Journal Vol – 14 No -5, October 2019

Abstract:

The wealth of organizations is being determined by the amount of quality data they possess. Organizations across the globe have recognised this phenomenon. With abundance of data along with advanced analytic tools and technologies, many organizations have embraced business analytics into their essential strategic and operational decision-making tools. Heart of business analytics is the data. The quality and value of decision-making outcomes lie with the data inputs supplied. Big data and social media analytics have given impetus to the expansion of business analytics into all critical functional areas of the organization. Though a little late, the domain of HR has also caught up the trend of applying analytics. This new area is termed as people analytics or HR analytics. In this paper, an attempt is made to understand the extent of data availability and usage in analytics, termed as data sophistication in HR analytics in the organizations. As there are no definite ways to determine the data sophistication levels, a response sheet with a set of 20 items is developed based on previous literature. Data is collected from HR professionals. This data is subjected to exploratory factor analysis to capture the important dimensions from the items. Using structural equation modelling, confirmatory factor analysis was carried out to assess the model fit. Based on the resultant model, data is subjected to ridit analysis to interpret the treatment effect intuitively. The findings of the study add to the field of study in the area of data analytics, HR analytics, and decision-making domains. New approaches and study opportunities in related areas can be explored in this area due to its fast-emerging nature.

Keywords:

Data analytics,Data sophistication,HR analytics,ridit analysis,

Refference:

I. Agresti A. (1984). Analysis of Ordinal Categorical Data, N.Y.:John Wiley
andSons.
II. Akerkar, R. (2014). Analytics on Big Aviation Data: Turning Data into
Insights. IJCSA, 11(3), 116-127.
III. Barton, D., & Court, D. (2012). Making advanced analytics work for
you. Harvard business review, 90(10), 78-83.
IV. Bross I. (1958).How to Use RIDIT Analysis, Biometrics, 14(1), 18-38.
V. Banerjee, A., Bandyopadhyay, T., &Acharya, P. (2013). Data analytics:
Hyped up aspirations or true potential?. Vikalpa, 38(4), 1-12.

VI. Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent
analytics. Harvard business review, 88(10), 52-58.
VII. DeRomree, H., Fecheyr-Lippens, B., &Schaninger, B. (2016). People
analytics reveals three things HR may be getting wrong. McKinsey Quarterly.
VIII. Earley, S. (2015). Analytics, machine learning, and the internet of things. IT
Professional, 17(1), 10-13.
IX. Fink, A. A., &Sturman, M. C. (2017). HR Metrics and Talent Analytics. The
Oxford Handbook of Talent Management, 375-390.
X. Hair, J. F., Black, W.C., Babin, B.J. and Anderson, R.E. (2013), Multivariate
Data Analysis: A Global Perspective, 7/e., Pearson Education: New Delhi.
XI. Kohavi, R., Rothleder, N. J., &Simoudis, E. (2002). Emerging trends in
business analytics. Communications of the ACM, 45(8), 45-48.
XII. Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., &Haydock, M. (2012).
Analytics: The widening divide. MIT Sloan Management Review, 53(2), 1.
XIII. Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big
data: Issues and challenges moving forward. In 2013 46th Hawaii
International Conference on System Sciences (pp. 995-1004). IEEE.
XIV. Lahrmann, G., Marx, F., Winter, R., &Wortmann, F. (2010, October).
Business intelligence maturity models: an overview. In VII conference of the
Italian chapter of AIS (itAIS 2010). Italian chapter of AIS, Naples.
XV. Levenson, A. (2011): Using targeted analytics to improve talent decisions.
People & Strategy, Vol. 34, pp. 34–43.
XVI. Malladi, S. (2013). Adoption of business intelligence & analytics in
organizations–an empirical study of antecedents.
XVII. Miller, L., Schiller, D., & Rhone, M. (2011). Data warehouse maturity
assessment service. TERADATA. In.
XVIII. Molefe, M. (2014). From data to insights: HR analytics in organisations
(Doctoral dissertation, University of Pretoria).
XIX. Poreca, S. (2018). The 4 Sophistication Levels of Data Analysis – Level Blog.
Retrieved from
https://www.northeastern.edu/levelblog/2018/03/28/sophistication-levelsdata-
analysis/
XX. Phillips-Wren, G. E., Iyer, L. S., Kulkarni, U. R., &Ariyachandra, T. (2015).
Business Analytics in the Context of Big Data: A Roadmap for
Research. CAIS, 37, 23.
XXI. Ransbotham, S., Kiron, D., & Prentice, P. K. (2016). Beyond the hype: the
hard work behind analytics success. MIT Sloan Management Review, 57(3).
XXII. Rex B. Kline. (2005). Principles and Practice of Structural Equation
Modeling, The Guilford Press

XXIII. Sahay, B. S., &Ranjan, J. (2008). Real time business intelligence in supply
chain analytics. Information Management & Computer Security, 16(1), 28-
48.
XXIV. Russom, P. (2011). Big data analytics. TDWI best practices report, fourth
quarter, 19(4), 1-34.
XXV. Shanks, G., Bekmamedov, N., & Sharma, R. (2011). Creating value from
business analytics systems: a process-oriented theoretical framework and case
study.
XXVI. Tabachnick, B.G. &Fidell, L.S. (2013). Using multivariate statistics (6thedn).
Boston: Pearson Education.
XXVII. Wu, C. H. (2007). An empirical study on the transformation of Likert-scale
data to numerical scores. Applied Mathematical Sciences, 1(58), 2851-2862.
XXVIII. Watkins, M.W. (2000). Monte Carlo PCA for parallel analysis [computer
software]. State College, PA: Ed & Psych Associates.
XXIX. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding
its capabilities and potential benefits for healthcare
organizations. Technological Forecasting and Social Change, 126, 3-13.

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Abstract:

Intelligent systems based on mathematical theories have proved to be efficient in diagnosing various diseases. In this paper we used an expert system based on “soft set theory” and “fuzzy set theory” named as soft expert system to diagnose tropical disease dengue. This study discuss the role of “Soft set theory” as system which worked on the basis of knowledge in medical field. Study used “soft expert system” to predict the risk level or chances of a patient causing dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure. The proposed method explicitly demonstrates the exact percentage of the risk level of dengue fever automatically circumventing for all possible (medical) imprecisions.

Keywords:

dengue fever,soft set theory,fuzzy set theory,intelligent systems,

Refference:

I. D. A. Molodtsov, soft set theory- first results, Comput. Math. Appl. 37
(1999) 19-31.
II. D. Chen, E. C. C. Tsang, D. S. Yeung, X. Wang, the parameterization
reduction of soft sets and its applications, Comput. Math. Appl. 49
(2005) 757-763.
III. F. M. E. Uzoka and K. Barker, Expert system and uncertainty in medical
diagnosis: A proposal for fuzzy-AHP hybridization, an international
journal of Medical Engg. And informatics, 2 (2010) 329-342.
IV. H. T. Nguyen and E. A. Walker, A first course in fuzzy logic,
Application in intelligent systems Boston; Kluwer Academic.
V. J. Durkin, Expert System Design and Development, New Jersy: Prentice
Hall (1994).
VI. L. A. Zadeh, Fuzzy sets, Inform. and Control 8 (1965) 338-353.
VII. L. Boullart, A. Krijgsman, R. A. Vingerhoeds, editors Applications of
Artificial Intelligence in process control program press, (1992).
VIII. M. I. Ali, F. Feng, X. Liu, W. K. Min, M. Shabbir, On new operation in
soft set theory. Comput. Math. Appl. 57 (2009) 1547-1553.

IX. P. K. Maji, R. Biswas, A. R. Roy, Soft set theory, Comput. Math. Appl.
45 (2003) 555-562.
X. P. Sharam, DBV. Singh, M. K. Bandil, and N. Mishra, Decision Support
System for Malaria and Dengue Diagnosis, International Journal of
information and Computational Technology, 3 (2013) 633-640.
XI. P. Szolovits, R. S. Patil and W. B. Schwartz, Artificial intelligence in
medical diagnosis, Journal of international medicine, 108 (1988) 80-87.
XII. S. B. Halsted, V. A. Suaya, D. S. Shepard, The burden of dengue
infection Lancet, 369 (2007) 1410-1411.
XIII. V. Pabbi: Fuzzy Expert System for Medical Diagnosis. IJSRP, 5 (2015).
XIV. X. Ma, N. Sulaiman, H. Qin, T. Herawan, V. M Zain, A new efficient
normal parameter reduction algorithm of soft sets, Comput. Math. Appl.
62 (2011) 588-598.
XV. Y. Zou, Z. Xiao, Data analysis approaches of soft sets under incomplete
information, known. Based Syst. 59 (2008)2128-2137.

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Abstract:

Digital watermarking is an innovation for the hiding a secret information into an object. It can be utilized ascopyright protection and secure concern for multimedia and digital information. This article presents a new video watermarking using redundant discrete wavelet transform (RDWT) in singular value decomposition (SVD) domain. Further, it is also computed several image quality metrics lie peak signal-tonoise ratio (PSNR), structural similarity (SSIM) index and root mean square error (RMSE) to disclose the imperceptibility and robustness of proposed watermarking approach compared to conventional approaches. Extensive simulation results show that the proposed algorithm have performed superior to the conventional water marking algorithms.

