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FLEXIBLE SCHEME FOR PROTECTING BIG DATA AND ENABLE SEARCH AND MODIFICATIONS OVER ENCRYPTED DATA DIRECTLY

Authors:

Sirisha N, K. V. D. Kiran

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00022

Abstract:

Secure data storage and retrieval is essential to safeguard data from different kinds of attacks. It is part of information security which enables a system to avoid unauthorized access to data. The data storage destinations are diversified which includes the latest Internet computing phenomenon known as cloud computing as well. Whatever be the storage destination, cryptographic primitives are widely used to protect data from malicious attacks. There are other methods like auditing for data integrity. However, cryptography is the technique which has witnessed many variants of algorithms. However, most of the cryptographic algorithms do not support search and data modifications directly on the encrypted data. Homomorphic encryption and its variants showed promising solution towards flexibility in data dynamics. Motivated by this cryptographic technique, in this paper we proposed an algorithm known as Flexible Data Encryption (FDE) which supports encryption, decryption, search operation directly on encrypted data besides allowing modifications. This improves performance and flexibility in data management activities. Moreover, the proposed algorithm supports different kinds of data like relational and non-relational data. The proposed big data security methodology uses Jalastic cloud as the storage destination. Empirical results revealed that the proposed algorithm outperforms baseline cryptographic algorithms.

Keywords:

Big data,big data security,Jelastic cloud,flexible encryption,homomorphic encryption,

Refference:

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III. Gai, K., &Qiu, M. (2018). Blend Arithmetic Operations on Tensor-Based Fully Homomorphic Encryption Over Real Numbers. IEEE Transactions on Industrial Informatics, 14(8), 3590–3598.
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VIII. J.S. Rauthan, K.S. Vaisla, VRS-DB: Preserve confidentiality of users’ data using encryption approach, Digital Communications and Networks, p1-14.
IX. Kuzu, M., Islam, M. S., &Kantarcioglu, M. (2015). Distributed Search over Encrypted Big Data. Proceedings of the 5th ACM Conference on Data and Application Security and Privacy – CODASPY ’15. P1-8.
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XI. Kim, H.-Y., Myung, R., Hong, B., Yu, H., Suh, T., Xu, L., & Shi, W. (2019). SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection. 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). P1-8.
XII. KashiSai Prasad, S Pasupathy, “Real-time Data Streaming using Apache Spark on Fully Configured Hadoop Cluster”, J.Mech.Cont.& Math. Sci., Vol.-13, No.-5, November-December (2018) Pages 164-176.
XIII. K. Sai Prasad, Dr. S Pasupathy, “Deep Learning Concepts and Libraries Used in Image Analysis and Classification”, TEST Engineering & Management, Volume 82, ISSN: 0193 – 4120 Page No. 7907 – 7913.
XIV. K. Sai Prasad &RajenderMiryala “Histopathological Image Classification Using Deep Learning Techniques” International Journal on Emerging Technologies 10(2): 467-473(2019)
XV. Li, Y., Gai, K., Qiu, L., Qiu, M., & Zhao, H. (2017). Intelligent cryptography approach for secure distributed big data storage in cloud computing. Information Sciences, 387, 103–115.
XVI. Maha TEBAA, Said EL HAJII, “Cloud Computing through Homomorphic Encryption”, International Journal of Advancements (IJACT), Vol. 8, No. 3, March – April 2017.
XVII. Ogburn, M., Turner, C., &Dahal, P. (2013). Homomorphic Encryption. Procedia Computer Science, 20, 502–509.
XVIII. PeterPietzuch and Valerio Schiavoni. (2019). Using Trusted Execution Environments for Secure Stream Processing of Medical Data, p1-16.
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XXIV. Sirisha, N., &Kiran, K. V. D. (2017), “Protection of encroachment on bigdata aspects”, International Journal of Mechanical Engineering and Technology, 8(7), 550- 558.
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PREDICTING THE PRICE OF CRYPTOCURRENCY USING SUPPORT VECTOR REGRESSION METHODS

Authors:

Saad Ali. Alahmari

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00023

Abstract:

The rising profit potential in virtual currency has made forecasting the prices of crypto currency a fascinating subject of study. Numerous studies have already been conducted to predict future prices of a specific virtual currency using a machine-learning model. However, very few have focused on using different kernels of a “Support Vector Regression” (SVR) model. This study applies the Linear, Polynomial and “Radial Basis Function”(RBF) kernels to predict the prices of the three major crypto currencies, Bitcoin, XRP and Ethereum, using a bivariate time series method employing the cryptocurrency (daily-Closed Price) as the continuous dependent variable and the “Morgan Stanley Capital International” (MSCI) World Index (MSCI-WI) and the (daily-Closed Price) as the predictor variable. The results demonstrated that ‘RBF’ outperforms most other kernel methods in predicting cryptocurrency prices in terms of “Mean Absolute Error”(MAE), “Mean Squared Error” (MSE), “Root Mean Squared Error” (RMSE) and R-squared (

Keywords:

Support Vector Regression,Cryptocurrency,Machine Learning,Time-series Analysis. Non-linear,

Refference:

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VI. Das, Debojyoti, and KannadhasanManoharan. “Emerging stock market co-movements in South Asia: wavelet approach.” International Journal of Managerial Finance 15, no. 2 (2019): 236-256.

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VIII. H. Drucker, C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in M. Mozer, M. Jordan, and T. Petsche, Eds., Advances in Neural Information Processing Systems 9, Cambridge, MA, USA: MIT Press, 1997, pp. 155–161.

IX. H. Sun and B. Yu, “Forecasting financial returns volatility: A GARCH-SVR model,” Computational Economics, pp. 1–21, 2019.

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XI. J. Huisu and J. Lee. “An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information.” IEEE Access, 6 ,pp. 5427-5437.2017.

XII. J. Rebane, I. Karlsson and P. Papapetrou, “Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: A comparative study,” in Proceedings of SIGKDD Workshop on Fintech (SIGKDD Fintech’18), London, UK, Association for Computing Machinery (ACM), 2018, article id 4.

XIII. K. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik, “Predicting time series with support vector machines,” in International Conference on Artificial Neural Networks, Berlin,Heidelberg: Springer, pp. 999–1004, 1997.

XIV. L. Catania, S. Grassi, and F. Ravazzolo, “Forecasting cryptocurrencies under model and parameter instability,” International Journal of Forecasting, vol. 35, no. 2, pp. 485–501, 2019.

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XVI. M. Suganyadevi and C. K. Babulal, “Support vector regression model for the prediction of loadability margin of a power system,” Applied Soft Computing, vol. 24, pp. 304–315, 2014.

XVII. S. Alahmari, “Using machine learning ARIMA to predict the price of cryptocurrencies,” The ISC International Journal of Information Security, vol. 11, no. 3, pp. 139–144, 2019, doi: 10.22042/isecure.2019.11.0.18.

XVIII. S. McNally, J. Roche and S. Caton, “Predicting the price of Bitcoin using machine learning,” in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, 2018, pp. 339–343, doi: 10.1109/PDP2018.2018.00060.

XIX. S. Wang, R. Li, and M. Guo, “Application of nonparametric regression in predicting traffic incident duration,” Transport, vol. 33, no. 1, pp. 22–31, 2018.

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XXI. V. Derbentsev, N. Datsenko, O. Stepanenko, and V. Bezkorovainyi, “Forecasting cryptocurrency prices time series using machine learning approach,” in SHS Web of Conferences, vol. 65, pp. 02001, 201.

XXII. Y. Peng, P. Albuquerque, J. de Sá, A. Padula, and M. Montenegro, The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression, 2018. Expert Systems with Applications, 97, pp. 177–192.

