Search results for: social network potentials

Authors: Omar Alshehri, Vic Lally

Abstract:

This paper examined the attitudes of using social media tools to support learning at a university in Saudi Arabia. Moreover, it investigated the students’ current usage of these tools and examined the barriers they could face during the use of social media tools in the education process. Participants in this study were 42 university students. A web-based survey was used to collect data for this study. The results indicate that all of the students were familiar with social media and had used at least one type of social media for learning. It was found out that all students had very positive attitudes towards the use of social media and welcomed using these tools as a supplementary to the curriculum. However, the results indicated that the major barriers to using these tools in learning were distraction, opposing Islamic religious teachings, privacy issues, and cyberbullying. The study recommended that this study could be replicated at other Saudi universities to investigate factors and barriers that might affect Saudi students’ attitudes toward using social media to support learning.

Keywords: barriers to social media use, benefits of social media use, higher education, Saudi Arabia, social media

Procedia PDF Downloads 148

Authors: D. Thawinkarn, S. Wongbutlee

Abstract:

The Social Change Leadership model is strongly aligned with administration’s mission. This research aims to examine the elements of social change leadership, build and develop leadership for social change, and evaluate effectiveness of leadership development model for social change. The research operation has 3 phases: model studies by in-depth interviews and survey research; drafting and creating model which verified by the experts; and trial of model in schools. The results showed that administrators and teachers have the elements of leadership for social change in moderate level. These elements are ranged descending from consciousness of self, common purpose, congruence, collaboration, commitment, citizenship, and controversy with civility. Model of leadership for social change is included the principles, objectives, content, process. Workshop process: Results show that the model of leadership development for social change in administrators and teachers leads to higher score in leadership evaluation prior to administering the operation.

Keywords: leadership, social change model, organization, administrators

Procedia PDF Downloads 400

Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe

Abstract:

The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.

Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults

Procedia PDF Downloads 522

Authors: Gintaras Mitrulevičius

Abstract:

This text, which is based on the conference report, seeks to briefly examine the relationship between the concepts of social democracy and democratic socialism, drawing attention to the essential aspects of its development and, in particular, discussing the contradictions in the relationship between these concepts in the modern period. In the preparation of this text, such research methods as historical, historical-comparative methods were used, as well as methods of analyzing, synthesizing, and generalizing texts. The history of the use of terms in social democracy and democratic socialism shows that these terms were used alternately and almost synonymously. At the end of the 20th century, traditional social democracy was transformed into the so-called "new social democracy." Many of the new social democrats do not consider themselves democratic socialists and avoid the historically characteristic identification of social democracy with democratic socialism. It has become quite popular to believe that social democracy is a separate ideology from democratic socialism. Or that it has become a variant of the ideology of liberalism. This is a testimony to the crisis of ideological self-awareness of social democracy. Since the beginning of the 21st century, social democracy has also experienced a growing crisis of electoral support. This, among other things, led to her slight shift to the left. In this context, some social democrats are once again talking about democratic socialism. The rise of the ideas of democratic socialism in the United States was catalyzed by Bernie Sanders. But the proponents of democratic socialism in the United States have different concepts of democratic socialism. In modern Europe, democratic socialism is also spoken of by leftists of non-social democratic origin, whose understanding is different from that of democratic socialism inherent in classical social democracy. Some political scientists also single out the concepts in question. Analysis of the problem shows that there are currently several concepts of democratic socialism on the spectrum of the political left, both social-democratic and non-social-democratic.

Keywords: democratic socializm, socializm, social democracy, new social democracy, political ideologies

Procedia PDF Downloads 101

Authors: J. Areerut

Abstract:

Experimental Film Class Project is supported by the Institute for Research and Development at Suan Sunandha Rajabhat University. This project is purported to provide academic and professional services to improve the quality standards of the community and locals in accordance with the mission of the university, which is to improve and expand knowledge for the community and to develop and transfer such knowledge and professions to the next generation. Eventually, it leads to sustainable development because the development of human resources is deemed as the key for sustainable development. Moreover, the Experimental Film Class is an integral part of the teaching of film production at Suan Sunandha International School of Art (SISA). By means of giving opportunities to students for participation in projects by sharing experience, skill and knowledge and participation in field activities, it helps students in the film production major to enhance their abilities and potentials as preparation for their readiness in the marketplace. Additionally, in this class, we provide basic film knowledge, screenwriting techniques, editing and subtitles including uploading videos on social media such as YouTube and Facebook for the participant students.

