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In recent research, recognition of fetal stress is significant in fetal monitoring during pregnancy. In order to evaluate the health of the fetus, a non-invasive fetal monitoring method should be used for measuring the Fetal Heart Rate (FHR).Many methods are available for assessing the FHR. Among these methods, Cardiotocography (CTG) and Fetal Electrocardiogram (FECG) are common methods for monitoring FHR. The most important parameter for determining the fetal health is the FHR. Lot of algorithms are available extracting FECG signals from mothers abdomen. An algorithm is proposed to extract FECG from signals measured from the maternal abdomen. Wavelet transform technique is the most popular and efficient method for determining ECG characteristics. Independent Component Analysis (ICA) and Principle component Analysis (PCA) techniques are used in wavelet transform. This proposed work consists of three algorithms for finding out the fetal heart rate and stress. The proposed algorithm consists of three steps: 1) Abdominal ECG signal (AECG) is acquired from maternal abdomen i.e. Maternal ECG (MECG). 2) FECG signal is extracted by subtracting MECG signal from AECG signal by PCA method. 3) Then fetal R peaks are calculated in extracted FECG signal to detect fetal heart rate. Finally fetal stress is monitored from the measured FHR. The main contribution of our work is the fetal stress analysis from the fetal heart rate.

Fetal Heart Rate, Cardiotocography, Fetal Electrocardiogram, PCA, ICA, ECG.

[1] J.Shamira and R.Tamilselvi, “Extraction of Fetal Heart Rate and Respiratory Rate from Abdominal Electrocardiogram”, Pak. J. Biotechnol. Volume 13 (special issue on Innovations in information Embedded and communication Systems) pp. 400- 404, 2016.
[2] Reza Sameni, Mohammad B. Shamsollahi, Christian Jutten and Gari D.Clifford, “A Nonlinear Bayesian Filtering Framework for ECG Denoising”, IEEE Transactions On Biomedical Engineering, Volume 54, Issue 12,2007.
[3] N P. Joshi, Shweta A.Tambe1, P.S. Topannavar, “Morphological &Dynamic Feature Based Heartbeat Classification”, International Journal of Engineering Research and General Science, Volume 2, Issue 3, 2014.
[4] RezaSameni, Christian Juttenand, Mohammad B.Shamsollahi, “Multichannel Electrocardiogram Decomposition Using Periodic Component Analysis”, IEEE Transactions on Biomedical Engineering. Volume 55, Issue 8, 2008.
[5] Camargo-Olivares J. L, R. Martin-Clemente, Hornillo-Mellado S, M. M.Elena, and I. Roman, “The Maternal Abdominal ECG as Input to MICA in the Fetal ECG Extraction Problem”, IEEE Signal Processing Letters, Volume 18,Issue 3,2011.
[6] C. Saritha,V. Sukanya,Y. Narasimha Murthy, “ ECG SignalAnalysis Using Wavelet Transforms”, Bulg. J. Phys. Volume 35 pp. 68–77,2008.
[7] Yatindra Kumar et al, “Performance Analysis of different Filters for Power Line Interface Reduction in ECG Signal” International Journal of Computer Applications ,Volume 3,Issue.7,pp.1-6, 2010.

R. Tamilselvi, M. Parisa Beham, A. Merline, S.M.M.Roomi, B. Saravanan and T. Ruba, "Detection of Fetal Stress from Maternal Abdominal Electrocardiogram Signal", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.65-70, 2018.