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ANALYSIS OF HEART RATE AND OXYGEN SATURATION IN ADOLESCENTS AT THE TIME OF NETWORK PLAY

Authors:

Wilver Auccahuasi, Orlando Aiquipa, Edward Flores, FernandoSernaqué, Sergio Arroyo, Ingrid Ginocchio, Aly Auccahuasi, Felipe Gutarra, Nabilt Moggiano

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00024

Abstract:

Technology is changing people's daily lives because of the electrical devices that make people's day-to-day life easier. One of the most influential fields is the entertainment field, proof of this is the variety of video games. These are constantly evolving both in the technical requirements and in the complexity of the games that nowadays, strategy games are booming. These games have new ways of interacting with the player. The most characteristic is the level that the player occupies the game and proof of this are the long times that young people devote to the moment of playing. This excess time causes a change in the personality of adolescents as well as causing certain changes in cardiorespiratory effects. Sudden changes of the emotions associated with a high level of stress at the time of playing are causing the heart to react differently to these sudden changes in oxygen requirement. In this paper, we analyze the strategy games that are in full swing at this time such as the famous FORTNITE game. The research consists of a monitoring of 10 young people to whom they have been subjected at long game times. On an average 5 hours in a row, in which they have been evaluated for oxygen saturationand heart rate at the times that players are developing various emotions such as stress, frustration, joy among others. The results show that when young people win and are promoted to higher levels, they present positive emotions such as tranquility and are happy, while when they lose and lower them, they present negative changes presenting frustration, they deny, in some cases they present aggressive attitudes, throwing things. These changes are reflected in an excess of oxygen consumption reaching saturation at 99% and presenting of high heart count greater than 85 beats per minute. It should be noted that young people who are under study, do not present any type of health problem and we end with some recommendations to take into account when playing these video games that require time prolonged subjected to video games.

Keywords:

Video game,Saturation,Oxygen,Heart rate,Frustration,

Refference:

I. García Cernaz, S. (2018). Videojuegos y violencia: una revisión de la línea de investigación de los efectos.
II. González-Vázquez, A., &Igartua, J. J. (2018). ¿ Por qué los adolescentes juegan videojuegos? Propuesta de una escala de motivos para jugar videojuegos a partir de la teoría de usos y gratificaciones. Cuadernos. info, (42), 135-146.
III. Irles, D. L., Gomis, R. M., Campos, J. C. M., & González, S. T. (2018). Validación española de la Escala de Adicción a Videojuegos para Adolescentes (GASA). AtenciónPrimaria, 50(6), 350-358.
IV. Rauber, S. B., Brandão, P. S., Moraes, J. F. V. N. D., Madrid, B., Barbosa, D. F., Simões, H. G., …& Campbell, C. S. G. (2018). Oxygen consumption and energy expenditure during and after street games, active video games and tv. RevistaBrasileira de Medicina do Esporte, 24(5), 338-342.
V. Santana, M., Pina, J., Duarte, G., Neto, M., Machado, A., &Dominguez-Ferraz, D. (2016). Efectos de la Nintendo Wii sobre el estado cardiorrespiratorio de adultos mayores: ensayo clínico aleatorizado. Estudiopiloto. Fisioterapia, 38(2), 71-77.
VI. Soares, L. M. D. M. M., Moreira, L. C. M., & de Souza, W. I. M. (2018). Respostascardiorrespiratórias e percepção subjetiva do esforço de hemiparéticossubmetidos à prática de exergames/Cardiorrespiratory responses and subjetive perception of the effort in hemipareticsafterexergamespractice/Respuestas… JOURNAL HEALTH NPEPS, 3(2), 492-505.

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OBJECT CLASSIFICATION IN HIGH RESOLUTION OPTICAL SATELLITE IMAGES BASED ON DEEP LEARNING TECHNIQUES

Authors:

Wilver Auccahuasi, Percy Castro, Edward Flores, Fernando SernaquÉ, Sergio Arroyo, Javier Flores, Michael Flores, Felipe Gutarra, Nabilt Moggiano9

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00025

Abstract:

The classification of objects that are present in the images or in the videos, is being developed progressively obtaining good results thanks to the use of Convolutional Networks, in this work we also use the convolutional networks for detection of objects that are present in high resolution satellite images, tests were carried out on ships that are on the high seas and in the ports, this classification is useful for monitoring the coasts, as well as for analyzing the dynamics of the ships can be applied in the search of ships, to cover this task of classifying ships in the spectral images, the use of high resolution satellite images of coastal areas and with a large number of ships is used, in order to build a set of images, containing images of the ships, in order to be used for training setting and testing of the convolutional network, a very particular configuration of the convolutional network caused by the particularity of high resolution satellite images is presented, the methodology developed indicating the procedures performed is also presented, a set of images containing 300 was built images of ships that are in the sea or are anchored in the ports, the results obtained in the classification using the convolutional networks are acceptable to be able to be used in different applications.

Keywords:

Convolutional Networks,Satellite Image,Classification,High Resolution,Multispectral Image,

Refference:

I. Maiwald, F., Bruschke, J., Lehmann, C., &Niebling, F. (2019). A 4D information system for the exploration of multitemporal images and maps using photogrammetry, web technologies and VR/AR. Virtual Archaeology Review, 10(21), 1-13.
II. Peña, A., Bonet, I., Manzur, D., Góngora, M., &Caraffini, F. (2019, June). Validation of convolutional layers in deep learning models to identify patterns in multispectral images. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE.
III. Riveros, L., & Raquel, E. (2018). Detección de vehículos con aprendizajeprofundo en Cámara de Vigilancia.
IV. Sánchez Santiesteban, S. (2018). Recuperación de imágenesporcontenidousandodescriptoresgeneradosporRedesNeuronalesConvolucionales. RevistaCubana de CienciasInformáticas, 12(4), 78-90.
V. Weinstein, B. G. (2018). Scene‐specific convolutional neural networks for video‐based biodiversity detection. Methods in Ecology and Evolution, 9(6), 1435-1441.

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LOW-COST PLATFORM FOR THE PROCESSING AND CONTROL OF SENSORS THAT MAKE UP THE PAYLOAD IN REMOTE SENSING EQUIPMENT

Authors:

Wilver Auccahuasi, Fernando Sernaqué, Edward Flores, Michael Flores Mamani, Percy Castro, Felipe Gutarra, NabiltMoggiano

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00026

Abstract:

In the development of equipment to be used in the remote sensing environment, it is recommended to consider in the design certain technical aspects such as: energy consumption, device size, performance, computational capacity, connectivity, radiation tolerance, among others. Therefore, certain electronic components capable of providing these characteristics are used, which makes their cost high and it becomes difficult to acquire these electronic components for special use. The proposal presented in this investigation, is the use of the embedded card Tegra TK1 of the NVIDIA brand, to be used as a base device for remote sensing equipment. This card provides considerable computational capacity. This card is composed of a CPU and the GPU, as well as communication buses and the communication card expansion to connect certain devices such as sensors and actuators. Another feature is fault tolerance and critical execution times that are critical in these types of equipment, among the main tasks, are the sending of telemetry, control of navigation devices, and synchronization among other tasks that will depend on the payload of the equipment. As a result, it is proposed to install a real-time operating system on the TK1 card, which ensures that the tasks are fulfilled in the established times and with the criticality that is required.

Keywords:

Operating System,Real Time,Driver,Programming,Function,Task,

Refference:

I. https://www.tldp.org/HOWTO/RTLinux-HOWTO-3.html
II. https://www.rtai.org/
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IV. https://www.freertos.org/
V. https://ecss.nl/
VI. https://devblogs.nvidia.com/low-power-sensing-autonomy-nvidia-jetson-tk1/
VII. http://www.esa.int/esl/ESA_in_your_country/Spain/Microlanzadores_para_pequenos_satelites
VIII. https://kernel.googlesource.com/pub/scm/linux/kernel/git/rt/linux-rt-devel/+/linux-4.4.y-rt-rebase

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RAPID ACTION PROTOCOL TO PREVENT THE OUTBREAK OF VECTORS TRANSMITTING TROPICAL DISEASES, THROUGH HETEROGENEOUS PROCESSING OF GEOSPATIAL INFORMATION

Authors:

Wilver Auccahuasi, Percy Castro, Orlando Aiquipa, Edward Flores, Fernando Sernaqué, Felipe Gutarra, NabiltMoggiano

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00027

Abstract:

The analysis and processing of data is important in different areas, and we must pay more attention when it comes to the health of people, in the development of the protocol to prevent the outbreak of vectors transmitting tropical diseases with an emphasis on the mosquito “ AedesAegypti ”, being able to control its reproduction is of vital importance, and is one of the objectives of the protocol, understanding the reproduction times corresponds to the times where we must take necessary actions to be able to cut its reproduction cycle, within the mechanisms Technological we indicate the use of meteorological information to be able to analyze and predict the favorable conditions so that the mosquito can reproduce, added to the valuable information provided by earth observation satellites, in their access to satellite images, which will provide us with Current images of the area of interest, for rapid detection of bodies of water that will be the future nests of the mosquitoes, the heterogeneous processing is characterized by the analysis of the meteorological data in the CPU and the processing of the satellite images in the GPU both running in parallel processes in the same computer, with which we optimize the use of resources available in applications dedicated to health care.

