Search results for: project progress prediction

Authors: Catalin-Teodor Dogaru, Ana-Maria Dogaru

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Success means different things to different people. For us, project managers, it becomes even harder to find a definition. Many factors have to be included in the evaluation. Moreover, literature is not very helpful, lacking consensus and neutrality. Post-implementation reviews (PIR) can be an efficient tool in evaluating how things worked on a certain project. Despite the visible progress, PIR is not a very detailed subject yet and there is not a common understanding in this matter. This may be the reason that some organizations include it in the projects’ lifecycle and some do not. Through this paper, we point out the reasons why all project managers should pay proper attention to this important step and to the elements, which can be assessed, beside the already famous triple constraints: cost, budget, and time. It is essential to take notice that PIR is not a checklist. It brings the edge in eliminating subjectivity and judging projects based on actual proof. Based on our experience, our success indicator model, presented in this paper, contributes to the success of the project! In the same time, it increases trust among customers who will perceive success more objectively.

Keywords: project, post implementation, review, success, indicators

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Authors: Cedric Leong, Parth Desai, Parth Patel

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The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.

Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)

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Authors: Jagoda Bruni, Jan Müller-Lucanus, Gernot Stöger-Knes

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The purpose of this article is to demonstrate modern project management approaches in the SAP S/R Hana surrounding a programming environment composed of multiple focus-diversified projects. We would like to propose innovative and goal-oriented management standards based on the specificity of the SAP transformations and customer-driven expectations. Due to the regular sprint-based controlling and management tools' application, it has been data-proven that extensive analysis of productive hours of the employees as much as a thorough review of the project progress (per GAP, per business process, and per Lot) within the whole program, can have a positive impact on customer satisfaction and improvement for projects' budget. This has been a collaborative study based on real-life experience and measurements in collaboration with our customers.

Keywords: project management, program management, SAP, controlling

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: N. H. Adenan, M. S. M. Noorani

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River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources.

Keywords: river flow, nonlinear prediction method, phase space, local linear approximation

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Authors: G. Ramya

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Value engineering is a systematic approach, widely used to optimize the design or process or product in the designing stage. It used to achieve the client's obligation by increasing the functionality and attain the targeted cost in the cost planning. Value engineering effectiveness and benefits decrease along with the progress of the project since the change in the scope of the work and design will account for more cost all along the lifecycle of the project. Integrating the value engineering with other project management activities will promote cost minimization, client satisfaction, and ensure early completion of the project in time. Previous research studies suggested that value engineering can integrate with other project delivery activities, but research studies unable to frame a model that collaborates the project management activities with the job plan of value engineering approach. I analyzed various project management activities and their synergy between each other. The project management activities and processes like a)risk analysis b)lifecycle cost analysis c)lean construction d)facility management e)Building information modelling f)Contract administration, collaborated, and project delivery model planned along with the RIBA plan of work. The key outcome of the research is a value-driven project delivery model, which will succeed in dealing with the economic impact, constraints and conflicts arise due to the COVID-19 outbreak in the Indian construction sector. Benefits associated with the structured framework is construction project delivery that ensures early contractor involvement, mutual risk sharing, and reviving the project with a cost overrun and delay back on track ,are discussed. Keywords: Value-driven project delivery model, Integration, RIBA plan of work Themes: Design Economics

Keywords: value-driven project delivery model, Integration, RIBA

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Authors: Mouhoubi Nedjima, Sassi Boudemagh Souad

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In this paper, we will try to demonstrate the importance of the project approach in the urban to deal with uncertainty, the importance of the involvement of all stakeholders in the urban project process and that the absence of an actor can lead to project failure but also the importance of the urban project management. These points are handled through the following questions: Does the urban adhere to the theory of complexity? Does the project approach bring hope and solution to make urban planning "sustainable"? How converging visions of actors for the same project? Is the management of urban project the solution to support the urban project approach?

