Search results for: © Learning Network

Authors: Janet Holland

Abstract:

Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: Area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 878

Authors: Mat Syai'in, Adi Soeprijanto, Takashi Hiyama

Abstract:

An optimal power flow (OPF) based on particle swarm optimization (PSO) was developed with more realistic generator security constraint using the capability curve instead of only Pmin/Pmax and Qmin/Qmax. Neural network (NN) was used in designing digital capability curve and the security check algorithm. The algorithm is very simple and flexible especially for representing non linear generation operation limit near steady state stability limit and under excitation operation area. In effort to avoid local optimal power flow solution, the particle swarm optimization was implemented with enough widespread initial population. The objective function used in the optimization process is electric production cost which is dominated by fuel cost. The proposed method was implemented at Java Bali 500 kV power systems contain of 7 generators and 20 buses. The simulation result shows that the combination of generator power output resulted from the proposed method was more economic compared with the result using conventional constraint but operated at more marginal operating point.

Keywords: Optimal Power Flow, Generator Capability Curve, Particle Swarm Optimization, Neural Network

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2575

Authors: Bhaskar Basu

Abstract:

Universities and higher education institutes are finding it increasingly difficult to engage students fruitfully through traditional pedagogic tools. Web 2.0 technologies comprising social networking sites (SNSs) offer a platform for students to collaborate and share information, thereby enhancing their learning experience. Despite the potential and reach of SNSs, its use has been limited in academic settings promoting higher education. The purpose of this paper is to assess the perception of social networking sites among business school students in India and analyze its role in enhancing quality of student experiences in a business school leading to the proposal of an agenda for future research. In this study, more than 300 students of a reputed business school were involved in a survey of their preferences of different social networking sites and their perceptions and attitudes towards these sites. A questionnaire with three major sections was designed, validated and distributed among  a sample of students, the research method being descriptive in nature. Crucial questions were addressed to the students concerning time commitment, reasons for usage, nature of interaction on these sites, and the propensity to share information leading to direct and indirect modes of learning. It was further supplemented with focus group discussion to analyze the findings. The paper notes the resistance in the adoption of new technology by a section of business school faculty, who are staunch supporters of the classical “face-to-face” instruction. In conclusion, social networking sites like Facebook and LinkedIn provide new avenues for students to express themselves and to interact with one another. Universities could take advantage of the new ways  in which students are communicating with one another. Although interactive educational options such as Moodle exist, social networking sites are rarely used for academic purposes. Using this medium opens new ways of academically-oriented interactions where faculty could discover more about students' interests, and students, in turn, might express and develop more intellectual facets of their lives. hitherto unknown intellectual facets.  This study also throws up the enormous potential of mobile phones as a tool for “blended learning” in business schools going forward.

Keywords: Business school, India, learning, social media, social networking, university.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1428

Authors: R.Vinodha S. Abraham Lincoln, J. Prakash

Abstract:

Multi-loop (De-centralized) Proportional-Integral- Derivative (PID) controllers have been used extensively in process industries due to their simple structure for control of multivariable processes. The objective of this work is to design multiple-model adaptive multi-loop PID strategy (Multiple Model Adaptive-PID) and neural network based multi-loop PID strategy (Neural Net Adaptive-PID) for the control of multivariable system. The first method combines the output of multiple linear PID controllers, each describing process dynamics at a specific level of operation. The global output is an interpolation of the individual multi-loop PID controller outputs weighted based on the current value of the measured process variable. In the second method, neural network is used to calculate the PID controller parameters based on the scheduling variable that corresponds to major shift in the process dynamics. The proposed control schemes are simple in structure with less computational complexity. The effectiveness of the proposed control schemes have been demonstrated on the CSTR process, which exhibits dynamic non-linearity.

Keywords: Multiple-model Adaptive PID controller, Multivariableprocess, CSTR process.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2012

Authors: Reena Murali, David Peter S.

