درخت حوزه‌های تخصصی

Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.

Recently, Massive Open Online Courses have become sensational in the field of distance learning. There is a plethora of advantages being listed in learning through MOOCs but this pedagogy lacks in few areas when compared with traditional classes. One of those inabilities of MOOC is its support to prepare the students for laboratory-based courses. The authors of this study chose a MOOC course that teaches Digital Photography and created an Augmented Reality (AR) experience for a module that explains the different parts of a digital camera. The 2 nd year Multimedia students of Vellore Institute of Technology have been asked to experience the MOOC video followed by the AR experience. Their feedbacks before and after the AR experience has been statistically tested and reported. The results revealed that the students feel more confident and concentrate more when the instructional video was given as an AR experience. This study suggests that AR integrated MOOC modules might help in training students better for practice-based courses.

This paper focuses on the contribution of digital government (DGOV) to Whistleblowing (WB). While considerable efforts have been devoted to DGOV and WB separately, research work at the intersection of these two domains is very scarce; hence and a systematic DGOV for WB (DGOV4WB) research framework has yet to emerge. This paper aims to identify the potential issues in whistleblowing and explore how digital government has been used to address these issues. To this end, this paper uses explanatory case study research methodology and analyses four case studies of existing DGOV initiatives with explicit WB dimensions. The result of the cross-case analysis shows that DGOV4WB initiatives contribute to address goals of the different dimensions of whistleblowing. The most common WB problems addressed are: easily accessible reporting and response whistleblowing channels (Whistleblowing Procedure), easy and timely communication channel with top management (Whistleblowing Organizational culture), and anonymous and confidential communication platforms (Whistleblower Protection). In addition, based on the analysis of the case studies, all the initiatives are classified as either engagement or contextualization stage of the digital government evolution model.

Cloud computing is an emerging concept of information technology that in many countries has an influence on many companies. The research was conducted to evaluate cloud computing adoption in Saudi Arabia; Benefits and hindrances for small and medium-sized enterprises (SMEs). The qualitative research approach is performed by interviews with the management of a variety of SMEs active in the information and communication technology (ICT) industry. This paper illustrates a significant positive correlation between the use of cloud computing and organizational quality performances. The paper concluded that the knowledge level of SMEs on the accessibility of cloud services is below average scale. The greatest challenges about the cloud service are privacy and security in the cloud among providers and users for the Saudi Arabian firms.

The Special Issue of the Journal of Information Technology Management (JITM) is publishing very selective papers on information management, technology in higher education, integrated systems, enterprise management, cultural thoughts, strategic contributions, management information systems, and cloud computing. We received numerous papers for this special issue but after an extensive peer-review process, 10 papers were finally selected for publication. The current special issue consisted of areas viz Integrated Intelligent Systems, Analytical Systems, and Enterprise Management.

This study aimed to investigate the impact of the hospital information system (HIS) on nurses' satisfaction in Iranian public hospitals and to examine the moderating effect of computer literacy. The study population consisted of nurses working in public hospitals in Iran. A sample of 385 Iranian public hospitals was surveyed and a total of 1912 questionnaires were collected over 9 months. The analytical method used to empirically test the proposed hypotheses was the SEM technique using SmartPLS3. Results showed that all four variables of nurses' attitude, system quality, information quality, and service quality had significant positive effects on nurses' satisfaction with HIS. The findings also indicated that the effects of nurses' attitude, system quality, and particularly service quality increased on nurses' satisfaction with HIS by rising computer literacy, and only the effect of (computer literacy × information quality) was not significant on satisfaction. The use of FIMIX, CTA, and permutation test analyses is the innovation of the analysis.

A logo epitomizes a brand and depicts the picture of a product; consequently, attention, as an initial step of the AIDA model, to the logo is a good-looking index to survey the cognitive processing during consumers’ decision-making. Eye tracker extracts the visual attention data. For these reasons and appreciated from fixation duration, in the present study, the process of visual attention to the logo of a brand during decision-making was studied. To analyze the effects of the popularity of a brand on consumers’ decision-making, visual attention of 53 undergraduate students was studied. The brands were selected from two categories of beverages: soft drinks and non-alcoholic beers. Prior to the test, the participants had declared their favorite categories. The results showed that 75% of the participants selected a popular brand of their unfavorite categories. On the other hand, the difference of fixation duration between logos of popular and unknown brands did not directly relate to the choices of the participants. Therefore, these results did not correspond with the AIDA model. Additionally, cognitive processing toward the unknown brands’ logos was more than the popular brands’ logos. Moreover, the consumers who changed their decision were subjected to more cognitive processing compared to the consumers who insisted on their selection. In contrast to the AIDA model which implies that decision-making is triggered by attention, the present study indicated that decision making can also be influenced by the popularity of the brand. This can be due to optimization of the working memory usage by the brain. Finally, these results could be helpful in creating a new brand. According to the AIDA model, the more attractive logo and package of the brand, the more chance for being selected by the customers. However, our findings emphasize the importance of the popularity of the brand.

