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This review paper highlights the methodology followed for analysing the medical image by extracting the red blood cells from it. The image of blood cell sample is captured through microscope which consists of number of cells. Different techniques for segmentation of image such as edge detection, thresholding, Morphological processing etc. are used for the area evaluation of red blood cells for its efficient analysis. The main objective is to adopt the proposed methodology for discovering the red blood cells in the microscopic image.

Red blood cells (RBCs), thresholding, edge detection, Morphological processing, Hough transforms

[1] T. Balaji , "Robust and Realistic Classification of Massive Gray Level Thresholding in Remote Sensing Images", International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.31-38, 2014.
[2] L.A Bhavnani, U.K Jaliya, M.J Joshi,“Blood Cell Segmentation and counting: A Survey”, International Journal of Innovative and Emerging Research in Engineering , Vol. 2,Issue. 11, pp. 21-24,2015.
[3] S.S.S.K.R.Innani,Mahavidyalaya Karanja,“Red Blood Cells Classification using Image Processing” Science Research Reporter, Vol 1, Issue. 3 pp. 151-154,2011.
[4] Aruna N.S., Hariharan S.,”Edge Detection of Sickle Cells in Red Blood Cells”, International Journal of Computer Science and Information technologies ,Vol. 5,Issue. 3, pp. 4140-4144,2014.
[5] M. MumthajBegam, R. Geetha , A. Sagayaselvaraj,“Red Blood Cell Identification Using Watershed Technique”,International Journal for Research in Applied Science and Engineering Technology,Vol. 3, Issue. 4,pp. 41–51, 2015.
[6] Hemant Tulsani,Saransh Saxena,Ashok Yadav “Segmentation using Morphological Watershed Transformation for Counting Blood Cells” , Iternational Journal of Computer Applications and Information Technology,Vol. 2, Issue. 3, pp. 28-36, 2013.
[7] Mausumi Maitra, Raj Kumar Gupta,Manali Mukherjee,“ Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform”, International Journal of Computer Applications,Vol. 53,Issue. 61,pp. 13-17 ,2012.

Neeti Taneja, Kamaljeet Kaur, "Review on Image Segmentation Techniques for Red Blood cell Identification," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.138-142, 2017.

Data Mining is the extraction of useful information from a huge amount of datasets. As one of the most important tasks in data mining, clustering aims to group a set of objects such that the objects within the same cluster are more similar to each other than to the objects in another cluster. An extension of the K-Means Algorithm, K-Mode Algorithm, is partitioning based clustering algorithm does not guarantee for the optimal solution. To overcome this problem, entropy based similarity coefficient was introduced in order to find good initial center points and the accurate result of the clusters were obtained. The nature-inspired harmonic algorithm is hybridized to optimize the k-mode algorithm. In this paper, Harmonic K-Mode Algorithm is proposed that reduces the computation time and improves the accuracy for cluster generation. The experimental result shows that the proposed algorithm gives better results than the existing algorithms.