Keywords: experimental film class, Watbangkapom School, participant students, basic of film production, film workshop

Procedia PDF Downloads 324

Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

Abstract:

In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 96

Authors: Abigail Qian Zhou

Abstract:

In today's China, the well-educated middle class, with stable jobs and above-average income, are the driving force behind its Internet society. Through the analysis of data from the 2015 Chinese General Social Survey and 50 interviewees, this study investigates the current situation of this group’s specific internet usage. The findings of this study demonstrate that daily life among the members of this socioeconomic group is closely tied to the Internet. For Chinese middle class, the Internet is used to socialize and entertain self and others. It is also used to search for and share information as well as to build their identities. The empirical results of this study will provide a reference, supported by factual data, for enterprises seeking to target the Chinese middle class through online marketing efforts.

Keywords: middle class, Internet use, network behaviour, online marketing, China

Procedia PDF Downloads 100

Authors: Taha Benarbia

Abstract:

The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

Procedia PDF Downloads 55

Authors: Muhammad Abubakar Alhassan, Diane Pennington

Abstract:

Social networking sites have become a space for people to discuss public health issues such as non-suicidal self-injury (NSSI). There are thousands of tweets containing self-harm and self-injury hashtags on Twitter. It is difficult to distinguish between different users who participate in self-injury discussions on Twitter and how their opinions change over time. Also, it is challenging to understand the topics surrounding NSSI discussions on Twitter. We retrieved tweets using #selfham and #selfinjury hashtags and investigated those from the United kingdom. We applied inductive coding and grouped tweeters into different categories. This study used the Latent Dirichlet Allocation (LDA) algorithm to infer the optimum number of topics that describes our corpus. Our findings revealed that many of those participating in NSSI discussions are non-professional users as opposed to medical experts and academics. Support organisations, medical teams, and academics were campaigning positively on rais-ing self-injury awareness and recovery. Using LDAvis visualisation technique, we selected the top 20 most relevant terms from each topic and interpreted the topics as; children and youth well-being, self-harm misjudgement, mental health awareness, school and mental health support and, suicide and mental-health issues. More than 50% of these topics were discussed in England compared to Scotland, Wales, Ireland and Northern Ireland. Our findings highlight the advantages of using the Twitter social network in tackling the problem of self-injury through awareness. There is a need to study the potential risks associated with the use of social networks among self-injurers.

Keywords: self-harm, non-suicidal self-injury, Twitter, social networks

Procedia PDF Downloads 113

Authors: Ge Zheng, Jun Peng

Abstract:

Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

Procedia PDF Downloads 152

Authors: Pekka Makkonen, Georgios Lampropoulos, Kerstin Siakas

Abstract:

This paper examines the impact of social media on knowledge work. It discloses and highlights which specific aspects, areas and tasks of knowledge work can be improved by the use of social media. Moreover, the study includes a survey about higher education students’ viewpoints in regard to the use of social media as a means to enhance knowledge work and knowledge sharing. The analysis has been conducted based both on empirical data and on discussions about the sources dealing with knowledge work and how it can be enhanced by using social media. The results show that social media can improve knowledge work, knowledge building and maintenance tasks in which communication, information sharing and collaboration play a vital role. Additionally, by using social media, personal, collaborative and supplementary work activities can be enhanced. Based on the results of the study, we suggest how knowledge work can be enhanced when using the contemporary information and communications technologies (ICTs) of the 21^st century and recommend future directions towards improving knowledge work.

Keywords: knowledge work, social media, social media services, improving work performance

Procedia PDF Downloads 139

Authors: Mohd Adib Abd Muin, Shuhairimi Abdullah, Azizan Bahari

Abstract:

Problem: The research problem is lacking of model on social entrepreneurship that focus on Islamic perspective. Objective: The objective of this paper is to analyse the existing model on social entrepreneurship and to identify the research gap on Islamic perspective from existing models. Research Methodology: The research method used in this study is literature review and comparative analysis from 6 existing models of social entrepreneurship. Finding: The research finding shows that 6 existing models on social entrepreneurship has been analysed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: social entrepreneurship, Islamic perspective, research gap, business management

Procedia PDF Downloads 339

Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

Abstract:

The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

Procedia PDF Downloads 62

Authors: Marjan Golmaryami, Marzieh Behzadi

Abstract:

Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 530

Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

Abstract:

Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

Procedia PDF Downloads 75

Authors: Jehnyne Lalaine Bautista, Marquise Baldemor, Ciara Mendoza

Abstract:

This study is an attempt to investigate the extent to which pre-teens engage in social media, the effects of social media use on the different facets of their identity development such as physical, social, cognitive, aspirations, and personality, as well as the effects of parental regulation on their identity formation. Twelve Filipino children, ages from 9-12 years old and are either regulated, semi-regulated, or unregulated on social media use, participated in this study along with their parent or guardian. The data were gathered through in-depth interviews with the participants and were analyzed through the use of thematic analysis. Results show that despite accessing similar social media applications, the effects of these on children from different types of parental regulation vary since they have different levels of exposure to social media content. Those who have parents with high parental regulation on the use of social media tend to perform better in school, to find time for extracurricular activities, and to develop positive identity formation. The results of this study suggest that parental regulation on social media use has the positive influence on the identity development of children while there are dangers to unregulated use of social media.

