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Malicious nodes in MANETS can make considerable damage. As basic routing protocols does not provide any strong security against internal attacks nodes, they can easily become part of network and degrade the performance by launching attack.In this work, the main focus is to develop a method of security in communications between ad-hoc network nodes. This proposed model i.e Trust based Multipath Routing Protocol Scheme (TMAODV), firstly calculates transmission cost and then trust factor is calculated on the basis of number of packets forward and dropped. For this a new data structure is added into neighborhood table. Secondly, a different computation factors like Optimal Traffic Ratio, Remaining Energy value and transmission cost is calculated and this data structure is stored by routing table.

MANET,TMAODV, Optimal Traffic Ratio, Transmission Cost, Remaining Energy

[1] Kush, A., Taneja, S., & Sharma, D. (2010). Energy efficient routing for MANET. 2010 International Conference on Methods and Models in Computer Science (ICM2CS-2010).
[2] Guodong, W., Gang, W., & Jun, Z. (2010). ELGR: An Energy-efficiency and Load-balanced Geographic Routing Algorithm for Lossy Mobile Ad Hoc Networks. Chinese Journal of Aeronautics, 23(3), 334-340.
[3] Fareena, N., Mala, A. S., & Ramar, K. (2012). Mobility Based Energy Efficient Multicast Protocol for MANET. Procedia Engineering, 38, 2473-2483.
[4] Jamali, S., Rezaei, L., & Gudakahriz, S. J. (2013). An Energy-efficient Routing Protocol for MANETs: a Particle Swarm Optimization Approach. Journal of Applied Research and Technology, 11(6), 803-812.
[5] Choukri, A., Habbani, A., & Koutbi, M. E. (2014). An energy efficient clustering algorithm for MANETs. 2014 International Conference on Multimedia Computing and Systems (ICMCS).
[6] S.S. Basurra, M. De Vos, J. Padget, Y. Ji, T. Lewis, S. Armour,(2014). Energy Efficient Zone based Routing Protocol for MANETs, Ad Hoc Networks (2014)
[7] CHOUDHURY, D., KAR, D., BISWAS, K. R, SAHA, H. (2015). ENERGY EFFICIENT ROUTING IN MOBILE AD-HOC NETWORKS. 2015 INTERNATIONAL CONFERENCE AND WORKSHOP ON COMPUTING AND COMMUNICATION (IEMCON).
[8] DAS, S. K., & TRIPATHI, S. (2015). ENERGY EFFICIENT ROUTING PROTOCOL FOR MANET BASED ON VAGUE SET MEASUREMENT TECHNIQUE. PROCEDIA COMPUTER SCIENCE, 58, 348-355.
[9] Divya, M., Subasree, S., & Sakthivel, N. (2015). Performance Analysis of Efficient Energy Routing Protocols in MANET. Procedia Computer Science, 57, 890-897.
[10] Patil, M., Naik, S. R., Nikam, V. B., & Joshi, K. K. (2015). Extended ECDSR protocol for energy efficient MANET. 2015 International Conference on Advanced Computing and Communication Systems.
[11] Sara, Z., & Rachida, M. (2015). Energy-Efficient Inter-Domain Routing Protocol for MANETs. Procedia Computer Science, 52, 1059-1064.
[12] Sumathi, K., & Priyadharshini, A. (2015). Energy Optimization in Manets Using On-demand Routing Protocol. Procedia Computer Science, 47, 460-470.
[13] ashkaar, M., & Sharma, P. (2016). Enhanced energy efficient AODV routing protocol for MANET. 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS).
[14] Dodke, S., Mane, P. B., & Vanjale, M. (2016). A survey on energy efficient routing protocol for MANET. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).
[15] Kaliappan, M., Augustine, S., & Paramasivan, B. (2016). Enhancing energy efficiency and load balancing in mobile ad hoc network using dynamic genetic algorithms. Journal of Network and Computer Applications, 73, 35-43.
[16] Kuo, W., & CHU, S. (2016). Energy Efficiency Optimization for Mobile Ad Hoc Networks. IEEE Access, 4, 928-940.
