Review of Computer Engineering Research

Published by: Conscientia Beam
Online ISSN: 2410-9142
Print ISSN: 2412-4281
Quick Submission    Login/Submit/Track

No. 2

Intensive Patient Monitoring Using LabVIEW

Pages: 92-96
Find References

Finding References


Intensive Patient Monitoring Using LabVIEW

Search :
Google Scholor
Search :
Microsoft Academic Search
Cite

DOI: 10.18488/journal.76.2019.62.92.96

Manivasagam Rajendran

Export to    BibTeX   |   EndNote   |   RIS

[1]          J. G. Webster, Encyclopedia of medical devices and instrumentation. New York: Wiley & Sons, 1988.

[2]          H. Lee, S. Park, and E. Woo, "Remote patient monitoring service through World-Wide Web Proceeding," in IEEE EMBS ’97, 19th Annual International Conference., Chicago, 1997, p. 97.

[3]          R. Manivasagam and V. Dharmalingam, "Power quality problem mitigation by unified power quality conditioner: An adaptive hysteresis control technique," International Journal of Power Electronics, vol. 6, pp. 403-425, 2014.Available at: https://doi.org/10.1504/ijpelec.2014.067442.

[4]          J. C. Lin, "Applying telecommunication technology to health-care delivery," IEEE Engineering in Medicine and Biology Magazine, vol. 18, pp. 28-31, 1999.Available at: https://doi.org/10.1109/51.775486.

[5]          I. Lacovides, C. S. Pattichis, and C. N. Schizas, "Editorial: Special issue on emerging health telematics applications in Europe," IEEE Transactions on Information Technology in Biomedicine, vol. 2, pp. 2-8, 1998.

[6]          R. Manivasagam, "Saturation analysis on current transformer," International Journal of Pure and Applied Mathematics, vol. 118, pp. 2169-2176, 2018.

[7]          S. Tachakra, R. Istepanian, and K. Banistas, "Mobile e-health: The unwired evolution of telemedicine," in Proceedings of Health Com 2001, Italy, July, 2001.

[8]          R. Manivasagam, "Modeling of a grid connected new energy vehicle charging station," International Journal of Applied  Engineering Research, vol. 10, pp. 15870- 15875, 2015.

[9]          S. Pattiches and E. Kyriacou, "Wireless telemedicine systems: An overview," IEEE Antenna’s & Propagation Magazine, vol. 44, pp. 143-153, 2002.

[10]        T. S. Rappaport, Wireless communications: Principles and practice. USA: PHI Publication, 2008.

No any video found for this article.
Manivasagam Rajendran (2019). Intensive Patient Monitoring Using LabVIEW. Review of Computer Engineering Research, 6(2): 92-96. DOI: 10.18488/journal.76.2019.62.92.96
In modern years, numerous telemedicine applications have been effectively implemented over wired communication technologies like POTS (Plain Old Telephone Systems) and ISDN (Integrated Service Digital Network). However, nowadays, modern wireless telecommunication means, such as the GSM (Global System for Mobile communication), GPRS (General Packet Radio Service), the forthcoming UMTS (Universal Mobile Telecommunication Systems) mobile telephony standards, and satellite communications, allow the operation of wireless telemedicine systems, freeing the medical personnel and/or the subject monitored from being bound to fixed locations . To offer a comparably dependable and easy way of monitoring for those people using newly available telecommunication technologies, we have proposed an Intensive Patient Monitoring System with database trailing ability. It will observe the results of simulation and its Hardware implementation can coincide up to its maximum accuracy. The investigational results prove the continuous monitoring of vital signs and be corroborate to be successful as per the required conditions. Intensive Monitoring System for various parameters monitoring with database tracking capability promises to be a powerful aid in monitoring multiple patients concurrently.
Contribution/ Originality
This study is one of very few studies which have investigated a new modular concept of patient monitoring system with multiple parameter monitoring and database tracking by exploiting the recent telecommunication technology. The Prototype of the proposed monitoring service was built-up and tested under various normal and abnormal conditions.

