Journal Vol – 15 No -2, February 2020

SEMI-PRIMARY RΓ-SUBMODULE OF MULTIPLICATION RΓ- MODULES

Authors:

Nuhad Salim Al-Mothafar,Ali Abd Alhussein Zyarah,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00001

Abstract:

Let R be a Γ-ring and G be an RΓ-module. An RΓ-submodule S of an RΓ-module G is called semi-primary RΓ-submodule if  is prime ideal of Γ-ring R. The purpose of this paper is to introduce interesting result semi-primary RΓ-submodule of RΓ-module which represent generalization semi-primary submodules.

Keywords:

Γ–Ring,RΓ-Modules,RΓ-Submodule ,Primary RΓ-Submodule,

Refference:

I. A. S. Majbass, Semi-Primary Submodules, Science journal, University of Tikrit, 6 (2000).
II. Ameri. R and R. Sadeghi, “Gamma Modules”, Ratio Mathematica, 20 (2010), pp. 127-147.
III. A. Zyarah and N. S. AL-Mothafar, “Semiprime RΓ-Submodule of Multiplication RΓ-module”, Iraqi Journal of Science 61 (2020).
IV. H. A. Abbas, “Projective Gamma Modules and Some Related Concepts”, department of Mathematics, Al Mustansiryah University, Baghdad, Iraq, 2018.
V. M. S. Abbas, H. R. Hassan and H. A. Abbas, ΓR-Multiplication and ΓR-Projective Gamma Modules, International Journal of Contemporary Mathematical Sciences, Vol.13 (2018), pp. 87-94.
VI. M. S. Abbas and B. M. Hamad, The Stable Envelope of Gamma Modules, Journal of Mechanics of Continua and Mathematical Sciences, 14 (2019).
VII. N. Nobusawa, “On a Generalization of the Ring Theory”, Osaka Journal of Mathematics, 1 (1964), pp. 81-89.
VIII. U. T. U. Sengu, “On Prime ΓM-Submodules of ΓM-Modules”, International Journal of Pure and Applied Mathematics 19 (2005), pp. 123-128.

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FLOWER POLLINATION ALGORITHM BASED CAD APPROACH FOR SUPPLY NOISE REDUCTION IN SYSTEM-ON-CHIP

Authors:

Partha Mitra,Angsuman Sarkar,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00002

Abstract:

Designing an efficient power distribution network is a major challenge in modern day system-on-chip. During manufacturing, the signal integrity problems such as resistive voltage drop, inductive noise at pad locations and electro-migration may result silicon failures. This paper deals with the analysis of supply noise using multiple power supply and use of decoupling capacitors for reduction of supply noise.  In this work flower pollination algorithm has been used for decap estimation so that the supply noise can be reduced significantly and various design parameters remains at its best. The purpose of this work is to reduce the supply noise with effecting the other design parameters of the chip.  In this work the supply noise has been reduced upto 70.2% with reduction of 81.6% in power consumption and 17.07 % increment in delay parameters. This approach can be used for any system-on-chip.

Keywords:

Decoupling capacitor,Flower Pollination Algorithm,Multiple Power Supply,Power Distribution Network,System-on-chip,

Refference:

I. C. Tirumurti, S. Kundu, S. Sur-Kolay, Y.Chang, “A modeling approach for addressing power supply switching noise related failures of integrated circuits”, Proceedings of Design, Automation and Test in Europe Conference and Exhibition (DATE) pp.: 1078- 1083, 2004.
II. J. A. Strak, H. Tenhunen, “Investigation of timing jitter in NAND and NOR gates induced by power- supply noise”, Proceedings 13th IEEE International Conference on Electronics, Circuits and Systems 2007.
III. K. Shah, “Power Grid Analysis in VLSI Designs”, Dissertation in Master of Science (Engineering),Super Computer Education and Research Centre, Indian Institute of Science Bangalore, 2007.
IV. K. Shimazaki, T. Okumura, “A Minimum Decap Allocation Technique Based on Simultaneous Switching for nano-scale SoC”.Proceedings of IEEE Custom Integrated Circuits Conference, 2009.
V. M. Khellah, D. Khalil, D. Somasekhar, Y. Ismail, T. Karnik, V.De, “Effect of power supply noise on SRAM dynamic stability”, Proceedings of Symposium on VLSI Circuits 2007.
VI. M. Musab, S. Yellampalli, “Study and Implementation of Multi-VDD Power Reduction Technique” Proceedings of IEEE International Conference on Computer Communication and Informatics (ICCCI), pp.: 1-4, 2015.
VII. M. Saint-Laurent, M. Swaminathan, “Impact of power-supply noise on timing in high frequency microprocessors”, IEEE Transactions on Advanced Packaging, Vol.:27, pp.: 135-144, 2004.
VIII. P. Mitra, J. Bhaumik, “Pre-Layout Decap Allocation for Noise suppression and Performance Analysis for 512-Point FFT core”, Proceedings of 2017 Devices for Integrated Circuits (DevIC), pp.: 341-345, 2017.
IX. P. Mitra, J.Bhaumik, “A CAD Approach for Suppression of Power Supply Noise And Performance Analysis of Some Multi-core Processors in Pre-layout Stage”, Microsystem Technologies, Springer, Vol.: 25, Issue: 5, pp.: 1977-1986, 2019.
X. S. A.Tawfik, V. Kursun, “Multi-Vth Conversion circuits For Multi-VDD Systems”, Proceedings of IEEE International Symposium on Circuits and Systems, pp.: 1397-1400, 2007.
XI. S. Ghosh., B.P. De, R. Kar, D. Mandal, A. K. Mal, “Optimal design of a 5.5GHz lowpower highgain CMOS LNA using the Flower Pollination Algorithm”. Journal of Computational Electronics, Springer 2019. DOI: 10.1007/s10825-019-01322-6
XII. S. Pant, “Design and analysis of Power Distribution Networks in VLSI Circuits”, Ph.D Dissertation,University of Michigan, 2008.
XIII. S. Zhao, K. Roy, C.K. Koh, “Decoupling Capacitance Allocation and Its Application to Power-Supply Noise-Aware Flooring”, IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems , Vol.: 21, Issue: 1, pp.: 81-92, 2002.
XIV. T. Karim, “On-Chip Power Supply Noise: Scaling, Suppression and Detection”, Ph.D Dissertation, University of Waterloo, 2012.
XV. X.S. Yang, “Flower Pollination Algorithm for Global Optimization”, Lecture Notes In Computer Science, Springer, Vol. 7445, pp.: 240–249, 2012.
XVI. Y. Shi, J. Xiong, C. Liu, L. He,“Efficient Decoupling Capacitance Budgeting Considering Operation and Process Variations”, IEEE Trans. on CAD , Vol.: 27, Issue: 7, pp.: 1253-1263, 2008.