Keywords:

Vector,Biometeorological,Bodies of Water,Temperature,Humidity,

Refference:

I. GPGPU General-Purpose computation on Graphics Processing Units Web Site. http://gpgpu.org
II. http://www.nvidia.es/page/gpu_computing.html
III. https://es.windfinder.com/#10/-4.4477/-81.1189/temp
IV. https://www.senamhi.gob.pe/?p=datos-historicos
V. https://www.who.int/denguecontrol/mosquito/es/
VI. https://www.windy.com/es/-Mostrar-a%C3%B1adir-m%C3%A1s-capas/overlays?rh,-5.102,-80.706,7,m:dvkadSD
VII. https://www.windy.com/es/-Mostrar-a%C3%B1adir-m%C3%A1s-capas/overlays?temp,-5.173,-80.739,8,m:dxradTR
VIII. Jason Sanders, Edward Kandrot, CUDA by Example An Introduction to General – Purpose GPU Programming, Nvidia, Addison Wesley, Ann Arbor, Michigan, United States, First printing July 2010.
IX. Lillesand, T., Kiefer, R., Chipman, J., 2004. Remote Sensing and Image Interpretation. fifthed Willey & Sons, New York.
X. Lunetta, R.S., Lyon, J.G., 2004. Remote Sensing and GIS Accuracy Assessment. CRC press,
XI. Nvidia Web Site. http://www.nvidia.com
XII. Nvidia. GPU Computing.
XIII. Pal, M., Mather, P.M., 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 86, 554–565.
XIV. Russ, J.C., 1999. The Image Processing Handbook. third ed. CRC Press, Boca Raton, FL.
XV. Saha, S., Bandyopadhyay, S., 2010. Application of a multiseed-based clustering technique for automatic satellite image segmentation. IEEE Geosci. Remote Sens. Lett. 7, 306–308.
XVI. Sridhar, P.N., Surendran, A., Ramana, I.V., 2008. Auto-extraction technique-based digital classification of saltpans and aquaculture plots using satellite data. Int. J. Remote Sens. 29, 313–323.
XVII. Wilkinson, G.G., 2005. Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Trans. Geosci. Remote Sens. 43, 433–440.

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ANALYSIS OF ORGANIC FLOCCULANTS IN LEAD AND CADMIUM BIOSORPTION IN LABORATORY-LEVEL SAMPLES

Authors:

Fernando Sernaqué, Wilver Auccahuasi

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00028

Abstract:

In the present investigation, the efficiency of organic flocculants was evaluated in the biosorption of lead and cadmium in laboratory-level samples is evaluated, for which a standard solution of 1000 mg / l or ppm of Pb and Cd is prepared, which was the basis for the daughter solutions of 50 mg / l, 100 mg / l and 200 mg / l; respectively for each metal, for this work three concentrations were defined in case of Pb at 0.2, 0.5 and 1 mg / l and for cadmium at 0.05, 0.25, 0.5 mg / l. The was used as an instrument the jar test for the first treatment of the samples, considering constant the volume of 1L, while the concentration of the organic flocculant varied, it was carried out at 5 different doses for all the fruits (0.5 g, 1 g, 1.5 g, 2 g and 2.5 g), having as development for the test, first run (v1 = 250 RPM for 15 minutes), rest time 1 (tr1 = 5 minutes), second run (50 RPM for 5 minutes) , Final rest time (Trf = 30 minutes). It was determinedthat dose with the highest efficiency is presented with 2.5 g for each natural flocculant. After the sample was treated, it was taken to the heating plate, for which to 100 ml aliquot it was taken and 5ml of nitric acid was added, for the digestion of the sample at a temperature of 95 ° C, with an approximate time of 50 minutes, where it was observed that the volume has been reduced to 20 to 30 ml, then let it cool, to then use the atomic absorption spectrophotometer equipment. It was concluded that the organic flocculants in the removal of lead and cadmium have an efficiency of 28.37% to 88.33%, being the carambola which presented a 28.37% lower efficiency in the removal of lead while the orange, grape, cucumber, cocona and apples are fruits with greater efficiency in the treatment of lead, highlighting the efficiency of the apple with 88.33%. Also for cadmium fruits such as cocona, grapefruit, tangerine, cucumber and apple are those who presented a greater efficiency statistically, where stands out once again the apple with an efficiency of 83.83%, while the grape presented only a 41.93% lower efficiency in the removal of cadmium

Keywords:

FlocculantOrganic,Biosorption,Cadmium ,Lead,

Refference:

I. Marshall Sánchez, R, Espinoza Subía, J. (2016). Evaluación del poderabsorbente de las
cascaras de cítricos “limón y Toronja” paraeliminación de metalespesados; plomo (Pb) y Mercurio (Hg) en aguasresidualessintéticos. Ecuador. Recuperado en:
http://repositorio.ug.edu.ec/handle/redug/18100.
II. Ministerio de Agricultura (2012). Conformación de la comisiónmultisectorialpara la
recuperación de la cuenca del ríoRímac. AutoridadNacionaldel Agua, 17-18,21-22 pp. [4] GarcíaCernaz, S. (2018). Videojuegos y violencia: unarevisión de lalínea de investigación de los efectos.
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Ambiental. 26, 32 pp
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aguasresidualesutilizando la cáscara de la mandarina. Cuenca.

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IDENTIFYING, CLASSIFYING, AND PRIORITIZING THE RESEARCHERS’ STRATEGIC COMPETENCIES IN OIL INDUSTRY RESEARCH CENTERS

Authors:

Ahmad Farmahini Farahani , Mohsen Bahrami, Fatollah Moztarzadeh

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00029

Abstract:

Changes caused by the knowledge economy, including the emergence of new idea  flows in management, methods and structure of organizations, have led to a change in the roles and skills needed for researchers in organizations. As new age organizations focus on intellectual property, organizational aspirations and organizational change, the researchers, as the wealth creators, in order to quickly adapt to new situations and develop their competencies in the\ competitive market, need to constantly change and develop a new identity for themselves. Since competencies have a prudential feature through describing skills and behavioral approaches, identifying and explaining researchers’ competencies in oil industry research institutes is of particular importance. Accordingly, the present paper seeks to identify the factors and indicators of researchers’ competencies in oil industry research centers using scientific methods and surveys and then identify, classify, and prioritize researchers’ strategic competencies using statistical methods. According to the results obtained from the present study, creativity and innovation, integration, accountability and customer orientation competencies have higher priorities; however, all identified strategic competencies have a significant positive distance to mean. With the help of the results of this study, researchers and managers can clarify expectations about each other

Keywords:

Researchers’ Competencies Prioritization,Industrial Research Centers,Strategic Competencies,Oil Industry,

Refference:

