Proposing a Hybrid Approach to Predict, Schedule and Select the Most Robust Project Portfolio under Uncertainty

Suitable project portfolio selection in inconsistent economy that can reduce the portfolio risks and increasing utilities for investors has gained significant research attentions.   This article addresses the project portfolio selection in which conventional certain (1) prediction, (2) optimization and (3) clustering approaches cannot be used to face uncertainty. To predict the real value of affecting project risk parameters, neural network has been used; Then to determine the optimized sequences and procedures, the proposed model have been evaluated using the multi-objective shuffle frog leaping algorithm (SFLA) by robust optimization approach; To suggest different risk criteria, K-means algorithm utilized to categorize the candidate projects and differentiating the clusters.  As the proposed hybrid methodology is studied on 420 different construction projects in an Iranian construction company in two economic stable years and an instable year in Iran real estate market. The results show 96 percent prediction-optimization capability due to different desired criteria

6G secure quantum communication: a success probability prediction model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The emergence of 6G networks initiates significant transformations in the communication technology landscape. Yet, the melding of quantum computing (QC) with 6G networks although promising an array of benefits, particularly in secure communication. Adapting QC into 6G requires a rigorous focus on numerous critical variables. This study aims to identify key variables in secure quantum communication (SQC) in 6G and develop a model for predicting the success probability of 6G-SQC projects. We identified key 6G-SQC variables from existing literature to achieve these objectives and collected training data by conducting a questionnaire survey. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the 6G-SQC matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each 6G-SQC project variable determined using NBC and LR indicated a strong positive correlation (rs = 0.895). The t-test results (t = 0.752, p = 0.502 > 0.05) show no significant differences between the rankings calculated using both prediction models (NBC and LR). The results reveal that the developed success probability prediction model, based on 15 identified 6G-SQC project variables, highlights the areas where practitioners need to focus more to facilitate the cost-effective and successful implementation of 6G-SQC projects.Peer reviewe

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners

A Logistic regression analysis model for predicting the success of computer networking projects in Zimbabwe

Information and communication technology (ICT) greatly influence today’s business processes be it in public or private sectors. Everything that is done in business requires ICT in one way or the other. Research in ICTs is therefore critical. So much research was and is still carried out in projects that develop or enhance ICT but it is still apparent that the success rate of these projects is still very low. The extensive coverage of ICTs implies that if the success rate is still that low, many resources are being wasted in the failed projects; therefore, more research is needed to improve the success rate. Previous research has focussed on factors which are critical for the success of ICT projects, assuming that all ICT projects are the same. As a result, literature is full of different suggestions and guidelines of the factors critical to ICT projects’ success. This scenario brings challenges to project managers who end up using their own personal judgement to select which factors to consider for any project at hand. The end result is the high failure rate of ICT projects since there is a very high chance of applying the same critical success factors to different types of ICT projects. This research answered the question: which factors are critical to the success of computer networking projects in Zimbabwe and how these factors could be used for building a model that determines in advance the success of such projects? Literature reviewed indicated that most CSFs were not focused on specific types of ICT projects, hence were generalised. No literature was found on ICT projects’ CSFs in Zimbabwe. More so, no CSFs were found for computer networking projects as a specific instance of ICT projects. No model existed that predicts computer networking projects’ success. This study addressed the gaps by developing a CSF framework for ICT projects in Zimbabwe, determining CSFs for computer networking projects in Zimbabwe and the development of a logistic regression analysis model to predict computer networking projects’ success in Zimbabwe. Data was collected in Zimbabwe using a unique three-staged process which comprise metasynthesis analysis, questionnaire and interviews. The study was motivated by the fact that most available research focused on CSFs for general ICT projects and that no research was found on CSFs influencing projects in computer networking. Meta-synthesis analysis was therefore conducted on literature in order to identify CSFs as given in literature. The approach was appropriate since the researcher had noticed that there were extensive ICT projects’ CSFs and that no such research has been carried out in Zimbabwe. These CSFs formed the basis for the determination (using a questionnaire) of ICT projects CSFs for Zimbabwe in particular. Project practitioners’ viewpoints were sought through questionnaires. Once CSFs for ICT projects in Zimbabwe were determined, they formed the basis for the determination of unique critical success factors for computer networking projects in Zimbabwe. Interviews were used to get further information that would have been left out by questionnaires. The interview questions were set to clarify some unclear or conflicting responses from the questionnaire and providing in-depth insights into the factors critical to computer networking projects in Zimbabwe. The data i.e. critical success factors for computer networking projects guided the development of the logistic regression analysis model for the prediction of computer networking projects’ success in Zimbabwe. Data analysis from the questionnaire was analysed using SPSS Version 23.0. Factor analysis and principal component analysis were some of the techniques used in the analysis. Interview data was analysed through NVivo Version 10.0. From the results it was deduced that factors critical to ICT project management in Zimbabwe were closely related to those found in the literature. The only apparent difference was that CSFs for ICT projects in Zimbabwe were more specific thereby enhancing their applicability. Computer networking projects had fewer CSFs than general ICT projects. In addition, CSFs for general ICT projects were different from those critical to computer networking projects in Zimbabwe. The development of a comprehensive set of general ICT projects’ CSFs was the first contribution of this study. This was achieved through meta-synthesis analysis. The other contribution was the development of a CSF framework for ICT projects specific to Zimbabwe and those specific to computer networking projects in Zimbabwe. The major contribution was the development of the logistic regression analysis model that predicts computer networking projects’ success in Zimbabwe. These contributions will provide literature on ICT project management in Zimbabwe which will subsequently assist ICT project managers to concentrate on specific factors. The developed prediction model can be used by project managers to determine possible success or failure of ICT projects; thereby possible reducing wastage of resource.School of Computin