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This paper aims to proposed multi-level block code based image compression of continuous tone still image to achieve low bit rate and high quality. The algorithm has been proposed by combining fractal image and block code algorithm. Fractal image compression (FIC) is a new compression technique in the spatial domain. It is based on block based image compression technique which, detects and codes the existing similarities between different regions in the image. The parameters considered for evaluating the performance of the proposed methods are compression ratio and subjective quality of the reconstructed images. The performance of proposed algorithm including color image compression, progressive image transmission is quite good. The effectiveness of the proposed schemes is established by comparing the performance with that of the existing methods.

Block Code, Bit Map, Fractal Image Compression, Quantization, MRI Image

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Anshu Agrawal, Pushpraj Singh Chauhan , "Design Image Compression for Fractal Image using Block Code Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.451-455, 2018.

Occupational stress is recognized as one of the major factors leads to health problems. This will lower efficiency and productivity on the job in an organization so the assessment and management of work-related stress are very crucial. Several kinds of computational techniques have been used for modeling and prediction but currently, occupational stress prediction in an early stage is still a challenge. This study is based on secondary data. In this paper, a review analysis has been carried out to analyze what has been done so far in last 26 years related to occupational stress and where there is a need to carry the further research. The paper explores occupational stress evaluation modeling techniques related to Machine Learning as well as statistical method. Occupational stress and burnout related to different kind of sector for working professional reviewed. This survey reviewed the subjective as well as objective measurement of stress evaluation. Questionnaires and physiological sensors used to measure and evaluate stress and corresponding techniques for modeling occupational stress have been reviewed. Occupational stress modeling based on Machine learning techniques such as ANN, BN, and SVM, LDA, RSM statistical methods like Regression, MLR etc. reviewed. This survey concludes with a discussion and future work, summary and finally conclusion.

Stress, Machine Learning Techniques, Stress Questionnaire, Stress Sensor, Stress classification, Stress prediction, Computational stress model

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Arshad Hashmi, S.K. Yadav, "A Systematic Review of Computational Methods for Occupational Stress Modeling Based on Subjective and Objective Measures," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.456-470, 2018.

Vision-based human action recognition has an increasing importance among the computer vision community with applications to visual surveillance, video retrieval, Video Indexing, Robotics and human-computer interaction. This paper presents a survey on human recognition using neural networks and the popular datasets used for it. A detailed survey of learning based approaches for human action representation is presented in this paper which is the core of action recognition. The Experimental Evaluation of various papers are analyzed efficiently with the various performance of recent methods using KTH and UCF sports action dataset are also analyzed.

action recognition, convolution neural network, action representation

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C. Indhumathi, V. Murugan, "A Survey on Neural Network based Approaches and Datasets in Human Action Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.471-476, 2018.

with the ever increasing population, urbanization, migration issues, and change in lifestyle, municipal solid waste generation levels are increasing significantly. Waste management directly affects the lifestyle, healthcare, environment, recycling and disposal, and several other industries. Current waste management trends are not sophisticated enough to achieve a robust and efficient waste management mechanism. It is very important to have a smart way of managing waste, so that not only the waste status is notified in-time when to be collected, but also, all the stakeholders are made aware in timely fashion that what type of waste in what quantity is coming up at what particular time. This will not only help in attracting and identifying stakeholders, but also aids in creating more effective ways of recycling and minimizing waste also making the overall waste management more efficient and environment friendly. Keeping all this in mind, we propose a cloud-based smart waste management mechanism in which the waste bins are equipped with sensors, capable of notifying their waste level status and upload the status to the cloud. The stakeholders are able to access the desired data from the cloud. Moreover, for city administration and waste management, it will be possible to do route optimization and select path for waste collection according to the statuses of waste bins in a metropolis, helping in fuel and time efficiency.

WSN, IOT

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[6] Greene, Krista L., and David J. Tonjes. "Quantitative assessments of municipal waste management systems: using different indicators to compare and rank programs in New York State."

Sanket Thakre, Dipali Shende, "Automation of dry-wet waste collection to support Swachh Bharat Abhiyan and its monitoring over IOT enabled WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.477-479, 2018.

Now these days most of the electronic devices are connected to the internet through wired or wireless computer network. This is very popular networking terminology known as the internet of things IOT, In background each and every internet networking devices are connecting to the backbone networking server. If this backbone network route is congested then IOT devices are suffered from their services and all these things are lacking in performance. The high throughput and low latency internet services are required for each device to do well. There is a number of reasons behind the internet speed. The most important reasons are that the TCP/IP protocol does not completely use the actual channel bandwidth and congestion occurrence during the data transmission. TCP provides the connection-oriented connection. TCP may have a problem with utilizing the full bandwidth of the communication channel. Numbers of congestion control proposals have been suggested to reduce this problem. This paper presents the implementation of a quadric increase congestion control algorithm and it’s simulation through the NS3. This algorithm is based on binary increased congestion control algorithm (TCP BIC). In ns3 TCP QIC is separately implemented and tested with the different congestion control algorithms. These congestion control algorithms are TCP Westwood, BIC, NewReno, scalable and Illinois. The performance of the TCP QIC has the significance over the other congestion control algorithms in respect of throughput, goodput, delay variance and round-trip time.