Abstract:

The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies show that upregulation of mRNA because serious diseases like cancer. So designing effective siRNA with good knockdown effects plays an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (%G), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: Artificial Neural Network, Double Stranded RNA, RNA Interference, Short Interfering RNA.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2666

Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

Abstract:

Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: Multi-agent system, safety analysis, safety model.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1087

Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri

Abstract:

Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.

Keywords: Connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1104

Authors: Paul Crowther, Richard Hill

Abstract:

Much has been written about the difficulties students have with producing traditional dissertations. This includes both native English speakers (L1) and students with English as a second language (L2). The main emphasis of these papers has been on the structure of the dissertation, but in all cases, even when electronic versions are discussed, the dissertation is still in what most would regard as a traditional written form. Master of Science Degrees in computing disciplines require students to gain technical proficiency and apply their knowledge to a range of scenarios. The basis of this paper is that if a dissertation is a means of showing that such a student has met the criteria for a pass, which should be based on the learning outcomes of the dissertation module, does meeting those outcomes require a student to demonstrate their skills in a solely text based form, particularly in a highly technical research project? Could it be possible for a student to produce a series of related artifacts which form a cohesive package that meets the learning out comes of the dissertation?

Keywords: Computing, Masters dissertation, thesis, portfolio

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1355

Authors: A. Bouabid, B. Bielenberg, S. Ainane, N. Pasha

Abstract:

In this paper, a team of faculty members of the Petroleum Institute in Abu Dhabi, UAE representing six different courses across General Engineering (ENGR), Communication (COMM), and Design (STPS) worked together to establish a clear developmental progression of learning outcomes and performance indicators for targeted knowledge, areas of competency, and skills for the first three semesters of the Bachelor of Sciences in Engineering curriculum. The sequences of courses studied in this project were ENGR/COMM, COMM/STPS, and ENGR/STPS. For each course’s nine areas of knowledge, competency, and skills, the research team reviewed the existing learning outcomes and related performance indicators with a focus on identifying linkages across disciplines as well as within the courses of a discipline. The team reviewed existing performance indicators for developmental progression from semester to semester for same discipline related courses (vertical alignment) and for different discipline courses within the same semester (horizontal alignment). The results of this work have led to recommendations for modifications of the initial indicators when incoherence was identified, and/or for new indicators based on best practices (identified through literature searches) when gaps were identified. It also led to recommendations for modifications of the level of emphasis within each course to ensure developmental progression. The exercise has led to a revised Sequence Performance Indicator Mapping for the knowledge, skills, and competencies across the six core courses.

Keywords: Curriculum alignment, horizontal and vertical progression, performance indicators, skill level.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 838

Authors: Hema C. R., Paulraj M. P., S. Yaacob, A. H. Adom, R. Nagarajan

Abstract:

Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification

Keywords: Motor Imagery, Brain Machine Interfaces, Neural Networks, Particle Swarm Optimization, EEG signal processing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2456

Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

Abstract:

Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2053

Authors: Justin D. Olmanson, Fitsum F. Abebe, Valerie Jones, Eric Kyle, Lyrica Lucas, Katherine Robbins, Guieswende Rouamba, Xianquan Liu

Abstract:

While emerging technologies continue to emerge, research into their use in learning contexts often focuses on a subset of educational practices and ways of using technologies. In this study we begin to explore the extent to which educational designs are influenced by larger societal and education-related factors not usually explicitly considered when designing or identifying technology-supported education experiences for research study. We examine patterns within and between factors via a content analysis across ten years and 19 different journals of published peer-reviewed research on technology-supported writing. Our findings have implications for how researchers, designers, and educators approach technology-supported educational design within and beyond the field of writing and literacy.

