A Review of the Application of Decision Tree Analysis and Artificial Neural Networks in Project Management

The Artificial Neural Network (ANN) which is one of the Artificial Intelligence (AI) tools, has been identified as a great technique to be used for construction cost estimation in the project. With the optimum quality of data input into the ANN model, it could produce an optimum and reliable cost estimation output. Nonetheless, the construction industry is lack of breadth and depth of data that is required as input into ANN. Though many online databases have been made available for data consumers, data quality problems remain unresolved. Thus, this study aims to identify data quality issues that can hinder the implementation of ANN for cost estimation of a construction project. A literature review and semi-structured interview were employed for the data collection of this research. The content analysis method was used to analyse the information obtained through the literature review. Meanwhile, the data collected from the semi-structured interview with nine (9) respondents were analysed using both content analysis and descriptive statistics analysis methods. The findings revealed six data quality issues that can hinder the ANN implementation for cost estimation of construction projects in Malaysia which are inaccurate data, outdated data, data access barriers, insufficient data, noise in training data, and data input degree of influence. Academically, this study contributes to the body of knowledge about the implementation of ANN for cost estimation of construction projects in Malaysia. Keywords: Artificial Neural Network, Artificial Intelligence, Cost Estimation, Data Quality.

Wastewater treatment is essential because it reduces the pollutant in the water, promotes the water quantity, and protects the ecosystem from harmful and toxic elements in wastewater. Many uncertainties appear in wastewater treatment systems since the natural condition is complex, and the technology of wastewater treatment is limited. Artificial Intelligence (AI) is a novel and influential technology assisting with complicated work, including modeling. The advantages of AI are evident in wastewater treatment because of the high accuracy, which leads to cost, energy, and material saving. This article mainly focuses on introducing Artificial Intelligence in wastewater treatment, displaying the application of Artificial Intelligence Neural Networks in wastewater treatment, and analyzing the advantages and problems. Overall, the research demonstrates that applying Artificial Intelligence in wastewater treatment provides a promising future with benefits, such as cost-saving and high accuracy.

Contents: D. Sobajic, Foreword. Part I:Perspectives. Y-H. Pao, G-H. Park, Learning and Generalization Characteristics of the Random Vector Functional-Link Net. C-C. Liu, M. Damborg, Artificial Neural Networks and Expert Systems in the Power System Operation Environment. E. Bradley, A Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence. Part II:Neural Network Methodologies. B. Widrow, M.A. Lehr, Backpropagation and Its Applications. F. Beaufays, E.A. Wan, Using Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-Time. A. Guha, Neural Network Based Inferential Sensing and Instrumentation. S.A. Harp, T. Samad, Optimizing Neural Networks Using Genetic Algorithms. Part III:Nuclear Power Plants. R. Uhrig, Potential Use of Neural Networks in Nuclear Power Plants. M. Khadem, A. Ipakchi, F.J. Alexandro, R.W. Colley, Sensor Validation in Power Plants Using Neural Networks. A. Ikonomopoulos, L. Tsoukalas, R. Uhrig, Measuring Fuzzy Variables in a Nuclear Reactor Using Artificial Neural Networks. Y.D. Lukic, C.R. Stevens, J. Si, Application of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient Events. J.A. Boshers, C.H.M. Saylor, S. Kamadolli, R. Wood, C. Isik, Control Rod Wear Recognition Using Neural Nets. R. Doremus, Severe Accident Management System On-Line Network (SAMSON). Part IV:Power System Operation. H. Ren-mu, A.J. Germond, Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional Methods. B. Avramovic, On Neural Network Voltage Assessment. D. Sobajic, Y-H. Pao, M. Djukanovic, Neural Network Synthesis of Tangent Hypersurfaces for Transient Security Assessment of Electric Power Systems. D. Niebur, A.J. Germond, Power System Static Security Assessment Using the Kohonen Neural Network Classifier. H. Mori, Voltage Stability Monitoring with Artificial Neural Networks. D. Novosel, A.B. Boveri, R.L. King, Intelligent Load Shedding. E. Chan, N. Markushevich, R. Adapa, Considerations in Intelligent Alarm Processing. Part V:Modeling and Prediction. D.J. Sobajic, Y-H. Pao, D.T. Lee, Predictive Security Monitoring with Neural Networks. A.G. Parlos, A.D. Patton, Empirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron Network. T. Samad, Modeling and Identification with Neural Networks. E. Wan, Autoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors. Part VI:Control. B. Widrow, F. Beaufays, Neural Control Systems. R.L. King, M.L. Oatts, Potential Uses of Intelligent and Adaptive Controls for Electric Power System Operations in the Year 2000 and Beyond. F. Beaufays, B. Widrow, Load-Frequency Control Using Neural Networks. L.L. Adams, Reinforcement Learning for Adaptive Control. Part VII:Load Forecasting. A.J. Germond, N. Macabrey, T. Baumann, Application of Artificial Neural Networks to Load Forecasting. M. Khadem, A. Lago, E. Dobrowolski, Short-Term Electric Load Forecasting Using Neural Networks. J.Y. Cheung, J. Fagan, D.C. Chance, Load Forecasting by Hierarchical Neural Networks that Incorporate Known Load Characteristics. Part VIII:Scheduling and Optimization. H. Sasaki, Y. Takiuchi, J. Kubokawa, A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural Networks. H. Saitoh, Y. Shimotori, J. Toyoda, Generation Dispatch Algorithm Coordinating Economy and Stability by Using Artificial Neural Networks. Part IX:Fault Diagnosis. T. Baumann, A.J. Germond, D. Tschudi, Impulse Test Fault Diagnosis on Power Transformers Using Kohonen's Self-Organizing Neural Network. Y. Du, F. Wang, T.C. Cheng, A Case Study of Neural Network Application: Power Equipment Application Failure. A. Agogino, M-L. Tseng, P. Jain, Integrating Neural Networks with Influence Diagrams for Power Plant Monitoring and Diagnostics. W.L. Biach, Use of Neural Network in Optimizing RPV Bolting Procedures.