Hybrid TCP Illinois, Congestion Control Algorithm, TCP BIC, TCP New Reno, NS3, TCP (Transmission control protocol) , QIC: Quadric Increase Congestion control algorithm

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G. Paliwal, S. Taterh, "Simulation Windows Size Quadric Increase Congestion Control Algorithm Implementation using NS3 in Wired Computer Networks Scenario," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.480-485, 2018.

Social networks provides platform to user to share their pictures, videos and make new friends and follow a community and so on. There are different applications of social networks but mostly used applications are Facebook, Instagram, and twitter. A user can recommend a page or community to other user based on their interests but it is difficult to recognize which page or posts posted on page is original or not for this in this paper an attempt has been made to recommend a friend to follow a Facebook page or not. In proposed mechanism the posts are distinguished based on their popularity which is calculated various features like comments reactions shares. this popularity is calculated using python program. The proposed mechanism is analyzed using Gephi with performance metrics like modularity, centrality betweeness, page rank etc.

Social Networks, Facebook, User, Posts Popularity, Netvizz and Gephi.

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[3]. Ekta, Sanjeev Dhawan and Kulvinder Singh, “Feature Extraction and Content Investigation of Facebook User’s using Netvizz and Gephi”, Advances in Computer Science and Information Technology (ACSIT), ACSIT 2016, pp. 262-265.
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[16]. Sanjeev Dhawan, Kulvinder Singh, Honey Gupta “User Behavior Based Friend Recommendation in Facebook Social Networks” ,2018, International Journal of Computer Sciences and Engineering, vol 06, issue 03, pp 69-73,

Sanjeev Dhawan, Kulvinder Singh, Honey Gupta, "User Behaviour Based Friend Recommendation in Facebook Social Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.486-490, 2018.

For sharing and integrating knowledge on the web semantically, Ontology is one of the most significant technologies of the semantic web stack to provide structured information on the web in machine processable format. Ontology has various research issues where Ontology design and representation is the most fundamental. For Ontology design and representation, there are various methods and tools available. One of the crucial Meta model-based approaches is UML (Unified Modelling Language) which is a graphical notational language for ontology design and representation. The UML has been extensively followed through the software engineering network and its scope is broadening to consist of various extra modeling features. In UML, the flow of control and data through the different stages in a procedure is represented by using structural and behavioral notations like in activity diagrams, Use case and class diagrams. In this paper, first Ontology key issues and the role of UML for Ontology design and representation has been explored and discussed. Second, a related literature review of Ontology and UML for Ontology representation has been presented. Third, Ontology creation activities and building stages have been discussed with the help of diagrams along with UML usage and benefits. Fourth, a case study of Banking System has been chosen for Ontology design using UML which includes Use case diagram, Activity diagrams and Class diagram to represent Banking System Ontology.

Ontology, UML, Association, Generalization Aggregation, Use Case Diagram, Class Diagram, Activity Diagram, Banking System, Ontology Design

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[14] C. Roussey, F. Pinet, M. A. Kang, and O. Corcho,” An Introduction to Ontologies and Ontology Engineering”, Ontologies in Urban Development Projects, Advanced Information and Knowledge Processing, Springer-Verlag London Limited, 2011.
[15] Baclawski, et al. “ Extending the Unified Modeling Language for Ontology development “, International Journal Software and System Modeling (SoSyM),1(2),2001.
[16] X. Wang, C. W. Chan, H. J. Hamilton,” Design of Knowledge-based systems with the Ontology domain system approach”, Proceeding SEKE ’02 Proceedings of the 14th international conference on software engineering and knowledge engineering , Pages 233-236, 2002.
[17] P. Kogut et al. “UML for Ontology Development”, Knowledge Engineering and Management,2004.
[18] D. Nicola, A., Missikoff, M., and Navigli, R.” A software engineering approach to ontology building. Information Systems”, 34(2):258–275, 2009.
[19] R. B. S.López , M. S.López, M. Bravo, I. I. Gurrola, and V. G. S. Arias,” GODeM: A Graphical Ontology Design Methodology”, Research in Computing Science 84 , 2014.
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P. Rathee, S.K. Malik, "Proposed UML Approach for Ontology Design and Representation: A Banking System Case Study," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.491-499, 2018.