Keywords:

Digital Watermarking,discrete wavelet transform,singular value decomposition,redundant discrete wavelet and image quality metrics,

Refference:

I. A. Siddiqui and A. Kaur, “A secure and robust image
watermarking system using wavelet domain”, Int. Conf. on Cloud
Comp., Data Sci. and Eng., Noida, India, Jun. 2017.
II. D. Frederic, et al. “Robust 3D DFT video watermarking”, in Proc.
of Security and Watermarking of Multimedia Contents, Electronic
Imaging, San Jos, CA, USA, vol. 3657, Apr. 1999.
III. F. Y. Shih, “Digital Watermarking and Steganography:
Fundamentals and Techniques”, December 17, 2007.
IV. http://www.alpvision.com/watermarking.html
V. https://en.wikipedia.org/wiki/Digital_watermarking
VI. J. R. Hernandez, M. Amado and F. Perez-Gonzalez, “DCT-domain
watermarking techniques for still images: detector performance
analysis and a new structure”, IEEE Trans. On Imag. Proces., vol.
9, no. 1, pp. 55-68, 2000.
VII. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VIII. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square
Patch Antenna using Truncated Technique for WLAN
Applications”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
IX. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure
design for low profile steerable antenna applications” Journal of
advanced research in dynamical and control systems, Vol-10,
Special issue-03, 2018.
X. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG
structure for mutual coupling reduction in antenna arrays; a
comparitive study” International Journal of engineering and
technology, Vol-7, No-3.6, Special issue-06, 2018
XI. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression
band, In phase reflection band and High Impedance region of
3DEBG Characterization” International journal of applied
engineering research (IJAER), Vol 10, No 11, 2015.
XII. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna;
Design and Analysis”, International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Volume-8 Issue-
8 June, 2019.
XIII. K. Sudhir and M. S. Gopal, “Dual watermarking based on Multiple
parameter fractional Fourier transform and LSB technique”, Int.
Conf. on Image Info. Proces., Shimla, India, 2011, pp. 1-5.

XIV. L. Kedmenec, A. Poljicak and L. Mandic, “Copyright protection of
images on a social network”, in Proc. ELMAR, Zadar, Croatia,
2014, pp. 1 – 4.
XV. M. Abdullatif, et al. “Properties of digital image
watermarking”, 9th Int. Collo. on Sig. Proces. and Appl., Kuala
Lumpur, Malaysia, Jun. 2013, pp. 235–240.
XVI. P. V. Nagarjuna, L. Bhaskar and B. R. Reddy, “Non-Decimated
Wavelet Domain based Robust Blind Digital Image Watermarking
Scheme Using Singular Value Decomposition”, 3rd Int. Conf. on
Sig. Proces. and Integ. Net., Noida, India, 2016, pp. 389-393.
XVII. R. K. Arya, S. Singh and R. Saharan, “A secure non-blind block
based digital image watermarking technique using DWT and
DCT”, Int. Conf. on Adv. in Comput., Comm. and Infor., Kochi,
India, IEEE, Aug. 2015, pp. 2041-2048.
XVIII. S. Chandran and K. Bhattacharya, “Performance analysis of LSB,
DCT and DWT for digital image watermarking application using
steganography”, Int. Conf. on Elect., Electr., Sig., Comm. and
Opt., Visakhapatnam, Andhra Pradesh, vol. 1, 2015, pp. 1-5.
XIX. S. Kadu, et al. “Discrete Wavelet Transform based Video
Watermarking technique”, Int. Conf. on Micro., Comp. and
Comm., Durgapur, India, 2016, pp. 1-6.
XX. S. Kumar, et al. “RGB image steganography on multiple frame
video using LSB technique”, Int. Conf. on Comp. and Comput.
Sci., Noida, India, Dec. 2015, pp. 226–231.
XXI. S. Saxena, P. Soni and M. Gujar, “A Secure and Robust DWT
based Digital Image Watermarking Technique”, International
Journal of Applications, vol. 168, no. 11, pp. 21-24, 2017.
XXII. Z. Jiang-bin, et al. “Color image watermarking based on DCTdomains
of color channels”, 10th Conf. on Comp., Comm., Cont.
and Power Eng., Beijing, China, vol. 1, no. 1, 2002, pp. 281-284.

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Abstract:

Diabetes detection has become an important task for medical practitioners in India and abroad. Researchers and scientists have been working on this problem actively. Machine learning has been contributing majorly to systems, techniques and solutions for diabetes detection problem. Yet there are challenges which remain to be addressed. Recently convolution based machine learning techniques have evolved to give efficient results in various domains. They have shown applicability over range of problems. So here recent architectures of Convolution based machine learning models like Convolutional Neural Networks (CNN) and Capsule Networks (CapsNet) are discussed. Also, application of these recent models is presented here. Additionally, challenges faced by current Diabetes detection systems are discussed. Along with these challenges CapsNet architecture for text analytics is presented. This CapsNet architecture is closest to Diabetes detection problem in terms of structure and arrangement of data to be handled. Thus in future this architecture and its variants can be applied for Diabetes detection.

Keywords:

Diabetes detection,Convolutional Neural Networks,CNN,Capsule Networks,CapsNet,

Refference:

I. A. Mackiewicz and W. Ratajczak, “Principal components analysis
(pca),” Computers and Geosciences, vol. 19, pp. 303-342, 1993.
II. A. Mobiny and H. Van Nguyen, “Fast capsnet for lung cancer
screening,” arXiv preprint arXiv:1806.07416, 2018.
III. A. Shahroudnejad, A. Mohammadi, and K. N. Plataniotis,
“Improved explainability of capsule networks: Relevance path by
agreement,” arXiv preprint arXiv:1802.10204, 2018.
IV. B. D. Kanchan and M. M. Kishor, “Study of machine learning
algorithms for special disease prediction using principal of
component analysis,” in Global Trends in Signal Processing,
Information Computing and Communication (ICGTSPICC), 2016
International Conference on, pp. 5-10, IEEE, 2016.
IV. B. Sierra and P. Larranaga, “Predicting survival in malignant skin
melanoma using bayesian networks automatically induced by
genetic algorithms. an empirical comparison between different
approaches,” Artificial Intelligence in Medicine, vol. 14, no. 1-2,
pp. 215-230, 1998.
V. C. Bennett, M. Guo, and S. Dharmage, “Hba1c as a screening tool
for detection of type 2 diabetes: a systematic review,” Diabetic
medicine, vol. 24, no. 4, pp. 333-343, 2007.
VI. C. Willi, P. Bodenmann, W. A. Ghali, P. D. Faris, and J. Cornuz,
“Active smoking and the risk of type 2 diabetes: a systematic review
and meta-analysis,” Jama, vol. 298, no. 22, pp. 2654 -2664, 2007.
VII. D. B. Carr and S. Gabbe, “Gestational diabetes: detection,
management, and implications,” Clinical Diabetes, vol. 16, no. 1,
pp. 4-12, 1998.
IX. Deliege, A. Cioppa, and M. Van Droogenbroeck, “Hitnet: a neural
network with capsules embedded in a hit-or-miss layer, extended
with.
X. D. K. Choubey, S. Paul, S. Kumar, and S. Kumar, “Classification of
pima indian diabetes dataset using naive bayes with genetic
algorithm as an attribute selection,” in Communication and
Computing Systems: Proceedings of the International Conference
on Communication and Computing System (ICCCS 2016), pp. 451-
455, 2017.

XI. Dolz, X. Xu, J. Rony, J. Yuan, Y. Liu, E. Granger, C. Desrosiers, X.
Zhang, I. B. Ayed, and H. Lu, “Multi-region segmentation of
bladder cancer structures in mri with progressive dilated
convolutional networks,” arXiv preprint arXiv:1805.10720, 2018.
XII. dos Santos and M. Gatti, “Deep convolutional neural networks for
sentiment analysis of short texts,” in Proceedings of COLING 2014,
the 25th International Conference on Computational Linguistics:
Technical Papers, pp. 69-78, 2014.
XIII. D. Rawlinson, A. Ahmed, and G. Kowadlo, “Sparse unsupervised
capsules generalize better,” arXiv preprint arXiv:1804.06094, 2018.
XIV. E. Alpaydin, “Introduction to machine learning”. MIT press, 2014.
XV. E. Xi, S. Bing, and Y. Jin, “Capsule network performance on
complex data,” arXiv preprint arXiv:1712.03480, 2017.
XVI. F. Liang, H. Liu, X.Wang, and Y. Liu, “Hyperspectral image
recognition based on artificial neural network,” NeuroQuantology,
vol. 16, no. 5, 2018.
XVII. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally,
and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x
fewer parameters and< 0.5 mb model size,” arXiv preprint
arXiv:1602.07360, 2016.
XVIII. H. Uemura, A. A. Ghaibeh, S. Katsuura-Kamano, M. Yamaguchi,
T. Bahari, M. Ishizu, H. Moriguchi, and K. Arisawa, “Systemic in
ammation and family history in relation to the prevalence of type 2
diabetes based on an alternating decision tree,” Scientific reports,
vol. 7, p. 45502, 2017.
XIX. H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes
mellitus prediction model based on data mining,” Informatics in
Medicine Unlocked, vol. 10, pp. 100-107, 2018.
XX. H. Ze, A. Senior, and M. Schuster, “Statistical parametric speech
synthesis using deep neural networks,” in Acoustics, Speech and
Signal Processing (ICASSP), 2013 IEEE International Conference
on, pp. 7962{7966, IEEE, 2013.
XXI. Jaiswal, W. AbdAlmageed, and P. Natarajan, “Capsulegan:
Generative adversarial capsule network,” arXiv preprint
arXiv:1802.06167, 2018.
XXII J. Li, G. Li, and H. Fan, “Image dehazing using residual-based deep
cnn,” IEEE Access, 2018.
XXIII J.-S. Jang, “Anfis: adaptive-network-based fuzzy inference system,”
IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3,
pp. 665-685, 1993.

