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Wireless Sensor Networks (WSN) are used in a extensively large number of applications. Based on the advanced development of sensors nodes, routing of data becomes an interesting concept in WSN. Many routing protocols were designed by researchers all over the world concerning about battery power, sensing data, networking lifetime and transmitting data etc. This paper concentrates on routing the data in an intelligent manner than the existing routing protocols. Aggregating the data with the help of clusters from the nearby nodes and the cluster heads are responsible to send the collected data to one or more Sink Node(SN) that request for data. This paper ponders on clustering with neurons in the cluster heads and thereby gathers the needed and correctly sensed information very faster based on fuzzy inference system. Neuron in the form of cluster heads gathers and remembers the data. With the help of NS2 simulator this algorithm is very clear that it surpasses the other routing protocols in an abundant number of ways.

Neurons, Clustering, cluster head, SN, Routing, Energy efficient, Fuzzy

[1] A Multi-Aware Query Driven (MAQD) routing protocol for mobile wireless sensor networks based on neuro-fuzzy inference., Ahmed I. Saleh, Khaled M. Abo-Al-Ez, Ahmed A. Abdullah, Journal of Network and Computer Applications 88 (2017) 72–9.
[2] Abreu, C., Ricardo, M., Mendes, P., 2014. Energy-aware routing for biomedical wireless sensor networks. J. Netw. Comput. Appl. (Elsevier) 40, 270–278.
[3] Anastasi, G., Conti, M., Francesco, D.M, Passarella. A, (2009), "Energy Conservation in Wireless Sensor Networks: a Survey", Ad Hoc Networks, Vol.7, No.3, pp.537—568.
[4] Aslam N, Philips W, Robertson W, Siva Kumar SH, "A multicriterion optimization technique for energy efficient cluster formation in Wireless Sensor networks", Information Fusion,Elsevier (2010).
[5] Baranidharan, B., B. Santhi, 2016. DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach. Applied Soft Computing, Vol. 40, Pg. 495–506.
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[8] David Culler, Deborah Estrin, Mani Srivastava "Overview of Sensor Networks", IEEE Computer Society, August 2004, pp 41-49.
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[22] A Survey of Civilian Applications of WSN and Security ProtocolsChahat Aggarwal1*, B. B. Gupta2. 1 and 2 Dept. of Computer Engineering, National Institute of Technology, Kurukshetra, India. International Journal of Scientific Research Computer Science and EngineeringVol.6, Issue.3, pp.56-66 , June (2018)
[23] Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks, Manju Bhardwaj, Dep. of Electronics Engineering, Shri Ramswaroop Memorial University, Lucknow, India, IJSRNSC, Volume-5, Issue-3, June 2017

D. Lissy, S. Behin Sam, "Neuro-Fuzzy Routing with Clusters in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.474-479, 2018.

Since its inception, market profile has been used by traders as a way to assess the market value of a stock. By reading market profile charts, it is possible for traders to assess who is driving the market (buyers or sellers) and make trades accordingly. The spatiotemporal feature of market profile can be used to train a deep learning model for classifying sequences of market profile. This is a novel idea and one that needs to be examined and experimented upon. LSTM networks are structures capable of remembering long term dependencies in time series data. Convolutional Neural Networks, on the other hand help in figuring out patterns in multidimensional data. A python library is built to generate market profile from time series data. Leveraging the power of LSTMs and CNNs, two models are proposed for the classification: FC-LSTM and ConvLSTM. The results show that the proposed models are able to catch patterns amongst profiles and FC-LSTM performs better than ConvLSTM on this task.

