Search results for: interactive learning applications.
3711 Open Educational Resource in Online Mathematics Learning
Authors: Haohao Wang
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Technology, multimedia in Open Educational Resources, can contribute positively to student performance in an online instructional environment. Student performance data of past four years were obtained from an online course entitled Applied Calculus (MA139). This paper examined the data to determine whether multimedia (independent variable) had any impact on student performance (dependent variable) in online math learning, and how students felt about the value of the technology. Two groups of student data were analyzed, group 1 (control) from the online applied calculus course that did not use multimedia instructional materials, and group 2 (treatment) of the same online applied calculus course that used multimedia instructional materials. For the MA139 class, results indicate a statistically significant difference (p = .001) between the two groups, where group 1 had a final score mean of 56.36 (out of 100), group 2 of 70.68. Additionally, student testimonials were discussed in which students shared their experience in learning applied calculus online with multimedia instructional materials.Keywords: Online learning, Open Educational Resources, Multimedia, Technology.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21083710 Endogenous Fantasy – Based Serious Games: Intrinsic Motivation and Learning
Authors: Robert F. Kenny, Glenda A. Gunter
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Current technological advances pale in comparison to the changes in social behaviors and 'sense of place' that is being empowered since the Internet made it on the scene. Today-s students view the Internet as both a source of entertainment and an educational tool. The development of virtual environments is a conceptual framework that needs to be addressed by educators and it is important that they become familiar with who these virtual learners are and how they are motivated to learn. Massively multiplayer online role playing games (MMORPGs), if well designed, could become the vehicle of choice to deliver learning content. We suggest that these games, in order to accomplish these goals, must begin with well-established instructional design principles that are co-aligned with established principles of video game design. And have the opportunity to provide an instructional model of significant prescriptive power. The authors believe that game designers need to take advantage of the natural motivation player-learners have for playing games by developing them in such a way so as to promote, intrinsic motivation, content learning, transfer of knowledge, and naturalization.
Keywords: serious games, endogenous fantasy, intrinsic motivation, online learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22293709 Spatial Services in Cloud Environment
Authors: Sašo Pečnik, Borut Žalik
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Cloud Computing is an approach that provides computation and storage services on-demand to clients over the network, independent of device and location. In the last few years, cloud computing became a trend in information technology with many companies that transfer their business processes and applications in the cloud. Cloud computing with service oriented architecture has contributed to rapid development of Geographic Information Systems. Open Geospatial Consortium with its standards provides the interfaces for hosted spatial data and GIS functionality to integrated GIS applications. Furthermore, with the enormous processing power, clouds provide efficient environment for data intensive applications that can be performed efficiently, with higher precision, and greater reliability. This paper presents our work on the geospatial data services within the cloud computing environment and its technology. A cloud computing environment with the strengths and weaknesses of the geographic information system will be introduced. The OGC standards that solve our application interoperability are highlighted. Finally, we outline our system architecture with utilities for requesting and invoking our developed data intensive applications as a web service.
Keywords: Cloud Computing, Geographic Information System, Open Geospatial Consortium, Interoperability, Spatial data, Web- Services.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17073708 Learning Human-Like Color Categorization through Interaction
Authors: Rinaldo Christian Tanumara, Ming Xie, Chi Kit Au
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Human perceives color in categories, which may be identified using color name such as red, blue, etc. The categorization is unique for each human being. However despite the individual differences, the categorization is shared among members in society. This allows communication among them, especially when using color name. Sociable robot, to live coexist with human and become part of human society, must also have the shared color categorization, which can be achieved through learning. Many works have been done to enable computer, as brain of robot, to learn color categorization. Most of them rely on modeling of human color perception and mathematical complexities. Differently, in this work, the computer learns color categorization through interaction with humans. This work aims at developing the innate ability of the computer to learn the human-like color categorization. It focuses on the representation of color categorization and how it is built and developed without much mathematical complexity.Keywords: Color categorization, color learning, machinelearning, color naming.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15233707 Conceptual Model for Massive Open Online Blended Courses Based on Disciplines’ Concepts Capitalization and Obstacles’ Detection
Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane
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Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.