XXIV Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas,
and I. Chouvarda, “Machine learning and data mining methods in
diabetes research,” Computational and structural biotechnology
journal, vol. 15, pp. 104-116, 2017.
XXV Khan, I. Yaqoob, I. A. T. Hashem, Z. Inayat, M. Ali, W.
Kamaleldin, M. Alam, M. Shiraz, and A. Gani, “Big data: survey,
technologies, opportunities, and challenges,” The Scientific World
Journal, vol. 2014, 2014.
XXVI K. Kayaer and T. Yldrm, “Medical diagnosis on pima indian
diabetes using general regression neural networks,” in Proceedings
of the international conference on artificial neural networks and
neural information processing (ICANN/ICONIP), pp. 181-184,
2003.
XXVII K. Polat and S. Gunes, “An expert system approach based on
principal component analysis and adaptive neuro-fuzzy inference
system to diagnosis of diabetes disease,” Digital Signal Processing,
vol. 17, no. 4, pp. 702-710, 2007.
XXVIII K. Polat, S. Gunes, and A. Arslan, “A cascade learning system for
classification of diabetes disease: Generalized discriminant analysis
and least square support vector machine,” Expert systems with
applications, vol. 34, no. 1, pp. 482-487, 2008.
XXIX L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement
learning: A survey,” Journal of artificial intelligence research, vol.
4, pp. 237-285, 1996.
XXX M. Engelin, “Capsnet comprehension of objects in different
rotational views,” divaportal.org, 2018.
XXXI M. Fatima and M. Pasha, “Survey of machine learning algorithms
for disease diagnostic,” Journal of Intelligent Learning Systems and
Applications, vol. 9, no. 01, p. 1, 2017.
XXXII M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman,
“Reading text in the wild with convolutional neural networks,”
International Journal of Computer Vision, vol. 116, no. 1, pp. 1-20,
2016.
XXXIII M. Singh, “Classification of diabetic retinopathy stages using deep
learning,” Scientific reports, 2018.
XXXIV P. Afshar, A. Mohammadi, and K. N. Plataniotis, “Brain tumor type
classification via capsule networks,” arXiv preprint
arXiv:1802.10200, 2018.

XXXV Q.Wang, T. Ruan, Y. Zhou, D. Gao, and P. He, “An attention-based
bi-gru-capsnet model for hypernymy detection between compound
entities,” arXiv preprint arXiv:1805.04827, 2018.
XXXVI Q. Xuan, H. Xiao, C. Fu, and Y. Liu, “Evolving convolutional
neural network and its application in fine-grained visual
categorization,” IEEE Access, 2018.
XXXVII R. Johnson and T. Zhang, “Effective use of word order for text
categorization with convolutional neural networks,” arXiv preprint
arXiv:1412.1058, 2014.
XXXVIII R. J. Williams and D. Zipser, “A learning algorithm for continually
running fully recurrent neural networks,” Neural computation, vol.
1, no. 2, pp. 270-280, 1989.
XXXIX R. LaLonde and U. Bagci, “Capsules for object segmentation,”
arXiv preprint arXiv:1804.04241, 2018.
XL. Safran, M. Bloomrosen, W. E. Hammond, S. Labkoff, S. Markel-
Fox, P. C. Tang, and D. E. Detmer, “Toward a national framework
for the secondary use of health data: an american medical
informatics association white paper,” Journal of the American
Medical Informatics Association, vol. 14, no. 1, pp. 1-9, 2007.
XLI. S. Aich and I. Stavness, “Object counting with small datasets of
large images,” arXiv preprint arXiv:1805.11123, 2018.
XLII. Schmidhuber, “Deep learning in neural networks: An overview,”
Neural networks, vol. 61, pp. 85-117, 2015.
XLIII. S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
XLIV. S. Ibrahim, P. Chowriappa, S. Dua, U. R. Acharya, K. Noronha, S.
Bhandary, and H. Mugasa, “Classification of diabetes maculopathy
images using data-adaptive neuro-fuzzy inference classifier,”
Medical & biological engineering & computing, vol. 53, no. 12, pp.
1345-1360, 2015.
XLV. S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between
capsules,” in Advances in Neural Information Processing Systems,
pp. 3856-3866, 2017.
XLVI. S. Valverde, M. Salem, M. Cabezas, D. Pareto, J. C. Vilanova, L.
Ramio-Torrenta, A. Rovira, J. Salvi, A. Oliver, and X. Llado, “Oneshot
domain adaptation in multiple sclerosis lesion segmentation
using convolutional neural networks,” arXiv preprint
arXiv:1805.12415, 2018.
XLVII. T. Deshmukh and H. Fadewar, “Fuzzy deep learning for diabetes
detection,” in Computing, Communication and Signal Processing,
pp. 875-882, Springer, 2019.

XLVIII. W. Liu, E. Barsoum, and J. D. Owens, “Object localization and
motion transfer learning with capsules,” arXiv preprint
arXiv:1805.07706, 2018.
XLIX. X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural
networks,” in Proceedings of the fourteenth international conference
on artificial intelligence and statistics, pp. 315-323, 2011.
L. X. Li, T. Wu, X. Song, and H. Krim, “Aognets: Deep and-or
grammar networks for visual recognition,” arXiv preprint
arXiv:1711.05847, 2017.
LI. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based
learning applied to document recognition,” Proceedings of the
IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
LII. Y. Sebastian, X. T. Tiong, V. Raman, A. Y. Y. Fong, and P. H. H.
Then, “Advances in diabetes analytics from clinical and machine
learning perspectives,” International Journal of Design, Analysis
and Tools for Integrated Circuits and Systems, vol. 6, no. 1, pp. 32-
37, 2017.
LIII. Y. Wang, A. Sun, J. Han, Y. Liu, and X. Zhu, “Sentiment analysis
by capsules,” in Proceedings of the 2018 World Wide Web
Conference on World Wide Web, pp. 1165-1174, International
World Wide Web Conferences Steering Committee, 2018.
LIV. Y. Wang, W. Ke, and P. Wan, “A method of ultrasonic image
recognition for thyroid papillary carcinoma based on deep
convolution neural network,” NeuroQuantology, vol. 16, no. 5,
2018.
LV. Zahangir Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K.
Asari, “Recurrent residual convolutional neural network based on unet
(r2u-net) for medical image segmentation,” arXiv preprint
arXiv:1802.06955, 2018.
LVI. Z. Chen and D. Crandall, “Generalized capsule networks with
trainable routing procedure,” arXiv preprint arXiv:1808.08692,
2018.
LVII. Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, “Learning to
diagnose with lstm recurrent neural networks,” arXiv preprint
arXiv:1511.03677, 2015.

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Abstract:

Mobile ad-hoc network is a most significant job in military applications since it is explicitly planned system for on demand requirement and in circumstances where set up of physical system is beyond the realm of imagination. This special kind of network which takes control in infrastructure less correspondence handles genuine difficulties carefully, for example, exceedingly hearty and dynamic military work stations, devices and littler sub-arranges in the front line. In this manner, there is an intense interest of planning productive directing conventions guaranteeing security and unwavering quality for fruitful transmission of exceedingly touchy and secret military data in guard systems. With this target, a power effective system layer directing convention in the system for military application is structured and mimicked utilizing another cross layer approach of configuration to expand unwavering quality and system lifetime up to a more prominent degree. But here PDO-AODV approach not supports to optimal path selection. So we propose a new ACO-DAEE (Ant colony optimization with delay aware energy efficient) for optimal path selection and mitigating the delay time in network system. The main goal is to maintain the optimal routes in network, during data transmission in an efficient manner. Our simulation results indicate that ACO-ADEE performs extremely well in terms of packet delivery ratio, end to end delay, and throughput. Simulation results through NS2 software to verify the effectiveness of our method.

Keywords:

Mobile ad-hoc network,ACO-DAEE,optimal path selection,NS2 software,

Refference:

I. Agbaria, A.; Gershinsky, G.; Naaman N. & Shagin,K. Extrapolationbased
and QoS-aware real-timecommunication in wireless mobile ad hoc
networks. Inthe 8th IFIP Annual Mediterranean Adhoc
NetworkingWorkshop, Med-Hoc-Net 2009. pp.21-26.doi:
10.1109/MEDHOCNET.2009.5205201
II. Ahmed, M.; Elmoniem, Abd; Ibrahim, Hosny M.;Mohamed, Marghny
H. &Hedar, Abdel Rahman. Antcolony and load balancing optimizations
for AODVrouting protocol. Int. J. Sensor Networks Data
Commun.,2012, 1.doi: doi:10.4303/ijsndc/X110203.
III. Amulya Boyina, K. Praveen Kumar “Active Coplanar Wave guide Fed
Switchable Multimode Antenna Design and Analysis” Journal Of
Mechanics Of Continua And Mathematical Sciences (JMCMS), Vol.-14,
No.-4, July-August (2019) pp 188-196.
IV. B. Venkateswar Rao, Praveen Kumar Kancherla, Sunita Panda
“Multiband slotted Elliptical printed Antenna Design and Analysis”
Journal Of Mechanics Of Continua And Mathematical Sciences
(JMCMS), Vol.-14, No.-4, July-August (2019) pp 378-386.

V. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VI. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VII. K.Praveen Kumar, “Design of 3D EBG for L band Applications” IEEE
International conference on communication technology ICCT-April-
2015. Noor Ul Islam University Tamilnadu.
VIII. K.Praveen Kumar, Dr. Habibulla Khan ” Active progressive stacked
electromagnetic band gap structure (APSEBG) structure design for low
profile steerable antenna applications” International Conference on
Contemporary engineering and technology 2018 (ICCET-2018) March
10 – 11, 2018. Prince Shri Venkateshwara Padmavathy Engineering
College, Chennai.
IX. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design
for low profile steerable antenna applications” Journal of advanced
research in dynamical and control systems, Vol-10, Special issue-03,
2018.
X. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization of
Optimized stacked electromagnetic band gap ground plane for low
profile patch antennas” International journal of pure and applied
mathematics, Vol 118, No. 20, 2018, 4765-4776.
XI. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structures
for Mutual coupling reduction in antenna arrays; A comparative study”
International Conference on Contemporary engineering and technology
2018 (ICCET-2018) March 10 – 11, 2018. Prince Shri Venkateshwara
Padmavathy Engineering College, Chennai
XII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XIII. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band,
In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XIV. K.Praveen Kumar, Dr Habibulla Khan ” The surface properties of
TMMD-HIS material; a measurement” IEEE International conferance on
electrical, electronics, signals, communication & optimization EESCO –
January 2015

XV. K.Praveen Kumar, “Effect of 2DEBG structure on Monopole Antenna
Radiation and Analysis of It’s characteristics” IEEE International
conference on communication technology ICCT-April-2015. Noor Ul
Islam University Tamilnadu.
XVI. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna Design
for C-Band Applications”, International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Volume-8 Issue-8
June, 2019.
XVII. K.Praveen Kumar, “Mutual Coupling Reduction between antenna
elements using 3DEBG” IEEE International conference on
communication technology ICCT-April-2015. Noor Ul Islam University
Tamilnadu.
XVIII. K.Praveen Kumar, “The surface properties of TMMD-HIS material; A
measurement” IEEE International conference on communication
technology ICCT-April-2015. Noor Ul Islam University Tamilnadu.
XIX. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XX. K Satish Reddy a, K Praveen Kumar, Habibulla Khan, Harswaroop
Vaish “Measuring the surface properties of a Novel 3-D Artificial
Magnetic Material” 2nd International Conference on Nanomaterials and
Technologies (CNT 2014), Elsevier Procedia material Science.
XXI. Kumaraswami Gajula, Amulya Boyina, K. Praveen Kumar “Active Quad
band Antenna Design for Wireless Medical and Satellite Communication
Applications” Journal Of Mechanics Of Continua And Mathematical
Sciences (JMCMS), Vol.-14, No.-4, July-August (2019) pp 239-252.
XXII. Mamata Rath, Binod Kumar Pattanayak and Bidudhendu Pati, – Energy
efficient MANET Protocol using Cross Layer Design for Military
Applications -, vol.66, no.2, March 2016, pp.146-150.
XXIII. Mamata Rath, Binod Kumar, Pattanayak and Bibudhendu Pati, – Energy
efficient MANET Protocol Using Cross Layer Design for Military
Applications -, Vol.66.2, March 2016, pp.146-150,
DOI:10.14429/dsj.66.9705.
XXIV. Siva, K. & P. Duraiswamy, K. A QoS routing protocol formobile ad hoc
networks based on the load distribution. Inthe IEEE International
Conference on ComputationalIntelligence and Computing Research
(ICCIC), 2010,pp.1-6.doi: 10.1109/ICCIC.2010.5705724.
XXV. Srivastava, S.; Daniel, A.K.; Singh, R. & Saini, J.P.
Energyefficientposition based routing protocol for mobile ad
hocNetworks. In the IEEE International Conference on
RadarCommunication and Computing (ICRCC), 2012, pp.18-23.doi:
10.1109/ICRCC.2012.6450540.

 

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Abstract:

The Synthetic Aperture Radar (SAR) usually corrupted by some surplus speckle formed. These speckles having multiplicative noise, which appears likes a grainy pattern in the SAR image. This performs an accurate interpretation of SAR images. The aim of this work was to remove the noise and the accurate classifying the LULC facts with quality evolution with statistical operations. The SAR images to play an import key role on Earth Observation applications using high resolution for allweather conditions and all times. These Radar satellite collecting images have noise. To despeckle the noise, we propose the NEST Tool. Using this tool we (statistical operations) subtract band wise noisily one. The experiment results are better performance from the state of art techniques.

Keywords:

Despeckle learning,SAR,radar,noise,nest tool,

Refference:

I. C.A. Deledalle, L. Denis, and F. Tupin, “How to compare noisy
patches? patch similarity beyond gaussian noise,” International Journal
of Computer Vision, vol. 99, pp. 86–102, 2012.

II. C.A. Deledalle, L. Denis, and F. Tupin, “Iterative weighted maximum
likelihood denoising with probabilistic patch-based weights,” IEEE
Trans. on Image Process., vol. 18, no. 12, pp. 2661–2672, 2009.
III. C.A. Deledalle, L. Denis, M. Jager, A. Reigber, and F. Tupin, ¨ “NLSAR:
A unified nonlocal framework for resolution preserving
(Pol)(In)SAR denoising,” IEEE Trans. Geosci. Remote Sens., vol. 53,
no. 4, pp. 2021–2038, 2015.
IV. C.V. Angelino S. Parrilli, M. Poderico, and L. Verdoliva, “A nonlocal
SAR image denoising algorithm based on LLMMSE wavelet shrinkage,”
IEEE Trans. Geosci. Remote Sens., vol. 50, no. 2, pp. 606–616, Feb.
2012.
V. D. Cozzolino, S. Parrilli, G. Poggi, , G. Scarpa and L. Verdoliva, “Fast
adaptive nonlocal SAR despeckling,” IEEE Geoscience and Remote
Sensing Letters, vol. 11, no. 2, pp. 524– 528, 2014.
VI. D.P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
in International Conf. on Learning Representations (ICLR), 2015.
VII. F. Argenti, L. Alparone, T. Bianchi and A. Lapini, “A tutorial on speckle
reduction in synthetic aperture radar images,” IEEE Geosci. Remote
Sens. Mag., vol. 1, pp. 6–35, 2013.
VIII. G. Chierchia, D. Cozzolino, and G.Poggi, “SAR image despeckling
through convolutional neural networks,” IEEE Geosci. Remote Sens.,
vol. 1, pp.5438–5441, 2017.
IX. H.C. Burger, S. Harmeling, and C.J. Schuler, “Image denoising: Can
plain neural networks compete with BM3D?” in IEEE CVPR, 2012, pp.
2392–2399.
X. https://earth.esa.int/documents/507513/1077939/nest
XI. Jain and S. Seung, “Natural image denoising with convolutional
networks,” in Advances in Neural Information Processing Systems,
2009, pp. 769–776.
XII. K. He, S. Ren X. Zhang, and J. Sun, “Deep Residual Learning for Image
Recognition,” in IEEE CVPR, 2016, pp. 770–778.
XIII. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XIV. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.

XV. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design
for low profile steerable antenna applications” Journal of advanced
research in dynamical and control systems, Vol-10, Special issue-03,
2018.
XVI. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XVII. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band,
In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XVIII. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XIX. L. Alparone, F. Argenti, and T. Bianchi, “Segmentation-Based MAP
Despeckling of SAR Images in the Undecimated Wavelet Domain,”
IEEE Trans. Geosci. Remote Sens., vol. 46, no. 9, pp. 2728–2742, 2008.
XX. S. Foucher, “SAR image filtering via learned dictionaries and sparse
representations,” in IEEE IGARSS, 2008, pp. 229–232.
XXI. S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep
Network Training by Reducing Internal Covariate Shift,”
arXiv:1502.03167 v3, 2015.
XXII. Y. Chen, D. Meng K. Zhang, L. Zhang, and W. Zuo, “Beyond a
Gaussian Denoiser: Residual Learning of Deep CNN for Image
Denoising,” arXiv:1608.03981v1, 2016.

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Abstract:

The design of layout and source VLSI by way of low design rules is a difficult assignment before fabricating required device. A RF integrated circuit contains extensive applications is Ring Oscillator (RO). Current article focus a novel method of design, where a ring oscillator (RO) is simulated with Layout versus Source(LVS) report for physical verification using mentor graphics with Pyxis schematic, ELDOsimulation, EzWaves, Pyxis Layout and Calibre tools. Here RO circuit is designed with inverters of 5 stages operating at 9 GHz with the boundaries obligatory by gdk Generic 13 library. Simulated results, schematic, layout with LVS reports are presented here to verify design of RO with Mentor graphics EDA back end tool in efficient manner compared to Cadence.

Keywords:

Layout Vs Schematic (LVS),Inverter,Oscillator,Mentor Graphics,

Refference:

I. A. HAJIMIRI, S. LIMOTYRAKIS, T. LEE,‟Jitter and Phase Noise in
Ring Oscillators”, IEEE Journal of Solid State Circuits, vol.34, 6,
(1999), 790-804.
II. Asad A. Abidi, “Phase Noise and Jitter in CMOS Ring Oscillators,”
IEEE Journal Of Solid-State Circuits, Vol. 41, No. 8, August 2006.
III. G. JOVANOVI´C , M. STOJˇCEV, “A Method for Improvement
Stability of a CMOS Voltage Controlled Ring Oscillators”, ICEST 2007,
Proceedings of Papers, vol. 2, pp. 715-718, Ohrid, Jun 2007.