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ENERGY SAVING ENHANCEMENT USING WATER-COOLED ROOF WITH SOLAR ASSISTED SYSTEM FOR SPACE COOLING APPLICATION

Authors:

Abbas Ahmed Hasan Al-Jaberi ,Najim Abid Jassim ,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00003

Abstract:

Due to significant demand on electrical energy on residence sector, especially those spend on space cooling for extreme hot summer countries in the middle east; moreover, the national electrical shortage supply issue in Iraq, it is a kind of interested and challenge at the same time to research for solution and utilize the redundancy of solar energy in such region. A test room was constructed 4.7*2.5*2.85m subjected to solar radiation all day time situated in Baghdad, a bunch of experimental testes were conducted for parametric study over summer season from April till October, the experiment testes were recurrent on monthly basis. The roof of test room was cooled by circulated water in pipes, the water is pre-cooled by evaporative cooler in separate system whilst the relatively cooled air is blew towards the A/C outdoor unit and other stream directed on back side of solar panels to minimize its average temperature for promoting performance aspects. Results indicated the energy save 33.53% over entire season when cooling the roof by water circulation and 9.01% energy saved due to A/C COP enhanced from cooling the condenser by cold air. Therefore COP was enhanced due to cold air effect from 2.90 to 3.39The solar panel temperature was minimized about 11°C that results to enhance the efficiency of PV panels from 12.1% to 12.8 %, solar system can handle the operation for 3hrs peak time when A/C is off as more energy was saved.

Keywords:

Space Cooling,Energy Saving,COP Enhancement,Solar Assistance,Water-Cooled Roof,PV panel Enhancement,

Refference:

I. C. George Popovici, S. ValeriuHudișteanu, T. DorinMateescu, Nelu-Cristian C., “Efficiency improvement of photovoltaic panels by using air cooled heat sinks”, Sustainable Solutions for Energy and Environment, EENVIRO – YRC 2015, 18-20, Bucharest, Romania, November ,(2015).
II. C. Zhong Yi and F. Nasir Ani, “Performance of an Improved Ejector Airconditioning System”, JurnalMekanikal, Vol 38, 63-72, June (2015).
III. J. Lucero-Álvarez, Norma A. Rodríguez-Muñoz, I. R. Martín-Domínguez, ” The Effects of Roof and Wall Insulation on the Energy Costs of Low Income Housing in Mexico”, Sustainability, 8,950,(2016).
IV. K. Mohammed Ali, and Khalid A. Joudi, “Space Cooling and Heating Using Renewable Energy with Thermally Activated Roof for a Residence in Iraq”, Ph.D. Dissertation, University of Baghdad, Mechanical Engineering Department, July (2015).
V. K. Ahmed Joudi, “Fundamental Engineering of Air-conditioning and Refrigeration”, Textbook, University of Basra, (1991).
VI. M. Hassan Ammar and Najim A. Jassim, “Improving of Thermal Performance of Air Conditioning System Using Evaporative Cooling”, M.Sc. Thesis, University of Baghdad, Mechanical Engineering Department, December (2017).
VII. P. Payam Nejat, F. Jomehzadeh,M. Mahdi Taheri,M. Goharic, Muhd Z. Abd. Majidd, ” A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)”, (2014).
VIII. P. Lizica Simona, P. Spiru, Ion V. Ion, ” Increasing the energy efficiency of buildings by thermal insulation”, International Scientific Conference “Environmental and Climate Technologies”, Riga, Latvia, 10–12 May (2017).
IX. Solar Electric System Design, Operation and Installation, Washington State University, Extension Energy Program, An Overview for Builders in the U.S. Pacific Northwest, October (2009).
X. W. F. Stoecker and J. W. Jones, “Refrigeration and Air Conditioning”, Textbook, 2nd edition,
XI. W. Miller , G. Crompton, and J. Bell,” Analysis of Cool Roof Coatings for Residential Demand Side Management in Tropical Australia”, Engineers, ISSN 1996-1073, 8, 5303-5318, (2015).
XII. Y. Ali Cengel, “Heat Transfer: A Practical Approach”., Textbook, July. (2002).
XIII. Y. Ali Cengel, “Thermodynamic: An Engineering Approach”., Textbook, January. (2014).