Keywords: identity formation, parental regulation, pre-teens, social media

Procedia PDF Downloads 375

Authors: Shin-Pin Tseng

Abstract:

Nowadays, numerous spectral amplitude coding (SAC) fiber-optic code-division-multiple-access (FO-CDMA) techniques were appealing due to their capable of providing moderate security and relieving the effects of multiuser interference (MUI). Nonetheless, the performance of the previous network is degraded due to fixed in-phase cross-correlation (IPCC) value. Based on the above problems, a new SAC FO-CDMA network using partial M-sequence (PMS) code is presented in this study. Because the proposed PMS code is originated from M-sequence code, the system using the PMS code could effectively suppress the effects of MUI. In addition, two-code keying (TCK) scheme can applied in the proposed SAC FO-CDMA network and enhance the whole network performance. According to the consideration of system flexibility, simple optical encoders/decoders (codecs) using fiber Bragg gratings (FBGs) were also developed. First, we constructed a diagram of the SAC FO-CDMA network, including (N/2-1) optical transmitters, (N/2-1) optical receivers, and one N×N star coupler for broadcasting transmitted optical signals to arrive at the input port of each optical receiver. Note that the parameter N for the PMS code was the code length. In addition, the proposed SAC network was using superluminescent diodes (SLDs) as light sources, which then can save a lot of system cost compared with the other FO-CDMA methods. For the design of each optical transmitter, it is composed of an SLD, one optical switch, and two optical encoders according to assigned PMS codewords. On the other hand, each optical receivers includes a 1 × 2 splitter, two optical decoders, and one balanced photodiode for mitigating the effect of MUI. In order to simplify the next analysis, the some assumptions were used. First, the unipolarized SLD has flat power spectral density (PSD). Second, the received optical power at the input port of each optical receiver is the same. Third, all photodiodes in the proposed network have the same electrical properties. Fourth, transmitting '1' and '0' has an equal probability. Subsequently, by taking the factors of phase‐induced intensity noise (PIIN) and thermal noise, the corresponding performance was displayed and compared with the performance of the previous SAC FO-CDMA networks. From the numerical result, it shows that the proposed network improved about 25% performance than that using other codes at BER=10-9. This is because the effect of PIIN was effectively mitigated and the received power was enhanced by two times. As a result, the SAC FO-CDMA network using PMS codes has an opportunity to apply in applications of the next-generation optical network.

Keywords: spectral amplitude coding, SAC, fiber-optic code-division multiple-access, FO-CDMA, partial M-sequence, PMS code, fiber Bragg grating, FBG

Procedia PDF Downloads 375

Authors: Maya Kagan, Michal Itzick, Patricia Tal-Katz

Abstract:

Social workers hold a variety of roles and practices, and one of these involves the care, treatment, and rehabilitation of disabled people. The current study assesses the association between demographic factors, attitudes towards social workers, the stigma attached to seeking social worker help, perceived social support, and psychological distress - and the self-reported likelihood of seeking social worker help, among people with physical disabilities (PWPD) in Israel. Data collection utilized structured questionnaires, administered to a sample of 435 PWPD. Statistical analyses were done using SPSS software. The findings suggest that women, older respondents, people with more positive attitudes towards social workers, with higher levels of psychological distress and of social support, and with a lower level of stigma, reported a greater likelihood of seeking social worker help. The study's conclusion is that there are certain avoidance factors among PWPD that might discourage them from seeking professional social worker help. Therefore, it is important that social workers identify these factors and develop interventions aimed at encouraging PWPD to seek professional social worker help in case of need, and also develop practices adjusted to PWPD's unique needs.

Keywords: attitudes towards social workers, people with physical disabilities, perceived social support, psychological distress, seeking help, stigma

Procedia PDF Downloads 322

Authors: Debneil Saha Roy

Abstract:

Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

Procedia PDF Downloads 154

Authors: Lara Góes Da Costa

Abstract:

This paper analyzes the social dimension of the empowerment of women in Brazil, following the principles of human development of the UN WOMEN, in particular the sixth principle, which establishes the promotion of gender equality through social policy initiatives and activism in general aimed at community. In Brazil, women's empowerment has taken social networks through the creation of avatars and pages of dissemination and promotion of gender equality, as well as denunciations and educational posts such as 'Observe Gender', 'Empower Two Women', 'Black Intellectual Women', among others. At the same time, women's social inclusion bills in various sectors are trailing in the legislative apparatus, with little or no relation to the current discussion of gender diversity and intersectionality. In this sense, this article establishes an analytical parallel between the media manifestations of social networks and the social distance of the representatives of the legislative power. This parallelly shows the political failing to meet the social demands of inclusion, as to multiply the creation of laws and the effectiveness of the principle of promoting gender equality.