[17] Logambal, R., & Chitra, K. (2016). Energy efficient hierarchical routing algorithm in MANETs. 2016 IEEE International Conference on Advances in Computer Applications (ICACA).
[18] Tiwari, A., & Kaur, I. (2017). Performance evaluaron of energy efficient for MANET using AODV routing protocol. 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT).
[19] Zahary, A., & Ayesh, A. (2010). An analytical review for multipath routing in Mobile Ad Hoc Networks. International Journal of Ad Hoc and Ubiquitous Computing, 5(2), 69.
[20] Savitha, K., & Chandrasekar, C. (2013). An energy aware enhanced AODV routing protocol in MANET. International Journal of Communication Networks and Distributed Systems, 10(3), 233.
[21] Singh, R., & Gupta, S. (2014). EE-AODV: Energy Efficient AODV routing protocol by Optimizing route selection process . International Journal of Research in Computer and Communication Technology, 3(1), 158-163.
[22] Jain, H. R., & Sharma, S. K. (2014). Improved energy efficient secure multipath AODV routing protocol for MANET. 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014).
[23] Kanakala, S., Ananthula, V. R., & Vempaty, P. (2014). Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding. Journal of Computer Networks and Communications, 2014, 1-12.
[24] Jabbar, W. A., Ismail, M., & Nordin, R. (2014). On the Performance of the Current MANET Routing Protocols for VoIP, HTTP, and FTP Applications. Journal of Computer Networks and Communications, 2014, 1-16.
[25] Kaur, N., & Singh, T. (2015). Improving Performance of MANETs using Multi-Criteria Multipath Routing Protocol . International Journal of Computer Applications, 112(8), 36-41.
[26] Agrawal, H., Johri, P., & Kumar, A. (2015). Emerging trends in energy efficient routing protocols. International Conference on Computing, Communication & Automation.
[27] Mohapatra, S. K., Mahapatra, S. K., Kanoje, L., & Behera, S. (2015). A Low Energy consumed routing multipath protocol in MANETS. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).
[28] Peng, S., Chen, Y., Chang, R., & Chang, J. (2015). An Energy-Aware Random Multi-path Routing Protocol for MANETs. 2015 IEEE International Conference on Smart City/SocialCom/Sustain Com (Smart City)
[29] Tong, M., Chen, Y., Chen, F., Wu, X., & Shou, G. (2015). An Energy-Efficient Multipath Routing Algorithm Based on Ant Colony Optimization for Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 11(6), 642189.
[30] Tiwari, S. (2016). An Energy Saving Multipath AODV Routing Protocol In MANET. International Journal Of Engineering And Computer Science.
[31] Lutimath, N. M., L, S., & Naikodi, C. (2016). Energy Aware Multipath AODV Routing Protocol for Mobile Ad hoc Network. International Journal of Engineering Research , 5(4), 790 -991.
[32] Periyasamy, P., & Karthikeyan, E. (2016). End-to-End Link Reliable Energy Efficient Multipath Routing for Mobile Ad Hoc Networks. Wireless Personal Communications, 92(3), 825-841.
[33] Iqbal, Z., Khan, S., Mehmood, A., Lloret, J., & Alrajeh, N. A. (2016). Adaptive Cross-Layer Multipath Routing Protocol for Mobile Ad Hoc Networks. Journal of Sensors, 2016, 1-18.
[34] Jayavenkatesan, & Mariappan, A. (2017). ENERGY EFFICIENT MULTIPATH ROUTING FOR MANET BASED ON HYBRID ACO-FDRPSO . International Journal of Pure and Applied Mathematic, 115(6), 185 -191.
[35] Tareq, M., Alsaqour, R., Abdelhaq, M., & Uddin, M. (2017). Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony. Wireless Communications and Mobile Computing, 2017, 1-14
[36] M.Selladevi1, S. Duraisamy, Survey Paper on Various Security Attacks In Mobile Ad Hoc Network International Journal of Computer Sciences and Engineering, Volume-6, Issue-1 E-ISSN: 2347-2693, 2018.

