Volume-6 , Issue-12 , Dec - 2018 Go Back

Vessel blocking is one of the reasons behind the death of people universally, more people pass away from cardiovascular diseases than from any other cause annually. To stay away from heart disease or to those symptoms early. Many experts will be developing intelligent decision support systems related to medical to get the better ability of the doctors in the detection of heart disease. In heart disease diagnosis and treatment, single data image are providing reasonable accuracy. The Heart Vessel blocking Prediction proposed system guides through an intelligent decision support system. In our proposed model a predictive analysis is carried out on Heart Disease Data using K-means and ANT colony optimization (ACO) techniques. Medical data is a combination of image and data set. This classification is implemented by developing a model using ANT colony optimization. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of the vessel region. In this framework. The evaluation results prove that our method performs better in a much shorter time which can be verified in the mat lab environment. This section presents the simulation results for proposed Ant Colony Optimization Based Heart Disease Identification (ACO-HDI). A total of three simulations were conducted to evaluate the performance of the proposed approaches. In proposed model compare with two existing model they Are Particle Swarm Optimization with K-Means (PSOK)we evaluate a swarm intelligent K-algorithm for dental property diagnosis, a disease that is most commonly found at all age groups, and Artificial Fish Swarm Algorithm Based K-Means (AFSA)is the widely used K-Means technique. K-algorithms the performance of the algorithm depends on the availability of the original masonry centers and one for local refinance. The following metrics were adopted to evaluate the performance of the proposed schemes. Compare to PSOK, AFSA, ACO-HDI all other methods the accuracy will increased in proposed method, also give the better result for proposed method. Heart disease is a major life-threatening disease that causes to death and it has a serious long-term disability. The time taken to recover from heart disease depends on the patient’s severity. Heart disease diagnosis is a complex task which requires much experience and knowledge. Nowadays, the healthcare industry contains the huge amount of healthcare data, which contain hidden information we put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, (6)miscellaneous tube-like object detection approaches. some of these categories are further divided into subcategories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, pre-processing, user interaction, and result type.

Heart Vessel blocking Prediction, ANT Algorithm, k-means Clustering, MATLAB

[1]. Solanas Et Al., Aug 2014, "Smart Health: A Context-Aware Health Paradigm within Smart Cities," Ieee Communications Magazine, Vol. 52, No. 8, Pp. 7481.
[2]. K.K Revathi, K.K Kavitha - Comparison of Classification Techniques On Heart Disease Data Set. International Journal of Advanced Research in Computer Science, November - 2017, Vol.8, No.9.
[3]. K.Kavitha, A Kangaiammal, K Satheesh Analysis On Classification Techniques In Mammographic Mass Data Set. International Journal of Engineering Research and Applications 5 (7) January - 2015/1/1 Vol. 5, Issue 7, (Part - 3) July 2015, pp.32-35.
[4]. Jinn Ho, Wen-Liang Hwang, 2012, “Image Demising Using Wavelet Bayesian Network Models” In Icassp IEEE International Conf. Pp 1105-1108.
[5]. Dr. Aynor Unai Unal, 2014 “Security in big data of medical records”. 2014 Conference on IT in Business, Industry and (CSIBIG).
[6]. B. Zhao, Schwartz, Moskowitz, Ginsberg, Rizvi, and Kris, 2006, “Lung cancer: computerized quantification of tumor response--initial results.” Radiology, vol. 241.
[7]. IlyaLevner and Hong Zhang, May 2007” Classification- Driven Watershed Segmentation”, IEEE Transactions on Image Processing, Vol. 16, No. 5,.
[8]. Gould, Tang, Liu, Lee, Zheng, Danforth, 2015, "Recent Trends in the Identification of Incidental Pulmonary Nodules," American Journal of Respiratory and Critical Care Medicine, vol. 192, no. 10, pp. 1208-1214.
[9]. Dinggang Shen, Feihu Qi, Hidenori Matsuo, Liya Chen, Kyoko Ito, Yonghong Shi and Zhong Xue, April 2008, "Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-Based and Patient-Specific Shape Statistics", IEEE Transactions on medical Imaging, vol. 27, no. 4,.
[10]. L. Nayak, Lee, and Wen, 2012 “Epidemiology of brain metastases,” Curr. Oncol. Rep., vol. 14, no. 1, pp. 48–54.
[11]. B. Zhang, Vijaya Kumar, and D. Zhang, 2014 “Noninvasive Diabetes Mellitus Detection using Facial Block Color with a Sparse Representation Classifier,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1027-1033,.
[12]. J. E. Johnson, T. Takenaka, and T. Tanaka, Aug 2008 “Two-dimensional time-domain inverse scattering for quantitative analysis of breast composition,” IEEE Trans. Biomed. Eng., vol. 55, no. 8, pp. 1941–1945.
[13]. K. Yee., May 2010," Numeral Solution of Initial Boundary Value Problems Involving Maxwell`s Equations in Isotropic Media". IEEE Trans. on Antennas and Propagation, AP-14(8): pp. 302–307.
[14]. 14. Kaur, Jaspinder, Nidhi Garg, and Daljeet Kaur, 2014, "Segmentation and Feature Extraction of Lung Region for the Early Detection of Lung Tumor." International Journal of Science and Research (IJSR) Vol 3: 2327-2330.
[15]. Eun Ju Cha, Kyong Hwan Jin, Dong-wook Lee, 2016, "Improved temporal resolution of twist imaging using annihilating filter-based low rank hankel matrix approach". pp 978-1-4799-2349-6.
[16]. 16. WANG Lei, WANG Xi-lian, YUAN Ke-hong, 2013, "Design and Implementation of Remote Medical Image Reading and Diagnosis System Based on Cloud Services" pp 978-1-4673-5887-3.
[17]. He Med-X Research Institute ,2011,"Medical Image Registration using Normal Vector and Intensity Value" pp 978-0-7695-4623-0
[18]. Banavath Dhanalaxmi Research Scholar, ECE Department, Srinivasulu Tadisetty Professor and Supervisor, ECE Department " Multimedia Cryptography- A Review" pp 978-1-5386-0814-2
[19]. Michael Winkler, Michael Street, Klaus-Dieter Tuchs, Konrad Wrona, 2013, “Wireless Sensor Networks for Military Purposes” in Autonomous Sensor Networks Springer Series on Chemical Sensors and Biosensors Volume 13, , pp 365-394
[20]. Poornima, Gladis , 2018,“A Novel Approach For Diagnosing Heart Disease With Hybrid Classifier”Biomedical Research
[21]. Nilakshi P, Waghulde, Nilima P. Patil, 2014, “Genetic Neural Approach for Heart Disease Prediction”International Journal, Pp Issn (Online): 2277-7970.
[22]. Binu K Nair, Lokhande Dept. Of Electronics & Telecommunication, Scoe,Jun 2017, “Patient Monitoring System Using Image Processing” International Journal Of Advanced Research In Electrical, Electronics And Instrumentation Engineering, Vol. 6.

