Archive

Abstract:

In the present numerical investigation, the epidemic patterns of Covid-19 in India is studied from a mathematical modeling perspective. The study is based on the simple SIR (Susceptible-Infectious-Recovered) deterministic compartmental model. It is analyzed fully and then calibrated against publicly available epidemiological data from late January until 10 July 2020 for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19) in India. The purpose of this study is to give a tentative prediction of the epidemic peak and sizes in our country.

Keywords:

COVID-19,India,SIR Model,Parameter Estimation,Simulation,

Refference:

An Introduction to Mathematical Epidemiology by Maia Martcheva, Springer
II. An Introduction to Mathematical Modeling of Infectious Diseases, Michael Y. Li, Springer.
III. Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 S Gupta, G S Raghuwanshi , A Chanda, Science of the Total Environment, 728 (2020)
IV. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases.
V. Kermack WO, McKendrick AG. A contribution to the mathematical theory of
VI. Rajesh Ranjan, The Ohio State University, Predictions for COVID-19 outbreak in
VII. Solving applied mathematical problems with MATLAB / DingyuXue, Chapman & Hall/CRC.
VIII. United Nations. Department of Economic and Social Affairs; Population Dynamics https://population.un.org/wpp/Download/Standard/Population/ as on 20 May 2020.
IX. Ward, Alex (24 March 2020). “India’s coronavirus lockdown and its looming crisis, explained” (http s://www.vox.com/2020/3/24/21190868/coronavirus-india-modi-lockdown-kashmir).

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Abstract:

The need of proper medical diagnosis and treatment has been need of the day to deal with various infections caused by viruses and micro-organisms. To prevent the spread of the disease we need proper scientific approach and methods in place. This paper suggests one such method using Markov Process technique, in particular deciding how many patients should be allocated to respective doctors in a hospital.

Keywords:

Markov Process,Markov Decision Process,Transition Probabilities, Transition Matrix, Diagonalization of a matrix,, Equilibrium Distribution ,

Refference:

I 49(10):1021–1025, 1998.

II Amanda A. Honeycutt, James P. Boyle, Kristine R. Broglio, Theodore J. Thompson, Thomas J. Hoerger, Linda S. Geiss, and K. M. Venkat Narayan, A dynamic Markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management

III Behavioral Sciences, pages 9242–9250, 2004.

IV Chih-Ming Liu, Kuo-Ming Wang, and Yuh-Yuan Guh. A Markov chain model for medical

V Distribution under treatment. Mathematical and Computer Modeling, 19(11):53–66, 1994.

VI For discrete-time longitudinal data on human mixed-species infections. In Some Mathematical

VII J. E. Cohen and B. Singer. Malaria in Nigeria: Contrained continuous-time Markov models

VIII L. Billard. Markov models and social analysis, International Encyclopedia of the Social and

IX Questions in Biology, pages 69–133. Providence: American Mathematical Society, 1979.

X Record analysis. The Journal of the Operational Research Society, 42(5):357–364, 1991.

XI S. I. McClean, B. McAlea, and P. H. Millard. Using a Markov reward model to estimate

XII Science, 6:155–164, 2003.

XIII Spend-down costs for a geriatric department. The Journal of the Operational Research Society,

XIV Y. W. Tan. First passage probability distributions in Markov models and the HIV incubation

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Abstract:

We proposed  [0,1] truncated Lomax –Inverted Gamma ([0,1] TLIGD) distribution build on [0,1] truncated Lomax ([0,1] TLD) distribution. General expressions for the statistical properties are obtained, also The Shannon entropy , Relative entropy functions and  Stress- Strength model of the ([0,1] TLIGD)  are presented

Keywords:

[0,1] TLIGD,stress strength model, Shannon entropy and Relative entropy functions,

Refference:

I. Abid, Salah , K. Abdulrazak, Russul, “[0, 1] truncated fréchet-gamma and inverted gam-ma distributions”, International Journal of Scientific World , 2017.
II. Eugene, N., Lee, C., & Famoye, F.,“Beta-normal distribution and its applications. Communications in Statistics-Theory and methods”,vol. 31(4), pp: 497-512, 2002.
III. Gradshteyn, I. S., & Ryzhik, I. M., “Table of integrals, series, and products”: Academic press,2014.
IV. Gupta, A. K., & Nadarajah, S., “On the moments of the beta normal distribution.Communications in Statistics-Theory and methods”, vol. 33(1), pp: 1-13, 2005.
V. Jamjoom, A., & Al-Saiary, Z., “Computing the moments of order Statistics from independent nonidentically distributed exponentiated Frechet variables”. Journal of Probability and Statistics, 2012.
VI. Jones, M., “Families of distributions arising from distributions of order statistics”. Test, vol. 13(1),pp :1- 43,2004.
VII. Maria do Carmo, S. L., Cordeiro, G. M., & Ortega, E. M., “A new extension of the normal distribution. Journal of Data Science”, vol. 13(2), pp: 385-408,2014.