Keywords: gender, rights, justice, social networks

Procedia PDF Downloads 378

Authors: Deepika Verma, Runa Sarkar

Abstract:

One of the prominent debates in the social entrepreneurship literature has been to establish whether entrepreneurial work for social well-being by for-profit organizations can be classified as social entrepreneurship or not. Of late, the scholarship has reached a consensus. It concludes that there seems little sense in confining social entrepreneurship to just non-profit organizations. Boosted by this research, increasingly a lot of businesses engaged in filling the social infrastructure gaps in developing countries are calling themselves social enterprise. These organizations are diverse in their ownership, size, objectives, operations and business models. The lack of a comprehensive definition of social enterprise leads to three issues. Firstly, researchers may face difficulty in creating a database for social enterprises because the choice of an entity as a social enterprise becomes subjective or based on some pre-defined parameters by the researcher which is not replicable. Secondly, practitioners who use ‘social enterprise’ in their vision/mission statement(s) may find it difficult to adjust their business models accordingly especially during the times when they face the dilemma of choosing social well-being over business viability. Thirdly, social enterprise and social entrepreneurship attract a lot of donor funding and venture capital. In the paucity of a comprehensive definitional guide, the donors or investors may find assigning grants and investments difficult. It becomes necessary to develop an exhaustive and objective definition of social enterprise and examine whether the understanding of the academicians and practitioners about social enterprise match. This paper develops a dictionary of words often associated with social enterprise or (and) social entrepreneurship. It further compares two lexicographic definitions of social enterprise imputed from the abstracts of academic journal papers and trade publications extracted from the EBSCO database using the ‘tm’ package in R software.

Keywords: EBSCO database, lexicographic definition, social enterprise, text mining

Procedia PDF Downloads 372

Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

Abstract:

Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

Procedia PDF Downloads 90

Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

Abstract:

Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

Procedia PDF Downloads 95

Authors: Mahdi Jalili

Abstract:

Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

Procedia PDF Downloads 427

Authors: Hossain Samani, Mohammad Ehteram

Abstract:

In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.

Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation

Procedia PDF Downloads 333

Authors: Diogo Prosdocimi, Don Ross, Matthew Townshend

Abstract:

Road authorities are mandated within limited budgets to both deliver improved access to basic services and facilitate economic growth. This responsibility is further complicated if maintenance backlogs and funding shortfalls exist, as evident in many countries including South Africa. These conditions require authorities to make difficult prioritisation decisions, with the effect that Road Asset Management Systems with a one-dimensional focus on traffic volumes may overlook the maintenance of low-volume roads that provide isolated communities with vital access to basic services. Given these challenges, this paper overlays the full South African road network with geo-referenced information for population, primary and secondary schools, and healthcare facilities to identify the network of connective roads between communities and basic service centres. This connective network is then rationalised according to the Gross Value Added and number of jobs per mesozone, administrative and functional road classifications, speed limit, and road length, location, and name to estimate the Basic Access Road Network. A two-step floating catchment area (2SFCA) method, capturing a weighted assessment of drive-time to service centres and the ratio of people within a catchment area to teachers and healthcare workers, is subsequently applied to generate a Multivariate Road Index. This Index is used to assign higher maintenance priority to roads within the Basic Access Road Network that provide more people with better access to services. The relatively limited incidence of Basic Access Roads indicates that authorities could maintain the entire estimated network without exhausting the available road budget before practical economic considerations get any purchase. Despite this fact, a final case study modelling exercise is performed for the Namakwa District Municipality to demonstrate the extent to which optimal relocation of schools and healthcare facilities could minimise the Basic Access Road Network and thereby release budget for investment in roads that best promote GDP growth.

Keywords: basic access roads, multivariate road index, road prioritisation, two-step floating catchment area method

Procedia PDF Downloads 212

Authors: Baladitya Swaika, Rahul Khatry

Abstract:

Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

Procedia PDF Downloads 162

Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

Abstract:

Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

Procedia PDF Downloads 116

Authors: Seyed Mahdi Jameii

Abstract:

Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

Procedia PDF Downloads 401

Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang

Abstract:

Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.

Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks

Procedia PDF Downloads 584