G. Manikandan, K. K. Kavitha, "Analysis of Heart Vessel Segmentation Using Ant Colony Optimization Algorithm Based On Digital Image Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.189-197, 2018.

Vehicular Ad-Hoc Network (VANET) is a variation of MANET (Mobile Ad-hoc network). MANET has many nodes involved without the central network and the nodes are equipped with network skills. If node leaks through a large network of service disruptions that can produce one of the anomalies of the inter-domain route. Route leaks are among the routing policies of autonomous systems because of the violation. Unfortunately, there are not many studies that location and route analysis of the route leak issue were discussed. There exist few convention solutions that can be problem as a first route of defense, such as route filters. The proposed system is applied based on the Network Route Identification leaks based on Node Location estimation (NRINLE). The path trail attracts different colored network system simulation polyline or marker node customization. There is more network simulation travel leaks on routes that are recommended for traveling by location. The main method can be location spaces like node tripping path leaks. In the network we create a rather fundamental theoretical application, a specific way of active network fits with a path leak location. Then motive the probable occurrence of route leaks in different location scenarios, with the aim of formulating requirements for their identification. And hence thereof prevention to good performance improve location leaks reliability.

Location-based service security, point of interest, Network Route identification based on Node Location estimation, Optimal location route estimated

[1]. Vijayasathana. Vishnukumar. May 2015,’ Conscious Aware Routing Protocol for VANET’, International Journal of Science, Engineering and Technology Research (IJSETR), Vol.6, pp (1-7).
[2]. Shilpa Shetty A, Sannidhan, May 2016,’ Packet Drop Detection VANET system’, International Journal of Computer Science and Mobile Computing (Ijcsmc), Vol. 5, Pp (656-661).
[3]. Kaushik, Chowdhury, Akyildiz, April 2017,` Crp: A Routing Protocol for Cognitive Radio VANET,` IEEE Journal Areas in VANET, Vol. 29, No. 4, pp (794-804).
[4]. Hsien-Kai Hsin, En-Jui Chang 2014,` Ant Colony Optimization-Based Fault-Aware Routing In Mesh-Based Network-On-Chip Systems,` IEEE Transactions On Computer-Aided Design Of Integrated Circuits And Systems, Vol. 33, No. 11, pp (1693-1705).
[5]. Xiao Chen, Zanxun Dai, 2014,` ProHet: A Probabilistic Routing Protocol with Assured Delivery Rate for VANET network,` IEEE Transactions on VANET Communication, Vol. 14, No. 7, pp (1504-1531).
[6]. Shuguang Fu, 2015, ‘Identification of Radio Network Routing Method Based on Genetic Algorithm,` IEEE International Journal of Future Generation Communication VANET Networking System, Vol. 7, No. 4, pp (2629-2368).
[7]. Leela Priya, Lalitha2, 2016’ Energy Efficient Routing Models in location - A Recent Trend Survey,` International Journal of Pure and Applied Mathematics Vol.118, No. 16, pp (443-458).
[8]. Ping Yi, Zhoulin Dai 2013,` A New location Attack in Vehicle Ad Hoc Networks,` International Journal of Computer Technology Vol. 16 No. 22, Pp (1-142).
[9]. Li Qiang Tao and Feng Qi Yu, Aug 2010,’ ECODA: Enhanced Congestion Detection and Avoidance for Multiple Class of Traffic in location Networks,` IEEE, pp (1387-1394).
[10]. Wenfeng Xia, Yonggang Wen, 2015,` A Survey on Software-Defined Networking,` IEEE Communication Surveys & Tutorials, Vol. 17, No, 1, pp (27-51).
[11]. Bara’a Ali Attea, Feyza Yıldırım Okay, 2012,’ Multi-objective Evolutionary Algorithm Based on Decomposition for Efficient Coverage Control in leaks Networks,` IEEE, Vol.5, pp (1-6).
[12]. Fareen Farzana, Neduncheliyan, 2018,` Multi-objective Artificial Bee Colony based Routing in Mobile Wireless Sensor Network,` International Journal of Pure and Applied Mathematics, Volume 118, No. 8, pp (635-641).
[13]. Zesong Fei, Bin Li, 2016,` A Survey Of Multi-Objective Optimization In Wireless Sensor Networks: Metrics, Algorithms And Open Problems,` Accepted To Appear On IEEE Communications Surveys & Tutorials, Vol.1, Pp (1-38).
