Journal Vol – 15 No -4, April 2020

Journal > Journal > Journal Vol – 15 No -4, April 2020 > Page 3

TEXTURE CLASSIFICATION USING CSTC-MEL IDENTIFICATION MODEL FOR DIAGNOSIS OF MELANOMA

Authors:

Tammineni Sreelatha, M.V. Subramanyam, M. N. Giri Prasad

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00021

April 28, 2020

Abstract:

Texture in images can be utilized as a cue for different computer vision tasks as object identification and classification. This paper proposes CSTC-Mel Identification Model for texture classification, the feature representation which is low dimensional and training free, robust in nature for the texture description. The proposed technique is implemented in 3 phases such as ULL responses, feature computation, Feature encoding and the representation of image. Feature Computation is generated to categorize the texture structures and their connection by implementing linear and non-linear operators on the ULL responses of Gaussian Filter in the scale space, which is established based on steerable filters. Feature encoding through more than one level of thresholding or binary can be adopted to compute these feature computation into texture. Two encoding methods are designed which is robust in nature to the illumination changes and image rotation. The feature representation is explored to combine the discrete texture into the histogram representation. Our proposed model is tested on PH2 dataset. By comparing the experimental outcomes of proposed CSTC-Mel Identification Model with existing models, we can observe t at the proposed CSTC-Mel Identification Model identifies the skin cancer with accuracy of 93.81%.

Keywords:

Texture Classification,Steerable Filter,Gaussian Filter,Feature Computation,Feature Encoding,

Refference:

I. A. Madooei, M. S. Drew and H. Hajimirsadeghi, “Learning to Detect Blue–White Structures in Dermoscopy Images With Weak Supervision,” in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 779-786, March 2019.

II. A. Madooei, M. S. Drew, M. Sadeghi, and M. S. Atkins, “Automatic detection of blue-white veil by discrete colour matching in dermoscopy images,” in Medical Image Computing and Computer-Assisted Intervention MICCAI, ser. Lecture Notes in Computer Science, K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, Eds. Springer Berlin Heidelberg, 2013, no. 8151, pp. 453–460.

III. EbtihalAlmansour and M. ArfanJaffar, “Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features”, IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.4, April 2016.

IV. Fatima, R and Khan, Mohammed Zafar Ali and A, Govardhan and Dhruve, K P (2012) Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma. International Journal of Computer Science and Network Security, 12 (10). pp. 74-86. ISSN 1738-7906

V. G. A. S. Saroja and C. H. Sulochana, “Texture analysis of non-uniform images using GLCM,” 2013 IEEE Conference on Information & Communication Technologies, Thuckalay, Tamil Nadu, India, 2013, pp. 1319-1322.

VI. H. Ganster, A. Pinz, R. Rohrer, and E. W. ¨ et al., “Automated melanoma recognition,” Medical Imaging, IEEE Transactions on, vol. 20, no. 3, pp. 233–239, 2001. [11] I. Maglogiannis, S. Pavlopoulos, and D.
VII. H. Kittler, H. Pehamberger, K. Wolff, and M. Binder, “Diagnostic accuracy of dermoscopy,” The lancet oncology, vol. 3, pp. 159-165, 2002.

VIII. J. C. Kavitha and A. Suruliandi, “Texture and color feature extraction for classification of melanoma using SVM,” 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), Kovilpatti, 2016, pp. 1-6.

IX. Koutsouris, “An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images,” IEEE Transactions on Information Technology in Biomedicine, pp. 86–98, 2005.

X. Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., &Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLOS ONE, 14(2), e0212110.

XI. M. E. Celebi, H. Iyatomi, W. V. Stoecker, R. H. Moss, H. S. Rabinovitz, G. Argenziano, and H. P. Soyer, “Automatic detection of blue-white veil and related structures in dermoscopy images,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 670–677, 2008.

XII. M. Moncrieff, S. Cotton, P. Hall, R. Schiffner, U. Lepski, and E. Claridge, “SIAscopy assists in the diagnosis of melanoma by utilizing computer vision techniques to visualise the internal structure of the skin,” Med Image Understanding Analysis, pp. 53-56, 2001.

XIII. M. Rademaker and A. Oakley, “Digital monitoring by whole body photography and sequential digital dermoscopy detects thinner melanomas,” J Prim Health Care, vol. 2, pp. 268-72, 2010.

XIV. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, “Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention,” in Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island, 2014, pp. 1-6.