Nikhat Raza Khan, Sanjay Sharma, P.S. Patheja, "Trust based Multipath Routing Scheme (TMAODV)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.150-158, 2018.

The insufficiency in terms of energy density and efficient operation faced by the renewable energy sources, have resulted in the development of multiple source integrating systems which can do the needful and effectively fill the deficiency gap which are inherent to the previous works in the same field. In the present work, a power electronics based triple source integration system has been designed which utilizes a Solar Photo Voltaic (SPV) system, a Battery Energy Storage (BES) system and an Ultra-Capacitor (UC) bank to supply the load. The designed converter system not only effectively harvests energy from all the sources having different voltage–current characteristics but also ably provide the provision of connecting each source either individually or collectively to feed the load. The converter topology also bears the benefit of bidirectional power flow capability with the added feature of buck operation, boost operation and buck-boost operation. A pulse width modulated controller is designed for controlling the power electronic switches and the entire simulation study is performed in MATLAB / Simulink environment.

Solar Photo Voltaic (SPV), Battery Energy Storage (BES), Ultra-Capacitor (UC), Triple Input Converter (TIC), Hybrid Energy System.

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H. S. Thakur, R. N. Patel, "Triple-Input DC-DC Converter (TIC) Design for PV-Battery-UC Hybrid Power System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.159-167, 2018.

Vision is the way we access, appreciate and interpret the world. Diabetic retinopathy is one of the common diseases which if remain undetected; causes blindness. Micro aneurysm is the first visible sign of Diabetic retinopathy; which appears like a tiny blood droplet on retinal fundus images. The bunches of Micro aneurysms may be examined to indicate the severity of the disease. The incidence of blindness caused by diabetes mellitus reduces by the early detection of MAs. This paper presents a novel approach for the automated detection of DR from fundus images using blood vessel perimeter measurement and Micro aneurysms count. The suggested method correctly identifies Micro aneurism even in poor quality image.

blood vessel; micro-aneurysms; fundus; hemorrhage

[1] Sujith Kumar S B* “ Automatic Detection of Diabetic Retinopathy in Non-dilated RGB Retinal Fundus Images” ,International Journal of Computer Applications (0975 – 888) Volume 47–No.19, June 2012.
[2] Jyotiprava Dash” A Survey on Blood Vessel detection Methodologies in Retinal Images”, 2015 International Conference on Computational Intelligence & Networks, pp 166-171.
[3] Surbhi Sangwan,Vishal Sharma,and Misha Kakkar,” Identification of different stages of diabetic retinopathy”, International Conference on Computer and Computational Sciences (ICCCS),pp.232-237,2015.
[4] www.who.int/mediacentre/factsheets/fs312/en/
[5] Jiri Minar, Marek Pinkava, Kamil Riha“Automatic Extraction of Blood Vessels and Veins using Laplace Operator in Fundus Image”.
[6] N.S Dutta, P. Saha, H. S. Dutta, D. Sarkar “A New Contrast Enhancement Method of RetinalImages in Diabetic Screening System”, 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS),pp 255-260 .
[7] Manjiri B. Patwari, Ramesh R., Yogesh M. Rajput, ” Automatic Detection of Retinal Venous Beading and Tortuosity by using Image processing Techniques”, International Journal of Computer Applications (0975 – 8887) Recent Advances in Information Technology, 2014, pp 27-32.
[8] Sandra Morales, Valery Naranjo, Amparo Navea, and Mariano Alca˜niz, ” Computer-Aided Diagnosis Software for Hypertensive Risk DeterminationThrough Fundus Image Processing”,pp 1757-1763.
[9] Tsuyoshi Inoue, Yuji Hatanaka, Susumu Okumura, “Automated Microaneurysm Detection Method Based on Eigenvalue Analysis Using Hessian Matrix in Retinal Fundus Images”, 35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013,pp5873-5876.
[10] Dr. Pradeep Nijalingappa, Sandeep B. “Machine Learning Approach for the Identification of Diabetes Retinopathy and its Stages”, 2015 International Conference on Applied and Theoretical Computing and Communication Technology pp653-658.
[11] Akara Sopharak “.Automatic Microaneurysms Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Mathematical Morphology Methods”, IAENG International Journal of Computer Science.
[12] S.Chaudhuri, S.Chatterjee, N.Katz, N.Nelson and M.Goldbaum, ”Detection of blood vessels in retinal images using two-dimensional matched filter”, IEEE trans medical imaging,vol.8, no.3,pp.263-269,1989

Renu, Sachin Kumar, Sumita Mishra, Pragya, "Automated Detection of Diabetic Retinopathy through Blood Vessel and Micro-aneurysms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.168-175, 2018.

