Editor-In-Chief Chair Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel

CEO, Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Additional Director, Technocrats Institute of Technology and Science, Bhopal (MP), India

Associated Editor-In-Chief Members Dr. Hitesh Kumar

Ph.D.(ME), M.E.(ME), B.E. (ME)

Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science, Technology and Maritime

Transport, Egypt

Associated Editor-In-Chief Members Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India

Executive Editor Dr. Deepak Garg

Professor, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Technical Program Committee Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Manager Chair Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Editorial Members Dr. J. Gladson Maria Britto

Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education University, Amman- Jordan

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

Dr. Mohammad Mahdi Mansouri

Associate Professor, Department of High Voltage Substation Design & Development, Yazd Regional Electric Co., Yazd Province,

Iran.

Dr. Kaushik Pal

Youngest Scientist Faculty Fellow (Independent Researcher), (Physicist & Nano Technologist), Suite.108 Wuhan University, Hubei,

Republic of China.

Dr. Wan Aezwani Wan Abu Bakar

Lecturer, Faculty of Informatics & Computing, Universiti Sultan Zainal Abidin (Uni SZA), Terengganu, Malaysia.

Dr. P. Sumitra

Professor, Vivekanandha College of Arts and Sciences for Women (Autonomous), Elayampalayam, Namakkal (DT), Tiruchengode

(Tamil Nadu), India.

Dr. S. Devikala Rameshbabu

Principal & Professor, Department of Electronics and Electrical Engineering, Bharath College of Engineering and Technology for

Women Kadapa, (Andra Pradesh), India.

Dr. V. Lakshman Narayana

Associate Professor, Department of Computer Science and Engineering, Vignan’s Nirula Institute of Technology & Science for

women, Guntur, (Andra Pradesh), India.

S. No

Volume-8 Issue-5s, February 2020, ISSN: 2277-3878 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Amel Austine, R. Suji Pramila

Paper Title: Interference Management by Resource Exchange for D2D Communication in Cellular Network

Abstract: Scarcity in communication spectrum in cellular network is one of the major

challenges faced by the service providers around the world. Many efforts were put forth to

manage the efficient spectrum distribution among various cells in the network. Reusing the

resources by different cells in a safe distance without interference is the current mechanism to

improve spectrum efficiency. Spectrum efficiency can be even improved by reusing the

resources within a single cell. Device to Device (D2D) communication, a new technology

boosts the spectrum reuse inside a cell in 5G. The major hurdle in implementing D2D is in the

management of interference in a highly mobile environment. This high mobility risks the life-

time of a D2D link and frequent shifting from D2D mode to normal cellular mode will occur.

In this paper an effort is put forward to maintain the D2D link as long as possible through a

resource exchange mechanism. The exchange occurs when interference by a cellular or D2D

link threatens another D2D link. This approach can improve the life-time of a D2D link and

thereby improving the consistency of the network.

Keywords: Cellular Network, CQI, D2D Communication, Interference Management.

References:

1. Rohde & Schwarz. Device to Device Communication in LTE; Rohde & Schwartz Whitepaper; Rohde & Schwarz USA, Inc.:

Columbia, MD, USA, (2015).

2. Huan Tang, Zhi Ding, and Bernard C. Levy, "Enabling D2D communications through neighbor discovery in LTE cellular

networks" , IEEE Transactions on Signal Processing, Vol. 62, No. 19, (2014), available online:

https://doi.org/10.1109/TSP.2014.2348950.

3. Bentao Zhang, Yong Li, Depeng Jin, Pan Hui, and Zhu Han, "Social-aware peer discovery for D2D communications underlaying

cellular networks", IEEE Transactions on Wireless Communications, Vol. 14, No. 5, (2015).

4. Kun Zhu and Ekram Hossain, "Joint mode selection and spectrum partitioning for device-to-device communication: A Dynamic Stackelberg Game", IEEE Transactions on Wireless Communications, Vol. 14, No. 3, (2015).

5. Xingqin Lin, Jeffrey G. Andrews and Amitava Ghosh, "Spectrum sharing for device-to-device communication in cellular

networks", IEEE Transactions on Wireless Communications, Vol. 13, No. 12, (2014).

6. G. Nardini, G. Stea, A. Virdis, D. Sabella, M. Caretti "Resource allocation for network-controlled device-to-device

communications in LTE-Advanced", Springer Wireless Networks, The Journal of Mobile Communication, Computation and Information, online (2016),available online: last visit: 05.08.2018.

7. Yanru Zhang, Erte Pan, Lingyang Song, Walid Saad, Zaher Dawy, and Zhu Han, "Social network aware device-to-device

communication in wireless networks", IEEE Transactions on Wireless Communications, Vol. 14, No. 1, January 2015

8. Wentao Zhao, and Shaowei Wang, "Resource sharing scheme for device-to-device communication underlaying cellular

networks", IEEE Transactions on Communications, Vol. 63, No. 12, December 2015.

9. Setareh Maghsudi and Sławomir Sta´nczak, "Hybrid centralized–distributed resource allocation for device-to-device

communication underlaying cellular networks", IEEE Transactions on Vehicular Technology, Vol. 65, No. 4, April 2016

10. Chih-Yu Wang, Guan-Yu Lin, Ching-Chun Chou, Che-Wei Yeh, and Hung-Yu Wei, “Device-to-Device communication in LTE-

Advanced system: A strategy-proof resource exchange framework”, IEEE Transactions on Vehicular Technology, Vol. 65, No.

12, (2016), pp:10022-10036.

11. Sassan Ahmadi, “LTE-Advanced: A Practical Systems Approach to Understanding 3GPP LTE Releases 10 and 11 Radio Access Technologies” Academic Press Elsevier, USA, 2014, pp-708-709.

1-5

2.

Authors: Ashoka Wilson Dsouza, Ismail B

Paper Title: Mutual Fund Rating Prediction using Proportional Odds Logistic Regression with Imbalanced

Class

Abstract: Mutual funds ratings given by rating agencies, are very popular and helps new/first time investors to

select and invest in funds based on the ratings a fund takes without going through the detailed portfolio.

However sometimes these ratings could be biased or incorrect or in favor of specific fund and it could affect an

investor decision. New investors face a lot of problems while investingand choosing mutual funds due to poor

professional advice and lack of right tools and resources to assess a funds true performance. To overcome the

problem of incorrect rating and to help an investor to choose the funds wisely using machine learning, we have

attempted to predict the rating and classify mutual funds using proportional odds logistic regression which

classifies funds intorating classes from 1 to 5 with 5 being the high rated fund and 1 being the low rated fund.

While some prior studies have suggested methods of using clustering to classify based on performances using

Supervised/Unsupervised learning, this paper deals with supervised learning forpredicting the ratings using the

mutual fund financial ratios and also handles imbalanced classes.To handle imbalance class problem in a multi-

class setting, we propose a new class balancing hybrid methodology of using EM and Gauss-Smote sampling

that significantly improves the rating prediction.

