Search results for: Classification of Redevelopment Project

Authors: Nnadi Ezekiel Ejiofor

Abstract:

The study investigates the connection between strategic leadership and project management sustainability, with an emphasis on building projects Nigeria. The study set out to accomplish two specific goals: first, it sought to establish a link between creative project management and resource efficiency in construction projects in Nigeria; and second, it sought to establish a link between innovative thinking and waste minimization in those same projects. A structured questionnaire was used to collect primary data from 45 registered construction enterprises in the study area as part of the study's descriptive research approach. Due to the nonparametric nature of the data, Spearman Rank Order Correlation was used to evaluate the acquired data. The findings demonstrate that creative project management had a significant positive impact on resource efficiency in construction projects carried out by project management firms (r =.849; p.001), and that innovative thinking had a significant impact on waste reduction in those same projects (r =.849; p.001). It was determined that strategic leadership had a significant impact on the sustainability of project management, and it was thus advised that project managers should foresee, prepare for, and effectively communicate present and future developments to project staff in order to ensure that the objective of sustainable initiatives, such as recycling and reuse, is implemented in construction projects.

Keywords: Construction, project management, strategic leadership, sustainability, waste reduction.

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Authors: Sayed Amir Hamzeh Mirkheshti

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Offshore wind energy as a strategic renewable energy, has been growing rapidly due to availability, abundance and clean nature of it. On the other hand, budget of this project is incredibly higher in comparison with other renewable energies and it takes more duration. Accordingly, precise estimation of time and cost is needed in order to promote awareness in the developers and society and to convince them to develop this kind of energy despite its difficulties. Occurrence risks during on project would cause its duration and cost constantly changed. Therefore, to develop offshore wind power, it is critical to consider all potential risks which impacted project and to simulate their impact. Hence, knowing about these risks could be useful for the selection of most influencing strategies such as avoidance, transition, and act in order to decrease their probability and impact. This paper presents an evaluation of the feasibility of 500 MV offshore wind project in the Persian Gulf and compares its situation with uncertainty resources and risk. The purpose of this study is to evaluate time and cost of offshore wind project under risk circumstances and uncertain resources by using Monte Carlo simulation. We analyzed each risk and activity along with their distribution function and their effect on the project.

Keywords: Wind energy project; uncertain resources; risks; Monte Carlo simulation.

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: Intelligent transportation systems, object detection, video processing, road traffic, vehicle counting, vehicle classification.

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Authors: Minsoo Lee, Yearn Jeong Kim, Yun-mi Kim, Sujeung Cheong, Sookyung Song

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Bio-chips are used for experiments on genes and contain various information such as genes, samples and so on. The two-dimensional bio-chips, in which one axis represent genes and the other represent samples, are widely being used these days. Instead of experimenting with real genes which cost lots of money and much time to get the results, bio-chips are being used for biological experiments. And extracting data from the bio-chips with high accuracy and finding out the patterns or useful information from such data is very important. Bio-chip analysis systems extract data from various kinds of bio-chips and mine the data in order to get useful information. One of the commonly used methods to mine the data is classification. The algorithm that is used to classify the data can be various depending on the data types or number characteristics and so on. Considering that bio-chip data is extremely large, an algorithm that imitates the ecosystem such as the ant algorithm is suitable to use as an algorithm for classification. This paper focuses on finding the classification rules from the bio-chip data using the Ant Colony algorithm which imitates the ecosystem. The developed system takes in consideration the accuracy of the discovered rules when it applies it to the bio-chip data in order to predict the classes.

Keywords: Ant Colony System, DNA chip data, Classification.

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Authors: Tudor Barbu

Abstract:

An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Then, an automatic pixel classification approach is proposed. The feature vectors are clustered using an unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: Image segmentation, moment-based texture analysis, automatic classification, validity indexes.

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Authors: Mohsen Pirdashti, Arezou Ghadi, Mehrdad Mohammadi, Gholamreza Shojatalab

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Chemical industry project management involves complex decision making situations that require discerning abilities and methods to make sound decisions. Project managers are faced with decision environments and problems in projects that are complex. In this work, case study is Research and Development (R&D) project selection. R&D is an ongoing process for forward thinking technology-based chemical industries. R&D project selection is an important task for organizations with R&D project management. It is a multi-criteria problem which includes both tangible and intangible factors. The ability to make sound decisions is very important to success of R&D projects. Multiple-criteria decision making (MCDM) approaches are major parts of decision theory and analysis. This paper presents all of MCDM approaches for use in R&D project selection. It is hoped that this work will provide a ready reference on MCDM and this will encourage the application of the MCDM by chemical engineering management.

