Archive

Abstract:

Artificial Intelligence (AI) technology has been adopted rapidly in the industry. Various research initiatives have been carried out to innate the AI system characteristics as humans. In our concept paper [VI] we disclosed the “Reflex layer” to mimic human systems. A reflex layer would have the ability to differentiate the repetitive stimuli, its related responses and ability to process this through a separate layer. We discussed the key characteristics of reflex features of the following AI capabilities:

The vision interface
The audio interface
The kinematic interface
The sheath interface
The core layer

In this paper we baseline the scope to core and kinematic interface; elaborate key characteristics, provide solutions and results.

Keywords:

Artificial Intelligence,Distributed Artificial Intelligence,Reflex AI,

Refference:

I. Anna Melamed. “Distributed Systems Management on Wall Street- AI Technology needs”
II. B Thuraisingham, J Larson “ AI applications in Distributed System Design issues”
III. D Verma, G Bent “Policy Enabled Caching for Distributed AI”. 2017 IEEE International Conference on Big Data (BIGDATA)
IV. Du-Mim Yoon, Joo-Seon Lee, Hyun-SuSeon, Jeong-Hyeon Kim, Kyung-Joong Kim*. “Optimization of Angry Birds AI Controllers with Distributed Computing”. IEEE CIG 2015, Tainan, Taiwan August 31, 2015 – September 2, 2015
V. E Oyekanlu, K Scoles “Towards Low-Cost, Real-Time, Distributed Signal and Data Processing for Artificial Intelligence Applications at Edges of Large Industrial and Internet Networks”. 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
VI. Gandhi Sivakumar, G. Arumugam, “The Facets of Distributed And Artificial Intelligence”, Volume8, Issue 12, December 2019 Edition, International Journal of Scientific & Technology Research
VII. JSR Jang, “Self- learning fuzzy controllers based on temporal back propagation”
VIII. Sean Martin, Andrew slade “A methodology for distributed AI and its applicability for Data fusion applications”
IX. YAO Ping-xiang, FENG Wen-xian “DESIGN OF DISTRIBUTED MARINE AISCOMMUNICATION MECHANISM AND INTEGRATED MONITORING PLATFORM”
X. Z. A. Shah1, T. AbdulQayyum2, Dr. S. I. A. Shah “A Distributed Approach for Solving the Position Inverse Kinematics of Mobile Parallel Manipulators”, 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE) Islamabad, Pakistan, April 22-24, 2014

LOGISTIC REGRESSION BASED HUMAN ACTIVITIES RECOGNITION

Authors:

Zunash Zaki, Muhammad Arif Shah, Karzan Wakil, Falak Sher

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00018

Abstract:

Human activity recognition through smartphones is now beneficial for humans to recognize their daily activities. Many of the researches are introduced for recognition of activities but somehow the performance of the classifiers is low because of different problems with the data or the classifiers. This research study offers a method to achieve the best performing classifiers. The comparative analysis held between the supervised and ensemble learning classifiers. Based on the best performing classifier, a system is also introduced in this study. We evaluate the method by using two publicly available datasets of human activities recognition acquired from UCI Machine Learning repository. One is UCI-Human Activity Recognition and the second is Smartphone-Based Recognition of Human Activities and Postural Transitions. The activities selected for this research study are Walking, Standing, Sitting, Laying, Downstairs and Upstairs. These input signals are a 3-dimensional raw form of data that was difficult to handle. The Principle Component Analysis (PCA) technique is used to reduce the dimensionalities of the data features and extract the most substantial data features for the classification of human activities. A comparison is performed between the different supervised and ensemble machine learning classifiers on the selected datasets. The supervised learning classifiers that we used are Gaussian Naïve Bayes, K-Nearest Neighbor, and Logistic Regression while the ensemble learning classifiers are Random Forest and Gradient Boosting. The achieved result shows that the Logistic Regression is more accurate as compared to other selected classifiers in this study for human activity recognition. The higher accuracy rate of Logistic Regression is 96.1% for UCI-HAR and 94.5% for HAPT dataset among all the compared classifiers.

