Search results for: random process

Authors: Behnam Zahednejad, Saeed Kosari

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Widespread deployment of Internet of Things (IoT) has raised security and privacy issues in this environment. Designing a secure two-factor authentication scheme between the user and server is still a challenging task. In this paper, we focus on Cancelable Biometric (CB) as an authentication factor in IoT. We show that previous CB-based scheme fail to provide real two-factor security, Perfect Forward Secrecy (PFS) and suffer database attacks and traceability of the user. Then we propose our improved scheme based on CB and Physically Unclonable Functions (PUF), which can provide real two-factor security, PFS, user’s unlinkability, and resistance to database attack. In addition, Key Compromise Impersonation (KCI) resilience is achieved in our scheme. We also prove the security of our proposed scheme formally using both Real-Or-Random (RoR) model and the ProVerif analysis tool. For the usability of our scheme, we conducted a performance analysis and showed that our scheme has the least communication cost compared to the previous CB-based scheme. The computational cost of our scheme is also acceptable for the IoT environment.

Keywords: IoT, two-factor security, cancelable biometric, key compromise impersonation resilience, perfect forward secrecy, database attack, real-or-random model, ProVerif

Procedia PDF Downloads 102

Authors: Mohammad Ruhul Amin, Nusrat Jahan

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Natural gas is a cleaner fuel compared to the others. But it needs some treatment before it is in a state to be used. So natural gas purification is an integral part of any process where natural gas is used as raw material or fuel. There are several impurities in natural gas that have to be removed before use. CO2 is one of the major contaminants. In this project we have removed CO2 by amine process by using MEA solution. We have built up the whole amine process for removing CO2 in Aspen Hysys and simulated the process. At the end of simulation we have got very satisfactory results by using MEA solution for the removal of CO2. Simulation result shows that amine absorption process enables to reduce CO2 content from NG by 58%. HYSYS optimizer allowed us to get a perfect optimized plant. After optimization the profit of existing plant is increased by 2.34 %.Simulation and optimization by Aspen-HYSYS simulator makes available us to enormous information which will help us to further research in future.

Keywords: Aspen–Hysys, CO2 removal, flowsheet development, MEA solution, natural gas optimization

Procedia PDF Downloads 498

Authors: H. Al-Jabli

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Using the freeze-melting process for the disposing of high saline brines was the aim of the paper by confirming the performance estimation of the treatment system. A laboratory bench scale freezing technique test unit was designed, constructed, and tested at Doha Research Plant (DRP) in Kuwait. The principal unit operations that have been considered for the laboratory study are: ice crystallization, separation, washing, and melting. The applied process is characterized as “the secondary-refrigerant indirect freezing”, which is utilizing normal freezing concept. The high saline brine was used as definite feed water, i.e. average TDS of 250,000 ppm. Kuwait desalination plants were carried out in the experimental study to measure the performance of the proposed treatment system. Experimental analysis shows that the freeze-melting process is capable of dropping the TDS of the feed water from 249,482 ppm to 56,880 ppm of the freeze-melting process in the two-phase’s course, whereas overall recovery results of the salt passage and salt rejection are 31.11%, 19.05%, and 80.95%, correspondingly. Therefore, the freeze-melting process is encouraging for the proposed application, as it shows on the results, which approves the process capability of reducing a major amount of the dissolved salts of the high saline brine with reasonable sensible recovery. This process might be reasonable with other brine disposal processes.

Keywords: high saline brine, freeze-melting process, ice crystallization, brine disposal process

Procedia PDF Downloads 271

Authors: Hamzeh Soltanali, Abbas Rohani, A. H. S. Garmabaki, Mohammad Hossein Abbaspour-Fard, Adithya Thaduri

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Toward continuous innovation and high complexity of technological systems, the automotive manufacturing industry is also under pressure to implement adequate management strategies regarding availability and productivity. In this context, evaluation of system’s performance by considering reliability, availability and maintainability (RAM) methodologies can constitute for resilient operation, identifying the bottlenecks of manufacturing process and optimization of maintenance actions. In this paper, RAM parameters are evaluated for improving the operational performance of the fluid filling process. To evaluate the RAM factors through the behavior of states defined for such process, a systematic decision framework was developed. The results of RAM analysis revealed that that the improving reliability and maintainability of main bottlenecks for each filling workstation need to be considered as a priority. The results could be useful to improve operational performance and sustainability of production process.

Keywords: automotive, performance, reliability, RAM, fluid filling process

Procedia PDF Downloads 354

Authors: Ashok Kalagura

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This paper presents a data-mining model for fault-zone identification of flexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines.