Data Mining, Clustering, K-Means Algorithm, K-Mode Algorithm

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[5]. P. Arora, Deepali, S.Varshney, “Analysis of K-Means and K-Medoids Algorithm For Big Data”, International Conference on Information Security & Privacy, India, Science Direct, Vol. 78, pp. 507-512, 2016.
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[10]. F. Cao, J. Liang, L. Bai, “A new initialization method for categorical data clustering”, Expert Systems with Applications, Science Direct, Vol. 36, pp. 10223-10228, 2009.
[11]. J. Lee, Y. J. Lee, M. Park, “Clustering with Domain Value Dissimilarity for Categorical Data”, Advances in Data Mining, Applications and Theoretical Aspects, Lecture Notes in Computer Science, Springer, Vol. 5633, pp. 310-324, 2009.
[12]. D. Ienco, R. G. Pensa, R. Meo, “From Context to Distance: Learning Dissimilarity for Categorical Data Clustering”, ACM Transactions on Knowledge Discovery from Data, pp.1-22, 2011.
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[14]. F. Cao, J. Liang, D. Li, L. Bai, C. Dang, “A dissimilarity measure for the k-modes clustering algorithm”, Knowledge-Based Systems, Elsevier, Vol. 26, pp. 120–127, 2012.
[15]. Y. M. Cheung, H. Jia, “Categorical and numerical attribute data clustering based on a unified similarity metric without knowing cluster number”, Pattern Recognition, Elsevier, Vol. 46, pp. 2228–2238, 2013.
[16]. S. S. Khan, A. Ahmad, “Cluster Center Initialization for Categorical Data Using Multiple Attribute Clustering”, Expert Systems with Applications, Elsevier, Vol. 40, pp. 7444–7456, 2013.
[17]. R. S. Sangam, H. Om, “The k-modes algorithm with entropy based similarity coefficient”, 2nd International Symposium on Big Data and Cloud Computing, Procedia Computer Science, Elsevier, Vol. 50, pp. 93-98, 2015.
[18]. R.Viederyte, “Preconditions evaluation in Maritime Clustering research”, 3rd Global Conference on Business, Economics, Management and Tourism, Rome, Italy, Elsevier, Vol. 39, pp. 365-372, 2016.

Manisha Goyal, Shruti Aggarwal, "Optimized K-Mode Algorithm Using Harmonic Technique," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.143-148, 2017.

Data mining is the process of analysing data from many different perspectives or dimensions, categorize it and finally summarize it into useful information. This information can be used to increase profits, cut costs, or both. Data mining software is used for analysing data. It allows users to analyse data from many different perspectives, categorize it, and summarize the relationships discovered. Specially, data mining is the way of extracting valuable correlations or patterns among many number of fields in large relational databases. Pattern mining has become an important task in data mining. Mining frequent and infrequent itemsets from a dataset is the most important field of data mining. Mining frequent itemset is very expensive when minimum support threshold is low, and when a minimum support threshold is high mining in frequent itemsets is highly expensive. The proposed system uses multiple level minimum supports to constrain infrequent itemsets by giving different minimum supports to itemsets with different length in order to mine a number of infrequent itemsets in an appropriate degree. In this paper, we are implementing the concept of infrequent weighted itemset mining based on Hadoop-MapReduce model, which can handle massive datasets in mining in frequent itemsets, in that we proposed two novel algorithms based on IWI Miner, IWI Miner to drive the IWI mining process. This paper emphasis on the issue of discovering those itemsets which occurs rarely in large dataset called infrequent weighted itemset (IWI) mining problem.

Data Mining, frequent Itemset, Infrequent Itemset, Weighted Itemset, Hadoop , MapReduce

