Search results for: roulette wheel selection

Authors: Katarina Rogulj, Ivana Racetin, Jelena Kilic

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In this study, the fuzzy logic approach (FLA) was developed for construction project management (CPM) under uncertainty and duality. The focus was on decision making in selecting the type of the glass facade for a residential-commercial building in the main design. The adoption of fuzzy sets was capable of reflecting construction managers’ reliability level over subjective judgments, and thus the robustness of the system can be achieved. An α-cuts method was utilized for discretizing the fuzzy sets in FLA. This method can communicate all uncertain information in the optimization process, taking into account the values of this information. Furthermore, FLA provides in-depth analyses of diverse policy scenarios that are related to various levels of economic aspects when it comes to the construction projects' valid decision making. The developed approach is applied to CPM to demonstrate its applicability. Analyzing the materials of glass facades, variants were defined. The development of the FLA for the CPM included relevant construction projec'ts stakeholders that were involved in the criteria definition to evaluate each variant. Using fuzzy Decision-Making Trial and Evaluation Laboratory Method (DEMATEL) comparison of the glass facade was conducted. This way, a rank, according to the priorities for inclusion into the main design, of variants is obtained. The concept was tested on a residential-commercial building in the city of Rijeka, Croatia. The newly developed methodology was then compared with the existing one. The aim of the research was to define an approach that will improve current judgments and decisions when it comes to the material selection of buildings facade as one of the most important architectural and engineering tasks in the main design. The advantage of the new methodology compared to the old one is that it includes the subjective side of the managers’ decisions, as an inevitable factor in each decision making. The proposed approach can help construction projects managers to identify the desired type of glass facade according to their preference and practical conditions, as well as facilitate in-depth analyses of tradeoffs between economic efficiency and architectural design.

Keywords: construction projects management, DEMATEL, fuzzy logic approach, glass façade selection

Procedia PDF Downloads 137

Authors: Josua K. Junias, Fillemon N. Nangolo, Petrina T. Johaness

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The deterioration of pavement can lead to the formation of potholes, which cause the wheels of a vehicle to experience unusual and uneven movement. In addition, improper loading practices of heavy vehicles can result in dynamic loading of the pavement due to the vehicle's response to the irregular movement caused by the potholes. Previous studies have only focused on the effects of either the road's uneven surface or the asymmetrical loading of the vehicle, but not both. This study aimed to model the pavement's dynamic response to heavy vehicles under different loading configurations and wheel movements. A sample of 225 cases with symmetrical and asymmetrical loading and kinematic movements was used, and 27 validated 3D pavement-vehicle interactive models were developed using SIMWISE 4D. The study found that the type of kinematic movement experienced by the heavy vehicle affects the pavement's dynamic loading, with eccentrically loaded, asymmetrically kinematic heavy vehicles having a statistically significant impact. The study also suggests that the mass of the vehicle's suspension system plays a role in the pavement's dynamic loading.

Keywords: eccentricities, pavement dynamic loading, vertical displacement dynamic response, heavy vehicles

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Authors: Nino Abesadze, Marine Mindorashvili, Nino Paresashvili

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At present actual tourism indicators cannot be calculated in Georgia, making it impossible to perform their quantitative analysis. Therefore, the study conducted by us is highly important from a theoretical as well as practical standpoint. The main purpose of the article is to make complex statistical analysis of tourist expenses of foreign visitors and to calculate statistical attractiveness indices of the tourism potential of Georgia. During the research, the method involving random and proportional selection has been applied. Computer software SPSS was used to compute statistical data for corresponding analysis. Corresponding methodology of tourism statistics was implemented according to international standards. Important information was collected and grouped from major Georgian airports, and a representative population of foreign visitors and a rule of selection of respondents were determined. The results show a trend of growth in tourist numbers and the share of tourists from post-soviet countries are constantly increasing. The level of satisfaction with tourist facilities and quality of service has improved, but still we have a problem of disparity between the service quality and the prices. The design of tourist expenses of foreign visitors is diverse; competitiveness of tourist products of Georgian tourist companies is higher. Attractiveness of popular cities of Georgia has increased by 43%.