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Artificial intelligence techniques have been widely applied in many domains, such as image /sound/text recognition, manufacturing monitoring, etc. One of the requirements for an artificial intelligence modeling is massive datasets. However, it is often limited knowns in the beginning of the design phase.This paper studied the methods and the influence of building an artificial intelligence model from a limited number of inputs. The application of the artificial neural network (ANN) and the recurrent neural network (RNN) has been applied to the nonlinear mechanical FE, steady-state thermal FE and transient FE model, and a rather simple neural network model and accuracy/application of these models has been reported.

This paper focuses on the development of an artificial neural network based on power load in power engineering. The front section represents areas in power systems that artificial intelligence has been applied to. And then overviews the artificial intelligence techniques which have been used and makes suggestions for the improvement of existing artificial intelligence tools. Following this, the paper concentrates on neural networks and their applications to power systems. The multi-layer feed forward network is introduced and the problems in establishing neural network approaches based on this network for power system applications are discussed. Further subjects for further research in artificial intelligence and neural network applications in power systems are presented. This paper focuses on the development of an artificial neural network based on power load.KeywordsArtificial Neural Network (ANN)Power Load PredictionMake Model

Urolithiasis is one of the most common urological diseases worldwide. One of the most common therapy, extracorporeal shock wave lithotripsy (ESWL), has a high failure rate. The failure rate can be significantly reduced by identifying the candidates most likely to benefit from ESWL, for example, by using machine learning (ML) algorithms. Decision tree analysis (DTA), artificial neural networks (ANN), and random forests (RF) represent a few of the machine learning approaches employed to forecast the stone-free outcome following ESWL. 219 studies were searched through six electronic databases (CENTRAL, MEDLINE, EMBASE, EBSCO, Proquest, SCOPUS). We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and adhered to the Standards for Reporting Diagnostic Accuracy Studies (STARD). To evaluate the potential bias in all the studies, we utilized the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. 41,484 patients from 11 studies were included. The ML models highlight varying levels of diagnostic precision, with sensitivity spanning from 35-96%, and specificity ranging from 63-98.4%, and area under the curve falling between 0.49-0.96. It is shown in this study that the accuracy of RF and DTA in predicting stone-free status is superior than ANN. ML is a comparable predictive method to statistical analysis in predicting stone-free status. Random forest method and DTA are superior MLs compared to ANN. Stone size, density, and 3D texture analysis are the most important variables to be considered in the ML models and should be included in the models to ensure accuracy of stone-free status prediction.