Keywords: Writing, emerging technology, learning, curriculum, pedagogy.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1834

Authors: D. SuganyaDevi, G. Padmavathi

Abstract:

Reliable secure multicast communication in mobile adhoc networks is challenging due to its inherent characteristics of infrastructure-less architecture with lack of central authority, high packet loss rates and limited resources such as bandwidth, time and power. Many emerging commercial and military applications require secure multicast communication in adhoc environments. Hence key management is the fundamental challenge in achieving reliable secure communication using multicast key distribution for mobile adhoc networks. Thus in designing a reliable multicast key distribution scheme, reliability and congestion control over throughput are essential components. This paper proposes and evaluates the performance of an enhanced optimized multicast cluster tree algorithm with destination sequenced distance vector routing protocol to provide reliable multicast key distribution. Simulation results in NS2 accurately predict the performance of proposed scheme in terms of key delivery ratio and packet loss rate under varying network conditions. This proposed scheme achieves reliability, while exhibiting low packet loss rate with high key delivery ratio compared with the existing scheme.

Keywords: Key Distribution, Mobile Adhoc Network, Multicast and Reliability.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1637

Authors: Osagiede Mercy Afe

Abstract:

After the internet explosion in the 90’s, technology was immediately integrated into the school system. Technology which symbolizes advancement in human knowledge was seen as a setback by many educators. Efforts have been made to help stem this erroneous believes and help educators realize the benefits of technology and ways of implementing it in the classrooms especially in the sciences. This advancement created a constantly expanding gap between the pupil’s perception on the use of technology within the learning atmosphere and the teacher’s perception and limitations hence, the focus of this paper is on the need to refocus on the use of Science and Technology in enhancing children’s potentials in learning at school especially in Science for sustainable development in Nigeria. The paper recommended measures for facilitating the sustenance of science and technology in Nigerian schools so as to enhance the potentials of our children in Science and Technology for a better tomorrow.

Keywords: Children’s potential, Educational system, ICT, Sustainable development.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1710

Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

Abstract:

Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: Artificial Neural Network, Decision Support System, drug abuse, drug addiction, Multilayer Perceptron.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1680

Authors: Geir Sigurðsson

Abstract:

This paper compares ancient Daoist and Confucian approaches to the human body as a locus for learning, edification or personal cultivation. While pointing out some major differences between ancient Chinese and mainstream Western visions of the body, it seeks at the same time inspiration in some seminal Western phenomenological and post-structuralist writings, in particular from Maurice Merleau-Ponty and Pierre Bourdieu. By clarifying the somewhat dissimilar scopes of foci found in Daoist and Confucian philosophies with regard to the role of and attitude to the body, the conclusion is nevertheless that their approaches are comparable, and that both traditions take the physical body to play a vital role in the cultivation of excellence. Lastly, it will be argued that cosmological underpinnings prevent the Confucian li from being rigid and invariable and that it rather emerges as a flexible learning device to train through active embodiment a refined sensibility for one’s cultural environment.

Keywords: Body, Confucianism, Daoism, li, phenomenology, ritual.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 828

Authors: Florin Leon, Silvia Curteanu

Abstract:

Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: Adaptive sampling, batch bulk methyl methacrylate polymerization, large margin nearest neighbor regression, machine learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1400

Authors: Liang Wang, Wei Zhang

Abstract:

Nowadays sustainable development has increasingly become an important research topic of engineering education all over the world. Engineering Education for Sustainable Development (EESD) highlighted the importance of addressing sustainable development in engineering practice. However, whether and how the professional engineering learning and experience affect those perceptions is an interesting research topic especially in Chinese context. Our study fills this gap by investigating perceptions bias of EESD among first-grade engineering students, fourth-grade engineering students and experienced engineers using a triple-dimensional model. Our goal is to find the effect of engineering learning and experience on sustainable development and make these learning and experiences more accessible for students and engineers in school and workplace context. The data (n = 138) came from a Likert questionnaire based on the triple-dimensional model of EESD adopted from literature reviews and the data contain 48 first-grade students, 56 fourth-grade students and 34 engineers with rich working experience from Environmental Engineering, Energy Engineering, Chemical Engineering and Civil Engineering in or graduated from Zhejiang University, China. One-way ANOVA analysis was used to find the difference in different dimensions among the three groups. The statistical results show that both engineering students and engineers have a well understanding of sustainable development in ecology dimension of EESD while there are significant differences among three groups as to the socio-economy and value rationality dimensions of EESD. The findings provide empirical evidence that both engineering learning and professional engineering experience are helpful to cultivate the cognition and perception of sustainable development in engineering education. The results of this work indicate that more practical content should be added to students’ engineering education while more theoretical content should be added to engineers’ training in order to promote the engineering students’ and engineers’ perceptions of sustainable development. In addition, as to the design of engineering courses and professional practice system for sustainable development, we should not only pay attention to the ecological aspects, but also emphasize the coordination of ecological, socio-economic and human-centered sustainable development (e.g., engineer's ethical responsibility).