Keywords:

UCI-HAR dataset,HAPT dataset,Smartphones,Accelerometer and gyroscope Sensors,Classifiers,HAR,

Refference:

I. [vanSmeden, Maarten, et al. “No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.” BMC Medical Research Methodology 16.1 (2016): 163]

II. Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013, April). A public domain the dataset for human activity recognition using smartphones. In ESANN.

III. B. Yuan and J. Herbert, “Context-aware Hybrid Reasoning Framework for Pervasive Healthcare,” Pervasive Ubiquitous Computing., vol. 18, no. 4, pp.865–881, 2013.

IV. Bayat, Akram, Marc Pomplun, and Duc A. Tran. “A study on human activity recognition using accelerometer data from smartphones.” Procedia Computer Science 34 (2014): 450-457.

V. Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková,Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). “On the evaluation ofunsupervised outlier detection: measures, datasets, and an empirical study”. Data Mining andKnowledge Discovery. 30 (4): 891–927. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810

VI. D. Figo, P. C. Diniz, D. R. Ferreira, and J. M. P.Cardoso, “Preprocessing Techniques for Context Recognition from Accelerometer Data,” PervasiveUbiquitous Computing., vol. 14, no. 7, pp. 645–662,2010.

VII. Deshmukh, R., Aware, S., Picha, A., Agrawal, A., &Wable, S. D. (2018). Human ActivityRecognition using Embedded Smartphone Sensors.

VIII. Elith, Jane. “A working guide to boosted regression trees”. British Ecological Society. British Ecological Society. Retrieved 31 August 2018.

IX. F. Attal, S. Mohammed, M. Dedabrishvili, F. Chamroukhi, L. Oukhellou, and Y.Amirat, “Physical Human Activity Recognition Using Wearable Sensors,” Sensors, vol. 15, no.12, pp. 31314–31338, 2015.

X. Friedman, Jerome. “Multiple Additive Regression Trees with Application in Epidemiology”Thestatisticin Medicine. Wiley. Retrieved 31 August 2018.

XI. Fu, B., Kirchbuchner, F., Kuijper, A., Braun, A., &VaithyalingamGangatharan, D. (2018, June). Fitness Activity Recognition on Smartphones Using Doppler Measurements. In Informatics (Vol. 5, No. 2, p. 24). Multidisciplinary Digital Publishing Institute.

XII. G. Vavoulas, M. Pediaditis, E. G. Spanakis, and M.Tsiknakis, “The MobiFall dataset: An Initial Evaluation of Fall Detection Algorithms using Smartphones,” 13thIEEE Int. Conf. Bioinforma. Bioeng., no. November,pp. 1–4, 2013.

XIII. I. Farkas and E. Doran, “Activity Recognition from Acceleration Data Collected with a Tri-axial Accelerometer,” Acta Tech. Napocensis – Electron.Telecommun., vol. 52, no. 2, pp. 38–43, 2011.

XIV. Inoue, Masaya, Sozo Inoue, and Takeshi Nishida. “Deep recurrent neural network for mobilehuman activity recognition with high throughput.” Artificial Life and Robotics 23.2 (2018): 173- 185.

XV. J. Fu, C. Liu, Y. Hsu, and L. Fu, “Recognizing Context-aware Activities of Daily Living using RGBD Sensor,” Iros2013, pp. 2222–2227, 2013.

XVI. K. G. ManoshaChathuramali and R. Rodrigo, “Faster Human Activity Recognition with SVM,” in International Conference on Advances in ICT for Emerging Regions, ICTer 2012 – Conference Proceedings, 2012, pp. 197–203.

XVII. Le, Tuan Dinh, and Chung Van Nguyen. “Human activity recognition by smartphone. “Information and Computer Science (NICS), 2015 2nd National Foundation for Science andTechnology Development Conference on. IEEE, 2015.