Clustering is a widely studied problem in a variety of application domains such as neural network and statistics. It is the process of partitioning or grouping a set of patterns into disjoint clusters which show that patterns belonging to the same cluster are same or alike and patterns in different cluster are different. There are many ways to deal with the above problem of clustering. K-means is the simple and effective algorithm in producing good clustering results for many practical applications. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the fast global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Fuzzy C- means is also very popular for fuzzy based data clustering. But all such clustering algorithms are hugely effected by the imbalanced nature of data values. Each data in the dataset has multiple attributes and the value of some attributes may be so large that the importance of other attributes values may be completely ignored during the clustering process. In this paper we proposed a data balancing technique for both fast global k-means and fuzzy c-means algorithm. We balanced the attributes values of each data in such a way that all the attributes get importance during the clustering process.

k-Means, Global k-Means, Fast Global k-Means, Data Streaming

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[8] R. He, W. Xu, J. Sun, and B. Zu, “Balanced k-means algorithm for partitioning areas in large-scale vehicle routing problem,” in Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 03, ser. IITA ’09. IEEE Computer Society, 2009, pp. 87–90. [Online]. Available: http://dx.doi.org/10.1109/IITA.2009.307

Purnendu Das, Bishwa Ranjan Roy, Saptarshi Paul, "Balanced Data Clustering Algorithm for Both Hard and Soft Clustering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.176-183, 2018.

Swarm intelligence is an exploration ground that simulates the mutual behavior in groups of insects or animals. Some algorithms ascending from such models have been proposed to solve a widespread range of difficult optimization problems. Typical swarm intelligence algorithms including Particle Swarm Optimization (PSO), Ant Colony System (ACS), Honey bee mating optimization (HBMO), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), Bat algorithm (BA), and Firefly algorithm, have been proven to be noble methods to address difficult optimization problems under static environments. Maximum SI algorithms have been established to discourse static optimization problems and hence, they can meet on the optimum solution powerfully. Swarm intelligence (SI) is built based on the combined characteristics of self-systematized systems. Furthermore the uses to conventional optimization problems, SI can also be used in monitoring robots and automated vehicles, forecasting social behaviors, improving the telecommunication and computer networks, etc. To be precise, the usage of swarm optimization can be applied to the various fields in engineering.

Particle Swarm Optimization (PSO), Ant Colony System (ACS), Honey bee mating optimization (HBMO), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), Bat algorithm (BA), Firefly algorithm

[1] S. Keerthi, Ashwini K, Vijaykumar M.V, Survey Paper on Swarm Intelligence, International Journal of Computer Applications (0975 – 8887) Volume 115 – No. 5, April 2015
[2] Dr. Ajay Jangra, Adima Awasthi, Vandana Bhatia, A Study on Swarm Artificial Intelligence, U.I.E.T,K.U, India.
[3] Michalis Mavrovouniotis, Changhe Li and Shengxiang Yang, A survey of swarm intelligence for dynamic optimization: algorithms and applications , Swarm and Evolutionary Computation, http://dx.doi.org/10.1016/j.swevo.2016.12.005
[4] Swarm Intelligence Optimization Algorithms and Their Application, WHICEB 2015 Proceedings Wuhan International Conference on e-Business, Association for Information Systems AIS Electronic Library (AISeL).
[5] Particle Swarm Optimization and Firefly Algorithm: Performance Analysis, Bharat Bhushan and Sarath S. Pillai, 978-1-4673-4529-3/12/2012 IEEE
[6] Overview of Algorithms for Swarm Intelligence, Shu-Chuan Chu, Hsiang-Cheh Huang, John F. Roddick1, and Jeng-Shyang Pan, P. Jędrzejowicz et al. (Eds.): ICCCI 2011, Part I, LNCS 6922, pp. 28–41, 2011.© Springer-Verlag Berlin Heidelberg 2011.
[7] Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications, Swagatam Das, Arijit Biswas, Sambarta Dasgupta, and Ajith Abraham, http://www.softcomputing.net/bfoa/chapter.pdf
[8] A survey of swarm intelligence for dynamic optimization: Algorithms and applications, Michalis Mavrovouniotisa, Changhe Lib, Shengxiang Yangc, Preprint submitted to Journal of Swarm and Evolutionary Computation January 12, 2017
[9] https://en.wikipedia.org/wiki/Firefly_algorithm
[10] Journal of electrical engineering, vol. 64, no. 3, 2013, 133–142 comparison of honey bee mating optimization and genetic algorithm for coordinated design of pss and statcom based on damping of power system oscillation by Amin Safari, Ali Ahmadian, Masoud Aliakbar Golkar.
[11] Bat algorithm: Recent advances, Iztok Fister Jr. and Iztok Fister, Xin-She Yang, CINTI 2014, 15th IEEE International Symposium on Computational Intelligence and Informatics, 19–21 November, 2014, Budapest, Hungary.
[12] Artificial bee colony algorithm, its variants and applications: a survey, Asaju la’aro bolaji, Ahamad tajudin khader, Mohammed azmi al-betar and Mohammed A. Awadallah, Journal of Theoretical and Applied Information Technology 20th January 2013. Vol. 47 No.2
[13] https://en.wikipedia.org/wiki/Particle_swarm_optimization

Meghana. L, Jaya. R, "Swarm Intelligence Algorithms - A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.184-188, 2018.