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A SURVEY ON PRIVACY PRESERVING METHODS OF ELECTRONIC MEDICAL RECORD USING BLOCKCHAIN

Authors:

Yogesh Sharma,B. Balamurugan,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00004

Abstract:

A blockchain technology is one of the types of decentralized technology and is based on distributed ledger technology. A blockchain technology is a tamper proof and secure technology which make the technology suitable for data store. However, there are some question from the critics with the issues related to technical challenges like the storage space of blockchain and some security issues but the technology has shown the benefit in multiple sectors. Electronic Health Records (EHR), Electronic Medical Record (EMR), Patient Health Record (PHR) are the patients record that are need to be monitored continuously after the patient get discharged from the hospital specially the patients with heart diseases or cancer. The electronic medical record proves to be a great help for the patient and for the concerned doctor as well. These medical records need more security and privacy against the leak or misuse by some other person. There have been some incidents where it has seen the security breach in the electronic medical records of a patient. In order to provide privacy to these records blockchain technology can be beneficial. In this paper, we will provide a comprehensive survey of different methods for preserving the privacy of EMR using the blockchain technology.

Keywords:

Blockchain,Decentralized Technology,Electronic Medical Record,Patient,Security,Privacy,

Refference:

I. Abdullah Al Omar, M. S. (2017.). MediBchain: A Blockchain Based Privacy Preserving Platform for Healthcare Data. International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, (pp. pp. 534-543).
II. Ahmed Faeq Hussein, A. N.-G. (2018). A Medical Records Managing and Securing Blockchain Based System Supported by a Genetic Algorithm and Discrete Wavelet Transform. Cognitive Systems Research, (pp. 1-11).
III. AlevtinaDubovitskaya, Z. X. (2017). Secure and Trustable Electronic Medical Records Sharing using Blockchain. Annual Symposium proceedings .AMIA.
IV. Allison Ackerman Shrier, A. C.-t.( 2017, August 19). “Blockchain and Health IT: Algorithms, Privacy, and Data,”. Retrieved from http://www.truevaluemetrics.org: http://www.truevaluemetrics.org/DBpdfs/Technology/ Blockchain/1-78-blockchainandhealthitalgorithmsprivacydata_whitepaper.pdf,
V. Dylan Yaga, P. M. (2018). Blockchain Technology Overview.NISTIR 8202.
VI. Dylan Yaga, P. M. (2018, October). Blockchain Technology Overview . Retrieved from https://csrc.nist.gov: https://csrc.nist.gov/publications/detail/nistir/8202/final
VII. Gaby G. Daghera, J. M. (2018). Ancile: Privacy-preserving framework foraccess control and interoperability of electronic health records using blockchain technology . Sustainable Cities and Society, 283-297.
VIII. Guy Zyskind, O. N. (2015). Decentralizing Privacy: Using Blockchain to Protect Personal Data. IEEE CS Security and Privacy Workshops (pp. 180-184). IEEE.
IX. HaiboTian, J. Y. (2019). Medical Data Managementon Blockchain with Privacy. Journal of Medical systems, 26.
X. Jingwei Liu, X. L. (2018). BPDS: A Blockchain based Privacy-Preserving Data Sharing for Electronic Medical Records. Global Communications,, (pp. pp. 1-6).
XI. Kevin Peterson, R. D. (2016). A Blockchain-Based Approach to Health Information Exchange Networks.
XII. Kessler, G. C. (2019). An Overview of Cryptography.
XIII. Licheng Wang, X. S. (2019). Cryptographic primitives in blockchains. Journal of Network and Computer Applications, Pages 43-58.
XIV. Liehuang Zhu, Y. W.-K. (2018). Controllable and trustworthy blockchain-based cloud data management. Future Generation Computer Systems, 527-535.
XV. Madeira, A. (2016, November 29). What is the Block Size Limit. Retrieved from https://www.cryptocompare.com : https://www.cryptocompare.com/coins/guides/what-is-the-block-size-limit/
XVI. Marr, B. (2018, may 21). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Retrieved from forbes.com: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#5fa0d7e60ba9
XVII. MinalThakkar, D. C. (2006). Risks, Barriers, and Benefits of EHR Systems: A Comparative Study Based on Size of Hospital. Perspectives in Health Information Management.
XVIII. Nabil El Ioini, C. P. (2018). A Review of Distributed Ledger Technologies. OTM 2018 Conferences – Cloud and Trusted Computing (C&TC 2018).
XIX. Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System. Retrieved from www.bitcoin.org.
XX. NeeshaJothia, N. A. (2015). Data Mining in Healthcare – A Review .Procedia Computer Science . Penang Malaysia .
XXI. One-way Hash Function. (n.d.). Retrieved from http://www.aspencrypt.com: http://www.aspencrypt.com/crypto101_hash.html
XXII. P. Gareth, P. E. (November, 2015). “Understanding Modern Banking Ledgers through Blockchain Technologies: Future of Transaction Processing and Smart Contracts on the Internet of Money.”.SSRN Electronic Journal.
XXIII. Paul J. Taylor, T. D.-K. (2019). A systematic literature review of blockchain cyber security.Digital Communications and Networks.
XXIV. Popov, S. (2018, july 19). The Tangle. Retrieved from https://iota.org: https://iota.org/IOTA Whitepaper.pdf
XXV. Qi Xia, E. B. (2017). BBDS: Blockchain-Based Data Sharing for Electronic Medical Records in Cloud Environments. Information.
XXVI. Rouse, M. (2017, August). consensus algorithm. Retrieved from whatis.techtarget.com: https://whatis.techtarget.com/definition/consensus-algorithm
XXVII. RUI ZHANG, R. X. (2019). Security and Privacy on Blockchain.ACM Computing Surveys, Vol.1, 1-35.
XXVIII. S. Bakhtiari, R. S.-N. (2005). Cryptographic Hash Function: A Survey.
XXIX. SandroAmofa, E. B.-B. (2018). A Blockchain-based Architecture Framework for Secure Sharing of Personal Health Data.International Conference on e-Health Networking, Applications and Services.
XXX. Tian, H., He, J. and Ding, Y., “Medical Data Management on Blockchain with Privacy,” Journal of medical systems, vol.43, no.2, pp.26, 2019.
XXXI. Xiao Yue, H. W. (2016). Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control. Journal of medical systems, 218.
XXXII. XiaodongLin, A. (2018). TowardsSecureandPrivacy-Preserving Data Sharingine-Health SystemsviaConsortiumBlockchain.Journal of medical Systems, 140.
XXXIII. Xueping Liang, J. Z. (2017). Integrating Blockchain for Data Sharing and Collaboration in Mobile Healthcare Applications. Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (pp. 1-5). IEEE.
XXXIV. Yi Chen, S. D. (2018). Blockchain-Based Medical Records Secure Storage and Medical Service Framework.Journal of Medical Systems, 5.
XXXV. Yin Zhang, M. C. (2015). iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems.
XXXVI. Zhang, A. and Lin, X., “Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain,” Journal of medical systems, vol.42, no.8, pp.140, 2018.
XXXVII. Zhu, L., Wu, Y., Gai, K. and Choo, K.K.R., “Controllable and trustworthy blockchain-based cloud data management,” Future Generation Computer Systems, vol.91, pp.527-535, 2019.