Keywords: strategic planning, project, urban project stakeholders, management

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Authors: Mohammad Lemar Zalmai, Osman Hurol Turkakin, Cemil Akcay, Ekrem Manisali

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In public construction projects, determining the contract period in the award phase is one of the most important factors. The contract period establishes the baseline for creating the cash flow curve and progress payment planning in the post-award phase. If overestimated, project duration causes losses for both the owner and the contractor. Therefore, it is essential to base construction project duration on reliable forecasting. In Turkey, schedules are usually built using the bar chart (Gantt) schedule, especially for governmental construction agencies. The usage of these schedules is limited for bidding purposes. Although the bar-chart schedule is useful in some cases, it lacks logical connections between activities; it would be harder to obtain the activities that have more effects than others on the project's total duration, especially in large complex projects. In this study, a construction schedule is prepared with Critical Path Method (CPM) that addresses the above-mentioned discrepancies. CPM is a simple and effective method that displays project time and critical paths, showing results of forward and backward calculations with considering the logic relationships between activities; it is a powerful tool for planning and managing all kinds of construction projects and is a very convenient method for the construction industry. CPM provides a much more useful and precise approach than traditional bar-chart diagrams that form the basis of construction planning and control. CPM has two main application utilities in the construction field; the first one is obtaining project duration, which is called an as-planned schedule that includes as-planned activity durations with relationships between subsequent activities. Another utility is during the project execution; each activity is tracked, and their durations are recorded in order to obtain as-built schedule, which is named as a black box of the project. The latter is more useful for delay analysis, and conflict resolutions. These features of CPM have been popular around the world. However, it has not been yet extensively used in Turkey. In this study, a real construction project is investigated as a case study; CPM-based scheduling is used for establishing both of as-built and as-planned schedules. Problems that emerged during the construction phase are identified and categorized. Subsequently, solutions are suggested. Two scenarios were considered. In the first scenario, project progress was monitored based as CPM was used to track and manage progress; this was carried out based on real-time data. In the second scenario, project progress was supposedly tracked based on the assumption that the Gantt chart was used. The S-curves of the two scenarios are plotted and interpreted. Comparing the results, possible faults of the latter scenario are highlighted, and solutions are suggested. The importance of CPM implementation has been emphasized and it has been proposed to make it mandatory for preparation of construction schedule based on CPM for public construction projects contracts.

Keywords: as-built, case-study, critical path method, Turkish government sector projects

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: Şemsettin Şahin, Ahmet Oğuz Aktürk, İsmail Çelik

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This study investigated the views of teachers, students and students' parents on the FATIH (Movement of Enhancing Opportunities and Improving Technology) Project, which was put into service by the Ministry of National Education in cooperation with the Ministry of Transportation in Turkey in November 2010 for the purpose of increasing students' success and planned to be completed within 5 years. The study group consisted of teachers employed in a pilot school in the province of Karaman in central Turkey included within the scope of the FATIH Project, students attending this school and parents whose children are students in that school. The research data were collected through forms developed by the researchers to determine the views of teachers, students and students' parents on the FATIH Project. The descriptive analysis method, one of the qualitative research methods, was used in the study. An analysis of the data revealed that a large majority of the teachers and the students believed that if computers were used to serve their set purpose, then they could make considerable contributions to education. A large majority of the students' parents, on the other hand, regard the use of computers in education as a great opportunity for the students. The views of the teachers, students and students' parents on the FATIH Project usually overlap. Most of the participants in the study pointed out that the FATIH Project was intended to use technology effectively in education. Moreover, each individual participant described their role in the FATIH Project in accordance with their relative position and stated that they could perform whatever was expected of them for the effective and efficient use and progress of the project. The views of the participants regarding the FATİH Project vary according to the kind of the participants.