Keywords: MOOC, Massive Open Online Courses, Online learning, E-learning, Blended learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9373706 Context Detection in Spreadsheets Based on Automatically Inferred Table Schema
Authors: Alexander Wachtel, Michael T. Franzen, Walter F. Tichy
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Programming requires years of training. With natural language and end user development methods, programming could become available to everyone. It enables end users to program their own devices and extend the functionality of the existing system without any knowledge of programming languages. In this paper, we describe an Interactive Spreadsheet Processing Module (ISPM), a natural language interface to spreadsheets that allows users to address ranges within the spreadsheet based on inferred table schema. Using the ISPM, end users are able to search for values in the schema of the table and to address the data in spreadsheets implicitly. Furthermore, it enables them to select and sort the spreadsheet data by using natural language. ISPM uses a machine learning technique to automatically infer areas within a spreadsheet, including different kinds of headers and data ranges. Since ranges can be identified from natural language queries, the end users can query the data using natural language. During the evaluation 12 undergraduate students were asked to perform operations (sum, sort, group and select) using the system and also Excel without ISPM interface, and the time taken for task completion was compared across the two systems. Only for the selection task did users take less time in Excel (since they directly selected the cells using the mouse) than in ISPM, by using natural language for end user software engineering, to overcome the present bottleneck of professional developers.Keywords: Natural language processing, end user development; natural language interfaces, human computer interaction, data recognition, dialog systems, spreadsheet.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11183705 The Effects of Yield and Yield Components of Some Quality Increase Applications on Razakı Grape Variety
Authors: Şehri Çınar, Aydın Akın
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This study was conducted Razakı grape variety (Vitis vinifera L.) and its vine which was aged 19 was grown on 5 BB rootstock in a vegetation period of 2014 in Afyon province in Turkey. In this research, it was investigated whether the applications of Control (C), 1/3 Cluster Tip Reduction (1/3 CTR), Shoot Tip Reduction (STR), 1/3 CTR + STR, Boric Acid (BA), 1/3 CTR + BA, STR + BA, 1/3 CTR + STR + BA on yield and yield components of Razakı grape variety. The results were obtained as the highest fresh grape yield (7.74 kg/vine) with C application; as the highest cluster weight (244.62 g) with STR application; as the highest 100 berry weight (504.08 g) with C application; as the highest maturity index (36.89) with BA application; as the highest must yield (695.00 ml) with BA and (695.00 ml) with 1/3 CTR + STR + BA applications; as the highest intensity of L* color (46.93) with STR and (46.10) with 1/3 CTR + STR + BA applications; as the highest intensity of a* color (-5.37) with 1/3 CTR + STR and (-5.01) with STR, as the highest intensity of b* color (12.59) with STR application. The shoot tip reduction to increase cluster weight and boric acid application to increase maturity index of Razakı grape variety can be recommended.
Keywords: Razakı, 1/3 cluster tip reduction, shoot tip reduction, boric acid, yield and yield components.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35483704 Learning and Practicing Assessment in a Pre-service Teacher Education Program: Comparative Perspective of UK and Pakistani Universities
Authors: Malik Ghulam Behlol, Alison Fox, Faiza Masood, Sabiha Arshad
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This paper explores the barriers to the application of learning-supportive assessment at teaching practicum while investigating the role of university teachers (UT), cooperative teachers (CT), prospective teachers (PT) and heads of the practicum schools (HPS) in the selected universities of Pakistan and the UK. It is a qualitative case study and data were collected through the lesson observation of UT in the pre-service teacher education setting and PT in practicum schools. Interviews with UT, HPS, and Focus Group Discussions with PT were conducted too. The study has concluded that as compared to the UK counterpart, PTs in Pakistan face significant barriers in applying learning-supportive assessment in the school practicum settings because of large class sizes, lack of institutionalised collaboration between universities and schools, poor modelling of the lesson, ineffective feedback practices, lower order thinking assignments, and limited opportunities to use technology in school settings.