A Survey on Sentiment Analysis Algorithms and Datasets

Pages: 84-91
Find References

Finding References


A Survey on Sentiment Analysis Algorithms and Datasets

Search :
Google Scholor
Search :
Microsoft Academic Search
Cite

DOI: 10.18488/journal.76.2019.62.84.91

Reena G. Bhati

Export to    BibTeX   |   EndNote   |   RIS

[1]          M.-H. Chen, W.-F. Chen, and L.-W. Ku, "Application of sentiment analysis to language learning," IEEE Access, vol. 6, pp. 24433-24442, 2018. Available at: https://doi.org/10.1109/access.2018.2832137.

[2]          W. Lincy and K. M. Naveen, "A survey on challenges in sentiment analysis," International Journal of Emerging Technology in Computer Science & Electronics, vol. 21, pp. 409-412, 2016.

[3]          P. Chiranjeevi, D. T. Santosh, and B. Vishnuvardhan, Survey on sentiment analysis methods for reputation evaluation. In cognitive informatics and aoft computing. Singapore: Springer, 2019.

[4]          R. Liu, Y. Shi, C. Ji, and M. Jia, "A survey of sentiment analysis based on transfer learning," IEEE Access, vol. 7, pp. 85401-85412, 2019. Available at: 10.1109/ACCESS.2019.2925059.

[5]          P. Priyanka and Y. Pratibha, "Sentiment analysis levels and techniques: A survey," International Journal of Innovations in Engineering and Technology, vol. 6, pp. 523-528, 2016.

[6]          D. Abdullah and J. Anurag, "Survey paper on sentiment analysis: In general terms," International Journal of Emerging Research in Management &Technology, vol. 5, pp. 1093-1113, 2014.

[7]          M. Walaa, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol. 5, pp. 1093-1113, 2014. Available at: https://doi.org/10.1016/j.asej.2014.04.011.

[8]          D. M. E.-D. M. Hussein, "A survey on sentiment analysis challenges," Journal of King Saud University-Engineering Sciences, vol. 30, pp. 330-338, 2018.

[9]          M. Sadegh, R. Ibrahim, and Z. A. Othman, "Opinion mining and sentiment analysis: A survey," International Journal of Computers & Technology, vol. 2, pp. 171-178, 2012.

[10]        A. M. Dudhat, R. R. Badre, and K. Mayura, "A survey on sentiment analysis and opinion mining," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, pp. 6633-6639, 2014.

[11]        M. Al-Ayyoub, A. A. Khamaiseh, Y. Jararweh, and M. N. Al-Kabi, "A comprehensive survey of arabic sentiment analysis," Information Processing & Management, vol. 56, pp. 320-342, 2019. Available at: https://doi.org/10.1016/j.ipm.2018.07.006.

[12]        S. Archana and G. Deipali, "Sentiment analysis and challenges involved: A survey," International Journal of Science and Research, vol. 4, pp. 1928-1932, 2015.

[13]        C. Erion and M. Maurizio, "Word embeddings for sentiment analysis: A comprehensive empirical survey," arXiv:1902.00753v1, DBLP:journals/corr/abs-1902-00753, CoRR , abs/1902.00753, 2019.

[14]        S. Diksha, G. Shubham, J. Joy, and M. Richa, "Sentiment analysis," International Journal of Engineering, Science and Mathematics, vol. 8, pp. 46-52, 2019.

[15]        V. A. Kharde and P. S. Sonawane, "Sentiment analysis of twitter data: A survey of techniques. arXiv:1601.06971. Available: https://arxiv.org/abs/1601.06971 " 2016.

[16]        A. M. Yang, J. H. Lin, Y. M. Zhou, and J. Chen, "Research on building a Chinese sentiment lexicon based on SO-PMI," Applied Mechanics and Materials, vol. 263, pp. 1688-1693, 2013.

[17]        K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer learning," Journal of Big Data, vol. 1, pp. 1-40, 2016.

[18]        P. D. Turney and M. L. Littman, "Measuring praise and criticism: Inference of semantic orientation from association," ACM Transactions on Information Systems, vol. 21, pp. 315-346, 2003. Available at: https://doi.org/10.1145/944012.944013.