Keywords: distance relaying, fault-zone identification, random forests, RFs, support vector machine, SVM, thyristor-controlled series compensator, TCSC, unified power-flow controller, UPFC

Procedia PDF Downloads 424

Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

Procedia PDF Downloads 420

Authors: Jeremie Coullon, Robert J. Webber

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We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems.

Keywords: Bayesian inverse problems, Markov chain Monte Carlo, infinite-dimensional inverse problems, dimensionality reduction

Procedia PDF Downloads 154

Authors: Safeer Hussain Khan, H. Fukhar-ud-Din

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The concept of quasi-contractive type operators was given by Berinde and extended by Imoru and Olatinwo. They named this new type as contractive-like operators. On the other hand, Xu and Noo introduced a three-step-one-mappings iterative process which can be seen as a generalization of Mann and Ishikawa iterative processes. Approximating common fixed points has its own importance as it has a direct link with minimization problem. Motivated by this, in this paper, we first extend the iterative process of Xu and Noor to the case of three-step-three-mappings and then prove a strong convergence result using contractive-like operators for this iterative process. In general, this generalizes corresponding results using Mann, Ishikawa and Xu-Noor iterative processes with quasi-contractive type operators. It is to be pointed out that our results can also be proved with iterative process involving error terms.

Keywords: contractive-like operator, iterative process, common fixed point, strong convergence

Procedia PDF Downloads 594

Authors: Ravneet Kaur

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The concept of Agile Requirements Engineering and Management is not new. However, the struggle to figure out how traditional Requirements Management Process fits within an Agile framework remains complex. This paper talks about a process that can merge the organization’s traditional Requirements Management Process nicely into the Agile Software Development Process. This process provides Traceability of the Product Backlog to the external documents on one hand and User Stories on the other hand. It also gives sufficient evidence that the system will deliver the right functionality with good quality in the form of various statistics and reports. In the nutshell, by overlaying a process on top of Agile, without disturbing the Agility, we are able to get synergic benefits in terms of productivity, profitability, its reporting, and end to end visibility to all Stakeholders. The framework can be used for just-in-time requirements definition or to build a repository of requirements for future use. The goal is to make sure that the business (specifically, the product owner) can clearly articulate what needs to be built and define what is of high quality. To accomplish this, the requirements cycle follows a Scrum-like process that mirrors the development cycle but stays two to three steps ahead. The goal is to create a process by which requirements can be thoroughly vetted, organized, and communicated in a manner that is iterative, timely, and quality-focused. Agile is quickly becoming the most popular way of developing software because it fosters continuous improvement, time-boxed development cycles, and more quickly delivering value to the end users. That value will be driven to a large extent by the quality and clarity of requirements that feed the software development process. An agile, lean, and timely approach to requirements as the starting point will help to ensure that the process is optimized.

Keywords: requirements management, Agile

Procedia PDF Downloads 370

Authors: Laura Bishop, Isabel Jones, Linn Halvorsen, Angela Smith

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Phishing emails are a form of social engineering where attackers deceive email users into revealing sensitive information or installing malware such as ransomware. Scammers often use persuasion techniques to influence email users to interact with malicious content. This study will use eye-tracking equipment to analyze how participants respond to and process Cialdini’s persuasion principles when utilized within phishing emails. Eye tracking provides insights into what is happening on the subconscious level of the brain that the participant may not be aware of. An experiment is conducted to track participant eye movements, whilst interacting with and then filing a series of persuasive emails delivered at random. Eye tracking metrics will be analyzed in relation to whether a malicious email has been identified as phishing (filed as ‘suspicious’) or not phishing (filed in any other folder). This will help determine the most influential persuasion techniques and those 'areas of interest' within an email that require intervention. The results will aid further research on how to reduce the effects of persuasion on human decision-making when interacting with phishing emails.

Keywords: cybersecurity, human-centric, phishing, psychology

Procedia PDF Downloads 84

Authors: M. Reni Sagayaraj, S. Anand Gnana Selvam, R. Reynald Susainathan

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We analyze stochastic integrals associated with a mutation process. To be specific, we describe the cell population process and derive the differential equations for the joint generating functions for the number of mutants and their integrals in generating functions and their applications. We obtain first-order moments of the processes of the two-way mutation process in first-order moment structure of X (t) and Y (t) and the second-order moments of a one-way mutation process. In this paper, we obtain the limiting behaviour of the integrals in limiting distributions of X (t) and Y (t).

