Search results for: project classification

Authors: Natthawut Arin

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In forensic contexts, competency to stand trial assessments are the most common referrals. The defendants may attempt to endorse psychopathology symptoms and feign incompetent. Coaching, which can be teaching them test-taking strategies to avoid detection of psychopathological symptoms feigning. Recently, the Symptom Validity Testings (SVTs) were created to detect feigning. Moreover, the works of the literature showed that the effects of coaching on SVTs may be more robust to the effects of coaching. Thai Symptom Validity Test (SVT-Th) was designed as SVTs which demonstrated adequate psychometric properties and ability to classify between feigners and honest responders. Thus, the current study to examine the utility as the robustness of SVT-Th in the detection of feigned psychopathology. Participants consisted of 120 were recruited from undergraduate courses in psychology, randomly assigned to one of three groups. The SVT-Th was administered to those three scenario-experimental groups: (a) Uncoached group were asked to respond honestly (n=40), (b) Symptom-coached without warning group were asked to feign psychiatric symptoms to gain incompetency to stand trial (n=40), while (c) Test-coached with warning group were asked to feign psychiatric symptoms to avoid test detection but being incompetency to stand trial (n=40). Group differences were analyzed using one-way ANOVAs. The result revealed an uncoached group (M = 4.23, SD.= 5.20) had significantly lower SVT-Th mean scores than those both coached groups (M =185.00, SD.= 72.88 and M = 132.10, SD.= 54.06, respectively). Classification rates were calculated to determine the classification accuracy. Result indicated that SVT-Th had overall classification accuracy rates of 96.67% with acceptable of 95% sensitivity and 100% specificity rates. Overall, the results of the present study indicate that the SVT-Th yielded high adequate indices of accuracy and these findings suggest that the SVT-Th is robustness against coaching.

Keywords: incompetency to stand trial, coaching, robustness, classification accuracy

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara

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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.

Keywords: Gaussian mixture discriminant analysis, spectral mixture model, Worldview-2, land parcels

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Authors: Matthias Stange

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Building Information Modelling (BIM) is one of the most promising methods in the building design process and plays an important role in the digitalization of the Architectural, Engineering, and Construction (AEC) Industry. The application of BIM is seen as the key enabler for increasing productivity in the construction industry. The model-based collaboration using the BIM method is intended to significantly reduce cost increases, schedule delays, and quality problems in the planning and construction of buildings. Numerous qualitative studies based on expert interviews support this theory and report perceived benefits from the use of BIM in terms of achieving project objectives related to cost, schedule, and quality. However, there is a large research gap in analysing quantitative data collected from real construction projects regarding the actual benefits of applying BIM based on representative sample size and different application regions as well as different project typologies. In particular, the influence of the project-related BIM maturity level is completely unexplored. This research project examines primary data from 105 construction projects worldwide using quantitative research methods. Projects from the areas of residential, commercial, and industrial construction as well as infrastructure and hydraulic engineering were examined in application regions North America, Australia, Europe, Asia, MENA region, and South America. First, a descriptive data analysis of 6 independent project variables (BIM maturity level, application region, project category, project type, project size, and BIM level) were carried out using statistical methods. With the help of statisticaldata analyses, the influence of the project-related BIM maturity level on 6 dependent project variables (deviation in planning time, deviation in construction time, number of planning collisions, frequency of rework, number of RFIand number of changes) was investigated. The study revealed that most of the benefits of using BIM perceived through numerous qualitative studies have not been confirmed. The results of the examined sample show that the application of BIM did not have an improving influence on the dependent project variables, especially regarding the quality of the planning itself and the adherence to the schedule targets. The quantitative research suggests the conclusion that the BIM planning method in its current application has not (yet) become a recognizable increase in productivity within the planning and construction process. The empirical findings indicate that this is due to the overall low level of BIM maturity in the projects of the examined sample. As a quintessence, the author suggests that the further implementation of BIM should primarily focus on an application-oriented and consistent development of the project-related BIM maturity level instead of implementing BIM for its own sake. Apparently, there are still significant difficulties in the interweaving of people, processes, and technology.