The oil spills are one of the major pollutions in the marine environment which needs to monitor exactly. The satellite remote sensing especially the synthetic aperture radar (SAR) is the method used to check oil spills for wide area coverage. The edges of an image play an important role to detect the oil spill in water. The existing method has some drawbacks in terms of correctly extracting the oil spills from synthetic aperture radar (SAR) images, where speckle noise exists. Due to this heterogeneous background noise, the existing edge detection techniques, not able to detect the accurate edges of oil spills in water. This paper proposes an alternative method of an edge detection that first, pre-processes the oil spill SAR image and then acquires the threshold by gray value statistics. The oil can be separated from water by using the threshold that was attained. After the threshold segmentation, region growing method is applied in the segmented image and then the edge can be extracted completely by using the Canny edge detection to extract oil spill information more accurately. The perfect extraction of edges of oil spill gathers significant benefits, in terms of monitoring automatically for the risk management.

SAR, ENVISAT, RADARSAT-I, speckle noise

[1]. F. Yu, W. Sun, J. Li, Y. Zhao, Y. Zhang, G. Chen,” An improved Otsu method for oil spill detection from SAR images”, Oceanologia, Vol. 59, Issue.3, pp.311-317, 2017.
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[3]. E. Niharika1, H. Adeeba, A.S.R Krishna, P.Yugander, ”K means based Noisy SAR Image Segmentation using Median Filtering and Otsu Method” ,IEEE, 2017 International Conference on IoT and Application, India, 2017.
[4]. A.S. Banu, P. Vasuki, S.M.M. Roomi, A.Y. Khan, “SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement”, IJSRNSC, Vol. 6, Issue.3, pp. 13-17, 2018.
[5]. S. Pahadiya, R. Khatri, “Compare Modify Canny Edge Detection Method with Existing Edge Detection Methods”, IJCSE, Vol.6, Issue. 2, pp. 337-340, 2018.
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[7]. R.C. Gonzalez, R.E. Woods, S.L. Eddins, ”Digital Image Processing Using Matlab”, Pearson Publisher, U.S, pp.299-302, 2010.
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[11]. W.B. Wei, “Study on Edge Detection Method. Computer Engineering & Applications”, Vol. 42, Issue. 30, pp. 88-91, 2006.
[12]. G. Hu, X. Xiao, “Edge Detection of Oil Spill Using SAR Image”, IEEE, 2013 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, China, pp.466-469, 2013.
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[14]. W. Li, M. He, S. Zhang, “A New Canny-Based Edge Detector for SAR Image”, IEEE, 2008 Congress on Image and Signal Processing, China, pp.211-215, 2008.
[15]. B. Wang, S. Fan, “An improved CANNY edge detection algorithm ”, IEEE, 2009 Second International Workshop on Computer Science and Engineering, China, pp.497-500, 2010.
[16]. X. Yu, J. Yla-Jaaski, “A new algorithm for image segmentation based on region growing and edge detection”, IEEE International Sympoisum on Circuits and Systems, Singapore, pp.516-519, 2002.
[17]. B.D. Setiawan, A.N. Rusydi, K. Pradityo, “Lake edge detection using Canny algorithm and Otsu thresholding”, IEEE, 2017 International Symposium on Geoinformatics (Icy), Indonesia, pp.72-76, 2017.
[18]. E. Schwinger, A.Z. Munthe-Kaas , “Comparative analysis of various thresholding techniques on TerraSAR-X images in the presence of speckle noise”, IEEE, 2013 8th International Sympoisum on Image and Signal Processing and Analysis (ISPA), Italy, pp.36-42, 2013.

Dhrisya Krishna, Keerthi Krishnan K, "Oil Spill Detection from Synthetic Aperture Radar Image through Improved Edge Detection Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.500-505, 2018.

The idea of the conventional cellular system is that devices are not permitted to communicate with each other directly, especially, in the licensed bandwidth of the cellular network. The entire communications occur via base stations. This paper includes envisioning about a two-tier cellular network which also implicates a macro-cell tier (Base Station to Communication of Device) and also a device tier (Device to Device Communication). Relay of device terminal makes it possible for those devices which are in network and allow doing function them as transmission of relays. It realizes a massive network of ad-hoc. In these scenarios, the two-tier cellular system maintained security for the privacy just to ensure existing macro-cells of BSs performance’s minimal impact. This network needs a design with some smart strategies, proper resources, and intrusion management. Moreover, models of novel pricing have been smartly designed to tempt those devices to contribute to the communication of such types. This paper gives an overview of such challenges that may arrive in two-tier networks. It also proposes some schemes related to prices for several relay devices.

Device-to-Device Communication, Cellular Network, Ad Hoc, Transmission, base Stations.