Microaneurysms (MAs) is an earliest lesions in DR detection, its plays a challenging role in diabetic retinopathy (DR) diagnosis. It has been an active research in medical image processing and so many machine learning algorithms has been developed for MA detection. The First Stage of detection is consisting of clearer segmentation of optical disc area in retina using a new Minimum Intensity Maximum Solidity (MinIMas) algorithm on fundus dataset, then extract bright lesion and red lesion using Gaussian mixture models. Set of feature extracted that the second stage of the system, finally machine learning approach is a0pplied for lesion classification. In this paper, a hybrid of segmentation and unsupervised classification of sparse PCA (HM-SPCA) for MA detection is proposed so that enhanced output is obtained. This proposed algorithm achieves great analysis in lesion segmentation with minimum false alarm. Furthermore, effective features can be extracted due to sparse properties of PCA (Principle Component Analysis) which merge the elastic net penalty with PCA together. Thus, the projected DR detection system enhanced its performance by reducing false positives compared with existing algorithms in lesion classification, and hence this approach can be applied to improve the beneficence in earlier vision detection of patients for diabetic retinopathy.

Diabetic Retinopathy, MA, Optic disk, Blood vessel, Classification, Sparse, PCA

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D. Ashok kumar, A. Sankari, "HM-SPCA: Hybrid Method for Automatic Detection of MA Using MinIMas with Sparse PCA in Diabetic Retinopathy", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.71-80, 2018.

Block Truncation coding is a simple and efficient technique for compressing still images. Absolute Moment Block Truncation Coding (AMBTC), an improved form of BTC has been enhanced in this proposed method to achieve better results in terms of PSNR (Peak Signal to Noise Ratio) and bpp (bits per pixel). In BTC and AMBTC based techniques, only two quantizers are used. In the proposed method, four quantizers are used for improving the quality of reconstructed images, by categorizing the blocks based on the distribution of gray levels among the pixels. Adaptive bit-plane patterns are generated to improve the coding efficiency. This method is tested with standard benchmark images such as Lena, Cameraman, Boats, Bridge, Baboon and Kush. For all the images, the proposed method gives better results in terms of bpp and PSNR when compared to that of existing techniques.

AMBTC, bpp, Coding Efficiency, hMean, Image Compression, lMean, PSNR.

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S. Vimala, P. Uma, S. Saranya, "Adaptive AMBTC using Bit Plane Patterns for Compressing Still Images", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.81-85, 2018.

India is associate agricultural country and majority of its population is engaged in agricultural works and farming outcomes being their own supply of financial gain. Agriculture sector faces several challenges of enhancing production with accessible natural resources. Soft set theory plays an important role to show great ability in decision making model like crop selection, crop planning, irrigation planning, water resource management etc. Even if a near optimal solution is obtained, it will have a very large impact. This can be achieved by the use of soft set techniques. This paper brings in awareness on decision making model in crop cultivation by using soft set theory that helps to identify the crop to be cultivated by a farmer which suits best of his expectations by the questionnaire and it can be optimized by various statistical measures.

Soft Set, Quickreduct, K-Means, Statistical Measure

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S. Mohanambal, G. Jeyanthi and A. Pethalakshmi, "An Efficient Decision making in Crop cultivation using Soft Set Theory", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.86-92, 2018.

Deep learning has proven to be beneficial for complex tasks such as classifying the image, pattern recognition, speech recognition, natural language processing, and recommendation systems. Autoencoder, Restricted Boltzmann Machine, Deep belief Network and Convolutional Neural network are four different types of architecture used in deep learning. Deep Belief Network is now the new the state of the art for many fields of machine learning research. The main aim of this survey is to widely cover deep belief network architecture and their practical applications such as computer-aided diagnosis for the dreadful diseases, pattern recognition and also in the field of industry. The proposed work helps to improve the classification performance for breast cancer to a certain extent, which provides a good direction for the future classification of breast cancer. At last, the limitations of Deep Belief network and list of future research information has been given.

deep learning, autoencoder, restricted Boltzmann machine, deep belief network, convolutional neural network.

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M. Sornam and A. Radhika, "Deep Belief Network Architecture and Their Applications – A Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.93-98, 2018.

