Search results for: classification of redevelopment project

Authors: Kemal Polat

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In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Amna Khan, Zareena Kausar, Saad Malik

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Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA.

Keywords: classification accuracies, electromyography, linear discriminant analysis (LDA), Myo armband sensor, support vector machine (SVM)

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Mohammad Ashraf

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This research links the use of Building Information Modeling technology in constructions and infrastructure projects, starting from the moment when considering the establishment of a project to demolishing or renovating it, going through the design work, planning, procurement and implementation. BIM Software's which used are Revit, Navisworks and Asta Project in the case study for the Atletico Madrid Stadium project (Wanda Stadium). Also, the project improves through various phases of construction (planning - implementation - management). Besides, the level of the details managed within this project advances gradually. In addition, the construction process problems become about 30 % less than before, resulting from high coordination between designing, implementation and follow through that is done by the project management office (PMO). The current disposition in the industry is to tightly manage the detail contained within the planning and coordination phases of construction, but we miss the opportunity to manage that data as it matures and grows into the execution and commissioning phases.

Keywords: construction management, BIM technology, planning, design, procurements, critical path method

Procedia PDF Downloads 269

Authors: Fernanda Varella Borges, Silvio Burrattino Melhado

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Motivated by the importance of public infrastructure projects in the civil construction chain, this research shows the study of project management methods and the infrastructure projects’ characteristics. The research aims at the objective of improving management efficiency by proposing guidelines for the application of project management methods in infrastructure projects. Through literature review and case studies, the research analyses two major infrastructure projects underway in Brazil, identifying the critical points for achieving its success. As a result, the proposed guidelines indicate that special attention should be given to the management of stakeholders, focusing on their knowledge and experience, their different interests, the efficient management of their communication, and their behavior in the day-by-day project management process.

Keywords: construction, infrastructure, project management, public projects

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Authors: H. Hussain, N. H. Mohamad

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Practical experiences have been one of the successful criteria for the Project Management course for the art and design students. There are series of events that the students have to undergo as part of their practical exercises in the learning context for Project Management courses. These series have been divided into few mini programs that involved the whole individual in each group. Therefore, the events have been one of the bench marks for these students. Through the practical experience, the task that has been given to individual has been performed according to the needs of professional practice and ethics.

Keywords: practical experience, project management, art and design students, events, programs

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Authors: Garg Ramneesh, Uppal Sanjeev, Mittal Rajinder, Shah Sheerin, Jain Vikas, Singla Bhupinder

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Introduction: Despite advances in trauma care, a classification system for soft tissue injuries of the face still needs to be objectively defined. Aim: To develop a classification system for soft tissue injuries of the face; that is objective, easy to remember, reproducible, universally applicable, aids in surgical management and helps to develop a structured data that can be used for future use. Material and Methods: This classification system includes those patients that need surgical management of facial injuries. Associated underlying bony fractures have been intentionally excluded. Depending upon the severity of soft tissue injury, these can be graded from 0 to IV (O-Abrasions, I-lacerations, II-Avulsion injuries with no skin loss, III-Avulsion injuries with skin loss that would need graft or flap cover, and IV-complex injuries). Anatomically, the face has been divided into three zones (Zone 1/2/3), as per aesthetic subunits. Zone 1e stands for injury of eyebrows; Zones 2 a/b/c stand for nose, upper eyelid and lower eyelid respectively; Zones 3 a/b/c stand for upper lip, lower lip and cheek respectively. Suffices R and L stand for right or left involved side, B for presence of foreign body like glass or pellets, C for extensive contamination and D for depth which can be graded as D 1/2/3 if depth is still fat, muscle or bone respectively. I is for damage to facial nerve or parotid duct. Results and conclusions: This classification system is easy to remember, clinically applicable and would help in standardization of surgical management of soft tissue injuries of face. Certain inherent limitations of this classification system are inability to classify sutured wounds, hematomas and injuries along or against Langer’s lines.