[1] Höyhtyä, Marko, Olli Apilo, and Mika Lasanen. "Review of Latest Advances in 3GPP Standardization: D2D Communication in 5G Systems and Its Energy Consumption Models." Future Internet 10.1 (2018): 3.
[2] Hong, Zhen, et al. "Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network." Future Internet 9.4 (2017): 85.
[3] Jiang, Fan, et al. "Energy-Efficient Resource and Power Allocation for Underlay Multicast Device-to-Device Transmission." Future Internet 9.4 (2017): 84.
[4] Ban, Tae-Won. "A Practical Resource Management Scheme for Cellular Underlaid D2D Networks." Future Internet 9.4 (2017): 62.
[5] Katsinis, Georgios, Eirini Eleni Tsiropoulou, and Symeon Papavassiliou. "Multicell Interference Management in Device to Device Underlay Cellular Networks." Future Internet 9.3 (2017): 44.
[6] Khan, Muhidul Islam, et al. "Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication." Future Internet 9.4 (2017): 72.
[7] Anwar, Asim, Boon-Chong Seet, and Xue Jun Li. "Quality of Service Based NOMA Group D2D Communications." Future Internet 9.4 (2017): 73.
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Rocky Kumar, "Communication of Device-to-Device in 5G Cellular Network in LTE-Advanced Network and Advances in 3GPP Standardization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.506-510, 2018.

Multi atlas based image segmentation is a successful method in recent days in segmenting and labeling MRI images and is mainly based on registration and label fusion. In atlas-based label propagation, an pre labeled image is registered to the target image using image registration methods which produces a segmentation based on the labels in the atlas image. The segmentation of the unknown input image is then achieved by warping the atlas label to the target image space. A single atlas will produce a single segmentation which may prone to errors whereas use of N atlases gives N segmentations; then all N segmentations will be merged to get a final target segmentation. Use of N atlas gives more accurate result than use of single atlas. Many label fusion methods have been proposed. In this paper, the performance of two label fusion methods for segmenting three crucial subcortical brain structures using atlases is evaluated and compared. The result shows that joint label fusion outperforms majority voting method.

MR Images, atlas based segmentation, multiple atlases, brain structures, label fusion

[1]. Lotjonen, J., Wolz, R., Koikkalainen, J., Thurfjell, L., Waldemar, G., Soininen, H., Rueckert, D.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49, 2352–2365 (2010).
[2]. Heckemann R, Hajnal J, Aljabar P, Rueckert D, Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage. 2006;33(1):115–126.
[3]. A. F. Frangi, D. Rueckert, J. A. Schnabel, and W. J. Niessen, “Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling,” Medical Imaging, IEEE Transactions on, vol. 21, no. 9, pp. 1151–1166, 2002.
[4]. 4.M. Lorenzo-Vald´es, G. I. Sanchez-Ortiz, R. Mohiaddin, and D. Rueckert, “Atlas-Based Segmentation and Tracking of 3D Cardiac MR Images Using Non-rigid Registration,” in MICCAI (1), pp. 642–650,2002.
[5]. S. Joshi, B. Davis, M. Jomier, and G. Gerig, “Unbiased diffeomorphic atlas construction for computational anatomy.” Neuroimage, vol. 23 Suppl 1, 2004.
[6]. 6.P. Lorenzen, M. Prastawa, B. Davis, G. Gerig, E. Bullitt, and S. Joshi, “Multi-Modal Image Set Registration and Atlas Formation,” Medical Image Analisys, vol. 10, no. 3, pp. 440–451, June 2006.
[7]. 7. Dipanjan Moitra, Dr. Rakesh Kumar Mandal “SEGMENTATION STRATEGY OF PETBRAIN TUMOR IMAGE” Indian Journal of Computer Science and Engineering (IJCSE), ISSN : 0976-5166 Vol. 4 No.5 Oct-Nov 2013 .
[8]. 8. D. N. Lohare, R. Telgade, R. R. Manza, “Review Brain Tumor Detection Using Image Processing Techniques”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.735-740, 2018.
[9]. 9. Huo J., Wang G., Wu Q.M.J., Thangarajah A. (2015) Label Fusion for Multi-atlas Segmentation Based on Majority Voting. In: Kamel M., Campilho A. (eds) Image Analysis and Recognition. ICIAR 2015
[10]. 10. Wang, H., Suh, J. W., Das, S. R., Pluta, J., Craige, C., & Yushkevich, P. A. (2013). Multi-Atlas Segmentation with Joint Label Fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3), 611–623.

R. Neela, R. Kalaimagal, "Performance Evalutaion of Two Label Fusion Methods for Segmenting Subcortical Brain Structures using Pre-Labeled Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.511-513, 2018.