Keywords: AEC-process, building information modeling, BIM maturity level, project results, productivity of the construction industry

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Authors: Farah Wahida Binti Mohamad Said

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Islamic culture is the way of life a Muslim is defined by the Qur’an and Sunnah. On the other hand, Western culture is fashioned by a host of people; Capitalists, atheists, people who believe in same-gender marriages and others of a similar nature. The main issue that faced by the Muslim in Malaysia is the effect of western culture on Muslim lifestyle. This is because of the influence from western culture that dominates mind of the Muslim and also impressed on their lifestyle. Practically, majority all things have connected with western culture. However, the main objective for this project is to develop the effect of western culture on Muslim lifestyle. This project also focuses on a few aspects that relate with cultural of Muslim and western culture nowadays. This paper will include a few method .The methods for this project are a video, interview etc. Another methodology we will put on next paper for more detail information. As a result, this research found that western cultural will be effect on Muslim lifestyle.

Keywords: effect of western culture, Muslim lifestyle, western culture, western and Muslim culture

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Authors: Huang Hao, Li Weiwen

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Global biodiversity continues to decline as a result of global climate change and various human activities, such as habitat destruction, pollution, introduction of alien species and overfishing. Although there are connections between global marine organisms more or less, it is better to have clear geographical boundaries in order to facilitate the assessment and management of different biogeographical zones. And so area based management tools (ABMT) are considered as the most effective means for the conservation and sustainable use of marine biodiversity. On a large scale, the geographical gap (or barrier) is the main factor to influence the connectivity, diffusion, ecological and evolutionary process of marine organisms, which results in different distribution patterns. On a small scale, these factors include geographical location, geology, and geomorphology, water depth, current, temperature, salinity, etc. Therefore, the analysis on geographic and environmental factors is of great significance in the study of biodiversity characteristics. This paper summarizes the marine spatial classification and ABMTs used in coastal area, open oceans and deep sea. And analysis principles and methods of marine spatial classification based on biogeographic related factors, and take China Near Sea (CNS) area as case study, and select key biogeographic related factors, carry out marine spatial classification at biological region scale, ecological regionals scale and biogeographical scale. The research shows that CNS is divided into 5 biological regions by climate and geographical differences, the Yellow Sea, the Bohai Sea, the East China Sea, the Taiwan Straits, and the South China Sea. And the bioregions are then divided into 12 ecological regions according to the typical ecological and administrative factors, and finally the eco-regions are divided into 98 biogeographical units according to the benthic substrate types, depth, coastal types, water temperature, and salinity, given the integrity of biological and ecological process, the area of the biogeographical units is not less than 1,000 km². This research is of great use to the coastal management and biodiversity conservation for local and central government, and provide important scientific support for future spatial planning and management of coastal waters and sustainable use of marine biodiversity.

Keywords: spatial classification, marine biodiversity, bio-geographical, conservation

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Authors: Shang-Hua Wu, Rong-Chi Chang, Horng–Twu Liaw

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Nowadays, competencies of e-learning project personnel are very important in assisting them in offering courses, serving students in an effective way, leveraging advantages, strengthen their relationships with potential students, etc. among e-learning platforms, MOOCs has recently attracted increasing focuses in distance education since it can be conducted for a large numbers of virtual learners. Nonetheless, since MOOCs is a relatively new e-learning platform, top concerns have been paid to what competencies are important for e-learning personnel to consider. Taking this need, this research aimed to carry out an in-depth exploration of competency requirements of MOOCs (e-learning) project personnel in Taiwan vocational schools. Data were collected through thorough literature reviews and discussions and competency analysis was carried out using Delphi technique questionnaires. The results show that that MOOCs (e-learning) project personnel’ professional competency lie in three main dimensions, among which ‘demand analysis competency’ (i.e., containing 10 major competences and 48 subordinate capabilities) is the most important competency, followed by ‘project management competency’ (i.e., comprising 6 major competences and 31 secondary capabilities), and finally ‘digital content production competency’ (i.e., including 12 major competences and 79 secondary capabilities). As such, in Taiwan context with different organizational scales and market sizes, the e-learning competency items and unique experience/ achievements throughout the promotion process obtained in this research will provide useful references for academic institutions in promoting e-learning.

