SaaS Adoption Decision-Making Model Using a New Hybrid MCDM Approach. International Journal of Information Technology & Decision Making 2019, 18, 1845 -1874.
AMA StyleElaheh Yadegaridehkordi, Mehrbakhsh Nilashi, Liyana Shuib, Shahla Asadi, Othman Ibrahim. Development of a SaaS Adoption Decision-Making Model Using a New Hybrid MCDM Approach. International Journal of Information Technology & Decision Making. 2019; 18 (6):1845-1874.
Chicago/Turabian StyleElaheh Yadegaridehkordi; Mehrbakhsh Nilashi; Liyana Shuib; Shahla Asadi; Othman Ibrahim. 2019. "Development of a SaaS Adoption Decision-Making Model Using a New Hybrid MCDM Approach." International Journal of Information Technology & Decision Making 18, no. 6: 1845-1874.
In today’s digital world the information surges with the widespread use of the internet and global communication systems. Healthcare systems are also facing digital transformations with the enhancement in the utilization of healthcare information systems, electronic records in medical, wearable, smart devices, handheld devices, and so on. A bulk of data is produced from these digital transformations. The recent increase in medical big data and the development of computational techniques in the field of cardiology enables researchers and practitioners to extract and visualize medical big data in a new spectrum. The role of medical big data in cardiology becomes a challenging task. Early decision making in cardiac healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Therefore, to facilitate this process a detailed report of the existing literature will be feasible to help the doctors and practitioners in decision making for the purpose of identifying and treating cardiac diseases. This detailed study will summarize results from the existing literature on big data in the field cardiac disease. This research uses the systematic literature protocol as presented by Kitchenham et al. 1. The data was collected from the published materials from 2008 to 2018 as conference or journal publications, books, magazines and other online sources. 190 papers were included relying on the defined inclusion, exclusion, and checking the quality criteria. The current study helped to identify medical big data features, the application of medical big data, and the analytics of the big data in cardiology. The results of the proposed research shows that several studies exist that are associated to medical big data specifically to cardiology. This research summarizes and organizes the existing literature based on the defined keywords and research questions. The analysis will help doctors to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients with heart related diseases.
Shah Nazir; Muhammad Nawaz; Awais Adnan; Sara Shahzad; Shahla Asadi. Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review. IEEE Access 2019, 7, 143742 -143771.
AMA StyleShah Nazir, Muhammad Nawaz, Awais Adnan, Sara Shahzad, Shahla Asadi. Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review. IEEE Access. 2019; 7 (99):143742-143771.
Chicago/Turabian StyleShah Nazir; Muhammad Nawaz; Awais Adnan; Sara Shahzad; Shahla Asadi. 2019. "Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review." IEEE Access 7, no. 99: 143742-143771.
A lot of attention has been given to institutional repositories from scholars in various disciplines and from all over the world as they are considered as a novel and substitute technology for scholarly communication. The purposed study aimed to examine the factors that have an influence on the adoption and intention of the researchers to use institutional repositories. The adoption intention of researchers was assessed using the following factors: attitude, effort expectancy, performance expectancy, social influence, internet self-efficacy and resistance to change. Data for this analysis was obtained from 177 Malaysian researchers and the research model put forward was tested using the multi-analytical approach. The variables that significantly affected institutional repositories adoption was initially determined using structural equation modeling (SEM). The neural network model (NN) was then used to put the comparative impact of significant predictors identified from SEM in order. It was found that the strongest predictors of the intentional to employ institutional repositories were internet self-efficacy and social influence. The findings of this research play an important part in influencing the decision-making of executives by determining and ranking factors through which they are able to identify the way they can promote the use of institutional repositories in their university. In addition, the research outcomes also provide information regarding the most important factors that are vital for formulating an appropriate strategic model to improve adoption of institutional repositories.
Shahla Asadi; Rusli Abdullah; Yusmadi Yah Jusoh. An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption. Advances in Intelligent Systems and Computing 2019, 513 -532.
AMA StyleShahla Asadi, Rusli Abdullah, Yusmadi Yah Jusoh. An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption. Advances in Intelligent Systems and Computing. 2019; ():513-532.
Chicago/Turabian StyleShahla Asadi; Rusli Abdullah; Yusmadi Yah Jusoh. 2019. "An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption." Advances in Intelligent Systems and Computing , no. : 513-532.