I. Afkhami Ardakani, M, Baba Shahi.J, Tahmasebi HR, 2016, Providing a Tool for Identifying and Measuring Knowledge Jobs, A mixed approach research, Journal of Human Resource Management Research
II. Baba Shahi, J. et al., 360 Degree Assessment of Oil Industry exploration management employees and managers, 2017, Institute for International Energy Studies
III. Based human Resources management : A Holistic Approach, Fanny Klett, Knowledge management & E- Learning 2017
IV. Drey fus, c., Identifying. Competencies that predict effectiveness of R&d managers, DREFUS&Associates INC,USA.2007
V. Essential Copetencies for the Supervisors oil and gas indutria companies , mir hadi miazen jamshid, Elsevier, 2012
VI. Evelyn orr,sneltjes,c, G., Best practices in developing& implemming com pet ency models, the korn/ fcrry institute,2010
VII. Farmahini Farahani,A., Hyper jobs& future skills in the field energy, institute for international energy studies(IIES), tehran,2013
VIII. Hsieh,s., lin,j.,lee,h., Analysison literature review of competency, Departmena of international trade& logistics, over seas Chinese university, 2012
IX. Klett,F.,the design of a sustainable compentency- based human resource2012 management : A Holistic Approach, fraunhofer institute Digital media Technology, Germany, knowled,e management& E-learning,vol2,no3,
X. Liu, p., Tsai, C.,A study on R&D COMPETENCE FOR R&D Management personnel in Taiwan,s High-tech Indusry, departmem of Induustrial Enginccing and management, 2008
XI. Nalimi Devis ,Analysis on Literature Review of competency maping for American international yourrol of research in Humanitiws , Arts and Social Sciences , 2013
XII. Onet Competency Model, 2017 (www.onetonline.ory)
XIII. The Future of the Energy field, Farmahini et al., 2015
XIV. The Competency Models of Employment and Training Administration, Department of Labor Education and Training, 2016
XV. Torres p. & Auguto, M.(2016). The impact of experiential on managers, strategic competencies and decision style. Journal of innovation & knowledge

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SCIENTIFIC AND TECHNICAL RESEARCH ON THE EFFICIENCY OF ORGANIZATIONAL AND TECHNOLOGICAL PROCESSES OF INDUSTRIAL CLUSTERS RE-PROFILING

Authors:

Azariy Lapidus

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00030

Abstract:

Reprofiling industrial facilities allows companies to optimize their structure while also creating a competitive environment in the service sector. In addition, the portfolio of assets undergoes optimization during the reprofiling process. Because of the release of the production space, it would be possible to reduce the costs by preserving, selling, and leasing production space. Therefore, to achieve and strengthen a long-term competitiveness, companies are forced to adjust their activities with an emphasis on the changing demands of the period. The world is constantly changing, so it is very important to respond expediently and quickly to these changes.To date, international practice and experience of reprofiling in the Russian Federation have shown it as one of the most difficult managerial tasks. During this process, many restrictions, along with the unique characteristics of the company, in which it is conducted, should be considered. Consequently, it must be performed only in the presence of the clearly defined goals, the reprofiling concept, and an understanding of each stage and methods to be observed.The topic of this article is relevant since the model of the work performed during the reprofiling allows this process to go as smoothly and efficiently as possible, allowing the company to adapt to new market conditions.However, this issue is poorly covered nowadays. In fact, many sources consider the redesigning strategy only as a special case study of a restructuring strategy or as a strategy for updating the fixed assets. Therefore, regulatory documentation for capital construction projects as well as for reprofiling facilities should be improved.

Keywords:

Construction control,Redevelopment of industrial areas,Reprofiling industrial facilities,Scientific and technical renovation,urban development,

Refference:

I. A. Ginzburg. Sustainable building life cycle design. MATEC Web of Conferences. XV International conference «Topical problems of architecture, civil engineering, energy efficiency and ecology», Vol.: 02018, n.d.
II. A. Lapidus, D.Topchiy. Formation of Methods for Assessing the Effectiveness of Industrial Areas’ Renovation Projects. Proceedings of the IOP Conference Series: Materials Science and Engineering, Vol.: 471, pp. 1-6, n.d..
III. A. Lapidus, I. Abramov. Formation of production structural units within a construction company using the systemic integrated method when implementing high-rise development projects. E3S Web of Conferences, Vol.: 33, 2018.
IV. A. Volkov, A.Sedova, P.Chelyshkov, B. Titarenko, G.Malyha, E.Krylov. The theory of probabilities methods in the scenario simulation of buildings and construction operation. Research Journal of Pharmaceutical, Biological and Chemical Sciences, Vol.: 7, Issue: 3, pp. 2416-2420, 2016.
V. A. Volkov, V.Chulkov, R.Kazaryan, M.Fachratov, O.Kyzina, R.Gazaryan. Components and guidance for constructional rearrangement of buildings and structures within reorganization cycles. Applied Mechanics and Materials, pp.2281-2284, 2014.
VI. A.A. Lapidus, P.A.Govorukha. Organizational and technologic potential of setting of enclosing structures for residential buildings. International Journal of Applied Engineering Research,Vol.: 10, Issue:20, pp.40946-40949, 2015.
VII. A.N. Vlasov,V.P.Merzlyakov, S.B.Ukhov. Determination of deformation and strength properties of layered rock by asymptotic averaging. Soil Mechanics and Foundation Engineering, Vol.: 6, pp.197-205, 2003.
VIII. B.V. Gusev, Ch. Jenn-Chuan, A.A. Speransky. Waves of innovation, and sustainable development of industry, on an example of construction. Scientific Israel – Technological Advantages, Vol.: 1, pp. 163-173,2 016.
IX. D.D. Zueva, E.S.Babushkin, D.V.Topchiy,A.Yu.Yurgaitis. Construction supervision during capital construction, reconstruction and re-profiling. MATEC Web of Conferences, Vol.: 265, pp. 1-8, 2019.
X. I. Abramov, T. Poznakhirko, A. Sergeev. The analysis of the functionality of modern systems, methods and scheduling tools. MATEC Web Conf 86, Issue: 04063, pp. 1-5, 2016.
XI. I. Abramov. Formation of integrated structural units using the systematic and integrated method when implementing high-rise construction projects. HRC 2017 (HIGH-RISE CONSTRUCTION-2017). E3S Web of Conferences, Vol.: 33, pp. 1-7, 2018.
XII. P. Graham. Building Ecology: First Principles For A Sustainable Built Environment. Blackwell Science, 2003.
XIII. P. Oleynik, S.Sinenko, B.Zhadanovsky, V. Brodsky, M.Kuzhin. Construction of a complex object. MATEC Web of Conferences. 5th International Scientific Conference «Integration, Partnership and Innovation in Construction Science and Education», pp. 4059, 2016.
XIV. R.I. Fokov. Problems of ecological reconstruction of the urbanized environment. International Academy of Ecological Reconstruction, Vol.: 2, pp. 11-21, 2006.
XV. S. Shinri, T. Masamichi. Developing environmental load factors for construction materials used in social infrastructure LCA. Enviromental System Research Papers,Vol.: 38,pp.185-191, n.d.
XVI. S.B. Ukhov. Beds and foundations of high-rise buildings. Scientific aspects and geotechnical problems.Soil mechanics and foundation engineering. Springer New York Consultants Bureau. 2003.
XVII. V.A. Ilyichev, A.S. Aleshin, A.S.Dubovskoi A.S. Instrument problems of deformation monitoring in construction. Soil mechanics and foundation engineering, Vol.: 3, pp. 91-97, 2003.
XVIII. V.I. Telichenko, V.I. Andreev, V.I.Gagin. Civil engieneering education in Russia. RSP-seminar, pp. 21-28, 2005.
XIX. Z.G. Ter-Martirosyan. Fundamentals of settlement analysis for high-rise buildings constructed in deep excavations. Soil Mechanics and Foundation Engineering, Vol.: 5, pp. 190-194, 2003.

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APPROXIMATION OF CASSONFLUID IN CONDUCTING FIELD PAST A PLATE IN THE PRESENCE OF DUFOUR, RADIATION AND CHEMICAL REACTION EFFECTS

Authors:

S. Venkateswarlu, D. Dastagiri Babu, E. Keshava Reddy

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00001

Abstract:

We examine the unsteady MHD free convective flow of a chemically reacting incompressible fluid over a vertical permeable plate under the influence of thermal radiation, Dufour and heat source/sink. The dimensionless governing equations are solved analytically using the three term perturbation method. Expressions for velocity, temperature and concentration for the flow are obtained and presented graphically. The analysis shows that Casson fluid parameter  increases the velocity; Dufour number increases the velocity and velocity; magnetic field force decreases the velocity; Chemical reaction rate increases the temperature but decreases the velocity and concentration; Grashof numbers increase the velocity when their values are increasingly varied. Furthermore, skin fiction coefficient, Nusselt number and Sherwood number for different values of governing parameters are calculated and the results are summarized in tabular form.