Market Profile, Machine Learning, ConvLSTM

[1] Ratnesh Kumar Shukla, Ajay Agarwal, Anil Kumar Malviya, "An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43, 2018.
[2] P. Rutravigneshwaran, "A Study of Intrusion Detection System using Efficient Data Mining Techniques", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.5-8, 2017
[3] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink and J. Schmidhuber, "LSTM: A Search Space Odyssey," IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 10, pp. 2222-2232, Oct. 2017.
[4] T. Fischer and C. Krauss, ‘‘Deep learning with long short-term memory networks for financial market predictions’’, FAU Discussion Papers in Economics, 2017.
[5] Milosevic, Nikola. “Equity forecast: Predicting long term stock price movement using machine learning.” Journal of Economics Library, Vol. 3, No. 2, pp. 288-294, Jun. 2016.
[6] K. Joshi, H.N. Bharathi, Jyothi Rao, “Stock Trend Prediction using News Sentiment Analysis”, International Journal of Computer Science & Information Technology (IJCSIT), Vol. 8, No. 3, pp67-76, July 2016.
[7] Steidlmayer, J. P. & Hawkins, S. B. “Steidlmayer on Markets: Trading with Market Profile”, John Wiley & Sons, Inc. NJ, 2003.
[8] James F. Dalton, Robert Bevan Dalton, and Eric T. Jones, “Markets in Profile”, John Wiley, New York, 2007.
[9] Krauss, C., Do, X. A., Huck, N., “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500”, European Journal of Operational Research Vol 2, pp. 689-702, 2017.
[10] K. Amudha, S. Padmapriya, D. Saravanan, "Ensemble Forecasting Through Learning Process", International Journal of Computer Sciences and Engineering, Vol. 06, No. 02, pp. 332-335, 2018.
[11] S. Velankar, S. Valecha and S. Maji, "Bitcoin price prediction using machine learning," 20th International Conference on Advanced Communication Technology (ICACT), pp. 144-147, 2018
[12] Kondor, D., Posfai, M., Csabai, I. & Vattay, G. “Do the rich get richer? an empirical analysis of ´ the bitcoin transaction network.” Public Library of Science, Vol. 9, pp. 1–10, 2014.
[13] S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, S. C. Kremer, J. F. Kolen, "Gradient flow in recurrent nets: The difficulty of learning long-term dependencies" A Field Guide to Dynamical Recurrent Networks, Piscataway, NJ, USA: IEEE Press, 2001.
[14] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo, ‘‘Convolutional LSTM network: A machine learning approach for precipitation nowcasting,’’ Neural Information Processing Systems, pp. 802–810, 2015.
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Pranshu Rupesh Dave, Priti Srinivas Sajja, "Classifying Sequences of Market Profile using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.480-485, 2018.

Heart disease detection is considered as the most complicated task in the world of medical sciences. There arises a necessity to progress the work and to develop a decision support system to find out a heart disease of a patient. To achieve a correct and cost effective treatment computer-based and support systems can be developed to make good decision. These information which exists contains the huge amounts of data which are organized in the form of images, text, charts and numbers. Hence, there is necessity which is motivating to create an excellent and useful project which will help physicians and cardiologists to predict the heart disease before it damage the health. It can be able to solve complicated enquiries for detecting heart disease in a patient and as a result it will assist medical practitioners to make more accurate and precise clinical decisions which traditional decision support systems were not able to decide. By providing effective and respective solution, it will surely help to reduce costs of treatment.

Back Propagation, Cardiovascular disease Confusion Matrix, Genetic Algorithm, Neural Network

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[15] S. Sabeena, V. Sujitha “Prediction of Heart Disease Using AI and NLP Techniques”, International Journal of Computer Sciences and Engineering, Vol-6, Issue-2, Jan 2018

S. Shrivastava, S. Shrivastava, "Diagnosis of Heart Disease using Cultural Algorithm with Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.486-491, 2018.

Biometric recognition is the challenging area because security and user authentication is necessary for any login purpose. To identify a person, physiological characteristics are most widely used. This paper proposed a new fusion technique for the integration of finger knuckle print, palmprint and face biometrics. The present study concentrated on the multimodal biometric system due to its benefits. Initially the region of interest was obtained for the biometric traits. Then the features are extracted using Speeded Up Robust Features (SURF). Fusion of finger knuckle print, palmprint and face are done at score level using MeRank fusion rule. The MeRank technique uses classification result as well as matching scores of each trait. The performance of the proposed technique is evaluated and experimental results demonstrated that the fusion technique achieved 98.84% of accuracy for the multimodal biometric system.