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DETERMINATION OF DAILY WATER CONSUMPTION PATTERN (A CASE STUDY OF KHYBER PUKHTOONKHWA, PAKISTAN)

Authors:

Zohaib Hassanz,Manzoor Elahi,Hanif Ullah,Yaseen Mahmood,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00005

Abstract:

Water is not only necessary for life but it plays vital role in the social accompanied with economic growth of that specific country especially developing countries. Increasing population and rapid urbanization accompanied with climate change may reduce the supply of fresh water globally in twenty-first century. This study aims to understand current household water use and water use pattern in different five houses of different five places of KPK Pakistan for five months, to improve the efficiency of house hold water use, to encourage sustainable use and conservation of water resources. For the provision of new fresh water facilities. It’s necessary for water supply system planners to comprehend current water consumption behaviors of inhabitant, and how they use water of the new facility in future. The water consumption pattern is differs for the nations and societies and dependent on factors which may vary consumption on daily, weekly, monthly and yearly basis. These factors are availability of water source, economic, cultural, seasonal, climatic, and approachability to these water sources.

Daily water consumption pattern for five different houses in different areas of KPK for five months were found by carefully examining the time taken by pump to fill the Household overhead tanks. But in order to increase reliability of the acquired data the pumps were allowed to fill the tank till water flow for one minute at overflow pipe of the water tank was not recorded. During the period of research (March 2018 to July 2018) it was concluded that the average consumption in Charsadda (urban), Charsadda (rural), Mardan (urban), Mardan (rural) and Kohat (urban) was 102.84, 61.81, 105.99, 66.44 and 100 litres per captia per day (LPCD) respectively. Interestingly it was also observed that the trends of water consumption were almost the same in urban and rural areas of different districts of KPK

Keywords:

Daily water Consumption Pattern,Peak factors,LPCD,Overflow pipe,Flow rate,

Refference:

I. Abedin, S. B., &Rakib, Z. B. (2013). Generation and quality analysis of greywater at Dhaka City. Environmental Research, Engineering and Management, 64(2), 29-41.

II. Azad, A. P., & Ahmed, R. (2006). A Geographical Study of Land-Use in the Commercial Heart of Karachi (Saddar). Pakistan Geographical Review, 61(2), 64-82.
III. Falkenmark, M., Lundqvist, J., &Widstrand, C. (1989, November). Macro‐scale water scarcity requires micro‐scale approaches. In Natural resources forum (Vol. 13, No. 4, pp. 258-267). Blackwell Publishing Ltd.

IV. Haydar, S., Hussain, G., Nadeem, O., Aziz, J. A., Bari, A. J., & Asif, M. (2016). Water Conservation Initiatives and Performance Evaluation of Wastewater Treatment Facility in a Local Beverage Industry in Lahore. Pakistan Journal of Engineering and Applied Sciences.

V. Howard, G., Bartram, J., Water, S., & World Health Organization. (2003). Domestic water quantity, service level and health.

VI. Kumpel, E., Woelfle‐Erskine, C., Ray, I., & Nelson, K. L. (2017). Measuring household consumption and waste in unmetered, intermittent piped water systems. Water Resources Research, 53(1), 302-315.

VII. Mead, N. (2008). Investigation of domestic water end use.

VIII. Sadr, S. M., Memon, F. A., Jain, A., Gulati, S., Duncan, A. P., Hussein, W. E., & Butler, D. (2016). An Analysis of Domestic Water Consumption in Jaipur, India.

IX. Shankhwar, A. K., Ramola, S., Mishra, T., & Srivastava, R. K. (2015). Grey water pollutant loads in residential colony and its economic management. Renewables: Wind, Water, and Solar, 2(1), 5.

X. Singh, O., &Turkiya, S. (2013). A survey of household domestic water consumption patterns in rural semi-arid village, India. GeoJournal, 78(5), 777-790.

