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Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

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.

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%.

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.

The use of mobile medical apps in clinical settings has recently received considerable attention. While some practitioners are using this technology to optimize decision making, others, on the other hand, are indifferent about its usage. Therefore, this study has utilized a modified UTAUT2 model to determine factors that influence the acceptance and use of mobile medical apps among professional nurses in the Ghanaian setting. A web-based data collection tool (Google Forms) was used to solicit data from 216 health professionals. Of the 216 respondents, 126 (58.33%) of them were Public Health Nurses, with 85 (39.35%) being General Nurses, and the remaining 5 (2.32%) were midwives, resulting in an average age of 31.57±4.14 years. The study used a previously validated self-administered questionnaire (UTAUT 2) to find out factors that informed respondents to adopt and use mobile medical apps. The findings established that professional qualification, gender, and the number of years that the individual had used smartphones were key determinants in accepting and using mobile medical apps. That is why this study makes the case that using technology in clinical practice has enormous advantages. It is, however, important to understand the factors that will influence the intention to adopt such technologies and their possible use.

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.

Artificial intelligence (AI) is a discipline interested in the processes and methods that allow a machine to perform tasks related to human intelligence. It offers many opportunities related to problem solving, quick decision-making, increasing efficiency and reducing costs. Because of its so various fields of application, artificial intelligence is at the heart of the new industrial revolution. Algeria aims to present its AI strategy by 2020. In this paper, we are interested in defining AI, its potential fields of application, and in particular, its influence in the customer journey and position of RFID ( Radio-Frequency Identification ) in the chain; application in the aviation sector and its relationship to the Internet of Things are also described through examples.

The challenge of electricity distribution’s upgrade to incorporate new technologies is big, and electric utilities are mandated to work diligently on this agenda, thus making investments to ensure that current networks maintain their electricity supply commitments secure and reliable in face of disruptions and adverse environmental conditions from a variety of sources. The paper presents a new model based on energy homeostasis for power control and energy management using tariffs differentiation as incentive, considered by ENEL, the largest electric utility in Chile. The model optimizes grid-tied distributed generation (DG) systems with energy storage, in line with the utility’s green energy program, part of its Smart Grid Transformation, aimed at installing grid-tied DG systems with solar generation and energy storage in Santiago, Chile. Results present different tariff options, system’s capacity and energy storage alternatives, in order to compare proposed strategies with the actual case, where no green energy is present. The results show the advantage of the proposed tariffs scheme and power-energy management model based on different scenarios, providing a good and safe option for installing DG solutions to the grid.