Keywords: tourist, expenses, indexes, statistics, analysis

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Authors: Hasan Keshavarzian, Tayebeh Nesari

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Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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Authors: A. Khan, H. Mahmood

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In Underwater Wireless Sensor Networks (UWSNs), sensor nodes close to water surface (final destination) are often preferred for selection as forwarders. However, their frequent selection makes them depleted of their limited battery power. In consequence, these nodes die during early stage of network operation and create energy holes where forwarders are not available for packets forwarding. These holes severely affect network throughput. As a result, system performance significantly degrades. In this paper, a routing protocol is proposed to avoid energy holes during packets forwarding. The proposed protocol does not require the conventional position information (localization) of holes to avoid them. Localization is cumbersome; energy is inefficient and difficult to achieve in underwater environment where sensor nodes change their positions with water currents. Forwarders with the lowest water pressure level and the maximum number of neighbors are preferred to forward packets. These two parameters together minimize packet drop by following the paths where maximum forwarders are available. To avoid interference along the paths with the maximum forwarders, a packet holding time is defined for each forwarder. Simulation results reveal superior performance of the proposed scheme than the counterpart technique.

Keywords: energy holes, interference, routing, underwater

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Authors: Maria C. Proença, Marco Aniceto, Pedro N. Santos, José C. Freitas

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A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.

Keywords: automatic detection, defects, fracture lines, wavelets

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Indira priyadarshini, B. Tulja Lal, K. Anusha, P. Sai Varun

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Formula One cars are the fastest road course racing cars in the world, owing to very high cornering speeds achieved through the generation of large amounts of aerodynamic downforce. The main intentions and goals of this paper are to reduce the accidents and improving the safety without affecting the visibility of the driver by redesigning Halo that was developed by Mercedes in conjunction with the FIA to deflect flying debris, such as a loose wheel, away from a driver’s head while the hinged locking mechanism can quickly be removed for easy access. Halo design has been modified in order to reduce the weight without affecting the aerodynamics of the car. CFD simulation is carried out to observe the flow over the Halo. The velocity profile and pressure contours were analyzed. Halo is designed using SOLIDWORKS Furthermore, using the software ANSYS FLUENT 3D simulation of the airflow contour around the Halo in order to make changes in the geometry to improve the design by reducing air resistance and improving aerodynamics. According to our assumption, new 3D Halo model has better aerodynamic properties in order to analyse possible improvements compared to the initial design. Structural analysis is also done by using ANSYS by making an F1 tire colliding with Halo at 225 kmph in order to know the deflections in the structure.

Keywords: aerodynamics, Halo, safety, visibility

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Shahrokh Barati, Reza Ramezani

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Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: N. Mansouri, A. Asadi

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Data Grid is a geographically distributed environment that deals with data intensive application in scientific and enterprise computing. Data replication is a common method used to achieve efficient and fault-tolerant data access in Grids. In this paper, a dynamic data replication strategy, called Enhanced Latest Access Largest Weight (ELALW) is proposed. This strategy is an enhanced version of Latest Access Largest Weight strategy. However, replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement task. ELALW replaces replicas based on the number of requests in future, the size of the replica, and the number of copies of the file. It also improves access latency by selecting the best replica when various sites hold replicas. The proposed replica selection selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. Simulation results utilizing the OptorSim show our replication strategy achieve better performance overall than other strategies in terms of job execution time, effective network usage and storage resource usage.

Keywords: data grid, data replication, simulation, replica selection, replica placement

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Authors: Adijat Olubukola Olateju

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Women empowerment is an essential developmental tool in every economy especially in less developed countries; as it helps to enhance women's socio-economic well-being. Some empirical evidence has shown that microcredit has been an effective tool in enhancing women empowerment, especially in developing countries. This paper therefore, investigates the impact of microcredit programme on women empowerment in Lagos State, Nigeria. The study used Cowries Microfinance Bank (CMB) as a case study bank, and a total of 359 women entrepreneurs were selected by simple random sampling technique from the list of Cowries Microfinance Bank. Selection bias which could arise from non-random selection of participants or non-random placement of programme, was adjusted for by dividing the data into participant women entrepreneurs and non-participant women entrepreneurs. The data were analyzed with a Propensity Score Matching (PSM) technique. The result of the Average Treatment Effect on the Treated (ATT) obtained from the PSM indicates that the credit programme has a significant effect on the empowerment of women in the study area. It is therefore, recommended that microfinance banks should be encouraged to give loan to women and for more impact of the loan to be felt by the beneficiaries the loan programme should be complemented with other programmes such as training, grant, and periodic monitoring of programme should be encouraged.