Photogrammetry has evolved as an important technique for generating point clouds from 2D images especially in the absence of direct 3D sensors for acquiring 3D points. It is seen in several image processing applications that the quality of the result depends on several parameters including luminosity, color saturation, and hue. While the technical workflow of the photogrammetry is well defined but the coloring aspects of the image and its effect on the quality of the point cloud generation has been less explored. This paper studies the impact of color adjustments including contrast, brightness, and gamma variation on 3D point cloud reconstruction capability. The result shows that higher contrast above 40% gradually reduces the reconstruction quality in terms of points on the surface, defining vertex set and faces. Similar observations are also true for brightness values. On negative side beyond -15% tonal adjustments, the results degraded faster. Other interesting results and its potential applications have also been cited in this paper. RMSE observations on resulting dense point cloud, faces and control vertices exhibit substantial loss of quality of reconstructed surface for higher values of brightness and contrast. Several future pointers for improving the reconstruction quality have also been proposed.

3D Reconstruction, Photogrammetry, Color Adjustments, Point Cloud

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[7] O. Akcay, R. C. Erenoglu and E. O. Avsar, “The Effect of JPEG Compression In Close Range Photogrammetry”. International Journal of Engineering and Geosciences (IJEG), Vol.2, Issue.1, pp.35-40. 1994
[8] Luhmann, T. Hastedt, H. Tecklenburg, “Modelling Of Chromatic Aberration for High Precision Photogrammetry”. ISPRS Commission V Symposium `Image Engineering and Vision Metrology, Vol.36, Issue.5, pp.173-178, 2006
[9] F. John, L. M. Keny, “Enhancement of Image Resolution on Digital Photogrammetry”. ISPRS Commission V Symposium `Image Engineering and Vision Metrology, Vol.67, Issue.6, pp.741-749, 2001
[10] A. Ballabeni, M. Gaiani, “Intensity histogram equalisation, a colour-to-grey conversion strategy improving photogrammetric reconstruction of urban architectural heritage”, Journal of the International Colour Association, Vol.16, Issue.1, pp.2-23, 2016
[11] H. Cai, “High dynamic range photogrammetry for synchronous luminance and geometry measurement”. Lighting Research and Technology, Vol.45, Issue.2, pp.230-257, 2011
[12] H. Kukkonen, J. Rovamo, K. Tiippana, R. Nsnen, “Michelson contrast, RMS contrast and energy of various spatial stimuli at threshold. Vision Research”, Vol.33, Issue.10, pp.1431-1436. doi:10.1016/0042-6989(93)90049-3

Swati R Maurya, Ganesh M Magar , "Assessing the Impact of Color Adjustments on Photogrammetry-Based 3D Surface Reconstruction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.189-195, 2018.

Recently, reconfigurable digital image processing algorithm has become growing research area in field of real-time embedded system. The edge detection algorithms are one of key area in digital image processing for object recognition or detection. These algorithms are usually implemented in software but it can be also implemented in hardware for special purpose such as high computational speed and good accuracy. This paper describes the Sobel edge detection algorithm has been designed using Hardware Description Language (HDL) and then implemented it on Field Programmable Gate Array (FPGA) devices with an emphasis on the salient features of FPGA technology. The result analysis shows that hardware implementing Sobel edge operator provide higher speed compare to software simulation. The proposed implementation uses a modified architecture which effectively reduces hardware resources. The images are transferred from PC to FPGA device using UART serial communication. The FPGA device processes the given design and result back to the PC. In PC both the results are verified.