[1] Aruna J. Chamatkar and P.K. Butey , "Comparison on Different Data Mining Algorithms", International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.54-58, 2014.
[2] Akilandeswari. S and A.V.Senthil Kumar, "A Novel Low Utility Based Infrequent Weighted Itemset Mining Approach Using Frequent Pattern", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.181-185, 2015.
[3] Jeffery Dean and Sanjay Ghemawat, “MapReduce: simplified data processing on large clusters”, Communications of the ACM, Vol. 51, No.1, 2008, pp. 107-113.
[4] Dong, Z Zheng, Z Niu and Q Jiam,” Mining infrequent itemset based on multiple level minimum supports”, 2nd Int. Conf. on Innovative Computing, Information Control, 2007.
[5] He Jiang, Xiumei Luan, Xiangjun Dong,” Mining Weighted Negative Association Rules from Infrequent Itemsets Based on Multiple Supports”, 978-0-7695-4792-3/12 $26.00 © 2012 IEEE 2012 International Conference on Industrial Control and Electronics Engineering.
[6] A. Gupta, A. Mittal, and A. Bhattacharya, “Minimally Infrequent Itemset Mining Using Pattern-Growth Paradigm and Residual Trees”, Proc. Int’l Conf. Management of Data (COMAD), pp. 57-68, 2011.
[7] T Ramakrishnudu, R B V Subramanyam,” Mining Interesting Infrequent Itemsets from Very Large Data based on MapReduce Framework”, I.J. Intelligent Systems and Applications, 2015, 07, 44-49.
[8] David J. Haglin and Anna M. Manning, “On Minimal Infrequent Itemset Mining”.
[9] K. Sun and F. Bai, “Mining Weighted Association Rules Without Preassigned Weights,” IEEE Trans. Knowledge and Data Eng., vol. 20, no. 4, pp. 489-495, Apr. 2008.
[10] Ling Zhou, Stephen Yau ∗,” Efficient association rule mining among both frequent and infrequent items”, Computers and Mathematics with Applications 54 (2007) 737–749.
[11] J.Jaya1, S.V.Hemalatha2,” A Survey of Frequent and Infrequent Weighted Itemset Mining Approaches”.
[12] He Jiang, Xiumei Luan, Xiangjun Dong,” Mining Weighted Negative Association Rules from Infrequent Itemsets Based on Multiple Supports”, 978-0-7695-4792-3/12 $26.00 © 2012 IEEE 2012 International Conference on Industrial Control and Electronics Engineering.
[13] Junfeng Ding, Stephen S.T. Yau, “TCOM, an innovative data structure for mining association rules among infrequent items”, Computers and Mathematics with Applications, Vol. 57, No. 2, January 2009, pp. 290-301.
[14] Guru Prasad M.S., Nagesh H.R., Swathi Prabhu, "An Efficient Approach to Optimize the Performance of Massive Small Files in Hadoop MapReduce Framework", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.112-120, 2017.
[15] Nidhi Sethi and Pradeep Sharma, "Mining Frequent Pattern from Large Dynamic Database Using Compacting Data Sets", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.31-34, 2013.

R.B.M. Sayyad, P.S. Yalagi, "Infrequent Weighted Itemset Mining for Large Dataset," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.149-153, 2017.

Wireless sensor networks have gone through a lot of changes in recent years. Secrecy in data transmission and network standby time are a major concern in communication. Currently, DSR algorithm is used for transmission of data between nodes. The algorithm determines the path for data to be transmitted to the destination node. Once the path is determined, all data are transmitted through the path. This method of data transmission drains the battery of the nodes quickly when the nodes are stationary. All the data are transmitted in a single stretch through the data path selected. When someone gains access to this path, all the data can be collected, and the entire network is compromised. A theory is implemented for eliminating this game. The data path is selected such that the battery level of all the nodes is effectively used. The data are spit and transmitted using more than one path for enhancing data security.