IV. MATSUDA, T. et al.‟ A combined test structure with ring oscillator and
inverter chain for evaluating optimum high-speed/low-power operation”.
In proceeding of International Conference on Microelectronic Test
Structures, 2003.
IV. M K Mandal and B C Sarkar,“Ring oscillators: Characteristics and
applications”, Indian journal of pure and applied physics, vol 48, pp 136-
145, 2010.
V. P. M. Farahabadi, H. Miar-Naimi and A. Ebrahimzadeh, “A New
Solution to Analysis of CMOS Ring Oscillators” Iranian Journal of
Electrical & Electronic Engineering, Vol. 5, No. 1, March 2009.
VI. Prakash Kumar Rout, Debiprasad Priyabrata Acharya, “Design of
CMOS Ring Oscillator Using CMODE” National Institute of
Technology, Rourkela, Orissa, India ,pp.1-6,2011.
VII. S. DOCKING, AND M. SACHDEV, ‟An Analytical Equation for the
Oscillation Frequency of High-Frequency Ring Oscillators”, IEEE
Journal of Solid State Circuits, vol.39, 3, (2004), 533-537.
VIII. S. Docking, M. Sachdev, ‟A Method to Derive an Equation for the
Oscillation Frequency of a Ring Oscillator”, IEEE Trans. on Circuits and
Systems – I: Fundamental Theory and Applications, vol. 50, 2,(2003),
259-264.
IX. SEGURA, J., HAWKINS, C.F. ‟CMOS electronics, how it works, how it
fails (ch. 4)”. Book IEEE edition, ISBN 0- 471-47669-2, 2004.
X. Sushil Kumar and Dr. Gurjit Kaur, “Design and performance analysis of
nine stages cmos based ring oscillator” International Journal Of VLSI
Design & Communication Systems (VLSICS),Vol.3, No.3, June 2012.
XII. Vandna Sikarwar,Neha Yadav, Shyam Akashe, ‟Design and analysis of
CMOS ring oscillator using 45nm technology”, 2013 3rd IEEE
International Advance Computing Conference (IACC).

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Abstract:

Beam forming can be achieved by combining elements in an array in way that certain angles get constructive interference and some destructive interference, which can be utilized for both transmitter and receiver ends so that they can achieve spatial selectivity. Combination of antenna and digital technology as Digital Beam Forming (DBF) which was developed by workers in sonar and Radar systems which was enhanced by development of aperture synthesis methods leading to modern dipolar arrays improvement. Converting RF signals into cos and sin signals representing amplitude and phase values which are combined to get desired output this is done by converting analog signal into digital. Antenna is considered as a device which converts spatio signals into strictly temporal signals which makes it helpful for various signal processing techniques.

Keywords:

Beam Forming (DBF),Radar systems,signal processing techniques,

Refference:

I. Amulya Boyina, K. Praveen Kumar “Active Coplanar Wave guide Fed
Switchable Multimode Antenna Design and Analysis” Journal Of
Mechanics Of Continua And Mathematical Sciences (JMCMS), Vol.-
14, No.-4, July-August (2019) pp 188-196
II. B. Venkateswar Rao, Praveen Kumar Kancherla, Sunita Panda
“Multiband slotted Elliptical printed Antenna Design and Analysis”
Journal Of Mechanics Of Continua And Mathematical Sciences
(JMCMS), Vol.-14, No.-4, July-August (2019) pp 378-386
III. Chou, H.T., Chang, C.H., Chen, Y.T.: Ferrite circulator integrated
phased array antenna module for dual-link beamforming at millimeter
frequencies. IEEE Trans. Antennas Propag. 10, 1–9 (2018)

IV. H. Shokri-Ghadikolaei, F. Boccardi, C. Fischione, G. Fodor, and M.
Zorzi, “Spectrum sharing in mmwave cellular networks via cell
association, coordination, and beamforming,” IEEE Journal on
Selected Areas in Communications, vol. 34, no. 11, pp. 2902–2917,
2016
IV. J. Bechter, K. Eid, F. Roos, and C. Waldschmidt, “Digital
beamforming to mitigate automotive radar interference,” in Proc. IEEE
MTT-S Int. Conf. Microw. Intell. Mobility, May 2016, pp. 1–4.
V. Kim, K.H. Kim, H., Kim, D.Y., Kim, S.K., Chun, S.H., Park, S.J.,
Jang, S.M., Chong, M.K. Jin, H.S.: Development of planar active
phased array antenna for detecting and tracking radar. In: Proceedings
of IEEE Radar Conference (RadarConf’18), Oklahoma City, April 23–
27, pp. 0100–0103, 2018.
VI. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VII. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
IX. K.Praveen Kumar, “Design of 3D EBG for L band Applications”
IEEE International conference on communication technology ICCTApril-
2015. Noor Ul Islam University Tamilnadu.
X. K.Praveen Kumar, Dr. Habibulla Khan ” Active progressive stacked
electromagnetic band gap structure (APSEBG) structure design for low
profile steerable antenna applications” International Conference on
Contemporary engineering and technology 2018 (ICCET-2018) March
10 – 11, 2018. Prince Shri Venkateshwara Padmavathy Engineering
College, Chennai.
XI. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure
design for low profile steerable antenna applications” Journal of
advanced research in dynamical and control systems, Vol-10, Special
issue-03, 2018.
XII. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization
of Optimized stacked electromagnetic band gap ground plane for low
profile patch antennas” International journal of pure and applied
mathematics, Vol 118, No. 20, 2018, 4765-4776.
XIII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG
structures for Mutual coupling reduction in antenna arrays; A
comparative study” International Conference on Contemporary
engineering and technology 2018 (ICCET-2018) March 10 – 11, 2018.
Prince Shri Venkateshwara Padmavathy Engineering College, Chennai.

XIV. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XV. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression
band, In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XVI. K.Praveen Kumar, Dr Habibulla Khan ” The surface properties of
TMMD-HIS material; a measurement” IEEE International conferance
on electrical, electronics, signals, communication & optimization
EESCO – January 2015.
XVII. K.Praveen Kumar, “Effect of 2DEBG structure on Monopole Antenna
Radiation and Analysis of It’s characteristics” IEEE International
conference on communication technology ICCT-April-2015. Noor Ul
Islam University Tamilnadu.
XVIII. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna
Design for C-Band Applications”, International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Volume-8 Issue-8
June, 2019.
XIX. K.Praveen Kumar, “Mutual Coupling Reduction between antenna
elements using 3DEBG” IEEE International conference on
communication technology ICCT-April-2015. Noor Ul Islam
University Tamilnadu.
XX. K.Praveen Kumar, “The surface properties of TMMD-HIS material; A
measurement” IEEE International conference on communication
technology ICCT-April-2015. Noor Ul Islam University Tamilnadu.
XXI. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XXII. K Satish Reddy a, K Praveen Kumar, Habibulla Khan, Harswaroop
Vaish “Measuring the surface properties of a Novel 3-D Artificial
Magnetic Material” 2nd International Conference on Nanomaterials
and Technologies (CNT 2014), Elsevier Procedia material Science.
XXIII. Kumaraswami Gajula, Amulya Boyina, K. Praveen Kumar “Active
Quad band Antenna Design for Wireless Medical and Satellite
Communication Applications” Journal Of Mechanics Of Continua And
Mathematical Sciences (JMCMS), Vol.-14, No.-4, July-August (2019)
pp 239-252.

XXIV. M. A. Vazquez, L. Blanco, and A. I. P ´ erez-Neira, “Hybrid analog–
digital ´ transmit beamforming for spectrum sharing backhaul
networks,” IEEE transactions on signal processing, vol. 66, no. 9, p.
2273, 2018.
XXV. M. Rameez, M. Dahl, and M. I. Pettersson, “Adaptive digital
beamforming for interference suppression in automotive FMCW
radars,” in Proc. IEEE Radar Conf., Apr. 2018, pp. 252–256.
XXVI. P. Kumari, M. E. Eltayeb, R. W. Heath, “Sparsity-aware adaptive
beamforming design for IEEE 802.11ad-based joint communicationradar”,
Accepted to IEEE Radar Conference (RdarConf), pp. 4281-
4285, March 2018.
XXVII. W. Zhang and Z. S. He, “Comments on ‘Waveform optimization for
transmit beamforming with MIMO radar antenna array,”’ IEEE Trans.
Antennas Propag., vol. 66, no. 9, Sep. 2016.

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Abstract:

In this article the design of Photo-voltaic (PV) based ZETA converter has been presented to drive the Switched Reluctance Motor (SRM) for the agricultural based applications to pump the water. PV panels are the major power resource to supply a water pump driven by 8/6 SRM motor. A Perturbation & Observation (P & O) based maximum power point tracking (MPPT) scheme is adopted to improve the performance of PV system. A dc-dc ZETA is connected in between PV and SRM drive to provide steady and continuous supply to the SRM for efficient operation of the system. The variable DC-link capacitor voltage of the ZETA converter controls the speed of SRM drive from various environmental conditions and irradiation levels of solar PV array. ZETA converter has the added advantage over other types of buckboost converters is that, it does not require added circuitry for inrush current problem and overload protection is also not required. To reduce the stresses on the converter elements, the two inductors are chosen to operate in continuous current conduction mode (CCM). A four-phase 8/6 SRM drive is developed in the MATLAB/SIMULINK environment to demonstrate the effectiveness of specified system.