Keywords: soft tissue injuries, face, avulsion, classification

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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: Kwounghee Choi

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Technological convergence between the various sectors is expected to have a very large impact on future industrial and economy. This study attempts to do empirical approach between specific technologies’ classification. For technological convergence classification, it is necessary to set the target technology to be analyzed. This study selected target technology from national research and development plan. At first we found a source technology for analysis. Depending on the weight of source technology, NT-based, BT-based, IT-based, ET-based, CS-based convergence types were classified. This study aims to empirically show the concept of convergence technology and convergence types. If we use the source technology to classify convergence type, it will be useful to make practical strategies of convergence technology.

Keywords: technology convergence, source technology, convergence type, R&D strategy, technology classification

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Authors: Apisit Nawapanpong, Natthapat Boonjerm

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This project aims to analyze and identify the flight plan of low-altitude aviation in Thailand and other countries. The development of UAV technology has led the innovation and revolution in the aviation industry; this includes the development of new modes of passenger or freight transportation, and it has also affected other industries widely. At present, this technology is being developed rapidly and has been tested all over the world to make the most efficient for technology or innovation, and it is likely to grow more extensively. However, no flight plan for low-altitude operation has been published by the government organization; when compared with high-altitude aviation with manned aircraft, various unique factors are different, whether mission, operation, altitude range or airspace restrictions. In the study of the essential components of low-altitude operation measures to be practical and tangible, there were major problems, so the main consideration of this project is to analyze the components of low-altitude operations which are conducted up to the altitudes of 400 ft or 120 meters above ground level referring to the terrain, for example, air traffic management, classification of aircraft, basic necessity and safety, and control area. This research will focus on confirming the theory through qualitative and quantitative research combined with theoretical modeling and regulatory framework and by gaining insights from various positions in aviation industries, including aviation experts, government officials, air traffic controllers, pilots, and airline operators to identify the critical essential components of low-altitude flight operation. This project analyzes by using computer programs for science and statistics research to prove that the result is equivalent to the theory and be beneficial for regulating the flight plan for low-altitude operation by different essential components from this project and can be further developed for future studies and research in aviation industries.

Keywords: low-altitude aviation, UAV technology, flight plan, air traffic management, safety measures

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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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 system, object detection, vehicle couting, vehicle classification, video processing

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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: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Vidya Badrinarayanan, Appa Iyer Sivakumar

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The coronavirus pandemic has sent an important message that future project teams need to be trained to work under virtual conditions, which has already become the new norm in organizations across the world. As organizations increasingly rely on virtual teams to achieve project objectives, it is essential to comprehend how leadership functions in virtual project teams. The purpose of this research is to analyze the leadership behaviors exhibited by project managers for building trust and managing conflicts effectively in virtual project teams. This convergent parallel mixed method research was conducted by surveying 185 virtual leaders and conducting a semi-structured interview with 13 senior virtual leaders involved in managing projects across the industry sectors. The research findings indicate that establishing trust and managing conflicts were ranked as significant challenges in leading virtual project teams in the post-pandemic era. In contrast to earlier findings, our research findings suggest that productivity was not ranked as a significant challenge in leading virtual project teams. This indeed is a positive finding for organizations to consider adopting virtual project teams in the long run. Additionally, the research findings recommend that virtual leaders need to strive to build a high-trust environment and develop effective conflict resolution skills to improve the effectiveness of virtual project teams. As the project management profession struggles with low project success rates, mixed-method research aims to contribute to the knowledge in the growing research area of virtual project leadership. This research contributes to the knowledge by offering first-person accounts from senior virtual leaders on the innovative strategies they had implemented for building trust and resolving conflicts effectively in the virtual project when there were limited opportunities for face-to-face interaction on account of the pandemic. In addition, the leadership framework created as a part of this research for trust development and conflict management in virtual project teams will guide project managers to improve virtual project team effectiveness.

