iosr-JCE   Volume-21 ~ Issue-1 ~ Jan-Feb 2019

Abstract: Artificial intelligence has made its presence felt ubiquitously in different avenues of research and technology wherein the data is large and complex. In the proposed work, to forecast solar irradiation energy; whose structure uses the back-propagation concept and uses the Levenberg Marquardt algorithm is used. The system used hitherto a single layer of hidden neurons. The averaging approach is also been used with 2, 12- and 24-hour averaging scheme so as to increase the accuracy of prediction. The system attains a MAPE of 2.7%. Hence the accuracy attained is 97%. The mean square error has been chosen as the performance function for the proposed algorithm.

Keywords: Solar Energy Prediction, Artificial Neural Network (ANN), Back Propagation, Levenberg-Marquardt (LM) Algorithm, Mean Absolute Percentage Error (MAPE).

[1]. L.Saad Saoud, F.Rahmoune, V.Tourtchine, K.Baddari in the paper "Fully Complex Valued Wavelet Neural Network for Forecasting the Global Solar Irradiation", Springer 2016
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[3]. Vishal Sharma, Dazhi Yang, Wilfred Walsh, Thomas Reindl in the paper "Short Term Solar Irradiance Forecasting Using A Mixed Wavelet Neural Network" Elsevier 2016
[4]. Ozgur Kisi, Erdal Uncuoghlu, "Comparison of three backpropagation training algorithms for two case studies," Indian Journal of Engineering & Materials Sciences, Volume 12, pp. 434-442, 2005.
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Abstract: Error correction code (ECC) and built-in selfrepair(BISR) techniques by using redundancies have beenwidely used for improving the yield and reliability of embeddedmemories. The target faults of these two schemes are soft errorsand permanent (hard) faults, respectively. In recent works, thereare also some techniques integrating ECC and BISR to deal withsoft errors and hard defects simultaneously. However, this willcompromise reliability, since some of the ECC protection capabilityis used for repairing single hard faults. To cure this dilemma,we propose an ECC-enhanced BISR (EBISR) technique, whichuses ECC to repair single permanent faults first and sparesfor the remaining faults in the production/power-ON test andrepair stage. However, techniques are proposed to maintain.........

Index Terms—Built-in self-repair (BISR), error correctioncode (ECC), hard repair, reliability, yield.

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Abstract: Frequent itemset mining is one of the important aspects of association rule mining. The primary algorithm based for frequent itemset mining is mostly based on CPU and they generated a large set of items that are required to be kept in memory all the time while processing. In this dissertation thesis, we designed a parallel Eclat algorithm. The algorithm will run on GPU and perform the task of the frequentitemset mining in parallel. The proposed algorithm also uses the optimized candidate representation and the frequent item sets generated are stored in cache memory and are fetched directly from the cache memory. The proposed algorithm runs in parallel and also uses the optimized candidate representation and thus provides better performance than the classical eclat algorithm. Thus, the proposed algorithm runs much faster than the classical eclat algorithm and has better performance than classical eclat algorithm in terms of memory and time..

Keywords: FIM(Frequent Itemset Mining), Equivalent Class, Candidate sets, support count, Eclat

[1]. K. Jiawei, Han Micheline, Data Mining Concepts and Techniques.
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[3]. S. Dutt, N. Choudhary, and D. Singh, An Improved Apriori Algorithm based on Matrix Data Structure, Global Journal of Computer Science and Technology: C Software & Data Engineering,14(5), 2014.
[4]. U. Grag, and M. Kaur, ECLAT Algorithm for Frequent Itemsets Generation, International Journal of Computer Systems,1(3), 2014,1-4.
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Abstract: Nowadays, embedded systems are becoming critical applications and play important roles in modern society. Therefore, quality assurance for these types of systems has attracted many attentions in software engineering research and development community. In these systems, testing procedure needs high coverage criteria such that test data cover all possible run. However, it is difficult to generate test data to coverage all possible runs due to the complex embedded system. In this paper, we propose a method to automatically generate test data based on built-in source signal in order to obtain test suite with high coverage. The experimental show that the proposed method generates a better test data than that of random test and Matlab/MBT test tool.