Keywords: Learning supportive assessment, pre-service teacher education, theory-practice gap, teacher education.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1723703 Closing the Achievement Gap Within Reading and Mathematics Classrooms by Fostering Hispanic Students- Educational Resilience
Authors: Hersh C. Waxman, Yolanda N. Padrón, Jee-Young Shin, Héctor H. Rivera
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While many studies have conducted the achievement gap between groups of students in school districts, few studies have utilized resilience research to investigate achievement gaps within classrooms. This paper aims to summarize and discuss some recent studies Waxman, Padr├│n, and their colleagues conducted, in which they examined learning environment differences between resilient and nonresilient students in reading and mathematics classrooms. The classes consist of predominantly Hispanic elementary school students from low-income families. These studies all incorporated learning environment questionnaires and systematic observation methods. Significant differences were found between resilient and nonresilient students on their classroom learning environments and classroom behaviors. The observation results indicate that the amount and quality of teacher and student academic interaction are two of the most influential variables that promote student outcomes. This paper concludes by suggesting the following teacher practices to promote resiliency in schools: (a) using feedback from classroom observation and learning environment measures, (b) employing explicit teaching practices; and (c) understanding students on a social and personal level.Keywords: achievement gap, classroom learning environments, educational resilience, systematic classroom observation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19793702 CAD Model of Cole Cole Representation for Analyzing Performance of Microstrip Moisture Sensing Applications
Authors: Settapong Malisuwan, Jesada Sivaraks, Wasan Jaiwong, Veerapat Sanpanich
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In the past decade, the development of microstrip sensor application has evolved tremendously. Although cut and trial method was adopted to develop microstrip sensing applications in the past, Computer-Aided-Design (CAD) is a more effective as it ensures less time is consumed and cost saving is achieved in developing microstrip sensing applications. Therefore microstrip sensing applications has gained popularity as an effective tool adopted in continuous sensing of moisture content particularly in products that is administered mainly by liquid content. In this research, the Cole-Cole representation of reactive relaxation is applied to assess the performance of the microstrip sensor devices. The microstrip sensor application is an effective tool suitable for sensing the moisture content of dielectric material. Analogous to dielectric relaxation consideration of Cole-Cole diagrams as applied to dielectric materials, a “reactive relaxation concept” concept is introduced to represent the frequency-dependent and moisture content characteristics of microstrip sensor devices.
Keywords: Microstrip, Sensor, Cole-Cole Representation, Moisture content.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18223701 Enhancing Visual Basic GUI Applications using VRML Scenes
Authors: Bala Dhandayuthapani Veerasamy
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Rapid Application Development (RAD) enables ever expanding needs for speedy development of computer application programs that are sophisticated, reliable, and full-featured. Visual Basic was the first RAD tool for the Windows operating system, and too many people say still it is the best. To provide very good attraction in visual basic 6 applications, this paper directing to use VRML scenes over the visual basic environment.Keywords: Cortona Control, Interpolator, Route, Sensor, VisualBasic, VRML
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22643700 Implementation of a New Neural Network Function Block to Programmable Logic Controllers Library Function
Authors: Hamid Abdi, Abolfazl Salami, Abolfazl Ahmadi
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Programmable logic controllers are the main controllers in the today's industries; they are used for several applications in industrial control systems and there are lots of examples exist from the PLC applications in industries especially in big companies and plants such as refineries, power plants, petrochemical companies, steel companies, and food and production companies. In the PLCs there are some functions in the function library in software that can be used in PLC programs as basic program elements. The aim of this project are introducing and implementing a new function block of a neural network to the function library of PLC. This block can be applied for some control applications or nonlinear functions calculations after it has been trained for these applications. The implemented neural network is a Perceptron neural network with three layers, three input nodes and one output node. The block can be used in manual or automatic mode. In this paper the structure of the implemented function block, the parameters and the training method of the network are presented by considering the especial method of PLC programming and its complexities. Finally the application of the new block is compared with a classic simulated block and the results are presented.Keywords: Programmable Logic Controller, PLC Programming, Neural Networks, Perception Network, Intelligent Control.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 38003699 NANCY: Combining Adversarial Networks with Cycle-Consistency for Robust Multi-Modal Image Registration
Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith
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Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.Keywords: Multimodal image registration, GAN, cycle consistency, deep learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8003698 A Metametadata Architecture forPedagogic Data Description
Authors: A. Ismail, M. S. Joy, J. E. Sinclair, M. I. Hamzah
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This paper focuses on a novel method for semantic searching and retrieval of information about learning materials. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. A novel metametadata taxonomy has been developed which provides the basis for a semantic search engine to extract, match and map queries to retrieve relevant results. The use of ontological views is a foundation for viewing the pedagogical content of metadata extracted from learning objects by using the pedagogical attributes from the metametadata taxonomy. Using the ontological approach and metametadata (based on the metametadata taxonomy) we present a novel semantic searching mechanism.These three strands – the taxonomy, the ontological views, and the search algorithm – are incorporated into a novel architecture (OMESCOD) which has been implemented.Keywords: Metadata, metametadata, semantic, ontologies.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15103697 Prototype of an Interactive Toy from Lego Robotics Kits for Children with Autism