Keywords: Classification, Gauss-Smote, Imbalanced Classes, Mutual Fund Rating, Proportional Odds Model

6-10

References:

1. A.Astha, V.L.Herna & P. Eric, (2015), “SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based

Undersampling”, KDIR, SciTePress 226-234.

2. Brant, R. (1990) “Assessing proportionality in the proportional odds model for ordinal logistic regression”. Biometrics, vol. 46, 1171–1178.

3. Chawla, N., Bowyer, K, Hall and Kegelmeyer, W. P. (2002). “Smote: synthetic minority over-sampling technique”, Journal of artificial intelligence research, vol. 16(1):321-357.

4. D. Acharya, G. Sidana, "Classifying mutual funds in India: Some results from Clustering", Indian Journal of Economics and

Business, vol. 6, no. 1, pp. 71-79, 2007.

5. F. Pattarina, S. Paterlini, T. Minerva (2014), "Clustering financial time series: an application to mutual funds style analysis",

Computational Statistics & Data Analysis, vol. 47, 353-372.

6. Hereil, Pierre, M, Philippe, M, Nicolas & Roncalli, Thierry. (2010). “Mutual Fund Ratings and Performance Persistence”. SSRN

Electronic Journal.

7. Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), 373-380.

8. Jaime, Cardoso and Joaquim. d. Costa, (2007), “Learning to classify ordinal data: the data replication method,” Journal of Machine Learning Research, vol. 8, 1393–1429.

9. Lee, Hansoo & Kim, Jonggeun & Kim, S. (2017). “Gaussian-Based SMOTE Algorithm for Solving Skewed Class Distributions”.

International Journal of Fuzzy Logic and Intelligent Systems. 17. 229-234.

10. Lisi, Francesco & Otranto, Edoardo. (2008). “Clustering Mutual Funds by Return and Risk Levels”. Mathematical and Statistical

Methods for Actuarial Sciences and Finance. Vol 10. 978-988

11. Marathe, Achla & Shawky, Hany. (1999). “Categorizing mutual funds using clusters”. Advances in Quantitative Analysis of

Finance and Accounting. 7.

12. Michael C. Jensen, (1968), “The Performance of Mutual Funds in the period 1945-1964’, The Journal of Finance, 389-416

13. McCullagh P, (1980),“Regression models for ordinal data,” Journal of the Royal Statistical Society, Series B, vol. 42, Issue no. 2,

109– 142.

14. Dhume, P.S.Shenvi and Prof. B. Ramesh (2011), "Performance Analysis of Indian Mutual Funds with a Special Reference to

Sector Funds", The Indian Journal of Commerce, Vol. 64, No. 9.

15. S. Takumasa, M. Tohgoroh, Mutoh, Atsuko, Inuzuka, Nobuhiro. (2015). “Clustering Mutual Funds Based on Investment Similarity”. Procedia Computer Science.60.881-890

16. T, Grigorios & K, Ioannis. (2009), “Multi-Label Classification: An Overview”. International Journal of Data Warehousing and Mining. Vol 3. 1-13.

3.

Authors: Ganesh Pai, M Sharmila Kumari

Paper Title: Cumulative Mean Intensity Differential Transition Algorithm for Edge Detection

Abstract: Edge Detection plays a vital role in machine vision applications and thereby variety of edge detection

algorithms being developed over time for both grey scale and colour images. In this paper, a new technique for

edge detection called cumulative mean intensity differential transition algorithm (CuMIDT Algorithm) is

proposed. This approach focuses on learning variations in the local pixel intensities and predicting the possible

edge when the intensity deviation goes out of the stipulated window area. Ramps at the edge boundaries and

zero crossing are addressed using differential transition model. Experimentation are done on standard FDDB

dataset and real dataset. It is observed that the proposed approach gives better results when compared to the

recently proposed novel edge detection algorithms.

Keywords: Edge Detection, CuMIDT, Differential transition model.

References:

1. R. C. Gonzalez, R. E. Woods, Digital Image Processing, 3rd edition, Pearson Education.

2. J Canny, “A computational approach to edge detection.” IEEE Trans. PAMI (1986), 8, n ~ 6, pp. 679-698.

3. Qiucheng Sun, YueqianHou, Qingchang Tan, A subpixel edge detection method based on an arctangent edge model, Optik 127

(2016) 5702–5710

4. AssmaAzeroual, Karim Afdel, Fast Image Edge Detection based on Faber Schauder Wavelet and Otsu Threshold, Heliyon 3

(2017), https://doi.org/10.1016/j.heliyon.2017.e00485

5. EserSert, DeryaAvci, A new edge detection approach via neutrosophy based on maximum norm entropy, Expert Systems With

Applications 115 (2019) 499–511

6. Shaohu Peng et al., Subpixel Edge Detection Based on Edge Gradient Directional Interpolation and Zernike Moment, 2018

International Conference on Computer Science and Software Engineering, 2018

7. Le Wang, Li Zou, Shengmei Zhao, Edge detection based on subpixel-speckle-shifting ghost imaging, Optics Communications

407 (2018) 181–185

8. Changbao Wen et al., Edge detection with feature re-extraction deep convolutional neural network, J. Vis. Commun. Image R. 57

(2018) 84–90

9. Xiaowei Hu, Yun Liu, Kai Wang, Bo Ren, Learning Hybrid Convolutional Features for Edge Detection, Accepted Manuscript in

Neurocomputing, 2018

10. Lucia Romani, Milvia Rossini, Daniela Schenone, Edge detection methods based on RBF interpolation, Accepted Manuscript in

Journal of Computational and Applied Mathematics, 2018

11. Luyang Wang, Yuan Shen, Houde Liu, ZhenhuaGuo, An accurate and efficient multi-category edge detection method, Cognitive Systems Research 58 (2019) 160–172

11-17

4.

Authors: Hemalatha N, Anusha T A, Asha Nair

Paper Title: Identification of Plant disease in leaves, using Deep Neural Networks

Abstract: Plant diseases have become a concern as they can lead to a significant reduction in both the quality

and quantity of agricultural products.Immediate identification of plant diseases is a key research topic as it can

prove useful in the monitoring of large crop fields and thus automatically identify the signs of pathogens as soon 18-21

as they appear on plant leaves. The proposed efficient algorithm could successfully identify and recognize the

diseases under investigation and model could achieve an accuracy of 95.18.

Keywords: Tomato leaf diseases, Potato leaf diseases, Pepper leaf Diseases.

References:

1. A.K Rangarajan, R. Purushothaman, and A. Ramesh. “Tomato crop disease classification using pre-trained deep learning

algorithm”. Procedia computer science., vol 133, 2018, pp.1040-1047. 2. V. Singh, and A.K. Misra. “Detection of plant leaf diseases using image segmentation and soft computing techniques”.