[14]. Saleh, Oct 2017,’ Enhancing Multi-Objective Optimization for Wireless Sensor Networks Coverage using Swarm Bat Algorithm,` International Journal of Computer Applications (0975 – 8887) Volume 175 – No.9, pp (1-7).
[15]. Song, Gong, Aug 2017, ‘Coverage Hole Recovery Algorithm Based on Molecule Model in Heterogeneous WSNs,` International Journal of Computers Communications &controls, vol.12, No.4, pp (562-576).
[16]. Jinil Persis, and Paul Robert, May 2015,’ Ant Based Multi-objective Routing Optimization in Mobile AD-HOC Network,` Indian Journal of Science and Technology, Vol 8, No.9.pp 875–888.
[17]. Ali Norouzi, Ahmet Sertbas, 2015,’ Energy Efficient Coverage Optimization in Wireless Sensor Networks based on Genetic Algorithm,` Universal Journal of Communications and Network, Vol.3, No.4, pp (82-88).
[18]. Narendra Reddy, Vishnuvardhan May 2013,’ Routing Attacks In network system communication, Computer Science and Mobile computing system,` Ijcsmc, Vol. 2, Issue. 5, pp 360 – 367.
[19]. Aditi, Joy Karansingh, Jul 2016,’ Analysis of Various Security Attacks Mobile Adhoc Network,` International Research Journal of Engineering and Technology, Vol.3, Pp (1-4).
[20]. Ankita Gupta, Sanjay Prakash Ranga, July 2012,’Network Node Various Location Attacks in Mobile Ad-Hoc Networks, Journal of Computing and Corporate Research.

M. Subalakshmi, "Route Leak Identification: A Step toward Making Inter-Domain Node Location Estimation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.198-209, 2018.

Testability is not new term as quality issues for software issues. In order to test the software product, the quality of their software product must be using measuring. Testability is the impact analysis the software product imminent requirement. On the behalf of the existing extensive use of software, if used in isolation, testability is oftentimes too fine grained to quantify comprehensively an investigated aspect of the software architecture. This paper calculates the testability index.

Quality factors, Software Development Life Cycle, Object Oriented Property

[1]. Genero M., J. Olivas, M. Piattini and F. Romero, “A Controlled Experiment for Corroborating the Usefulness of Class Diagram Metrics at the early phases of Object Oriented Developments”, Proceedings of ADIS 2001, Workshop on decision support in Software Engineering, 2001.
[2]. M. Dagpinar and J. Jahnke, “Predicting Maintainability with Object- Oriented Metrics – an Empirical Comparison,” Proc. 5th Working Conference on Reverse Engineering (WCRE’03), 13 - 17 Nov. 2003, pp. 155 - 164, 2003.
[3]. A. Mishra, D. Agrwal & M. H. Khan, “Confidentiality Estimation Model: Fault Perspective” International Journal of Advanced Research in Computer Science (IJARCS), Volume.8 Issue. 4, June 2017.
[4]. Mohammad Zunnun Khan,M Akheela Khana &, M. H. K. “Requirement Modifiability Quantification Model of Object Oriented Software” , Special Edition on Applied Mathematics in Information and Communication Technology (AMICT), June 2017.
[5]. Natasha Sharygina , James C. Browne, and Robert P. Kurshan, “A Formal Object-Oriented Analysis for Software Reliability: Design for Verification”, 2011.
[6]. P. Jalote, An Integrated Approach to Software Engineering, 3rd Edition. Narosa Publishing House, India, 2005.
[7]. Bansiya, Jagdish, and Carl G. Davis. "A hierarchical model for object-oriented design quality assessment." Software Engineering, IEEE Transactions on 28.1 (2002): 4-17.
[8]. Khan, R.A., Mustafa, K.: Metric based Testability Model for Object Oriented Design (MTMOOD). SIGSOFT Software Engineering Notes 34(2) (March 2009).
[9]. Lee, Ming-Chang. "Software Quality Factors and Software Quality Metrics to Enhance Software Quality Assurance." British Journal of Applied Science & Technology 4.21 (2014).
[10]. Jibitesh Mishra, Ashok Mohanty “Software Engineering. Page No.21, Pearson, 2012. www.pearsoned.co.in/ jibiteshmishra
[11]. M. Nazir and R. A. Khan, “Software Design Testability Factors: A New Perspective,” in Proceddings of Naional Third Conference INDIACOM 2009, 2009, pp. 1–6.
[12]. Dr. Brijesh Kumar Bhardwaj, “Testability Analysis Phase: A Design Stage”, International Journal of Research, Volume 7, Issue11, November-2018.
[13]. Dr. Brijesh Kumar Bhardwaj, “Effective Factors of Software Testability”, American International Journal of Research in Science, Technology, Engineering & Mathematics, Volume 1, Issue 24, September-November 2018.

