Artificial intelligence (AI) delivers numerous chances to add to the prosperity of people and the stability of economies and society, yet besides, it adds up a variety of novel moral, legal, social, and innovative difficulties. Trustworthy AI (TAI) bases on the possibility that trust builds the establishment of various societies, economies, and sustainable turn of events, and that people, organizations, and societies can along these lines just at any point understand the maximum capacity of AI, if trust can be set up in its development, deployment, and use. The risks of unintended and negative outcomes related to AI are proportionately high, particularly at scale. Most AI is really artificial narrow intelligence, intended to achieve a specific task on previously curated information from a certain source. Since most AI models expand on correlations, predictions could fail to sum up to various populations or settings and might fuel existing disparities and biases. As the AI industry is amazingly imbalanced, and experts are as of now overpowered by other digital devices, there could be a little capacity to catch blunders. With this article, we aim to present the idea of TAI and its five essential standards (1) usefulness, (2) non-maleficence, (3) autonomy, (4) justice, and (5) logic. We further draw on these five standards to build up a data-driven analysis for TAI and present its application by portraying productive paths for future research, especially as to the distributed ledger technology-based acknowledgment of TAI.
The highest benefit of IT spans through the enabling of personnel to attain their organizational goals. However, acquiring the IT skills that were not aware of in the past will boost and enhance the personnel for greater performance. IoT technology gives understanding from novel data generated and gives solutions. Therefore, allowing organizations to access new strategies via technological innovation will bring about efficiency and productivity with the project lifecycle. However, this project aimed at assessing the impact of IoT on PM in project-based organizations. A qualitative method of investigation was adopted through interviews and discussions with 9 selected respondents. The result shows the benefit and the usefulness of IoT in project-based organizations. This was assessed using the five project management model namely initiating, planning, executing, control and monitoring, and closing. It is established that the impact of IoT can be seen using any of the five stages. Hence, this study identifies the most critical elements of any project-based organization to include people, possessing on personnel and how their impact the invention of project-based organizations.
In the face of the agricultural sector's challenges, food security with an increasing human population and high demand for food is a significant problem. Traditional methods used by farmers have not been sufficient to meet the food requirements of the growing population. As a result, the agricultural sector has begun to deploy artificial intelligence to meet the demand for food and sustainability. This study was conducted to examine how AI improves farmers' productivity and sustainability. Data were analyzed using centering resonance analysis, t-test, ANOVA, and text mining news articles from 2014-2019 in Africa, Asia, Europe, and North America. Results show that AI is used primarily to increase productivity and efficiency and secondarily to address labor shortages and environmental sustainability concerns. The results at the regional level reflect the active adoption of AI in North America and Europe, with increasing efforts in Asia and Africa.
Cloud data migration is the process of moving data, localhost applications, services, and data to the distributed cloud processing framework. The success of this data migration measure is relying upon a few viewpoints like planning and impact analysis of existing enterprise systems. Quite possibly the most widely recognized process is moving locally stored data in a public cloud computing environment. Cloud migration comes along with both challenges and advantages, so there are different academic research and technical applications on data migration to the cloud that will be discussed throughout this paper. By breaking down the research achievement and application status, we divide the existing migration techniques into three strategies as indicated by the cloud service models essentially. Various processes should be considered for different migration techniques, and various tasks will be included accordingly. The similarities and differences between the migration strategies are examined, and the challenges and future work about data migration to the cloud are proposed. This paper, through a research survey, recognizes the key benefits and challenges of migrating data into the cloud. There are different cloud migration procedures and models recommended to assess the presentation, identifying security requirements, choosing a cloud provider, calculating the expense, and making any essential organizational changes. The results of this research paper can give a roadmap for data migration and can help decision-makers towards a secure and productive migration to a cloud computing environment.
The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP.
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