XI. Tabassum, R., Arsalan, M. H., & Imam, N. (2016) Estimation of Water Demand For Commercial Units in Karachi City

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DISCRETIZATIONS OF A FRACTIONAL ORDER LOGISTIC EQUATION ARISING FROM A SIMPLE SI-TERRORIST MODEL

Authors:

Asep K. Supriatna,Asep Sholahuddin,Hennie Husniah,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00006

Abstract:

The simplest model of terrorist growth model consists of two subpopulations, namely the susceptible subpopulation (S) and the militant or infected and infectious subpopulation (I). The model is governed by a coupled of differential equation reflecting the growth of the susceptible and infected subpopulations. Assuming a constant human population, the system can be reduced to a logistic differential equation. In this paper  a fractional order delayed logistic equation is discussed and  the discretization  in the form of piecewise constant argument is used to find the solution. We use the first and the second order discretization method in the numerical scheme and investigate the effect of the fractional order in the growth of the underlying population modelled by the equation. We found that in general the discretization method can mimic the behavior of the original logistic equation for some parameters. However, destabilizing effect may occur depending on the combination of the values of related parameters, such as the fractional order, the intrinsic growth rate, and the piecewise constant argument parameter.

Keywords:

SI-terrorist model,logistic differential equation,fractional order,piecewise constant argument,

Refference:

I. Atangana, “Application of Fractional Calculus to Epidemiology,” in Fractional dynamic (Eds. C. Cattani, H. M. Srivastava and X. Yang), De Gruyter Open, pp: 174-190, 2015.
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III. K. Supriatna and H. P. Possingham, “Harvesting a two-patch predator-prey metapopulation”, Natural Resource Modeling, vol. 12, pp: 481-489, 1999.
IV. K. Supriatna, “Maximum sustainable yield for marine metapopulation governed by generalized coupled logistic equations”, J. Sustain. Sci. Manage., vol. 7, pp: 201-206, 2012.
V. K. Supriatna, A. P. Ramadhan and H. Husniah, “A decision support system for estimating growth parameters of commercial fish stock in fisheries industries”, Procedia Computer Science, vol. 59, pp: 331-339, 2015.
VI. K. Supriatna, A. Sholahuddin, A. P. Ramadhan and H. Husniah, “SOFish ver. 1.2 – a decision support system for fishery managers in managing complex fish stocks”, IOP Conf. Ser.: Earth Environ. Sci., vol. 2016, pp: 1-7, 2016.
VII. M. A. El-Sayed and S. M. Salman, “On a discretization process of fractional-order Riccati differential equation”, Journal of Fractional Calculus and Applications, vol. 4, pp: 251–259, 2013.
VIII. M. A. El-Sayed, S. M. Salman and N. A. Elabd, “On the fractional-order Nicholson equation, Applied Mathematical Sciences, vol. 10, pp: 503 – 518, 2016.
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X. N. Angstmann, B. I. Henry, B. A. Jacobs and A. V. McGann, “Discretization of fractional differential equations by a piecewise constant approximation, arXiv, vol. 1605.01815v1, pp: 1-8, 2016.
XI. Aru˘gaslan, “Dynamics of a harvested logistic type model with delay and piecewise constant argument”, J. Nonlinear Sci. Appl., vol. 8, pp: 507–517, 2015.
XII. F. Farokhi, M. Haeri and Tavazoei, “Comparing numerical methods for solving nonlinear fractional order differential equations”, In book: “New Trends in Nanotechnology and Fractional Calculus Applications”, pp: 171-179, 2010.
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XIV. G. Seifert, “Second-order neutral delay-differential equations with piecewise constant time dependence”, J. Math. Anal. Appl. vol. 281, pp: 1–9, 2003.
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XVI. H. Husniah, A. K. Supriatna and N. Anggriani, “System dynamics approach in managing complex biological resources”, ARPN Journal of Engineering and Applied Sciences, vol. 10, pp: 1685-1690, 2014.
XVII. H. Husniah, R. A. R. H. Anwar, D. Haspada and A. K. Supriatna, “Terrorist dynamics: a transient solution from theoretical point of view”, The International Conference on Policing and Society, Indonesia, pp: 255-262, 2018.
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SHORT TERM RAIN FORECASTING FROM RADIOMETRIC BRIGHTNESS TEMPERATURE DATA

Authors:

Kausik Bhattacharyya,Manabendra Maiti ,Salil Kumar Biswas,Md Anoarul Islam,Ayan Kanti Pradhan,Pradip Kumar Ghosh,Judhajit Sanyal,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00007

Abstract:

Prediction of rainfall is important in terms of the impact of a rain event on various systems such as communication systems. Traditional approaches used to predict rain events are often sensitive to fluctuations in the datasets on which the predictions are made. The present paper therefore develops a robust machine learning based technique for accurate short term rain forecasting, based on experimentally collected data. Ground based microwave radiometer allows continuous monitoring of ambient temperature, water vapour and liquid water, and other hydrometeors through measurement of radiometric brightness temperature at different frequencies in clear and cloudy weather conditions. The radiometric brightness temperature outputs at 23.834 and 30 GHz are used to establish a relation where data trends which are precursors to rain events can be identified using this parameter. Spline equations are modeled by partitioning the dataset. The predictability of the occurrence of precipitation and the rainfall intensity has been studied based on the rise of brightness temperature from clear to cloudy weather conditions. The rise of brightness temperature at 23.834 and 30 GHz show that the precursory variations of this parameter preceding rain events are observable from 29 to 47 minutes prior to precipitation depending upon the nature of rainfall patterns. The data collected empirically displays trends that are used in this paper to provide a clear forecast of short term precipitation. Spline regression based machine learning models incorporating monthly trends, proposed in this paper improve the accuracy of prediction of short term rain events.