Early detection of heart disease can be achieved by high disease prediction and diagnosis efficiency. Machine learning techniques can help the medical expert in decision making for providing the best treatment. In this paper, an autoencoder neural network classifier is developed for the classification of heart disease medical data sets. The autoencoder were trained to properly classify the clinical data. The proposed classifier is tested on heart disease data sets namely Cleveland and Statlog obtained from the UCI repository and also compared with conventional classification techniques namely Support Vector Machine, Random Forest, K-Nearest Neighbour, Naïve Bayes to concerning its outperformance. Experimental results show that the autoencoder neural network clas¬sifier offers much better classification accuracy, precision, recall and f-measure rates when compared with other conventional methods. The proposed method presents itself as an easily accessible and cost-effective alternative to traditional machine learning methods which are used for the diagnosis. In this study, the implementation of the developed model can potentially support in reducing heart disease among patients.

Artificial Neural Networks, Autoencoder, Heart disease, Classification

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[19] B.L. Betechuoh, T. Marwala, T. Tettey, “Autoencoder networks for HIV classification”, Current Science, Vol. 91, No. 11, pp. 1467-1473, 2006.
[20] R. Hata, M.A.H. Akhand, K. Murase, “Multi-valued autoencoders and classification of large-scale multi-class problem”, International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, pp. 19-26, 2017.
[21] F.M.Alakwaa,Kumar D.Chaudhary, L.X.Garmire, “Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data” J Proteome Res, Vol. 17(1), pp.337-347, 2017.

D. Rajeswari, K. Thangavel, "Heart Disease Detection using Autoencoder", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.99-103, 2018.

MR images are mostly used for clinical diagnosis for their accuracy. Even though the resolution, signal-to-noise ratio and acquisition speed have been increased, the MR images are still getting polluted. Thus, denoising is needed to be done in order to improve the accuracy of both the manual and computer aided diagnostic process. There are number of noises in digital images caused based on the nature of image acquisition or transformation. Rician noise is the kind of noise occurs in MR images. Numerous denoising techniques have been proposed to denoise Rician distribution in MR images. In this paper a survey about noises in digital images, non-local means (NLM) filtering and wavelet based MRI denoising techniques have been done. Finally, a Rician denoising method is proposed using wavelet thresholding and Rician NLM and compared with the existing methods. The PSNR values show that the proposed method yields better results.

Noises, Rician noise, MRI, Non local means, wavelet thresholding, PSNR.

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Authors Profile
T. Kalaiselvi is currently working as an Assistant Professor in Department of Computer Science and applications, Gandhigram Rural Institute, Dindigul, Tamilnadu, India. She received her Bachelor of Science (B. Sc) degree in Mathematics and Physics in 1994 & Master of Computer Applications (M.C.A) degree in 1997 from Avinashilingam University, Coimbatore, Tamilnadu, India. She received her Ph. D degree from Gandhigram Rural University in February 2010. She has completed a DST sponsored project under Young Scientist Scheme. She was a PDF in the same department during 2010-2011. An Android based application developed based on her research work has won First Position in National Student Research Convention, ANVESHAN-2013, organized by Association of Indian Universities (AUI), New Delhi, under Health Sciences Category. Her research focuses on MRI of human Brain Image Analysis to enrich the Computer Aided Diagnostic process, Telemedicine and Teleradiology Technologies.

N.Kalaichelvi received her Bachelor of Sciences (B.Sc) degree in Physics in 2007 and Master of Computer Science & Applications in 2010 from Gandhigram Rural University, Dindigul, Tamilnadu, India. She received her Master of Philosophy (M.Phil) degree in Computer Science in 2013 from Madurai Kamaraj University, Madurai, Tamilnadu, India. She was working as Assistant Professor from July 2010 – May2012 and from July 2015 – March2016 in the Centre for Geoinformatics, Department of Rural Development, Gandhigram Rural Institute – Deemed University, Dindigul, Tamilnadu, India. She was working as Assistant Professor from June – 2014 to June -2015 in the Department of computer Science in Prince Shri Venkateshwara Arts and Science College, Gowrivakkam, Chennai, Tamil nadu, India. Currently she is pursuing Ph.D. degree in Gandhigram Rural Institute – Deemed

T. Kalaiselvi, N. Kalaichelvi, "Investigation on Image Denoising Techniques of Magnetic Resonance Images", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.104-111, 2018.