Keywords: competency analysis, Delphi technique questionnaire, e-learning, massive open online courses

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Authors: Khaled Hesham Hyari, Amjad Almani

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Construction projects in the public sector are commonly awarded through competitive bidding. In the last decade, the Construction projects environment in the Middle East went through many changes. These changes have been caused by different factors including the economic crisis, delays in monthly payments, international competition and reduced number of projects. These factors had a great impact on the bidding behaviors of contractors and their pricing strategies. This paper examines the competition characteristics in public construction projects through an analysis of bidding results of contractors in public construction projects over a period of 6 years (2006-2011) in Jordan. The analyzed projects include all categories of projects such as infrastructure, buildings, transportation and engineering services (design and supervision contracts). Data for the projects were obtained from the General Tender’s Directorate in Jordan and includes 462 projects. The analysis performed in this projects includes, studying the bid spread in all projects as it is an indication of the level of competition in the analyzed bids. The analysis studied the factors that affect bid spread such as number of bidders, Value of the project, Project category and years. It also studying the “Signal to Noise Ratio” in all projects as it is an indication of the accuracy of cost estimating performed by competing bidders and bidder´s evaluation of project risks. The analysis performed includes the relationship between signal to noise ratio and different parameters such as project category, number of bidders and changes over years. Moreover, the analysis includes determining the bidder´s aggressiveness in bidding as it is an indication of competition level in such projects. This was performed by determining the pack price which can be considered as the true value of the project and comparing it with the lowest bid submitted for each project to determine the level of aggressiveness in submitted bids. The analysis performed in this project should prove to be useful to owners in understanding bidding behaviors of contractors and pointing out areas that needs improvement in preparing bidding documents. Also the project should be useful to contractors in understanding the competitive bidding environment and should help them to improve their bidding strategies to maximize the success rate in obtaining contracts.

Keywords: construction projects, competitive bidding, public construction, competition

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Authors: Hyung Jun Ahn

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With the growing importance of social media, it is imperative to analyze it to understand the users. Users share useful information and their experience through social media, where much of what is shared is in the form of texts. This study focused on blogs and aimed to test whether the psycho-linguistic characteristics of blog texts vary with the subject or the type of experience of the texts. For this goal, blog texts about four different types of experience, Go, skiing, reading, and musical were collected through the search API of the Tistory blog service. The analysis of the texts showed that various psycholinguistic characteristics of the texts are different across the four categories of the texts. Moreover, the machine learning experiment using the characteristics for automatic text classification showed significant performance. Specifically, the ensemble method, based on functional tree and bagging appeared to be most effective in classification.

Keywords: blog, social media, text analysis, psycholinguistics

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Aliseydi Bozkurt, Ugur Yılmaz

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Objective: It was aimed to evaluate the current status of semen parameters in fertile males with one or more children and whose wife having a pregnancy for the last 1-12 months in Malatya region. Methods: Sperm samples were obtained from 131 voluntary fertile men. In each analysis, sperm volume (ml), number of sperm (sperm/ml), sperm motility and sperm viscosity were examined with Makler device. Classification was made according to World Health Organization (WHO) criteria. Results: Mean ejaculate volume ranged from 1.5 ml to 5.5 ml, sperm count ranged from 27 to 180 million/ml and motility ranged from 35 to 90%. Sperm motility was found to be on average; 69.9% in A, 7.6% in B, 8.7% in C, 13.3% in D category. Conclusion: The mean spermiogram values of fertile males in Malatya region were found to be similar to those in fertile males determined by the WHO. This study has a regional classification value in terms of spermiogram values.

Keywords: fertile men, infertility, spermiogram, sperm motility

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Authors: Yunwei Tang, Linhai Jing, Hui Li, Qingjie Liu, Xiuxia Li, Qi Yan, Haifeng Ding