[19]        P. Routray, C. Kumar Swain, and S. Praya Mishra, "A survey on sentiment analysis," International Journal of Computer Applications, vol. 76, pp. 1-8, 2013.

[20]        S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, pp. 1345-1359, 2010.

[21]        A. Ruchika and G. Latika, "A hybrid approach for sentiment analysis using classification algorithm," International Journal of Computer Science and Mobile Computing, vol. 6, pp. 149-157, 2017.

[22]        J. Jeevanandam and S. Koteeswaran, "Sentiment analysis: A survey of current research and techniques," International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, pp. 3749-3757, 2015. Available at: https://doi.org/10.15680/ijircce.2015.0305002.

[23]        Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, "Hierarchical attention networks for document classification," in Proceedings of NAACL-HLT, 2016, pp. 1480-1489.

[24]        R. L. Vieriu, A. K. Rajagopal, R. Subramanian, O. Lanz, E. Ricci, N. Sebe, and K. Ramakrishnan, "Boosting-based transfer learning for multi-view head-pose classification from surveillance videos," in Proc. 20th Eur. Signal Process. Conf. (EUSIPCO), Aug, 2012, pp. 649-653.

[25]        Y. Kim, "Convolutional neural networks for sentence classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1746-1751.

[26]        B. McCann, J. Bradbury, C. Xiong, and R. Socher, "Learned in translation: Contextualized word vectors," in Advances in Neural Information Processing Systems, 2017, pp. 6294-6305.

[27]        M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, "Deep contextualized word representations," arXiv preprint arXiv:1802.05365, DBLP:journals/corr/abs-1802-05365, CoRR, volume = abs/1802.05365, 2018.

[28]        A. Conneau, H. Schwenk, L. Barrault, and Y. Lecun, "Very deep convolutional networks for text classification," arXiv preprint arXiv:1606.01781, CoRR volume = abs/1606.01781, 2016.

[29]        R. Johnson and T. Zhang, "Supervised and semi-supervised text categorization using LSTM for region embeddings," arXiv preprint arXiv:1602.02373, 2016.

[30]        A. M. Dai and Q. V. Le, "Semi-supervised sequence learning," in Processing Advances in Neural Information Processing Systems, 2015, pp. 3079-3087.

[31]        J. Howard and S. Ruder, "Universal language model fine-tuning for text classification," in arXiv preprint arXiv:1801.06146, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers), Melbourne, Australia, Association for Computational Linguistic, 2018, pp. 328–339.

[32]        A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, "Learning word vectors for sentiment analysis," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011.

[33]        R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, "Recursive deep models for semantic compositionality over a sentiment treebank," in Proceedings of the Conference Empirical Methods Natural Language Process, 2013, pp. 1631-642.

[34]        J. Blitzer, M. Dredze, and F. Pereira, "Domain adaptation for sentiment classification," in Proceedings of the 45th Annual Meeting of the Association Computational Linguistics, 2007.

[35]        A. Go, R. Bhayani, and L. Huang, "Twitter sentiment classification using distant supervision," Stanford, CA, USA, Tech. Rep. CS224N, vol. 1, 2009.

No any video found for this article.
Reena G. Bhati (2019). A Survey on Sentiment Analysis Algorithms and Datasets. Review of Computer Engineering Research, 6(2): 84-91. DOI: 10.18488/journal.76.2019.62.84.91
In this paper we present a deep literature review on existing system for sentimental analysis. Basically sentimental analysis (SA) is the measurement of preference of people’s thoughts via natural language processing. The main aim of sentiment analysis is to know the orientation of the sentiment described in script. In recent decades the researcher focuses on the study various algorithms for relevant research results of the sentiment analysis. This research paper provides a comprehensive overview of this field's latest update. In this review, some recent proposed improvements of algorithms and various SA applications are explored and briefly described. The aim of this paper is to provide knowledge about the different method related to sentimental analysis also how they are classified, what the applications of this analysis.
Contribution/ Originality
This study contributes to the existing literature by studying the existing systems and showing the disadvantages and comparative analysis based on various parameters.

