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International Conference on computer, Communications, and Control Technology (I4CT)

作者： Hasmanizam Abdul Majid Mazlina Abdul Majid Mohd Izham Ibrahim Wan Nurul Safawati Wan Manan Muhammed Ramiza Ramli Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang MY Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang

The advancement of computer and Internet technologies have brought teaching and learning activities to a new dimension. Learners were virtually moved out from their classrooms to a new learning environment where learning contents and materials were delivered electronically. This new environment, which is called e-learning environment uses the web and other Internet technologies to enhance teaching and learning experience. The success and failure of any e-learning system fall on how secure the system is. Security of an e-learning system is very important so that the information contained in the system is not compromised. However no matter how secure an e-learning system is the security threats always fall on human factor. Human is identified as the weakest link in information security and lack of security awareness such as password sharing will compromise the security of e-learning system. This paper studies the awareness level in information security among e-learning users, particularly students at Higher Education Institution. The study focuses on evaluating awareness level, perception and behavior of e-learning users from International Islamic University Malaysia. Results of this study helps the university authority in preparing effective and specific awareness program in e-learning security for their students.

关键词： Electronic learning Information security computers Internet Electronic mail

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Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

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Journal of Physics: Conference Series 2017年第1期892卷

作者： Debashish Das Ali Safa Sadiq Seyedali Mirjalili A. Noraziah Faculty of Computer Systems & Software Engineering University Malaysia Pahang Kuantan Pahang Darul Makmur Malaysia Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation TPM Bukit Jalil Kuala Lumpur 57000 Malaysia School of Information Technology Monash University 46150 Bandar Sunway Malaysia School of Information and Communication Technology Griffith University Nathan Campus Brisbane QLD 4111 Australia

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

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Time series forecasting of energy commodity using grey Wolf optimizer

Time series forecasting of energy commodity using grey Wolf ...

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International MultiConference of Engineers and computer Scientists 2015, IMECS 2015

作者： Yusof, Yuhanis Mustaffa, Zuriani School of Computing College of Arts and Sciences Universiti Utara Malaysia Sintok Kedah06010 Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Malaysia

ISBN: (纸本)9789881925329

The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices. In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting. The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price. Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. Furthermore, the proposed GWO produces a better forecast for gasoline price as compared to the ABC model, as well as being at par in crude oil. Such an achievement indicates that GWO may become a competitor in the domain of time series forecasting and would be useful for investors in planning their investment and projecting their profit.

关键词： Data mining

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Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance

Using an Enhanced Feed-Forward Neural Network Technique for ...

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3rd International Multi-Conference on computer, Electrical, Electronics and Mechanical engineering (IMCEEME’15);3rd International Multi-Conference on Agricultural Chemical, Biological and Ecosystems (IMACBE’15)

作者： Adeleke Ajiboye Ruzaini Abdullah-Arshah Hongwu Qin the Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang

The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experience some academic adjustment that is associated with stress;many factors have been attributed to this, which most times, results in the high percentage of failure and low Grade Point Average(GPA). Computing the earlier academic achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-informed of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network for the creation of a network model to predict the students' performance based on their historical data is proposed. In the course of experimentations with Matlab software, two network models are created using the existing and enhanced feed-forward neural network techniques. The ability of these models to generalize is measured using simulation methods. The enhanced network model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supported financially in the form of scholarship;while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies.

关键词： data partitioning,neural networks,predictive model,students' performance

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Anonymous yoking-group proofs 15

Anonymous yoking-group proofs

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10th ACM Symposium on Information, computer and Communications Security, ASIACCS 2015

作者： Li, Nan Mu, Yi Susilo, Willy Varadharajan, Vijay Centre for Computer and Information Security School of Computer Science and Software Engineering University of Wollongong Wollongong Australia Information and Networked Systems Security Research Department of Computing Faculty of Science Macquarie University Sydney Australia

ISBN: (纸本)9781450332453

Yoking-proofs show an interesting application in Radio Frequency Identification (RFID) that a verifier can check whether two tags are simultaneously scanned by a reader. We consider a scenario that multi-group of tags can be proved to be scanned simultaneously. Grouping-proof, which is an extension of yoking-proofs, allows multiple tags to be proved together, while existing protocols cannot support multiple groups. In this paper, we introduce a novel concept called "yoking-group proofs". Additionally, we propose an anonymous yoking-proof protocol and an anonymous yoking-group proof protocol and prove their security in Universal Composability framework. Copyright © 2015 ACM.