Keywords: stochastic integrals, single–server queue model, catastrophes, busy period

Procedia PDF Downloads 643

Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

Procedia PDF Downloads 149

Authors: Yusra Abdulsalam Alqamati, Ahmed Alkilany

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The teaching staff is a distinct group whose impact is on the educational process and which plays an important role in enhancing the quality of the academic education process. To improve the management effectiveness of the academy, the Teaching Staff Management System (TSMS) proposes that all teacher processes be digitized. Since the BPMN approach can accurately describe the processes, it lacks a clear picture of the process flow map, something that the process mining approach has, which is extracting information from event logs for discovery, monitoring, and model enhancement. Therefore, these two methodologies were combined to create the most accurate representation of system operations, the ability to extract data records and mining processes, recreate them in the form of a Petri net, and then generate them in a BPMN model for a more in-depth view of process flow. Additionally, the TSMS processes will be orchestrated to handle all requests in a guaranteed small-time manner thanks to the integration of the Google Cloud Platform (GCP), the BPM engine, and allowing business owners to take part throughout the entire TSMS project development lifecycle.

Keywords: process mining, BPM, business process model and notation, Petri net, teaching staff, Google Cloud Platform

Procedia PDF Downloads 142

Authors: Natalina, Hwai Chyuan Onga, W. T. Chonga

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Oxidation stability of biodiesel is very important in fuel handling especially for remote location of biodiesel application. Variety of feedstocks and biodiesel production process resulted many variation of biodiesel oxidation stability. The current study relates to investigation of the impact of fatty acid composition that caused by natural and production process of calophyllum inophyllum palm oil methyl ester that correlated with improvement of biodiesel oxidation stability. Firstly, biodiesel was produced from crude oil of palm oil, calophyllum inophyllum and mixing of calophyllum inophyllum and palm oil. The production process of calophyllum inophyllum palm oil methyl ester (CIPOME) was divided by including washing process and without washing. Secondly, the oxidation stability was measured from the palm oil methyl ester (POME), calophyllum inophyllum methyl ester (CIME), CIPOME with washing process and CIPOME without washing process. Then, in order to find the differences of fatty acid compositions all of the biodiesels were measured by gas chromatography analysis. It was found that mixing calophyllum inophyllum into palm oil increased the oxidation stability. Washing process influenced the CIPOME fatty acid composition, and reduction of washing process during the production process gave significant oxidation stability number of CIPOME (38 h to 114 h).

Keywords: biodiesel, oxidation stability, calophyllum inophyllum, water content

Procedia PDF Downloads 270

Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

Procedia PDF Downloads 413

Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

Procedia PDF Downloads 237

Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

Procedia PDF Downloads 32

Authors: B. Ravi Kumar, S. Gajanana, K. Hemachandra Reddy, K. Udayani

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Physically into various derived shapes and sizes under the effect of externally applied forces. The spinning process is an advanced plastic working technology and is frequently used for manufacturing axisymmetric shapes. Over the last few decades, Sheet metal spinning has developed significantly and spun products have widely used in various industries. Nowadays the process has been expanded to new horizons in industries, since tendency to use minimum tool and equipment costs and also using lower forces with the output of excellent surface quality and good mechanical properties. The automation of the process is of greater importance, due to its wider applications like decorative household goods, rocket nose cones, gas cylinders, etc. This paper aims to gain insight into the conventional spinning process by employing experimental and numerical methods. The present work proposes an approach for optimizing process parameters are mandrel speed (rpm), roller nose radius (mm), thickness of the sheet (mm). Forming force, surface roughness and strain are the responses.in spinning of Aluminum (2024-T3) using DOE-Response Surface Methodology (RSM) and Analysis of variance (ANOVA). The FEA software is used for modeling and analysis. The process parameters considered in the experimentation.

Keywords: FEA, RSM, process parameters, sheet metal spinning

Procedia PDF Downloads 319

Authors: Iman Dwi Almunandar, Nellawaty A. Tewu, Anshari Al Ghaniyy

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The emergence of academic procrastination behavior among students in Indonesian, especially the students of Faculty of Psychology at YARSI University becomes a habit to be underestimated, so often interfere with the effectiveness of learning process. The lecturers at the Faculty of Psychology YARSI University have very often warned students to be able to do and collect assignments accordance to predetermined deadline. However, they are still violated it. According to researchers, this problem needs to do a proper training for the solution to minimize academic procrastination behavior on students. In this study, researchers conducted analyze needs for deciding whether need the training or not. Number of sample is 30 respondents which being choose with a simple random sampling. Measurement of academic procrastination behavior is using the theory by McCloskey (2011), there are six dimensions: Psychological Belief about Abilities, Distractions, Social Factor of Procrastination, Time Management, Personal Initiative, Laziness. Methods of analyze needs are using Questioner, Interview, Observations, Focus Group Discussion (FGD), Intelligence Tests. The result of analyze needs shows that psychology students generation of 2015 at the Faculty of Psychology YARSI University need for training on Time Management.