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Abstract:

Aadhaar based identification systems are gaining momentum and it isused in several authentication mechanisms. In many democratic countries, the electoral system is still in its juvenile stage and operating in a manual mechanism which consumes huge resources for every voting. In this work, we propose a mechanism which uses Aadhaar based identification to enable a voter to vote. The connection between the voting machine and Aadhaar database is fully secured and encrypted. To avoid intentional hacking, the whole system is computerized and does not require human intervention.

Keywords:

Refference:

I. Ankita R Kasliwal, Jaya S. Gadekar, Manjiri A. Lavadkar, Pallavi K. Thorat and Prapti Deshmukh,“Aadhar Based Election Voting System”IOSR Journal of Computer Engineering, pp.18-21, 2017.
II. K. Dinakaran, P. Aravind Kumar, E. Bagavathi, M. Kathiresh Kumar, R. Madhankumar,“Smart Electronic Voting Machine Using Raspberry Pi”, International Journal of AdvancedResearch in Electrical, Electronics and Instrumentation Engineering, Vol. No. 8, pp. 829-834, March 2019.
III. Kolluru Venkata Nagendra, Palem Chandrakala, Palicherla Anusha, Dampuru Ramesh,“Implementing Aadhar Voting System in Elections Using Raspberry Pi”, InternationalJournal of Scientific Research and Review, Vol. No.7, pp. 500-507, 2018.
IV. Latha V. and Satheesh Thirumalal, “Aadhar Based Electronic Voting System andProviding Authentication on Internet of Things”, International Journal of Engineering andManufacturing Science, Vol. No. 8, pp.102-108, 2018.
V. Lingamallu Naga Srinivas and K. Srinivasa Rao, “Aadhaar Card Voting System”, Proceedings of International Conference on Computational Intelligence and Data Engineering, Vol. No. 9, pp. 159 -172, December 2018.
VI. N. N Nagamma, M. V. Lakshmaiah and T. Narmada, “Aadhar based Finger print EVMSystem”,International Journal of Electronics Engineering Research,Vol. No. 9, pp. 923-930,2017.
VII. R. Murali Prasad, Polaiah Bojja and Madhu Nakirekanti, “Aadhar based ElectronicVoting Machine using Arduino”, International Journal of Computer Applications, Vol. No.145, pp. 39-42, July 2016.
VIII. Rakesh S. Raj, Reshma, Madhushree and Bhargavi, “An Online Voting System Using Biometric Fingerprint and Aadhaar Card”, International Journal of Computing and Technology, Vol. No. 1, pp.87-92, May 2014.
IX. Sneha S.Lad, Pranav N.Tonape, Rohit S.Bhosale, Jayesh A.Shingole, Vinayak S.Kumar, “E-Voting and Presentee Muster Using Raspberry Pi 2 Modules”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. No. 4, pp. 475-482, May 2016.
X. Syed Mahmud Hasan, Arafa Mohd Anis, Hamidur Rahman, Jennifer Sherry Alam, Sohel Islam Nabil and Md KhalilurRhaman, “Development of Electronic Voting Machine with the Inclusion of Near Field Communication ID cards and Biometric fingerprint identifier”,17th International Conference on Computer and Information Technology, pp. 383-387, 2014.
XI. Tabish Ansari, Brijesh Chaurasia, Niraj Kumar, Nilesh Yadav, SonaliSuryawanshi, “Online Voting System linked with Aadhaar Card”, International Journal of Advanced Research in Computer and Communication and Communication Engineering, Vol. No. 6, pp. 204-207, September 2017.

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Abstract:

Frequent failure in production systems is one of the most important problems facing maintenance planners. In this paper, the methodology for estimating failure in an electrical energy production system has been proposed.Consisting of a number of related sub-systems, respectively, failure of any one causes the rest to stop producing.Operating data were collected and the type of failure identified, which was classified into three types (mechanical failure, electrical failure, and control failure). The software (Matlab) was used in generating and training an artificial neural network (ANN) to estimate the type of failure, through the data collected for each sub-system of the unit under study, use 90% of the data for training, 5% for testing, and 5% for valuation. The target matrix was built and trained, with a mean square error (MSE) its(6.54 E-16), and regression (91%), and adopted to estimate the type of future failure for subsequent years(2019),conformance results were for the subsequent year between (82%-87%) for all the subsystems. Using the artificial neural network, failure types were estimated for another subsequent year (2020), the failure ratios were for subsystems for every ten days during the year of estimation, were (33%) for the generator, (22%) for the boiler, (31%) for the turbine, and (13%) for the condenser. High percentages, which can be reduced by taking advantage of the proposed methodology that gave an understanding of the type of failure, the time it occurred, and the location of the failure, by building an overlapping preventive maintenance plan whose application is approved in reducing the failuretimes of the unit under study.The proposed methodology can also be applied to all other systems of different production

Keywords:

Matlab software, Generator,Artificial Intelligent (AI),

Refference:

I. Devika Chhachhiya, Amita Sharma, Manish Gupta “Case Study on Classification of Glass Using Neural Network Tool in MATLAB” International Journal of Computer Applications, 0975 – 8887),(2014).
II. D. Bose, G. Ghosh, K. Mandal, S.P. Sau4 and S. Kunar “Measurement and Evaluation of Reliability, Availability and Maintainability of a Diesel Locomotive Engine” International Journal of Engineering Research and Technology, Volume 6, Number 4,pp. 515-534, 2003.