Keywords: education, FATIH project, technology, students

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Bennani Zoubir Nada, El Hiri Abderrazak, El Hajri Aimad

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In context of economic and health crisis, sustainable entrepreneurship has become one of the best solutions to economic recovery. This study is about the third program of the Moroccan national human development initiative in her third phase which began in 2019 and continuous until 2023, and which deals with income improvement and social inclusion of young people, under the high patronage of his majesty the king of Morocco. What is the approach of this program and how entrepreneurship training of young people can be a pillar to generate income and create jobs? Starting on the effectuation theory, we adopted an exploratory qualitative approach through semi-structured interviews with national human development initiative stakeholders in the area of Meknes-Morocco, which allowed us the state of progress of this program. We carried out a survey based on a grid of questions to collect information that we processed using NVIVO software. The most relevant results are that people eligible are jobless young people, who are between 18 and 35 years old, who reside in Meknes and surroundings and who have a project idea. They are trained by experts in entrepreneurship and management through targeted and diversified courses. To ensure the sustainability of projects, the project organisers have provided measures to ensure the sustainability of the companies through continuous monitoring and evaluation as well as support during all phases from the project idea to the realisation and progress.

Keywords: sustainable entrepreneurship, training, social inclusion, national human development initiative in Morocco (INDH), youth entrepreneurship, the effectuation theory

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Mohammad Reza Oftadeh

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The purpose of the article is to describe project objective structure (POS) concept that was developed on research activities and experiences about project management, Balanced Scorecard (BSC) and European Foundation Quality Management Excellence Model (EFQM Excellence Model). Furthermore, this paper tries to define a balanced, systematic, and integrated measurement approach to meet project objectives and project strategic goals based on a process-oriented model. In this paper, POS is suggested in order to measure project performance in the project life cycle. After using the POS model, the project manager can ensure in order to achieve the project objectives on the project charter. This concept can help project managers to implement integrated and balanced monitoring and control project work.

Keywords: project objectives, project performance management, PMBOK, key performance indicators, integration management

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Mouhoubi Nedjima, Sassi Boudemagh Souad

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In this article, and through the modernization project of metropolis of Constantine (PMMC) experience in Algeria, discussed to highlight the importance of management in an urban project at various levels: strategic and operational. The statement we attended to reach is to evaluate the modernization project of metropolis of Constantine in the light of management and prove the relation between a good urban management and the success of an urban project.

Keywords: urban project, strategic management, operational management, the modernization project of constantine

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Authors: Wenzheng Fu, Yue Fu, Zhijiang Dong, Yujian Fu

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The integration of artificial intelligence (AI) and machine learning (ML) into project management is transforming how engineering projects are executed, monitored, and controlled. This paper provides a comprehensive survey of AI and ML applications in project management, systematically categorizing their use in key areas such as project data analytics, monitoring, tracking, scheduling, and reporting. As project management becomes increasingly data-driven, AI and ML offer powerful tools for improving decision-making, optimizing resource allocation, and predicting risks, leading to enhanced project outcomes. The review highlights recent research that demonstrates the ability of AI and ML to automate routine tasks, provide predictive insights, and support dynamic decision-making, which in turn increases project efficiency and reduces the likelihood of costly delays. This paper also examines the emerging trends and future opportunities in AI-driven project management, such as the growing emphasis on transparency, ethical governance, and data privacy concerns. The research suggests that AI and ML will continue to shape the future of project management by driving further automation and offering intelligent solutions for real-time project control. Additionally, the review underscores the need for ongoing innovation and the development of governance frameworks to ensure responsible AI deployment in project management. The significance of this review lies in its comprehensive analysis of AI and ML’s current contributions to project management, providing valuable insights for both researchers and practitioners. By offering a structured overview of AI applications across various project phases, this paper serves as a guide for the adoption of intelligent systems, helping organizations achieve greater efficiency, adaptability, and resilience in an increasingly complex project management landscape.