Keywords:

Radiometer,brightness temperature,microwave,propagation,rain,weather forecasting,

Refference:

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III. Bosisio, A. V., Fionda, E., Basili, P., Carlesimo, G., Martellucci, A., “Identification of rainy periods from ground based microwave radiometry,” European Journal of Remote Sensing, Vol. 45, pp: 41-50, 2012.
IV. Bosisio, A. V., Fionda, E., Ciotti, P., Fionda, E., Martellucci, A., “Rainy events detection by means of observed brightness temperature ratio,” “Rainy events detection by means of observed brightness temperature ratio,” 2012 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), Rome, pp. 1-4, 2012.
V. Chan, P.W., & Tam, C.M., “Performance and application of a multi-wavelength, ground-based microwave radiometer in rain now-casting”, 9th IOAS-AOLS of AMS., 2005.
VI. Chan, P.W., “Performance and application of a multi-wavelength, ground based microwave radiometer in intense convective weather,” Meteorol. Z., Vol. 18, no.3,pp. 253–265, 2009.
VII. Chan, P.W., & Hon, K.K., “Application of ground-based, multichannel microwave radiometer in the now casting of intense convective weather through instability indices of the atmosphere,” Meteorol. Z., Vol. 20, no.4, pp. 431–440, 2011.
VIII. Doran, J.C., Zhong, S., Liljegren, J. C., &Jakob, C., “A comparison of cloud properties at a coastal and inland site at the North Slope of Alaska,” J. Geophys. Res.,Vol. 107 (D11), pp. 4120, doi:10.1029/2001JD000819., 2002.
IX. Dvorak, P., Mazanek, M., Zvanovec, S., “Short-term Prediction and Detection of Dynamic Atmospheric Phenomena by Microwave Radiometer,” Radioengineering, Vol. 21, no. 4, Dec. 2012.
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XIII. Hye, Y.W., Yeon-Hee, K., &Hee-Sang, L., “An application of brightness temperature received from a ground-based microwave radiometer to estimation of precipitation occurrences and rainfall intensity,” Asia-Pacific Journal of Atmospheric Sciences, Vol. 45, no. 1, pp: 55-69, 2009.
XIV. Karmakar, P. K., Maiti, M., Calheiros, A. J. P., Angelis, C. F., Machado, L. A. T., Da Costa, S. S., ”Ground based single frequency micro -wave radiometric measurement of water vapour ,” International Journal of remote sensing(UK) , vol. 32, No. 23, pp 1-11, 2011.
XV. Knupp, K., Ware, R., Cimni, D., Vandenberghe F., Vivekanandan, J., Westwater, E., Coleman, T., “Ground-based passive microwave profiling during dynamic weather conditions,” J. Atmos. Oceanic Technol., Vol. 26, pp. 1057–1072, 2009.
XVI. Liu, G. R., “Rainfall intensity estimation by ground-based dual-frequency microwave radiometers,” Journal of Applied Meteorology, Vol. 40, pp: 1035-1041, 2001.
XVII. Lee, O.S.M., “Forecast of strong gusts associated with thunderstorms based on data from radiosonde ascents and automatic weather stations in China”, 21st Guangdong–Hong Kong–Macao Technical Seminar on Meteorological Science and Technology, Hong Kong, 24–26 Jan. 2007.
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A NEW STRATEGY FOR PHASE SWAPPING LOAD BALANCING RELYING ON A META-HEURISTIC MOGWO ALGORITHM

Authors:

Ibrahim H. Al-Kharsan,Ali. F. Marhoon,Jawad Radhi Mahmood,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00008

Abstract:

The significant spread of the single-phase loads in the consumer homes make the distribution network suffering from many dangerous problems like the load unbalancing. This problem comes because the single-phase devices continuously plugged in and out to different phases each time. This paper cared about this problem and solved it efficiently by the new meta-heuristic algorithm called GWO that applied for the first time to solve the load balancing issue. The algorithm has the ability based on the smart meter included swapping mechanism to disconnects the appropriate home phases from their initial connection to specific feeders and reconnected again to other feeders for satisfying the balancing in the secondary distribution network. The algorithm adapted to reaching the balancing with a minimal number of swaps and take care of the online PVs if the consumer likes to buy energy to the national utility. It distributed all the PVs in a manner that not cause a balancing problem or lead to a stability issue. The proposed method has been applied to some unbalanced areas with random data generated in MATLAB to confirm the efficacy of the proposed algorithm.

Keywords:

Load balancing,Gray Wolf algorithm,phase swapping, solar energy,swapping factor,unbalanced feeders,

Refference:

I. A. R. Malekpour and A. Pahwa, “Radial Test Feeder including primary and secondary distribution network,” in 2015 North American Power Symposium (NAPS), 2015, pp. 1–9.
II. A. Raminfard and S. M. Shahrtash, “A Practical Method for Load Balancing in the LV Distribution Networks Case study : Tabriz Electrical Network,” vol. 2, no. 6, pp. 1193–1198, 2010.
III. H. M. Khodr, I. J. Zerpa, P. M. De Oliveira-De Jesús, and M. A. Matos, “Optimal phase balancing in distribution system using mixed-integer linear programming,” 2006 IEEE PES Transm. Distrib. Conf. Expo. Lat. Am. TDC’06, vol. 00, pp. 1–5, 2006.
IV. J. J. Burke, Power distribution engineering: fundamentals and applications. CRC Press, 2017.
V. J. Zhu, M. Y. Chow, and F. Zhang, “Phase balancing using mixed-integer programming,” IEEE Trans. Power Syst., vol. 13, no. 4, pp. 1487–1492, 1998.
VI. K. P. Schneider et al., “Analytic considerations and design basis for the IEEE distribution test feeders,” IEEE Trans. Power Syst., vol. 33, no. 3, pp. 3181–3188, 2017.
VII. K. Wang, S. Skiena, and T. G. Robertazzi, “Phase balancing algorithms,” Electr. Power Syst. Res., vol. 96, pp. 218–224, 2013.
VIII. L. R. De Araujo, D. R. R. Penido, S. CarneiroJr, and J. L. R. Pereira, “Optimal unbalanced capacitor placement in distribution systems for voltage control and energy losses minimization,” Electr. Power Syst. Res., vol. 154, pp. 110–121, 2018.
IX. M. M. A. Salama, “Multi-Objective Optimization for the Operation of an Electric Distribution System With a Large Number of Single Phase Solar Generators,” vol. 4, no. 2, pp. 1038–1047, 2013.
X. N. Gupta, A. Swarnkar, and K. R. Niazi, “A novel strategy for phase balancing in three-phase four-wire distribution systems,” IEEE Power Energy Soc. Gen. Meet., pp. 1–7, 2011.