III. Emilia Sipos, Laura-Nicoleta Ivanciu”Failure Analysis and Prediction Using Neural Networks in the Chip Manufacturing Process “ResearchGate, DOI: 10.1109/ISSE.2017.8000931, May 2017

IV. Erdi Tosun, Ahmet C¸ alık”Failure load prediction of single lap adhesive joints using artificial neural networks”Alexandria Engineering Journal vol. 55, pp1341–1346,2016
V. Farhad Hooshyaripora, Ahmad Tahershamsib, and Kourosh Behzadian”Estimation of Peak Outflow in Dam Failure Using Neural Network Approach under Uncertainty Analysis” Pleiades Publishing, Vol. 42, No. 5, 2015
VI. Gustavo Scalabrini Sampaio, Arnaldo Rabello de Aguiar Vallim Filho,Leilton Santos da Silva and Leandro Augusto da Silva” Prediction of Motor Failure Time Using An Artificial Neural Network” Sensors, 19, 4342; doi:10.3390/s19194342, 2019
VII. Laurene V. Fausett, “Fundamentals of Neural Networks: Architecture, Algorithm, and Application”, Florida Institute of Technology, First Edition, December, 1993.
VIII. Mahdi Saghafi , Mohammad B. Ghofrani “Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network” Nuclear Engineering and Technology doi.org/10.1016/j.net.2018.11.017
IX. M. Goya-Martinez, “The Emulation of Emotions in Artificial Intelligence,” Emotions, Technology, and Design. pp. 171–186, 2016
X. Walter, E.; Pronzato L., “Identification of Parametric Models from Experimental Data”, London, England: Springer-Verlag, 1997.

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Abstract:

IoT is a globally accepted smart technology that has the ability to connect each and almost every physical devices through the network. It acts as a bridge between cloud environment and physical environment. It is mainly used to connect the hardware devices like sensors, actuators, storage, hardware, and software to acquire or exchange data. These devices collect the information from the physical world and convert this into useful information that can help in decision making. Since IoT connects everything to the network, so it may face the problem of a large amount of energy loss. In this respect, this paper mainly focuses on reducing the energy loss problem and designing of an energy efficient data transfer scenario between cloud and IoT devices. For this reason, a Complex Proportional Assessment (COPRAS) based clustering approach has been proposed in this work to select the cluster premier effectively and form the set of best clusters for maximizing the network lifetime. The proposed work deals with data transmission model between IoT and cloud that confirms the improvement in energy efficiency, network lifetime, and latency. Furthermore, the sensitivity analysis has also been carried out and satisfactory results has been obtained.

Keywords:

Cloud Computing, Clustering, MCDM, IoT,

Refference:

