Historical Project Data Research Articles

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

Machine learning algorithms for safer construction sites: Critical review

Machine learning, a key thruster of Construction 4.0, has seen exponential publication growth in the last ten years. Many studies have identified ML as the future, but few have critically examined the applications and limitations of various algorithms in construction management. Therefore, this article comprehensively reviewed the top 100 articles from 2018 to 2023 about ML algorithms applied in construction risk management, provided their strengths and limitations, and identified areas for improvement. The study found that integrating various data sources, including historical project data, environmental factors, and stakeholder information, has become a common trend in construction risk. However, the challenges associated with the need for extensive and high-quality datasets, models’ interpretability, and construction projects’ dynamic nature pose significant barriers. The recommendations presented in this paper can facilitate interdisciplinary collaboration between traditional construction and machine learning, thereby enhancing the development of specialized algorithms for real-world projects.

Big Data Analytics-Driven Project Management Strategies

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuring that risks and resource constraints are identified and addressed before they impact project delivery. By analyzing massive datasets, including historical performance metrics, resource availability, and project timelines, AI-driven systems can forecast delays, assess risk levels dynamically, and adapt schedules in real-time. This proactive approach facilitates better decision-making, optimized resource allocation, and improved project outcomes. The study is anchored on the premise that the sheer volume of data generated in large-scale projects often overwhelms traditional project management systems. By incorporating AI and BDA, project managers can better utilize this data, turning it into actionable insights that inform intelligent decision-making. Machine learning algorithms, particularly those specializing in predictive analytics, are capable of identifying patterns that elude human analysis, allowing for the accurate forecasting of project risks, schedule slippage, and task dependencies. This ability to predict potential issues, such as resource bottlenecks or unforeseen delays, enables project teams to implement mitigative actions in advance, thus reducing the likelihood of project failure. Furthermore, dynamic scheduling is a key focus of this research, as AI-powered models can continuously adjust project timelines based on real-time data. These models consider variables such as resource utilization rates, task dependencies, and evolving project constraints, offering adaptive scheduling mechanisms that evolve throughout the project lifecycle. The automated task prioritization system, powered by BDA, ensures that the most critical tasks receive the appropriate level of attention at the right time, improving project performance and enhancing resource efficiency. Through natural language processing (NLP) and advanced data mining techniques, AI models can also analyze project documentation and communication channels to detect potential risks and suggest task adjustments. The paper also discusses the application of AI in risk prediction, focusing on how AI models can analyze risk factors from historical data, including resource constraints, financial limitations, and market volatility, to produce risk profiles that project managers can use for strategic planning. Real-time risk assessments, made possible by the integration of AI and BDA, can help project teams stay ahead of potential disruptions. This allows for more accurate contingency planning and reduces the overall risk to project timelines and budgets. Practical applications of these AI-driven strategies are presented through case studies of large-scale projects in various industries, including construction, information technology, and healthcare. These case studies demonstrate how AI-powered analytics have been successfully implemented to enhance project efficiency, optimize resource allocation, and minimize risks in complex projects. The study underscores the importance of integrating these technologies into modern project management frameworks to cope with the increasing complexity of projects in today’s fast-paced business environment. While the potential benefits of AI and BDA in project management are substantial, this paper also addresses the challenges associated with their implementation. One significant challenge is the quality and availability of data required to train AI models effectively. Incomplete or inaccurate data can lead to unreliable forecasts, compromising the project’s success. Additionally, the paper explores the issues of data privacy and security in AI-driven project management systems, highlighting the need for robust data governance frameworks to ensure the ethical use of AI technologies. Another key consideration is the resistance to change within organizations, where traditional project management methods are deeply ingrained. The paper emphasizes the need for a cultural shift towards data-driven decision-making and suggests strategies for fostering an environment conducive to AI adoption. This includes training project management teams to work alongside AI systems and fostering collaboration between AI experts and project managers to ensure smooth implementation and operation. Finally, this research outlines future trends in AI and BDA for project management, suggesting that further advancements in AI technologies, such as reinforcement learning and more sophisticated natural language processing algorithms, will drive the next generation of intelligent project management systems. These future systems are expected to be even more adept at handling the complexities of large-scale projects, offering real-time solutions to unforeseen challenges and adapting dynamically to changing project requirements.

A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.

Numerical Simulation and Design of Ensemble Learning Based Improved Software Development Effort Estimation System

This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation.