Keywords: Chemical Engineering, R&D Project, MCDM, Selection.

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Authors: Rami N. Khushaba, Adel Al-Jumaily

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The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: Essam Al-Daoud

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Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.

Keywords: AdaBoost, Bayesian neural network, Signal-to-Noise, support vector machine, MCMC.

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Authors: F.Alilat, S.Loumi, H.Merrad, B.Sansal

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In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.

Keywords: Neural Networks, fuzzy ART, fuzzy ARTMAP, Remote sensing, multispectral Classification.

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Authors: Jameela Ali Alkrimi, Loay E. George, Azizah Suliman, Abdul Rahim Ahmad, Karim Al-Jashamy

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used.

Keywords: Red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC.

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Authors: Grzegorz Sierpiński, Ireneusz Celiński, Marcin Staniek

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The article discusses multimodal mobility in contemporary societies as a main planning and organization issue in the functioning of administrative bodies, a problem which really exists in the space of contemporary cities in terms of shaping modern transport systems. The article presents classification of available resources and initiatives undertaken for developing multimodal mobility. Solutions can be divided into three groups of measures – physical measures in the form of changes of the transport network infrastructure, organizational ones (including transport policy) and information measures. The latter ones include in particular direct support for people travelling in the transport network by providing information about ways of using available means of transport. A special measure contributing to this end is a trip planner. The article compares several selected planners. It includes a short description of the Green Travelling Project, which aims at developing a planner supporting environmentally friendly solutions in terms of transport network operation. The article summarizes preliminary findings of the project.

Keywords: Mobility, modal split, multimodal trip, multimodal platforms, sustainable transport.

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Authors: Sajith Sajeev

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The application of Computational Fluid Dynamics (CFD) is widespread in engineering and industry, including aerospace, automotive, and energy. CFD simulations necessitate the use of intricate mathematical models and a substantial amount of computational power to accurately describe the behavior of fluids. The implementation of CFD projects can be difficult, and a well-structured approach to project management is required to assure the timely and cost-effective delivery of high-quality results. This paper's objective is to provide an overview of project management in CFD, including its problems, methodologies, and best practices. The study opens with a discussion of the difficulties connected with CFD project management, such as the complexity of the mathematical models, the need for extensive computational resources, and the difficulties associated with validating and verifying the results. In addition, the study examines the project management methodologies typically employed in CFD, such as the Traditional/Waterfall model, Agile and Scrum. Comparisons are made between the advantages and disadvantages of each technique, and suggestions are made for their effective implementation in CFD projects. The study concludes with a discussion of the best practices for project management in CFD, including the utilization of a well-defined project scope, a clear project plan, and effective teamwork. In addition, it highlights the significance of continuous process improvement and the utilization of metrics to monitor progress and discover improvement opportunities. This article is a resource for project managers, researchers, and practitioners in the field of CFD. It can aid in enhancing project outcomes, reducing risks, and enhancing the productivity of CFD projects. This paper provides a complete overview of project management in CFD and is a great resource for individuals who wish to implement efficient project management methods in CFD projects.

Keywords: Project management, Computational Fluid Dynamics, Traditional/Waterfall methodology, agile methodology, scrum methodology.

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Authors: Irina Pyrlina

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This article presents the results of a study conducted to identify operational risks for information systems (IS) with service-oriented architecture (SOA). Analysis of current approaches to risk and system error classifications revealed that the system error classes were never used for SOA risk estimation. Additionally system error classes are not normallyexperimentally supported with realenterprise error data. Through the study several categories of various existing error classifications systems are applied and three new error categories with sub-categories are identified. As a part of operational risks a new error classification scheme is proposed for SOA applications. It is based on errors of real information systems which are service providers for application with service-oriented architecture. The proposed classification approach has been used to classify SOA system errors for two different enterprises (oil and gas industry, metal and mining industry). In addition we have conducted a research to identify possible losses from operational risks.

Keywords: Enterprise architecture, Error classification, Oil&Gas and Metal&Mining industries, Operational risks, Serviceoriented architecture

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Authors: Rana Yousef, Khalil el Hindi

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The behavior of Radial Basis Function (RBF) Networks greatly depends on how the center points of the basis functions are selected. In this work we investigate the use of instance reduction techniques, originally developed to reduce the storage requirements of instance based learners, for this purpose. Five Instance-Based Reduction Techniques were used to determine the set of center points, and RBF networks were trained using these sets of centers. The performance of the RBF networks is studied in terms of classification accuracy and training time. The results obtained were compared with two Radial Basis Function Networks: RBF networks that use all instances of the training set as center points (RBF-ALL) and Probabilistic Neural Networks (PNN). The former achieves high classification accuracies and the latter requires smaller training time. Results showed that RBF networks trained using sets of centers located by noise-filtering techniques (ALLKNN and ENN) rather than pure reduction techniques produce the best results in terms of classification accuracy. The results show that these networks require smaller training time than that of RBF-ALL and higher classification accuracy than that of PNN. Thus, using ALLKNN and ENN to select center points gives better combination of classification accuracy and training time. Our experiments also show that using the reduced sets to train the networks is beneficial especially in the presence of noise in the original training sets.