Keywords: artificial intelligence, decision support systems, machine learning, project management, resource optimization, risk prediction

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Authors: Jeonghwan Jeon

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With the advent of IPTV in the fierce competition with existing broadcasting system, it is emerged as an important issue to predict how much the adoption of IPTV service will be. This paper aims to suggest a prediction model for adopting IPTV using classification and Ranking Belief Simplex (CaRBS). A simplex plot method of representing data allows a clear visual representation to the degree of interaction of the support from the variables to the prediction of the objects. CaRBS is applied to the survey data on the IPTV adoption.

Keywords: prediction, adoption, IPTV, CaRBS

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Authors: Karthick Prasad Gunasekaran, B. Chase Babrich, Saurabh Shirodkar, Hee Hwang

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Construction preparation is crucial for the success of a construction project. By involving project participants early in the construction phase, project managers can plan ahead and resolve issues early, resulting in project success and satisfaction. This study uses quantitative data from construction management projects to determine the relationship between the pre-construction phase, construction schedule, and customer satisfaction. This study examined a total of 65 construction projects and 93 clients per job to (a) identify the relationship between the pre-construction phase and program reduction and (b) the pre-construction phase and customer retention. Based on a quantitative analysis, this study found a negative correlation between pre-construction status and project schedule in 65 construction projects. This finding means that the more preparatory work done on a particular project, the shorter the total construction time. The Net Promoter Score of 93 clients from 65 projects was then used to determine the relationship between construction preparation and client satisfaction. The pre-construction status and the projects were further analyzed, and a positive correlation between them was found. This shows that customers are happier with projects with a higher ready-to-build ratio than projects with less ready-to-build.

Keywords: NLP, BERT, LLM, deep learning, classification

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Authors: Maciej Zareba, Pawel Dawid

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This article provides a case study exploring the use of serious games in educational settings, indicating their potential to transform conventional teaching methods into interactive and engaging learning experiences. By incorporating game elements such as points, leaderboards and progress indicators, serious games establish clear goals, provide real-time feedback and give a sense of progress. These elements enable students to solve complex problems in simulated environments, fostering critical thinking, creativity and contextual learning. The article provides a case study of the feasibility of using the 4FactryManager serious game in a selected educational context, demonstrating its effectiveness in increasing student motivation, improving academic performance and promoting knowledge consolidation. The study and presentation are based on the results of industrial research and development work conducted as part of the project titled (4FM) 4FACTORY Manager – an innovative simulation game for managing real production processes using a novel gameplay model based on the interaction between the virtual and real worlds, applying the Industry 4.0 concept (Project number: POIR.01.02.00-00-0057/19).

Keywords: gamification, serious games, education, elearning

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: Michael Lousis

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The systematic identification of the most conspicuous and significant errors made by learners during three-years of testing of their progress in learning Arithmetic throughout the development of the Kassel Project in England and Greece was accomplished. How much retentive these errors were over three-years in the officially provided school instruction of Arithmetic in these countries has also been shown. The learners’ errors in Arithmetic stemmed from a sample, which was comprised of two hundred (200) English students and one hundred and fifty (150) Greek students. The sample was purposefully selected according to the students’ participation in each testing session in the development of the three-year project, in both domains simultaneously in Arithmetic and Algebra. Specific teaching practices have been invented and are presented in this study for subverting these learners’ errors, which were found out to be retentive to the level of the nationally provided mathematical education of each country. The invention and the development of these proposed teaching practices were founded on the rationality of the theoretical accounts concerning the explanation, prediction and control of the errors, on the conceptual metaphor and on an analysis, which tried to identify the required cognitive components and skills of the specific tasks, in terms of Psychology and Cognitive Science as applied to information-processing. The aim of the implementation of these instructional practices is not only the subversion of these errors but the achievement of the mathematical competence, as this was defined to be constituted of three elements: appropriate representations - appropriate meaning - appropriately developed schemata. However, praxis is of paramount importance, because there is no independent of science ‘real-truth’ and because praxis serves as quality control when it takes the form of a cognitive method.