Keywords: Engineering education, sustainable development, experienced engineers, engineering students.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 591

Authors: Ebrahim Panah, Muhammad Yasir Babar

Abstract:

Thanks to the development of online technology and social networks, people are able to communicate as well as learn. WhatsApp is a popular social network which is growingly gaining popularity. This app can be used for communication as well as education. It can be used for instructor-learner, learner-learner, and learner-content interactions; however, very little knowledge is available on these potentials of WhatsApp. The current study was undertaken to investigate university students’ perceptions of WhatsApp used as a tool for instructor-learner dialogue, learner-content dialogue, and learner-learner dialogue. The study adopted a survey approach and distributed the questionnaire developed by Google Forms to 54 (11 males and 43 females) university students. The obtained data were analyzed using SPSS version 20. The result of data analysis indicates that students have positive attitudes towards WhatsApp as a tool for Instructor-Learner Dialogue: it easy to reach the lecturer (4.07), the instructor gives me valuable feedback on my assignment (4.02), the instructor is supportive during course discussion and offers continuous support with the class (4.00). Learner-Content Dialogue: WhatsApp allows me to academically engage with lecturers anytime, anywhere (4.00), it helps to send graphics such as pictures or charts directly to the students (3.98), it also provides out of class, extra learning materials and homework (3.96), and Learner-Learner Dialogue: WhatsApp is a good tool for sharing knowledge with others (4.09), WhatsApp allows me to academically engage with peers anytime, anywhere (4.07), and we can interact with others through the use of group discussion (4.02). It was also found that there are significant positive correlations between students’ perceptions of Instructor-Learner Dialogue (ILD), Learner-Content Dialogue (LCD), Learner-Learner Dialogue (LLD) and WhatsApp Application in classroom. The findings of the study have implications for lectures, policy makers and curriculum developers.

Keywords: Instructor-learner dialogue, learners-contents dialogue, learner-learner dialogue, WhatsApp.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 681

Authors: Helen Zhang

Abstract:

Accurate segmentation of the airways from CT scans is crucial for early diagnosis of lung cancer. However, the existing airway segmentation algorithms often rely on thin-slice CT scans, which can be inconvenient and costly. This paper presents a set of machine learning-based 3D super-resolution algorithms along heterogenous dimensions to improve the resolution of thicker CT scans to reduce the reliance on thin-slice scans. To evaluate the efficacy of the super-resolution algorithms, quantitative assessments using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) were performed. The impact of super-resolution on airway segmentation accuracy is also studied. The proposed approach has the potential to make airway segmentation more accessible and affordable, thereby facilitating early diagnosis and treatment of lung cancer.

Keywords: 3D super-resolution, airway segmentation, thin-slice CT scans, machine learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 287

Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

Abstract:

Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 997

Authors: W. Zekri

Abstract:

This paper explores Zohra Drif’s memoir, Inside the Battle of Algiers, which narrates her desires as a student to become a revolutionary activist. She exemplified, in her narrative, the different roles, she and her fellows performed as combatants in the Casbah during the Algerian Revolution 1954-1962. This book review aims to evaluate the concept of women’s agency through education and language learning, and its impact on empowering women’s desires. Close-reading method and thematic analysis are used to explore the text. The analysis identified themes that refine the meaning of agency which are social and cultural supports, education, and language proficiency. These themes aim to contribute to the representation in Inside the Battle of Algiers of a woman guerrilla who engaged herself to perform national acts of resistance.