Keywords: procrastination, psychology, analyze needs, behavior

Procedia PDF Downloads 382

Authors: Mandira Mishra, Mark Allen

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This meta-analysis explored whether rejection sensitivity relates to facets of romantic relationships. A comprehensive literature search identified 60 studies (147 effect sizes; 16,955 participants) that met inclusion criteria. Data were analysed using inverse-variance weighted random effects meta-analysis. Mean effect sizes from 21 meta-analyses provided evidence that more rejection sensitive individuals report lower levels of relationship satisfaction and relationship closeness, lower levels of perceived partner satisfaction, a greater likelihood of intimate partner violence (perpetration and victimization), higher levels of relationship concerns and relationship conflict, and higher levels of jealousy and self-silencing behaviours. There was also some evidence that rejection sensitive individuals are more likely to engage in risky sexual behaviour and are more prone to sexual compulsivity. There was no evidence of publication bias and various levels of heterogeneity in computed averages. Random effects meta-regression identified participant age and sex as important moderators of pooled mean effects. These findings provide a foundation for the theoretical development of rejection sensitivity in romantic relationships and should be of interest to relationship and marriage counsellors and other relationship professionals.

Keywords: intimate partner violence, relationship satisfaction, commitment, sexual orientation, risky sexual behaviour

Procedia PDF Downloads 82

Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

Procedia PDF Downloads 129

Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

Procedia PDF Downloads 95

Authors: Suppara Charoenpoom

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The objectives of this research was to design and develop a quality management of the school buildings and environment. A quantitative and qualitative mixed research methodology was used. The population sample included 14 directors of primary schools. Two research tools were used. The first research tool included an in-depth interview and questionnaire. The second research tool included the Quality Business Process and Quality Work Procedure, and a Key Performance Indicator of each activity. The statistics included mean and standard deviation. The findings for the development of a quality management process of buildings and environment administration of the basic schools consisted of one quality business process (QBP) and seven quality work processes (QWP). The result from the experts’ evaluation revealed that the process and implementation of quality management of the school buildings and environment has passed the inspection process with consensus. This implies that the process of quality management of the school buildings and environment is suitable for implementation. Moreover, the level of agreement in the feasibility of the implementation of this plan had the mean in the range of 0.64-1.00 which suggests the design of the new plan is acceptable.

Keywords: process, building, environment, management

Procedia PDF Downloads 241

Authors: Alisha Sinha, Laxmi Kant Sharma

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Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters.

Keywords: wildfire susceptibility mapping, LST, random forest, GEE, MODIS, climatic parameters

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Authors: A. Javorova, M. Matusova, K. Velisek

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The article summarizes the experience of laboratory technical subjects teaching methodologies using a number of software products. The main aim is to modernize the teaching process in accordance with the requirements of today - based on information technology. Increasing of the study attractiveness and effectiveness is due to the introduction of CA technologies in the learning process. This paper discussed the areas where individual CA system used. Environment using CA systems are briefly presented in each chapter.

Keywords: education, CA systems, simulation, technology

Procedia PDF Downloads 396

Authors: Gerald Todd

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Recent advances in random epistemologies and ubiquitous theory have paved the way for web services. Given the current status of linear-time communication, cyberinformaticians compellingly desire the exploration of link-level acknowledgements. In order to realize this purpose, we concentrate our efforts on disconfirming that DHTs and model checking are mostly incompatible.