Kavita Srivastava, "An Impact Analysis of Testability Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.210-212, 2018.

River is main source of water for drinking and domestic usage. Over exploitation and discharge of surface water in river stream has ecologically stressed the rivers. In view to manage river health, it is essential to carry out river engineering periodically. River follows complex structure; goes through dense forest and valleys. Therefore, measuring water quality and several other parameters associated with the river is a discouraging job. Mostly river follows longest path and generating data for such large geographical area is very challenging. Scientific study of the river requires data for several consecutive years. Having such large data requirement and expecting data generation simply through field-work is highly burdened and never ending process. Therefore, in this paper we introduced auto data generation techniques like: data extraction, data generation through public-partnership, data estimation and data generation using GIS (Geographic information system) based utility software. Lastly, we illustrate complete data generated by using these auto data generation techniques.

Information system, River modelling, Kanhan River; Geo-mapping, River engineering, Water quality

[1] Soumita Mitraa, Swayambhu Ghosh, Kamala Kanta Satpathy, Bhaskar Deb, Bhattacharya, Santosh KumarSarkar, PravakarMishra, P.Rajae, “Water quality assessment of the ecologically stressed Hooghly River Estuary, India: A multivariate approach”, 126(2018) 592-599
[2] Stuart E. Bunn, Angela H. Arthington, “Basic Principles and Ecological Consequences of Altered Flow Regimes for Aquatic Biodiversity1”, DOI: 10.1007/s00267-002-2737-0
[3] Nilamadhab Mishra, "Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams" International Journal of Scientific Research in computer science and Engineering, Vol.6, Issue.1, pp.30-36, Feb (2018)
[4] Dr. G. K. Khadse, P. M. Patni, P.S. Kelkar, S. Devotta, "Qualitative evaluation of Kanhan River and its tributaries flowing over central Indian plateau", Environ Monit Assess. 2008 Dec; 147 (1-3):83-92. Epub 2007 Dec 22.
[5] Poonam Devi, "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Volume-6, Issue-2, April 2018
[6] P.V. Nikam, D.S. Deshpande, "Different Approaches for Frequent Itemset Mining", International Journal of Scientific Research in computer science and Engineering, Vol.6, Issue.2, pp. 10-14, April (2018)

Dinesh A. Lingote, Girish S. Katkar, Ritesh Vijay, R. B. Biniwale, "Responsive Information generation system for Kanhan River, an effective information system for river modeling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.213-221, 2018.

Nowadays, the mechanisms are growing among the high speed and novel developments are accomplished day by day. Every day as a huge amount of data shared within the diverse users on the internet so the sharing of data is enhanced. In order to hide the occurrence of the communication, the steganography is utilized. In a suitable carrier such as image, video or audio this mechanism deals among the insertion of the secret message. Due to its broad utilization on the internet, the digital images are majorly preferred over the other carriers. For the implementation, the MATLAB software is utilized. In this paper, a couple of methods are utilized to embed the secret message. Those methods are original binary form and complemented form of a binary converted message, to advance security. For the embedding purpose, fuzzy edge detection technique is utilized and in order to hide the information into an image, the LSB mechanism is proposed. In the proposed work fuzzy technique will be utilized to offer more sharp edges having more data and to acquire the continuity. Huffman coding compression technique is utilized to compress the data and also to send more data on less space. The MSE, BER, and PSNR are utilized as a calculation of performance analysis.

