Query: Artificial intelligence in project management

title: Data Science and Artificial Intelligence in Project Management: The Past, Present and Future
abstract: The contemporary landscape of Project Management is continually morphing as a response to the changing demands as well as the developing technology available. This article discusses and uncovers the current state of Data Science and Artificial Intelligence in relation to the Project Management sector. Drawing from this context, the possible future applications and uses are then delineated. Several trends are identified, and the overall findings are synthesised to provide a realistic impression of what the future topography of Project Management holds. The increasingly sophisticated applications of technology will assist in streamlining current industry standard procedures.

title: The Last State of Artificial Intelligence in Project Management
abstract: Artificial intelligence (AI) has been used to advance different fields, such as education, healthcare, and finance. However, the application of AI in the field of project management (PM) has not progressed equally. This paper reports on a systematic review of the published studies used to investigate the application of AI in PM. This systematic review identified relevant papers using Web of Science, Science Direct, and Google Scholar databases. Of the 652 articles found, 58 met the predefined criteria and were included in the review. Included papers were classified per the following dimensions: PM knowledge areas, PM processes, and AI techniques. The results indicated that the application of AI in PM was in its early stages and AI models have not applied for multiple PM processes especially in processes groups of project stakeholder management, project procurements management, and project communication management. However, the most popular PM processes among included papers were project effort prediction and cost estimation, and the most popular AI techniques were support vector machine, neural networks, and genetic

title: Artificial Intelligence in Project Management

title: Emerging Roles of Artificial Intelligence in Project Management

title: Applying Artificial Intelligence techniques in Project Management
abstract: The main purpose of this thesis is to create a model like Earned Value Management (EVM) model in the Project Management for better forecasting. Some machine learning concepts will be used to better predict the project duration and minimize the cost. Traditional EVM model formulas to track Projects progress relies on too many assumptions and simplifications. This oversimplification can affect the accuracy of the forecasting which can cause delay and in turn increase project costs. There is also bias related to human perceptions in the EVM model which must be eliminated. The goal is to develop a new dynamic monitoring and optimization tool to track progress of the project. Furthermore, to increase forecasting, rescheduling and reallocation processes efficiency with new DSS (decision support system) Machine learning an ever growing field and implementation of it in project management can change how projects are tracked. Machine learning models are trained with data already available so that the computer can make a more informed decision by itself. It should identify all the combinations and provide best scheduling option which minimizes cost and financial exposures. The ultimate goal is to minimize the project duration and project costs. The another aspect which will be focused and that is the financial exposure. The aim is to minimize the financial exposure which traditional methods do not account for. This is more practical than theoretical as banks are reluctant to fund the project if the cash flow is not smooth. The financial need should be minimized.

title: The Role of Artificial Intelligence in the future of E learning and Project Management

title: Application of artificial intelligence in project management under risk condition
abstract: Risk management problem was shown in the paper. Relationship between risk management and production process scheduling was analyzed. Different types of data analysis were presented. Toothed gear production process was taken as an example of task timing estimation. 1.

title: Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management
abstract: This paper aims to illustrate an example of how to set up a work injury database: the Smart Work Injury Management (SWIM) system. It is a secure and centralized cloud platform containing a set of management tools for data storage, data analytics, and machine learning. It employs artificial intelligence to perform in depth analysis via text mining techniques in order to extract both dynamic and static data from work injury case files. When it is fully developed, this system can provide a more accurate prediction model for cost of work injuries. It can also predict return to work (RTW) trajectory and provide advice on medical care and RTW interventions to all RTW stakeholders. The project will comprise three stages. Stage one: to identify human factors in terms of both facilitators and barriers RTW through face to face interviews and focus group discussions with different RTW stakeholders in order to collect opinions related to facilitators, barriers, and essential interventions for RTW of injured workers; Stage two: to develop a machine learning model which employs artificial intelligence to perform in depth analysis. The technologies used will include: 1. Text mining techniques including English and Chinese work segmentation as well as N Gram to extract both dynamic and static data from free style text as well as sociodemographic information from work injury case files; 2. Principle component/independent component analysis to identify features of significant relationships with RTW outcomes or combine raw features into new features; 3. A machine learning model that combines Variational Autoencoder, Long and Short Term Memory, and Neural Turning Machines. Stage two will also include the development of an interactive dashboard and website to query the trained machine learning model. Stage three: to field test the SWIM system.  SWIM ia secure and centralized cloud platform containing a set of management tools for data storage, data analytics, and machine learning. When it is fully developed, SWIM can provide a more accurate prediction model for the cost of work injuries and advice on medical care and RTW interventions to all RTW stakeholders. Ethics The project has been approved by the Ethics Committee for Human Subjects at the Hong Kong Polytechnic University and is funded by the Innovation and Technology Commission (Grant ITS/249/18FX)

title: Technologies of Artificial Intelligence in Educational Management
abstract: This research paper presents the analysis of artificial intelligence techniques employed in the field of educational management. The author reviews intelligent learning systems. Particular attention is drawn to the expert systems that are integrated into the design of platforms for on line learning. Highlighted are the features of intelligent systems allowing for organization of the optimized digital learning process. By way of illustration, suggested is the creation of an interactive training project aiming at the successful acquisition of knowledge by students taking courses in philosophy, as well as at improving the quality of this knowledge. The methods of the project implementation include design and development of the software necessary to create and operate the on line platforms; systematic approach including the databank formation and classification of the data; approach to the intelligent data processing to activate the interactive learning models.

title: Artificial Intelligence and Project Management