Exploring Internet of Thing on PCA Algorithm for Optimization of Facial Detection and Tracking

Pages: 76-83
Find References

Finding References


Exploring Internet of Thing on PCA Algorithm for Optimization of Facial Detection and Tracking

Search :
Google Scholor
Search :
Microsoft Academic Search
Cite

DOI: 10.18488/journal.76.2019.62.76.83

Adeniji, Kehinde , Awosika, Olawoyin , Ajibade, Adedayo , Onibonoje, Moses

Export to    BibTeX   |   EndNote   |   RIS

[1]          M. Follman, G. Aronsen, and D. Pan, A guide to mass shootings in America, 2nd ed. Washington: Mojo Readers, 2012.

[2]          J. Fox and M. DeLateur, "Mass shootings in America," Homicide Studies, vol. 18, pp. 125-145, 2013.

[3]          A. Lankford, "Public mass shooters and firearms: A cross-national study of 171 countries," Violence and Victims, vol. 31, pp. 187-199, 2016.

[4]          R. Middleton, Piracy in Somalia threatening global trade, feeding local wars. London: Chatham House, 2018.

[5]          H. Onapajo and U. Uzodike, "Your haram terrorism in Nigeria," African Security Review, vol. 21, pp. 24-39, 2012.

[6]          B. Obichie, "Oil exploration in Chad Basin: NNPC seeks collaboration with military.Legit.ng - Nigeria news." Available: https://www.legit.ng/1253066-oil-exploration-chad-basin-nnpc-seeks-collaboration-military.html [Accessed 19 Aug. 2019], 2019.

[7]          M. A. O. Vasilescu and D. Terzopoulos, "Multilinear image analysis for facial recognition. In Object recognition supported by user interaction for service robots," IEEE, vol. 2, pp. 511-514, 2002.

[8]          E. M. Hand and R. Chellappa, "Attributes for improved attributes: A multi-task network utilizing implicit and explicit relationships for facial attribute classification," in AAAI-17. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017, pp. 1-3.

[9]          A. Jadhav, V. Namboodiri, and K. Venkatesh, Deep attributes for one-shot face recognition, 1st ed.: 2-4, n.d.

[10]        C. Gurel and A. Erden, "Design of a face recognition system," in International Conference on Machine Design and Production. [online] Denizli: Researcher Gate, 2012, pp. 1-20.

[11]        H. Kanchwala and V. Vaidyanathan, "Facial recognition: Definition, history, working, and applications." Science ABC. Available at: https://www.scienceabc.com/innovation/facial-recognition-works.html [Accessed 4 Aug. 2019], 2019.

[12]        Z. Liu, Z. You, A. Jain, and Y. Wang, "Face detection and facial feature extraction in color image," in International Conference on Computational Intelligence and Multimedia Applications, 2003, pp. 126-130.

[13]        C. Lin, "Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network," Pattern Recognition Letters, vol. 28, pp. 2190-2200, 2007. Available at: https://doi.org/10.1016/j.patrec.2007.07.003.

[14]        Q.-X. Ye, J.-B. Jiao, and S.-Q. Jiang, "Fast and robust pedestrian detection algorithm with multi-scale orientation features," Ruanjian Xuebao Journal of Software, vol. 22, pp. 3004-3014, 2011. Available at: https://doi.org/10.3724/sp.j.1001.2011.03987.

[15]        S.-H. Lin, S.-Y. Kung, and L.-J. Lin, "Face recognition/detection by probabilistic decision-based neural network," IEEE Transactions on Neural Networks, vol. 8, pp. 114-132, 1997. Available at: https://doi.org/10.1109/72.554196.

[16]        S. Ufldl, "PCA - Ufldl." Available: http://ufldl.stanford.edu/wiki/index.php/PCA, 2018.

[17]        Dezyre, "Principal component analysis tutorial. Available: https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial," n.d.