Keywords:

Solar PV system,MPPT,ZETA converter,SRM drive,agricultural applications,Perturbation and Observation (P&O),

Refference:

I. B. Singh, A. K. Mishra, and R. Kumar, “Solar powered water pumping
system employing switched reluctance motor drive,” IEEE Trans. Ind.
Appl., vol. 52, pp. 3949-3957, Sep./Oct 2016.
II. B. Singh and G. D. Chaturvedi, “Analysis, design and development of a
single switch fly-back Buck-Boost AC-DC converter for low power battery
charging applications,” Journal of Power Electronics, Vol. 7, No. 4, pp. 318-
327, Oct. 2007.
III. D.C. Martins, “Zeta Converter Operating in Continuous Conduction Mode
Using the Unity Power Factor Technique”, in Proc. IEE PEVSD’96, 1996,
pp.7-11.
IV. Falin, J. Designing DC/DC converters based on ZETA topology. Analog
Appl. J. Texas Instruments Incorporated, 2Q. Texas, USA. 2010, 16–21.
V. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VI. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”, International
Journal of Innovative Technology and Exploring Engineering (IJITEE),
Volume-8 Issue-8 June, 2019.
VII. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design for
low profile steerable antenna applications” Journal of advanced research in
dynamical and control systems, Vol-10, Special issue-03, 2018.
VIII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure for
mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6, Special
issue-06, 2018.

IX. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band, In
phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
X. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XI. Krishnan, Ramu. Switched reluctance motor drives: modeling, simulation,
analysis, design, and applications. CRC press, 2001.
XII. Kumar, Rajan, and Bhim Singh, “BLDC motor-driven solar PV array-fed
water pumping system employing zeta converter”, IEEE Transactions on
Industry Applications, vol. 52, no. 3, (2016), pp. 2315-2322.
XIII. Mahmoud, Samia M., et al.”Studying different types of power converters
fed switched reluctance motor.” International Journal of Electronics and
Electrical Engineering 1.4, pp. 281-290, 2013.
XIV. Miller, Timothy John Eastham, ed. et al.” Electronic control of switched
reluctance machines”. Newnes, 2001.
XV. Mishra A. K, & Singh B, “Solar photovoltaic array dependent dual output
converter based water pumping using Switched Reluctance Motor drive”,
IEEE Transactions on Industry Applications, 53(6), 5615-5623, 2017.
XVI. Nabil Farah, M.H.N. Talib, Jurifa Lazi, Majed Abo Ali, Z. Ibrahim,
“Multilevel Inverter Fed Switched Reluctance Motors (SRMs) 6/4, 8/6 and
10/8 SRM Geometric Types”, International Journal of Power Electronics
and Drive System (IJPEDS) Vol. 8, No. 2, June 2017, pp. 584~592 ISSN:
2088-8694, DOI: 10.11591/ijpeds.v8i2.pp584-592.
XVII. Nagi Reddy. B, A. Pandian, O. Chandra Sekhar, M. Rammoorty, “Design of
Non-isolated integrated type AC-DC converter with extended voltage gain
and high power factor for Class-C&D applications”. International Journal of
Recent Technology and Engineering (IJRTE), Vol. 7, No. 5, Jan 2019, pp
230-236.
XVIII. Nagi Reddy. B, O. Chandra Sekhar, M. Ramamoorty, “Implementation of
zero-current switch turn-ON based buck-boost buck type rectifier for low
power applications”. International Journal of electronics – Taylor & Francis
publication, Vol.106, No.8.
https://www.tandfonline.com/doi/full/10.1080/00207217.2019.1582711.
XIX. Qingguo Sun, Jianhua Wu*, Chun Gan and Jing Wang”A Novel Boost
Chopper Converter-Based Torque Sharing Function Control Strategy for
Switched Reluctance Motors,” 978-1-5386-3246-8/17/$31.00 © 2017 IEEE.
XX. Tai, Chang Yan, and Cheng KW Eric. et al.”A simulation model for a 4
phase switched reluctance motor for PSIM.” Power Electronics Systems and
Applications (PESA), 2011 4th International Conference on, IEEE, pp. 1-5,
2011.

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Abstract:

De-noising is the reconstruction of an original image once all useless noise that is from affected images are eliminated. The image de-noising is a major challenge to researchers since the removal of noise can introduce artefacts that can result in the blurring of all images. The techniques based on the wavelet were to identify better applicability in the removal of noise owing to the capability of spacefrequency and its localization. The techniques inspired by nature have an important role to play in image processing. This will bring down image blurring, noise and improves enhancement of image, image fusion, image thresholding, and image pattern recognition. The algorithm known as Grey Wolf Optimization (GWO) falls under the category of swarm intelligence and thus initiates the process of optimization using random solutions.

Keywords:

Denoising,Image denoising,Wavelet-based techniques,Grey Wolf Optimization (GWO) algorithm,

Refference:

[I] Chaudhari, Y. P., & Mahajan, P. M. (2017). Image denoising of various
images using wavelet transform and thresholding techniques. Int. Res. J. Eng.
Technol.(IRJET), 4(2).
[II] E. Daniel, J. Anitha K. K Kamaleshwaran and Indu Rani, “Optimum
spectrum mask based medical image fusion using Gray Wolf Optimization,”
Biomedical Signal Processing and Control, vol. 34, pp. 36–43, Apr. 2017.
[III] E.Daniel, J. Anitha, Optimum green plane masking for the contrast
enhancement of retinal images using enhanced genetic algorithm, Optik 126
(2015) 1726–1730.
[IV] Faris, H., Aljarah, I., Al-Betar, M. A., &Mirjalili, S. (2018). Grey wolf
optimizer: a review of recent variants and applications. Neural computing and
applications, 30(2), 413-435.
[V] K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
[VI] K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”, International
Journal of Innovative Technology and Exploring Engineering (IJITEE),
Volume-8 Issue-8 June, 2019.
[VII] K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design
for low profile steerable antenna applications” Journal of advanced research in
dynamical and control systems, Vol-10, Special issue-03, 2018.
[IX] K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6, Special
issue-06, 2018.
[X] K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band,
In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
[XI] K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
[XII] Misra., A, Kartikeyan., B (2015) “DENOISING TECHNIQUES FOR
SYNTHETIC APERTURE RADAR DATA – A REVIEW”, International
Journal of Computer Engineering & Technology (IJCET) Volume 6, Issue 9,
Sep 2015, pp. 01-11.
[XIII] Misra., I, A, Kartikeyan., B, and Garg., B. (2014) “Denoising Of Sar
Imagery In The Wavelet Framework: Performance Analysis”, International
Journal of Remote Sensing & Geoscience (IJRSG), Volume 3, Issue 2, March
2014, pp (1 to 11).

[XIV] Mustafa, N., Khan, S. A., Li, J. P., Khalil, M., Kumar, K., &Giess, M.
(2014, December). Medical image de-noising schemes using wavelet
transform with fixed form thresholding. In 2014 11th International Computer
Conference on Wavelet Actiev Media Technology and Information Processing
(ICCWAMTIP) (pp. 397-402). IEEE.
[XV] Nair, J. J., &Bhadran, B. (2014, April). Denoising of sar images using
maximum likelihood estimation. In 2014 International Conference on
Communication and Signal Processing (pp. 853-857). IEEE.
[XVI] Ramson, S. J., Raju, K. L., Vishnu, S., &Anagnostopoulos, T. (2019).
Nature Inspired Optimization Techniques for Image Processing—A Short
Review. In Nature Inspired Optimization Techniques for Image Processing
Applications (pp. 113-145). Springer, Cham.
[XVII] Ramson, S. J., Raju, K. L., Vishnu, S., &Anagnostopoulos, T. (2019).
Nature Inspired Optimization Techniques for Image Processing—A Short
Review. In Nature Inspired Optimization Techniques for Image Processing
Applications (pp. 113-145). Springer, Cham.
[XVIII] Xizhi, Z. (2008, December). The application of wavelet transform in
digital image processing. In 2008 International Conference on MultiMedia and
Information Technology (pp. 326-329). IEEE.
[XIX] Xue, X. (2016). An Effective Method of SAR Image
Denoising. REVISTA DE LA FACULTAD DE INGENIERIA, 31(12), 161-
166.
[XX] Zhao, H. H., Lopez Jr, J. F., Martinez, A., &Qiao, Z. J. (2013). SAR
Image Denoising Based on Wavelet Packet and Median Filter. In Applied
Mechanics and Materials (Vol. 333, pp. 916-919). Trans Tech Publications.

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Abstract:

Human resources are the most precious asset of society and any civil engineering and construction firms. Experience shows that successful construction companies always focus on recruitment and human resources training. Thus, ranking main factors in measuring the performance of construction engineers is one of the most critical determinations in the success of civil engineering and construction projects. However, traditional methods of evaluating key factors performance of construction engineers are usually based on subjective opinions, resulting in irrational and inappropriate outcomes. Therefore, this paper presents a fuzzy model for ranking critical factors in measuring the performance of construction engineers. The results show that there are five essential factors in measuring the performance of construction engineers in Vietnam: (1) Ability to perform work in accordance with project procedures and accept overtime work; (2) Ability to improve knowledge and skills; and (3) Ability to meet and make a decision with the consensus of a project team; and (4) Ability to communicate exchange and persuade and build good relationships with project members; (5) Planning and scheduling ability.