Network Life Time, Data Encryption, Game Theory

[1] Xiangwen Zhang; Fei-Yue Wang “Key Technologies of Passive Wireless Sensor Networks Based on Surface Acoustic Wave Resonators”. Networking, Sensing and Control, IEEE International Conference (ICNSC) on 6-8 April 2008, pp. 1253 – 1258.
[2] Shi-Wei Li; Dong-Qian Ma; Qiang-Yi Li ; Ju-Wei Zhang; X. Zhang “Nodes deployment algorithm based on perceived probability of heterogeneous wireless network”. International Conference on Advanced Mechatronic Systems (ICAMechS), 25 – 27 Sept. 2013 pp: 374 – 378.
[3] Keisuke Nakatsuka, Kenzo Nakamura, Yuichi Hirata, Takeshi Hattori “A Proposal of the Co-existence MAC of IEEE 802.11b/g and 802.15.4 used for The Wireless Sensor Network” 5th IEEE Conference on EXCO, Daegu, Korea October 22-25, 2006.
[4] Vaibhav V. Deshpande; Arvind R. Bhagat Patil “Energy efficient clustering in Wireless Sensor Network using Cluster of Cluster heads”, Wireless and Optical Communication Networks (WOCN), 2013, Tenth International IEEE Conference on 26 – 28 July 2013, pp: 1 – 5.
[5] Wei. Zhao; Yao. Liang “Kernel-based Markov random fields learning for wireless sensor networks”, Local Computer Networks (LCN) IEEE 36th Conference on 4 -7 Oct 2011, pp: 155 – 158.
[6] M. Jeyalakshmi “Location aware end-end data security using Mac for secured wireless sensor networks”. International Conference on Advances in Engineering, Science, and Management (ICAESM), 2012.
[7] Md. Anisur Rahman, Mitu Kumar Debnath “An energy-efficient data security system for Wireless Sensor Network”, 11th International IEEE Conference on Computer and Information Technology, (ICCIT) 24 – 27 Dec. 2008, pp: 381 – 386.
[8] Akshay S. Nagdive, Piyush K. Ingole “An implementation of energy efficient data compression & security mechanism in clustered Wireless Sensor Network”, International IEEE Conference on Advances in Computer Engineering and Applications (ICACEA), 19 – 20 March 2015, pp: 375 – 380.
[9] M. Panda “Data Security in Wireless Sensor Networks via AES algorithm”. IEEE 9th International Conference on Intelligent Systems and Control (ISCO), 9 – 10 Jan. 2015. pp: 1 – 5.
[10] R. Velayutham, J. Mary Suganya “Security Authentication through AES and fine-grained distributed Data Access Control using Clustering in Wireless Sensor Networks”. Third International Conference on Computing Communication and Networking Technologies, 26 – 28 July 2012, pp: 1- 6.

A. Muruganandam, R. Anitha, "Game Theory Based Security Approach in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.154-158, 2017.

In traditional wireless sensor networks all the nodes communicate data with the central node. The path for data transmission is mainly the AODV protocol used. Each time the nodes communicate with each other before data transmission and the shortest path is selected to enable minimizing the battery usage in data transmission. This type of protocol uses considerable energy of all nodes in the network and the network traffic is increased at times depending on the number of nodes in the network. Convex approximation is introduced for overcoming this problem. In this method the nodes are grouped. Every group has a group head. Which determines the path for data transmission, also holding all the data about the nodes present under it. The group heads also communicate with other group heads to determine the path for data transmission.

Convex Approximation, Network Life Time, Data path selection

[1]. A. Garg, N. Batra, I. Taneja, A. Bhatnagar, A. Yadav, S. Kumar, "Cluster Formation based Comparison of Genetic Algorithm and Particle swarm Optimization Algorithm in Wireless Sensor Network", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.14-20, 2017.
[2]. Jinghon. Guo; J. Yao; T. Song; J. Hu; M. Liu “A routing algorithm to long lifetime network for the intelligent power distribution network in smart grid”, IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 19 – 20 Dec. 2015, pp: 1077 – 1082.
[3]. S. Sharma, D. Kumar and K. Kishore, "Wireless Sensor Networks- A Review on Topologies and Node Architecture", International Journal of Computer Sciences and Engineering, Vol.1, Issue.2, pp.19-25, 2013.
[4]. Aditya Singh Mandloi and Vinita Choudhary, "An Efficient Clustering Technique for Deterministically Deployed Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.1, pp.6-10, 2013.
[5]. X. Chen; C. He; L. Jiang “The tradeoff between transmission cost and network lifetime of data gathering tree in Wireless Sensor Networks”, IEEE International Conference on Communications (ICC), 9 – 13 June 2013, pp: 1790 – 1794.
[6]. P. Wang; C. Li; J. Zheng “Combined Data Aggregation and Encryption Using Clustered Slepian-Wolf Coding for Wireless Sensor Networks”, IEEE Global Telecommunications Conference GLOBECOM `07, 26 – 30 Nov. 2007, pp: 920 – 925.
[7]. T. D. Ramotsoela; G. P. Hancke “Data aggregation using Homomorphic Encryption in Wireless Sensor Networks”, International Conference on Information Security for South Africa (ISSA), 16 – 18 Sept. 2011, pp: 2983 – 2986.
[8]. G.R. Shahmohammadi, Kh.Mohammadi, "Key Management in Hierarchical Sensor Networks Using Improved Evolutionary Algorithm", International Journal of Scientific Research in Network Security and Communication, Vol.4, Issue.2, pp.5-14, 2016.
[9]. G. Rohini “Dynamic router selection and encryption for data secure in Wireless Sensor Networks”, International Conference on Information Communication and Embedded Systems (ICICES) 21 – 22 Feb. 2013 pp: 256 – 259.
[10]. M. Abdallah; J. Pogge; A. Simpson “Intrinsic Cognitive Network Addressing”, IEEE International Conference on Long Island Systems, Applications and Technology Conference (LISAT), 3 May 2013 pp: 1 – 4.