[18]        K. Susheel Kumar, S. Prasad, V. Bhaskar Semwal, and R. Tripathi, "Real time face recognition using Ada Boost improved fast PCA algorithm," International Journal of Artificial Intelligence & Applications, vol. 2, pp. 45-58, 2011. Available at: https://doi.org/10.5121/ijaia.2011.2305.

[19]        A. Lakhina, M. Crovella, and C. Diot, "Diagnosing network-wide traffic anomalies. In ACM SIGCOMM computer communication review," Association for Computing Machinery, vol. 34, pp. 219-230, 2004.

[20]        I. Abdel-Qader, S. Pashaie-Rad, O. Abudayyeh, and S. Yehia, "PCA-based algorithm for unsupervised bridge crack detection," Advances in Engineering Software, vol. 37, pp. 771-778, 2006. Available at: https://doi.org/10.1016/j.advengsoft.2006.06.002.

No any video found for this article.
Adeniji, Kehinde , Awosika, Olawoyin , Ajibade, Adedayo , Onibonoje, Moses (2019). Exploring Internet of Thing on PCA Algorithm for Optimization of Facial Detection and Tracking. Review of Computer Engineering Research, 6(2): 76-83. DOI: 10.18488/journal.76.2019.62.76.83
This work was able to integrate Internet of Things expertise on Principal Count Analysis (PCA) algorithm for facial recognition optimization. An internet-based real-time facial recognition system was developed using PCA algorithm that can detect and track faces, a label name that indicates the face identified was also incorporated to easily track the face in situations where multiple faces are identified at the same time. A router was used to set up a wireless connection with an internet protocol (IP)-camera via the IP-camera firmware. As the router broadcast this connection, a link is set up with a personal computer through the network and sharing centre tab on the computer system, thereby creating a wireless connection between the computer and the camera on the internet. Graphical interface designed on MATLAB was used to access the feed from the camera which PCA algorithm explored in detecting and tracking faces in real-time. Security features such as timestamp and database were also integrated with the system developed.
Contribution/ Originality
This study integrates PCA algorithm to detect, recognize and match faces detected with a GUI design on MATLAB in real-time. Most importantly a label was created to easily identify the face when multiple faces are detected concurrently.

A Review of Machine Learning Models for Software Cost Estimation

Pages: 64-75
Find References

Finding References


A Review of Machine Learning Models for Software Cost Estimation

Search :
Google Scholor
Search :
Microsoft Academic Search
Cite

DOI: 10.18488/journal.76.2019.62.64.75

Farrukh Arslan

Export to    BibTeX   |   EndNote   |   RIS

[1]          S. Kumari and S. Pushkar, "Cuckoo search based hybrid models for improving the accuracy of software effort estimation," Microsystem Technologies, vol. 24, pp. 4767-4774, 2018. Available at: https://doi.org/10.1007/s00542-018-3871-9.

[2]          P. Pospieszny, B. Czarnacka-Chrobot, and A. Kobylinski, "An effective approach for software project effort and duration estimation with machine learning algorithms," The Journal of Systems & Software, vol. 137, pp. 184–196, 2018. Available at: https://doi.org/10.1016/j.jss.2017.11.066.

[3]          K. Langsari, R. Sarno, and Sholiq, "Optimizing effort parameter of COCOMO II using particle swarm optimization method," Telkomnika, vol. 16, pp. 2208-2216, 2018. Available at: https://doi.org/10.12928/telkomnika.v16i5.9703.

[4]          I. Attarzadeh and S. H. Ow, "Improving estimation accuracy of the COCOMO II using an adaptive fuzzy logic model," presented at the 2011 IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, 2011.

[5]          R. Litoriya, N. Sharma, and D. A. Kothari, "Incorporating cost driver substitution to improve the effort using Agile COCOMO II," presented at the 2012 CSI Sixth International Conference on Software Engineering, 2012.

[6]          R. Saljoughinejad and V. Khatibi, "A new optimized hybrid model based On COCOMO to increase the accuracy of software cost estimation," Journal of Advances in Computer Engineering and Technology, vol. 4, pp. 27-40, 2018.