Keywords: empowerment, microcredit, socio-economic wellbeing, development

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Authors: M. Taghavi Zavareh, H. Saremi

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Railway transportation is one of the types of transportation systems which, due to the advantages such as the ability to transport a large number of passengers, environmental protection, low energy consumption, and contribution to tourism, has importance. The existence of suitable and accessible stations is one of the requirements that leads to better performance and plays a significant role in the economic, social, political, and cultural development of urban areas. This paper aims to propose a framework for locating railway passenger stations. This research used descriptive-analytical methods and library tools to answer which definitions and theoretical approaches are suitable for the location of railway passenger stations. The results showed that theoretical approaches such as Transit-Oriented Development and Urban Regeneration are of the utmost importance theoretical bases in the field of research. Moreover, we studied three stations in Iran to find out about real trends and criteria in this research. This study also proposed four major criteria including accessibility, development, rail related and economics, and environmental harmony. Ultimately with an emphasis on the proposed criteria, the study concludes that the combination of Transit-Oriented Development and Urban Regeneration is the most suitable framework to locate railway passenger stations.

Keywords: railway passenger station, railway station, site selection, transit-oriented development, urban regeneration

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Authors: Iman Farasat, Howard M. Salis

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The CRISPR/Cas9 system has revolutionized genome engineering by enabling site-directed and high-throughput genome editing, genome insertion, and gene knockdowns in several species, including bacteria, yeast, flies, worms, and human cell lines. This technology has the potential to enable human gene therapy to treat genetic diseases and cancer at the molecular level; however, the current CRISPR/Cas9 system suffers from seemingly sporadic off-target genome mutagenesis that prevents its use in gene therapy. A comprehensive mechanistic model that explains how the CRISPR/Cas9 functions would enable the rational design of the guide-RNAs responsible for target site selection while minimizing unexpected genome mutagenesis. Here, we present the first quantitative model of the CRISPR/Cas9 genome mutagenesis system that predicts how guide-RNA sequences (crRNAs) control target site selection and cleavage activity. We used statistical thermodynamics and law of mass action to develop a five-step biophysical model of cas9 cleavage, and examined it in vivo and in vitro. To predict a crRNA's binding specificities and cleavage rates, we then compiled a nearest neighbor (NN) energy model that accounts for all possible base pairings and mismatches between the crRNA and the possible genomic DNA sites. These calculations correctly predicted crRNA specificity across 5518 sites. Our analysis reveals that cas9 activity and specificity are anti-correlated, and, the trade-off between them is the determining factor in performing an RNA-mediated cleavage with minimal off-targets. To find an optimal solution, we first created a scheme of safe-design criteria for Cas9 target selection by systematic analysis of available high throughput measurements. We then used our biophysical model to determine the optimal Cas9 expression levels and timing that maximizes on-target cleavage and minimizes off-target activity. We successfully applied this approach in bacterial and mammalian cell lines to reduce off-target activity to near background mutagenesis level while maintaining high on-target cleavage rate.