Edge Detection, FPGA, Sobel Operator, VHDL,MATLAB

[1] R. Gonzalez and R. Woods, Digital Image Processing, Prentice Hall, 2008.
[2] H. C. Roth, Circuit Design with VHDL. Cambridge, MA: MIT Press, 2004.
[3] W. Wolf, FPGA-Based System Design. Englewood Cliffs, NJ: Prentice- Hall, 2004.
[4] D. Nguyen, D.Halupka, P. Aarabi and A. Sheikholeslami, “Real- Time Face Detection and Lip Feature Extraction Using Field-Programmable Gate Arrays,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, pp. 902-912, 2006.
[5] S. Mittal, S Gupta and S. Dasgupta, “FPGA:An efficient And Promising Platform For Real-Time Image Processing Applications,” Proceedings of National Conference on Research and Development in Hardware & Systems, June 20-21,2008.
[6] J.Wu, J. Sun, and W. Liu, “Design and Implementation of Video Image edge Detection System Based on FPGA,” In Proceedings of 3rd IEEE International Congress on Image and Signal Processing, 2010.
[7] W. He and K. Yuan, “An Improved Canny Edge Detector and Its Realization on FPGA,” In Proceedings of 7th IEEE World Congress on Intelligent Control and Automation, 2008.
[8] M. N. Haque, “Implementation of a FPGA based Architecture of Prewitt Edge detection Algorithm using Verilog HDL,” In Proceedings of Conference on Electronic and Telecommunication, 2010.
[9] Z. Guo, W. Xu and Z. Chai, “Image Edge Detection Based on FPGA,” In Proceedings of Ninth IEEE International Symposium on Distributed Computing and Applications to Business, Engineering and Science, pp. 169-171, 2010.
[10] T. A. Abbasi and M. U. Abbasi, “A novel FPGAbased architecture for Sobel edge detection operator,” International Journal of Electronics, Taylor & Francis, pp.889–896, 2007.
[11] I. Yasri, N. H. Hamid and V. V Yap, “ Performance Analysis of FPGA Based Sobel Edge Detection Operator,” International Conference on Electronic Design, 2008.
[12] A. Nosrat and Y. S. Kavian, “Hardware description of multidirectional fast sobel edge detection processor by VHDL for implementing on FPGA,” International Journal of Computer Applications,vol.47,no.25,pp.1–7,2012.
[13] Z. Guo, W. Xu, and Z. Chai, “Image edge detection based on FPGA,” in Proceedings of the 9th International Symposium on Distributed Computing and Applications to Business, Engineering andScience,pp.169–171,August2010.
[14] A. Nosrat and Y. S. Kavian, “Hardware description of multidirectional fast sobel edge detection processor by VHDL for implementing on FPGA,” International Journal of Computer Applications,Vol.47,no.25,pp.1–7,2012.
[15] V. Sanduja and R. Patial, “Sobel Edge Detection using Parallel Architecture Based on FPGA,” International Journal of Applied Information Systems, Vol. 3,no.4, 2012.
[16] A. Nosrat and Y. S. Kavian, “Hardware Description of Multi-Directional Fast Sobel Edge Detection Processor by VHDL for Implementing on FPGA,” International Journal of Computer Applications, Vol. 47, 2012

S. Nandy, B. Datta, D. Datta, "Implementation Sobel Edge Detector on FPGA," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.196-200, 2018.

“Improvement usually means doing something that we have never done before”, says Shigeo Shingo, world’s leading expert on manufacturing practices. This principle has been followed, consciously or unconsciously, in almost every industry for the development of the self and the society. The manufacturing industry is one of the fastest growing industries ensuing from this doctrine. With the advancements in Science and Technology, better techniques and efficient methods are being developed to minimize the outright efforts for manufacturing a product. Before the coinage of 3D printing(3DP) technologies, one has to depend on classical plots which are less efficient and time-consuming. But now, the implications of 3D printing technologies have changed the fate of the manufacturing industry. Over and above Manufacturing, 3DP has a profound influence on other industries such as Aerospace industry, Consumer goods industry, Medical industry, Education and so on. This paper elaborates various steps and processes involved in manufacturing a product using 3DP, types of 3DP and their boundless applications in various sectors.

Aerospace, Industry, Manufacture, Technique, Technology, 3D printing.