[7]          Z. Chen, T. Menzies, D. Port, and B. Boehm, "Feature subset selection can improve software cost estimation accuracy," ACM SIGSOFT Software Engineering Notes, vol. 30, pp. 1-6, 2005. Available at: https://doi.org/10.1145/1082983.1083171.

[8]          Z. A. Khalifelu and F. S. Gharehchopogh, "Comparison and evaluation of data mining techniques with algorithmic models in software cost estimation," Procedia Technology, vol. 1, pp. 65-71, 2012. Available at: https://doi.org/10.1016/j.protcy.2012.02.013.

[9]          P. A. Whigham, C. A. Owen, and S. G. Macdonell, "A baseline model for software effort estimation," ACM Transactions on Software Engineering and Methodology, vol. 24, pp. 1-11, 2015. Available at: https://doi.org/10.1145/2738037.

[10]        F. Sarro, A. Petrozziello, and M. Harman, "Multi-objective software effort estimation," presented at the ACM 38th IEEE International Conference on Software Engineering, 2016.

[11]        Y. Masoudi-Sobhanzadeh, H. Motieghader, and A. Masoudi-Nejad, "Feature select: A software for feature selection based on machine learning approaches," BMC Bioinformatics, vol. 20, pp. 1-17, 2019. Available at: https://doi.org/10.1186/s12859-019-2754-0.

[12]        V. Vig and A. Kaur, "Test effort estimation and prediction of traditional and rapid release models using machine learning algorithms," Journal of Intelligent & Fuzzy Systems, vol. 35, pp. 1657-1669, 2018. Available at: https://doi.org/10.3233/jifs-169703.

[13]        A. Khalid, M. A. Latif, and M. Adnan, "An approach to estimate the duration of software project through machine learning techniques," Gomal University Journal of Research, vol. 33, pp. 1-13, 2017.

[14]        T.-H. Yeh and S. Deng, "Application of machine learning methods to cost estimation of product life cycle," International Journal of Computer Integrated Manufacturing, vol. 25, pp. 340-352, 2012. Available at: https://doi.org/10.1080/0951192x.2011.645381.

[15]        M. D. Ganggayah, N. A. Taib, Y. C. Har, P. Lio, and S. K. Dhillon, "Predicting factors for survival of breast cancer patients using machine learning techniques," BMC Medical Informatics and Decision Making, vol. 19, pp. 1-17, 2019. Available at: https://doi.org/10.1186/s12911-019-0801-4.

[16]        P. Pandey, "Analysis of the techniques for software cost estimation," presented at the 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT), Rohtak, India, 2013.

[17]        B. Başkeleş, B. Turhan, and A. Bener, "Software effort estimation using machine learning methods," presented at the 2007 22nd International Symposium on Computer & Information Sciences, 2007.

[18]        J. Rahikkala, S. Hyrynsalmi, V. Leppänen, and I. Porres, "The role of organisational phenomena in software cost estimation: A case study of supporting and hindering factors," E-Informatica Software Engineering Journal, vol. 12, pp. 167–198, 2018.

[19]        M. Vyas, A. Bohra, D. C. Lamba, and A. Vyas, "A review on software cost and effort estimation techniques for agile development process," International Journal of Recent Research Aspects, vol. 5, pp. 612-618, 2016.

[20]        S. A. Woznicki, J. Baynes, S. Panlasigui, M. Mehaffey, and A. Neale, "Development of a spatially complete floodplain map of the conterminous United States using random forest," Science of the Total Environment, vol. 647, pp. 942-953, 2019. Available at: https://doi.org/10.1016/j.scitotenv.2018.07.353.

[21]        S. Kalmegh, "Analysis of weka data mining algorithm reptree, simple cart and randomtree for classification of Indian news," International Journal of Innovative Science, Engineering & Technology, vol. 2, pp. 438-446, 2015.

[22]        S.-A. Blaifi, S. Moulahoum, R. Benkercha, B. Taghezouit, and A. Saim, "M5P model tree based fast fuzzy maximum power point tracker," Solar Energy, vol. 163, pp. 405-424, 2018. Available at: https://doi.org/10.1016/j.solener.2018.01.071.