I. A. Majumdar, T. Debnath, S. K. Sood, K. L. Baishnab, “Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment”, Journal of medical systems, Springer, vol. 42, no.10, pp.187, 2018.
II. A. Majumdar, A., Biswas, K. L. Baishnab, S. K. Sood, “DNA Based Cloud Storage Security Framework Using Fuzzy Decision Making Technique”, KSII Transactions on Internet & Information Systems, vol.13, no.7, pp. 3794-3820, 2019.
III. A. Majumdar, N. M. Laskar, A. Biswas, S. K. Sood, K. L. Baishnab, “Energy efficient e-healthcare framework using HWPSO-based clustering approach”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 36, no. 5, pp. 3957-3969, 2019.
IV. A. Biswas, A. Majumdar, S. Nath, A. Dutta, K. L. Baishnab, “LRBC: a lightweight block cipher design for resource constrained IoT devices”, Journal of Ambient Intelligence and Humanized Computing, Springer pp.1-15, 2020.
V. A. V. Dhumane and R. S. Prasad, “Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT”, Wireless Personal Communications, Springer, vol. 102, no. 1, pp. 411-36, 2018.
VI. A. V. Dhumane, R. S. Prasad, and J. R. Prasad, “An optimal routing algorithm for internet of thing enabling technologies”, International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 4, no. 3, pp. 1-16, 2017.
VII. A. Orsino, G. Araniti, L. Militano, J. Alonso-Zarate, A. Molinaro, A. Iera,. “Energy efficient IoT data collection in smart cities exploiting D2D communications”, Sensors, vol. 16, no. 6, p.836, 2016.
VIII. D. Wei, S. Kaplan, H.A. Chan, “Energy efficient clustering algorithms for wireless sensor networks”, In Communications Workshops, 2008. ICC Workshops’ 08. IEEE International Conference on, pp. 236-240, 2008.
IX. G. L. da Silva Fré, J. de Carvalho Silva, F.A. Reis, and L.D.P. Mendes, “Particle Swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the internet of things,” in International Workshop on Telecommunications (IWT), pp. 1–7, 2015.
X. I. Yaqoob, E. Ahmed, I.A.T. Hashem, A.I.A. Ahmed, A. Gani, M. Imran, M. Guizani, “Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges”, IEEE wireless communications, vol. 24, no. 3, pp.10-16, 2017.
XI. J. H. Kwon, M. Cha, S. B. Lee, and E. J. Kim, “Variable-categorized clustering algorithm using fuzzy logic for Internet of things local networks”, Multimedia Tools and Applications, Springer, vol. 78, no.3, pp. 2963-82, 2019.
XII. J. A. Martins, A. Mazayev, N. Correia, G. Schütz, and A. Barradas, “GACN: Self-clustering genetic algorithm for constrained networks”, IEEE Communications Letters, vol. 21, no. 3, pp. 628-31, 2017.
XIII. J.M. Liang, J.J. Chen, H.H. Cheng, Y.C. Tseng, “An energy-efficient sleep scheduling with qos consideration in 3gpp lte-advanced networks for internet of things,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 1, pp.13-22, 2013.
XIV. J. Tang, Z. Zhou, J. Niu, Q. Wang, “An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things”, Journal of Network and Computer Applications, vol. 40, pp.1-11, 2014.
XV. L. Song, K. K. Chai, Y. Chen, J. Loo, S. Jimaa, and J. Schormans, “QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems”, in Wireless Communications and Networking Conference, IEEE, April 2016, pp. 1-6.
XVI. L. Song, K.K. Chai, Y. Chen, J. Schormans, J. Loo, A. Vinel, “QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems”, IEEE Systems Journal, vol. 11, no. 3, pp.1447-1455, 2017.
XVII. M. P. K. Reddy and M. R. Babu, “Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things”, Cluster Computing, Springer, pp. 1-12, 2018.
XVIII. M. P. K. Reddy and M. R. Babu, “Energy Efficient Cluster Head Selection for Internet of Things”, New Review of Information Networking, Taylor & Francis, vol. 22, no. 1, pp. 54-70, 2017.
XIX. M. P. K. Reddy and M. R. Babu, “An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things”, International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 337-46, 2017.
XX. N. T. Van, T. T. Huynh, and B. An, “An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 35, no. 6, pp. 5845-5852, 2018.
XXI. N. Kaur, and S.K. Sood, “An Energy-Efficient Architecture for the Internet of Things (IoT)”, IEEE Systems Journal, vol.11, no.2, pp.796-805, 2017.
XXII. Ö.U. Akgül, B. Canberk, “Self-Organized Things (SoT): An energy efficient next generation network management,” Computer Communications, vol. 74, pp.52-62, 2016.
XXIII. S. K. Singh, M.P. Singh, D.K. Singh, “Energy-efficient homogeneous clustering algorithm for wireless sensor network”, International Journal of Wireless & Mobile Networks (IJWMN), vol. 2, no. 3, pp.49-61, 2010.
XXIV. S. Rani, R. Talwar, J. Malhotra, S.H. Ahmed, M. Sarkar, H. Song, “A novel scheme for an energy efficient Internet of Things based on wireless sensor networks”, Sensors, vol. 15, no. 11, pp.28603-28626, 2015.
XXV. S. D. Muruganathan, D. C. Ma, R. I. Bhasin, A. O. Fapojuwo, “A centralized energy-efficient routing protocol for wireless sensor networks”, IEEE Communications Magazine, vol. 43, no. 3, pp. S8-13, 2005.
XXVI. T. Ayesha, S. Sadaf, D. Sinha, and A. K. Das. “Secure Anti-Void Energy-Efficient Routing (SAVEER) Protocol for WSN-Based IoT Network”, In Advances in Computational Intelligence, pp. 129-142. Springer, Singapore, 2020.
XXVII. Z. Zhou, J. Tang, L.J. Zhang, K. Ning, Q. Wang, “EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things”, Personal and Ubiquitous computing, vol.18, no.4, pp.951-966, 2014.
XXVIII. A. Majumdar, T. Debnath, K. L. Baishnab, S. K. Sood, “An Energy Efficient e-Healthcare Framework Supported by HEO-µGA (Hybrid Extremal Optimization Tuned Micro-GeneticAlgorithm)”, Information System Frontiers, Springer, 2020, DoI: https://doi.org/10.1007/s10796020-10016-5.
XXIX. A. Majumdar, M. Sharma, “Enhanced information security using DNA cryptographic approach. International Journal of Innovative Technology and Exploring Engineering”, vol.4, no.2, pp. 72-76, 2020.