XV. O. Abuzaghleh, M. Faezipour and B. D. Barkana, “A comparison of feature sets for an automated skin lesion analysis system for melanoma early detection and prevention,” 2015 Long Island Systems, Applications and Technology, Farmingdale, NY, 2015, pp1-6.

XVI. S. Joseph and J. R. Panicker, “Skin lesion analysis system for melanoma detection with an effective hair segmentation method,” 2016 International Conference on Information Science (ICIS), Kochi, 2016, pp. 91-96.

XVII. S. K. Vengalil, N. Sinha and G. S. Raghavan, “Modified oriented Gaussian derivative filter based texture detection algorithm and parameter estimation,” 2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, 2015, pp. 1-6.

XVIII. S.Suer, S.Kockara, and M.Mete, “An improved border detection in dermoscopy images for density based clustering”, BMC bioinformatics.,vol.12, No.10,p.S12,2011.

XIX. Saha, Sujaya and Dr. Rajat Gupta. “An Automated Skin Lesion Diagnosis by using Image Processing Techniques.” (2014).
XX. Sreelatha, T., Subramanyam, M.V. & Prasad, M.N.G., “Early detection of skin cancer using Melanoma Segmentation Technique”, Journal of Medical Systems (May 2019) 43:190.

XXI. T. Mendonca, P. Ferreira, J. Marques, A. Marcal, and J. Rozeira, “PH2- a dermoscopic image database for research and benchmarking,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). IEEE, 2013, pp. 5437–5440.

XXII. T. Wadhawan, N. Situ, K. Lancaster, X. Yuan, and G. Zouridakis, “SkinScan©: A portable library for melanoma detection on handheld devices,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, 2011, pp. 133-136l.

XXIII. T. Y. Satheesha, D. Satyanarayana, M. N. G. Prasad and K. D. Dhruve, “Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification,” in IEEE Journal of Translational Engineering in Health and Medicine, vol. 5, pp. 1-17, 2017, Art no. 4300117.

XXIV. TammineniSreelatha, M.V.Subramanyam, M.N.Giri Prasad. A Survey work on Early Detection methods of Melanoma Skin Cancer. Research J.Pharm. and Tech. 2019; 12(5): 2589-2596.

XXV. Torkashvand, Fatemeh and Mehdi Fartash. “Automatic Segmentation Of Skin Lesion Using Markov Random Field.” (2015).

XXVI. W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 9, pp. 891–906, Sep. 1991.

XXVII. W.Stoecker, M.Wronkiwwiez, R.Chowdhury, R. J. Stanley, J. Xu, A. Bangert, B. Shrestha, D.A. Calcara, H.S. Rabinovitz, M. Oliviero, F. Ahmed, L.A. Perry and R. Drugge, “Detection of Granularity in dermoscopy images of malignant melanoma using color and texture features”, Compu.Med Imaging Graph, Vol.32.N0.8, pp 670-677, Dec.2008.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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:

I. DING, Wenxiu; YAN, Zheng; and DENG, Robert H.. Encrypted data processing with Homomorphic Re-Encryption. (2017). Information Sciences, 35-55.
II. Elhoseny, M., Elminir, H., Riad, A., & Yuan, X. (2016). A secure data routing schema for WSN using Elliptic Curve Cryptography and homomorphic encryption. Journal of King Saud University – Computer and Information Sciences, 28(3), 262–275.
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.
IV. Graepel, T., Lauter, K., &Naehrig, M. (2013). ML Confidential: Machine Learning on Encrypted Data. Information Security and Cryptology – ICISC 2012, 1–21.
V. Hen, C., Zhu, X., Shen, P., & Hu, J. (2014). A hierarchical clustering method for big data oriented ciphertext search. 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). P1-6.
VI. Helsloot, L. J., Tillem, G., &Erkin, Z. (2017). AHEad: Privacy-preserving online behavioural advertising using homomorphic encryption. 2017 IEEE Workshop on Information Forensics and Security (WIFS). P1-6.
VII. Jiang, R., Lu, R., &Choo, K.-K. R. (2018). Achieving high performance and privacy-preserving query over encrypted multidimensional big metering data. Future Generation Computer Systems, 78, 392–401.
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.
X. Khedr, Alhassan, et al. “SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers.” IEEE Transactions on Computers 65, 9, 2848–2858.
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.
XIX. R.Hariharan, S. Saran Raj and R. Vimala. (2018). A Novel Approach for Privacy Preservation in Bigdata Using Data Perturbation in Nested Clustering in Apache Spark. Journal of Computational and Theoretical Nanoscience. 15 (.), p1-6.
XX. Rawat, D. B., Doku, R., &Garuba, M. (2019). Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security. IEEE Transactions on Services Computing, 1–18.
XXI. Shiyuan Wang, DivyakantAgrawal and Amr El Abbadi. (2012). Is Homomorphic Encryption the Holy Grail for Database Queries on Encrypted Data, p1-18.
XXII. Sirisha, N., &Kiran, K. V. D. (2018), “Authorization of Data In Hadoop Using Apache Sentry”, International Journal of Engineering and Technology, 7(3), 234-236.
XXIII. Sirisha, N., Kiran, K. V. D., &Karthik, R, (2018), ”Hadoop security challenges and its solution using KNOX”, Indonesian Journal of Electrical Engineering and Computer Science,12(1), 107-116.
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.
XXV. Tan Soo Fun and AzmanSamsudin. (2016). A Survey of Homomorphic Encryption for Outsourced Big Data Computation. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS. 10 (8), p1-26.
XXVI. T.Ramaporkalai, “Security Algorithms in Cloud Computing”, International Journal of Computer Science Trends and Technology Vol. 5. Issue 2, Mar – Apr 2017.
XXVII. Tourky, D., ElKawkagy, M., &Keshk, A. (2016). Homomorphic encryption the “Holy Grail” of cryptography. 2016 2nd IEEE International Conference on Computer and Communications (ICCC). P1-6.
XXVIII. V. Biksham, D. Vasumathi“Homomorphic Encryption Techniques for securing Data in Cloud Computing: A Survey”, International Journal of Computer Applications Vol.160, February 2017.
XXIX. Wei Dai, Doroz, Y., &Sunar, B. (2014). Accelerating NTRU based homomorphic encryption using GPUs. 2014 IEEE High Performance Extreme Computing Conference (HPEC). P1-6.
XXX. Wei Wang,&Xinming Huang. (2013). FPGA implementation of a large-number multiplier for fully homomorphic encryption. 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013). P1-4.
XXXI. Yu, S. (2016). Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data. IEEE Access, 4, 2751–2763.
XXXII. Yadav, D., Maheshwari, D. H., & Chandra, D. U. (2019). Big Data Hadoop: Security and Privacy. SSRN Electronic Journal. P1-8.
XXXIII. Zhiqiang, G., &Longjun, Z. (2017). Privacy Preserving Data Mining on Big Data Computing Platform: Trends and Future. Lecture Notes on Data Engineering and Communications Technologies, 491–502.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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:

I. “Coinmarktcap,” http://www. coinmarketcap.com (accessed 18 Dec. 2018).

II. “Investing,” https://www.investing.com/indices/msci-world-stock-historical-data (accessed 15 June 2018).

III. “Kaggle,” http://www.kaggle.com (accessed 15 June 2018).

IV. B. Alex Greaves, “Using the Bitcoin transaction graph to predict the price of Bitcoin.”

V. C. Giakloglou and P. Newbold, “Empirical evidence on Dickey‐Fuller‐type tests,” Journal of Time Series Analysis, vol. 13, pp. 471–483, 1992, doi:10.1111/j.1467-9892.1992.tb00121.x.

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.

VII. . 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.

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.

X. H. Wang and D. Xu, “Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function,”Journal of Control Science and Engineering, 2017.

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.

XV. M. Razi and K. Athappilly, “A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models,” Expert Systems with Applications, vol. 29, no. 1, pp. 65–74, 2005.

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.

XX. T. Phaladisailoed and T. Numnonda, “Machine learning models comparison for Bitcoin price prediction,” in 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), IEEE, 2018, pp. 506–511.

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.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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/
III. https://www.osrtos.com/rtos/chibios-rt/
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

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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.
III. Ministerio de Salud (2010). Evaluación de los resultados de los monitoreosrealizadosalos Recursos Hídricos en la cuenca del ríoRímac, Dirección General de Salud
Ambiental. 26, 32 pp
IV. PNUD (2016) Objetivos del desarrollosostenible. Recuperado de
file:///C:/Users/Fernando/Desktop/SDG_6_Spanish.p
V. Verdugo Vergara, J. (2017). Bioadsorción de iones de plomo y cromoprocedentes de
aguasresidualesutilizando la cáscara de la mandarina. Cuenca.

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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

View Download

Journal Vol – 15 No -4, April 2020

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

April 28, 2020

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.

View Download

Journal Vol – 15 No -4, April 2020