The digital transformations and use of healthcare information system, electronic medical records, wearable technology, and smart devices are increasing with the passage of time. A variety of sources of big data in healthcare are available, such as biometric data, registration data, electronic health record, medical imaging, patient reported data, biomarker data, clinical data, and administrative data. Visualization of data is a key tool for producing images, diagrams, or animations to convey messages from the viewed insight. The role of cardiology in healthcare is obvious for living and life. The function of heart is the control of blood supply to the entire parts of the body. Recent speedy growth in healthcare and the development of computation in the field of cardiology enable researchers and practitioners to mine and visualize new insights from patient data. The role of visualization is to capture the important information from the data and to visualize it for the easiness of doctors and practitioners. To help the doctors and practitioners, the proposed study presents a detailed report of the existing literature on visualization of data in the field of cardiology. This report will support the doctors and practitioners in decision-making process and to make it easier. This detailed study will eventually summarize the results of the existing literature published related to visualization of data in the cardiology. This research uses the systematic literature protocol and the data was collected from the studies published during the year 2009 to 2018 (10 years). The proposed study selected 53 primary studies from different repositories according to the defined exclusion, inclusion, and quality criteria. The proposed study focused mainly on the research work been done on visualization of big data in the field of cardiology, presented a summary of the techniques used for visualization of data in cardiology, and highlight the benefits of visualizations in cardiology. The current research summarizes and organizes the available literature in the form of published materials related to big data visualization in cardiology. The proposed research will help the researchers to view the available research studies on the subject of medical big data in cardiology and then can ultimately be used as evidence in future research. The results of the proposed research show that there is an increase in articles published yearly wise and several studies exist related to medical big data in cardiology. The derivations from the studies are presented in the paper.
Shah Nazir; Muhammad Nawaz Khan; Sajid Anwar; Awais Adnan; Shahla Asadi; Sara Shahzad; Shaukat Ali. Big Data Visualization in Cardiology—A Systematic Review and Future Directions. IEEE Access 2019, 7, 115945 -115958.
AMA StyleShah Nazir, Muhammad Nawaz Khan, Sajid Anwar, Awais Adnan, Shahla Asadi, Sara Shahzad, Shaukat Ali. Big Data Visualization in Cardiology—A Systematic Review and Future Directions. IEEE Access. 2019; 7 (99):115945-115958.
Chicago/Turabian StyleShah Nazir; Muhammad Nawaz Khan; Sajid Anwar; Awais Adnan; Shahla Asadi; Sara Shahzad; Shaukat Ali. 2019. "Big Data Visualization in Cardiology—A Systematic Review and Future Directions." IEEE Access 7, no. 99: 115945-115958.
Green IT has attracted policy makers and IT managers within organizations to use IT resources in cost-effective and energy-efficient ways. Investigating the factors that influence decision-makers’ intention towards the adoption of Green IT is important in the development of strategies that promote the organizations to use Green IT. Therefore, the objective of this study stands to understand potential factors that drive decisions makers in Malaysian manufacturing sector to adopt Green IT. This research accordingly developed a model by integrating two theoretical models, Theory of Planned Behavior and Norm Activation Theory, to explore individual factors that influence decision’ makers in manufacturing sector in Malaysia to adopt Green IT via the mediation of personal norms. Accordingly, to determine predictive factors that influence managerial intention toward Green IT adoption, the researchers conducted a comprehensive literature review. The data was collected from 183 decision-makers from Malaysian manufacturing sector and analyzed by Structural Equation Modelling. This research provides important preliminary insights in understanding the most significant factors that determined managerial intention towards Green IT adoption. The model of Green IT adoption explained factors which encourages individual decision-makers in the Malaysian organizations to adopt Green IT initiatives for environment sustainability.
Shahla Asadi; Mehrbakhsh Nilashi; Mahmood Safaei; Rusli Abdullah; Faisal Saeed; Elaheh Yadegaridehkordi; Sarminah Samad. Investigating factors influencing decision-makers’ intention to adopt Green IT in Malaysian manufacturing industry. Resources, Conservation and Recycling 2019, 148, 36 -54.
AMA StyleShahla Asadi, Mehrbakhsh Nilashi, Mahmood Safaei, Rusli Abdullah, Faisal Saeed, Elaheh Yadegaridehkordi, Sarminah Samad. Investigating factors influencing decision-makers’ intention to adopt Green IT in Malaysian manufacturing industry. Resources, Conservation and Recycling. 2019; 148 ():36-54.
Chicago/Turabian StyleShahla Asadi; Mehrbakhsh Nilashi; Mahmood Safaei; Rusli Abdullah; Faisal Saeed; Elaheh Yadegaridehkordi; Sarminah Samad. 2019. "Investigating factors influencing decision-makers’ intention to adopt Green IT in Malaysian manufacturing industry." Resources, Conservation and Recycling 148, no. : 36-54.