PSNR, Bit Error Rate, MSE, Stegnography, Huffman Coding

[1] Soni, A.; Jain, J.; Roshan, R., "Image Steganography using discrete fractional Fourier transform," Intelligent Systems and Signal Processing (ISSP), 2013 International Conference on , vol., no., pp.97,100, 1-2 March 2013.
[2] Masud Karim, S.M.; Rahman, M.S.; Hossain, M.I., "A new approach for LSB based image Steganography using secret key," Computer and Information Technology (ICCIT), 2011 14th International Conference on , vol., no., pp.286,291, 22-24 Dec. 2011.
[3] Shrutika Suri,” Comparative Analysis of Steganography for Coloured Images”, JCSE, vol 2(4), Pp 180-184, 2014
[4] Sabyasachi Pramanik,” Image Steganography Using Wavelet Transform And Genetic Algorithm”, IJIRAE, vol 1(1), Pp 17-20,2014
[5] Chin-Chen Chang,” Meaningful Shadows for Image Secret Sharing with Steganography and Authentication Techniques”, journal of information hiding and data processing, vol 5(3), Pp 342-352, 2014
[6] Komal Hirachandani, “New Approach of Information Security through Steganography by using Wavelet Transformation and Symmetric Encryption”, IJCSIT, vol 5(5), Pp 6253-6260,
[7] Shemi P B,” An Enhanced Image Steganography Technique in Art Images”, IJCSMC, Vol.3 Issue.8, August- 2014, pg. 613-621
[8] Mohammad Sajid Khan, “Encryption Based Steganography- Modern Approach for Information Security”, IJCSIT, Vol. 5 (3) , Pp 2914-2917, 2014
[9] Takashi Mihara,” A New Framework of Steganography Using the Content of Cover Data”, Journal of information hiding and multimedia signal processing, Vol 5(2), Pp 117-123, 2014
[10] Prof.Pramod Khandare,” Data Hiding Technique Using Steganography”, IJCSIT, Vol. 5 (2) , Pp 1785-1787, 2014
[11] Shikha Mohan,” Image Steganography: Classification, Application and Algorithms”, IJCEM, Vol 1(10), Pp 93-97, 2015
[12] M. Kameswara Rao, ” Security Enhancement in Image Steganography a MATLAB Approach”, Journal of scientific research, Vol 23(2), Pp 357-361, 2015
[13] Chaitali R. Gaidhani, ” Image Steganography for Message Hiding Using Genetic”, IJCSE, vol 2(3), Pp 67-70, 2014
[14] Mamta Juneja, “Improved LSB based Steganography Techniques for Color Images in Spatial Domain”, IJNS, vol 16(6), Pp 452-462, 2014
[15] Bhattacharjee, T., Nov. 2014 “Progressive quality access through secret sharing and data hiding scheme”Pp 5-7,2014
[16] Preeti Parashar ,2014“A Survey: Digital Image Watermarking Techniques”, ijsp, Vol. 7(6), pp. 111-124, 2014
[17] Monika Patel,Priti Srinivas Sajja,” Analysis and Survey of Digital Watermarking Techniques”, ijarcsse, Vol 3, Pp 203-210, 2013
[18] Sahar A. El_Rahman, “A comparative analysis of image steganography based on DCT algorithm and steganography tool to hide nuclear reactors confidential information”, ELSEVIER, Pp 1-20, 2016
[19] Abhinav Shrivastav,”Survey report on Different Techniques of Image Encrption”, IJETAE, vol 2, Issue 6, Pp 163-167, 2012
[20] Lauren Dubreuil,”Spread Spectrum, Cryptography and information Hiding”,
[21] Swati malik, Ajit “Securing Data by Using Cryptography with Steganography”,IJARCSSE Volume 3, Issue 5, May 2013
[22] Anil Kumar (2013), “A Secure Image Steganography Based on RSA Algorithm and Hash-LSB Technique”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, Pp 363-372
[23] Mehdi Hussain , A “Survey of Image Steganography Techniques”, IJAST Vol. 54,Pp 113-124, 2013
[24] Dr. Mahesh Kumar,”Image Steganography using Frequency domain”, IJST, vol 3, Issue 9, Pp 226-230, 2014
[25] Akanksha Kaushal,”Secured Image steganography using Different Transform Domain”, IJCA, vol 77, Issue 2, Pp 24-38, 2013
[26] C.P.Sumathi, “A Study of Various Steganography Techniques Used for Information Hiding “,IJCSES) Vol.4, No.6, Issue 9,Pp 9-25, 2013
[27] Amritpal Singh,”An overview of Image Steganography Techniques”, IJECS, vol 3, Issue 7, Pp 7341-7345, 2014
[28] Parmar Ajit Kumar Maganbha,” A Study and literature Review on Image Steganography”, IJCSIT, vol 6, Issue 1, Pp 686-688, 2015

Kirti Chopra, Ishpreet Singh Virk, "Image Steganography Using Edge Detection Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.222-227, 2018.

Work on simplifying the drawing of Artwork required for making of prototype PCB has been carried out and reported in the present article. The students of electronics find PCB Artwork making procedure tedious and time consuming. To overcome this problem, we have presented here the simplified as well as motivating procedure. Work started with a selection of circuit diagram that contains various types of components and connectors. After that the rough layout is planned with consideration of dimensions of components. Various steps in drawing of Artwork like placement of footprint of components, making netlist as per the circuit diagram, manual routing, etc. are discussed with example. Utilisation of various facilities provided by computer aided PCB layout making softwares are also discussed in the present work. Procedure of making of postscript file and its printout using postscript editor like corel draw is also discussed. Present work is helpful to the hobbyist and students of electronics for making of their project work.