[23]        T. Rajasekaran, P. Jayasheelan, and K. S. Preethaa, "Predictive analysis in agriculture to improve the crop productivity using zeroR algorithm," International Journal of Computer Science and Engineering Communications, vol. 4, pp. 1397-1401, 2016.

[24]        B. G. Becker, "Visualizing decision table classifiers," in Proceedings IEEE Symposium on Information Visualization, 1998.

[25]        Class Input Mapped Classifier, Available: http://weka.sourceforge.net, 2019.

[26]        Additive Regression, Available: https://www.cs.waikato.ac.nz/ml/weka/, 2019.

[27]        Gerardnico, "Machine learning - K-nearest neighbors (KNN) algorithm - instance based learning." Available: https://gerardnico.com/, 2017.

[28]        University of Konstanz, "K*  Algorithm  (K  Star)." Available: https://www.sen.uni-konstanz.de/, 2019.

[29]        M. Krasser, "Gaussian processes." Available: http://krasserm.github.io, 2018.

[30]        Geeksforgeeks, "ML linear regression." Available: https://www.geeksforgeeks.org, 2019.

[31]        P. Singh and S. Agrawal, "Node localization in wireless sensor networks using the M5P tree and SMOreg algorithms," presented at the 2013 5th International Conference and Computational Intelligence and Communication Networks. IEEE, 2013.

No any video found for this article.
Farrukh Arslan (2019). A Review of Machine Learning Models for Software Cost Estimation. Review of Computer Engineering Research, 6(2): 64-75. DOI: 10.18488/journal.76.2019.62.64.75
Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. The aim of such a process is to have a better future sight of the project progress and its phases. Another main objective is to have clear project details and specifications to assist stakeholders in managing the project in terms of human resources, assets, software, data and even in the feasibility study. Accurate estimation results with definitely helps the project manager to do better estimation for the project cost, the time required for various project phases and resources or assets. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate.
Contribution/ Originality
This study contributes to the existing literature by enhancing the results of thirteen Machine Learning algorithms on two datasets. The evaluation criteria used in this work are R², MAE, RMAE, RAE, and RRSE. The aim of the proposed model is to predict the effort using dataset attributes and compare them with the actual effort in order to measure the error using different criteria.

Stability Analysis of Type-2 Fuzzy Process Control Using LMI

Pages: 57-63
Find References

Finding References


Stability Analysis of Type-2 Fuzzy Process Control Using LMI

Search :
Google Scholor
Search :
Microsoft Academic Search
Cite

DOI: 10.18488/journal.76.2019.62.57.63

Manivasagam Rajendran , Prabhu Aruchunan

Export to    BibTeX   |   EndNote   |   RIS

[1]          K. Gu, V. L. Kharitonov, and J. Chen, Stability of time-delay systems. Berlin, Germany: Birkhauser, 2003.

[2]          J.-P. Richard, "Time-delay systems: An overview of some recent advances and open problems," Automatica, vol. 39, pp. 1667-1694, 2003. Available at: https://doi.org/10.1016/s0005-1098(03)00167-5.

[3]          M. S. Mahmoud and N. F. Al-Muthairi, "Design of robust controllers for time-delay systems," IEEE Transactions on Automatic Control, vol. 39, pp. 995-999, 1994.

[4]          T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, pp. 116-132, 1985.

[5]          Y.-Y. Cao and P. M. Frank, "Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach," IEEE Transactions on Fuzzy Systems, vol. 8, pp. 200-211, 2000. Available at: https://doi.org/10.1109/91.842153.

[6]          R. Manivasagam and V. Dharmalingam, "Power quality problem mitigation by unified power quality conditioner: An adaptive hysteresis control technique," International Journal of Power Electronics, vol. 6, pp. 403-425, 2014. Available at: https://doi.org/10.1504/ijpelec.2014.067442.

[7]          P. A. Birkin and J. M. Garibaldi, "A comparison of type-1 and type-2 fuzzy controllers in a micro-robot context," presented at the 2009 IEEE international conference on fuzzy systems, 2009.