Keywords:

Construction projects,civil engineers,fuzzy logic,Job performance,

Refference:

I. D. L. Luong, D.-H. Tran, and P. T. Nguyen, “Optimizing multi-mode timecost-
quality trade-off of construction project using opposition multiple
objective difference evolution,” International Journal of Construction
Management, pp. 01-13, 2018.
II. J. J. Buckley, “The fuzzy mathematics of finance,” Fuzzy Sets and Systems,
vol. 21, no. 3, pp. 257-273, 1987.
III. J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, vol. 17,
no. 3, pp. 233-247, 1985.
IV. N. Hao, Y. Feng, K. Zhang, G. Tian, L. Zhang, and H. Jia, “Evaluation of
traffic congestion degree: An integrated approach,” International Journal of
Distributed Sensor Networks, vol. 13, no. 7, 2017.
V. N. T. Phong, V. N. Phuc, and T. T. H. L. N. Quyen, “Application of Fuzzy
Analytic Network Process and TOPSIS Method for Material Supplier
Selection,” Key Engineering Materials, vol. 728, pp. 411-415, 2017.
VI. P. T. Nguyen et al., “Construction Project Quality Management using Building
Information Modeling 360 Field,” International Journal of Advanced
Computer Science and Applications, vol. 9, no. 10, pp. 228-233, 2018.
VII. P. Van Nguyen, P. T. Nguyen, Q. L. H. Thuy, T. Nguyen, and V. D. B. Huynh,
“Calculating Weights of Social Capital Index Using Analytic Hierarchy
Process,” International Journal of Economics and Financial Issues, vol. 6, no.
3, pp. 1189-1193, 2016.
VIII. T. A. Nguyen and P. T. Nguyen, “Explaining model for supervisor’s behavior
on safety action based on their perceptions,” ARPN Journal of Engineering and
Applied Sciences, Article vol. 10, no. 20, pp. 9562-9572, 2015.
IX. V. D. B. Huynh, P. V. Nguyen, Q. L. H. T. T. Nguyen, and P. T. Nguyen,
“Application of Fuzzy Analytical Hierarchy Process based on Geometric Mean
Method to Prioritize Social Capital Network Indicators,” International Journal
of Advanced Computer Science and Applications, vol. 9, no. 12, pp. 182-186,
2018.

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Abstract:

Project comes up with a lot of hurdles and unnecessary obstacles due to situational and appropriate eccentricity of engineering which usually left manager and engineers to take on these challenges with their exceptional managerial skills and to work effectively in the given scenarios. Engineering Project Health management (EPHM) is an important term for management engineers which is based on framework for observation of engineered program/structure with in context understanding. This paper presents a novel framework approach of integrated vehicle health management (IVHM) in engineering management. It is applied to four industrial cases through which mutual understanding of project activity is increased. The purpose of implementing IVHM protocol in management position is reduce the analytical efforts and to increase the reliability of project analysis.

Keywords:

Project Engineering,Engineering Management,integrated vehicle health management (IVHM),Project analysis,Reliability,

Refference:

I. A. I. Lavagnon, “Project success as a topic in project management journals,”
Project Manage. J., vol. 40, no. 4, pp. 6–19, 2009.
II. C. Chapman and S. Ward, Project Risk Management: Processes, Techniques
and Insights. Chichester, U.K.: Wiley, 1996.
III. C. Earl, C. Eckert, and J. Clarkson, “Design change and complexity,” in
Proc. 2nd Workshop Complexity Des. Eng., 2005, pp. 24–33.
IV. E. Baroth et al., “IVHM (Integrated vehicle health management) techniques
for future space vehicles,” in Proc. 37th Joint Propulsion Conf.
Exhibit, 2001, pp. 1–10.
V. I. Egan, J. M. Ritchie, and P. D. Gardiner, “Measuring performance
change in the mechanical design process arena,” Proc. Inst. Mech. Eng.
Part B, J. Eng. Manuf., vol. 219, no. 12, pp. 851–863, 2005.
VI. J. K. Pinto and S. J. Mantel, Jr., “The causes of project failure,” IEEE
Trans. Eng. Manage., vol. 37, no. 4, pp. 269–276, Nov. 1990.
VII. J. Watson, “Joseph Black Lecture, Design Exhibition,” University of
Bath, 2012. [Online]. Available: https://wiki.bath.ac.uk/display/
MechEngDesignExhibition/Home

VIII. L. Wallace and M. Keil, “How software project risk affects project performance:
An investigation of the dimensions of risk and an exploratory
model,” Decis. Sci., vol. 35, no. 2, pp. 289–321, 2004.
IX. M. Engwall, “No project is an island: Linking projects to history and
context,” Res. Policy, vol. 32, no. 2003, pp. 789–808, 2002.
X. M. Cataldo and S. Nambiar, “The impact of geographic distribution and
the nature of technical coupling on the quality of global software
development projects,” J. Softw. Evol. Process, vol. 24, pp. 153–168,
2012.
XI. M. Hobday, “The project-based organisation: An ideal form for
managing complex products and systems?,” Res. Policy, vol. 29, no. 7–8,
pp. 871– 893, Aug. 2000.
XII. S. D. Eppinger and A. R. Chitkara, “The new practice of global prod- uct
development,” MIT Sloan Manage. Rev., vol. 47, no. 4, pp. 22–30, 2006.
XIII. S. Floricel and R. Miller, “Strategizing for anticipated risks and
turbulence in large-scale engineering projects,” Int. J. Project Manage.,
vol. 19, no. 8, pp. 445–455, Nov. 2001.
XIV. W. Al-Ahmad, K. Al-Fagih, K. Khanfar, K. Alsamara, S. Abuleil, and H.
Abu-Salem, “A taxonomy of an IT project failure: Root causes,” Int.
Manage. Rev., vol. 5, no. 1, pp. 93–104, 2009.
XV. S.-R. Toor and S. O. Ogunlana, “Beyond the ‘iron triangle’: Stakeholder
perception of key performance indicators (KPIs) for large-scale public
sector development projects,” Int. J. Project Manage., vol. 28, no. 3, pp.
228–236, Apr. 2010.

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Abstract:

Product accessibility is a serious feature of client package for sellers and industrialists. While product being out of stock (OOS), both sellers and manufacturers may agonize relying on demand-side features, such as whether customers are extra dependable to the store. Though together sellers and producers contribute to OOS conditions, the supply-side features, like whether seller or producer is accountable for in-store contentment might effect OOS situation. Direct store delivery (DSD) includes the producer sidestepping the seller’s delivery center and transporting product straight to the seller’s distinct warehouses. This article presents the Supply and Demand (SAD) concerns concurrently to define the insinuations of stock outs for both sellers and producers using an agent-based simulation. An agent-based simulation permits reflection of such concerns under recurring OOS circumstances to define the general and complete effect to seller and producer performance.

Keywords:

Supply and Demand,Producers,Agent based simulation,product reliability,Out of Stock (OOS),

Refference:

I. A. Musalem, M. Olivares, E. T. Bradlow, C. Terwiesch, and D. Corsten,
“Structural estimates of the effect of out-of-stocks,” Manage. Sci., vol. 56,
no. 7, pp. 1180–1197, 2010.
II. A. Bonfrer and P. K. Chintagunta, “Store brands: Who buys them and
what happens to retail prices when they are introduced?,” Review Ind.
Org., vol. 24, no. 2, pp. 195–218, 2004.
III. D. Papakiriakopoulos and G. Doukidis, “Classification performance of
making decisions about products missing from the shelf,” Adv. Decis.
Sci., pp. 1–13, 2011, Art.ID 515978.
IV. E. T. Anderson, G. J. Fitzsimons, and D. Simester, D, “Measuring and
mitigating the costs of stockouts,” Manage. Sci., vol. 52, no. 11, pp. 1751–
1763, 2006.
V. ECR Europe and RolandBerger Strategy Consultants. ECR— Optimal
Shelf Availability: Increasing Shopper Satisfaction at the Moment
of Truth. ECR Europe, 2003. [Online]. Avail- able: http://ecrall.
org/files/pub_2003_osa_blue_book.pdf. Accessed on: Jun. 30, 2014.
VI. GMA Direct Store Delivery Committee, AMR Research, and Clark- ston
Consulting. Powering Growth Through Direct Store Delivery (Ver- sion
1.1), Grocery Manufacturers of America, Washington, DC, USA, 2008.
VII. GMA Direct Store Delivery Committee and Willard Bishop. Optimiz- ing
the Value of Integrated DSD, Grocery Manufacturers of America,
Washington, DC, USA, 2011.
VIII. J. Aastrup and H. Kotzab, “Analyzing out-of-stock in independent grocery
stores: An empirical study,” Int. J. Retail Distrib. Manage., vol. 37, no. 3,
pp. 765–789, 2009.
IX. J. Aastrup and H. Kotzab, “Forty years of out-of-stock research – and
shelves are still empty,” Int. Rev. Retail, Distrib. Consumer Res., vol. 20,
no. 1, pp. 147–164, 2010
X. K. Campos, E. Gijsbrechts, and P. Nisol, “The impact of retailer stockouts
on whether, how much, and what to buy,” Int. J. Res. Marketing, vol. 20,
no. 3, pp. 273–286, 2003.
XI. K. Xu, R. Yin, and Y. Dong, “Stockout recovery under consignment: The
role of inventory ownership in supply chains,” Decis., Sci., vol. 47, no. 1,
pp. 94–124, 2016.
XII. R. C. Basole and M. A. Bellamy, “Supply network structure, visibility,
and risk diffusion: A computational approach,” Decis. Sci. J., vol. 45, no.
4, pp. 753–789, 2014.
XIII. T. W. Gruen, D. S. Corsten, and S. Bharadwaj, Retail Out-of-Stocks: A
Worldwide Examination of Extent, Causes, and Consumer Responses,
Grocery Manufacturers of America: Washington, DC, USA, 2002.
XIV. T. Wu, S. Huang, J. Blackhurst, X. Zhang, and S. Wang, “Supply chain
risk management: An agent-based simulation to study the impact of retail
stockouts,” IEEE Trans. Eng. Manage., vol. 60, no. 4, pp. 676–686, Nov.
2013.