Keywords: biophysical model, CRISPR, Cas9, genome editing

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Authors: M. Golzade Gangraj, A. Abdi, N. ganji

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Aim: The aim of this study was to evaluate the Effect of aerobic training with Coriandrum sativum extract on selection of oxidative stress markers in diabetic rats. Methods: The population of male Wistar rats is the Pasteur Institute. Forty rats were randomly selected as subjects. After moving the mouse in vitro and stay for a week in a cage for sustainability, were diabetic. Diabetes induced by injection STZ (55 mg per kg of body weight of mice) was performed. According blood glucose was randomly divided into four experimental groups (control, training, extract and training-extract). Extract group consumed 150 mg per kg of body weight per day coriander juice. Training group performed aerobic training (50-55% VO2max). Result: The results showed that aerobic exercise training and coriander seed extract caused a significant increase in total antioxidant; superoxide dismutase and catalase were significantly decreased malondialdehyde. Conclusion: the research findings can be stated that the exercise with coriander seed extract has the ability to inhibit free radicals and can have beneficial effects on the body's antioxidant defense system and reduce oxidative stress in diabetic rats with STZ. Because it improves the body's antioxidant defense by increasing serum levels of antioxidant enzymes.

Keywords: aerobic training, coriandrum sativum, antioxidant, diabetes

Procedia PDF Downloads 513

Authors: Donatien Callon, Ian Lalonde, Mathieu Nadeau, Alexandre Girard

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Patient transfer is a challenging, critical task because it exposes caregivers to injury risks. Available transfer devices, like floor lifts, lead to improvements but are far from perfect. They do not eliminate the caregivers’ risk of musculoskeletal disorders, and they can be burdensome to use due to their poor maneuverability. This paper presents a new motorized floor lift with a single central motorized wheel connected to an instrumented handle. Admittance controllers are designed to 1) improve the device maneuverability, 2) reduce the required caregiver effort, and 3) ensure the security and comfort of patients. Two controller designs, one with a linear admittance law and a non-linear admittance law with variable damping, were developed and implemented on a prototype. Tests were performed on seven participants to evaluate the performance of the assistance system and the controllers. The experimental results show that 1) the motorized assistance with the variable damping controller improves maneuverability by 28%, 2) reduces the amount of effort required to push the lift by 66%, and 3) provides the same level of patient comfort compared to a standard unassisted floor lift.

Keywords: floor lift, human robot interaction, admittance controller, variable admittance

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Authors: Sowntharya L.

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Aerodynamics is the most important factor when it comes to resistive forces such as lift, drag and side forces acting on the vehicle. In passenger vehicles, reducing the drag will not only unlock the door for higher achievable speed but will also reduce the fuel consumption of the vehicle. Generally, tyre contributes significantly to the overall aerodynamics of the vehicle. Hence, understanding the air-flow behaviour around the tyre is vital to optimize the aerodynamic performance in the early stage of design process. Nowadays, aerodynamic simulation employing Computational Fluid Dynamics (CFD) is gaining more importance as it reduces the number of physical wind-tunnel experiments during vehicle development process. This research develops a methodology to predict aerodynamic drag of a standalone tyre using Numerical CFD Solver and to validate the same using a wind tunnel experiment. A parametric study was carried out on different tread pattern tyres such as slick, circumferential groove & patterned tyre in stationary and rotating boundary conditions. In order to represent wheel rotation contact with the ground, moving reference frame (MRF) approach was used in this study. Aerodynamic parameters such as drag lift & air flow behaviour around the tire were simulated and compared with experimental results.

Keywords: aerodynamics, CFD, drag, MRF, wind-tunnel

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Hoora Dehghani, Meisam Shahbazi, Reza Zare

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During the previous decade, a significant evolution has occurred in the number of private educational centers and, accordingly, the increase in the number of providers and students of these centers around the world. The number of language teaching institutes in Iran that are considered private educational sectors is also growing exponentially as the request for learning foreign languages has extremely increased in recent years. This fact caused competition among the institutions in improving better services tailored to the students’ demands to win the competition. Along with the growth in the industry of education, higher education institutes should apply the marketing-related concepts and view students as customers because students’ outlooks are similar to consumers with education. Studying the influential factors in the selection of an institute has multiple benefits. Firstly, it acknowledges the institutions of the students’ choice factors. Secondly, the institutions use the obtained information to improve their marketing methods. It also helps institutions know students’ outlooks that can be applied to expand the student know-how. Moreover, it provides practical evidence for educational centers to plan useful amenities and programs, and use efficient policies to cater to the market, and also helps them execute the methods that increase students’ feeling of contentment and assurance. Thus, this study explored the influencing factors in the selection of a language learning institute by language learners and examined and compared the importance among the varying age groups and genders. In the first phase of the study, the researchers selected 15 language learners as representative cases within the specified age ranges and genders purposefully and interviewed them to explore the comprising elements in their language institute selection process and analyzed the results qualitatively. In the second phase, the researchers identified elements as specified items of a questionnaire, and 1000 English learners across varying educational contexts rated them. The TOPSIS method was used to analyze the data quantitatively by representing the level of importance of the items for the participants generally and specifically in each subcategory; genders and age groups. The results indicated that the educational quality, teaching method, duration of training course, establishing need-oriented courses, and easy access were the most important elements. On the other hand, offering training in different languages, the specialized education of only one language, the uniform and appropriate appearance of office staff, having native professors to the language of instruction, applying Computer or online tests instead of the usual paper tests respectively as the least important choice factors in selecting a language institute. Besides, some comparisons among different groups’ ratings of choice factors were made, which revealed the differences among different groups' priorities in choosing a language institute.