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Abstract:

Despite the world’s 20th largest economy, Thailand's innovation ecosystem is questionable, ranked the world’s 43rd in Global Innovation Index 2019 report.  The paper aims at investigating the innovation performance and trend of Thailand based on 7 aspects of innovation inputs and outputs.  Referred to the data dated back to 2011, knowledge and technology outputs, human capital and research, institutions, and creative inputs are considered Thai strengths with progressive improvement.  Market sophistication is strong but there has been no significant improvement.  Business sophistication is considerably weak but there is a sign of improvement.  Infrastructure is the most concerning issue.

Keywords:

Thailand,Global Innovation Index ,innovation performance and trend,

Refference:

I. A. Limcharoen, V.Jangkrajarng, W.Wisittipanich, S. Ramingwong, “Thailand logistics trend: Logistics performance index”. International Journalof Applied Engineering Research, Vol: 12, Pages: 4882-4885, 2017.
II. A. Sopadang, N. Chonsawat, S. Ramingwong, “Smart SME 4.0 Implementation Toolkit”. in Industry 4.0 for SMEs. Palgrave Macmillan, Cham, 2020.
III. B. Å.Lundvall, “Why study national systems and national styles of innovation?”. Technology Analysis & Strategic Management, Vol: 10, Issue: 4, Pages: 403-422, 1998.
IV. B. Mercan, D. Goktas, “Components of innovation ecosystems: a cross-country study”. International Research Journal of Finance and Economics, Vol: 76, Issue: 16, Pages: 102-112, 2011.
V. C. Chaminade, P.Intarakumnerd, K. Sapprasert, “Measuring systemic problems in national innovation systems”. An application to Thailand. Research Policy, Vol: 41, Issue: 8, Pages: 1476-1488, 2012.
VI. C. Jones, P.Pimdee, “Innovative ideas: Thailand 4.0 and the fourth industrial revolution”. Asian International Journal of Social Sciences, Vol: 17, Issue: 1, Pages: 4-35, 2017.
VII. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2013: The Local Dynamics of Innovation”. Geneva, Ithaca, and Fontainebleau, 2013.
VIII. Cornell University, INSEAD, WIPO,“The Global Innovation Index 2014: The Human Factor In innovation”. Fontainebleau, Ithaca, and Geneva, 2014.
IX. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2015: Effective Innovation Policies for Development”. Fontainebleau, Ithaca, and Geneva, 2015.
X. Cornell University, INSEAD,WIPO, “The Global Innovation Index 2016: Winning with Global Innovation”. Ithaca. Fontainebleau, and Geneva, 2016.
XI. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2017: Innovation Feeding the World”. Ithaca, Fontainebleau, and Geneva, 2017.
XII. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2018: Energizing the World with Innovation”. Ithaca, Fontainebleau, and Geneva, 2018.
XIII. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2019: Creating Healthy Lives – The Future of Medical Innovation”. Ithaca, Fontainebleau, and Geneva, 2019.
XIV. D. J. Jackson, “What is an innovation ecosystem”. National Science Foundation, Vol: 1, Issue: 2. 2011.
XV. D. Schiller, “Nascent innovation systems in developing countries: University responses to regional needs in Thailand”. Industry and Innovation, Vol: 13, Issue: 4, Pages: 481-504, 2006.
XVI. D. Schiller, “The potential to upgrade the Thai innovation system by university‐industry linkages”. Asian Journal of Technology Innovation, Vol: 14, Issue: 2, Pages: 67-91, 2006.
XVII. E. G. Carayannis, D. F. J. Cambell, “’Mode 3’and’Quadruple Helix’: toward a 21st century fractal innovation ecosystem”. International Journal of technology management, Vol: 46, Issue: 3-4, Pages: 201-234, 2009.
XVIII. E. Rauch, P. Dallasega, M. Unterhofer, “Requirements and Barriers for Introducing Smart Manufacturing in Small and Medium-Sized Enterprises”. IEEE Engineering Management Review, Vol: 47, Issue: 3, Pages: 87-94, 2019.
XIX. H. Zsifkovits, M.Woschank, S. Ramingwong, W. Wisittipanich, “State-of-the-Art Analysis of the Usage and Potential of Automation in Logistics”. In Industry 4.0 for SMEs (pp. 193-212). Palgrave Macmillan, Cham, 2020.
XX. INSEAD, CII,“Global Innovation Index 2008-2009”, 2008.