An IoT network is rendered reliable when it is tolerant to faults. Fault management involves fault prevention, detection and recovery of which prevention is considered to be a significant phase. The main objective of this work is fault prevention and detection in IoT networks. The existing IoT fault prevention system finds only one reliable path identified using the goodness value of different available paths and no alternate solutions are provided in case of failure of the reliable path. The proposed system provides a solution by choosing the non-discarded path with the highest goodness value for transmission in case the chosen path fails. The proposed framework has a set of observer nodes connected to a smart gateway and a set of sensor nodes connected to the observers. The gateway acts as the interface between the network and the Internet. This framework comprises prevention and detection algorithms to prevent communication failures between sensor node and gateway, to provide alternative reliable paths for transmission and to detect node faults in early stages. The proposed gateway also provides a solution for hardware failure, software failure, connection failure and also manages the overall load balance of the network.

IoT, Smart gateway, Fault-tolerant, Fault management system, Goodness value

[1] Q. Zhu, R. Wang,Q. Chen, Y. Liu & W. Qin, "Iot gateway: Bridgingwireless sensor networks into internet of things", Embedded and Ubiquitous Computing (EUC), 2010 IEEE/IFIP 8th International Conference on. IEEE, 2010
[2] H. Suo, J. Wan, C. Zou, & J. Liu, “Security in the internet of things: a review”, In Computer Science and Electronics Engineering (ICCSEE), 2012 international conference on (Vol. 3, pp. 648-651). IEEE
[3] G.White,V. Nallur, & S. Clarke,” Quality of Service Approaches in IoT": A Systematic Mapping”, Journal of Systems and Software, 132, pp.186-203 ,2017
[4] M.C. León, H. Meyer, D. Rexachs, & E. Luque, “Fault tolerance at system level based on RADIC architecture”, Journal of Parallel and Distributed Computing, 86, 98-111, 2015.
[5] W. Chen, R.F. da Silva, E. Deelman, & T. Fahringer,” Dynamic and fault-tolerant clustering for scientific workflows”, IEEE Transactions on Cloud Computing, 4(1), 49-62,2016.
[6] Y. Challal, A. Ouadjaout., N. Lasla, M. Bagaa & A. Hadjidj, “Secure and efficient disjoint multipath construction for fault tolerant routing in wireless sensor networks”, Journal of network and computer applications, 34(4), pp.1380-1397, 2011.
[7] T.N. Gia, A.M. Rahmani, T. Westerlund, P. Liljeberg, & H. Tenhunen, “Fault tolerant and scalable IoT-based architecture for health monitoring”, In Sensors Applications Symposium (SAS), 2015 IEEE (pp. 1-6). IEEE.
[8] S. Misra, A. Gupta, P.V. Krishna, H. Agarwal, & M.S. Obaidat, “An adaptive learning approach for fault-tolerant routing in Internet of things”, In Wireless Communications and Networking Conference (WCNC), 2012 IEEE (pp. 815-819). IEEE.K.
[9] M.W. Woo, J. Lee, & K. Park, “A reliable IoT system for Personal Healthcare Devices. Future Generation Computer Systems” , 78, pp.626-640 ,2017.
[10] S.A. Karthikeya, Vijeth, J. K., & C.S.R. Murthy,”Leveraging Solution-Specific Gateways for cost-effective and fault-tolerant IoT networking” In Wireless Communications and Networking Conference (WCNC), 2016 IEEE (pp. 1-6). IEEE.
[11] S. Misra, P.V. Krishna,A. Bhiwal, A.S. Chawala, B.E. Wolfinger, & C. Lee. "A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks." The Journal of Supercomputing 1-20,2012.

T. Saha, R. Sunitha, "A Framework for Fault Prevention and Detection in IoT using Smart Gateway", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.112-122, 2018.