Keywords: conflict management, trust building, virtual leadership, virtual teams

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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: 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: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Nahed Al-Hajeri

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There are several reasons which lead to a delay in project completion, out of all, one main reason is the delay in deliverable processing, i.e. submission and review of documents. Most of the project cycles start with a list of deliverables but without a sequence of submission of the same, means without a direction to move, leading to overlapping of activities and more interdependencies. Hence Project Design Deliverables (PDD) is developed as a solution to Organize Transmittals (Documents/Drawings) received from contractors/consultants during different phases of an EPC (Engineering, Procurement, and Construction) projects, which gives proper direction to the stakeholders from the beginning, to reduce inter-discipline dependency, avoid overlapping of activities, provide a list of deliverables, sequence of activities, etc. PDD attempts to provide a list and sequencing of the engineering documents/drawings required during different phases of a Project which will benefit both client and Contractor in performing planned activities through timely submission and review of deliverables. This helps in ensuring improved quality and completion of Project in time. The successful implementation begins with a detailed understanding the specific challenges and requirements of the project. PDD will help to learn about vendor document submissions including general workflow, sequence and monitor the submission and review of the deliverables from the early stages of Project. This will provide an overview for the Submission of deliverables by the concerned during the projects in proper sequence. The goal of PDD is also to hold responsible and accountability of all stakeholders during complete project cycle. We believe that successful implementation of PDD with a detailed list of documents and their sequence will help organizations to achieve the project target.

Keywords: EPC (Engineering, Procurement, and Construction), project design deliverables (PDD), econometrics sciences, management sciences

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Authors: Meng Sun, Bin Wei

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In the past decades, design-build has become a more popular project delivery system especially for the large scaled infrastructure project in North America. It provides a one-stop shopping system for the client therefore improves the efficiency of construction, and reduces the risks and overall cost for the clients. Compared to the project with traditional delivery method, design-build project requires contractor and designer to work together efficiently to deliver the best-value solutions through the construction process. How to facilitate a solid integration and efficient interaction between contractor and designer often affects the schedule, budget and quality of the construction therefore becomes a key factor to the success of a design-build project. This paper presents a concept of using modern mobile cloud technology to provide an integrated solution during the design-build construction. It uses mobile cloud architecture to provide a platform for real-time field progress, change request approval, job progress log, and project time entry with devices integration for field information and communications. The paper uses a real filed change notice as an example to demonstrate how mobile cloud technology applies in a design-build project and how it can improve the project efficiency.

Keywords: cloud, design-build, field change notice, mobile application

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Authors: Theophilus O. Odunlami, Hasan Haroglu, Nader Saleh-Matter

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The challenges facing the construction industry are often worsened by the compounded nature of projects coupled with the complexity of key stakeholders involved at different stages of the project. Projects are planned to achieve outlined benefits in line with the business case; however, a lack of effective management of key stakeholders can result in unrealistic delivery aspirations, unnecessary re-works, and overruns. The aim of this study is to examine the early stages of a project lifecycle and investigate the stakeholder management and involvement processes and their impact on the successful delivery of the project. The research engaged with conventional construction organisations and project personnel and stakeholders on diverse projects, using a research strategy to analyse existing project case studies, narrative enquiries, interviews, and surveys using a combined qualitative, quantitative, and mixed method of analysis. Research findings have shown that the involvement of stakeholders at different levels during the early stages has pronounced effects on project delivery; it helps to forge synergy and promotes a clear understanding of individual responsibilities, strengths, and weaknesses. This has often fostered a positive sense of productive collaboration right through the early stages of the project. These research findings intend to contribute to the development of a process framework for stakeholder and project team involvement in the early stages of a project. This framework will align with the selection criteria for stakeholders, contractors, and resources, ultimately contributing to the successful completion of projects. The primary question addressed in this study is stakeholder involvement and management of the early stages of a building project life cycle impacts project delivery. Findings showed that early-stage stakeholder involvement and collaboration between project teams and contractors significantly contribute to project success. However, a strong and healthy communication strategy would be required to maintain the flow of value-added ideas among stakeholders at the early stages to benefit the project at the execution stage.