Information processing in Agriculture, vol. 4(1), 2017, pp.41-49.

3. S. Arivazhagan, R. N. Shebiah, S. Ananthi, and S.V. Varthini, 2013. “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features.”, Agricultural Engineering International: CIGR Journal, vol 15(1),

pp.211-217.

4. J. Amara, B. Bouaziz, and A. Algergawy, 2017. “A Deep Learning-based Approach for Banana Leaf Diseases Classification.” In BTW (Workshops) (pp. 79-88).

5. A. Fuentes, S. Yoon, S. Kim and D. Park, 2017. “A robust deep-learning-based detector for real-time tomato plant diseases and

pests recognition”. Sensors, 17(9), p.2022. 6. S. Sladojevic, M. Arsenovic, A Anderla,, D. Culibrk, and D. Stefanovic. “Deep neural networks-based recognition of plant

diseases by leaf image classification”. Computational intelligence and neuroscience, 2016.

5.

Authors: Hemalatha N, Nausheeda B.S, Athul K.P, Navaneeth

Paper Title: Detection of Skin Cancer using Deep CNN

Abstract: Development of abnormal cells are the cause of skin cancer that have the ability to attack or spread to

various parts of the body. The skin cancer signs may include mole that has varied in size, shape, color, and may

also haveno –uniform edges, might be having multiple colours, and would itch orevn bleed in some cases. The

exposure to the UV-rays from the sun is considered to be accountable for more than 90% of the Skin Cancer

cases which are recorded.In this paper, the development of a classificiation system for skin cancer, is discussed,

using Convolutional Neural Network which would help in classifying the cancer usingTensorFlow and Keras as

Malignantor Benign. The collected images from the data set are fed into the system and it is processed to classify

the skin cancer. After the implementation the accuracy of the Convolutional 2-D layer system developed is

found to be 78%.

Keywords: Skin Cancer, convolutional neural network, keras, TensorFlow, Benign, Malignant.

References:

1. Ho Tak Lau, Adel Al-Jumaily, “Early Detection of Skin Cancer: Study Based on Neural Network Classification”.

2. L Chen. et al, "A. L. 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs"

3. Yunzhu Li, et al,"Skin Cancer Detection and Tracking"

4. RahimehRouhia, Mehdi Jafarib, ShohrehKasaeic, PeimanKeshavarziana, ”Benign and malignant breast tumors classification

based on region growing and CNN segmentation Skin Cancer Detectionand Tracking Using Data Synthesisand Deep Learning”.

5. www.kaggle.com

6. Tri-Cong Pham, Chi-Mai Luong Muriel Visani, and Van-Dung Hoangwh “Deep CNN and Data Augmentation for Skin L`esion

Classification”.

7. Long, J, et al,"Fully convolutional networks for semantic segmentation”.

22-24

6.

Authors: Mani Bushan Dszoua, Manjaiha D.H.

Paper Title: Energy and Congestion Aware Multipath Routing in MANET

Abstract: An interconnection of wireless nodes in motion is called as Mobile AdHoc Network (MANET). One

of the problems facing MANET is the route failure due to dynamic movement of nodes. Route failure leads to

frequent path search and extra effort for maintaining existing path. An effective routing protocol should choose

an elite path for dispatching data and consume less resources. Ad-hoc On-demand Multipath Distance Vector

(AOMDV) protocol can sustain more than one path between the communicating nodes and switches between

them, whenever communication fails over selected path. This way, it reduces the effort of discovering new path,

whenever an existing path fails. However, while choosing alternative paths the protocol only considers the hop

count as a deciding factor and it does not take into consideration the energy associated with node nor the

congestion along the chosen path. In this paper, we consider both residual energy and active load while selecting

path for communication. Performance of both protocols are tested on NS2 simulator. It was found that, the

enhancement does provide an improvement in performance than the existing protocol.

Keywords: AOMDV, Congestion, Energy, Cost function, ECAOMDV, Power Factor, Residual Energy,

Routing, MANET.

References: 1. V T ajne, "MultipathNode-DisjointRoutingProtocol to Minimize End to End Delay and Routing Overhead for Manets,"

International Journal of Engineering Research and Applications, vol. 3, no. 4, August 2013.

2. Yumei Liu, Lili Guo, Huizhu Ma and Tao Jiang, "Energy efficient on demand multipath ng protocol for hop ad hoc networks," in

ISSSTA-08, IEEE 10th InternationalsymposiumonSpreadspectrum and applications, Bologna, Italy, August

3. L en and YonghongKuo, "Multipath Routing Protocol for Networks Lifetime Maximization in Ad-Hoc Networks. Proc. of the 5th

International Conference on Wireless Communications"Networking and Mobile Computing (WiCom '09), pp. 1- 1

4. May ho Aye and A ye Moe A ng,"A Modified Energy Constrained Protocol based AO MDV for MobileAd-HocNetworks,"

25-29

International Journal of Scientific Engineering and Technology Research, vol. 3, November 2014.

5. A. P. Patil, B. V. Chandan, S. Aparna and R. Greeshma, "An improved energyefficient AODV routing protocol for MANETs,"

Eleventh International Conference on Wireless and Optical Communications O 1- 1

6. O A F z M Ob P D - M - path Routing Protocol for Ad Hoc Networks," 2014 International Conference on Computer, Information

and Telecommunication IT 1-6 1

7. K ka Khan and Wayne dridge, "Energy Aware Ad Hoc On-Demand Multipath Distance Vector Routing," International Journal of

Intelligent Systems and Applications (IJISA),2015.

8. Sivaraman T. and Karthikeyan E., "EE-BWA-AOMDV: Energy A O -demand Multipath Routing protocol for Mobile Ad hoc Networks," International Journal of Computer Application, vol. 6, no. 2, pp. 85-99, 2016.

