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Semantic Predictive Model of Student Dynamic Profile Using Fuzzy Concept

Procedia computer science 2018年 132卷 1592-1601页

作者： T. Sheeba Reshmy Krishnan Research Scholar Department of Computer Science and Engineering Karpagam University Coimbatore-641021 India Associate Professor Department of Computing Muscat College P.O.Box: 2910 P.C:112 Ruwi Sultanate of Oman

Student profile describes the best way a student prefers to learn. It includes information on student’s characteristics such as background knowledge, learning preference, styles, interest, goals etc. The major challenge that the students face in learning system is that they are unable to retrieve relevant information based on their requirements. One of the methods used to obtain the requirement of the students is to construct an efficient student profile which would reflect the true student needs. The proposed work is to develop an intelligent ontology-based dynamic student profile that provides semantic retrieval using fuzzy concepts. The approach starts with the collection of both static and dynamic data of students. The dynamic dataof students particularly student interest and learning style are obtained by weblog analysis using algorithms such as semantic based representation using WordNet and decision tree classifier algorithm based on Felder-Silverman learning style model (FSLSM). The retrieved data is then used to construct student profile using ontology in which automatic student profile updating is obtained using ontology -based semantic similarity algorithm using WordNet. Finally, semantic retrieval of student information from ontology is achieved by integrating fuzzy concepts using fuzzy linguistic variable and ‘fuzzy IF THEN’ rules. Fuzzy linguistic variable is used to make precise representation on the existing ontology concepts which facilitate more specific classification and semantic retrieval of information. The predictive model of student profile is designed with the implementation of ‘fuzzy IF THEN’ rules using forward chaining reasoning process in the existing ontology model. The inference engine predicts the preference of a new student based on the reasoning process done for specific conditions particularly on student interest and learning style. The experiments were performed using NetBeans IDE, OWL API and Protégé 4.2 beta editor. The experime

关键词： Dynamic studentprofile Ontology WordNet Reasoning Fuzzy linguistic variable Fuzzy IF THEN rules

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Ensemble feature analysis classifier for sentiment analysis using convolutional neural networks

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Journal of Physics: Conference Series 2019年第1期1228卷

作者： M Arunasafali Chittineni Suneetha Research Scholar Dept Of Computer Science &Engg. Acharya Nagarjuna University India associate Professor Rvr & Jc College Of Engineering GunturIndia

Text mining is the worldwide fast growing domain in research. Sentiment analysis is the one of the sub domain in the text mining to extract the sentiment from the various texts available in the internet and from other sources. Various existing systems are implemented to get the sentiment analysis with the migration of natural language processing algorithms (NLP) and artificial intelligence *** issues identified in the text mining with sentiment analysis are solved very rarely. According to the previous research, deep-learning and artificial intelligencebased TSA prediction method that comprises of a stacked auto encoder (SAE) model that is used to learn generic linguistic and text semantic features But the system not reached up to the mark. In this paper, Ensemble Feature Analysis Classifier to incorporate the new domain dimension within the rating and text based sentiment analyzer. Implementation of this proposed prototype validates our claim and highlights our efficiency in supporting multiple dimensions during sentiment analysis.

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Investigation of Power Networks and their Patterns by Mathematical methods in order to Voltage Stability Enhancement

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Journal of Physics: Conference Series 2019年第1期1391卷

作者： Seyed Morteza Moghimi Ali Bostani Abolfazl Elahimanesh Department of Electrical Engineering Islamic Azad University Iran. Associate Professor Department of Electrical and Computer Engineering American University of Kuwait Salmiya Lecturer Department of Electrical Engineering University of Applied Science and Technology (UAST) Qom Iran

Authors in the research paper has considered IEEE 14-bus and 34-bus test network for creation generation and load patterns. This paper aims at analysing the potential impacts that distributed generation (DG) might have on voltage stability of electrical power networks. It is expected that increasing amounts of new generation technologies will be connected to electrical power systems in the near future. Most of these technologies are considerably smaller scale than conventional synchronous generators. In this paper, the impact of DG pattern (size and location of DG) on the voltage stability of two test system is investigated. The simulation methodology is discussed as well. It is found that the effects of DG the voltage stability of a power system strongly depend on the size and location of the distributed generators. The results show that install of DG on the weak bus, improve loadability limit and voltage stability and decrease loss of system. By investigating and reviewing the results of similar patterns in these two systems, the different generation and load patterns impact mechanism of DG units over the behaviour of the systems in terms loadability limit and voltage stability is achieved.