Keywords:

MHD,Casson fluid,Dufour effect,Free convection,Chemical reaction,

Refference:

I. Alao, F. I., A. I. Fagbade, and B. O. Falodun. “Effects of thermal radiation, Soret and Dufour on an unsteady heat and mass transfer flow of a chemically reacting fluid past a semi-infinite vertical plate with viscous dissipation.” Journal of the Nigerian mathematical Society 35.1 (2016): 142-158.

II. Charankumar, G., et al. “Chemical Reaction and Soret Effects on Casson MHD Fluid Flow over a Vertical Plate.” Int. J. Chem. Sci 14.1 (2016): 213-221.

III. Choudhary, Sunita, and MamtaGoyal. “Unsteady MhdCasson Fluid Flow Through Porous Medium With Heat Source/Sink And Time Dependent Suction.”Volume 56 Issue 6- April 2018.

IV. Falana, A., O. A. Ojewale, and T. B. Adeboje. “Effect of Brownian motion and thermophoresis on a nonlinearly stretching permeable sheet in a nanofluid.” Advances in Nanoparticles 5.01 (2016): 123.

V. Ferdows, M., MdJashimUddin, and A. A. Afify. “Scaling group transformation for MHD boundary layer free convective heat and mass transfer flow past a convectively heated nonlinear radiating stretching sheet.” International Journal of Heat and Mass Transfer 56.1-2 (2013): 181-187.

VI. Ibrahim, F. Hassanien, and A. Bakr. “Unsteady magnetohydrodynamic micropolar fluid flow and heat transfer over a vertical porous plate through a porous medium in the presence of thermal and mass diffusion with a constant heat source.” Canadian Journal of Physics 82.10 (2004): 775-790.

VII. Idowu, A. S., and B. O. Falodun. “Soret–Dufour effects on MHD heat and mass transfer of Walter’sB viscoelastic fluid over a semi-infinite vertical plate: spectral relaxation analysis.” Journal of Taibah University for Science 13.1 (2019): 49-62.

VIII. Kim, Youn J. “Heat and mass transfer in MHD micropolar flow over a vertical moving porous plate in a porous medium.” Transport in Porous Media 56.1 (2004): 17-37.

IX. Mahmud, Alam, and SattarAbdus. “Unsteady MHD free convection and mass transfer flow in a rotating system with Hall current, viscous dissipation and Joule heating.” Journal of Energy, Heat and Mass Transfer 22.2 (2000): 31-39.

X. Mohan, S. Rama, G. Viswanatha Reddy, and S. Balakrishna. “An Unsteady MHD Free Convection Flow of Casson Fluid Past an Exponentially Accelerated Infinite Vertical Plate through a Porous Media in the Presence of Thermal Radiation, Chemical Reaction and Heat Source or Sink” International Journal of Engineering and Techniques 4.4 (2018): 16-27.

XI. Narayana, PV Satya, B. Venkateswarlu, and S. Venkataramana. “Effects of Hall current and radiation absorption on MHD micropolar fluid in a rotating system.” Ain Shams Engineering Journal 4.4 (2013): 843-854.

XII. Ojjela, Odelu, and N. Naresh Kumar. “Unsteady MHD mixed convective flow of chemically reacting and radiating couple stress fluid in a porous medium between parallel plates with Soret and Dufour effects.” Arabian Journal for Science and Engineering 41.5 (2016): 1941-1953.

XIII. Okuyade, W. I. A., T. M. Abbey, and A. T. Gima-Laabel. “Unsteady MHD free convective chemically reacting fluid flow over a vertical plate with thermal radiation, Dufour, Soret and constant suction effects.” Alexandria engineering journal 57.4 (2018): 3863-3871.

XIV. Pal, Dulal, and BabulalTalukdar. “Perturbation technique for unsteady MHD mixed convection periodic flow, heat and mass transfer in micropolar fluid with chemical reaction in the presence of thermal radiation.” Open Physics 10.5 (2012): 1150-1167.

XV. Rajakumar, K. V. B., et al. “Radiation, dissipation and Dufour effects on MHD free convection Casson fluid flow through a vertical oscillatory porous plate with ion-slip current.” International Journal of Heat and Technology 36 (2018): 494-508.

XVI. Raju, M. C., N. Ananda Reddy, and S. V. K. Varma. “Analytical study of MHD free convective, dissipative boundary layer flow past a porous vertical surface in the presence of thermal radiation, chemical reaction and constant suction.” Ain Shams Engineering Journal 5.4 (2014): 1361-1369.

XVII. Sattar, Md, and MdMaleque. “Unsteady MHD natural convection flow along an accelerated porous plate with Hall current and mass transfer in a rotating porous medium.” Journal of Energy, Heat and Mass Transfer 22.2 (2000): 67-72.

XVIII. Seth, G. S., R. Sharma, and B. Kumbhakar. “Heat and Mass Transfer Effects on Unsteady MHD Natural Convection Flow of a Chemically Reactive and Radiating Fluid through a Porous Medium Past a Moving Vertical Plate with Arbitrary Ramped Temperature.” Journal of Applied Fluid Mechanics 9.1 (2016).

XIX. Seth, G. S., S. M. Hussain, and S. Sarkar. “Effects of Hall current and rotation on unsteady MHD natural convection flow with heat and mass transfer past an impulsively moving vertical plate in the presence of radiation and chemical reaction.” Bulgarian Chemical Communications 46.4 (2014): 704-718.

XX. Sharma, Bhupendra K., et al. “Soret and Dufour effects on unsteady MHD mixed convection flow past a radiative vertical porous plate embedded in a porous medium with chemical reaction.” Applied Mathematics 3.7 (2012): 717.

XXI. Srinivas, Suripeddi, ChallaKalyan Kumar, and AnalaSubramanyam Reddy. “Pulsating flow of Casson fluid in a porous channel with thermal radiation, chemical reaction and applied magnetic field.” Nonlinear Analysis: Modeling and Control 23.2 (2018): 213-233.

XXII. Ullah, Imran, Ilyas Khan, and SharidanShafie. “Soret and Dufour effects on unsteady mixed convection slip flow of Casson fluid over a nonlinearly stretching sheet with convective boundary condition.” Scientific reports 7.1 (2017): 1113.

XXIII. Vedavathi, N., et al. “Chemical Reaction, Radiation and Dufour effects on casson magneto hydro dynamics fluid flow over a vertical plate with heat source/sink.” Global Journal of Pure and Applied Mathematics 12.1 (2016): 191-200.

XXIV. Venkateswarlu, B., and P. V. SatyaNarayana. “Effects of thermal radiation on unsteady MHD micropolar fluid past a vertical porous plate in the presence of radiation absorption.” International Journal of Engineering Science and Computing 6.9 (2016).

XXV. Vijayaragavan, R. “Heat and Mass Transfer in Radiative Casson Fluid Flow Caused by a Vertical Plate with Variable Magnetic Field Effect.” Journal of Global Research in Mathematical Archives (JGRMA) 5.4 (2018): 48-66.

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AN EXTENSIVE STUDY ON CLASSIFICATION BASED PLANT DISEASE DETECTION SYSTEM

Authors:

Ms. Sri Silpa Padmanabhuni, Pradeepini Gera

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00002

Abstract:

Agriculture plays an important role in the Indian economy, therefore early prediction of plant diseases will help in increasing the productivity of crops thereby contributing to the economy’s growth. However, Manual identification of diseases in plants at every stage is very difficult since it involves huge manpower and requires extensive knowledge about plants. Multi disease patterns and pest identification can be automated using computer vision and deep learning techniques and by observing the controlled environmental parameters. Using, Internet of things the model can continuously monitor the temperature, humidity and water levels.