Finite State GUI Testing with Test Case Prioritization Using Z-BES and GK-GRU

To deliver user-friendly experiences, modern software applications rely heavily on graphical user interfaces (GUIs). However, it is paramount to ensure the quality of these GUIs through effective testing. This paper proposes a novel “Finite state testing for GUI with test case prioritization using ZScore-Bald Eagle Search (Z-BES) and Gini Kernel-Gated recurrent unit (GK-GRU)” approach to enhance GUI testing accuracy and efficiency. First, historical project data is collected. Subsequently, by utilizing the Z-BES algorithm, test cases are prioritized, aiding in improving GUI testing. Attributes are then extracted from prioritized test cases, which contain crucial details. Additionally, a state transition diagram (STD) is generated to visualize system behavior. The state activity score (SAS) is then computed to quantify state importance using reinforcement learning (RL). Next, GUI components are identified, and their text values are extracted. Similarity scores between GUI text values and test case attributes are computed. Grounded on similarity scores and SAS, a fuzzy algorithm labels the test cases. Data representation is enhanced by word embedding using GS-BERT. Finally, the test case outcomes are predicted by the GK-GRU, validating the GUI performance. The proposed work attains 98% accuracy, precision, recall, f-measure, and sensitivity, and low FPR and FNR error rates of 14.2 and 7.5, demonstrating the reliability of the model. The proposed Z-BES requires only 5587 ms to prioritize the test cases, retaining less time complexity. Meanwhile, the GK-GRU technique requires 38945 ms to train the neurons, thus enhancing the computational efficiency of the system. In conclusion, experimental outcomes demonstrate that, compared with the prevailing approaches, the proposed technique attains superior performance.

Применение систем искусственного интеллекта для оценки времени и стоимости строительного проекта

The purpose of this article is to determine whether various artificial intelligence models can be used to estimate the time and cost of a construction project. Construction projects are complex and time-consuming, they include many factors that can affect the overall cost and duration of the project. Accurately estimating the time and cost of a construction project is critical to project management and planning. However, traditional methods of estimating these factors are often inefficient because of their dependence on historical data and limited scope. Artificial intelligence (AI) has emerged as a potential solution for improving construction project estimating. AI models can analyze a myriad of data, including historical project data, weather data, labor, and material costs, to provide more accurate predictions. Regression algorithms, in particular, have been shown to be effective in predicting project completion dates and costs based on various input factors. One advantage of using AI to evaluate construction projects is that it can account for complex and dynamic factors that traditional methods often overlook. For example, weather conditions can have a significant impact on construction projects, and AI models can incorporate this factor into their predictions. AI can also analyze data in real time, allowing for more timely adjustments to project plans and budgets. However, using AI to evaluate construction projects also presents some challenges. One potential challenge is the quality and reliability of the data used to train the models. AI models are only as good as the data they are trained on, so it is important to ensure that the data used is accurate and complete. In addition, AI models can be complex and require expertise to develop and maintain.

Cross-Project Defect Prediction with Metrics Selection and Balancing Approach

Abstract In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/fault/error). Thus, several experiments have been suggested for defect prediction (DP) techniques. Mainly DP method utilises historical project data for constructing prediction models. SDP performs well within projects until there is an adequate amount of data accessible to train the models. However, if the data are inadequate or limited for the same project, the researchers mainly use Cross-Project Defect Prediction (CPDP). CPDP is a possible alternative option that refers to anticipating defects using prediction models built on historical data from other projects. CPDP is challenging due to its data distribution and domain difference problem. The proposed framework is an effective two-stage approach for CPDP, i.e., model generation and prediction process. In model generation phase, the conglomeration of different pre-processing, including feature selection and class reweights technique, is used to improve the initial data quality. Finally, a fine-tuned efficient bagging and boosting based hybrid ensemble model is developed, which avoids model over -fitting/under-fitting and helps enhance the prediction performance. In the prediction process phase, the generated model predicts the historical data from other projects, which has defects or clean. The framework is evaluated using25 software projects obtained from public repositories. The result analysis shows that the proposed model has achieved a 0.71±0.03 f1-score, which significantly improves the state-of-the-art approaches by 23 % to 60 %.