9. K. Ragunathan and T. Kathiravelu, "A - count and time-based MANETrouting protocol," 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1-6 16

10. A. T a, R. A our, M. Uddin, M. Abdelhaq and T. Saba, "Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc

Network Using the Fitness Function," IEEE Access, vol. 5, pp. 10369-10381, 1

11. AlhamaliMasoudAlfrgani Ali, RaghavYadav and HariMohan Singh, "Congestion Control Technique for Wireless Networks," IOSR Journal of Computer Engineering(IOSR-JCE), vol. 16, no. 2, Ver. II, pp. 31-33, M -A 1

12. Anju and SugandhaSingh, "Modified AODV for Congestion Control in MANET," International Journal of Computer Science and

Mobile Computing v 6 -1 1 - 1

13. Juan-Carlos C and Dongkyun Kim, "Investigating Performance of Power-aware Routing Protocols for Mobile Adhoc Networks,"

in International Mobility and Wireless Access Workshop, Fort Worth, TX, A

14. Diwan B. and Sumalatha M.R., "Transmission and Reception Based Relay Deployment for Database Transactions in MANETs,"

in Advancesin Computing,Communication and Control. ICAC32013. Communications in Computer and Information Science, vol 361., H b 13

15. M Bheemalingaiah, MM. Naidu, D. Sreenivasa Rao and P.Vishvapathi,"Energy Aware On-Demand Multipath Routing Protocolin Mobile Ad Hoc Networks," IRACST - International Journal of Computer Networks and Wireless Communications

(IJCNWC), vol. 6, no. 5, pp. 14-31, 16

16. M. Tekaya, N. Tabbane and S. Tabbane, "Multipath routing mechanism with load balancing in ad hoc network," in The 2010

International Conference 6 - 1

17. M.K.Marina and S.R.Das, "On-Demand multipath distance vector routing in ad hoc networks," in 9th

IEEEInternationalConference on Network Protocols (ICNP), Riverside, CA, USA, 1 1 - 3

18. Y. HaroldRobinson, E. Golden Julie, KrishnanSaravanan, RaghvendraKumar and Le Hoang Son, "FD-AOMDV: fault-tolerantdisjointad-hocon-demand multipath distance vector routingalgorithmin mobile ad-hoc networks," Journal of Ambient

Intelligence and Humanized Computing, Springer Berlin Heidelberg, pp. 1-18, 1

19. Dipika Sarkar, SwagataChoudhury, AbhishekMajumder. "Enhanced-Ant-AODVfor optimal route selection in mobile ad-hoc

network",Journalof King Saud University - Computer and Information Sciences, 2018

20. S. M. Benakappa, M. Kiran. "An energy-aware node disjoint multipath routing protocol for MANETs with dynamictransmission

range adjustment",2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques(ICEECCOT),2017

7.

Authors: Mohith Rajendra, Susobhit Panigrahi, Rashmi Joyappa Kand Shreya Sridhar

Paper Title: PyTorch YOLOv3 Object Detection for Vehicle Identification

Abstract: Detecting real-world vehicle objects captured from car-mounted cameras requires manual labelling

of video images. Previous vehicle object detection papers such as the winners of the 2018 AI City Challenge [1]

used a training set of over 4,500 hand labelled images. In this paper, we attempt to automate this task by

applying transfer learning to a YOLOv3 model trained on Imagenet and then re-trained on a set of stock car

images and a small subset of hand labelled images taken from front-mounted dashboard camera videos. The

mean Average Precision (mAP) of the validation set is used to determine the effectiveness of model vehicle

classification. There is a significant variance issue between the validation and training set because the video

images are taken in 1) various weather and lighting conditions and 2) the stock images have different image

perspectives. The experimental results demonstrate that the YOLOv3 model can reach an overall 16.07% mAP

after 60 epochs of training and can identify classes of vehicles that had few training examples in the dataset.

Keywords: Object detection, image processing, pytorch, YOLOv3, R-CNN, Fast R-CNN, Faster R-CNN, deep

learning, mAP, IOU.

References:

1. Z. Tang, G. Wang, H. Xiao, A. Zheng, J.N. Hwang. “Single-camera vehicle tracking and 3D speed estimation based on fusion of

visual and semantic features”. 2018 AI City Challenge.

2. J. Sang, Z. Wu, P. Guo, H. Hu, H. Xiang, Q. Zhang, B. Cai. “An Improved YOLOv2 for Vehicle Detection”. Sensors December

2018.

3. ]J. Redmon, A. Farhadi. “YOLOv3: An Incremental Improvement”, University of Washington. 2018.

4. NexarNEXETdataset. https://www.getnexar.com/challenge-2/.

5. Stanford cars dataset. https://ai.stanford.edu/~jkrause/cars/car_dataset.html.

6. J. Hui. “Real-time Object Detection with Yolo, YOLOv2, and now YOLOv3”. www.medium.com. 2018.

7. R. Girshick, J. Donahue, T. Darrell, J. Malik. “Rich feature hierarchies for accurate object detection and semantic segmentation”. 2014 IEEE Conference on Computer Vision and Pattern Recognition, June 2014. p 580-587.

8. R. Girshick. “Fast R-CNN”. 2015 IEEE Conference on Computer Vision and Pattern Recognition, December 2015. p 1440-1448.

9. S. Ren, K. He, R. Girshick, J. Sun. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, NIPS 2015.

10. S. Azam, A. Rafique, M. Jeon. “Vehicle pose detection using region based convolutional neural network”. International

Conference on Control, Automation, and Information Sciences (ICCAIS). October 2016. p 194-198

11. J. Redmon, S. Divvala, R. Girshick, A. Farhadi. “You only look once: Unified, real-time object detection”. June 2016. p 779-788

12. J. Redmon, A. Farhadi. “YOLO9000: Better, Faster, Stronger”. IEEE Conference on Computer Vision and Pattern Recognition

(CVPR). July 2017. p 6517-6525.

30-32

8. Authors: Neenu Daniel, A. Anitha

Paper Title: Detection of Face Spoofing using Color Texture and Edge Features

Abstract: The wide scale use of facial recognition systems has caused concerns about spoofing attacks.

Security is essential requirement for a face recognition system to provide reliable protection against spoofing

attacks. Spoofing happens in situations where someone tries to behave as an authorized user to obtain illicitly

access the protected system to gain advantage over it. In order to identify spoofing attacks, face spoofing

detection approaches have been used. Traditional face spoofing detection techniques are not good enough as

most of them focus only on the gray scale information and discarding the color information. Here a face

spoofing detection approach with color texture and edge analysis is presented. The approach for investigating the

texture of input images, Local binary pattern and Edge Histogram descriptor are proposed. Experiments on a

publicly available dataset, Replay attack, showed excellent results compared to existing works.

Keywords: Face Recognition, Color texture analysis, Spoofing attacks, Spoofing detection.

References:

1. Z.Boulkenafet, J. Komulainen, A. Hadid, "Face spoofing detection using color texture analysis", IEEE Transactions on.

Information. Forensics Security, Vol. 11, No. 8,(2016), pp: 1818-1830.

2. D. Wen , H. Han, and A. K. Jain, “Face spoof detection with image distortion analysis,” IEEE Transactions on. Information and.

Forensics Security, Vol. 10, No. 4,(2014),pp: 746–761.

3. P. P. K. Chan et al., “Face liveness detection using a flash against 2D spoofing attack,” IEEE Trans. Inf. Forensics Security, Vol. 13, No. 2, (2017), pp: 521–534B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished.

4. De Souza, G.B.; Da Silva Santos, D.F.; Pires, R.G.; Marana, A.N.; Papa, J.P.,” Deep texture features for robust face spoofing detection.” IEEE Transactions on Circuits and Syst. II Exp.ress Briefs ,Vol.64,No.2,(2017), pp: 1397 – 1401.