PCB, Artwork, footprint, layout

[1]. Walter C. Bosshart, “Printed Circuit Board Design and Technology”,1st edn., McGraw Hill Education, (1984).
[2]. R. Sharma, S. Singh, “Swarm Intelligence Based Automated Testing for MTAAS”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.475-477, 2018
[3]. Kraig Mitzner, “Complete PCB Design Using OrCAD Capture and Layout”, Elsevier, Armsterdam. (2007).
[4]. Mehzabeen Kaur, Surender Jangra,” Execution Time of Quick Sort on Different C Compilers: A Benchmark”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.786-788, 2018
[5]. Orcad layout user’s guide, Orcad Inc. Oregon, (1998).
[6]. Pravin Bhadane, Suchita Bhangale and Aparna Lal, ‘Understanding the parts of PCB Layout’, International Journal of Electrical and Electronics Research, Vol.6, Issue 3, pp.1-3, (2018).

Pravin Bhadane, Aparna Lal, "Simplified Procedure for Drawing of Artwork of Prototype PCB," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.228-231, 2018.

There is rise in new Intrusion Detection Systems (IDSs) due to increasing frequency of various malicious activities over network and certain network policy violations. IDS, being an advanced tool and equipment to secure the network parameter by surveillance from the different network risks, is capable of detecting various attacks due to advancements in Computer Science. These advancements include machine learning models which can be integrated into an IDS for increasing the Detection Rate of attacks and minimizing the False Alarm Rate (false positives). In this paper, Hybrid Distributed IDS (HDIDS) is proposed in which strengths of Signature-based and Anomaly-based detection are combined together to detect different types of Denial of Service (DoS) attacks. HDIDS is presented by combining an anomaly-based detection algorithm and multiple signature-based detection algorithms. The signature-based multiple classifiers ensemble and can detect real time attack based on majority of votes from each classifier. Ensembled output use voting technique which are simplest to implement and produce favourable results. Anomaly based classifier has intensive focus over new and unknown attacks in distributed network. The dataset used for training the classifiers is ISCX CICIDS-17 consisting of latest attacks and 88 features providing better options for feature selection with respect to each classifier.

— Machine Learning, Hybrid, Intrusion Detection System, Anomaly-based classifier, Signature-based classifier, Ensemble

[1] C. Guo, Y. Ping, N. Liu, S. Luo, “A two-level hybrid approach for intrusion detection”, Science Direct, Neurocomputing, Appl. 214, pp. 391–400, June 2016
[2] G. P. Spathoulas and S. K. Katsikas, “Reducing false positives in intrusion detection systems”, Science Direct, Computers and Security, Appl. 29, pp. 35-44, July 2009
[3] P. Casas, J. Mazel, P. Owezarski, “Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge”, Science Direct, Computer Communication, pp. 772-783, Jan 2012
[4] H. Sarvari, M M. Keikha, “Improving the Accuracy of Intrusion Detection Systems by Using the Combination of
Machine Learning Approaches”, IEEE, International Conference of Soft Computing and Pattern Recognition, pp. 334-337, June 2010
[5] A. Shenfield, D. Day, A. Ayesh, “Intelligent intrusion detection systems using artificial neural networks”, Science Direct, ICT Express 4, pp. 95-99, May 2018
[6] P. Aggarwala, S. Sharma, “Analysis of KDD Dataset Attributes - Class wise For Intrusion Detection”, Science Direct, Computer Science, Appl. 57, pp. 842-851, 2015
[7] R. Ashfaq, X. Wang, J. Z. Huang, H. Abbas, Y. He, “Fuzziness based semi-supervised learning approach for intrusion detection system”, Science Direct, Information Science, Appl. 378, pp. 484-497, May 2016
[8] S. Peddabachigaria, A. Abrahamb, C. Grosanc, J. Thomasa, “Modelling intrusion detection system using hybrid intelligent systems”, Science Direct, Journal of Network and Computer Applications, Appl. 30, pp. 114-132, June 2005
[9] S. Khonde, V. Ulagamuthalvi, “A Machine Learning Approach for Intrusion Detection using Ensemble Techniques - A survey”, International journal of scientific research in computer science, Engineering and Information Technology, Vol 3. Issue 1, ISSN - 2456-3307, pp. 328 – 338, 2018
[10] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Volume-5, Issue-6, December 2017

A.A. Ujeniya, R.D. Pawar, S.A. Sonawane, S.B. Shingade, S.R. Khonde, "Hybrid Distributed Intrusion Detection System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.232-237, 2018.

The Assamese language is broadly classified in three major eras viz- early Assamese period, middle Assamese and modern Assamese. Influence of Sanskrit and the Magadhi Prakrit is visible in the vocabulary of the language. Banikanta Kakati has broadly classified the Assamese language into two divisions –namely Eastern Assamese and Western Assamese. Understanding the difference in the phonology, play an important role in the development of emotional TTS. (MFCC) is most popular features of speech recognition. This is a feature of vocal tract. Here the variations on this process are observed using MFCC. MFCC plays an important role in identifying speakers.