Keywords: early stages, project lifecycle, stakeholders, decision-making strategy, project framework

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Authors: Nuruddeen Usman, Mohammad Sani

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The performance of project delivery methods has been an issue of concern to various stakeholders in the construction industry. The contracting system of project delivery is the traditional system used in the delivery of most public projects in Nigeria. The direct labor system is used most times as an alternative to the traditional system. There were so many complain about the performance of contracting system and the suitability of direct labor as an alternative to the delivery of public projects. Therefore, this paper is aimed at investigating the effect of project size on the project delivery methods in the completed public buildings. Questionnaires were self-administered to managerial staff in the study area and analyzed using descriptive statistics. The findings reveals that contracting system was choosing for large size building construction project delivery with higher frequency (F) of 40 (76.9%) against direct labor with 12 (23.1%). While the small size project, the result revealed a frequency (F) of 26 (50%) for contracting system and direct labor system respectively. Base on the research findings, the contracting system, was recommended for all sizes of building construction project delivery while direct labor system can only use as an alternative for small size building construction projects delivery.

Keywords: construction size, contracting system, direct labour, effect

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Abdulsalam Abiodun Salman, Abdulmumin Isah

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The study investigated the motivational factors that could enhance the teaching and understanding of ICT-driven cataloguing and classification (Cat and Class) practical classes among students of library and information science (LIS) in Kwara State Library Schools, Nigeria. It deployed a positivist research paradigm using a quantitative method by deploying the use of questionnaires for data collection. The population of the study is one thousand, one hundred and twenty-five (1,125) which was obtained from the department of each respective library school (the University of Ilorin, Ilorin (Unilorin); Federal Polytechnic Offa, (Fedpoffa); and Kwara State University (KWASU). The sample size was determined using the research advisor table. Hence, the study sample of one hundred and ten (110) was used. The findings revealed that LIS students were averagely motivated toward ICT-driven Cataloguing and Classification practical classes. The study recommended that modern cataloguing and classification tools for practical classes should be made available in the laboratories as motivational incentives for students. It was also recommended that library schools should motivate the students beyond the provision of these ICT-driven tools but also extend the practical class periods. Availability and access to medical treatment in case of injuries during the practical classes should be made available. Technologists/Tutors of Cat and Class practical classes should also be exposed to further training in modern trends, especially emerging digital knowledge and skills in cataloguing and classification. This will keep both the tutors and students abreast of the new development in the technological arena.

Keywords: cataloguing and classification, motivational factors, ICT-driven practical classes, LIS students, Nigeria

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

Abstract:

Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Maher Aburrous, Adel Khelifi, Manar Abu Talib

Abstract:

Transactions performed by financial institutions on daily basis require XML encryption on large scale. Encrypting large volume of message fully will result both performance and resource issues. In this paper a novel approach is presented for securing financial XML transactions using classification data mining (DM) algorithms. Our strategy defines the complete process of classifying XML transactions by using set of classification algorithms, classified XML documents processed at later stage using element-wise encryption. Classification algorithms were used to identify the XML transaction rules and factors in order to classify the message content fetching important elements within. We have implemented four classification algorithms to fetch the importance level value within each XML document. Classified content is processed using element-wise encryption for selected parts with "High", "Medium" or “Low” importance level values. Element-wise encryption is performed using AES symmetric encryption algorithm and proposed modified algorithm for AES to overcome the problem of computational overhead, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. An implementation has been conducted using data set fetched from e-banking service to present system functionality and efficiency. Results from our implementation showed a clear improvement in processing time encrypting XML documents.

Keywords: XML transaction, encryption, Advanced Encryption Standard (AES), XML classification, e-banking security, fuzzy classification, cryptography, intelligent encryption

Procedia PDF Downloads 398