Many algorithms in the field of image processing support high degree of inherent parallelism. Edge detection is one of the most important processes in medical image processing. Edge detection is an independent process to support parallel computation of each pixel intensity changes by their neighbourhood pixels. Magnetic resonance imaging (MRI) scanner provides stack of 2D slices with millions of pixels and thus require much time for edge detection process in central processing unit (CPU) systems. In the proposed work, graphics processing unit (GPU) based parallel edge detection methods are developed for MRI volume using compute unified device architecture (CUDA). Each pixel operation in edge detection is independent and thus GPU provides high level data parallelism using threads per voxel method. Basic edge detection operators such as Roberts, Prewitt, Sobel, Marr- Hildreth and Canny are used in this experiment. The computational time of parallel GPU-CUDA based methods were compared with the serial CPU implementation. Results showed that parallel implementation is about 11× to 98× times faster than the serial CPU implementation.

Edge detection, GPU, CUDA, Roberts, Prewitt; Sobel, Marr- Hildreth, Canny

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[3] T.Kalaiselvi, P. Sriramakrishnan, “Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine”, International Journal of Imaging System and Technology, pp. 1–12, 2018. DOI: 10.1002/ima.22267
[4] M. Chouchene, F.E. Sayadi, Y. Said, M. Atri, R. Tourki, “Efficient implementation of Sobel edge detection algorithm on CPU, GPU and FPGA”, International Journal of Advanced Media and Communication, Vol. 5, No. 2, pp. 105-117, 2014.

[5] H.X. Gong, L Hao, “Roberts edge detection algorithm based on GPU”, Journal of Chemical and Pharmaceutical Research, Vol. 6, No. 7, pp. 1308-1314, 2014
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[7] M. Kossain, M.A. Ashique, M.A. Ibtehaz, J. Uddin, “Parallel Edge Detection using Sobel Algorithm with Contract-time Anytime Algorithm in CUDA”, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), CSREA Press, pp. 130-135, 2016
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[9] T.L.B. Cheikh, G. Beltrame, G. Nicolescu, F. Cheriet, S. Tahar, “Parallelization strategies of the canny edge detector for multi-core CPUs and many-core GPUs”, In the Proceedings of the International on New Circuits and Systems Conference (NEWCAS), Canada, pp. 49-52, 2012.
[10] Z. Emrani, S. Bateni, H. Rabbani, “A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms”, Journal of medical signals and sensors, Vol. 7, No. 1, pp. 33-42, 2017.
[11] T. Kalaiselvi, P. Sriramakrishnan, K. Somasundaram, “Survey of using GPU CUDA programming model in medical image analysis”, Informatics in Medicine Unlocked, Vol. 9, pp. 133 – 144, 2017
[12] F. Rob, CUDA application design and development, 1st Ed. Elsevier, pp. 1–336, 2011.
[13] L. Roberts, J. Tippet, “Machine Perception of Three Dimensional Solids”, Optical and Electro-Optical Information Processing, Cambridge, MA: IT Press; 1965.
[14] J.M. Prewitt, “Object Enhancement and Extraction”, Vol. 75, New York, Academic Press 1970.
[15] D. Marr, E. Hildreth, “Theory of Edge Detection”, Proceedings of the Royal Society of London, Series B, Biological Sciences, Vol. 207, No. 1167, pp. 187–217, 1980.
[16] J.F. Canny, “A computation approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 769-798, 1986.
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[18] V. Podlozhnyuk, “Image convolution with CUDA”, 2007.

P. Sriramakrishnan, T. Kalaiselvi and K. Somasundaram, "Parallel Processing Edge Detection Methods for MR Imagery Volumes using CUDA Enabled GPU Machine", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.123-130, 2018.

Deep Learning is currently being used for a variety of different applications. It has drawn increasing research interest because of the capability of overcoming the drawback of traditional algorithms. Some of the important applications are pattern recognition, computer vision, speech recognition, natural language processing, handwriting recognition, face recognition, IoT and medical. There are several researches has been done in the area of deep learning from the last decade of the nineteenth century and still many more to come. This paper gives survey on deep learning and some of the recent research that has been done in the area of deep learning.