P. Bagyalakshmi, R. Anitha, "Successive Convex Approximation for Efficient Energy Utilisation in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.159-163, 2017.

Most of the facial features recognition, say for an example, character, gender and expression has been broadly envisioned. Programmed age assessment and prediction of future expressions have once in a while been examined. With the increase in age of human beings, we can see some gradual changes in their facial features. This paper aims to give a procedure to gauge age gathering that makes use of facial features. This procedure takes account of three stages namely Location, Feature Extraction and Classification. The geometric components of face pictures such as face edge, wrinkle topography, left eye to right eye separation, eye to nose separation, eye to jaw separation and eye to lip separation are calculated. By considering the surface and shape data, age grouping is done making use of K-Means bunching calculation. Age features are further ordered progressively based on the gathered data making use of K-Means bunching calculation. The acquired results are pretty fast and efficient. This paper can further be utilized for anticipating future confronts, arranging gender orientation, and expression recognition from images of the various faces.

Age Estimation, Eyeball Recognition, Face Detection and Wrinkle Features

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A. Tomar, J.S. Kumare, "An Improved Method for Age Group Classification using Facial Features," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.164-172, 2017.

Image compression is a technique to reduce the file size of an image. The main objective of a compression algorithm is to remove redundancy in an image. Compression ratio and Peak signal to noise ratio (PSNR) are the two parameters used to evaluate the efficiency of a particular algorithm. In this paper, we introduce some popular and a new compression algorithm. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the existing algorithms used for image compression. Hybrid compression is a new algorithm which is a combination of DWT and DCT. It takes advantages of both DWT and DCT by discarding their limitations. The purpose of the hybrid algorithm is to keep the balance between compression ratio and quality of the reconstructed image. On comparison of results of the Hybrid compression algorithm with DWT-based compression algorithm, it shows that there are high compression ratio and high quality of reconstructed image using Hybrid compression.

Image Compression, Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Hybrid Compression, Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

[1] C. S. Rawat and S. Meher, “A Hybrid Image Compression Scheme Using DCT and Fractal Image Compression”, The International Arab Journal of Information Technology, Vol. 10, No. 6, pp. 553- 561, 2013..
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[3] A.M.Raid , W.M.Khedr , M. A. El-dosuky and Wesam Ahmed “Jpeg Image Compression Using Discrete Cosine Transform - A Survey” International Journal of Computer Science & Engineering Survey (IJCSES), Vol.5, No.2, pp. 39-47, April 2014.
[4] S.P. Bagal and V.B. Raskar “JPEG Image Compression Using DCT” International Journal of Computer Sciences and Engineering, Vol. 4, Issue 4, pp. 34-38, April 2016.
[5] M. Mozammel Hoque Chowdhury and Amina Khatun “Image Compression Using Discrete Wavelet Transform” International Journal of Computer Science Issues, Vol. 9, Issue 4, No 1, pp. 327-330, July 2012.
[6] N. Saroya, Parbhpreet Kaur, “Analysis of image compression using DCT and DWT transforms”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 2, pp. 879-900, 2014.
[7] P. Telagarapu, V. J. Naveen, A. L. Prasanthi, G. V. Santhi “Image Compression Using DCT and Wavelet Transformations”, International Journal of Signal Processing, Image Processing and Pattern Recoginition, Vol. 4, No. 3, pp. 61-73, 2011.
[8] K.Ayyappa Swamy, C.Somasundar Reddy, K. Durga Sreenivas, “Image compression using Hybrid DWT DCT transform” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5, Issue 5, pp. 1655-1658, May 2015.
[9] R.P. Jasmi, B. Permul and M.P. Rajasekaran, “Comparison of image compression techniques using Huffman Coding, DWT and Fractal Algorithm”, International Conference on Computer Communication and Informatics, Coimbatore (India), pp. 4799-6805, 2015