[8]          P. Manikandan, M. Geetha, T. K. Vijaya, K. S. Elamurugan, and V. Silambarasan, "Real-time implementation and performance analysis of an intelligent fuzzy logic controller for level process," presented at the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013.

[9]          R. Manivasagam and D. Aarthi, "Design of UPFC using ten switch converter with switch reduction," International  Journal for Scientific Research & Evelopment, vol. 5, pp. 490-493, 2017.

[10]        H.-K. Lam and L. D. Seneviratne, "Stability analysis of interval type-2 fuzzy-model-based control systems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, pp. 617-628, 2008.

[11]        D. Wu, "Fundamental differences between interval type-2 and type-1 fuzzy logic controllers," IEEE Transactions on Fuzzy Systems, vol. 20, pp. 832-848, 2012.

[12]        H. Zhou and H. Ying, "A method for deriving the analytical structure of a broad class of typical interval type-2 Mamdani fuzzy controllers," IEEE Transactions on Fuzzy Systems, vol. 21, pp. 447-458, 2012. Available at: https://doi.org/10.1109/tfuzz.2012.2226891.

[13]        R. Manivasagam and R. Raghavi, "Modeling of a grid connected new energy vehicle charging station," International Journal of Applied Engineering Research, vol. 10, pp. 15870-15875, 2015.

[14]        T. Kumbasar, "Robust stability analysis and systematic design of single-input interval type-2 fuzzy logic controllers," IEEE Transactions on Fuzzy Systems, vol. 24, pp. 675-694, 2015. Available at: https://doi.org/10.1109/tfuzz.2015.2471805.

[15]        D. H. Lee, Y. H. Joo, and M. H. Tak, "LMI conditions for local stability and stabilization of continuous-time TS fuzzy systems," International Journal of Control, Automation and Systems, vol. 13, pp. 986-994, 2015. Available at: https://doi.org/10.1109/tfuzz.2015.2471805.

[16]        G. Pascal, N. Arkadi, J. L. Alan, and C. Mahmoud, "LMI control toolbox user's guide," IEEE Xplore, The MathWorks, Inc, vol. 1, pp. 1-12, 1995.

[17]        W. Dongrui and W. T. Woei, "Interval type-2 fuzzy pi controllers: Why they are more robust," presented at the IEEE International Conference on Granular Computing, 2010.

[18]        R. Manivasagam, P. Parthasarathy, and R. Anbumozhi, "Robust analysis of T-S fuzzy controller for nonlinear system using H-infinity," Advances in Intelligent Systems and Computing, vol. 949, pp. 643-651, 2019. Available at: https://doi.org/10.1007/978-981-13-8196-6_56.

Manivasagam Rajendran , Prabhu Aruchunan (2019). Stability Analysis of Type-2 Fuzzy Process Control Using LMI. Review of Computer Engineering Research, 6(2): 57-63. DOI: 10.18488/journal.76.2019.62.57.63
This paper exhibits a type of fuzzy robust plan designed for nonlinear time-delay system based on the fuzzy Lyapunov method. In addition, the obtainable delay-self-governing state is changed into linear matrix inequalities (LMIs) consequently the fuzzy state response gain and regular solutions are numerically possible by nature inspired optimization algorithm. LMI approach for determining robust stability of non-linear system such as to an exothermic continuous-time stirred tank reactor (CSTR) by parametric uncertainties. Essential and satisfactory circumstances intended for stabilization of a linear continuous-time uncertain system through static output feedback are specified at earliest. After that the difficulty of robust static output feedback control plan is transformed to resolution of linear matrix inequalities (LMIs) and two LMI based algorithms, iterative and non-iterative ones are used. The design process guarantee by satisfactory circumstances the healthy quadratic stability and assured price. The opportunity to utilize a healthy static output response for control of CSTRs with suspicions is verified via simulation results.
Contribution/ Originality
This study is one of the very few studies which have investigated about T-S Fuzzy Technique. Here two techniques are used to control, nonlinear plant CSTR model. LMI approach for determining robust stability of non-linear system such as to an exothermic continuous-time stirred tank reactor (CSTR) by parametric uncertainties.