Keywords: choice factors, EFL institute selection, english learning, need analysis, TOPSIS

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Authors: Shidrokh Goudarzi, Wan Haslina Hassan

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Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers or vertical handoffs are necessary for seamless mobility. In this paper, we conduct a review of existing vertical handover decision-making mechanisms that aim to provide ubiquitous connectivity to mobile users. To offer a systematic comparison, we categorize these vertical handover measurement and decision structures based on their respective methodology and parameters. Subsequently, we analyze several vertical handover approaches in the literature and compare them according to their advantages and weaknesses. The paper compares the algorithms based on the network selection methods, complexity of the technologies used and efficiency in order to introduce our vertical handover decision framework. We find that vertical handovers on heterogeneous wireless networks suffer from the lack of a standard and efficient method to satisfy both user and network quality of service requirements at different levels including architectural, decision-making and protocols. Also, the consolidation of network terminal, cross-layer information, multi packet casting and intelligent network selection algorithm appears to be an optimum solution for achieving seamless service continuity in order to facilitate seamless connectivity.

Keywords: heterogeneous wireless networks, vertical handovers, vertical handover metric, decision-making algorithms

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Authors: Mohammed Gamil Montasser, Angelo Battaglia

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Tourism is making its presence felt across the Sultanate of Oman. The story is one of the most recognized phenomena as a sustainable solid growth and is considered a remarkable outcome for any destination. The competitive situation and challenges within the tourism industry worldwide entail a better understanding of the destination position and its image to achieve Oman’s aspiration to retain its international reputation as one of the most desirable destinations in the Middle East. To access general perceptions of Oman’s attributes, their importance and their influences among U.S. tourists, an online survey was conducted with 522 American travelers who have traveled internationally, including non-visitors, virtual-visitors and visitors to Oman. This research involved a total of 36 attributes in the survey. Participants were asked to rate their agreement on how each attribute represented Oman and how important each attribute was for selecting destinations on 5- point Likert Scale. They also indicated if each attribute has a positive, neutral or negative influence on their destination selection. Descriptive statistics and importance performance analysis (IPA) were conducted. IPA illustrated U.S. tourists’ perceptions of Oman’s destination attributes and their importance in destination selection on a matrix with four quadrants, divided by actual mean value in each grid for importance (M=3.51) and performance (M=3.57). Oman tourism organizations and destination managers may use these research findings for future marketing and management efforts toward the U.S. travel market.

Keywords: analysis of importance, performance, destination attributes, Oman's position, U.S. tourists

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Authors: Tanveer Hussain, Khan Muhammad, Amin Ullah, Mi Young Lee, Sung Wook Baik

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The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities.