XVIII. Li, F., Shirahama, K., Nisar, M. A., Köping, L., &Grzegorzek, M. (2018). Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors, 18(2), 679.

XIX. M. N. S. Zainuddin, N. Sulaiman, N. Mustapha, and T. Perumal, “ActivityRecognitionbasedonAccelerometerSensorusingCombinational Classifiers,” pp. 68–73, 2015.

XX. Machado, Inês P., et al. “Human activity data discovery from triaxial accelerometer sensor:Non-supervised learning sensitivity to feature extraction parametrization.” InformationProcessing & Management 51.2 (2015): 204-214.

XXI. Milenkoski, Martin, et al. “Real-time human activity recognition on smartphones using LSTMNetworks.” 2018 41st International Convention on Information and CommunicationTechnology, Electronics and Microelectronics (MIPRO). IEEE, 2018.

XXII. R. Cilla, M. A. Patricio, J. García, A. Berlanga, and J.M. Molina, “Recognizing Human Activities from Sensors using Hidden Markov Models Constructed by Feature Selection Techniques,” Algorithms, vol. 2, no. 1, pp. 282–300, 2009.

XXIII. Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235-244.

XXIV. S. Chernbumroong, A. S. Atkins, and H. Yu, “Perception of Smart Home Technologies to Assist Elderly People,” 4th Int. Conf. Software, Knowledge,Inf. Manag. Appl. (SKIMA 2010),
no. March 2016, pp.1–7, 2010.

XXV. Shah, Muhammad Arif, et al. “Ensembling Artificial Bee Colony with Analogy-Based Estimation to Improve Software Development Effort Prediction.” IEEE Access (2020).

XXVI. Shah, Muhammad Arif, et al. “Communication management guidelines for software organizations in Pakistan with clients from Afghanistan.” IOP Conference Series: Materials Science and Engineering. Vol. 160. No. 1. IOP Publishing, 2016.

XXVII. Shoaib, M., Bosch, S., Incel, O. D., Scholten, H., &Havinga, P. J. (2016). Complex humanactivity recognition using a smartphone and wrist-worn motion sensors. Sensors, 16(4), 426.

XXVIII. Sukor, AS Abdul, A. Zakaria, and N. Abdul Rahim. “Activity recognition using accelerometer sensor and machine learning classifiers.” Signal Processing & Its Applications (CSPA), 2018 IEEE 14th International Colloquium on. IEEE, 2018.

XXIX. T. Shi, X. Sun, Z. Xia, L. Chen, and J. Liu, “Fall Detection Algorithm Based on TriaxialAccelerometer and Magnetometer,” no.2, May 2016.

XXX. T. Sztyler, “On-body Localization of Wearable Devices : An Investigation of Position-AwareActivity Recognition,” 2016.

XXXI. Vellampalli, Haritha. “Physical Human Activity Recognition Using Machine LearningAlgorithms.” (2017).

XXXII. W. Xiao and Y. Lu, “Daily Human Physical Activity Recognition Based on KernelDiscriminant Analysis and Extreme Learning Machine,” Math. Probl. Eng., vol. 2015, 2015.

CLASSIFICATION OF MULTI-LABEL OBJECT BASED ON MSIFT FEATURE PROBABILISTIC FUZZY C-MEANS CLUSTERING CLASSIFIED BY GSVM

Authors:

Damodara Krishna Kishore Galla, BabuReddyMukkamalla

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00019

Abstract:

Face analysis is a requisite notion for dissimilar appeal allied to artificial intelligence has made possible for Classification of Gender. Facial Data images are still an arduous task for biometric systems due to diverse expressions, dimensions, pose, illustrations and age in facial and other affiliated images includes dissimilar object label classifications. In this paper, SIFT Probabilistic Fuzzy C-means Clustering Approach (SPFCA) proposed to intensify the stratification methodology in object classification for dissimilar images using GSVM. This approach extremely used for recognition and classification of an object due to its fundamental properties which make decorous contrasting object classification in divergent types of robust in facial and other related images. SPFCA is robust clustering approach to diminish uproar insensitivity and assists to group the vicinity ages, male, female and objects. It also assists to find a solution for coinciding cluster complications which may face preceding clustering approaches. Consequently the proficiency can also be used to increase the comprehensive robustness of face recognition and multi-label object classification system and the result increases its invariance and make it a reliably passable biometric.