Keywords: arithmetic, cognitive science, cognitive psychology, information-processing paradigm, Kassel project, level of the nationally provided mathematical education, praxis, remedial mathematical teaching practices, retentiveness of errors

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Authors: Nor Zila Abd Hamid, Mohd Salmi M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: chaotic approach, phase space, Cao method, local linear approximation method

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: Supriya H. S., Chandrakala B. M.

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Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach.

Keywords: MLHN, network traffic prediction

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Authors: A. A. Omonori, K. T. Alade, A. F. Lawal

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This research was conducted to identify the causes of project abandonment in Nigeria and the effect it has on the host community. The aim of the research was to identify the causes and effects of project failure and abandonment. Project abandonment is a major course of concern in the country as different projects fail and are abandoned at various levels. These projects do not fulfill the purpose for which they were initiated. This is the absolute definition of failure and hence the selection of the Ajaokuta Steel Project as an interesting case study and a typical example of project failure and abandonment. This has been done by conducting field study through the administration of questionnaires. This study was carried out on the Ajaokuta Steel Project to investigate the causes of the abandonment of the project and the effects it has had on the people of Ajaokuta community. Qualitative method of data analysis was used to analyze the findings through frequency tables and ranking. This study brought to light the major factors that led to the abandonment of the Ajaokuta Steel Project. The effects the abandonment of the project has had on the immediate community were identified and recommendations made to prevent the menace of Project abandonment.

Keywords: abandonment, case-study, Nigeria, project

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Authors: Soheila Sadeghi

Abstract:

Infrastructure projects often encounter contract variations that can significantly deviate from the original tender estimates, leading to cost overruns, schedule delays, and financial implications. This research aims to quantitatively assess the impact of changes in contract variations on project performance by conducting an in-depth analysis of a comprehensive dataset from the Regional Airport Car Park project. The dataset includes tender budget, contract quantities, rates, claims, and revenue data, providing a unique opportunity to investigate the effects of variations on project outcomes. The study focuses on 21 specific variations identified in the dataset, which represent changes or additions to the project scope. The research methodology involves establishing a baseline for the project's planned cost and scope by examining the tender budget and contract quantities. Each variation is then analyzed in detail, comparing the actual quantities and rates against the tender estimates to determine their impact on project cost and schedule. The claims data is utilized to track the progress of work and identify deviations from the planned schedule. The study employs statistical analysis using R to examine the dataset, including tender budget, contract quantities, rates, claims, and revenue data. Time series analysis is applied to the claims data to track progress and detect variations from the planned schedule. Regression analysis is utilized to investigate the relationship between variations and project performance indicators, such as cost overruns and schedule delays. The research findings highlight the significance of effective variation management in construction projects. The analysis reveals that variations can have a substantial impact on project cost, schedule, and financial outcomes. The study identifies specific variations that had the most significant influence on the Regional Airport Car Park project's performance, such as PV03 (additional fill, road base gravel, spray seal, and asphalt), PV06 (extension to the commercial car park), and PV07 (additional box out and general fill). These variations contributed to increased costs, schedule delays, and changes in the project's revenue profile. The study also examines the effectiveness of project management practices in managing variations and mitigating their impact. The research suggests that proactive risk management, thorough scope definition, and effective communication among project stakeholders can help minimize the negative consequences of variations. The findings emphasize the importance of establishing clear procedures for identifying, assessing, and managing variations throughout the project lifecycle. The outcomes of this research contribute to the body of knowledge in construction project management by demonstrating the value of analyzing tender, contract, claims, and revenue data in variation impact assessment. However, the research acknowledges the limitations imposed by the dataset, particularly the absence of detailed contract and tender documents. This constraint restricts the depth of analysis possible in investigating the root causes and full extent of variations' impact on the project. Future research could build upon this study by incorporating more comprehensive data sources to further explore the dynamics of variations in construction projects.

Keywords: contract variation impact, quantitative analysis, project performance, claims analysis

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