The Applications of Artificial Intelligence in Managing Project Processes and Targets: A Systematic Analysis

Artificial intelligence (AI) has emerged as the defining technology of the 21st century and has far-reaching impacts on project management (PM). This study assesses the applications of AI in managing project processes and targets through a systematic analysis of publications from 2017 to 2021. The analysis has revealed interesting insights, trends, gaps, and issues. This study informs the researchers and practitioners of the status of AI applications in the management of project processes and targets. It helps stimulate research efforts that can lead to more advances in applying AI to augment PM practices

Software Development Effort Estimation Using Regression Fuzzy Models

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call regression fuzzy logic. State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.Comment: This paper has been accepted in January 2019 in Computational Intelligence and Neuroscience Journal (In Press

Transfer Learning using Computational Intelligence: A Survey

Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..

Effort Estimation Methods in Software Development Using Machine Learning Algorithms

Estimation of effort for the proposed software is a standout amongst the most essential activities in project management. Proper estimation of effort is often desirable in order to avoid any sort of failures in a project and is the practice to adopted by developers at the very beginning stage of the software development life cycle. Estimating the effort and schedule with a higher accuracy is a challenge that attracts attention of researchers as well as practitioners. Predicting the effort required to develop a software to a certain level of accuracy is definitely a difficult assignment for a manager or system analyst, when the requirements are not very clearly identified. Effort estimation helps project managers to determine time and effort required for the successful completion of the project. In order to help the organization in developing qualitative products within a planned time frame, the job of appropriate software effort estimation is of primary requirement. For measuring the cost and effort of software development, traditional software estimation techniques like Constructive Cost Estimation (COCOMO) model and Function Point Analysis (FPA) have not been proved very much satisfactory, because of uncertainties associated with parameters such as Line Of Code (LOC) and Function Point (FP) respectively, used for procedural programming concept. The procedural oriented design splits the data and procedure, whereas accepted practice of present day i.e., the object-oriented design combines both of them Since class and use case are the basic logical units of an object-oriented system, the use of Class Point (CP) and Use Case Point (UCP) approach to estimate the project effort helps to get more accurate result. For projects based on the aspect of Web Engineering, effort estimation practice is identified as a critical issue Considering these facts, there is a strong need for formal estimation of web-based projects, which can be accomplished by the help of International Software Benchmarking Standards Group (ISBSG) dataset. Similarly, in case of agile projects, Story Point Approach (SPA) is used to measure the effort required to implement a user story. By adding up the estimates of user stories which were nished during an iteration (story point iteration), the project velocity is obtained. The dataset related to CP, UCP and SPA are collected from previous projects mentioned in few research articles or from industries in order to assess the results. In order to create results of estimation with more accuracy, when managing issues of complex connections in the middle of inputs as well as yields, and where, there is a distortion in the inputs by high noise levels, the application of machine learning (ML) techniques helps to bring out results with more accuracy. A number of past research studies indicate that no single technique turns out to be the best for all cases. This is because of the dependency of system's execution altogether on the predicted function types, variations in properties of collected data, number of tests, noise ratio and so on. Hence the use of ML techniques in order to cope with issues arises in real-life situation is considered to be worthwhile. The research work carried out here presents the use of various ML techniques for software effort estimation using CP, UCP, Web-based and SPA approaches. The ML techniques are implemented taking into consideration of related dataset to predict the required effort

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Machine learning to generate soil information

This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

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