XXI. INSEAD, CII, “Global Innovation Index 2009-2010”, 2009.
XXII. INSEAD, “The Global Innovation Index 2011: Accelerating Growth and Development”. Fontainebleau, 2011.
XXIII. INSEAD, WIPO, “The Global Innovation Index 2012: Stronger Innovation Linkages for Global Growth”. Fontainebleau, 2012.
XXIV. J. Jintana, A. Limcharoen, Y. Patsopa, S. Ramingwong, “Innovation Ecosystem of ASEAN Countries”. Amazonia Investiga, Vol: 9, Issue: 28, Pages: 356-364, 2020.
XXV. J.R. Bessant, J. Tidd. “Innovation and Entrepreneurship”. John Wiley & Sons, 2001.
XXVI. J. Wonglimpiyarat, “Government programmes in financing innovations: Comparative innovation system cases of Malaysia and Thailand”. Technology in Society, Vol: 33, Issue: 1-2, Pages: 156-164, 2011.
XXVII. K. B. Kahn, “Understanding innovation”. Business Horizons, Vol: 61, Issue: 3, Pages: 453-460, 2018.
XXVIII. L.M. Branscomb, J.H. Keller, “Investing in innovation: Creating a research and innovation policy that works”. MIT Press, 1999.
XXIX. M. A. R. Garcia, R. Rojas, L. Gualtieri, E. Rauch, D. Matt, “A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing”. Procedia CIRP, Vol: 81, Pages: 600-605, 2019.
XXX. M. Lindberg, M. Lindgren, J.Packendorff, “Quadruple Helix as a Way to Bridge the Gender Gap in Entrepreneurship: The Case of an Innovation System Project in the Baltic Sea Region”. Journal of the Knowledge Economy, Vol: 5, Pages: 94-113, 2014.
XXXI. M. Woschank, E. Rauch, H. Zsifkovits, “A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics”. Sustainability, Vol: 12, Issue: 9, Pages: 3760, 2020.
XXXII. N. Chonsawat, A. Sopadang, “The Development of the Maturity Model to evaluate the Smart SMEs 4.0 Readiness”. In Proceedings of the International Conference on Industrial Engineering and Operations Management Bangkok, Thailand, March 5-7, 2019.
XXXIII. NESDB.“The Twelfth National Economic and Social Development Plan (2017-2021)”, 2017.
XXXIV. NESDB, “Gross Domestic Product, Chain Volume Measures: Q1/2019”, 2019.
XXXV. P. Cooke, M.G. Uranga, G.Etxebarria, “Regional innovation systems: Institutional and organizational dimensions”. Research Policy, Vol: 26, Issue: 4-5, Pages: 475-491, 1997.
XXXVI. P. Intarakumnerd, P. A.Chairatana, T. Tangchitpiboon, “National innovation system in less successful developing countries: the case of Thailand”. Research Policy, Vol: 31, Issue: 8-9, Pages: 1445-1457, 2002.
XXXVII. R. R. Nelson, “National innovation systems: a comparative analysis”. Oxford University Press on Demand, 1993.
XXXVIII. S. Durst, P. Poutanen, “Success factors of innovation ecosystems-Initial insights from a literature review”. Co-create,Pages: 27-38, 2013.
XXXIX. S. Dutta, INSEAD, S. Caulkin, “The World’s Top Innovators”. World Business, Vol: 8, Pages: 26-37, 2007.
XL. S. J. Kline, N. Rosenberg, “An overview of innovation”. in Studies On Science And The Innovation Process: Selected Works of Nathan Rosenberg, Pages: 173-203, 2010.
XLI. S. Klaus, “The Global Competitiveness Report 2019”. World Economic Forum, Geneva, 2019.
XLII. S. Ramingwong, W.Manopiniwes, “Supportment for organization and management competences of ASEAN community and European Union toward Industry 4.0”. International Journal of Advanced and Applied Sciences, Vol: 6, Issue: 3, Pages: 96-101, 2019.
XLIII. S. Ramingwong, W.Manopiniwes, V.Jangkrajarng, “Human Factors of Thailand Toward Industry 4.0”. Management Research and Practice, Vol: 11, Issue: 1, Pages: 15-25, 2019.
XLIV. S. Santiteerakul, K. Y.Tippayawong, P.Dallasega, K.Nimanand, S. Ramingwong, “Logistics performance review: European Union and ASEAN community”. Journal of Applied Economic Sciences, Vol: 13, Pages: 1175-1180, 2018.
XLV. S. Tiwong, S. Ramingwong, K. Y. Tippayawong, “On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs”. Sustainability, Vol: 12, Issue: 6, Pages: 2394, 2020.
XLVI. SDPD, “NESDC Economic Report: Thai Economic Performance in Q3 and Outlook for 2019 – 2020”, 2019.
XLVII. W. Manopiniwes, K. Y.Tippayawong, J.Numkid, S.Santiteerakul, S. Ramingwong, P.Dallasega, “On Logistics Potential of Thai Industry in Identifying Gap to Logistics 4.0”. Journal of Engineering and Applied Sciences, Vol: 14, Pages: 1608-1613, 2019.