Keywords: embedded system, Matlab/simulink, MCDC coverage, testing, continuous signal

[1]. Godboley, S., Sridhar, A., Kharpuse, B., Mohapatra, D.P. and Majhi, B., 2013. Generation of branch coverage test data for simulink/stateflow models using crest tool. International Journal of Advanced Computer Research, 3(4), p.222..
[2]. Godefroid, P., Klarlund, N. and Sen, K., 2005, June. DART: directed automated random testing. In ACM Sigplan Notices (Vol. 40, No. 6, pp. 213-223). ACM.
[3]. Holling, D., Pretschner, A. and Gemmar, M., 2014, September. 8cage: lightweight fault-based test generation for simulink. In Proceedings of the 29th ACM/IEEE international conference on Automated software engineering (pp. 859-862). ACM..
[4]. Matinnejad, R., Nejati, S., Briand, L.C. and Bruckmann, T., 2016, May. Automated test suite generation for time-continuous simulink models. In Proceedings of the 38th international conference on software engineering (pp. 595-606). ACM.
[5]. Matinnejad, R., Nejati, S., Briand, L. C., &Bruckmann, T. (2016, May). SimCoTest: A test suite generation tool for Simulink/Stateflow controllers. In Proceedings of the 38th International Conference on Software Engineering Companion (pp. 585-588). ACM.

Abstract: In this study, an Intrusion Detection System (IDS) is proposed based on the use of machine learning and distributed computing. The proposed system uses classification techniques that are implemented in the built-in machine learning library in Apache Spark distributed computing framework. As the use of distributed computing allows the proposed method to provide rapid predictions for the packets flowing in the network, two classifiers are cascaded in order to combine their decisions for more accurate decisions. The Multi-Layer Perceptron (MLP) classifier is used as a binary classifier, where the output of this classifier only indicates whether the packet is a normal or........

Keywords: Distributed Computing, Machine Learning, Anomaly Detection, Network Security

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Abstract: At present,Point of Sale (POS) software is used widely in retail business. It has changed the manual system of business to computerized system. The main goal of this paper is to implement point of sale software which is used in mobile shop for purchasing and selling as well as generating all necessary reports.Human faults and paper works are decreased by using this software. Even the system works faster than any time.Any operating system can be used to run the software properly.The software is developed following incremental model. HTML, CSS, JavaScript, Ajax& PHP are used to design & develop the software. MySQL is also used here as database to store the data........

Keywords: Cash Memo, Mobile Shop, Point of Sale (POS), Purchase, Software, Stock

[1]. Amber Gillum, Mohammad A. Rob, IT project management: class project of a point of sale (POS) system implementation in a restaurant, International Association for Computer Information Systems- Issues in Information Systems,ISSN 1529-7314, Volume XII, No. 2, pp.67-73, 2011.

[2]. Manion, C., DeMicco, F. J., Handheld wireless point of sale systems in the restaurant industry, Journal of food service business research, 7(2),pp.103-111, 2004.

[3]. http://posspot.com/various_pos/electronic-store/
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Abstract: The cervical cancer refers to the uncontrolled growth of cells in the internal lining of the cervix part that connects the uterus region to the vaginal part of the women. This cervical cancer needs to be screened for identifying pre-cancers before they get transformed into invasive cancer. The Pap Smear Test is considered as the potential screening test essential for detecting pre-cancerous cells in the cervix, such that the uncontrolled growth of inter-uterine wall cells can be prevented. The majority of the segmentation schemes proposed in the literature for detecting cervical cancer failed in predominant localization of cytoplasm and nucleus boundaries from the pap smear extracted during.......

Keywords: Cervical Cancer, Improvised Kernel Graph Cuts, Continuous Max-Flow Optimization, Cytoplasm Boundaries, Pap Smear Cells.

[1]. Plissiti, M. E., Nikou, C., & Charchanti, A. (2011). Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognition Letters, 32(6), 838-853.