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The giant panda (Ailuropoda melanoleuca) is an endangered species, mainly live in central China, where bamboos act as the main food source of wild giant pandas. Knowledge of spatial distribution of bamboos therefore becomes important for identifying the habitat of giant pandas. There have been ongoing studies for mapping bamboos and other tree species using remote sensing. WorldView-2 (WV-2) is the first high resolution commercial satellite with eight Multi-Spectral (MS) bands. Recent studies demonstrated that WV-2 imagery has a high potential in classification of tree species. The advanced classification techniques are important for utilising high spatial resolution imagery. It is generally agreed that object-based image analysis is a more desirable method than pixel-based analysis in processing high spatial resolution remotely sensed data. Classifiers that use spatial information combined with spectral information are known as contextual classifiers. It is suggested that contextual classifiers can achieve greater accuracy than non-contextual classifiers. Thus, spatial correlation can be incorporated into classifiers to improve classification results. The study area is located at Wuyipeng area in Wolong, Sichuan Province. The complex environment makes it difficult for information extraction since bamboos are sparsely distributed, mixed with brushes, and covered by other trees. Extensive fieldworks in Wuyingpeng were carried out twice. The first one was on 11th June, 2014, aiming at sampling feature locations for geometric correction and collecting training samples for classification. The second fieldwork was on 11th September, 2014, for the purposes of testing the classification results. In this study, spectral separability analysis was first performed to select appropriate MS bands for classification. Also, the reflectance analysis provided information for expanding sample points under the circumstance of knowing only a few. Then, a spatially weighted object-based k-nearest neighbour (k-NN) classifier was applied to the selected MS bands to identify seven land cover types (bamboo, conifer, broadleaf, mixed forest, brush, bare land, and shadow), accounting for spatial correlation within classes using geostatistical modelling. The spatially weighted k-NN method was compared with three alternatives: the traditional k-NN classifier, the Support Vector Machine (SVM) method and the Classification and Regression Tree (CART). Through field validation, it was proved that the classification result obtained using the spatially weighted k-NN method has the highest overall classification accuracy (77.61%) and Kappa coefficient (0.729); the producer’s accuracy and user’s accuracy achieve 81.25% and 95.12% for the bamboo class, respectively, also higher than the other methods. Photos of tree crowns were taken at sample locations using a fisheye camera, so the canopy density could be estimated. It is found that it is difficult to identify bamboo in the areas with a large canopy density (over 0.70); it is possible to extract bamboos in the areas with a median canopy density (from 0.2 to 0.7) and in a sparse forest (canopy density is less than 0.2). In summary, this study explores the ability of WV-2 imagery for bamboo extraction in a mountainous region in Sichuan. The study successfully identified the bamboo distribution, providing supporting knowledge for assessing the habitats of giant pandas.

Keywords: bamboo mapping, classification, geostatistics, k-NN, worldview-2

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: Faisel Elramalli, Ibrahim Althomali Amjad Sabbagh, Dhananjay Tambe

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SPEC CPU benchmarks are used to evaluate the performance of CPUs on computer systems. In our project we are going to use SPEC CPU suite that contains several benchmarks running on two different compilers gcc and icc in different optimizations levels to evaluate the performance of a CPU. The motivation of this project is to find out which compiler and in which optimization level makes the CPU reaches the best performance. The results of that evaluation will help users of these compilers to choose the best compiler and optimization level that perform efficiently for their work. In other words, it will give users the best performance of the CPU while doing their works. This project is interesting since it will provide the method used to measure the performance of CPU and how different optimization levels of compilers can help achieve a higher performance. Moreover, it will give a good understanding of how benchmarks are used to evaluate a CPU performance. For the reader, in reality SPEC CPU benchmarks are used to measure the performance of new released CPUs to be compared to other CPUs.

Keywords: SPEC, CPU, GCC, ICC, copilers

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Authors: Maria Lucília Borges

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This article presents the creation and design process of the "patchwork map" made for the project “Santo Amaro em Rede” (Santo Amaro on Web). The project was carried out in 2009 by SESC – SP – Brazil (Social Service for the Commerce of São Paulo) in partnership with Instituto Pólis. It is a mapping of socio-cultural dynamics of São Paulo’s South Zone and neighboring municipalities.

Keywords: affective map, cartography, São Paulo city, space, patchwork

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Authors: Zhongjie Yu, Hancheng Yu

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In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles.

Keywords: concentric circle detection, gradient, contour, edge pre-classification, RANSAC

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Authors: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

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Authors: Pintip Vajarothai, Saad Al-Jibouri, Johannes I. M. Halman