Keywords: Radial basis function networks, Instance-based reduction, PNN.

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Authors: P. F. Wong, H. Salleh, F. A. Rahim

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The adoption of building information modeling (BIM) is increasing in the construction industry. However, quantity surveyors are slow in adoption compared to other professions due to lack of awareness of the BIM’s potential in their profession. It is still unclear on how BIM application can enhance quantity surveyors’ work performance and project performance. The aim of this research is to identify the capabilities of BIM in quantity surveying practices and examine the relationship between BIM capabilities and project performance. Questionnaire survey and interviews were adopted for data collection. Literature reviews identified there are eleven BIM capabilities in quantity surveying practice. Questionnaire results showed that there are several BIM capabilities significantly correlated with project performance in time, cost and quality aspects and the results were validated through interviews. These findings show that BIM has the capabilities to enhance quantity surveyors’ performances and subsequently improved project performance.

Keywords: Building information modeling (BIM), quantity surveyors, capability, project performance.

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Authors: Mamoon Mousa Atout

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Projects managers in construction industry usually face a difficult organizational environment especially if the project is unique. The organization lacks the processes to practice construction management correctly, and the executive’s technical managers who have lack of experience in playing their role and responsibilities correctly. Project managers need to adopt best practices that allow them to do things effectively to make sure that the project can be delivered without any delay even though the executive’s technical managers should follow a certain process to avoid any factor might cause any delay during the project life cycle. The purpose of the paper is to examine the awareness level of projects managers about construction management processes, tools, techniques and implications to complete projects on time. The outcome and the results of the study are prepared based on the designed questionnaires and interviews conducted with many project managers. The method used in this paper is a quantitative study. A survey with a sample of 100 respondents was prepared and distributed in a construction company in Dubai, which includes nine questions to examine the level of their awareness. This research will also identify the necessary benefits of processes of construction management that has to be adopted by projects managers to mitigate the maximum potential problems which might cause any delay to the project life cycle.

Keywords: Construction Methodology, Design Process, Project Managers, Scheduling and Resource Planning.

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: Hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection.

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Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan

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The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.

Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK

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Authors: Feras Al-Obeidat, Nabil Belacel, Juan A. Carretero, Prabhat Mahanti,

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In this paper, a new learning algorithm based on a hybrid metaheuristic integrating Differential Evolution (DE) and Reduced Variable Neighborhood Search (RVNS) is introduced to train the classification method PROAFTN. To apply PROAFTN, values of several parameters need to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attribute. Based on these requirements, the hybrid approach, named DEPRO-RVNS, is presented in this study. In some cases, the major problem when applying DE to some classification problems was the premature convergence of some individuals to local optima. To eliminate this shortcoming and to improve the exploration and exploitation capabilities of DE, such individuals were set to iteratively re-explored using RVNS. Based on the generated results on both training and testing data, it is shown that the performance of PROAFTN is significantly improved. Furthermore, the experimental study shows that DEPRO-RVNS outperforms well-known machine learning classifiers in a variety of problems.

Keywords: Knowledge Discovery, Differential Evolution, Reduced Variable Neighborhood Search, Multiple criteria classification, PROAFTN, Supervised Learning.

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Authors: Jaehwan Kang, Haejung Lee

Abstract:

This paper introduces an automatic voice classification system for the diagnosis of individual constitution based on Sasang Constitutional Medicine (SCM) in Traditional Korean Medicine (TKM). For the developing of this algorithm, we used the voices of 309 female speakers and extracted a total of 134 speech features from the voice data consisting of 5 sustained vowels and one sentence. The classification system, based on a rule-based algorithm that is derived from a non parametric statistical method, presents 3 types of decisions: reserved, positive and negative decisions. In conclusion, 71.5% of the voice data were diagnosed by this system, of which 47.7% were correct positive decisions and 69.7% were correct negative decisions.

Keywords: Voice Classifier, Sasang Constitution Medicine, Traditional Korean Medicine, SCM, TKM.