XI. P. V. Prasad, S. Sivanagaraju, and N. Sreenivasulu, “Network reconfiguration for load balancing in radial distribution systems using genetic algorithm,” Electr. Power Components Syst., vol. 36, no. 1, pp. 63–72, 2008.
XII. R. A. Hooshmand and S. Soltani, “Fuzzy optimal phase balancing of radial and meshed distribution networks using BF-PSO algorithm,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 47–57, 2012.
XIII. R. E. Brown, Electric power distribution reliability. CRC press, 2017.
XIV. R. Hooshmand and S. H. Soltani, “Simultaneous optimization of phase balancing and reconfiguration in distribution networks using BF-NM algorithm,” Int. J. Electr. Power Energy Syst., vol. 41, no. 1, pp. 76–86, 2012.
XV. S. C. Ross, G. Vuylsteke, and J. L. Mathieu, “Effects of load control for real-time energy balancing on distribution network constraints,” in 2017 IEEE Manchester PowerTech, 2017, pp. 1–6.
XVI. S. Mirjalili, S. Mirjalili, A. L.-A. in engineering Software, and U. 2014, “Grey wolf optimizer,” Elsevier, vol. 69, pp. 46–61, 2014.(14)
XVII. T. A. Short, Electric power distribution equipment and systems. CRC press, 2018.
XVIII. V. Zdraveski, M. Todorovski, and L. Kocarev, “Dynamic intelligent load balancing in power distribution networks,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 157–162, 2015.

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A NOVEL 3D-CNN BASED FEATURE EXTRACTION BASED CLASSIFICATION FOR DIABETIC RETINOPATHY (DR) DETECTION

Authors:

Shaik Akbar,Divya Midhunchakkaravarthy,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00009

Abstract:

Diabetic retinopathy (DR) is one of the eye diseases that results in vision loss if not diagnosed earlier. The automated computer aided models on the DR images help in accurate treatment disease prevention. Microaneurysms (MA) and red spots are the indicators of DR for disease diagnosis. Many DR classification approaches have been proposed in the literature with deep learning framework and non-linear functionality. Also, these models are not applicable to large feature space due to high true negative rate. To optimize these problems, a hybrid feature selection based deep learning classifier is used to detect the MA and red spots disease severity on the large image dataset. In this paper, a new feature extraction approach is implemented to find the essential positive bag features to the deep learning framework. A hybrid SVM classification model is used to classify the disease patterns with high true positive rate. Experimental results are simulated on different DR image class labels; results show that the hybrid deep learning classification model is better than the traditional models under various statistical metrics on large dataset.

Keywords:

Diabetic Retinopathy,Deep learning,Feature Extraction,Classification,

Refference:

I. B. Dashtbozorg, J. Zhang, F. Huang, and B. M. ter Haar Romeny, “Retinal Microaneurysms Detection Using Local Convergence Index Features,” IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3300–3315, Jul. 2018.
II. J. Xu et al., “Automatic Analysis of Microaneurysms Turnover to Diagnose the Progression of Diabetic Retinopathy,” IEEE Access, vol. 6, pp. 9632–9642, 2018.
III. K. M. Adal, P. G. van Etten, J. P. Martinez, K. W. Rouwen, K. A. Vermeer, and L. J. van Vliet, “An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 6, pp. 1382–1390, Jun. 2018.
IV. L. Zhou, Y. Zhao, J. Yang, Q. Yu, and X. Xu, “Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images,” IET Image Processing, vol. 12, no. 4, pp. 563–571, 2018.
V. M. Leeza and H. Farooq, “Detection of severity level of diabetic retinopathy using Bag of features model,” IET Computer Vision, vol. 13, no. 5, pp. 523–530, 2019.
VI. P. Costa, A. Galdran, A. Smailagic, and A. Campilho, “A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images,” IEEE Access, vol. 6, pp. 18747–18758, 2018.
VII. R. Bhoopalan and S. Sundaramoorthy, “Efficient approach for the automatic detection of haemorrhages in colour retinal images,” IET Image Processing, vol. 12, no. 9, pp. 1540–1544, 2018.
VIII. R. F. Mansour, “Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey,” IEEE Reviews in Biomedical Engineering, vol. 10, pp. 334–349, 2017.
IX. R. Pires, S. Avila, H. F. Jelinek, J. Wainer, E. Valle, and A. Rocha, “Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 193–200, Jan. 2017.
X. S. Lahmiri, “High-frequency-based features for low and high retina haemorrhage classification,” Healthcare Technology Letters, vol. 4, no. 1, pp. 20–24, 2017.
XI. S. Manivannan, C. Cobb, S. Burgess, and E. Trucco, “Subcategory Classifiers for Multiple-Instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification,” IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1140–1150, May 2017.
XII. S. Qummar et al., “A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection,” IEEE Access, vol. 7, pp. 150530–150539, 2019.
XIII. S. Sil Kar and S. P. Maity, “Gradation of diabetic retinopathy on reconstructed image using compressed sensing,” IET Image Processing, vol. 12, no. 11, pp. 1956–1963, 2018.
XIV. Seoud, T. Hurtut, J. Chelbi, F. Cheriet, and J. M. P. Langlois, “Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening,” IEEE Transactions on Medical Imaging, vol. 35, no. 4, pp. 1116–1126, Apr. 2016.
XV. W. Cao, N. Czarnek, J. Shan, and L. Li, “Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods,” IEEE Transactions on NanoBioscience, vol. 17, no. 3, pp. 191–198, Jul. 2018.
XVI. W. Zhou, C. Wu, D. Chen, Y. Yi, and W. Du, “Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method,” IEEE Access, vol. 5, pp. 2563–2572, 2017.
XVII. Zeng, H. Chen, Y. Luo, and W. Ye, “Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network,” IEEE Access, vol. 7, pp. 30744–30753, 2019.