Keywords:

Computer Vision,Deep Learning,Segmentation,Classification,

Refference:

I. Humeau-Heurtier, “Texture Feature Extraction Methods: A Survey,” in IEEE Access, vol. 7, pp. 8975-9000, 2019. doi: 10.1109/ACCESS.2018.2890743.

II. AIP Conference Proceedings 2095, 030018 (2019);https://doi.org/10.1063/1.5097529. Published Online:09 April 2019.

III. Aitor Gutierrez, Ander Ansuategi, Loreto Susperregi, Carlos Tubío, Ivan Rankić, and Libor Lenža, “A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases,” Journal of Sensors, vol. 2019, Article ID 5219471, 15 pages, 2019.

IV. Akram, Tallha&Naqvi, Syed & Kamran, Muhammad & Kamran, Muhammad. (2017). Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision. Computers & Electrical Engineering. 59. 15-26. 10.1016/j.compeleceng.2017.02.020.

V. Arsenovic, M.; Karanovic, M.; Sladojevic, S.; Anderla, A.; Stefanovic, D. Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection. Symmetry 2019, 11, 939.

VI. Azad, Dr&Hasan, Md& K, Mohammed. (2017). Color Image Processing on Digital Image. International Journal of New Technology and Research. 3. 56-62.

VII. Banchhor, C. &Srinivasu, N. 2018, “FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification”, Journal of Intelligent Systems.

VIII. Baranwal, Saraansh&Khandelwal, Siddhant&Arora, Anuja. (2019). Deep Learning Convolutional Neural Network for Apple Leaves Disease Detection. SSRN Electronic Journal. 10.2139/ssrn.3351641.

IX. B. Dhruv, N. Mittal and M. Modi, “Analysis of different filters for noise reduction in images,” 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, 2017, pp. 410-415.

X. BOMMADEVARA, H.S.A., SOWMYA, Y. and PRADEEPINI, G., 2019. Heart disease prediction using machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8(5), pp. 270-272.

XI. Chandana, K., Prasanth, Y. &Prabhu Das, J. 2016, “A decision support system for predicting diabetic retinopathy using neural networks”, Journal of Theoretical and Applied Information Technology, vol. 88, no. 3, pp. 598-606.

XII. Chen, Jiansheng&Bai, Gaocheng& Liang, Shaoheng& Li, Zhengqin. (2016). Automatic Image Cropping: A Computational Complexity Study. 507-515. 10.1109/CVPR.2016.61.

XIII. Chouhan, Siddharth&Koul, Ajay & Singh, Dr. Uday& Jain, Sanjeev. (2018). Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology. IEEE Aceess.

XIV. Chouhan, Siddharth& Singh, Dr. Uday& Jain, Sanjeev. (2019). Applications of Computer Vision in Plant Pathology: A Survey. Archives of Computational Methods in Engineering. 10.1007/s11831-019-09324-0.

XV. Dey, Abhishek&Bhoumik, Debasmita&Dey, Kashi. (2019). Automatic Multi-class Classification of Beetle Pest Using Statistical Feature Extraction and Support Vector Machine: Proceedings of IEMIS 2018, Volume 2. 10.1007/978-981-13-1498-8_47.

XVI. Ferreira, Alessandro &Freitas, Daniel & Silva, Gercina&Pistori, Hemerson&Folhes, Marcelo. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture. 143. 314-324. 10.1016/j.compag.2017.10.027.

XVII. Hassanien, Aboul Ella &Gaber, Tarek&Mokhtar, Usama&Hefny, Hesham. (2017). An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Computers and Electronics in Agriculture. 136. 86-96. 10.1016/j.compag.2017.02.026.

XVIII. Hossain, Eftekhar&Hossain, Md&Rahaman, Mohammad. (2019). A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier. 1-6. 10.1109/ECACE.2019.8679247.

XIX. I. M. Krishna, C. Narasimham and T. B. Reddy, “Image super resolution and contrast enhancement using curvlet’s with cycle spinning,” 2016 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, 2016, pp. 1-6. doi: 10.1109/CESYS.2016.7889926.

XX. I.Murali Krishna, Dr. ChallaNarsimham and Dr.A.S.N. Chakravarthy Published a paper on ” A Novel Feature Selection based Classification Model for Disease Severity Prediction on Alzheimer’s Database”,2018,JARDCS,Volume-10,Issue-4 Page no: 245-255 ISSN: 1943023X.

XXI. Jha, Kirtan&Doshi, Aalap& Patel, Poojan& Shah, Manan. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2. 10.1016/j.aiia.2019.05.004.

XXII. Kaur, Sukhvir&Pandey, Shreelekha&Goel, Shivani. (2018). Plants Disease Identification and Classification Through Leaf Images: A Survey. Archives of Computational Methods in Engineering. 26. 10.1007/s11831-018-9255-6.

XXIII. Kiani, Ehsan&Mamedov, Tofik. (2017). Identification of plant disease infection using soft-computing: Application to modern botany. Procedia Computer Science. 120. 893-900. 10.1016/j.procs.2017.11.323.

XXIV. Kishore, P.V.V., Kumar, K.V.V., Kiran Kumar, E., Sastry, A.S.C.S., TejaKiran, M., Anil Kumar, D. & Prasad, M.V.D. 2018, “Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks”, Advances in Multimedia, vol. 2018.

XXV. Konstantinos P. Ferentinos, Deep learning models for plant disease detection and diagnosis, Computers and Electronics in Agriculture, Volume 145, 2018, Pages 311-318, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2018.01.009.

XXVI. Kour, Vippon&Arora, Sakshi. (2019). Fruit Disease Detection Using Rule-Based Classification: Proceedings of ICSICCS-2018. 10.1007/978-981-13-2414-7_28.

XXVII. Lu, Yang & Yi, Shujuan&Zeng, Nianyin& Liu, Yurong& Zhang, Yong. (2017). Identification of Rice Diseases using Deep Convolutional Neural Networks. Neurocomputing. 267. 10.1016/j.neucom.2017.06.023.

XXVIII. Ma, Juncheng& Du, Keming&Zheng, Feixiang& Zhang, Lingxian& Sun, Zhongfu. (2018). A Segmentation Method for Processing Greenhouse Vegetable Foliar Disease Symptom Images. Information Processing in Agriculture. 6. 10.1016/j.inpa.2018.08.010.

XXIX. Mondal, Dhiman&Kole, Dipak& Roy, Kusal. (2017). Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves. Computers and Electronics in Agriculture. 142. 10.1016/j.compag.2017.11.024.

XXX. M. Sardogan, A. Tuncer and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, 2018, pp. 382-385. doi: 10.1109/UBMK.2018.8566635.

XXXI. Narmadha, R. &Arulvadivu, G.. (2017). Detection and measurement of paddy leaf disease symptoms using image processing. 1-4. 10.1109/ICCCI.2017.8117730.

XXXII. Narottambhai, Mitisha&Tandel, Purvi. (2016). A Survey on Feature Extraction Techniques for Shape based Object Recognition. International Journal of Computer Applications.137.16-20.10.5120/ijca2016908782.

XXXIII. PadmajaGrandhe, Dr. E. Sreenivasa Reddy, Dr.D.Vasumathi . (2016). An Adaptive Cluster Based Image Search And Retrieve For Interactive Roi To Mri Image Filtering, Segmentation, And Registration (Vol. 94,. No.1). Journal of Theoretical and Applied Information Technology.

XXXIV. Pantazi, X. E., Moshou, D., &Tamouridou, A. A. (2019). Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Computers and Electronics in Agriculture, 156, 96–104.

XXXV. Pearline, Anubha& Kumar, Sathiesh&Harini, S.. (2019). A study on plant recognition using conventional image processing and deep learning approaches. Journal of Intelligent & Fuzzy Systems. 36. 1-8. 10.3233/JIFS-169911.

XXXVI. Rahman, Ziaur& PU, Yi-Fei&Aamir, Muhammad &Ullah, Farhan. (2018). A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter. International Journal of Computers and Applications. 1-11. 10.1080/1206212X.2017.1422358.