5. X. Zhao, Y. Lin, and J. Heikkila, “Dynamic texture recognition using ¨ volume local binary count patterns with an application to

2d face spoofing detection,” IEEE Transactions on Multimedia,Vol20,No.3(2018).

6. T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," in

IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, Dec. 2006.

7. C. S. Won, D. K. Park, and S. J. Park, Efficient use of MPEG-7 edgehistogram descriptor, ETRI J., vol.

24, no. 1, pp. 23-30, 2002C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.

8. I. Chingovska, A. Anjos, S. Marcel, "On the effectiveness of local binary patterns in face anti-spoofing", Proc. IEEE Int. Conf.

Biometrics. Special Interest Group, pp. 1-7, 2012-Sep

9. S. Tirunagari, N. Poh, D. Windridge, A. Iorliam, N. Suki, and A. T. Ho,“Detection of face spoofing using visual dynamics,”

IEEE Transactions on Information Forensics Security, Vol. 10, No. 4, (2015), pp: 762–777.

10. A. Pinto, H. Pedrini, W. R. Schwartz, A. Rocha, "Face spoofing detection through visual codebooks of spectral temporal cubes",

IEEE Trans. Image Process.,

33-37

9.

Authors: Rajashree S, Sheetal V A, Soman K S, Bhuvankumar P

Paper Title: Digital Voting System as Internet of Things Application

Abstract: The objective for the efficient functioning of the Indian democracy is purely dependent on the

decisions made by the citizens of our country. To avoid duplicate or illegal votes we need a secure system which

uniquely identifies our citizen. In India AADHAR uniquely identifies the citizens of INDIA by their thumb

impression and also provides the other details like Date of birth, address, gender, father’s name, Spouse details

etc. The election process is carried out in 3 steps Creation of voter list, actual voting process, and counting of

votes. Creation of voter list can be done by database which is efficient to store big data with the person’s name

and his AADHAR number. In actual voting process verification can be done by using fingerprint recognition

and votes should be stored depending on ward numbers. Counting is the last process which can be done very

easily if previous steps are digitized. In the world of Internet of things a voter should be able to cast his vote

from anywhere by validating his credentials. This paper describes a voting system with 3 possible ways for voter

to cast his vote.

Keywords: Digital election system, Aadhar card, voter list, IoT Application.

References:

1. Shah, P.M.a.D.P., Machine to Machine Metamorphosis to the IOT. Ausjournal,. vol. 1, (no. 1, pp. 31-34 ): p. pp. 31-34 2. Manjunath, P., M. Prakruthi, and P.G. Shah. IoT Driven with Big Data Analytics and Block Chain Application Scenarios. in 2018

Second International Conference on Green Computing and Internet of Things (ICGCIoT). 2018.

3. Bhuvanapriya, R., et al. Smart voting. in 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). 2017.

4. Awathankar, R.V., R.D. Raut, and S. Rukmini. Ad-hoc network based smart I-voting system: An application to cognitive radio

technology. in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). 2016.

5. N. Kavitha, S., K. Shahila, and S.C. Prasanna Kumar, Biometrics Secured Voting System with Finger Print, Face and Iris Verification. 2018. 743-746.

6. Lakshmi, C.J. and S. Kalpana. Secured and transparent voting system using biometrics. in 2018 2nd International Conference on

Inventive Systems and Control (ICISC). 2018.

7. Madhuri, B., et al., Secured Smart Voting System using Aadhar. 2017. 1-3.

8. Patil, P.S., et al. E-Smart Voting System with Secure Data Identification Using Cryptography. in 2018 3rd International

Conference for Convergence in Technology (I2CT). 2018.

9. [cited 2019; Available from: https://github.com/miracl/MIRACL/blob/master/source/mrshs256.c.

10. ; Available from: https://en.wikipedia.org/wiki/Electronic_fingerprint_recognition.

11. Technology, M.S.B.O.B.; Available from: http://www.m2sys.com/blog/fingerprint-scanner/how-to-integrate-fingerprint-scanner-with-web-application/.

12. NewGenApps. Available from: https://www.newgenapps.com/blog/bid/219838/10-steps-to-create-a-successful-mobile-

application.

38-41

10.

Authors: Laveena D’Costa, Ashoka Wilson D’Souza, Abhijith K, Deepthi Maria Varghese

Paper Title: Predicting True Value of Used Car using Multiple Linear Regression Model

Abstract: Predicting the true value of used cars requires lot of analysis. This prediction takes into account

variables such as car model, fuel type, number of owner and so on. In this paper we are applying machine

learning algorithms to determine the true value of cars when selling them to the dealers. We have used multiple

linear regression model by dividing the data into training and test. Vehicle price forecast is both a critical and

significant job, particularly when the car is used and does not come directly from the factory.

Keywords: Multiple Linear Regression, True value.

References:

1. Noor, Kanwal, and Sadaqat Jan. "Vehicle price prediction system using machine learning techniques." International Journal of

Computer Applications 167.9 (2017): 27-31.

2. Pal, Nabarun, et al. "How Much Is My Car Worth? AMethodology for Predicting Used Cars’ Prices UsingRandom Forest."

Future of Information andCommunication Conference. Springer, Cham, 2018.

3. Monburinon, Nitis, et al. "Prediction of prices for used carby using regression models." 2018 5th InternationalConference on

Business and Industrial Research (ICBIR), IEEE, 2018

42-45

11.

Authors: Roshan D Suvaris, S Sathyanarayana

Paper Title: Broken Character Recognition using Connected Components and Convolutional Neural Network

Abstract: Recognizing broken characters in scanned and ancient scanned text document is not easy because the

characters may be broken and unclear. Many researches have been carried to recognize these broken characters.

In this research paper we have described a new broken characters recognition method for English text documents

only. The proposed method uses a hybrid approach which uses connected component concepts and

convolutional neural network to identify the broken characters. The input to the approach is scanned or ancient

text document which contains unclear text that is difficult to recognize and hence our new proposed

methodology will recognize these characters with greater accuracy and it will give the recognized characters to

the user. The projected technique has attained a precision up to 92% in recognition.

Keywords: Connected Components, Convolutional Neural Network, Image Processing.

References:

1. Charles Jacobs, James Rinker, Paul viola and Patrice Y. Simard “Text recognition of Low-resolution Document Images”

International ijournal ion iDocument ianalysis iand iRecognition iICDAR i2005.

2. Michael Droettboom “Correcting broken characters in the recognition of historical printed documents” In iProc. iof iJoint iConf.

ion iDigital iLibraries, ipp.364-366, i2003.

3. Laurence Likforman-Sulem, Marc Sigelle “Recognition of broken characters from historical printed books using Dynamic

Bayesian Networks” International iConference ion iDocument ianalysis iand iRecognition iICDAR i2007.