Feature extraction, score level fusion, SURF, Multimodal system and MeRank

[1] C. Busch, “Finger Knuckle Recognition”, Technical University of Denmark, pp. 1-4, 2011.
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[9] The Hong Kong Polytechnic University, PolyU Palmprint Database, Available at: http:// www. Comp. polyu. edu.hk/biometrics.
[10] PolyU Finger Knuckle Print Database, Available at: http://www.comp.polyu.edu.hk/ biometrics/FKP.htm.
[11]PolyU FaceDatabase, Available at:
http://www.comp.polyu.edu.hk/ biometrics/
[12] M. Nageshkumar, P. Mahesh, M. Swamy, “An Efficient Secure Multimodal Biometric Fusion using Palmprint and Face Image”, The International Journal of Computer Science Issue, vol. 1, pp. 49-53, 2009.
[13] M. M. Kazi, Y. S. Rode, S. B. Dabhade, N. N. H. Al-Dawla, A. V. Mane, R. R. Manza, K. V. Kale, “Multimodal biometric system using face and signature: a score level fusion approach”, Advances in Computational Research, vol. 4, no. 1, pp. 99-103, 2012
[14] S. Chaudhary, R. Nath, “A New Multimodal Biometric Recognition System Integrating Iris, Face and Voice” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 4, pp.145-150, 2015.

J. Anne Wincy, Y. Jacob Vetha Raj, "A Fusion Technique for The Multimodal Biometric System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.492-495, 2018.

Video based Face Recognition (VFR) has significantly more challenges when compared to Still Image-based Face Recognition (SIFR). The objective of this paper is to identify faces in video more precisely. In this paper, the minute details of the face are identified by block based technique. It is classified using neural network. The proposed method is tested with four publicly available datasets: Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), Honda/UCSD and UMD Comcast10 datasets. The proposed method achieves higher recognition rate when compared to other recent methods.

Keyframe, Block matching algorithm, face recognition

[1] Z. Huang, S. Shan, R.Wang, H. Zhang, S. Lao, A. Kuerban, and X. Chen, “A benchmark and comparative study of video-based face recognition on cox face database,” IEEE Trans. Image Process., vol. 24, no. 12, pp. 5967–5981, 2015.
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[17] Lenin, Wilson S, “An Efficient Key frame Extraction Method in Video based Face Recognition” IPASJ International Journal of Computer Science (IIJCS), Volume 6, Issue 2, February 2018, ISSN 2321-5992.
[18] Lenin, Wilson S, “An Efficient Key frame Extraction Method in Video based Face Recognition” IPASJ International Journal of Computer Science (IIJCS), Volume 6, Issue 2, February 2018, ISSN 2321-5992.
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S.Wilson, A. Lenin Fred, "Simulation Based Exploration of SKC Block Cipher Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.496-501, 2018.

In this paper, the performance to optimize feed-forward neural network has been evaluated for the classification of voice signals of English alphabets. There are various feed forward neural network models have been used earlier but the selection of optimize architecture is a challenge. In this paper we are implementing a optimize architecture which is best suitable for the classification of voice signals. Digital signal processing operations are applied on analog speech signals to convert them into digital form and then to make them suitable for further processing by neural network models.

Digital signal processing, Optimize neural network, Pattern classification

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Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal, "Optimized Neural Network Architecture for The Classification of Voice Signals," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.502-506, 2018.

PIM-DM is a multicast routing protocol that uses the underlying unicast routing information base to flood multicast datagrams to all multicast routers. End-to-End Multicast Congestion Control (MCC) is a complex problem. TFMCC (TCP Friendly Multicast Congestion Control) is a congestion control mechanism for multicast transmissions. Where the sending rate is adapted to the receiver experiencing the worst network conditions TFMCC shows better performance. TFMCC is stable and responsive under a wide range of network conditions and scales to receiver sets on the order of several thousand receivers. In this paper, we implemented TFMCC protocol to PIM-DM model and also we compare TFMCC with TCP behaviour. TFMCC is designed to be reasonably fair when competing for bandwidth, sending rate, varying number of links, different receiver capacity and scalability. Experimental results show tremendous performance improvement in throughput without affecting the TCP fairness of the protocol.