A Realistic and Integrated Model for Evaluating Offshore Oil Development

With the rising consumption of oil resources, major oil companies around the world have increasingly engaged in offshore oil exploration and development, and offshore oil resources have accounted for an increasing proportion. Offshore oil engineering projects are capital intensive, and the development of offshore oil fields faces a tough battle, especially in a period of low oil prices. Thus, a comprehensive evaluation model is highly needed to help assess economic benefits and provide meaningful and valuable information for operators and investors to make sensible decisions. This study firstly proposed a realistic and integrated evaluation model for offshore oil development based on actual historical project data. This evaluation model incorporated modules from the underwater system to the platform system and processes from oil reservoir extraction to oil, gas and water treatment. The uncertain parameters in the evaluation process are dealt with by sensitivity analysis and Monte Carlo simulation. The proposed model is applied to a typical offshore oil development project in Bohai Bay, China. The results reveal that the recovery factor and oil price have the greatest impact on the economic benefits. In the case of deterministic analysis, the breakeven oil price of the project is 40.59 USD/bbl. After considering the uncertainty of project parameters, the higher the oil price, the greater the probability of NPV > 0. When the oil price is higher than 70 USD/bbl, even with uncertain project parameters, the probability of NPV > 0 can still be as high as 97.39%.

A Predictive Analytics Framework for Mobile Crane Configuration Selection in Heavy Industrial Construction Projects

Predictive analytics have been used to improve efficiency and productivity in the construction industry by leveraging the insights from historical data with a variety of applications in project management. In the planning process of heavy industrial construction projects, mobile crane selection plays a critical role in the project’s success, and poor choice of mobile crane configurations can lead to unnecessary cost-overrun and delayed schedules. In this research, the authors propose a predictive analytics framework for crane configuration selection using combined heuristic search and artificial neural network (ANN) approaches for heavy industrial construction projects. The heuristic search allows the practitioners to select the crane configurations based on engineering rules, while the ANN model utilizes the historical project data to help select crane configurations. The K-fold cross-validation is conducted to validate the designed ANN model and improve the accuracy of predictions. The results from the cross-validation test set have shown 70% accuracy.

Early warning system for highway construction projects using GA-SVM

Construction project changes vis-a-vice their impact on the cost and time performance has been the main concern of practitioners and scholars for decades; hence, many attempts have been made to develop early warning mechanisms for decision-makers. Since many factors drive project changes, various strategies can be adopted to mitigate further alterations after awarding the contract. This study aimed at developing multiple Support Vector Machine (SVM) classifiers to enhance change prediction accuracy. 5,628 completed construction-oriented projects datasets comprising preconstruction information, obtained from the Indiana Department of Transportation (INDOT), were used to develop the models. All free kernel parameters were determined using Genetic Algorithm (GA) optimization. The results show that the models built using SVM classifiers with Radial Basis Function (RBF) kernel function were successful in decreasing the uncertainty associated with change order occurrence in the early phases of the projects. Therefore, the study recommends that agencies, developers, and contractors store historical project data to build early warning systems using machine learning techniques.

United states department of defense (DoD) real property repair, alterations, maintenance, and construction project contract data: 2009-2020.

Nearly one-half of all construction projects exceed planned costs and schedule, globally [1]. Owners and construction managers can analyze historical project performance data to inform cost and schedule overrun risk-reduction strategies. Though, the majority of open-source project datasets are limited by the number of projects, data dimensionality, and location. A significant global customer of the construction industry, the Department of Defense (DoD) maintains a vast database of historical project data that can be used to determine the sources and magnitude of construction schedule and cost overruns for many continental and international locations. The selection of data provided by the authors is a subset of the U.S. Federal Procurement Data System-Next Generation (FPDS-NG), which stores contractual obligations made by the U.S. Federal Government [2]. The data comprises more than ten fiscal years (1 Oct 2009 – 04 June 2020) of construction contract attributes that will enable researchers to investigate spatiotemporal schedule and cost performance by, but not limited to: contract type, construction type, delivery method, award date, and award value. To the knowledge of the authors, this is the most extensive open-source dataset of its kind, as it provides access to the contract data of 132,662 uniquely identified construction projects totaling $865 billion. Because the DoD's facilities and infrastructure construction requirements and use of private construction firms are congruent with the remainder of the public sector and the private sector, results obtained from analyses of this dataset may be appropriate for broader application.

Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase

The current literature discusses the methods to estimate the costs and cost contingency. The literature also distinguishes “known unknowns” and “unknown unknowns” contingencies. Little is written, however, about the evolvement of total project cost estimates during the preconstruction phase of construction projects. Moreover, not many studies are investigating the “known unknowns” and “unknown unknowns” contingencies in real construction projects. Practice expressed the need for getting more insight into the development of the estimated costs of the projects in the preconstruction phase. This paper, therefore, discusses the estimate of the total project costs (and cost contingency) in the preconstruction phases of 29 Dutch flood defense projects using a case study approach. Altogether, the projects have experienced an 11.51% increase in the estimated costs compared to the initial estimates, which is low compared to previous studies. This increase in the cost estimates of the flood defense projects can be explained by “technical” reasons. The investigation of “known unknowns” and “unknown unknowns” contingencies shows that the percentage of the “unknown unknowns” contingency has increased in the preconstruction phase while a reduction was expected. This increase suggests that the projects were not confident about their estimates and the increase can be explained by a lack of experience, organizations’ culture, or the phenomenon of “pessimistic bias.” Practitioners can avoid “pessimistic bias” behavior by asking for opinions about their estimates and using historical project data. Further research is suggested into realized cost contingency after project execution.

A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects

PurposeThe purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints.Design/methodology/approachThis paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs.FindingsThe methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately.Research limitations/implicationsThe research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management.Practical implicationsThe model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives.Originality/valueThe authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.

Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application

Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

Determining relevant training data for effort estimation using Window-based COCOMO calibration

ContextA software estimation model is often built using historical project data. As software development practices change over time, however, a model based on past data may not make accurate predictions for a new project. ObjectivesWe investigate the use of moving windows to determine relevant training data for COCOMO calibration. MethodWe present a windowing calibration approach to calibrating COCOMO and assess performance of effort estimation models calibrated using windows and all data. ResultsOur results show that calibrating COCOMO using small windows of the most recently completed projects generates superior estimates than using all available historical projects. Large windows tend to produce worse estimates. ConclusionsThis study provides empirical evidence to support the use of small windows of projects completed so far to calibrate models when COCOMO-like data is available. Additionally, when the change in software development over time is rapid, the use of windows is more justifiable for improving estimation accuracy.

Task Allocation in Distributed Software Development: A Systematic Literature Review

Distributed development is a constant need in the software industry. The allocation of tasks, in turn, is a critical activity for any project, especially in a distributed setting. Assigning a task to an individual or team can be done in a rigorous manner, taking into account a number of factors. In this scenario, the more data on factors that influence task assignment are available to support the assignment decision, the more likely the project will succeed. As a result, companies have been looking to build their big data with historical project data, aiming to better their chances of success. Some studies about task allocation in distributed software development (DSD) have been carried out along the years aiming at mapping this topic and its features. Also, some approaches, models, and frameworks to task allocation in DSD have been proposed. This paper presents a systematic review of the literature whose objective is to identify works based on multicriteria models for assigning tasks in DDS projects, especially those based on qualitative decision-making methods, considering the cognitive validity present in some of these methods. The review results allowed identifying several interesting aspects and classifying the works according to a set of questions. In addition, it has made some important findings that may be very useful for those who intend to conduct research with qualitative multicriteria methods, since some of them employ techniques considered psychologically valid to address the complexity of the decision about the task allocation in distributed projects.

Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects

PurposeThe purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage.Design/methodology/approachThis paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model.FindingsThe result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data.Research limitations/implicationsBecause the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied.Originality/valueThe study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.

Algorithmic optimization method for effort estimation

This paper presents an improvement of an effort estimation method that can be used to predict the level of effort for software development projects. A new estimation approach based on a two-phase algorithm is used. In the first phase, we apply a calculation based on use case points (UCPs). In the second phase, we add correction values (a1, a2) obtained via least squares regression. This approach employs historical project data to refine the estimate. By applying the least squares regression approach, the algorithm filters out estimation errors caused by human factors and company practice.

Fuzzy Inference Modeling with the Help of Fuzzy Clustering for Predicting the Occurrence of Adverse Events in an Active Theater of War

This study investigated the relationship between adverse events and infrastructure development projects in an active theater of war using fuzzy inference systems (FIS) with the help of fuzzy clustering that directly benefits from its prediction accuracy. Fourteen developmental and economic improvement projects were selected as independent variables. These were based on allocated budgets and included a number of projects from different time periods, urban and rural population density, and total number of adverse events during the previous month. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, or hijacked and the total number of adverse events has been estimated. The performance of each model was investigated and compared to all other models with calculated mean absolute error (MAE) values. Prediction accuracy was also tested within ±1 (difference between actual and predicted value) with values around 90%. Based on the results, it was concluded that FIS is a useful modeling technique for predicting the number of adverse events based on historical development or economic project data.