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Abstract:

In this work, the effect of electromagnetic field on natural fluid flow within the absorbent tube in the parabolic solar collector was numerical investigated.Where a solar collector with parabolic reflector was used. Water was used in the first and the flow was free as the results showed high efficiency of the device. Then a magnetic iron oxide (Fe₃O₄) nanoparticle was added to make the fluid subject to influence in the electromagnetic field, where three concentrations (0.9%, 0.5%, and 0.3%) were used to study the effect of magnetic flux on each concentration and to make a comparison. The results showed a slight effect of the electromagnetic field in the case of water use, as the efficiency of the solar collector improved by (8.8%) in the case of using the concentration (0.9%) and an electromagnetic overflow (7970 Gauss).

Keywords:

Magnetic field, ,solar collector,solar collecto,Solar energy,Ferrfluid, Nano Particles,Nanofluid Properties,Nanofluid,

Refference:

I Abu-Nada, E, “Application of nanofluids for heat transfer enhancement of separated flows encountered in a backward-facing step”, International Journal of Heat and Fluid Flow.; 242-24,. (2008).
II Aminfar H., Mohammad P. M., Mohseni F., “Two-phase mixture model simulation of the hydro-thermal behavior of an electrical conductive ferrofluid in the presence of magnetic fields”, Journal Magn. Magn. Mater.;324, 830-842, (2012).
III Duffie J A., Beckman W A., “Solar energy thermal processes”, in, University of Wisconsin- Madison, Solar Energy Laboratory, Madison, WI, (1974).
IV Hussein A. K., Ashorynejad H. R., Sheikholeslami M., Sivasankaran S., “Lattice Boltzmann simulation of natural convection heat transfer in an open enclosure filled with Cu–water nanofluid in a presence of magnetic field”,Nucl. Eng. Des.;268,10-17, (2014).
V Maiga S. E. B., Cong T. N., “Heat transfer enhancement in turbulent tube flow using Al2O3 nanoparticle suspension”, International Journal of Numerical Methods for Heat and Fluid Flow.; 275-29, (2006).
VI Mohsen S., Mofid G. B., Ellahibc A. Z., “Simulation of MHD CuO–water nanofluid flow and convective heat transfer considering Lorentz forces”, Journal of Magnetism and Magnetic Materials.; 369, 69-80, (2014).
VII Nagarajan P K., Subramani J., Suyambazhahan S., Sathyamurthy R., “Nanofluids for solar collector applications: A Review”, Energy Procedia; 61: 2416 – 2434,(2014).
VIII Sheikhzadeh G A, Sebdani1 M S, Mahmoodi M, Elham S, Hashemi S E. “Effect of a Magnetic Field on Mixed Convection of a Nanofluid in a Square Cavity”, Journal of Magnetics.;18, 321-325, (2012).
IX Titan C., Morshed A. M., Jamil A.K., “Nanoparticle enhanced ionic liquids (NEILS) as working fluid for the next generation solar collector”, Procedia Engineering, 5th BSME International Conference on thermal engineering.; 56, 631-636, (2013).
X Tyagi H., Phelan P., Prasher R., “Predicted Efficiency of a Low-Temperature Nanofluid–Based Direct Absorption Solar Collector”, Journal of Solar Energy Eng. 131, 041004, (2009).
XI Zhang Z., Gu H., Fujii M., “Effective thermal conductivity and thermal diffusivity of nanofluids containing spherical and cylindrical nanoparticles”, Exp. Therm. Fluid Sci.; 31, 5593-5599, (2007).

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Abstract:

The main purpose of segmentation is to partition an image based on features into different regions. Unsupervised classification algorithms K means, K-nearest neighbor, neural networks can be used to perform efficient image segmentation. Image segmentation is an important step to perform classification of images. Segmentation algorithms such as watershed segmentation, support vector machines can be used to find the region of interest. A genetic algorithm based image segmentation algorithm, ant colony optimization algorithm is proposed and we compare it with k-means segmentation. We apply some segmentation algorithms in industry standard datasets and view the results of our segmentation algorithms. Segmentation is a basic task in image processing and can be applied in large number of domains. We emphasize on how a segmentation algorithm can be developed to segment out tum ours from medical magnetic resonance images. We have used the open CV python package for our image processing tasks.