Background Urolithiasis is one of the most common urological diseases worldwide. One of the most common therapy, extracorporeal shock wave lithotripsy (ESWL), has a high failure rate. The failure rate can be significantly reduced by identifying the candidates most likely to benefit from ESWL, for example, by using machine learning (ML) algorithms. Decision tree analysis (DTA), artificial neural networks (ANN), and random forests (RF) represent a few of the machine learning approaches employed to forecast the stone-free outcome following ESWL. Methods 219 studies were searched through six electronic databases (CENTRAL, MEDLINE, EMBASE, EBSCO, Proquest, SCOPUS). We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and adhered to the Standards for Reporting Diagnostic Accuracy Studies (STARD). To evaluate the potential bias in all the studies, we utilized the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results 41,484 patients from 11 studies were included. The ML models highlight varying levels of diagnostic precision, with sensitivity spanning from 35-96%, and specificity ranging from 63-98.4%, and area under the curve falling between 0.49-0.96. It is shown in this study that the accuracy of RF and DTA in predicting stone-free status is superior than ANN. Conclusion ML is a comparable predictive method to statistical analysis in predicting stone-free status. Random forest method and DTA are superior MLs compared to ANN. Stone size, density, and 3D texture analysis are the most important variables to be considered in the ML models and should be included in the models to ensure accuracy of stone-free status prediction.

Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

Minimising the loss of treated water from water supply systems due to burst and leakage is an ongoing issue for water service providers around the world. Flow monitoring techniques are currently used by the water industry to monitor leakage, generally offline through the application of mass balance type calculations or through observations of change in night line values. The data for such analysis has, until recently, been at best collected 24 hourly via SMS technology. The objective of the study reported here was to assess the online application of an AI system to a real distribution system and the potential benefits of so doing. Specifically the application of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FIS), which are computational techniques in the field of Artificial Intelligence (AI). The online hybrid ANN/FIS system developed uniquely uses DMA (District Meter Areas) level flow data for the detection of leaks/bursts as they occur. The ANN model (a Mixture Density Network) was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A FIS, used for classification, compared observed flows with the probability density function of predicted flows over time windows such that confidence intervals could be assigned to alerts and further, an accurate estimate of likely burst size provided. A Water Supply System in the UK was used for the case study. The case study pilot area has near real-time flow data provided by General Packet Radio Service (GPRS). The online AI leak/burst detection system was constructed to operate along side an existing flat line alarm system, and continuously analyse every twelve hours a set of 50 DMAs of various size, complexity and connectivity within the case study area. Results are presented from a six month period. The new system identified a number of events and alerts were raised prior to their notification in the control room; either through flat line alarms or customer contacts. Examples are given of their correlation with burst reports and subsequent mains repairs. 56% of AI alerts were found to correspond to bursts confirmed by repair data or customer contacts reporting bursts. The study shows that the integration of the AI system with near real time communications can facilitate rapid determination (i.e. before customers are impact) of abnormal flow patterns. It is concluded from the study that the system is an effective and viable tool for online burst detection in water distribution systems.