Keywords: Agency, education, learning, women.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1063

Authors: Watcharawalee Lertlum, Borworn Papasratorn

Abstract:

Previous researches found that conventional WBL is effective for meaningful learner, because rote learner learn by repeating without thinking or trying to understand. It is impossible to have full benefit from conventional WBL. Understanding of rote learner-s intention and what influences it becomes important. Poorly designed user interface will discourage rote learner-s cultivation and intention to use WBL. Thus, user interface design is an important factor especially when WBL is used as comprehensive replacement of conventional teaching. This research proposes the influencing factors that can enhance learner-s intention to use the system. The enhanced TAM is used for evaluating the proposed factors. The research result points out that factors influencing rote learner-s intention are Perceived Usefulness of Homepage Content Structure, Perceived User Friendly Interface, Perceived Hedonic Component, and Perceived (homepage) Visual Attractiveness.

Keywords: Web-Based learning, Electronic learning, Intentionto use, Rote learner, Influencing

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1585

Authors: Ismail Algelli Sassi Ehtiwesh, Željko Đurović

Abstract:

The main goal of the study is to analyze all relevant properties of the electro hydraulic systems and based on that to make a proper choice of the control strategy that may be used for the control of the servomechanism system. A combination of electronic and hydraulic systems is widely used since it combines the advantages of both. Hydraulic systems are widely spread because of their properties as accuracy, flexibility, high horsepower-to-weight ratio, fast starting, stopping and reversal with smoothness and precision, and simplicity of operations. On the other hand, the modern control of hydraulic systems is based on control of the circuit fed to the inductive solenoid that controls the position of the hydraulic valve. Since this circuit may be easily handled by PWM (Pulse Width Modulation) signal with a proper frequency, the combination of electrical and hydraulic systems became very fruitful and usable in specific areas as airplane and military industry. The study shows and discusses the experimental results obtained by the control strategy (classical feedback (PID) & neural network) using MATLAB and SIMULINK [1]. Finally, the special attention was paid to the possibility of neuro-controller design and its application to control of electro-hydraulic systems and to make comparative with classical control.

Keywords: Electro-hydraulic systems, PID, Neural network controller.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1839

Authors: Hassan Haghighi, Sajad Ghorbani, Maryam Mohebati, Mohammad Mahdi Javanmard

Abstract:

One of the most important aspects expected from ERP systems is to integrate various operations existing in administrative, financial, commercial, human resources, and production departments of the consumer organization. Also, it is often needed to integrate the new ERP system with the organization legacy systems when implementing the ERP package in the organization. Without relying on an appropriate software architecture to realize the required integration, ERP implementation processes become error prone and time consuming; in some cases, the ERP implementation may even encounters serious risks. In this paper, we propose a new architecture that is based on the agent oriented vision and supplies the integration expected from ERP systems using several independent but cooperator agents. Besides integration which is the main issue of this paper, the presented architecture will address some aspects of intelligence and learning capabilities existing in ERP systems

Keywords: enterprise resource planning, software architecture, agent oriented architecture, integration, intelligence, learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1838

Authors: Ashagrie Getnet Flattie

Abstract:

Line-of-sight (LOS) information, data rates, good quality, and flexible network service are limited by the fact that, for the duration of any given connection, they experience severe variation in signal strength due to fading and path loss. Wireless system faces major challenges in achieving wide coverage and capacity without affecting the system performance and to access data everywhere, all the time. In this paper, the cell coverage and edge rate of different Multiple-input multiple-output (MIMO) schemes in 20 MHz Long Term Evolution (LTE) system under Unmanned Air Vehicles (UAV) platform are investigated. After some background on the enormous potential of UAV, MIMO, and LTE in wireless links, the paper highlights the presented system model which attempts to realize the various benefits of MIMO being incorporated into UAV platform. The performances of the three MIMO LTE schemes are compared with the performance of 4x4 MIMO LTE in UAV scheme carried out to evaluate the improvement in cell radius, BER, and data throughput of the system in different morphology. The results show that significant performance gains such as bit error rate (BER), data rate, and coverage can be achieved by using the presented scenario.