[2]. Marinakis, Y., Dounias, G., & Jantzen, J. (2009). Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine, 39(1), 69-78.

[3]. Chen, S., Zhao, M., Wu, G., Yao, C., & Zhang, J. (2012). Recent Advances in Morphological Cell Image Analysis. Computational and Mathematical Methods in Medicine, 2012, 1-10.

[4]. Bergmeir, C., García Silvente, M., & Benítez, J. M. (2012). Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework. Computer Methods and Programs in Biomedicine, 107(3), 497-512.

[5]. Chankong, T., Theera-Umpon, N., & Auephanwiriyakul, S. (2014). Automatic cervical cell segmentation and classification in Pap smears. Computer Methods and Programs in Biomedicine, 113(2), 539-556..

Abstract: In this paper, a method is presented to find fuzzy critical path using parametric interval valued function. Here network model has been developed in fuzzy environment. All activities are considered as a Trapezoidal fuzzy number. This fuzzy number has been transform as an interval number using Nearest Interval Approximation method. In this work, we develop a parametric interval-valued functional form of an interval number and then solve the parametric network problem. To illustrate the technique, an airport's cargo ground operation system is considered here.

Keywords: Network Problem, Critical Path Method, Fuzzy Number, Trapezoidal Fuzzy Number

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[4]. Shankar, N. R., Sireesha,V. and Rao, P. B.,An analytical method for finding critical path in a fuzzy project network, International Journal of Contemporary Mathematical Sciences,5,953-962,(2010).
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Abstract:This paper discuss about system implementation of image processing technique for weed detection and removal. The system presents a machine vision system for weed detection in vegetable crops using outdoor images. The purpose of this paper is to develop a useful algorithm to discriminate weed, using image filtering to extract color and area features, then, a process to label each object in the scene is implemented, finally, a classification based on area is proposed, including sensitivity, specificity, positive and negative predicted values in order to evaluate algorithm performance.

Keywords: machine vision, weed identification, classical classifier, neuronal networks, unwanted weed, weed in crops

[1]. M. Mustafa, A. Hussain, K. Ghazali and S. Riyadi, "Implementation of Image Processing Technique in Real Time Vision System for Automatic Weeding Strategy", in IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt, 2007, pp. 632-635
[2]. A. Tellaeche, G. Pajares, X. Burgos and A. Ribeiro, "A computer vision approach for weeds identification through Support Vector Machines", Applied Soft Computing, vol. 11, pp. 908-915, 2011.
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[4]. H. Liu, S. Lee and C. Saunders, "Development of a machine vision system for weed detection during both of off-season and in-season in broadacre no-tillage cropping lands", American Journal of Agricultural and Biological Sciences, vol. 9, pp. 174-193, 2014.
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Abstract: Cloud computing is a new technology that brings new challenges to all organizations around the world. Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development of the scale and number of applications, this access is often threatened by overload. Therefore, this paper our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud computing to improve performance for end-user. We have simulated the proposed algorithm with the CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the cloud data center.

Keywords: Load balancing; response time; cloud computing; processing time

[1]. Syed Hamid Hussain Madni (2016), "Recent advancements in resource allocation techniques for cloud computing environment: a systematic review", Springer Science+Business Media New York 2016, DOI 10.1007/s10586-016-0684-4,.
[2]. Shubham Sidana, Neha Tiwari, Anurag Gupta Inall Singh Kushwaha (2016), "NBST Algorithm: A load balancing algorithm in cloud computing", International Conference on Computing, Communication and Automation (ICCCA2016), DOI: 10.1109/CCAA.2016.7813914, 29-30 April, Noida, India.
[3]. Feilong Tang, Laurence T. Yang, Can Tang, Jie Li and Minyi Guo (2016), "A Dynamical and Load-Balanced Flow Scheduling Approach for Big Data Centers in Clouds", DOI 10.1109/TCC.2016.2543722,IEEE TRANSACTIONS ON CLOUD COMPUTING.
[4]. Sambit Kumar Mishra, Md Akram Khan, Bibhudatta Sahoo, Deepak Puthal, Mohammad S. Obaidat, and KF Hsiao (2017), "Time efficient dynamic threshold-based load balancing technique for Cloud Computing" IEEE International Conference on Computer, Information and Telecommunication Systems (CITS), 21-23 July, Dalian, China.
[5]. Sobhan Omranian-Khorasani and Mahmoud Naghibzadeh (2017), "Deadline Constrained Load Balancing Level Based Workflow Scheduling for Cost Optimization", 2017 2nd IEEE International Conference on Computational Intelligence and Applications, Beijing, China 8-11 Sept