Dialect variation, Emotional TTS, G2P, MFCC

[1] Dr. Golukchandra Goswami, “Structure of Assamese”, Department of publication, Gauhati University, pp 25-32
[2] http://www.iitg.ernet.in/rcilts/pdf/assamese.pdf .P7.
[3] Dr. Pran Hari Talukdar, “Speech Production, Analysis and Coding Introduction to speech processing”, Lap Lambert Academic Publishing, pp36-64.
[4] Bhargab Medhi, Prof P. H. Talukdar, LPC and MFCC Analysis of Assamese Vowel Phonemes, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 1, January 2015 ISSN: 2277 128X,2015
[5] Md. Sahidullah , Goutam Saha, Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition, Speech Communication 54 (2012) pp-543–565,2012
[6] Vibha Tiwari, MFCC and its applications in speaker recognition, International Journal on Emerging Technologies 1(1): 19-22(2010) ISSN : 0975-8364

Bitopi Sharma, P.H. Talukdar, "Phoneme based Dialect variation of Assamese Language," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.238-240, 2018.

Cloud computing is rising as a leading edge that delivers secure data storage center, unlimited computational resources, network connection and flexible data processing capabilities. The main aim of cloud data centers is how to distribute and maintain the tasks coming from request group efficiently and accurately. Load balancing is one of the major issues in cloud computing which stabilizes the dynamic workload evenly spread across all the virtual machines (VMs) of the cloud data centers. It helps to upgrade the performance of the cloud system and make proper utilization of resources by legitimate allocation. This paper focuses on developing hybrid load balancing algorithm which disseminates the arriving requests in cloud data centers. The proposed algorithm will be implemented using Cloud Analyst simulator and the performance of this algorithm will compare with Throttled, Round Robin and ESCS (equally spread current execution) algorithms on the basis of overall response time and data center processing time. The analysis carried out in the paper shows that the proposed algorithm performs better than the existing algorithms.

Load balancing, Existing Algorithms, Hybrid Load Balancing Algorithm, Cloud Analyst

[1] Zhen Xiao, Weijia Song and Qi Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment”, IEEE transactions on parallel and distributed systems , vol. 24, no. 6, 2013.
[2] Dhinesh Babu L.D., P. Venkata Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments”, Applied Soft Computing Science Direct, 2292–2303, 2013.
[3] Apoorva Tripathi, Saurabh Shukla and Deepak Arora, “A Hybrid Optimization Approach for Load Balancing in Cloud Computing”, Advances in Computer and Computational Sciences, Advances in Intelligent Systems and Computing, Springer,554, 2018.
[4] Shridhar G.Domanal and G. Ram Mohana Reddy,“Load Balancing in Cloud Environment using a Novel Hybrid Scheduling Algorithm”, IEEE International Conference on Cloud Computing in Emerging Markets, 2015.
[5] Rajkumar Somani, Jyotsana Ojha, “A Hybrid Approach for VM Load Balancing in Cloud Using CloudSim, International Journal of Science”, Engineering and Technology Research (IJSETR), vol 3, Issue 6, 2014.
[6] S. Mohapatra, K. S. Rekha and S. Mohanty, “A comparison of four popular heuristics for Load balancing of Virtual Machines in Cloud Computing”, International journal of Computer Applications, vol 68, no 6, pp–38, 2013.
[7] Sukrati Jain, Dr. Ashendra K. Saxena , “A Survey of Load Balancing Challenges in Cloud Environment”, IEEE 5th International Conference on System Modeling Advancement in Research Trends, 2016.
[8] B. Wickremasinghe, “CloudAnalyst: A CloudSim-based Tool for Modeling and Analysis of Large Scale Cloud Computing Environments”, MEDC Distributed Computing Project, CSSE Dept., University Of Melbourne,2009.
[9] R. Buyya, R. Ranjan, and R. N. Calheiros, “Modeling And Simulation Of Scalable Cloud Computing Environments And The Cloudsim Toolkit: Challenges And Opportunities”, Proc. Of The 7th High Performance Computing And Simulation Conference (HPCS 09), IEEE Computer Society, June 2009.
[10] Meeta Singh, Poonam Nandal, and Deepa Bura, “Comparative Analysis of Different Load Balancing Algorithm Using Cloud Analyst”, Springer Nature Singapore Pte Ltd. B. Panda et al. (Eds.): REDSET 2017, CCIS 799, pp. 321–329, 2018.
[11] Bhathiya Wickremasinghe ,Roderigo N. Calherios, “Cloud Analyst: A Cloud-Sim-Based Visual Modeler For Analyzing Cloud Computing Environments And Applications”, Proc Of IEEE International Conference On Advance Information Networking And Applications,2010.
[12] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic, “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility”, Future Generation Computer Systems Elsevier Science Direct, , vol 25, no 6, pg. 599–616, ISSN: 0167-739X, ,Amsterdam, The Netherlands June 2009.
[13] Shang-Liang Chen , Yun-Yao Chen , Suang-Hong Kuo, “CLB: A novel load balancing architecture and algorithm for cloud services”, Computersand Electrical Engineering, Elsevier Science Direct, 2016.
[14] Sushil Kumar, Deepak Singh Rana, “Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey”, International Journal of Computer Applications, 0975–8887 Vol 129 No 6 2015.