Keywords:

Magnetic Resonance Imaging,K-means algorithm,Genetic Algorithms,Ant Colony Optimization ,Image segmentation,unsupervised classification,support vector machine,Medical Image processing,

Refference:

I A Markov random field image segmentation model for color textured images Zoltan Kato a,*, Ting-Chuen Pong b,1
II Bradski, G., 2000. The Open CV Library. Dr. Dobb Journal of Software Tools.
III Colour Image Segmentation Using SVM Pixel Classification Image K. Sakthivel, R. Nallusamy, C. Kavitha TW World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:8, No:10, 2014
IV Dorigo, Marco & Birattari, Mauro & Stützle, Thomas. (2006). Ant Colony Optimization. Computational Intelligence Magazine, IEEE. 1. pp 28-39. 10.1109/MCI.2006.329691.
V Digital Image Processing and Analysis by Bhabatosh Chanda and Dwijesh Dutta Majumder PDF Online. ISBN 9788120343252 from PHI Learning.
VI Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. Upper Saddle River, N.J.: Prentice Hall, 2002.pp700-809
VII M. Haseyama, M. Kumagai and H. Kitajima, “A genetic algorithm based image segmentation for image analysis,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA, 1999,
VIII Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011

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Abstract:

There are serious obstacles in resolving a people’s present position and movement state inside an indoor situation. Position and movement action report of people becomes a business. For particular, it can resort movement accelerometer information to scan how patients are adapted to practices, for example, strolling or standing. Position following data can be for ensuring the preservation of mature consideration cases. The designed system applied for patient’s localization, tracking and investigation services within healthcare institutes through a wireless sensor network based on IoT. The personal monitoring module based on optional sensors which analyzes the movements of the patients is detecting hazardous incidents, and the wireless communication framework to send the data. Two methodologies are contrasted with the usage of the limitation and following motor a unified execution where confinement is executed halfway out of data gathered at the local area and a result where the localization is observed at nodes and the result is given to the central administrator connected through IOT which provides global accesses monitoring to the authorized personnel at anytime and anywhere. It displays strong and poor positions of the both the results from a system viewpoint in calls of localization efficiency, energy performance and traffic capacities. These sensor systems are examined in a specific situation using testing kits. The key outcomes are average localization faults fewer than 2 m in 80% of the experiments and an operation’s analysis efficiency as significant as 90%. This paper presents patient localization, tracking and information services within healthcare institutes through a WSN based on IoT. Particle Swarm Optimization Adaptive Extended Kalman Filter (PSO-AKF) have been recommended for localization and having a path of victim’s position. A particular observation module based on optional sensors that analyzes the actions of the patients eventually detecting hazardous incidents, and a wireless communication framework to transmit the data remotely.

Keywords:

Localization, E-Health,Particle Swarm Optimization Adaptive Extended Kalman Filter (PSO-AKF), IoT,Wireless Sensor Networks,

Refference:

I. E.K. Antonsson, R.W. Mann, The frequency content of gait, Journal of Biomechanics 18 (1) (1985) 39–47, http://dx.doi.org/10.1016/0021929 (85)90043-0.

II. G. Currie, D. Rafferty, G. Duncan, E. Bell, A. Evans, Measurement of gait by accelerometer and walkway: a comparison study, Medical & Biological Engineering & Computing 30 (1992) 669670.

III. J. Ko, C. Lu, M. Srivastava, J. Stankovic, A. Terzis, M. Welsh, Wireless sensor networks for healthcare, Proceedings of the IEEE 98 (11) (2010) 1947–1960, http://dx.doi.org/10.1109/JPROC.2010.2065210.

IV. Janapati, Ravichander, and K. Soundararajan. “Enhancement of Indoor Localization in WSN using PSO tuned EKF.” International Journal of Intelligent Systems and Applications 9.2 (2017): 10.

V. Janapati, Ravichander, et al. “Indoor localization of cooperative WSN using PSO assisted AKF with optimum references.” Procedia Computer Science 92 (2016): 282-291.

VI. L. Klingbeil and T. Wark, “A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring“ in International Conference on Information Processing in Sensor Networks, 2008.

VII. M. Mathie, A. Coster, B. Celler, N. Lovell, Classification of basic daily movements using a triaxial accelerometer, Medical and Biological Engineering and Computing 42 (2004) 670687.

VIII. M. McCarthy, P. Duff and H. L. Muller, C. Randell, C. “Accessible Ultrasonic Positioning”, IEEE Pervasive Computing, Vol 5, pp 86-93, 2006

IX. M. Sugano, T. Kawazoe, Y. Ohta, M. Murata, Indoor localization system using rssi measurement of wireless sensor network based on zigbee standard, in: Wireless and Optical Communications, IASTED/ ACTA Press, 2006, pp. 1–6.

X. Prasad, C. R., & Bojja, P. (2020). The energy-aware hybrid routing protocol in WBBSNs for IoT framework. International Journal of Advanced Science and Technology, 29(4), 1020–1028.

XI. Pravalika, V., & Rajendra Prasad, C. (2019). Internet of things based home monitoring and device control using Esp32. International Journal of Recent Technology and Engineering, 8(1 Special Issue 4), 58–62.

XII. V. Otsason, A. Varshavsky, A. LaMarca and E. de Lara, “Accurate GSM indoor localization.”, in Ubiquitous Computing 7th International Conference, Proceedings (Lecture Notes in Computer Science Vol. 3660) . Springer-Verlag, pp 141-58, 2005

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