Cloud computing (CC) is a recently developed computing paradigm that can be utilized to deliver everything-as-a-service to various businesses. In higher education institutions (HEIs), CC is rapidly being deployed and becoming an integral part of institution experience. CC adoption in HEIs is accompanied by numerous scientific contributions that address the topic from different perspectives. A systematic review of these heterogeneous contributions, which provide a coherent taxonomy, can be considered interesting for HEIs to identify opportunities to use CC in its own context. Therefore, this systematic literature review aims to analyze existing research on adopting and using CC in HEIs, review background research to develop a coherent taxonomy and provide a landscape for future research on CC in HEIs. The outcomes of this study include a coherent taxonomy and an overview of the basic characteristics of this emerging field in terms of motivation and barriers of adopting CC in HEIs, existing individual and organizational theoretical models to understand the future requirements for extensively adopting and using CC in HEIs, and factors that influence the adoption of CC in HEIs at individual and organizational levels. Considerable information is available in relation to adopting and using CC in HEIs. This review will enhance this information by offering an in-depth analysis of the existing data to bridge any gap and expand on existing literature.
Yousef A. M. Qasem; Rusli Abdullah; Yusmadi Yah Jusoh; Rodziah Atan; Shahla Asadi. Cloud Computing Adoption in Higher Education Institutions: A Systematic Review. IEEE Access 2019, 7, 63722 -63744.
AMA StyleYousef A. M. Qasem, Rusli Abdullah, Yusmadi Yah Jusoh, Rodziah Atan, Shahla Asadi. Cloud Computing Adoption in Higher Education Institutions: A Systematic Review. IEEE Access. 2019; 7 (99):63722-63744.
Chicago/Turabian StyleYousef A. M. Qasem; Rusli Abdullah; Yusmadi Yah Jusoh; Rodziah Atan; Shahla Asadi. 2019. "Cloud Computing Adoption in Higher Education Institutions: A Systematic Review." IEEE Access 7, no. 99: 63722-63744.
The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.
Shahla Asadi; Rusli Abdullah; Mahmood Safaei; Shah Nazir. An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices. Mobile Information Systems 2019, 2019, 1 -9.
AMA StyleShahla Asadi, Rusli Abdullah, Mahmood Safaei, Shah Nazir. An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices. Mobile Information Systems. 2019; 2019 ():1-9.
Chicago/Turabian StyleShahla Asadi; Rusli Abdullah; Mahmood Safaei; Shah Nazir. 2019. "An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices." Mobile Information Systems 2019, no. : 1-9.
Shahla Asadi; Rusli Abdullah; Yusmadi Yah; Shah Nazir. Understanding Institutional Repository in Higher Learning Institutions: A Systematic Literature Review and Directions for Future Research. IEEE Access 2019, 7, 35242 -35263.
AMA StyleShahla Asadi, Rusli Abdullah, Yusmadi Yah, Shah Nazir. Understanding Institutional Repository in Higher Learning Institutions: A Systematic Literature Review and Directions for Future Research. IEEE Access. 2019; 7 ():35242-35263.
Chicago/Turabian StyleShahla Asadi; Rusli Abdullah; Yusmadi Yah; Shah Nazir. 2019. "Understanding Institutional Repository in Higher Learning Institutions: A Systematic Literature Review and Directions for Future Research." IEEE Access 7, no. : 35242-35263.
Recently cloud computing has received significant attention, but its adoption is still far from reaching its full potential, especially in educational contexts. Only a few studies have considered the students’ behavior toward adoption of cloud technology in particular for online collaborative learning purposes. Therefore, this research seeks to develop an adoption model for online collaborative learning tools in cloud environment. To this end, Technology Acceptance Model (TAM) is extended by adding mobility, collaboration, and personalization as external variables. A sample of 209 respondents is collected from four top Malaysian universities and Structural Equation Modelling (SEM) is utilized to assess the research model. The findings show that intention to adopt is significantly affected by perceived usefulness. Although, perceived ease of use does not perform a direct impact on intention to adopt, its indirect influence through perceived usefulness is supported. Mobility and personalization significantly influence perceived ease of use, but they have insignificant impacts on perceived usefulness. Furthermore, perceived usefulness and perceived ease of use are significantly influenced by collaboration. This study rounds off with discussion and conclusions, highlighting implications. The findings provide a baseline for cloud service providers and education institutions in providing effective online collaborative learning tools.