Megha Dubey, Pradeep Singh Chauhan, "An Optimal Load Balancing Strategy for Virtual Machines in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.241-245, 2018.

Plants are cultivated for food, medicine, clothing, shelter, fiber, and beauty for thousands of years. Fungi, bacteria, and viruses are the causing source of plant disease. So, need of an Automatic detection of plant disease for this problem. A traditional method of plant disease detection is not efficient and also unreliable. Due to pest attack, nearly 18% of crop yield is lost in worldwide during every year. Identification of plant disease is difficult in manually but which is a key factor to preventing the losses. In existing, a module is applied in a farm, that contains large number of different sensors and also a device is used for converting and transfer data for monitoring and controlling purposes. And then Image processing is showing the disease visually. In this, we approach a Convolutional Neural Network (CNN) classification model deployed in a smart phone app and also responsible to predict the plant disease for dynamic plants image. This method is generic and useful. Frequently, we should adding and updating new diseases in the datasets and then cloud computing is used for storing, retrieving and serving data. Captured image of normal plants are stored in the cloud server, and these images are compared with the diseased plant leaves in the cloud campus. This paper presents a automated detection of various diseases associated with crops and also given a proposed methodology for computing amount of diseases in various crops.

Convolutional Neural Network (CNN),Cloud Computing, Advanced Neural Network

[1] Nasir, Fakhri A. M. Nordin Rahman A., and A. Mamat Rasid. "A study of image processing in agriculture application under high performance computing environment." International Journal of Computer Science and Telecommunications 3, no. 8 (2012): 16-24.
[2] Prasad, Shitala, K. Peddoju Sateesh, and Ghosh Debashis. "AgroMobile: a cloud-based framework for agriculturists on mobile platform." International Journal of advanced science and technology 59 (2013): 41-52
[3] Mohanty, Sharada, David P. Hughes, and Salathé Marcel. "Using deep learning for image-based plant disease detection." Frontiers in plant science 7 (2016): 1419.
[4] Radford, Alec, Metz uke, and Chintala Soumith. "Unsupervised representation learning with deep convolutional generative adversarial networks (2015)." arXiv preprint arXiv:1511.06434
[5] Yan Simon, Karen, and Zisserman Andrew. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[6] David Hughes, , and Salathé Marcel. "An open access repository of images on plant health to enable the development of mobile disease diagnostics." arXiv preprint arXiv:1511.08060 (2015).
[7] Szegedy, Christian, Vanhoucke Vincent, Ioffe Sergey, Shlens Jon, and Zbigniew Wojna. "Rethinking the inception architecture for computer vision." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826. 2016.
[8] Andrew Howard G., Zhu Menglong, Bo Chen, Dmitry Kalenichenko, Wang Weijun, Tobias Weyand, Andreetto Marco and Hartwig Adam. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
[9] Giusti, Alessandro, Guzzi jérôme, Dan C. Ciresan, Fang-Lin He, Rodríguez Juan P, Flavio Fontana, Matthias Faessler et al. "A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots." IEEE Robotics and Automation Letters 1, no. 2 (2016): 661-667.
[10] Augustus Odena. "Semi-supervised learning with generative adversarial networks." arXiv preprint arXiv:1606.01583 (2016).
[11] Zhao, Fuqing, Zongyi Ren, Dongmei Yu, and Yahong. "Application of an improved particle swarm optimization algorithm for neural network training." In Neural Networks and Brain, 2005. ICNN&B`05. International Conference on, vol. 3, pp. 1693-1698. IEEE, 2005.
[12] Vinod Nair, and E. Hinton Geoffrey. "Rectified linear units improve restricted boltzmann machines." In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807-814. 2010.
[13] Yann LeCun, Patrick Haffner, Bottou Léon, and Yoshua Bengio. "Object recognition with gradient-based learning." In Shape, contour and grouping in computer vision, pp. 319-345. Springer, Berlin, Heidelberg, 1999.
[14] Kaibo Duan, , S. Keerthi Sathiya, Wei Chu, Krishnaj Shirish Shevade, Neow and Aun Poo. "Multi-category classification by soft-max combination of binary classifiers." In International Workshop on Multiple Classifier Systems, pp. 125-134. Springer, Berlin, Heidelberg, 2003.
[15] S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna Calculation of Texture Features for Polluted Leaves “International Journal of Scientific Research in Computer Sciences and Engineering” Vol.6 , Issue.1 , pp.11-21, Feb-2018
[16] Sakshi kathuria “A Survey on Security Provided by Multi-Clouds in Cloud Computing” International Journal of Scientific Research in Network Security and Communication, Vol.6 , Issue.1, pp.23-27, Feb-2018

Raghavendran.S, P.Kumar, Silambarasan. K, "Plant Disease Detection System for Agricultural Application in Cloud Using CNN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.246-249, 2018.