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Breaking google reCaptcha V2

Journal of Computing Sciences in Colleges

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Journal of Computing sciences in Colleges 2018年第1期34卷

作者： Yuan Zhou Zesun Yang Chenxu Wang Matthew Boutell Rose-Hulman Institute of Technology Associate Professor of Computer Science and Software Engineering

reCaptcha is a service designed to protect websites from spam and abuse by telling humans and robots apart. In this research, we focused on maximizing accuracy on classifying road signs vs background, in order to break the road sign puzzles of Google's reCaptcha version 2. We trained two different convolutional neural networks, did extensive dataset collection, and developed a tool to visualize the results. Our transfer learning neural network based on GoogLeNet with 144 layers has a success rate of 94.25% for classifying road signs, while our self-built neural network with 24 layers has a success rate of 77.2%. Based on our results, we believe that we have successfully broken the road sign category in Google reCaptcha version 2.

关键词： convolutional neural network image augmenter image resolution reCaptcha v2 road signs

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Keep Your Friends Close and Your Colleagues Nearby: The Hidden Ties That Improve STEM Education

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Change 2019年第1期51卷 42页

作者： Mestre, Jose P. Herman, Geoffrey L. Tomkin, Jonathan H. West, Matthew Jose P. Mestre (mestre@illinois.edu). A theoretical nuclear physicist by training Mestre’s research focuses on the learning of physics contributing in areas such as the acquisition and use of knowledge by experts and novices transfer of learning problem solving and instructional design. He has offered Congressional testimony on The Science of Learning has published numerous articles and books and is a Fellow of the American Physical Society.http://***/0000-0003-2110-1954 Geoffrey L. Herman (glherman@illinois.edu). Herman has a Ph.D. in Electrical and Computer Engineering and is a Teaching Assistant Professor in Computer Science and a Research Assistant Professor in the Department of Curriculum & Instruction at the University of Illinois. His research focuses on how students learn engineering and computing concepts and studying processes for creating systemic instructional change in college settings. Previously he was a postdoctoral researcher in Engineering Education at Purdue University.http://***/0000-0002-9501-2295 Jonathan H. Tomkin (tomkin@illinois.edu). Tomkin is a mathematically based geoscientist with two research focus areas: first he uses models and large data sets to determine how climates glaciers tectonics and landscapes interact. Second he explores how new technological and organizational approaches can improve STEM education. He produced the first geoscience MOOC is the editor of an open-source textbook and has published widely in both geoscience and STEM education. Matthew West (mwest@illinois.edu). Matthew West is an Associate Professor of Mechanical Science and Engineering at the University of Illinois with research interests in scientific computing that include time integration of deterministic and stochastic systems distributed computation and particle methods for simulation and estimation. In education he researches computer-based assessment and the development of innovative teaching cultures. Professor West is a University of Illinois Distinguished Teacher-Scholar and Colleg

A science department on the campus of the University of Illinois has received numerous national awards for excellence in undergraduate teaching. The secret to their success? For nearly two decades, they have collaboratively used evidence-based instructional practices (EBIP) in their introductory level courses. Despite these successes and efforts to spread their EBIP, no other departments followed this department's lead. Then, suddenly six years ago, many instructors from other departments began adopting EBIP in their classrooms. This article asks what was the catalyst for this change? Why had previous attempts at institutional change failed? The authors answer these questions by analyzing their efforts to transform the teaching practices in large-enrollment science and engineering introductory courses on their campus.

关键词： STEM Education Evidence Based Practice Teaching Methods Undergraduate Study College Faculty Educational Change Communities of Practice Mentors School CultureIllinois

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Design Of Novel Cloud Architecture For Energy Aware Cost Computation In Cloud Computing Environment

Design Of Novel Cloud Architecture For Energy Aware Cost Com...

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International Conference on Innovations in Power and Advanced Computing Technologies

作者： S. Kaushik Srijan Singh Raquib Khan Pathan Associate Professor School of Computer Science and Engineering VIT University Vellore India B.Tech CSE - Student School of Computer Science and Engineering VIT University Vellore India

ISBN: (纸本)9781509056835

Cloud computing mainly aims to deliver the storage and retrieval of large number of data over the internet for cloud consumers. Many researchers and developers are into the deployment of Cloud architecture by analyzing it in the cloud data centers power consumption models. The challenges faced by the data analytics are to reduce the computational power for the data, as the data growth is infinite over the period. In order to solve this problem a novel approach is needed for the cloud computing for cost computational methods. Our proposed model is an energy aware cost computation for solving the complex data computation through load balancing in the cloud. Our experimental outcomes through Cloudsim have demonstrated the effectiveness and competence of the proposed cost computational algorithm in the Cloud environment in terms of response time, throughput and other QoS metrics.