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Authors: Kamolchanok Panishkan, Kanokporn Swangjang, Natdhera Sanmanee, Daoroong Sungthong

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The objective of this research is to study principal component analysis for classification of 67 soil samples collected from different agricultural areas in the western part of Thailand. Six soil properties were measured on the soil samples and are used as original variables. Principal component analysis is applied to reduce the number of original variables. A model based on the first two principal components accounts for 72.24% of total variance. Score plots of first two principal components were used to map with agricultural areas divided into horticulture, field crops and wetland. The results showed some relationships between soil properties and agricultural areas. PCA was shown to be a useful tool for agricultural areas classification based on soil properties.

Keywords: soil organic matter, soil properties, classification, principal components

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline which ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

Keywords: Machine learning, healthcare, classification, explainability.

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Authors: Samir Brahim Belhaouari

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By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.

Keywords: Pattern recognition, Time series, k-Nearest Neighbor, k-means cluster, Gaussian Mixture Model, Classification

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

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Authors: Kahraman Ayyildiz, Stefan Conrad

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In this paper we illuminate a frequency domain based classification method for video scenes. Videos from certain topical areas often contain activities with repeating movements. Sports videos, home improvement videos, or videos showing mechanical motion are some example areas. Assessing main and side frequencies of each repeating movement gives rise to the motion type. We obtain the frequency domain by transforming spatio-temporal motion trajectories. Further on we explain how to compute frequency features for video clips and how to use them for classifying. The focus of the experimental phase is on transforms utilized for our system. By comparing various transforms, experiments show the optimal transform for a motion frequency based approach.

Keywords: action recognition, frequency, transform, motion recognition, repeating movement, video classification

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Authors: Kasim Terzic, Bernd Neumann

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Scene interpretation systems need to match (often ambiguous) low-level input data to concepts from a high-level ontology. In many domains, these decisions are uncertain and benefit greatly from proper context. This paper demonstrates the use of decision trees for estimating class probabilities for regions described by feature vectors, and shows how context can be introduced in order to improve the matching performance.

Keywords: Classification, Decision Trees, Interpretation, Vision

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Authors: Abdullahi Mohamud Sharif, Mohd. Zaidi Abd. Rozan

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Risk Assessment Tool (RAT) is an expert system that assesses, monitors, and gives preliminary treatments automatically based on the project plan. In this paper, a review was taken out for the current project time management risk assessment tools for SME software development projects, analyze risk assessment parameters, conditions, scenarios, and finally propose risk assessment tool (RAT) model to assess, treat, and monitor risks. An implementation prototype system is developed to validate the model.

Keywords: Project Time Management, Risk Assessment Tool(RAT), Small and Medium Enterprises (SME).

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Authors: Chien-Hua Wang, Meng-Ying Chou, Chin-Tzong Pang

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In the era of great competition, understanding and satisfying customers- requirements are the critical tasks for a company to make a profits. Customer relationship management (CRM) thus becomes an important business issue at present. With the help of the data mining techniques, the manager can explore and analyze from a large quantity of data to discover meaningful patterns and rules. Among all methods, well-known association rule is most commonly seen. This paper is based on Apriori algorithm and uses genetic algorithms combining a data mining method to discover fuzzy classification rules. The mined results can be applied in CRM to help decision marker make correct business decisions for marketing strategies.

Keywords: Customer relationship management (CRM), Data mining, Apriori algorithm, Genetic algorithm, Fuzzy classification rules.

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Authors: A. Elbaz, K. Spang

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Infrastructure projects are often subjected to delays and cost overruns and mistakenly described as unsuccessful projects. These projects have many peculiarities such as public attention, impact on the environment, subjected to special regulations, etc. They also deal with several stakeholders with different motivations and face unique risks. With this in mind we need to reconsider our approach to manage them, define their success factors and implement these success factors. Infrastructure projects are not only lacking a unified meaning of project success or a definition of success factors, but also a clear method to implement these factors. This paper investigates this gap and introduces a concept to implement success factors in an efficient way, taking into consideration the specific characteristics of infrastructure projects. This concept consists of six enablers such as project organization, project team, project management workflow, contract management, communication and knowledge transfer and project documentations. These enablers allow other success factors to be efficiently implemented in projects. In conclusion, this paper provides project managers as well as company managers with a tool to define and implement success factors efficiently in their projects, along with upgrading their assets for the coming projects. This tool consists of processes and validated checklists to ensure the best use of company resources and knowledge. Due to the special features of infrastructure projects this tool will be tested in the German infrastructure market. However, it is meant to be adaptable to other markets and industries.

Keywords: Infrastructure projects, enablers, project success, success factors, transportation projects.

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

Abstract:

Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: Machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation.

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