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Public investment projects are primarily aimed at achieving development strategies to increase national economies of scale and overall improvement in a country. However, experience shows that public projects, particularly in developing countries, struggle or fail to fulfill the immediate needs of local communities. In many cases, the reason for that is that projects are selected in a subjective manner and that a major part of the problem is related to the evaluation criteria and techniques used. The evaluation process is often based on a broad strategic economic effects rather than real benefits of projects to society or on the various needs from different levels (e.g. national, regional, local) and conditions (e.g. long-term and short-term requirements). In this paper, an extensive literature review of the types of criteria used in the past by various researchers in project evaluation and selection process is carried out and the effectiveness of such criteria and techniques is discussed. The paper proposes substitute social and project sustainability criteria to improve the conditions of local people and in particular the disadvantaged groups of the communities. Furthermore, it puts forward a way for modelling the interaction between the selected criteria and the achievement of the social goals of the affected community groups. The described work is part of developing a broader decision model for public investment project selection by integrating various aspects and techniques into a practical methodology. The paper uses Thailand as a case to review what and how the various evaluation techniques are currently used and how to improve the project evaluation and selection process related to social and sustainability issues in the country. The paper also uses an example to demonstrates how to test the feasibility of various criteria and how to model the interaction between projects and communities. The proposed model could be applied to other developing and developed countries in the project evaluation and selection process to improve its effectiveness in the long run.

Keywords: evaluation criteria, developing countries, public investment, project selection methodology

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Amir Reza Talaghat

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Since 2015, a new approach and policy regarding energy resources protection and using renewable energies has been started in Iran which was developing new projects. Investigating about the feasibility study of these new projects helped to figure out five steps to prepare an executive feasibility study of the concerned projects, which are proper site selections, authorizations, design and simulation, economic study and programming, respectively. The results were interesting and essential for decision makers and investors to start implementing of these projects in reliable condition. The research is obtained through collection and study of the project's documents as well as recalculation to review conformity of the results with GIS data and the technical information of the bidders. In this paper, it is attempted to describe the result of the performed research by describing the five steps as an executive methodology, for preparing a feasible study of installing a 10 MW – solar farm project. The corresponding results of the research also help decision makers to start similar projects is explained in this paper as follows: selecting the best location for the concerned PV plant, reliable and safe conditions for investment and the required authorizations to start implementing the solar farm project in the concerned region, selecting suitable component to achieve the best possible performance for the plant, economic profit of the investment, proper programming to implement the project on time.

Keywords: solar farm, solar energy, execution of PV power plant PV power plant

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Rui Serôdio, Alexandra Serra, José Albino Lima, Luísa Catita, Paula Lopes

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We will present the outline of the Project SIA (Social Inclusion through Accessibility) focusing in one of its core components: how our applied research model contributes to define community participation as a pillar for strategic and political agenda amongst local authorities. Project ISA, supported by EU regional funding, was design as part of a broader model developed by SIMLab–Social Inclusion Monitoring Laboratory, in which the relation University-Community is a core element. The project illustrates how University of Porto developed a large scale project of applied psychology research in a close partnership with 18 municipalities that cover almost all regions of Portugal, and with a private architecture enterprise, specialized in inclusive accessibility and “design for all”. Three fundamental goals were defined: (1) creation of a model that would promote the effective civic participation of local citizens; (2) the “voice” of such participation should be both individual and collective; (3) the scientific and technical framework should serve as one of the bases for political decision on inclusive accessibility local planning. The two main studies were run in a standardized model across all municipalities and the samples of the three modalities of community participation were the following: individual participation based on 543 semi-structured interviews and 6373 inquiries; collective participation based on group session with 302 local citizens. We present some of the broader findings of Project SIA and discuss how they relate to our applied research model.

Keywords: applied psychology, collective identity, community participation, inclusive accessibility

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Authors: Zhuoyuan Wang, Benson T. H. Lim, Imriyas Kamardeen

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Effective service quality management has long been touted as a means of improving project and organisational performance. Particularly, in construction projects, main contractors are often seen as a broker between clients and subcontractors, and their service quality is thus associated with the overall project affinity and outcomes. While a considerable amount of research has focused on the aspect of clients-main contractors, very little research has been done to explore the effect of contractors’ service quality on subcontractors’ behaviours and so project outcomes. In addressing this gap, this study surveyed 97 subcontractors in the Chinese Construction industry and data was analysed using the Partial Least Square (PLS) Structural Equation Modelling (SEM) technique. The overall findings reveal that subcontractors categorised main contractors’ service quality into three dimensions: assurance; responsiveness; reliability and empathy. Of these, it is found that main contractors’ ‘assurance’ and ‘responsiveness’ positively influence subcontractors’ intention to engage in contractual behaviours. The results further show that the subcontractors’ intention to engage in organizational citizenship behaviours is associated with how flexible and committed the main contractors are in reliability and empathy. Collectively, both subcontractors’ contractual and organizational citizenship behaviours positively influence the overall project outcomes. In conclusion, the findings inform contractors different strategies towards managing and gaining subcontractors’ behaviour commitment in a socially connected, yet complex and uncertain, business environment.