4. Rakesh Kumar Mandal, N R Manna “Handwritten English character recognition using column-wise segmentation of image matrix” wseas itransaction ion icomputers iIssue i5, iVolume i11, iMay i2012.

5. Dileep Kumar Patel,Manoj Kumar Singh, Sushil Kumar Yadav Tanmoy Som, “Handwritten character recognition using

multiresolution technique and Euclidean distance metric” Journal iof iSignal iand iinformation iprocessing, i2012.

6. Dickson neoh Tze How, Khairul salleh Mohammed Sahari “character recognition of Malaysian vehicle license plate with deep

convolutional neural networks” IEEE iInternational iSymposium ion iRobotics iand iIntelligent iSensors i(IRIS2016) iDecember

i2016.

7. P Rajendra, Rahul Boadh and K sudheer Kumar “Design of a Recognition System automatic vehicle License plate through a

convolutional neural network” International iJournal iof iComputer iApplications, iVolume i177 iNo.3, i2017.

8. XinHao Liu, Kunio kashino, Xiaomeng Wu and Takahito Kawanishi “Scene Text Recognition with CNN classifier and WFST-based word labelling” International iConference ion iPattern iRecognition i(ICPR) i2016.

9. Savitha Choudhary, Sanjay Chichadwani and Nikhil Kumar Singh “Text Detection and Recognition from Scene images using MSER and CNN” International iconference ion iadvances iin ielectronics, iComputer iand iCommunications i(ICAECC i–

i2018).

10. Yejun tang, Akio furuhata, Yanwei Wang, Qian Xu, and liangrui Peng “CNN based transfer learning for historical Chinese

character recognition” IAPR iworkshop ion iDocument ianalysis isystems iIEEE i2016.

11. Nallapareddy Priyanka,Ranju Mandal and Srikanta Pal, “Line and Word segmentaion approach for printed Documents” IJCA ispecial iissue ion iRecent itrends iin iImage iProcessing iand iPattern iRecognition iRTIPPR i2010.

46-49

12.

Authors: Ruban S, Elreena Maria Pinto, Valerie Roselyn Cardozo, Kavya S.

Paper Title: Non-Invasive Prediction Model to Detect Sepsis using Supervised Machine Learning Algorithms

Abstract: Sepsis is a life-threatening disease that causes tissue damage, organ failure and results in the death of

millions of people. Sepsis is one of the highest risky diseases identified globally. A large proportion of these

deaths occur in developing countries due to inaccessibility of hospitals or lack of resources. Blood samples are

taken to confirm sepsis, but it requires the presence of laboratory and is time-consuming. The aim and objective

of this study is to develop a practical, non-invasive sepsis prediction model that can be used to detect sepsis

using supervised machine Learning algorithms. For this retrospective analysis, we used the data available from

Physio-Net database.

Keywords: Sepsis, Prediction model, Physio-Net dataset, Non-invasive.

50-52

References:

1. Martin, G. S. Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes. Expert review of anti-

infective therapy 10, 701–706 (2012).

2. Johnson, T. Pollard, L. Shen, L. Lehman, and M. Feng, “The MIMIC III Clinical Database”,01-Oct2015.[Online].

vailable:https://physionet.org

3. Henry, K. E., Hager, D. N., Pronovost, P. J. & Saria, S. A targeted real-time early warning score (trewscore) for septic shock. Science translational medicine 7, 299ra122–299ra122 (2015).

4. Saeed, M. et al. Multiparameter intelligent monitoring in intensive care ii (mimic-a public-access intensive care unit database.

Critical care medicine 39, 952 (2011). 5. Calvert, J. S. et al. A computational approach to early sepsis detection. Computers in biology and medicine 74, 69–73 (2016).

13.

Authors: Ruban, Vivek, Krithi

Paper Title: Heart Disease Prediction using Machine Learning Models

Abstract: Healthcare has become one of the most important concerns in the world. The cases of heart disease

are increasing on a rapid scale among the people especially among the young generation. We can save the lives

of the people if we could detect the heart disease on/before time, by getting them treated. In this matter artificial

intelligence can be of a great help. Here we have collected a data set and then we have built a prediction model

to detect heart disease based on the various algorithms that are available for machine learning.we have used

Logistic regression, K-NN, SVM, Decision Tree, Random Forest with the accuracy values of K-Neighbors

Classifier (0.956194%), Support Vector Machine (0.9561945%), Decision Tree (0.91050%), Random Forest

Classifier (0.95404%) and Logistic Regression (0.95592%). The best value given by the Machine Learning

model is by Logistic regression followed by K-NN.

Keywords: Heart Disease, Predictive Model, Machine Learning, Artificial Intelligence.

References:

1. 1.TheresaiPrincy,iJ.iThomas,i“HumaniHeartiDiseaseiPredictioniSystemsiUsingiDataiMiningiTechniques”iIEEEiInternationaliCo

nferenceioniCircuit,iPoweriandiComputingiTechnologies,i(ICCPCTi2016i),iDoi:i10.1109/ICCPCT.2016.7530265.

2. 2.HimanshuiSharma,iMiAiRizvi,i“PredictioniofiHeartiDiseaseiusingiMachineiLearningiAlgorithms:iAiSurvey”,iIJRITCC,ivolume:5,iIssue:8,iAugusti2017,iiPP:99i–i103,.

3. 3.RamandeepiKaurietial,i“AiReviewiiHeartiDiseaseiForecastingiPatterniusingiVariousiDataiMiningiTechniques”,iInternationaliJ

ournaliofiComputeriScienceiandiMobileiComputing,iVol.5iIssue.6,iJune-i2016,ipp.i350-354i.

4. BenjaminiEJiet.al,i“HeartiDiseaseiandiStrokeiStatistics-

2018iUpdate:iAiReportiFromitheiAmericaniHeartiAssociationi2018”,iMari20;i137(12):ie67-e492. doi:i10.1161/iCIR.0000000i000000558i.iEpubi2018iJani31.i

5. 5.AbhayiKishoreietial,”iHeartiAttackiPredictioniUsingiDeepiLearning”,iInternationaliResearchiJournaliofiEngineeringiandiTech

nologyi(IRJET)ie-ISSN:i2395-0056iVolume:i05iIssue:i04,iApr-2018i.iPP:4420-4423.

6. M.NikhiliKumar,iK.V.SiKoushik,iK.Deepak,i“PredictioniHeartiDiseasesiusingiDataiminingiandimachineilearningialgorithmsian

ditools”,iInternationaliJournaliofiScientificiResearchiiniComputeriScience,iEngineeringiandiInformationiTechnology,iIJSRCSEITi,Volumei3i,iIssuei3i,iISSNi:i2456-3307,iAprili2018.

53-57

14.