The new paradigm of good governance has an emphasis on international transparency, and this in this study takes into account the actual impacts of organizational intelligence in policymaker organizations. This study primarily aims to design an intelligent model of transparent governance in policymaker organizations with the approach of good governance. The study is a fundamental research in terms of objectives. In the qualitative section, data collection was done through Delphi interview questions, and the statistical population was senior managers, specialists, and policymakers with targeted sampling. In the quantitative part, the population was mid-level managers and organizational intelligence experts. The random sampling method was via the Cochran formula, and 432 individuals were selected. Data gathering tools were interviews and researcher-made survey questionnaires. The content and face validity and Cronbach alpha reliability were employed. The data were analyzed by descriptive and inferential statistics, including factor analysis, regression, and structural equations. Model fit and Friedman test were employed. The findings indicated three dimensions in the proposed model design, including transparency, knowledge creation, and knowledge translation, along with six components, five subcomponents, and 23 indicators. The results suggested that there is a relatively strong and appropriate relationship between organizational intelligence and organizational transparency. Furthermore, sense-making had the highest correlation with organizational transparency. Also, the strongest predictor was the sense-making variable.

This study aimed to examine the impact of parenting mediation strategies on family relationships and social media use among Saudi adolescents. To achieve the aim, a quantitative research design was used, involving questionnaires with data collected from 393 Saudi students aged 13-18 years. Pearson correlation and hierarchical multiple regression analyses were performed. The key findings of this study showed that Snapchat and Instagram were the most popular social media sites among Saudi adolescents, and parenting mediation strategies were affected by family relationships. Just over a third of participants (35.62%) reported that they spent 3-5 hours per day on social media with another 30.79% spending more than 6 hours per day on social media. Family relationships were found to strongly predict the social integration and social media addiction. The data showed a significant negative correlation between excessive use of social media and two components of family relationships (cohesion and expressiveness). Moreover, the results suggest that lower levels of family expressiveness and higher levels of family conflict were associated with social media addiction. The parenting mediation strategies were shown to predict the cohesiveness and expressiveness of family relationships. Finally, technical and monitoring parenting mediation strategies were found significant associated with the social media use and the family relationships. These results contribute to formulating guidelines for parents and policymakers in developing countries such as Saudi Arabia to protect their children from the social media risks.

Handwriting Analysis has been used for a very long time to analyze an individual’s suitability for a job, and is in recent times, gaining popularity as a valid means of a person’s evaluation. Extensive Research has been done in the field of determining the Personality Traits of a person through handwriting. We intend to analyze an individual’s personality by breaking it down into the Big Five Personality Traits using their handwriting samples. We present a dataset that links personality traits to the handwriting features. We then propose our algorithm - consisting of one ANN based model and PersonaNet, a CNN based model. The paper evaluates our algorithm’s performance with baseline machine learning models on our dataset. Testing our novel architecture on this dataset, we compare our algorithm based on various metrics, and show that our novel algorithm performs better than the baseline Machine Learning models.

Today, news texts can be created automatically and presented to readers without human participation through technologies and methods such as big data, deep learning, and natural language generation. With this research, we have developed an application that can contribute to the literature regarding The Studies on Robot Journalism Applications with a technology-reductionist perspective. Robot journalism application named Robottan Al Haberi (the English equivalent of the application name is “get the news from the robot”) produces news text by placing weather, exchange rates, and earthquake data in certain templates. The news texts, which are produced by placing the data in appropriate spaces on the template and with a maximum length of 280 characters, are automatically shared via the Twitter account @robottanalhaber. The weather information is shared once a day, the exchange rate information is shared three times a day, and the earthquake information is shared instantly. Here, we aim to produce automatic and short news by using the available structured data by placing them in specific news templates suggesting different options or a combination of them for different situations.

The continuing outbreak of COVID-19 pneumonia is globally concerning. Timely detection of infection ensures prompt quarantine of patient which is crucial for preventing the rapid spread of this contagious disease and also supports the patient with necessary medication. Due to the high infection rate of COVID-19, our health management system needs an automatic diagnosis tool that equips the health workers to pay immediate attention to the needy person. Chest CT is an essential imaging technique for diagnosis and staging of 2019 novel coronavirus disease (COVID-19). The identification of COVID-19 CT findings assists health workers on further clinical evaluation, especially when the findings on CT scans are trivial, the person may be recommended for Reverse-transcription polymerase chain reaction (RT-PCR) tests. Literature reported that the ground-glass opacity (GGO) with or without consolidation are dominant CT findings in COVID-19 patients. In this paper, the U-Net based segmentation approach is proposed to automatically segment and analyze the GGO and consolidation findings in the chest CT scan. The performance of this system is evaluated by comparing the auto-segmented infection regions with the manually-outlines ones on 100 axial chests CT scans of around 40 COVID-19 patients from SIRM dataset. The proposed U-Net with pre-process approach yields specificity of 0.91 ± 0.09 and sensitivity of 0.87 ± 0.07 on segmenting GGO region and specificity of 0.81 ± 0.13 and sensitivity of 0.44 ± 0.17 on segmenting consolidation region. Also the experimental results confirmed that the automatic detection method identifies the CT finding with a precise opacification percentage from the chest CT image.