Keywords: BER, LTE, MIMO, path loss, UAV.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1391

Authors: Dyah Titisari Widyastuti

Abstract:

Rail-station area development that is based on the concept of TOD (Transit Oriented Development) is principally oriented to pedestrian accessibility for daily mobility. The aim of this research is elaborating how far the existing physical elements of a rail-station district could facilitate pedestrian mobility and establish a pedestrian friendly district toward implementation of a TOD concept. This research was conducted through some steps: (i) mapping the rail-station area pedestrian sidewalk and pedestrian network as well as activity nodes and transit nodes, (ii) assessing the level of pedestrian sidewalk connectivity joining trip origin and destination. The research area coverage in this case is limited to walking distance of the rail station (around 500 meters or 10-15 minutes walking). The findings of this research on the current condition of the street and pedestrian sidewalk network and connectivity, show good preference for the foot modal share (more than 50%) is achieved. Nevertheless, it depends on the distance from the trip origin to destination.

Keywords: Accessibility of daily mobility, pedestrian friendly district, rail-station district, Transit Oriented Development.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 882

Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

Abstract:

Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: Conditional Generative Adversarial Net, market and credit risk management, neural network, time series.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1199

Authors: Roberto Iglesias, Theocharis Kyriacou, Ulrich Nehmzow, Steve Billings

Abstract:

Fundamental sensor-motor couplings form the backbone of most mobile robot control tasks, and often need to be implemented fast, efficiently and nevertheless reliably. Machine learning techniques are therefore often used to obtain the desired sensor-motor competences. In this paper we present an alternative to established machine learning methods such as artificial neural networks, that is very fast, easy to implement, and has the distinct advantage that it generates transparent, analysable sensor-motor couplings: system identification through nonlinear polynomial mapping. This work, which is part of the RobotMODIC project at the universities of Essex and Sheffield, aims to develop a theoretical understanding of the interaction between the robot and its environment. One of the purposes of this research is to enable the principled design of robot control programs. As a first step towards this aim we model the behaviour of the robot, as this emerges from its interaction with the environment, with the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving Average models with eXogenous inputs). This method produces explicit polynomial functions that can be subsequently analysed using established mathematical methods. In this paper we demonstrate the fidelity of the obtained NARMAX models in the challenging task of robot route learning; we present a set of experiments in which a Magellan Pro mobile robot was taught to follow four different routes, always using the same mechanism to obtain the required control law.

Keywords: Mobile robotics, system identification, non-linear modelling, NARMAX.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1721

Authors: Farhan Beg, Raymond Moberly

Abstract:

The Indian subcontinent is facing a massive challenge with regards to energy security in its member countries; to provide reliable electricity to facilitate development across various sectors of the economy and consequently achieve the developmental targets. The instability of the current precarious situation is observable in the frequent system failures and blackouts.

The deployment of interconnected electricity ‘Supergrid’ designed to carry huge quanta of power across the Indian sub-continent is proposed in this paper. Not only enabling energy security in the subcontinent it will also provide a platform for Renewable Energy Sources (RES) integration. This paper assesses the need and conditions for a Supergrid deployment and consequently proposes a meshed topology based on Voltage Source High Voltage Direct Current (VSC- HVDC) converters for the Supergrid modeling. Various control schemes for the control of voltage and power are utilized for the regulation of the network parameters. A 3 terminal Multi Terminal Direct Current (MTDC) network is used for the simulations.

Keywords: Super grid, Wind and Solar energy, High Voltage Direct Current, Electricity management, Load Flow Analysis.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2811