Authors: Ricardo A. Martins, Matheus S. da Silva, Gabriel H. F. Iarossi, Helen C. M. Senefonte, Cinthyan R. S. C. de Barbosa
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This paper is the development of a concept of the man/robot interaction. More accurately in developing of an autistic child that have more troubles with interaction, here offers an efficient solution, even though simple; however, less studied for this public. This concept is based on code applied thought out the Lego NXT kit, built for the interpretation of the robot, thereby can create this interaction in a constructive way for children suffering with Autism.Keywords: Lego NXT, autism, ANN (Artificial Neural Network), Backpropagation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8763696 Moving Data Mining Tools toward a Business Intelligence System
Authors: Nittaya Kerdprasop, Kittisak Kerdprasop
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Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.Keywords: Business intelligence, data mining, functionalprogramming, intelligent system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17333695 A Static Android Malware Detection Based on Actual Used Permissions Combination and API Calls
Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu
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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.Keywords: Android, permissions combination, API calls, machine learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19123694 Eclectic Rule-Extraction from Support Vector Machines
Authors: Nahla Barakat, Joachim Diederich
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Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule-extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity.Keywords: Data mining, hybrid rule-extraction algorithms, medical diagnosis, SVMs
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17023693 Different Views and Evaluations of IT Artifacts
Authors: Sameh Al-Natour, Izak Benbasat
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The introduction of a multitude of new and interactive e-commerce information technology (IT) artifacts has impacted adoption research. Rather than solely functioning as productivity tools, new IT artifacts assume the roles of interaction mediators and social actors. This paper describes the varying roles assumed by IT artifacts, and proposes and distinguishes between four distinct foci of how the artifacts are evaluated. It further proposes a theoretical model that maps the different views of IT artifacts to four distinct types of evaluations.Keywords: IT adoption, IT artifacts, similarity, social actor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13483692 A Learner-Centred or Artefact-Centred Classroom? Impact of Technology, Artefacts, and Environment on Task Processes in an English as a Foreign Language Classroom
Authors: Nobue T. Ellis
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This preliminary study attempts to see if a learning environment influences instructor’s teaching strategies and learners’ in-class activities in a foreign language class at a university in Japan. The class under study was conducted in a computer room, while the majority of classes of the same course were offered in traditional classrooms without computers. The study also sees if the unplanned blended learning environment, enhanced, or worked against, in achieving course goals, by paying close attention to in-class artefacts, such as computers. In the macro-level analysis, the course syllabus and weekly itinerary of the course were looked at; and in the microlevel analysis, nonhuman actors in their environments were named and analyzed to see how they influenced the learners’ task processes. The result indicated that students were heavily influenced by the presence of computers, which lead them to disregard some aspects of intended learning objectives.
Keywords: Computer-assisted language learning, actor-network theory, English as a foreign language, task-based teaching.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16073691 Promoting Complex Systems Learning through the use of Computer Modeling
Authors: Kamel Hashem, David Mioduser
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This paper describes part of a project about Learningby- Modeling (LbM). Studying complex systems is increasingly important in teaching and learning many science domains. Many features of complex systems make it difficult for students to develop deep understanding. Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning. Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students. This study will investigate the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students- mental model from the perspective of structure, behavior and function. Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self-organized and decentralized patterns. Results show that LbM plays a major role in students' concept formation about complexity concepts.Keywords: Complexity, Educational technology, Learning by modeling, Mental models
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15693690 Improved Back Propagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model
Authors: Kavita Burse, Manish Manoria, Vishnu P. S. Kirar
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The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm. We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and compared with the standard back propagation training algorithm.Keywords: Three term back propagation, multiplicative neuralnetwork, proportional factor, local minima.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28103689 Meta Random Forests
Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti
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Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques to the random forests. We experiment the working of the ensembles of random forests on the standard data sets available in UCI data sets. We compare the original random forest algorithm with their ensemble counterparts and discuss the results.Keywords: Random Forests [RF], ensembles, UCI.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27053688 W-CAS: A Central Users Authentication and Authorization System for Enterprise Wide Web Applications
Authors: Sharil Tumin, Sylvia Encheva
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Centrally controlled authentication and authorization services can provide enterprise with an increase in security, more flexible access control solutions and an increased users' trust. By using redirections, users of all Web-based applications within an organization are authenticated at a single well known and secure Web site and using secure communication protocol. Users are first authenticated at the central server using their domain wide credentials before being redirected to a particular Web-based application. The central authentication server will then provide others with pertinence authorization related particulars and credentials of the authenticated user to the specific application. The trust between the clients and the server hosts is established by secure session keys exchange. Case- studies are provided to demonstrate the usefulness and flexibility of the proposed solution.