PIM-DM,TFMCC, Congestion control, Scalability

[1] Mohammed A. Al-Jendary1, Maher A. Al-Sanabani2 “Congestion
Avoidance in Wireless Mesh Networks Based On Multicast
Routing Protocols” IJCSE vol-6 Issue-8 Aug 2018 E-ISSN: 2347-2693
[2] Christos Bouras1, postolos Gkamas1 and Georgios Kioumourtzis2.
“ Smooth Multicast Congestion Control for Adaptive Multimedia
Transmission" IEEE Infocom 2003.Twenty-second Annual Joint
Confenence Society (IEEE Cat.No.03CH37428)
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V B Deepa, M B Ushadevi, "Adapting TFMCC Protocol in PIM-DM for avoiding Congestion Control in Wired Multicast," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.507-512, 2018.

In this paper, we propose an artificial neural network-based approach for developing the model for software reliability estimation. The use of intelligent neural network and hybrid techniques in place of the traditional statistical techniques has shown a remarkable improvement in the development of prediction models for software reliability in the recent years. Among the intelligent and the statistical techniques, it is not easy to identify the best one since their performance varies with the change in data. In this paper, firstly the neural network from the mathematical viewpoints of software reliability modeling is explained. Then it is show how to apply neural network to develop a model for the prediction of software reliability. The implementation of proposed model is done with real software failure data sets. From simulation results, the proposed model significantly outperforms the traditional software reliability models.

Software Reliability, Statistical, Artificial Neural network, Reliability Prediction

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Dipak D. Shudhalwar, Pallavi Agrawal, "Software Reliability Modeling Using Neural Network Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.513-524, 2018.

The image enhancement is the well-known concept among the researchers as in the area of image enhancement a lot of work has been done by several authors but also more amendments can be done. In the image enhancement method not only a large number of techniques but also some ways to enhance the image are existed. The contrast enhancement or to improve the brightness of the image is one of the way to improve the quality of the image. This study develops a new approach for image contrast enhancement by considering HSI colour model and fuzzy inference system to improve the intensity of the image pixels. The simulation is done by taking a set of four different images into an account. The simulation results with respect to the Detail Variance and Background Variance of the images describes that the proposed work performs outstanding comparative to the traditional methods that are GLE, Enhanced AHE and original image.

Image Enhancement, Contrast Enhancement, Color Model, HIS Model, Fuzzy Inference Model

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[2]. N. Naik,, “Low Contrast Image Enhancement using Wavelet Transform based Algorithms: A Literature Review” International Journal of Engineering and Technical Research (IJETR), Vol. 3, pp. 123-128, 2015.
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Mehzabeen Kaur, "Soft Computing Approach for Image Contrast Enhancement for Improving Image Visuality," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.525-528, 2018.

Semantic Web is spread by World Wide Web Consortium (W3C) and international standardization body of the web. It is an extended form of the current web which provides an easier way to search, reuse, combine and share the information. Therefore Semantic Web is subsequently viewed as an integrator crosswise over various content, data applications, and frameworks. Today’s big data is usually pronounced as consisting of 3 V’s: volume, variety, and velocity. Variety of data discusses to deal with different formats of data and a large number of various data sources. Thus the problems of big data variety are important for solving many real-world difficulties. Semantic Web is used as an integrator to incorporate data from various kinds of sources like web services, relational databases, and spreadsheets etc. and in different formats. Due to the presence of data heterogeneity, this work presents various difficulties that may not be totally settled with the existing system. This paper is an attempt to focus on the various challenges that involved in integrating data from different types of sources and how different semantic web technologies and tools are used for the integration of disparate data.

Semantic Web, Big Data, Disparate Data

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Jeelani Ahmed, Muqeem Ahmed, "Semantic Web Approach of Integrating Big Data- A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.529-532, 2018.