XXXVII. R. Gandhi, S. Nimbalkar, N. Yelamanchili and S. Ponkshe, “Plant disease detection using CNNs and GANs as an augmentative approach,” 2018 IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, 2018, pp. 1-5. doi: 10.1109/ICIRD.2018.8376321.

XXXVIII. Sandhu, Gittaly& Kumar, Vinay& Joshi, Hemdutt. (2017). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications. 1-50. 10.1007/s11042-017-54458.

XXXIX. Shanwen Zhang, Wenzhun Huang, Chuanlei Zhang, Three-channel convolutional neural networks for vegetable leaf disease recognition, Cognitive Systems Research, Volume 53, 2019, Pages 31-41, ISSN 1389-0417, https://doi.org/10.1016/j.cogsys.2018.04.006.

XL. Shuli, Xing & Lee, Marely& Lee, Keun-kwang. (2019). Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network. Sensors. 19. 3195. 10.3390/s19143195.

XLI. Tetila, Everton & Machado, Bruno &Belete, Nícolas Alessandro &Guimaraes, David &Pistori, Hemerson. (2017). Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images. IEEE Geoscience and Remote Sensing Letters. PP. 1-5. 10.1109/LGRS.2017.2743715.

XLII. Thangaiyan, Jayasankar. (2019). AN IDENTIFICATION OF CROP DISEASE USING IMAGE SEGMENTATION. 10.13040/IJPSR.0975-8232.10(3).1054-64.

XLIII. Yuheng, Song &Hao, Yan. (2017). Image Segmentation Algorithms Overview.

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THE RELIABLE ESTIMATION FOR THE LASER WELD BY THE H- AND P- REFINEMENT OF THE FINITE ELEMENT METHOD

Authors:

Long Nguyen-Nhut-Phi, Son Nguyen-Hoai, Quan Nguyen, Phong Le-Thanh, Dai Mai-Duc

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00003

Abstract:

The finite element (FE) solutions are different from the exact ones due to the presence of various error sources, such as computer round-off error, error due to discrete of the displacement field, etc. This paper uses the h- and p-refinement of the finite element method for the laser butt weld problem, with the base metal is AISI 1018 steel highness 8 mm. The objective is to present estimation techniques the strain energy relative error and evaluate its reliability through two indices: the affectivity index and the uniformity index SD. The numerical results achieve to meet the conditions for reliability assessment. Specifically, the, , SD values of h- refinement, and p- refinement respectively: less than 6%, 0.535667, 0.019528, and less than 4%, 0.506616, 0.103834.

Keywords:

Finite element method (FEM),Laser butt weld,Relative error,Reliability,h- refinement,p- refinement,

Refference:

I. A. Düster & E. Rank, “The p-version of the finite element method compared to an adaptive h-version for the deformation theory of plasticity”, Computer Methods in Applied Mechanics and Engineering, 190(15-17), 1925–1935, 2001 (https://doi.org/10.1016/s0045-7825(00)00215-2)
II. B. A. Szabó, “Mesh design for the p-version of the finite element method”, Computer Methods in Applied Mechanics and Engineering, 55(1-2), 181–197 (https://doi.org/10.1016/0045-7825(86)90091-5), 1986
III. B. A. Szabo, P. K. Basu, and M. P. Rossow, “Adaptive Finite Element Analysis Based on P-Convergence”, Symposium on Future Trends in Computerized Structural Analysis and Synthesis, Washington, D.C., NASA Conference Publication 2059, pp. 43-50, 1978
IV. Claudio Canuto, Ricardo H. Nochetto, Rob P. Stevenson, and Marco Verani, “Convergence and Optimality of hp-AFEM”, Numer. Math. 135, 1073–1119 (https://doi.org/10.1007/s00211-016-0826-x), 2017
V. F.Cugnon & P.Beckers, “Error estimation for h- and p-methods”, 8th Mechanical Engineering Chilean Congress”, Concepción, pp.183-188, 1998
VI. F.Cugnon: Automatisation des calculs elements finis dans le cadre de la methode-p, these de doctorat, ULG, 2000
VII. F.Cugnon, M. Meyers, P.Beckers& G. Warzee, “Iterative solvers for the p-version of the finite element method”, first international conference on Advanced Computational Methods in Engineering – ACOMEN’ 98, Ghent, pp. 737-744, 1988
VIII. I. Babuška and B. A. Szabó,“On the Rates of Convergence of the Finite Element Method”, Int. J. Numer. Meth. Engng., 18, 323-341, 1982
IX. I. Babuška and W.C. Rheinboldt, “A‐posteriori error estimates for the finite element method”, Int. J. Numer. Meth. Engng, 12: 1597-1615, 1978 (http://dx.doi.org/10.1002/nme.1620121010)
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XII. Information on http://www.engr.mun.ca/~katna/5931/ STRAIN%20 ENERGY-Impact Loading.pdf
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XIV. J. E. Flaherty: Finite element analysis, Troy, New York, 2002
XV. Jae S. Ahn, Seung H. Yang, and Kwang S. Woo.,“Free Vibration Analysis of Patch Repaired Plates with a Through Crack by p-Convergent Layer-wise Element”, The Scientific World Journal, 2014. Article ID 427879. (http://dx.doi.org/10.1155/2014/427879)
XVI. L. Demkowicz, Ph. Devloo, J.T. Oden,“On an h-type mesh-refinement strategy based on minimization of interpolation errors”, Computer Methods in Applied Mechanics and Engineering, Volume 53, Issue 1, Pages 67-89, ISSN 0045-7825, 1985 (https://doi.org/10.1016/0045-7825(85)90076-3)
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SOME CRITERIA OF COMMUTATIVITY OF SEMIRINGS

Authors:

Muhammad Nadeem

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00004

Abstract:

In this article, we discuss some functional identities of certain semirings which enable us to induce commutativitiy in them. This will be helpful to extend some remarkable results of ring theory in the canvas of semirings. We also study some other useful functional identities which are trivial in ordinary rings.

Keywords:

Semiring,Inversesemiring,MA-semiring,Derivation,

Refference:

I. Bell H. E., DaifM. N., “On derivation and commutativity in prime rings”,ActaMathematicaHungarica, vol. 66,pp: 337-343, 1995.

II. Bistarelli S., MontanariU.and Rossi F., “Semiring-based constraint logic programming:syntax and semantics”,ACMTransactions on Programming Languages and Systems, vol. 23, pp:1-29, 2001.

III. Glazek, A., Guide to the Literature on Semirings and their Applications in Mathematics and Information Sciences,Kluwer, Dordrecht, 2000.

IV. Golan J. S., Semirings and Affine Equations over Them: Theory and Applications, Kluwer, Dordrecht, 2003.

V. HersteinI. N., “A note on derivations”,Canadian Mathematical Society,vol.21, pp:369-370, 1978.

VI. Javed M. A., AslamM., HussainM., “On condition (A2) of Bandletand Petrich for inverse semirings”,International Mathematical Forum,vol.59, pp: 2903-2914, 2012.

VII. Javed M.A., AslamM., “Some commutativity conditions in prime MA-semirings”, Ars Combinatoriavol.114,pp:373-384, 2014.

VIII. Pouly M. “Generic solution construction in valuation-based systems.Advances in Artificial Intelligence”,vol.6657, pp: 335-346, 2011.

IX. Karvellas P.H., “Inversivesemirings”, Journal of the Australian Mathematical Society, vol: 18, pp: 277-287,1974.

X. Posner E. C., “Derivations in prime rings”, Proceedings of the American Mathematical Society, vol. 8, pp:1093-1100, 1957.

XI. Vandiver H. S., “Note on a simple type of algebra in which cancellation law of addition does not hold”,Bulletin of The American Mathematical Society, vol. 40,pp: 914-920, 1934.

XII. VukmanJ., “Commutating and centralizing mappings in primerings”, Proceedings of the American Mathematical Society, vol.109,pp: 47-52, 1990.