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PREDICTION OF GENDER FROM FACIAL IMAGE USING DEEP LEARNING TECHNIQUES

Authors:

Ramalakshmi K,T. Jemima Jebaseeli,Venkatesan R,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00010

Abstract:

Gender recognition is a process of recognizing a person’s gender from their facial image using deep learning. The posed variation, illumination, and occlusion are some of the factors that affect in recognizing faces. These are reduced by increasing the accuracy of prediction. The network used for training the system is Convolutional Neural Network (CNN). For improving accuracy, the faces are detected and cropped from the image. Face detection is done using Open CV which detects the face by the frontal features of the face. This is done during training the network. The dataset used for training has cropped images. The proposed system predicts the person’s gender without compromising accuracy.

Keywords:

Gender recognition,convolutional neural network,VGGNet,

Refference:

I. Amit Dhomne, Ranjit Kumar and Vijay Bhan. Gender Recognition through Face using Deep Learning. Procedia Computer Science. 2018; 132: 2-10.
II. Biao Shi, HuaijuanZang, RongshengZheng and ShuZhan.An efficient 3D face recognition approach using Frenet feature of iso-geodesic curves. Journal of Visual Communication and Image Representation.2019; 59: 455-460.
III. Dhriti. K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion.International Journal of Computer Applications. 2012; 60(14): 0975 – 8887.

IV. DongshunCui, GuanghaoZhang, KaiHu, WeiHan and Guang-BinHuan, Face recognition using total loss function on face database with ID photos. Optics & Laser Technology. 2019; 110: 227-233.

V. Gil Levi and Tal Hassner. Age and Gender Classification Using Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2015.

VI. Hui-Cheng Lian, Bao-Liang Lu, ErinaTakikawa, and Satoshi Hosoi. Gender Recognition Using a Min-Max Modular Support Vector Machine. Lecture Notes in Computer Science. 2006; 210-215.
VII. HuiZhi and Sanyang Liu.Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation.2019; 58: 495-502.

VIII. Kevin Santoso and Gede Putra Kusuma.Face Recognition using Modified OpenFace. Procedia Computer Science.2018; 135: 510-517.

IX. MaafiriAyyad andChougdali Khalid.New fusion of SVD and Relevance Weighted LDA for face recognition, Procedia Computer Science.2019; 148: 380-388.

X. MaafiriAyyad andChougdali Khalid.New fusion of SVD and Relevance Weighted LDA for face recognition, Procedia Computer Science.2019; 148: 380-388.
XI. Rai P and Khanna P. Gender Classification Techniques: A Review. Advances in Computer Science, Engineering & Applications. Advances in Computer Science, Engineering & Applications. 2012; 51-59.

XII. Ranjeet Singh and Mohit Kumar Goel. Gender Classification Techniques-From Machine Learning to Deep Learning. International Journal of Computer Technology and Applications. 2016; 9(41): 77-88.
XIII. RupaliSandipKute,VibhaVyas and AlwinAnuse. Component-based face recognition under transfer learning for forensic applications.Information Sciences.2019; 476: 176-191.

XIV. Samik Banerjee and Sukhendu Das.LR-GAN for degraded Face Recognition.Pattern Recognition Letters.2018; 116: 246-253.
XV. SwaroopGuntupalliJandM. Ida Gobbini. Reading Faces: From Features to Recognition.Spotlight. 2017; 21(12): 915-916.

XVI. Vito Santarcangelo, Giovanni Maria Farinella and SebastianoBattiat. Gender Recognition: Methods, Datasets and Results. Conference: International Workshop on Video Analytics for Audience Measurement. 2015.
XVII. YangLi, WenmingZheng and ZhenCuiTong Zhang.Face recognition based on recurrent regression neural network. Neurocomputing. 2018; 297: 50-58.
XVIII. Yan Liang, Yun Zhang andXian-Xian Zeng.Pose-invariant 3D face recognition using half face. Signal Processing: Image Communication. 2017; 57: 84-90.
XIX. Yihua Chen, Ali Rahimi and Luca Cazzanti. Similarity-based Classification: Concepts and Algorithms, Journal of Machine Learning Research. 2009; 747-776.

XX. ZahraddeenSufyanu, FatmaSusilawatiMohamad, Abdulganiyu Abdu Yusuf, AbdulbasitNuhu Musa and RabiuAbdulkadir. Feature Extraction Methods for Face Recognition.International Review of Applied Engineering Research. 2016; 5(3): 5658-5668.

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