Keywords: construction firms, organisational citizenship behaviour, service quality, social exchange theory

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Authors: C. W. F. Che Wan Putra, M. Alshawi, M. S. Al Ahbabi, M. Jabakhanji

Abstract:

The construction industry is a multi-disciplinary and multi-national industry, which has an important role to play within the overall economy of any country. There are major challenges to an improved performance within the industry. Particularly lacking is, the ability to capture the large amounts of information generated during the life-cycle of projects and to make these available, in the right format, so that professionals can then evaluate alternative solutions based on life-cycle analysis. The fragmented nature of the industry is the main reason behind the unavailability and ill utilisation of project information. The lack of adequately engaging clients and managing their requirements contributes adversely to construction budget and schedule overruns. This is a difficult task to achieve, particularly if clients are not continuously and formally involved in the design and construction process, which means that the design intent is left to designers that may not always satisfy clients’ requirements. Client lead is strongly recognised in bringing change through better collaboration between project stakeholders. However, one of the major challenges is that collaboration is operated under conventional procurement methods, which hugely limit the stakeholders’ roles and responsibilities to bring about the required level of collaboration. A research has been conducted with a typical project in the UAE. A qualitative research work was conducted including semi-structured interviews with project partners to discover the real reasons behind this delay. The case study also investigated the real causes of the problems and if they can be adequately addressed by BIM and IPD. Special focus was also placed on the Client leadership and the role the Client can play to eliminate/minimize these problems. It was found that part of the ‘key elements’ from which the problems exist can be attributed to the client leadership and the collaborative environment and BIM.

Keywords: client leadership, building information modelling (BIM), integrated project delivery (IPD), case study

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Authors: Andi Fallahi, Bona Ryan Situmeang

Abstract:

The Construction Industry is notoriously risky. Much of the preparatory paperwork that precedes construction project can be viewed as the formulation of risk allocation between the owner and the Contractor. The Owner is taking the risk that his project will not get built on the schedule that it will not get built for what he has budgeted and that it will not be of the quality he expected. The Contractor Face a multitude of risk. One of them is an unforeseen condition at the construction site. The Owner usually has the upper hand here if the unforeseen condition occurred. Site data contained in Ground Investigation report is often of significant contractual importance in disputes related to the unforeseen ground condition. A ground investigation can never fully disclose all the details of the underground condition (Risk of an unknown ground condition can never be 100% eliminated). Adipala Coal Fired Steam Power Plant (CSFPP) 1 x 660 project is one of the large CSFPP project in Indonesia based on Engineering, Procurement, and Construction (EPC) Contract. Unforeseen Ground Condition it’s responsible by the Contractor has stipulated in the clausal of Contract. In the implementation, there’s indicated unforeseen ground condition at Circulating Water Pump House (CWPH) area which caused the Contractor should be changed the Method of Work that give big impact against Time of Completion and Cost Project. This paper tries to analyze the best way for allocating the risk between The Owner and The Contractor. All parties that allocating of sharing risk fairly can ultimately save time and money for all parties, and get the job done on schedule for the least overall cost.

Keywords: unforeseen ground condition, coal fired steam power plant, circulating water pump house, Indonesia

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Authors: Kemal Polat

Abstract:

In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Andriy Didenko, Zanin Kavazovic

Abstract:

Engineering students often have certain difficulties in mastering major theoretical concepts in mathematical courses such as differential equations. Student projects were proposed to motivate students’ learning and can be used as a tool to promote students’ interest in the material. Authors propose a student project that includes the use of Microsoft Excel. This instructional tool is often overlooked by both educators and students. An integral component of the experimental part of such a project is the exploration of an interactive spreadsheet. The aim is to assist engineering students in better understanding of Euler’s method. This method is employed to numerically solve first order differential equations. At first, students are invited to select classic equations from a list presented in a form of a drop-down menu. For each of these equations, students can select and modify certain key parameters and observe the influence of initial condition on the solution. This will give students an insight into the behavior of the method in different configurations as solutions to equations are given in numerical and graphical forms. Further, students could also create their own equations by providing functions of their own choice and a variety of initial conditions. Moreover, they can visualize and explore the impact of the length of the time step on the convergence of a sequence of numerical solutions to the exact solution of the equation. As a final stage of the project, students are encouraged to develop their own spreadsheets for other numerical methods and other types of equations. Such projects promote students’ interest in mathematical applications and further improve their mathematical and programming skills.