Keywords: LAN, cyberinformatics, model checking, communication

Procedia PDF Downloads 403

Authors: Yu-Chieh Huang, Pei-Shih Chen, Tao-Hsin Tung

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Background: Urinary tract infection is the most common bacterial infection to our best knowledge. Objective: This study is to investigate whether cranberry ingesting could improve the urinary tract infection. Methods: We searched the PubMed and Cochrane Library for relevant randomized controlled trials without language limitations between 9 March 1994 and June 30, 2017, with a priori defined inclusion and exclusion criteria. The search terms included (cranberry OR Vaccinium macrocarpon OR Vaccinium oxy-coccus OR Vaccinium microcarpum OR Vaccinium erythrocarpum OR Vaccinium) AND (urinary tract infection OR bacteriuria OR pyuria) AND (effect OR effective-ness OR efficacy) AND (random OR randomized). Results: There were 26 studies met the selection criteria included among 4709 eligible participants. We analyzed all trials in meta-analysis. The random-effects pooled risk ratio (RR) for the group using cranberry versus using placebo was 0.75; 95%CI[0.63, 0.880]; p-value=0.0002) and heterogeneity was 56%. Furthermore, we divided the subjects into different subgroup to analysis. Ingesting cranberry seemed to be more effective in some subgroups, including the patients with recurrent UTI (RR, 0.71; 95%CI[0.54,0.93]; p-value=0.002) (I²= 65%) and female population (RR, 0.73, 95%CI[0.58,0.92]; p-value=0.002) (I²= 59%). The prevention effect was not different between cranberry and trimethoprim (RR, 1.25, 95%CI[0.67, 2.33]; p-value=0.49) (I²= 68%). No matter the forms of cranberry were capsules or juice, the efficacy was useful. Conclusions: It is showed that cranberry ingesting is usefully associated with prevention UTI. There are more effective in prevention of UTI in some groups.

Keywords: cranberry, effectiveness, prevention, urinary tract infect

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Authors: Peiyuan Guan, Tao Wan, Dewei Chu

Abstract:

With the flash memory approaching its physical limit, the emerging resistive random-access memory (RRAM) has been considered as one of the most promising candidates for the next-generation non-volatile memory. One selector-one resistor configuration has shown the most promising way to resolve the crosstalk issue without affecting the scalability and high-density integration of the RRAM array. By comparison with other candidates of selectors (such as diodes and nonlinear devices), threshold switching selectors dominated by formation/spontaneous rupture of fragile conductive filaments have been proved to possess low voltages, high selectivity, and ultra-low current leakage. However, the flexibility and transparency of selectors are barely mentioned. Therefore, it is a matter of urgency to develop a selector with highly flexible and transparent properties to assist the application of RRAM for a diversity of memory devices. In this work, threshold switching selectors were designed using a facilely solution-processed fabrication on AgNWs@PDMS composite films, which show high flexibility, transparency and scratch resistance. As-fabricated threshold switching selectors also have revealed relatively high selectivity (~107), low operating voltages (Vth < 1 V) and good switching performance.

Keywords: flexible and transparent, resistive random-access memory, silver nanowires, threshold switching selector

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Authors: Naritphol Boonyakiat

Abstract:

This research attempts to investigate the effects of corporate social responsibility and knowledge management factors on students’ learning process of the Silpakorn University. The goal of this study is to fill the literature gap by gaining an understanding of corporate social responsibility and the knowledge management factors that fundamentally relate to students’ learning process within the university context. Thus, this study will focus on the outcomes that derive from a set of quantitative data that were obtained using Silpakorn university’s database of 200 students. The results represent the perceptions of students regarding the impact of corporate social responsibility and knowledge management factors on their learning process within the university. The findings indicate that corporate social responsibility and knowledge management have significant effects on students’ learning process. This study may assist us in gaining a better understanding of the integrated aspects of university and learning environments to discover how to allocate optimally university’s resources and management approaches to gain benefits from corporate social responsibility and knowledge management practices toward students’ learning process within the university bodies. Therefore, there is a sufficient reason to believe that the findings can contribute to research in the area of CSR, KM and students’ learning process as an essential aspect of university’s stakeholder.

Keywords: corporate social responsibility, knowledge management, learning process, university’s students

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Authors: Sylwia Wiewiorowska, Zbigniew Muskalski

Abstract:

The article presents part of research on the development of nickel plated welding wire production technology, whose application will enable the elimination of the flaws of currently manufactured welding wires. The nickel plated welding wire will be distinguished by high quality, because the Ni layer which is deposited electrochemically onto it from acid baths is characterized by very good adhesion to the steel wire surface, while the ductile nickel well deforms plastically in the drawing process and the adhesion of the Ni layer increases in the drawing process due to the occurring process of diffusion between the Ni and the steel. The Ni layer obtained in the proposed technology, despite a smaller thickness than when the wire is coated with copper, is continuous and tight, thus ensuring high corrosion resistance, as well as unsusceptible to scaling, which should provide a product that meets requirements imposed by the market. The product will also reduce, to some extent, the amount of copper brought in to steel through recycling, while the wire coating nickel introduced to the weld in the welding process is expected, to a degree, to favorably influence its mechanical properties. The paper describes the tests of the process of nickel plating of f1.96 mm-diameter wires using various nickel plating baths with different process parameters.

Keywords: steel wire, properties, welding process, Ni layer

Procedia PDF Downloads 147