关键词： Radio frequency identification (RFID)

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A formulation of rod based nonlinear model predictive control of nuclear reaction with temperature effects and Xenon poisoning 23

A formulation of rod based nonlinear model predictive contro...

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23rd International Conference on Nuclear engineering: Nuclear Power - Reliable Global Energy, ICONE 2015

作者： Teatro, Timothy A.V. McNelles, Phillip Eklund, J. Mikael Department of Electrical Computer and Software Engineering University of Ontario Institute of Technology OshawaON Canada Faculty of Energy Systems and Nuclear Science University of Ontario Institute of Technology OshawaON Canada

ISBN: (纸本)9784888982566

This paper presents a formulation of a nonlinear model predictive controller for steering of a nuclear reaction in a pressurized water reactor via control rod displacement. The predictive model, based on point kinetic equations, includes an averaged delayed neutron group, thermal dependence on reactivity, heat exchange between temperature and moderator and Xenon poisoning. From the model, a finite horizon optimal control problem is cast in the form of a control Hamiltonian minimization. A basic gradient descent scheme is derived to solve the optimal control problem. The predictive model and gradient descent are used to form the basis from which the model predictive controller is formulated. Copyright © 2015 by JSME.

关键词： Model predictive control

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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A separability-entanglement classifier via machine learning

arXiv

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arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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Land cover classification using Landsat data (study area: Pekan district, Pahang state, Malaysia) 4

Land cover classification using Landsat data (study area: Pe...

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4th International Conference on software engineering and computer systems, ICSECS 2015

作者： Jamaruppin, Mohamad Eliyass Bin Pebrianti, Dwi Bayuaji, Luhur Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Abdul Razak Kuantan Malaysia Faculty of Electrical and Electronic Engineering Universiti Malaysia Pahang Malaysia University Malaysia Pahang Lebuhraya Tun Abdul Razak Kuantan Malaysia

ISBN: (纸本)9781467367226

In order to investigate the terrain changed in areas of urban, vegetation, bare land, and water type mapping in Pekan, Pahang of Malaysia using Landsat data. The data of a district of educational and research on years 2000 and 2013, were analyzed. First, will evaluating the quality of the image of Landsat data. There were no obvious errors noticeable on the geometric and radiometric. In order to estimate the accuracy of the map derived from classification, a ground truth map was prepared using strip sampling on map. Image classification was performed using original and synthetic bands for following four land types: urban, water, bare land, vegetation, and cloud types. Classification was performed using maximum likelihood (ML). The highest accuracy was obtained by using ML classifier. According to the results of this study, the spectral data of Landsat data have a large potential for land type mapping in Pekan area mapping. © 2015 IEEE.

关键词： Landsat

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Solving examination timetabling problem using partial exam assignment with hill climbing search

Solving examination timetabling problem using partial exam a...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Ashis Kumar Mandal M N M Kahar Faculty of Computer Systems & Software Engineering University Malaysia Pahang Kuantan Pahang

This paper describes a method that combines graph heuristics and hill climbing for addressing the examination timetable problem. In this approach, all exams are ordered with graph heuristic ordering approach and partial exams are considered for scheduling. These partial scheduled exams are then improved using hill climbing until all exams have been successfully scheduled. Various exam assignment values with different graph heuristics ordering have been investigated. The proposed approach has been tested over the twelve Toronto benchmark datasets. The experimental results and comparison with other methods demonstrate that the proposed approach is able to produce good quality timetable.

关键词： Benchmark testing Simulated annealing Search problems Evolutionary computation Genetic algorithms computers Scheduling

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