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Abstract:

Direct torque control (DTC) of Induction Motor (IM) is primal requirement in traction and vehicle applications which has been employed for fast torque response in dynamic performance of IM drives. DTC decouple flux and torque control there for to achieve fast torque response, optimal inverter voltage selection is taken. In this paper, DTC method is employed for fast torque response utilizing PI-controller based on the voltage vector selection for inverter. Moreover, performance of IM is evaluated in term of torque response, speed response, stator current, rotor and stator flux in both time and d-q flux. Analysis reveals that PI-controller response fairly match the reference torque curve faster there for considered as best choice for DTC of IM.

Keywords:

Direct torque control,Induction Motor,PI-Controller,Electric drive,

Refference:

I. Achalhi, D. Ouoba, M. Bezza, N. Belbounaguia and F. Dkhichi,
“Application of direct torque control of induction motor in a
photovoltaic water pumping system,” 2015 3rd International Renewable
and Sustainable Energy Conference (IRSEC), Marrakech, 2015, pp. 1-5.
II. FatihaZidani, RachidNait said, “Direct Torque Control of Induction
Motor with Fuzzy Minimization Torque Ripple”, Journal of Electrical
Engineering.,vol 56(7-8): pp. 183-188. 2005.
III. K. L. Butler, M. Ehsani, P. Kamath, “A Matlab-based modeling and
simulation package for electric and hybrid electric vehicle design”, IEEE
Trans. Vehicular Technol., vol. 48, no. 6, pp. 1770-1118, Nov. 1999.
IV. Kazmierkowski M P and Giuseppe Buja, “Review of Direct Torque
Control Methods for Voltage Source Inverter-Fed Induction Motors”,
Conf. Rec. IEEE-IAS. pp. 981-991, 2003
V. Kang Jun-Koo, Chung Dae-Woong and Seung-Ki Sul, “Direct Torque
Control of Induction Machine with Variable Amplitude Control of Flux
and Torque Hysteresis Bands”, Conf. Rec. IEEE-IAS.pp. 640-642, 1999.
VI. Malik E. Elbuluk, “Torque Ripple Minimization in Direct Torque
Control of Induction Machines”, IEEE-IAS annual meeting, 1: 12-16,
2003.
VII. Muhammad Aamir Aman, 2Hamza Umar Afridi, 3Muhammad
ZulqarnainAbbasi, 4Akhtar Khan, 5Muhammad Salman. Power
Generation from Piezoelectric Footstep Technique 1,2,3,4,5 Department
of Electrical Engineering, Iqra National University, Pakistan Email:
aamiraman@inu.edu.pk *Corresponding author: Muhammad Aamir
Aman, E-mail: aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-
13, No.-4, September-October (2018) Pages 67-72 67

VIII. Muhammad Aamir Aman, 2Muhammad ZulqarnainAbbasi, 3Akhtar
Khan, 4Waleed Jan, 5Mehr-e-Munir Power Generator Automation,
Monitoring and Protection System 1,2,3,4,5Department of Electrical
Engineering, Iqra National University, Pakistan
Email:mehre.munir@inu.edu.pk *Corresponding author: Mehr-e-Munir,
E-mail: mehre.munir@inu.edu.pk J.Mech.Cont.& Math. Sci., Vol.-13,
No.-4, September-October (2018) Pages 122-133
IX. Muhammad Aamir Aman, 2Muhammad ZulqarnainAbbasi, 3Hamza
Umar Afridi, 4Khushal Muhammad, 5Mehr-e-Munir Prevailing
Pakistan’s Energy Crises.1,2,3,4,5 Department of Electrical Engineering,
Iqra National University, Pakistan Email: aamiraman@inu.edu.pk
*Corresponding author: Muhammad Aamir Aman, E-mail:
aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-13, No.-4,
September-October (2018) Pages 147-154
X. Muhammad Aamir Aman, 2Muhammad ZulqarnainAbbasi, 3Murad Ali,
4Akhtar Khan.To Negate the influences of Un-deterministic Dispersed
Generation on Interconnection to the Distributed System considering
Power Losses of the system 1 Department of Electrical Engineering,
Iqra National University, Pakistan Email : aamiraman@inu.edu.pk
*Corresponding author: Muhammad Aamir Aman, E-mail:
aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-13, No.-3, July-
August (2018) Pages 117-132
XI. Muhammad Aamir Aman1, Ali Shahab2, Fazl e Jamil3, Muhammad
Nauman Naeem4, Mehre Munir5 Intelligent Home Automation System
Using BitVoicer Department of Electrical Engineering, Iqra National
University, Pakistan. Email: aamiraman@inu.edu.pk *Corresponding
author: Muhammad Aamir Aman, E-mail:
aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-14, No.-4, July-
August (2019)
XII. Pengcheng Zhu, Yong Kang and Jian Chen, “Improve Direct Torque
Control Performance of Induction Motor with Duty Ratio Modulation”,
Conf. Rec. IEEE-IEMDC, 03. 1: 994-998, 2003.
XIII. T. Ramesh and A. K. Panda, “Direct flux and torque control of three
phase induction motor drive using PI and fuzzy logic controllers for
speed regulator and low torque ripple,” 2012 Students Conference on
Engineering and Systems, Allahabad, Uttar Pradesh, 2012, pp. 1-6.
XIV. Vaez-Zadeh S and G.H. Mazarei, “Open Loop Control of Hysteresis
Band Amplitudes in Direct Torque Control of Induction Machines”,
Conf. Rec. IEEE-IAS. pp. 1519-1524, 2000.
XV. Y. Srinivasa Kishore Babu, G. Tulasi Ram Das, “Improvement in Direct
Torque Control of Induction Motor using Fuzzy Logic duty ratio
controller”, ARPN Journal of Engineering and Applied Sciences, vol. 5,
no. 4, April 2010

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Abstract:

Non-Banking Finance Companies (NBFCs) are playing a key role in providing financial support to the unsupported and secure the unsecured population. The broad objective of the paper is to study how NBFCs can increase customer base and its retention. There are various variables investigated, which were anticipated to be the reasons for increase in customer base. These variables involved both employee and customer satisfaction. The data collected is both primary and secondary in nature. The variables investigated are Interest Rate, customer satisfaction over various parameters likeservice offered, interest rate, staff behavior, and documentation process. For employee satisfaction there were factors investigated like salary, incentives, working hours, flexibility with work and personal interest. Also, there were factors which were analyzed to know how customers can be increased and retained. Factors such as referral program for customers, maintain regular interaction with customers, provide benefits offered by other competitors and provide benefits to loyal customers. The study attempts to identify the relations between above factors and how we can increase customer base and retention thereafter.

Keywords:

NBFC,MSME,Customer engagement,customer loyalty,customer satisfaction,customer retention,

Refference:

I. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards commodities market
with reference to GUNTUR city, Andhra Pradesh.International Journal of Engineering
and Technology(UAE). 7(2), pp. 1104-1106.
II. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian commodity
market: An empirical analysis with reference to Amaravathi region of Andhra Pradesh.
International Journal of Innovative Technology and Exploring Engineering.8(7), pp.
1708-1714.
III. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds selection
behavior: An analysis among the selected mutual fund investors. International Journal of
Engineering and Advanced Technology.8(4), pp. 1913-1920.
IV. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite wellness
interventions towards occupational stress. International Journal of Recent
Technology and Engineering.8(1), pp. 2874-2879.
V. Lakshmi Narahari, C., Koneru, K. (2018). Stress at work place and its impact on
employee performance. International Journal of Engineering and Technology(UAE).
7(2), pp. 1066-1071.
VI. Manukonda et al. (2019).What Motivates Students To Attend Guest Lectures?.The
International Journal of Learning in Higher Education.Volume 26, Issue 1. 23-34.
VII. Neelima, J., Koneru, K.(2019). Assessing the role of organizational culture in
determining the employee performance – empirical evidence from Indian pharmaceutical
sector.International Journal of Innovative Technology and Exploring Engineering. 8(7),
pp. 1701-1707.
VIII. Sivakoti Reddy, M. (2019).Impact of RSERVQUAL on customer satisfaction: A
comparative analysis between traditional and multi-channel retailing. International
Journal of Recent Technology and Engineering. 8(1), pp. 2917-2920.
IX. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship management
practices and their impact over customer purchase decisions: A study on the selected
private sector banks housing finance schemes. International Journal of Innovative
Technology and Exploring Engineering. 8(7), pp. 1720-1728.
X. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail service quality
in food and grocery retailing: A comparative study between traditional and multi-channel
retailing. International Journal of Management and Business Research. 9(2), pp. 68-73.
XI. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016).Saga of silicon plate:
An empirical analysis on the impact of socio economic factors of farmers on inception of solar plants.
International Journal of Control Theory and Applications. 9(29), pp. 257-
266.
XII. Suhasini, T., Koneru, K. (2019).Employee engagement through HRD practices on
employee satisfaction and employee loyalty: An empirical evidence from Indian IT
industry.
International Journal of Engineering and Advanced Technology.8(4), pp. 1788-1794.
XIII. Suhasini, T., Koneru, K. (2018).A study on employee engagement driving factors and
their impact over employee satisfaction – An empirical evidence from Indian it industry.
International Journal of Mechanical Engineering and Technology.9(4), pp. 725-732.
XIV. Y. V. Rao and SrinivasaRaoBudde.Banking Technology Innovations in India:
EnhancingCustomer Value and Satisfaction.Indian Journal of Science and Technology,
Vol 8(33), DOI: 10.17485/ijst/2015/v8i33/78280, December 2015.1-10.

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