Authors: Sreenivasa B.L, S Sathyanarayana

Paper Title: VMS Mean and FFD VM Allocation Algorithm for Cloud Datacenter

Abstract: The datacenter power consumption is increasing speedily, because of the scalable and dynamic

provisioning of resources to the remote consumers. Not efficiently utilizing the resource of host machine is the

main reason for increase in power consumption. By efficiently placing the number of virtual machine in fewer

active host and shutdown the inactive host could reduce the consumption of power in datacenter. In this paper

VMs Mean and FFD Bin Packing Virtual Machine Placement Algorithm is proposed.FFD preprocess VM’s and

Host’s by sorting all the VM’s by MIPS and Host’s by their power. To place the VM’s in host machine VMs

Mean method is used which finds the lowest CPU utilizing VM. The main objective is to increase the host

machine utilization by efficiently placing the VM’s into few active Host’s and also reduced the power

consumption. To test this algorithm CloudSim Toolkit is been used. The parameter such as EC, SLAV, PDM,

ESV, VM migration and SLATAH is used as a metric to evaluate the Algorithm efficiency.

Keywords: Energy Consumption (EC), SLA Violation (SLAV), performance degradation due to migration

(PDM), Energy SLA Violation (ESV), VM migration and SLA Violation Time per active host (SLATAH), First

Fit Decreasing (FFD), Virtual machine (VM).

References:

1. Garima iBatra, iHarshita iSingh, iIshu iGupta, iAshutosh iKumar iSingh,“Best iFit iSharing iand iPower iAware i(BFSPA)

iAlgorithm ifor iVMPlacement iin iCloud iEnvironment”, i978-15090-6403-8/17/$31.00 i©2017 iIEEE.

2. Zhou iZhou, iZhigang iHu, iand iKeqin iLi, i“Virtual iMachinePlacement iAlgorithm ifor iBoth iEnergy-Awareness iand iSLAViolation iReduction iin iCloud iData iCenters”, iHindawi iPublishingCorporation iScientific iProgramming iVolume i2016,

iArticle iID5612039, i11 ipages ihttp://dx.doi.org/10.1155/2016/5612039

3. Jing iV. iWang_, iNuwan iGanganath, iChi-Tsun iCheng, iand iChi iK.Tse, i“A iHeuristics-based iVM iAllocation iMechanism

ifor iCloudData iCenters”, i2017 iIEEE iInternational iSymposium ion iCircuitsand iSystems i(ISCAS), iBaltimore, iMD, iUSA, i2017, ipp. i1-4 iisavailable iat ihttp://dx.doi.org/10.1109/ISCAS.2017.8050470

4. Aneeba iKhalil iSoomro, iMohammad iArshad iShaikh, iHameedullah iKazi, i“ iFFD iVariants ifor iVirtual iMachine iPlacement

iin iCloud iComputing iData iCenters”, i(IJACSA) iInternational iJournal iof iAdvanced iComputer iScience iand iApplications,

iVol. i8, iNo. i10, i2017.

58-62

5. Pradeep iKumar, iDilbag iSingh, iAnkur iKaushik, i“Power iand iData iAware iBest iFit iAlgorithm ifor iEnergy iSaving iin

iCloud iComputing”, i(IJCSIT) iInternational iJournal iof iComputer iScience iand iInformation iTechnologies, iVol. i5 i(5) i, i2014, i6712-6715.

6. Anton iBeloglazov ia,∗, iJemal iAbawajyb, iRajkumar iBuyyaa, i“Energy-aware iresource iallocation iheuristics ifor iefficient

imanagement iof idata icenters ifor iCloud icomputing”, i0167-739X/$ i– isee ifront imatter i© i2011 iElsevier iB.V. iAll irights

ireserved. idoi:10.1016/j.future. i2011.04.017

7. L. iShi, iJ. iFurlong, iand iR. iWang. i“Empirical ievaluation iof ivector ibin ipacking ialgorithms ifor ienergy ie_cient idata icenters”, iIn iIEEE iSymposium ion iComputers iand iCommunications, ipages i9{15, i2013.

8. Jay iH. iSheth, iProf. iKrunal iN. iVaghela, i“Technical iReview ion iLive iVirtual iMachine iMigration iTechniques ifor

iEucalyptus iCloud”, iJay iH. iSheth iInt. iJournal iof iEngineering iResearch iand iApplications, iISSN i: i2248-9622, iVol. i5, iIssue i3, i( iPart i-2) iMarch i2015, ipp.50-52.

9. Server iVirtualization: iA istep itoward iCost iEfficiency iand iBusiness iAgility, iAvanade iperspective, i2009.

10. Dabiah iAhmed iAlboaneen1, iBernardi iPranggono iand iHuaglory iTianfield,“ iEnergy-aware iVirtual iMachine iConsolidation

ifor iCloud iData iCenters2014 iIEEE/ACM i7th iInternational iConference ion iUtility iand iCloud iComputing.

11. Nicolo iMaria iCalcavecchia, iOfer iBiran, iErez iHadad, iand iYosef iMoatti. iVM iplacement istrategies ifor icloud iscenarios.

iIn iIEEE i5th iInternational iConference ion iCloud iComputing i(CLOUD), ipages i852{859, i2012.

15.

Authors: Sreenivasa B.L, S Sathyanarayana

Paper Title: VMS Mean and Minimum Utilization VM Selection Algorithm for Cloud Datacenter

Abstract: Cloud datacenter provides the various resources on-demand for the end consumers on pay-per-use

through internet connection. The request for computing resources has increased which leads to the growth of

power consumption inside datacenters. The major challenge is how to balance the profits by decreasing the

consumption of energy and violation of SLA. VM Consolidation techniques helps VMs to migrate between the

host machines in reducing energy consumption, but again which VM to select for migration between the host

machine is again the challenging task. In this paper VMs Mean and Minimum Utilization VM Selection

Algorithm is proposed.To select the suitable VM’s in host machine for the migration, VMs Mean method is used

which finds the lowest CPU utilizing VM. To test this algorithm CloudSim Toolkit is been used. The parameter

such as EC, SLAV, PDM, ESV, VM migration and SLATAH is used as a metric to evaluate the Algorithm

efficiency.

Keywords: Energy Consumption (EC), SLA Violation (SLAV), performance degradation due to migration

(PDM), Energy SLA Violation (ESV), VM migration and SLA Violation Time per active host (SLATAH),

Minimum Utilization (MU), Virtual machine (VM).

References:

1. SuhibBaniMelhem, Anjali Agarwal, NshithGoel, MarziaZaman. ” Minimizing Biased VM Selection in Live VM Migration”,

978-1-5386-1115-9/17/$31.00@2017 IEEE.

2. SuhibiBaniiMelhem,iAnjaliiAgarwal,iNshithiGoel,iMarziaiZaman.i”iMinimizingiBiasediVMiSelectioniiniLiveiVMiMigration”,i

978-1-5386-1115-9/17/$31.00@2017iIEEE.