Machine Learning, Deep learning, Fog Computing, Genetic Algorithm, Pattern Recognition.

[1] Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, Fuad E. Alsaadi, “A survey of deep neural network architecture and their applications,” Neurocomputing, vol. 234, pp. 11-26, 2017.
[2] Vrizlynn L.L. Thing, “IEEE 802.11 Network Anomaly Detection: A Deep Learning Approach,” IEEE Wireless Communication and Networking Conference, pp. 1-6, 2017.
[3] AsanthaThilina, Shakthi Atanayake, SacithSamarakoon, DahamiNawodya, LakmalRupasinghe, NadithPathirage, TharinduEdirisinghe, KesavanKrishnadeva, “Intruder Detection using Deep Learning and Association Rule Mining,” IEEE Conference on Computer and Information Technology, pp. 615-620, 2016.
[4] Chuanlong Yin, Yuefei Zhu, Jinlong Fei, Xinzheng He, “A Deep Learning Approach for Intrusion Detection using Recurrent Neural Networks,” IEEE Access, vol.5, pp. 21954-21961, 2017.
[5] Nency Patel, Narendra Sekokar, “Implementation of pattern matching algorithm to defend SQLIA,” Procedia Computer Science, vol.45, pp. 453-459, 2015
[6] Youbiao He, Gihan J, Mendis, Jin Wei, “Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism,” IEEE Transaction on Smart Grid, vol. 8, no. 5, pp. 2505-2516, 2017.
[7] Hongmei He, Tim Watson, Carsten Maple, JornMehnen, Ashutosh Tiwari, “A New Semantic Deep Learning with a Linguistic Attribute Hierarchy for Spam Detection,” International Joint Conference on Neural Networks, pp. 3862-3869, 2017.
[8] A.ADiro, N. Chilamkurti, “Distributed Attack Detection Scheme using Deep Learning Approach for Internet of Things,” Future Generation Computer Systems DOI=http”//dx.doi.org/10.1016/j.future.2017.08.043

[9] Dayieng Liu, JianchengLv, Xiaofeng Qi and Jiangshhu Wei, “A Neural Words Encoding Model,” International Joint Conference on Neural Network, pp. 532-536, 2016.
[10] Xiaoguang Chen, Xuan Yang, Maosen Wang, Jiaancheng Zou, “Convolutional Neural Network for Automatic Facial Expression Recognition,” International Conference on Applied System Innovation, pp. 814-817, 2017.
[11] Zijing Mao, Wan Xiang Yao, Yufei Huang, “EEG-based biometric identification with deep learning,” International IEEE/EMBS Conference on Neural Engineering, pp. 609-612, 2017.
[12] Daniele Ravi, Charence Wong, Benny Lo, Guang-Zhong Yang, “A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no.1, pp. 56-64, 2017.
[13] Na Lu, Tengei Li, Xiaodong Ren, Hongyu Miao, “A Deep Learning Scheme or Motor Imagery Classification based on Restricted Boltzman Machines,” Transaction on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 566-576, 2017.
[14] M. Sornam, C. Vishnu Priya, “Deep Convolutional Neural Network for Handwritten Tamil Character Recognition using Principal Component Analysis,” International Conference on Next Generation Computing Technologies, University of Petroleum and Energy Studies, Dehradun, Oct 30th and 31st 2017, pp. 102-112.
[15] Mohammed Amine JanatiIdrissi, Hassan Ramchoun, Youssef Ghanou, “Genetic Algorithm for Neural Network Architecture Optimization,” International Conference on Logistics Operation Management, pp.1-4, 2017.
[16] YausiKanada, “Optimizing Neural Network Learning Rate by Using a Genetic Algorithm with Per-epoch Mutations,” International Joint Conference on Neural Networks, pp. 1472-1479, 2016.

M.Sornam, E. Panneer Selvam, "Recent Advances in Deep Learning Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.131-135, 2018.