Keywords:

Object classification,fuzzy c-means clustering,Eigenvalues,shape,corner,wavelet transform,face recognition ,principal component analysis,

Refference:

I. Chang Huang ,Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, “CNN-RNN: A Unified Framework for Multi-label Image Classification”, Neural Networks and Learning Systems, IEEE Transactions on,2014.
II. Chih-Chin Lai, Chih-Hung Wu1, Shing-Tai Pan, Shie-Jue Lee, and Bor-Haur Lin, “Gender Recognition Using Local Block Difference Pattern”, Advances in Intelligent Information Hiding and Multimedia Signal Processing, page:45-54,Springer International Publishing AG 2017.
III. Chunyu Zhang , Hui Ding ,Yuanyuan Shang et.al , Gender Classification Based on Multiscale Facial Fusion Feature,Hindawi Mathematical Problems in Engineering Volume 2018, ISSN: 1563-5147.
IV. Dai D Q, Yuen P C. Wavelet-based discriminant analysis for face recognition. Applied Mathematics and Computation, 175,1(2006):307-318.
V. Dogucan Yaman1,FevziyeIremEyiokur,NurdanSezgin,”Age and Gender Classification from Ear Images”ACCEPTED FOR IAPR/IEEE IWBF 2018.
VI. D. Ramanan ,T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, P. Doll´ar, and C. L. Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014, pages 740–755. Springer, 2014
VII. G. Ding ,Z. Lin, M. Hu, Y. Lin and S. S. Ge. Image tag completion via dual-view linear sparse reconstructions. Computer Vision and Image Understanding, 124:42–60, 2014.
VIII. G. D. K. Kishore, M.Babu Reddy, Analysis and Prototype Sequences of Face Recognition Techniques in Real-Time Picture Processing, Intelligent Engineering Informatics, Advances in Intelligent Systems and Computing 695, Springer Nature Singapore Pte Ltd-2018.pp.323-335,2018.
IX. G. D. K. Kishore, M.Babu Reddy,” Gender classification based on similarity features through SURF and SVM”,Int. J. Knowledge Engineering and Data Mining, pg No:89-104, Vol. 6, No. 1, 2019.
X. G.D.K. Kishore, M.Babu Reddy, “ Detecting Human and classification of Gender using Facial Images MSIFT Features Based GSVM”, International Journal of Recent Technology and Engineering(IJRTE), pg No:1466-1471, Vol.8, Issue3, Sep2019.
XI. I.Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 61, SIAM Press, Philadelphia, 1992.
XII. J.Bezdek, “A convergence theorem for the fuzzy ISODATA clustering algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, no.1, pp. 1–8, Jan. 1981
XIII. J.Kittler, M. Hatef, R.P. Duin, J.G. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3) (1998) 226–239.
XIV. J.Mao, W. Xu, Y. Yang, J. Wang, and A. Yuille. Deep captioning with multimodal recurrent neural networks (m-rnn). In ICLR, 2015.
XV. J.Sullivan ,A. S. Razavian, H. Azizpour, and S. Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pages 512–519.IEEE, 2014.
XVI. K.W. Wong, K. M. Lam, and W. C. Siu, “An efficient algorithm for face detection and facial feature extraction under different conditions,” Pattern Recognit., vol. 34, no. 10, pp. 1993–2004, 2001.
XVII. LI Xiao-Fei, “Wavelet Transform for Face Recognition Based on Improved Fuzzy C-Means”, Applied Mechanics and Materials Vols. 602-605 (2014) pp 2170-2173.
XVIII. Mingyuan Xin, Yong Wang “Research on image classification model based on deep convolution neural network”, EURASIP Journal on Image and Video Processing, Springer, page no:2-11,2019.
XIX. MinjSalen Kujur1, Prof. Prashant Jain, “Performance Parameter Analysis of Face Recognition Based On Fuzzy C-Means Clustering, SIFTDetection”, MinjSalenKujur et al. Int. Journal of Engineering Research and Applications” ISSN: 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.515-520.
XX. P.J. Phillips, H. Moon, S.A. Rizvi, P.J. Rauss, The FERET evaluation methodology for face-recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (10) (2000) 1090–1104.
XXI. PuQ,Gao C B, Zhou J L. Theory of fractional covariance matrix and its applications in PCA and 2D-PCA. Expert systems with applications, 40(2013):5395-5401.
XXII. Sandeep Kumar,Sukhwinder Singh,Jagdish Kumar,”A Study on Face Recognition Techniques with Age and Gender Classification” IEEE conference, at Greater Noida, May 2017.
XXIII. S J Raudys, A K Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13 (1991):252–264.
XXIV. T. Sim, S. Baker, M. Bsat, TheCMUpose, illumination, and expression (PIE) database, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, May 2002.
XXV. W J Krzanowski, P Jonathan, W V McCarthy, M R Thomas. iscriminant analysis with singular covariance matrices: methods and applications to spectroscopic data, Appl. Statist. 44 (1995) :101–115.
XXVI. W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Netw., vol. 9, no. 4, pp.601–612, Jul. 1998.
XXVII. Yang, J, Zhang D. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26,1( 2004):131–137.