Abstract: In this paper, we propose a fast real time clustering method and an algorithm for detecting polygon lines and their orientations. The content is presented in two parts. In part I, a new clustering method based on density peeling technique is proposed, which sets up the basis of objects features clustering; in part II, a new polygon detection method is proposed based on the peeling technique and a statistical histogram of directions of pixels of the polygon lines; the effective pixels, which contributes to the orientations and positions of polygon lines, are extracted as soon as the polygon shape is obtained. The methods proposed in these two parts are based on the principle that iterative searching and comparisons should be maximally avoided and thus are fast algorithms suitable for real time application, especially for embedded systems

Keywords: clustering; contour tracing; peeling technique; polygon detection; direction histogram

[1]. Wikipedia - Edge Detection (\url{https://en.wikipedia.org/wiki/Edge_detection})
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Abstract: Although catheterization is an important tool in the diagnosis and the treatment of cardiovascular diseases, it may cause different complications such as death or myocardial infarction during diagnosis. Data mining techniques are used for the construction of a cardiac catheterization Prediction System (CCPS) for whom catheterization is needed; therefore, it can decrease the complications of Cardiac catheterization Procedure. The aim of this study is to predict whether a patient needs a cardiac catheterization procedure or not. WEKA software was used in this experimental evaluation study of the Home dataset. Five classification algorithms were used for the prediction of catheterization procedure based on the prediction Accuracy, True Positive, True Negative, and ROC area. This study concluded that J48 without smoker attribute was a well-suited model for the prediction of whether a patient needs a cardiac catheterization procedure or not.

Keywords: Data mining, supervised Classification Algorithms, Machine Learning, Cardiac catheterization

[1]. World Health Organization. Cardiovascular diseases (CVDs). https://www.who.int/cardiovascular_diseases/en/. [Accessed 3rd January 2019].
[2]. K. T. Keerthana. "Heart Disease Prediction System using Data Mining Method". International Journal of Engineering Trends and Technology (IJETT), Vol. 47, Issue 6, 2017, pp. 361-63.
[3]. G.W. Reed, M.L. Tushman, and S.R. Kapadia. "Effective Operational Management in the Cardiac Catheterization Laboratory". Journal of the American College of Cardiology, Vol. 72, Issue 20, 2018, pp. 2507-17.
[4]. B. Srinivasan and K. Pavya. "A STUDY ON DATA MINING PREDICTION TECHNIQUES IN HEALTHCARE SECTOR". International Research Journal of Engineering and Technology (IRJET). Vol. 3, Issue 3, 2016, pp. 552-56.
[5]. M. Gera and S. Goel. "Data Mining - Techniques, Methods and Algorithms: A Review on Tools and their Validity". International Journal of Computer Applications, Vol. 113, Issue 18, 2015, pp. 22-29..

Abstract: This study examines to identify the demand and the development of machine learning-based mobile big data analysis by exploring the insights of obstacles in the mobile big data space. The goal of this investigation is to ultimately build machine learning-based mobile big data analysis (MBD). In addition to this, it examines the most recent developments in the field of MBD about the applications of data analysis. To begin, we will describe how MBD came into being. In the second step of the process, the most common approaches to data analysis are examined. There are three common applications of MBD analysis that are introduced in this article. These applications include wireless channel modelling, human online and offline behaviour analysis, and voice recognition

Keywords: supervised and unsupervised learning machines, data analysis, and mobile data

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