Keywords: Authentication, Authorization, Security, Protected Web-based Applications
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15493687 Bayesian Deep Learning Algorithms for Classifying COVID-19 Images
Authors: I. Oloyede
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The study investigates the accuracy and loss of deep learning algorithms with the set of coronavirus (COVID-19) images dataset by comparing Bayesian convolutional neural network and traditional convolutional neural network in low dimensional dataset. 50 sets of X-ray images out of which 25 were COVID-19 and the remaining 20 were normal, twenty images were set as training while five were set as validation that were used to ascertained the accuracy of the model. The study found out that Bayesian convolution neural network outperformed conventional neural network at low dimensional dataset that could have exhibited under fitting. The study therefore recommended Bayesian Convolutional neural network (BCNN) for android apps in computer vision for image detection.Keywords: BCNN, CNN, Images, COVID-19, Deep Learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8613686 Socio-Demographic Effects on Digital Libraries Preference and Use: A Case Study at Higher Learning Institutions
Authors: A. K. Razilan, A. B. Amzari, B. Ap-azli, A. R. Safawi
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Explosion in information management and information system technology has brought dramatic changes in learning and library system environments. The use of academic digital libraries does witness the spectacular impact on academic societies’ way of performing their study in Malaysia, a country with a multi-racial people. This paper highlights a research on examining the socio-demographic differences on the preference and use of academic digital libraries as compared to physical libraries at higher learning institutions. Findings indicate that preference towards digital libraries differed between ethnicity, gender and university. However none of the socio-demographic factors is statistically significant in terms of the use of digital libraries.
Keywords: Socio-demographic, academic digital library, preference, use.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14843685 Simulation of Non-Crimp 3D Orthogonal Carbon Fabric Composite for Aerospace Applications Using Finite Element Method
Authors: Sh. Minapoor, S. Ajeli, M. Javadi Toghchi
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Non-crimp 3D orthogonal fabric composite is one of the textile-based composite materials that are rapidly developing light-weight engineering materials. The present paper focuses on geometric and micromechanical modeling of non-crimp 3D orthogonal carbon fabric and composites reinforced with it for aerospace applications. In this research meso-finite element (FE) modeling employs for stress analysis in different load conditions. Since mechanical testing of expensive textile carbon composites with specific application isn't affordable, simulation composite in a virtual environment is a helpful way to investigate its mechanical properties in different conditions.Keywords: 3D orthogonal woven composite, Aerospace applications, Finite element method, Mechanical properties.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30553684 An Energy Aware Dispatch Scheme WSNs
Authors: Siddhartha Chauhan, Kumar S. Pandey, Prateek Chandra
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One of the key research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we present a Fishnet Based Dispatch Scheme (FiBDS) with energy aware mobility and interest based sensing angle. We propose two algorithms, one is FiBDS centralized algorithm and another is FiBDS distributed algorithm. The centralized algorithm is designed specifically for the non-time critical applications, commonly known as non real-time applications while the distributed algorithm is designed specifically for the time critical applications, commonly known as real-time applications. The proposed dispatch scheme works in a phase-selection manner. In this in each phase a specific constraint is dealt with according to the specified priority and then moved onto the next phase and at the end of each only the best suited nodes for the phase are chosen. Simulation results are presented to verify their effectiveness.
Keywords: Dispatch Scheme, Energy Aware Mobility, Interest based Sensing, Wireless Sensor Networks (WSNs).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16193683 Prediction on Housing Price Based on Deep Learning
Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang
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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.
Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 49853682 Evaluation of Curriculum Quality of Postgraduate Studies of Actuarial Science Field at Public Universities of Iran
Authors: F. Havas Beigi, M. Vafaee Yeganeh, E. Mohammadi
Abstract:
Evaluation and survey of curriculum quality as one of the most important components of universities system is necessary for different levels in higher education. The main purpose of this study was to survey of the curriculum quality of Actuarial science field. Case: University of SHahid Beheshti and Higher education institute of Eco insurance (according to viewpoint of students, alumni, employers and faculty members). Descriptive statistics (mean, tables, percentage, and frequency distribution) and inferential statistics (CHI SQUARE) were used to analyze the data. Six criteria considered for the Quality of curriculum: objectives, content, teaching and learning methods, space and facilities, Time, assessment of learning. Content, teaching and learning methods, space and facilities, assessment of learning criteria were relatively desirable level, objectives and time criterions were desirable level. The quality of curriculum of Actuarial Science field was relatively desirable level.
Keywords: Quality, curriculum, Actuarial science, higher education
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