Keywords: big video data analysis, fuzzy logic, multi-view video summarization, saliency detection

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Authors: Hamad Albadr

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With the development that has occurred in the orientation of universities from liability cognitive and maintain the culture of the community to responsibility job formation graduates to work according to the needs of the community development; representing universities in today's world, the prime motivator for the wheel of development in the community and find appropriate solutions to the problems they are facing and adapt to the demands of the changing environment. In this paper review of the reality of engineering education in the Kingdom of Saudi Arabia and its suitability to the requirements of the labor market, where they will be looking at the university as a system administrator educational using System Analysis Approach as one of the methods of modern management to analyze the performance of organizations and institutions, administrative and quality assessment. According to this approach is to deal with the system as a set of subsystems as components of the main divided into : input, process, and outputs, and the surrounding environment, will also be used research descriptive method and analytical , to gather information, data and analysis answers of the study population that consisting of a random sample of the beneficiaries of these services that the universities provided that about 500 professionals about employment in the business sector.

Keywords: universities in Saudi Arabia, engineering education, labor market, administrative, quality assessment

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Authors: S. P. Akinbogun, P. Oloruntoyin

Abstract:

One of the millennium development goals is the reduction of illiteracy. The state of user friendliness of the educational buildings is expected to play a significant role in the quest, particularly among the physically challenged. This study considers the state of access of educational buildings to disabled on wheel chair and crutches. It draws context from one of the federal universities in Nigeria. The study is basically qualitative; data were collected through structured interview and observation to assess compliance with the prescribed accessibility standard of academic buildings in the Federal University of Technology Akure. The study found that narrow entrances and routes of buildings, raised steps at entrances of the buildings, and ramps were absent. This implies exclusion as it renders most of the buildings inaccessible to wheelchair users. Perhaps, it accounts for low enrolment of wheelchair users in the institution despite many of them in the city. The implication is a challenge in the achievement of the millennium development goal concerning the reduction in the level of illiteracy in the country. The study suggests that government should strictly ensure that public buildings should satisfy or retrofitted to meet disabled access before development approval. This should be followed with the issuance of certificate of compliance upon completion.

Keywords: public building, accessibility, physically challenged, education

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Authors: Ambra Giovannelli, Erika Maria Archilei

Abstract:

The amount of the electrical power required by refrigeration systems is relevant worldwide. It is evaluated in the order of 15% of the total electricity production taking refrigeration and air-conditioning into consideration. For this reason, in the last years several energy saving techniques have been proposed to reduce the power demand of such plants. The paper deals with the development of an innovative internal recovery system for cryogenic cooling plants. Such a system consists in a Compressor-Expander Group (CEG) designed on the basis of the automotive turbocharging technology. In particular, the paper is focused on the design of the expander, the critical component of the CEG system. Due to the low volumetric flow entering the expander and the high expansion ratio, a commercial turbocharger expander wheel was strongly modified. It was equipped with a transonic nozzle, designed to have a radially inflow full admission. To verify the performance of such a machine and suggest improvements, two different set of nozzles have been designed and modelled by means of the commercial Ansys-CFX software. steady-state 3D CFD simulations of the second-generation prototype are presented and compared with the initial ones.

Keywords: vapour cCompression systems, energy saving, refrigeration plant, organic fluids, radial turbine

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Authors: Muhammad Ali

Abstract:

Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Gia Sirbiladze

Abstract:

Route planning problems are among the activities that have the highest impact on logistical planning, transportation, and distribution because of their effects on efficiency in resource management, service levels, and client satisfaction. In extreme conditions, the difficulty of vehicle movement between different customers causes the imprecision of time of movement and the uncertainty of the feasibility of movement. A feasibility level of vehicle movement on the closed route of the disaster-streaking zone is defined for the construction of an objective function. Experts’ evaluations of the uncertain parameters in q-rung ortho-pair fuzzy numbers (q-ROFNs) are presented. A fuzzy bi-objective combinatorial optimization problem of fuzzy vehicle routine problem (FVRP) is constructed based on the technique of possibility theory. The FVRP is reduced to the bi-criteria partitioning problem for the so-called “promising” routes which were selected from the all-admissible closed routes. The convenient selection of the “promising” routes allows us to solve the reduced problem in real-time computing. For the numerical solution of the bi-criteria partitioning problem, the -constraint approach is used. The main results' support software is designed. The constructed model is illustrated with a numerical example.

Keywords: q-rung ortho-pair fuzzy sets, facility location selection problem, multi-objective combinatorial optimization problem, partitioning problem

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

Abstract:

The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

Procedia PDF Downloads 240