3. HananiA.iNadeem,iMaiiA.iFadeliandiHananiElazhary,i“iPriority-

AwareiVirtualiMachineiSelectioniAlgorithmiiniDynamiciConsolidation”,i(IJACSA)iInternationaliJournaliofiAdvancediComputeriScienceiandiApplications,iVol.i9,iNo.i11,i2018.

4. 4.VershaiLodhi,iProf.iSarveshiRai,iGaneshiKumariVishwakarma,i“EnhancediMinimumiUtilizationiVMiSelectioniMechanismifo

riClouds”,i(IJCSIT)iInternationaliJournaliofiComputeriScienceiandiInformationiTechnologies,iVol.i6i(3)i,i2015,i2975-2977.

5. 5.SonaliNamdev,iProf.iNeelamiSain,iProf.iAnjuliKiSiRai,i“iImprovediMinimumiMigrationiTimeiVMiSelectioniPolicyiforiCloudiDataiCenter”,iInternationaliJournaliofiApplicationioriInnovationiiniEngineeringi&iManagementi(IJAIEM),iwww.ijaiem.orgiE

mail:ieditor@ijaiem.org,iVolumei4,iIssuei4,iAprili2015iISSNi2319i–i4847

6. ZhouiiZhou,iiZhigangiiHu,iiandiiKeqiniiLi,ii“VirtualiiMachineiPlacementiiAlgorithmiiforiiBothiiEnergy-

AwarenessiiandiiSLAiViolationiiReductioniiiniiCloudiiDataiiCenters”,iiHindawiiiPublishingiCorporationiiScientificiiProgrammi

ngiiVolumeii2016,iiArticleiiIDi5612039,ii11iipagesiihttp://dx.doi.org/10.1155/2016/5612039

7. 7.MohammadiAlauliHaque,iMonil,iRomasaiQasim,iRasheduriMiRahman,i“IncorporatingiMigrationiControliiniVMiSelectioniStr

ategiesitoiEnhanceiPerformance”,iInternationaliJournaliofiInformationiWebiApplicationsiVolumei6iNumberi4iDecemberi2014.

8. AntoniiBeloglazoviia,iiJemaliiAbawajyb,iiRajkumariiBuyyaa,ii“Energy-

awareiiresourceiiallocationiiheuristicsiiforiiefficientiimanagementiiofiidataiicentersiiforiiCloudiicomputing”,ii0167-739X/$ii–iiseeiifrontiimatterii©ii2011iiElsevieriiB.V.iiAlliirightsiireserved.iidoi:10.1016/j.future.ii2011.04.017

9. 9.L.iShi,iJ.iFurlong,iandiR.iWang.i“Empiricalievaluationiofivectoribinipackingialgorithmsiforienergyie_cientidataicenters”,iIniI

EEEiSymposium ioniComputersiandiCommunications,ipages i9{15, i2013.

10. JayiH.iSheth,iProf.iKrunaliN.iVaghela,i“TechnicaliReview ioniLiveiVirtualiMachineiMigrationiTechniquesiforiEucalyptus

iCloud”,iJayiH.iShethiInt.iJournaliofiEngineeringiResearchiandiApplications,iISSN i: i2248-9622,iVol. i5,iIssue i3,i(iPart i-2)iMarch i2015, ipp.50-52.

11. ServeriVirtualization:iAistepitowardiCostiEfficiencyiandiBusinessiAgility,iAvanadeiperspective, i2009.

12. DabiahiAhmed iAlboaneen1,iBernardiiPranggonoiandiHuagloryiTianfield,“iEnergy-awareiVirtualiMachineiConsolidationifor

iCloudiData iCenters2014iIEEE/ACM i7thiInternationaliConference ioniUtilityiand iCloudiComputing.

13. NicoloiMariaiCalcavecchia,iOferiBiran,iEreziHadad,iandiYosefiMoatti.iVMiplacementistrategiesiforicloudiscenarios.iIniIEEE

i5thiInternationaliConference ion iCloudiComputingi(CLOUD),ipages i852{859, i2012.

63-67

16.

Authors: V. Bala Raju, Ch. Chengaiah

Paper Title: A Novel T-C-T Solar Photovoltaic Array Configurations using Rearrangement of PV Modules with

Shade Dispersion Technique for Enhancing the Array Power

Abstract: Total-Cross-Tied (TCT) solar array configuration has more output power under uniform irradiance

condition (un-shade case) among all conventional solar photovoltaic (SPV) array configurations but reduced

array power under non-uniform irradiance cases (shading cases). To improve the performance of TCT array

configuration under shading cases by using rearrangement or repositioning of existing photovoltaic (PV)

modules in TCT configuration to new optimal locations within a TCT array configuration with shade dispersion

technique. In this rearranged method, repositioning the modules based on puzzle pattern without altering the

68-78

electrical connections among modules in an SPV array. The shading on PV modules are dispersed by changing

the position of PV modules to optimal locations within SPV array so the performance of conventional TCT

configuration will be improved. In this paper proposed an optimal TCT configurations, it requires a minimum

number of electrical connections or ties between array modules and it depends on the shaded modules location in

SPV array and also the proposed method reduces the wiring losses, mismatch losses. For this analysis,

MATLAB/Simulink software is used for modeling and simulation of 6x6 size different rearrangement based

TCT and proposed optimal SPV array configurations under one un-shaded case and fourteen different shading

cases.

Keywords: PV modules, array power, optimal interconnections, wiring losses, Partial shading cases, shaded

modules.

References:

1. Du Bolun, Yang Ruizhen, He Yunze, Wang Feng, Huang Shoudao. Nondestructive inspection, testing and evaluation for si-

based, thin film and multijunction solar cells: an overview. Renew Sustain Energy Rev 2017; 78: 1117e51.

2. Villalva, M.G., Gazoli, J.R., Ruppert Filho, E., 2009. Comprehensive approach to modeling and simulation of photovoltaic

arrays. IEEE Trans. Power Electron. 24(5), 1198–1208.

3. Balato, M., Costanzo, L., Vitelli, M., “Reconfiguration of PV modules: a tool to get the best compromise between maximization

of the extracted power and minimization of localized heating phenomena”, Solar Energy 138, 105–118.

4. M. Z. Shams El-Dein, Mehrdad Kazerani, M. M. A. Salama” Optimal Photovoltaic Array Reconfiguration to Reduce Partial Shading Losses”, IEEE Transactions on Sustainable Energy, Vol. 4, No. 1, January 2013.

5. Ishaque, K., Salam, Z., Taheri, H., 2011. Modeling and simulation of photovoltaic (PV) system during partial shading based on a

two-diode model. Simul. Model. Pract. Theory 19 (7), 1613–1626.

6. Bai, J., Cao, Y., Hao, Y., Zhang, Z., Liu, S., Cao, F., 2015. Characteristic output of PV systems under partial shading or mismatch

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