INVESTIGATION OF MICRO STRUCTURE AND MECHANICAL PROPERTIES OF FRICTION STIR WELDED AA6061 ALLOY WITH DIFFERENT PARTICULATE REINFORCEMENTS ADDITION

Authors:

Radhika chada, N. Shyam Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00020

Abstract:

Joining of heat-treated alloys(AA6061-T6) by Welding process often results a deterioration of mechanical properties because of the coarsening and dissolution of the strengthening precipitates(Mg₂Si,Al₃FeSi,Al₁₂FeSi) at the weld nugget. However, its scares the applications of AA6061-T6 alloy. In order to enhance mechanical properties of Friction stir welded(FSW) AA6061-T6 alloy and to minimize the loss of T6 condition , four butt joints (FSW-SiC, FSW- B₄C, FSW- Zn and FSW- Al₂O₃)were fabricate with the addition of harder reinforcement materials such as SiC, B₄C,Zn and Al₂O₃ particles. In this study, the microstructure, tensile strength and hardness of reinforced friction stir welded AA6061-T6 alloy joints were investigated, while the base metal and the welded joint prepared without reinforcement material were utilized as reference to control the process. The grains refinement ,which had been the reason for improved mechanical properties was increased with the addition of reinforced particles in the weld region. Due to the high density of homogeneous dispersion of harder reinforcement particles and considerably increased grain refinement in the entire welded joints, all the reinforced welded joints resulted improvements over the unreinforced joint in terms of strength and hardness. The addition of SiC, B₄C,Zn and Al₂O₃reinforcements particles increases the tensile strength by 24.2% ,1.79%,32.46 and 10.83% respectively, whereas the elongation decreased as compared to unreinforced welded. Due to extremely high hardness value and homogeneous dispersion of B₄C particles in the FSW- B₄C joint .It showed the highest percentage of hardness enhancement that was about 54.9% followed by Al₂O₃, SiC and Zn with improved hardness percentage as 50.37% 40.9%, and 23.2% respectively.