关键词： Cloud computing cloud data center energy aware cost computation load balancing response time throughput

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Investigation of Surface Roughness for Inconel 625 using Wire Electric Discharge Machining

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IOP Conference Series: Materials science and engineering 2018年第1期377卷

作者： Ashish Goyal Anand Pandey Pooja Sharma Assistant Professor Department of Mechanical Engineering Manipal University Jaipur 303007 India Associate Professor Department of Mechanical Engineering Manipal University Jaipur 303007 India Associate Professor Department of Computer Science & Engineering Manipal University Jaipur 303007 India

Due to the increase in the demand of materials with good surface finish and complex shapes, conventional machining processes became out of date. This directed to the increasing demand of non-conventional machining processes. This research work is focuses on the optimization of Surface Roughness (SR) using Wire Electrical Discharge Machining (WEDM) method on Inconel 625 super alloy material. The diffused wire tool electrode (plain and cryo-treated) is used for experimental work. Taguchi's L18 (21*35) orthogonal array's has been used to design for experimental run. The machining parameters selected for optimization are tool electrode, current (Ip), pulse on time (Ton), pulse off time (Toff), wire feed (Wf) and wire tension (Wt). Rectangular slits has been fabricated, which are useful for many industrial application like increase the heat transfer rate of fluids, micro-channels, micro-fins etc. Design of Experiment (DoE) optimization technique is used to find the most significant factor.

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An efficient collaborative approach for black hole attack discovery and mitigating its impact in manet

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AIP Conference Proceedings 2018年第1期1952卷

作者： K. Devipriya B. Persis Urbana Ivy D. Prabha 1Assistant Professor Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India 2Professor and Head Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India 3Associate Professor Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

A mobile ad hoc network (MANET) is an assemblage of nodes composed of mobile devices coupled in various ways wirelessly which do not have any central administration. Each node in MANET cooperates in forwarding packets in the network. This type of collaboration incurs high cost but there exits nodes that declines to cooperate leading to selfish conduct of nodes which effects overall network performance. To discover the attacks caused by such nodes, a renowned mechanism using watchdog can be deployed. In infrastructure less network attack detection and reaction and high false positives, false negatives initiating black hole attack becomes major issue in watchdog. This paper put forward a collaborative approach for identifying such attacks in MANET. Through abstract analysis and extensive simulation of this approach, the detection time of misbehaved nodes is reduced and substantial enhancement in overhead and throughput is witnessed.

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Software Citations and the ACAT Community

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Journal of Physics: Conference Series 2018年第2期1085卷

作者： Daniel S. Katz Assistant Director for Scientific Software and Applications National Center for Supercomputing Applications (NCSA) Research Associate Professor Computer Science (CS) Research Associate Professor Electrical and Computer Engineering (ECE) Research Associate Professor School of Information Sciences (iSchool) University of Illinois Urbana-Champaign Urbana Illinois 61801 USA

Software is essential for the bulk of research today. It appears in the research cycle as infrastructure (both inputs and outputs, software obtained from others before the research is performed and software provided to others after the research is complete), as well as being part of the research itself (e.g., new software development). To measure and give credit for software contributions, the simplest path appears to be to overload the current paper citation system so that it also can support citations of software. A multidisciplinary working group built a set of principles for software citation in late 2016. Now, in ACAT 2017 and its proceedings, we want to experimentally encourage those principles to be followed, both to provide credit to the software developers and maintainers in the ACAT community and to try out the process, potentially finding flaws and places where it needs to be improved.

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Analysis on Various Machine Learning based Approaches with a Perspective on the Performance

Analysis on Various Machine Learning based Approaches with a...

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International Conference on Innovations in Power and Advanced Computing Technologies

作者： Meesala Shobha Rani Sumathy. S Research Scholar School of Computer Science and Engineering VIT University Vellore India Associate Professor School of Information Technology and Engineering VIT University Vellore India

ISBN: (纸本)9781509056835

With the rapid development of web 2.0, increased users of online social media show keen interest to express their opinion and reviews on numerous aspects such as microblogs, various products, hotels, movies and political issues. At present, text mining plays a vital role in various application domains such as online media, healthcare, security applications, business, marketing and industrial applications. In text mining, sentiment analysis or opinion mining is a task carried over to extract or classify the information. This paper presents an exhaustive study on the performance factors highlighting the current state-of art techniques and the open issues on various machine learning based text mining approaches.

关键词： Machine Learning Supervised Semi-supervised Unsupervised Sentiment Analysis

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