BackgroundUrolithiasis is one of the most common urological diseases worldwide. One of the most common therapy, extracorporeal shock wave lithotripsy (ESWL), has a high failure rate. The failure rate can be significantly reduced by identifying the candidates most likely to benefit from ESWL, for example, by using machine learning (ML) algorithms. Decision tree analysis (DTA), artificial neural networks (ANN), and random forests (RF) represent a few of the machine learning approaches employed to forecast the stone-free outcome following ESWL.Methods219 studies were searched through six electronic databases (CENTRAL, MEDLINE, EMBASE, EBSCO, Proquest, SCOPUS). We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and adhered to the Standards for Reporting Diagnostic Accuracy Studies (STARD). To evaluate the potential bias in all the studies, we utilized the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.Results41,484 patients from 11 studies were included. The ML models highlight varying levels of diagnostic precision, with sensitivity spanning from 35-96%, and specificity ranging from 63-98.4%, and area under the curve falling between 0.49-0.96. It is shown in this study that the accuracy of RF and DTA in predicting stone-free status is superior than ANN.ConclusionML is a comparable predictive method to statistical analysis in predicting stone-free status. Random forest method and DTA are superior MLs compared to ANN. Stone size, density, and 3D texture analysis are the most important variables to be considered in the ML models and should be included in the models to ensure accuracy of stone-free status prediction.

The search for better climate change adaptation techniques for addressing environmental and economic issues due to changing climate is of paramount interest in the current era. One of the many ways Pacific Island regions and its people get affected is by dry spells and drought events from extreme climates. A drought is simply a prolonged shortage of water supply in an area. The impact of drought varies both temporally and spatially that can be catastrophic for such regions with lack of resources and facilities to mitigate the drought impacts. Therefore, forecasting drought events using predictive models that have practical implications for understanding drought hydrology and water resources management can allow enough time to take appropriate adaption measures. This study investigates the feasibility of the Artificial Neural Network (ANN) algorithms for prediction of a drought index: Standardized Precipitation-Evapotranspiration Index (SPEI). The purpose of the study was to develop an ANN model to predict the index in two selected regions in Queensland, Australia. The first region, is named as the grassland and the second as the temperate region. The monthly gridded meteorological variables (precipitation, maximum and minimum temperature) that acted as input parameters in ANN model were obtained from Australian Water Availability Project (AWAP) for 1915–2013 period. The potential evapotranspiration (PET), calculated using thornthwaite method, was also an input variable, while SPEI was the predictand for the ANN model. The input data were divided into training (80%), validation (10%) and testing (10%) sets. To determine the optimum ANN model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton backpropagation algorithms were used for training the ANN network and the tangent sigmoid, logarithmic sigmoid and linear activation algorithms were used for hidden transfer and output functions. The best architecture of input-hidden neuron-output neurons was 4-28-1 and 4-27-1 for grassland and temperate region, respectively. For evaluation and selection of the optimum ANN model, the statistical metrics: Coefficient of Determination (R 2 ), Willmott’s Index of Agreement (d), Nash-Sutcliffe Coefficient of Efficiency (E), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed. The R 2 , d, E, RMSE and MAE for optimum ANN models were 0.9839, 0.9909, 0.9838, 0.1338, 0.0882 and 0.9886, 0.9935, 0.9874, 0.1198, 0.0814 for grassland and temperate region, respectively. When prediction errors were analysed, a value of 0.0025 to 0.8224 was obtained for the grassland region, and a value of 0.0113 to 0.6667 was obtained for the temperate region, indicating that the ANN model exhibit a good skill in predicting the monthly SPEI. Based on the evaluation and statistical analysis of the predicted SPEI and its errors in the test period, we conclude that the ANN model can be used as a useful data-driven tool for forecasting drought events. Broadly, the ANN model can be applied for prediction of other climate related variables, and therefore can play a vital role in the development of climate change adaptation and mitigation plans in developed and developing nations, and most importantly, in the Pacific Island Nations where drought events have a detrimental impact on economic development.

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. As the computing power has substantially improved over last decade, the ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. Therefore, the application of ANN is more widespread in textiles and clothing industries over last decade. In this chapter, it aims to review current application of ANN in textiles and clothing industries over last decade. Based on literature reviews, the challenges encountered by ANN used in the industries will be discussed and the potential future application of ANN in the industries will also be addressed. The structure of this chapter comprises of seven sections. The first section includes background of ANN, importance of ANN in textiles and clothing and the arrangement of this chapter. In forthcoming three sections, they include review of applications of ANN in fibres and yarns, in chemical processing, and in clothing over last decade. Afterwards, challenges encountered by ANN used in textiles and clothing industries will be discussed and potential future application of ANN in textiles and clothing industries will be addressed in last section.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.