Keywords:

Friction Stir welding (FSW) AA 6061-T6 Hardness Reinforcement particles Microstructure,

Refference:

I. Ahmadnia .M, Seidanloo .A, Teimouri .R, Rostamiyan .Y, Titrashi.k.G,:Determining inﬂuence of ultrasonic-assisted friction stir welding parameters on mechanical and tribological properties of AA6061 joints. Int. J. Adv. Manuf. Technol, Vol.78 (9e12),pp.2009-2024,2015.
II. Bahrami .M, Dehghani.K, MKB Givi , A novel approach to develop aluminum matrix nano-composite employing friction stir welding technique, Materials & Design .Vol. 53, pp. 217-225, 2014.
III. Bodaghi M, Dehgani K. Friction stir welding of AA5052: the effects of SiC nano-particles addition. Int J Adv Manuf Technol ,Vol.88,pp.2651–60,2017.
IV. Cavaliere.P, SantisA.D, Panella.F, A. Squillace, Effect of welding parameters on mechanical and microstructural properties of dissimilar AA6082–AA2024 joints produced by friction stir welding, Mater. Des. pp.609–616,2009.
V. Dinaharan.I, Murugan .N,: Effect of friction stir welding on microstructure, mechanical and wear properties of AA6061/ZrB2 in situ cast composites Materials Science and Engineering: Vol. 543, pp. 257-266, 2012.
VI. Dixitm M, Newkirk. J.W, Mishra. R.S, :Properties of friction stir-processed Al1100-NiTi composite, Scr. Mater. Vol.56,pp. 541–544,2007.
VII. Dong.Y.Y, Ai-P.W, Juergen.S,Qing.Y.S,: Predicting residual distortion of aluminum alloy stiffened sheet after friction stir welding by numerical simulation Materials & Design ,Vol. 32, pp. 2284-2291 ,l 2011
VIII. Dursun.T, Soutis. C, : Recent developments in advanced aircraft aluminium alloys. Materials & Design. Vol. 56, pp. 862–871, 2013
IX. Elangovan .K, Balasubramanian. V,: Influences of post-weld heat treatment on tensile properties of friction stir-welded AA6061 aluminum alloy joints: M a t e r i a l s c h a r a c t e r i z a t i o n ,Vol.5 9 ,pp. 1 1 6 8 – 1 1 7 7, 2 0 0 8 .
X. Elangovan. K, Balasubramanian. V, Babu. S,: Predicting tensile strength of friction stir welded AA6061 aluminium alloy joints by a mathematical model. Materials &Design,Vol.30, , pp. 188-193 , 2009.
XI. Faraji.G, Asadi.P, :Studying microstructure and mechanical properties of SiC-incorporated AZ31 joints fabricated through FSW: the effects of rotational and traveling speeds,Mater. Sci. Eng. A,Vol. 527 ,pp.2320–2330,2011.
XII. XII.Ghamdi. K.A.Al, Hussain .G, Hashemi.R,: Fabrication ofmetal-matrixAL7075T651/TiN nano composite employing friction stir process, Proc. IME B J. Eng. Manufact. Vol.231 (8), 1319-1331,2017.
XIII. Gopalakrishnan.S, Murugan .N,: Prediction of tensile strength of friction stir welded aluminium matrix TiCp particulate reinforced composite J. Mater Des, Vol.32,pp. 462–467 ,2011.

XIV. Guo.J, Gougeon .P, Chen X.G, :Microstructure evolution and mechanical propertiesof dissimilar friction stir welded joints between AA1100-B4C MMC and AA6063 alloy, Mater. Sci. Eng. A.,Vol. 553,pp.149–156,2012.
XV. Heydarian. Arash, Dehghani Kamran &SlamkishTaymor. Optimizing powder distribution in production of surface nano-composite via friction stir processing. Metallurgical and Materials Transactions .B,Vol. 45, pp.821-826 ,2014.

XVI. Khodaverdizadeh H, Heidarzadeh A &Saeid T. Effect of tool pin profile on microstructure and mechanical properties of friction stir welded pure copper joints. Materials &Design,Vol. 45, pp.265-270,2013.