[1] http://powerup.or.kr/archives/279
[2] https://in.pinterest.com/pin/546061523545613809/
[3] https://blog.factoryfinder.io/recent-developments-in-3d-printing-material-selection-f06467ab46d
[4] https://in.pinterest.com/pin/537335799272289794/
[5] http://www.ams3d.co.za/lasersintering.html
[6] http://www.metalbot.org/metalbot-wiki/index.php?titl=File:SLS_Method.PNG
[7] http://www.silloptics.de/unternehmen/forschung/
[8] http://www.novint.co.kr/?p=526
[9] https://www.foundry-planet.com/equipment/detail-view/3d-printing-from-prototype-to-production/?cHash=cf00400c182d6cf8d0b3fcbc5a6d64e0
[10] http://medicalfuturist.com/3d-printing-in-medicine-and-healthcare/

Vaka Vamshi Krishna Reddy, Devavarapu Sreenivasarao, Shaik Khasim Saheb, "Process, Types and Applications of 3D Printing Technologies," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.201-205, 2018.

The main objective behind data mining applications is to specify that data, a fact, number, text etc. can be processed by a software system which results out as a useful knowledge. Data mining is interactive and iterative process. It is a discovery of association changes, automatic and semi-automatic patterns, anomalies, different structures and also events in data. The main purpose behind the implementation of data mining classification techniques on mental health care data set is to develop an automated tool for recognition, identification and publication of relevant mental health care information. This paper aims to help experts in healthcare domain in decision making by doing predictive analysis on mental healthcare dataset using classifiers in weka. We have mainly applied 3 classifiers- Naïve Bayes, J48 and Multilayer Perceptron. Naïve Bayes is an advanced form of Bayesian’s theorem, J48 is a decision tree based approach and Multilayer Perceptron is the simplest form in Neural networks. Dataset to be supplied to weka is Mental Healthcare survey with respect to IT industry all around the world. Data mining thus improves the quality of decision making process in its various applicative domains. Finally, this paper concludes by determining the major objective by illustrating data mining techniques and processes, methodologies and also the performance and accuracy observed in determining the best possible result from each existing technique so as to get the authentic information from the data set that we have supplied.

Predictive analysis, Comparative study, Weka, Naïve Bayes, J48, Neural Network, Mental healthcare dataset

[1] Mental healthcare dataset- https://www.kaggle.com/osmi/mental-health-in-tech-survey
[2] L. L. Dhande and Dr. Prof. G. K. Patnaik, “Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier” , International Journal of Emerging Trends & Technology in Computer Science , Volume-3, Issue 4 July-August 2014, pg:313-319
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[7] K. Ara Shakil, S. Anis and M. Alam, “Dengue Disease Prediction Using Weka Data Mining Tool”, pg:1-26
[8] R. Kirkby & E. Frank, “WEKA Explorer User Guide for Version 3-4-3”, November 9, 2004, pg:1-13
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[13] G.K Gupta, “Introduction to data mining with case studies”, Monash University, Clayton, Australia, Prentice Hall of India pvt ltd., 3rd edition, 2011, pg: 1-2

S. Sharma, S. Anand, A. K. Jaiswal, M. K.Goyal, "Predictive analysis using classification techniques in healthcare domain," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.206-212, 2018.

We all knows with increase in Traffic Congestion and growing population increases the demand for emergency vehicles services arises, time can be friend or a worst enemy in emergency lesser the time to travel greater will be efficiency each second turn by the red light and traffic blocking is the critical thing which routes the emergency site. So how to prioritized emergency vehicles is the question for our mind. How much the seconds matter is one doctors who can save life, no even we engineers can save the life. In this human care about time but not a life. Even when ambulance is stuck in traffic people don’t give the way for ambulance to move safely. So traffic congestion has become major problem around us. As the result of the rapid growth of technology and engineering field the life of mankind has got automated.

IoT, Google cloud app engine, emergency medical service, GPS, mobile computing platform

Faisal A. Al- Nasser,Hosam Rowaihy “Simulation of Dynamic Traffic control system based on Wireless sensor network”, IEEE Symposium on Computers & Informatics.
Dr.A.Balamurugan –” Automated Emergency System in Ambulance to Control Traffic Signals using IoT”.


Devyani Bajaj, Neelesh Gupta, “GPS Based Automatic Vehicle Tracking Using RFID
Dr. Khalifa A. Salim, Ibrahim Mohammed Idrees,“Design and Implementation of Web-Based GPS-GPRS Vehicle Tracking System”.
Obuhuma, J. I.,Moturi, C. A, “Use of GPS With Road Mapping For Traffic Analysis”.
Joseph Owusu, Francis Afukaar and B.E.K. Prah, “Urban Traffic Speed Management: The use of GPS/GIS”.

Vishal S. Patil, Priyanka Sawale, Pooja Kale, "Emergency Traffic Signal Control System for Ambulance," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.213-216, 2018.