Elaheh Yadegaridehkordi; Liyana Shuib; Mehrbakhsh Nilashi; Shahla Asadi. Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies 2018, 24, 79 -102.
AMA StyleElaheh Yadegaridehkordi, Liyana Shuib, Mehrbakhsh Nilashi, Shahla Asadi. Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies. 2018; 24 (1):79-102.
Chicago/Turabian StyleElaheh Yadegaridehkordi; Liyana Shuib; Mehrbakhsh Nilashi; Shahla Asadi. 2018. "Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities." Education and Information Technologies 24, no. 1: 79-102.
Many smart healthcare centers are deploying long distance, high bandwidth networks in their computer network infrastructure and operation. Transmission control protocol (TCP) is responsible for reliable and secure communication of data in these medial infrastructure networks. TCP is reliable and secure due to its congestion control mechanism, which is responsible for detecting and reacting to the congestion in the network. Many TCP congestion control mechanisms have been developed previously for different operating systems. TCP CUBIC, TCP Compound, and TCP Fusion are the default congestion control mechanism in Linux, Microsoft Windows, and Sun Solaris operating systems, respectively. The earliest congestion control mechanism Standard TCP acts as the trademark congestion control mechanism. The exponential growth of congestion window (cwnd) in slow start phase of the TCP CUBIC causes burst losses of packets, and TCP flows did not share available link bandwidth fairly. The prime aim of this paper is to enhance the performance of TCP CUBIC for long distance, high bandwidth secured networks to achieve better performance in medical infrastructure, concerning packet loss rate, protocol fairness, and convergence time. In this paper, congestion control module for slow start is proposed, which reduces the effect of the exponential growth of cwnd by designing the new limits of cwnd size in slow start phase, which in turn decreases the packet loss rate in healthcare networks. NS-2 is used to simulate the experiments of enhanced TCP CUBIC and state-of-the-art congestion control mechanisms. Results show that the performance of enhanced TCP CUBIC outperforms by 18% as compared with the state-of-the-art congestion control mechanisms.
Mudassar Ahmad; Majid Hussain; Beenish Abbas; Omar Aldabbas; Uzma Jamil; Rehan Ashraf; Shahla Asadi. End-to-End Loss Based TCP Congestion Control Mechanism as a Secured Communication Technology for Smart Healthcare Enterprises. IEEE Access 2018, 6, 11641 -11656.
AMA StyleMudassar Ahmad, Majid Hussain, Beenish Abbas, Omar Aldabbas, Uzma Jamil, Rehan Ashraf, Shahla Asadi. End-to-End Loss Based TCP Congestion Control Mechanism as a Secured Communication Technology for Smart Healthcare Enterprises. IEEE Access. 2018; 6 ():11641-11656.
Chicago/Turabian StyleMudassar Ahmad; Majid Hussain; Beenish Abbas; Omar Aldabbas; Uzma Jamil; Rehan Ashraf; Shahla Asadi. 2018. "End-to-End Loss Based TCP Congestion Control Mechanism as a Secured Communication Technology for Smart Healthcare Enterprises." IEEE Access 6, no. : 11641-11656.
The environmental issues had drawn attention from governments, societies as well as business organisations. Indeed, organisations have put effort on challenging the environment issues. One of the most important logical efforts that organisations put to deal with environmental issues is Green IT which, in turn, leads to enhancement in the organisation's performance and improvement. Using Green IT by organisations will be advantageous to the society. There is plethora of research on Green IT in organisations and the adoption of Green IT from managers' pro-environmental intention was only explored by a small number of them. The norm activation theory and the theory of planned behaviour have been used in this study. The study provides the available recent knowledge about information system which is necessary to monitoring the decision maker's intention for adoption of Green IT and sustainability by developing a research model which recognises the important aspects for Green IT adoption.
Shahla Asadi; Ab Razak Che Hussin; Halina Mohamed Dahlan. Toward Green IT adoption: from managerial perspective. International Journal of Business Information Systems 2018, 29, 106 .
AMA StyleShahla Asadi, Ab Razak Che Hussin, Halina Mohamed Dahlan. Toward Green IT adoption: from managerial perspective. International Journal of Business Information Systems. 2018; 29 (1):106.
Chicago/Turabian StyleShahla Asadi; Ab Razak Che Hussin; Halina Mohamed Dahlan. 2018. "Toward Green IT adoption: from managerial perspective." International Journal of Business Information Systems 29, no. 1: 106.