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THE MODIFIED DECOMPOSITION METHOD FOR SOLVING LINEAR SECOND-ORDER FREDHOLM INTEGRO-DIFFERENTIAL EQUATIONS

Authors:

Anas Al-Haboobi, Ghassan A. Al-Guaifri

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00005

Abstract:

This paper applies Modifed Decomposition Method (MDM) as numerical analysis linear second-order FredholmIntegro-differential Equations. The calculation of the approximate solutions are computed by mathematical package. The main aim of this paper is to demonstrate how effective this method minimizes the size of calculations and reaching the final solution in the shortest time and best result. When com paring the results with the (ADM) and with the exact solution, we will note how effective this method minimizes the size of calculations of the solution and reaches the exact solution. Accordingly, the (MDM) is the best method to be used to solve linear second-order FredholmIntegro-Differential equation. The convertion to the exact solution is notably fast and also a time saver, as it requires less computational work in solving equations. This is why the (MDM) is more efficient in solving this kind of equations.

Keywords:

MDM,Integro-differential Equations,Fredholm integral Equation,approximate solutions,

Refference:

I. A. Mohsen, M. El-Gamel. “A Sinc–Collocation method for the linear Fredholmintegro-differential equations”, in Zeits chrift fürangewandte Mathematik und Physik, pp.380-390, 2007.

II. A.M Wazwaz, “A reliable modification of Adomian decomposition method”, in Applied Mathematics and Computation, pp.77-86, 1999.

III. D.D Bainov, M.B Dimitrova, A.B Dishliev, “Oscillation of the bounded solutions of impulsive differential-difference equations. of second order”, in Applied Mathematics and Computation, pp.61-68, 2000.

IV. E. Aruchunan, J. Sulaiman, “Numerical Solution of First-Order Linear FredholmIntegro-Differential Equations using Conjugate Gradient Method”, in International Symposium on Geology, pp.11-13, 2009.

V. E. Aruchunan, J. Sulaiman, “Numerical solution of second-order linear fredholmintegro-differential equation using generalized minimal residual method”, in American Journal of Applied Science, pp.780-783,2010.

VI. H. Safdari, Y.E Aghdam, “Numerical Solution of Second-Order Linear FredholmIntegro-Differetial Equations by Trigonometric Scaling Functions”, in Open Journal of Applied Sciences, pp.135-144, 2015.

VII. M. Gülsu, M. Sezer, “A Taylor polynomial approach for solving differential-difference equations”, in Journal of Computational and Applied Mathematics, pp.349-364, 2006.

VIII. M. Fathy, M. El-Gamel, M.S El-Azab, “Legendre–Galerkin method for the linear Fredholmintegro-differential equations”, in Applied Mathematics and Computation, pp.789-800, 2014.

IX. M.FKarim, M. Mohamad, M.S Rusiman, N. Che-Him, R.Roslan, K. Khalid, “ADM For Solving Linear Second-Order FredholmIntegro-Differential Equations”, in Journal of Physics: Conference Series, pp.012009, 2018.

X. S. Yalçinbaş, M. Sezer,“The approximate solution of high-order linear Volterra–Fredholmintegro-differential equations in terms of Taylor polynomials”,in Applied Mathematics and Computation, pp.291-308, 2000.

XI. S.M Hosseini, S. Shahmorad,“Tau numerical solution of Fredholmintegro-differential equations with arbitrary polynomial bases”, in Applied Mathematical Modelling,pp.145-154, 2003.

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THERMOPHORESIS AND DIFFUSION THERMO EFFECTS ON SHEAR THICKENNING AND SHEAR THINING CASES OF FLUID MOTION PAST A PERMEABLE SURFACE

Authors:

Kamal Debnath, Debasish Dey, Rupjyoti Borah

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00006

Abstract:

An effort has been prepared numerically to investigate thermophoresis and diffusion thermo effects on liquid motion past a permeable surface. Motion is managed by the constitutive equation of power law fluid model. External forces appeared in the flow system are Lorentz force due to external magnetic field, buoyancy force. Similarity transformation has been utilized in the methodology part and MATLAB built in bvp4c solver scheme has been adopted to carry out the numerical solutions. Impacts of flow parameters on flow characteristics have been outlined by figures and diagrams.

Keywords:

MHD,Power-law fluid,Soret Effect (thermophoresis),Dufoureffect (diffusion thermo),thermal and mass transfer,

Refference:

I. Acrious, A., Shah, M.J., Peterson E.E., “Momentum and heat transfer in laminar boundary layer flow on non-newtonian fluids past external surfaces”, AIChE Journal. vol. 6, pp: 312–316, 1960.
II. Andersson, H.I., Bech, K.H. and Dandapat, B.S., “Magnetohydrodynamic flow of a power law fluid over a stretching sheet”, International Journal of Non-Linear Mechanics. vol. 72, pp: 929–936, 1992
III. Aziz, A., Ali, Y., Aziz, T. and Siddique, J., “Heat Transfer analysis for stationary boundary layer slip flow of a power low fluid in a Darcy porous medium with plate suction/injection”, PLoS ONE. Vol.10 (9), doi:10.1371/journal.pone.0138855, 2015
IV. Cheng, C.Y., “Soret and Dufour effects on mixed convection heat and mass transfer from a vertical wedge in a porous medium with constant wall temperature and concentration”, Transport in Porous Media. vol. 94, pp: 123–32, 2012.
V. Dey, D., “Non-Newtonian effects on hydromagnetic dusty stratified fluid flow through a porous medium with volume fraction”, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 86(1), pp: 47-56, 2016.
VI. Hirschhorn J., Madsen M., Mastroberardino A. and Siddique J.I., “Magnetohydrodynamic boundary layer slip flow and heat transfer of power-law fluid over a flat plate”, Journal of Applied Fluid Mechanics, 9(1), pp: 11-17, 2016.
VII. Huang, C.J. and Yih, K.A., “Heat and Mass Transfer on the Mixed Convection of non-Newtonian fluids over a vertical wedge with Soret/Dufour effects and Internal Heat Generation: Variable wall Temperature/Concentration”, Transport in Porous Media. vol.130, pp: 559-576, 2019.
VIII. Jafarimughaddam, A. and Aberoumand, S., “Exact approximations for skin friction coefficient and convective heat transfer coefficient for a class of power-law fluids flow over a semi-infinite plate: Results from similarity solution”, Engineering Science and Technology: an International Journal. vol. 20(3), pp: 1115-1121, 2016.
IX. Lee, S.Y., Ames, W.F., “Similar solutions for non-Newtonian fluids”, AIChE Journal. vol. 12, pp: 700–708, 1960.
X. Saritha, K., Rajasekhar, M.N. and Reddy, B.S. “Combined effects of soret and dufour on mhd flow of a Power-law fluid over flat plate in slip flow regime”, International Journal of Applied Mechanics and Engineering. vol. 23(3), pp: 689-705, 2018
XI. Schowalter, W.R., “The application of boundary layer theory to power law pseudo plastic fluids: similar solutions”, AIChE Journal. vol. 6(1), pp: 24-28, 1960.
XII. Sharma, B.K., Gupta, S., Vamsikrishna, V. and Bhargavi, R.J., “Soret and Dufour effects on an unsteady MHD mixed convective flow past an infinite vertical plate with Ohmic dissipation and heat source”, AfrikaMathematika, vol. 25, pp. 799–821, 2014.
XIII. Shateyi, S., Motsa, S.S. and Sibanda, P., “The effects of thermal radiation, Hall currents, Soret and Dufour on MHD flow by mixed convection over a vertical surface in porous media”, Mathematical Problems in Engineering, Article ID 627475, 2010
XIV. Tai, B.C. and Char, I.M. “Soret and Dufour effects on free convection flow of non-Newtonian fluids along a vertical plate embedded in a porous medium with thermal radiation”, International Communications in Heat and Mass Transfer. vol. 37, pp. 480-483, 2010
XV. Zhang, H., Kang, Y., and Xu, T., “Study on Heat Transfer of non-Newtonian Power-law fluid in pipes with different cross sections”, Procedia Engineering. vol. 205, pp. 3381-3388, 2017.

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