XVII. Li. Y, Liu. Y, Liu. C, Li. C, Ma. Z, Huang .Y, Wang. Z, Li. W, Microstructure evolution and mechanical properties of linear friction welded S31042 heat-resistant steel, J. Mater. Sci. Technol. Vol.34 (4),pp.653–659, 2018.
XVIII. Morisada Y, Fujii H, Nagaoka T &Fukusumi M. Effect of friction stir processing with SiC particles on microstructure and hardness of AZ31. Materials Science and Engineering: A .Vol.433, pp.50-54,2006.

XIX. Reimann. M, Gartner .T, Suhuddin. U, Göbel. J, dos Santos JF, :Keyhole closure using friction spot welding in aluminum alloy 6061-T6. J Mater Process Technol,Vol.237,pp .12–8,2016.
XX. Rhodes .C.G, Mahoney .M.W, Bingel .W.H, Spurling .R.A, Bampton .C.C, : Effects of friction stir welding on microstructure of 7075 aluminium. Scr Mater.Vol.36,pp.69–75,1997.

XXI. Safeen W, Hussain S, Wasim A, Jahanzaib M, Aziz H, AbdallaH. Predicting the tensile strength, impact toughness and hardness of friction stir-welded AA6061-T6 using response surface methodology. Int J Adv Manuf Technol ,Vol.87,pp.1765–81,2016.
XXII. Sahraeinejad ,Izad.H, Meysam.H. Fabrication of metal matrix composites by friction stir processing with different Particles and processing parameters. Materials Science and Engineering A•Vol. 626,pp.505-513, 2015.
XXIII. Scialpi .D.F, Cuomo.P, Summa.P.D,: Micro-friction stir welding of 2024–6082 aluminium alloys, Weld. Int.Vol. 21 (1),pp. 16–22,2008.
XXIV. Senthil.V, Balasubramanian. E,Effect of tool pin profile on corrosion behavior of friction stir processed LM25AA 10% SiCp metal matrix composites, Metal Powder Report, 2019

XXV. Singh. T, Tiwari.S.K, Shukla.D.K, Friction stir welding of AA6061-T6: the effects of Al2O3nano-particles addition, Results in Materials .1. 100005,2019.
XXVI. Sun. Y.F ,Fujii. H, :The effect of SiC particles on the microstructure and mechanical properties of friction stir welded pure copper joints. Mater SciEng .A.Vol.528,pp. 5470–5475,2011.
XXVII. T.E. Abioyea,b, H. Zuhailawatia,∗, A.S. Anasyidaa, S.A. Yahayac, B.K. Dhindaw,:Investigation of the microstructure, mechanical and wear properties of AA6061-T6 friction stir weldments with different particulate reinforcements addition Journal of Materials Research and Technology.Vol. 8(5) , 2019
XXVIII. Tutunchilar S, Haghpanahi M, Givi MK Besharati, Asadi P &Bahemmat P. Simulation of material flow in friction stir processing of a cast Al–Si alloy. Materials &Design,Vol. 40,pp. 415-426,2012.
XXIX. Yokoyama T, Nakai K, Katoh K. Tensile properties of 6061-T6 friction stir welds and constitutive modelling in transverse and longitudinal orientations. Weld Int ,Vol.32,pp.161–71,2018.
XXX. Yuvaraj. N, Aravindan S, Vipin. Fabrication of Al5083/B4C surface composite by friction stir processing and its tribological characterisation. J Mater Res Technol ,Vol.4(4),pp.398–410,2015.
XXXI. Zhang.C, Cui.L , Wang.D, Liu.Y, Liu.C, Li.H, The heterogeneous microstructure of heat affect zone and its effect on creep resistance for friction stir joints on 9Cr–1.5W heat resistant steel, Scr. Mater. Vol.158 ,pp.6–10, 2019.
XXXII. Zhang .H.J, Liu .H.J, Huang .Y.X, Yu. L,: Effect of water cooling on performance of friction stir welded heat affected zone, J. Mater. Eng. Perform,Vol. 21,pp.1182-1187,2012.

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

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.

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:

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.

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:

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.

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.

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.

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/
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