Paramjeet Singh, Binni Garg, Shaveta Rani, "Image Compression Using Hybrid Technique," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.173-176, 2017.

Image enhancement is a digital processing technique which does its best to get a better image. It is a simple and most engaging range in image processing. Image enhancement is a process by which we got an improved and high-quality image through the low contrast and low-quality image. Image Contrast enhancement without disturbing other parameters of the image is one of the difficult tasks in image. This paper focused on several enhancement techniques that deal with contrast of image. Image enhancement dealt with improvement in the appearance and quality of the given image without losing the information of the image. This paper includes several approaches of image enhancement these approaches are used for improving the given image which incorporates manipulation of gray scale, Histogram Equalization (HE) and Filtering. HE is the basic method of image enhancement. All the techniques presented by this study is simulated on Intel I3 64 bit processor using MATLAB R2013b. For quality measurement of contrast enhanced image Peak to signal noise ratio and Mean Square Error parameter for each of the methods is additionally computed.

Image Enhancement, Histogram, Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Brightness Preserving Dynamic Histogram Equalization.

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P. Gupta, J.S. Kumare, U.P. Singh, R.K. Singh, "Histogram Based Image Enhancement Techniques: A Survey," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.177-182, 2017.

Sentiment Analysis is an area of interest over the last decade. The social networking is one of the important sources for users to know express the views on different organizations, product, and politics. In this work, we focus on mining sentiments and analyzing public review on demonetization. Demonetization was one of the biggest political decisions taken in year 2016 which affected each and every person in India. In demonetization 500 and 1000 rupees currency was banned more over a new 2000 rupees note was introduced in currency. This affected economy, market and exposed black money also. We worked on twitter data for demonetization. It aims to analyzing positive and negative of tweets reviews as sentiment classification task. The raw dataset collected is preprocessed by cleaning unwanted text, tokenized and used for polarity classification of data corpus.

Sentiment Analysis , Classification, SVM, Machine learning

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Uma Aggarwal, Gaurav Aggarwal , "Sentiment Analysis on Demonetization using SVM," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.183-187, 2017.

This manuscript shows a novel routing protocol for Active-Inactive networks (AINs) called Signal-to-Interference-plus-Noise-Ratio (SINR). SINR sagaciously coordinates the sending and buffer administration arrangements into a versatile protocol that incorporates a neighborhood organize parameters estimation instrument. It powerfully changes the delivery likelihood for messages as indicated by another metric. In the interim, SINR organizes the sending arrangement and the dropping need in light of their doled out weight. The weight is controlled by the Replication Density (RD), the Message Length (ML), and Message Excess Life Time (MELT). A broad recreation of SINR was done and its execution was contrasted with surely understood AIN routing protocols: PRoPHET, and Epidemic Routing protocols. Reenactment comes about demonstrate that the proposed routing protocol beats them as far as bundle delivery proportion, delivery deferral and message overhead.

AINs, Opportunistic Network, Adaptive Routing Protocols, Forwarding and Dropping, New Routing Protocols

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S. Vaishnavi, A. Pavithra2, "Beamforcing for Efficient Spectrum Sensing Based TRMS," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.188-192, 2017.