Keywords: student project, Euler's method, spreadsheet, engineering education

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Authors: Ekaterina Posokhova, Boris Gavrilov

Abstract:

Priority national projects have become an essential phenomenon in the Russian Federation. Both regional and local government institutions and a significant part of the society have been involved in their implementation. The scale and multispectricity of the national projects give a reason to believe that their concept is beyond the scope of the individual state programs. The national project “environment” contains federal projects on waste management, water, and air quality, ecotourism development, and biodiversity conservation highlights the importance of the preservation and restoration of Volga River and Lake Baikal ecosystems. This study assesses the national projects according to their relativeness with the current SDGs (i.e., SGD 14 and 15), evaluates the methodology of the projects. The paper considers the peculiarities of the national projects as strategic management tools as well as the possibility of amending the project objective indicators. Conclusion on the effectiveness of NP in terms of achieving SDGs is provided.

Keywords: management, SDP, russia, conservation, law

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Authors: Espinosa-Garza G., Loera-Hernandez I., Antonyan N.

Abstract:

To innovate, it is necessary to perform projects directed towards the search of improvement. The agricultural technique and the design of greenhouses have been studied by undergraduate engineering students from the Tecnológico de Monterrey using the campus areas. The purpose of this project was to incite students to create innovations and help rural populations of the state to solve one of the problems that they are dealing with nowadays. The main objective of the project was to search for an alternative technique that will allow the planting of the “chile piquín” plant, also known as Capsicum annuum, to grow quicker as it germinates. The “chile piquín” is one of the original crops of Mexico and forms the basis of the Mesoamerican cultures’ diet since the pre-hispanic era. To fulfill with today’s demand, it is required to implement new alternative methods to increase the “chile piquín’s” growth. The project lasted one semester with the participation of engineering students from multiple majors. The most important results from this academic experience were that, from the proposed goal, the students could analyze the needs of their town and were capable of introducing new and innovative ideas with the aim of resolving them. In the present article the pedagogic methodologies that allowed to carry out this project will be discussed.

Keywords: academic experience, chile piquín, engineering education, greenhouse design, innovation

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Authors: Fatemeh Jaferi, Moslem Parsa, Heshmatolah Shams Khorramabadi

Abstract:

In this modern fluctuating world, organizations need to outsource some parts of their activities (project) to providers in order to show a quick response to their changing requirements. In fact, a number of companies and institutes have contractors do their projects and have some specific criteria in contractor selection. Therefore, a set of scientific tools is needed to select the best contractors to execute the project according to appropriate criteria. Multi-criteria decision making (MCDM) has been employed in the present study as a powerful tool in ranking and selecting the appropriate contractor. In this study, devolving second-source (civil) project to contractors in the National Gas Company of Lorestan Province (Iran) has been found and therefore, 5 civil companies have been evaluated. Evaluation criteria include executive experience, qualification of technical staff, good experience and company's rate, technical interview, affordability, equipment and machinery. Criteria's weights are found through experts' opinions along with AHP and contractors ranked through TOPSIS and AHP. The order of ranking contractors based on MCDM methods differs by changing the formula in the study. In the next phase, the number of criteria and their weights has been sensitivity analysed through using AHP. Adding each criterion changed contractors' ranking. Similarly, changing weights resulted in a change in ranking. Adopting the stated strategy resulted in the facts that not only is an appropriate scientific method available to select the most qualified contractors to execute gas project, but also a great attention is paid to picking needed criteria for selecting contractors. Consequently, executing such project is undertaken by most qualified contractors resulted in optimum use of limited resource, accelerating the implementation of project, increasing quality and finally boosting organizational efficiency.

Keywords: multi-criteria decision making, project, management, contractor selection, gas company

Procedia PDF Downloads 405