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This paper presented the new work to minimize the mean absolute error of mammogram breast image which is highly corrupted by impulse noise density. The proposed methodology is implemented with the Adaptive Switching Weighted Median (ASWM) Filter cascaded with Cuckoo Search (CS) optimization algorithm. The efficient adaptive filter de-noises the medical image by detecting the corrupted pixel and replaces them with the median value. The CS algorithm minimizes the error rate between the ASWM filter image and corrupted image. It minimizes the Mean Absolute Error (MAE) percentage and also maximizes the Peak Signal to Noise Ratio (PSNR). This method removes the highly corrupted impulse noise of 90%. The experimental analysis is made and it is observed from the result that the proposed method is far superior to the other conventional techniques in terms of qualitative and quantitative factors. In terms of visual quality, it yields a well sharp edge region and better visual perception of the image quality.

Switching filter, image de-noising, impulse noise, optimization technique, Cuckoo search algorithm

[1] Ramya, A., Murugan, V. and Murugan, D., 2017. Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification. International Journal of Computer Applications, 163(7).
[2] Bhandari, A.K., Kumar, D., Kumar, A. and Singh, G.K., 2016. Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm. Neurocomputing, 174, pp.698-721.
[3] Javaid, Q., Arif, M., Shah, M.A. and Nadeem, M., 2018. A hybrid technique for De-Noising multi-modality medical images by employing cuckoo`s search with curvelet transform. Mehran University Research Journal Of Engineering & Technology, 37(1), p.29.
[4] Borra, S.R., Reddy, G.J. and Reddy, E.S., 2016. An efficient fingerprint enhancement technique using wave atom transform and mcs algorithm. Procedia Computer Science, 89, pp.785-793.
[5] Faragallah, O.S. and Ibrahem, H.M., 2016. Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-International Journal of Electronics and Communications, 70(8), pp.1034-1040.
[6] Yang, X.S. and Deb, S., 2009, December. Cuckoo search via Lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.
[7] Yang, X.S., 2014. Nature-inspired optimization algorithms. Elsevier.
[8] Mareli, M. and Twala, B., 2017. An adaptive Cuckoo search algorithm for optimisation. Applied Computing and Informatics.
[9] Ye, Z., Wang, M., Hu, Z. and Liu, W., 2015. An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm. Computational intelligence and neuroscience, 2015, p.13.
[10] Babu, R.K. and Sunitha, K.V.N., 2015. Enhancing digital images through cuckoo search algorithm in combination with morphological operation. Journal of Computer Science, 11(1), p.7.
[11] Ashour, A.S., Samanta, S., Dey, N., Kausar, N., Abdessalemkaraa, W.B. and Hassanien, A.E., 2015. Computed tomography image enhancement using cuckoo search: a log transform based approach. Journal of Signal and Information Processing, 6(03), p.244.
[12] Wang, Z. and Zhang, D., 1999. Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 46(1), pp.78-80.
[13] Srinivasan, K.S. and Ebenezer, D., 2007. A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE signal processing letters, 14(3), pp.189-192.
[14] Meher, S.K. and Singhawat, B., 2014. An improved recursive and adaptive median filter for high density impulse noise. AEU-International Journal of Electronics and Communications, 68(12), pp.1173-1179.
[15] Nair, M.S. and Mol, P.A., 2013. Direction based adaptive weighted switching median filter for removing high density impulse noise. Computers & Electrical Engineering, 39(2), pp.663-689.
[16] Gupta, V., Chaurasia, V. and Shandilya, M., 2015. Random-valued impulse noise removal using adaptive dual threshold median filter. Journal of visual communication and image representation, 26, pp.296-304.
[17] Gandomi, A.H., Yang, X.S. and Alavi, A.H., 2013. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, 29(1), pp.17-35.

A. Ramya, D. Murugan , T. Ganesh Kumar, S. Vijaya Kumar , "Adaptive Switching De-noising Filter Cascaded with Cuckoo Search Algorithm to Minimize the Mean Error – Medical Image Application", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.1-7, 2018.

The research work presents detection of different types of text in scene images based on extensive segmentation (proposed method) to generate character candidate regions. We usually consider many connected regions as candidates, which aim to capture character regions as many as possible. Key feature of exhaustive segmentation technique, which exactly segments character candidate region in scene images from non-character candidate region also. First, we detect candidate text regions using Maximally Stable External Region (MSER method) where the scene image has been converted to gray image and to find the text region. Second, the geometric properties of text on the image are used to filter out non-text regions using simple thresholds. Third, we remove the rest Non-Text region based on Stroke Width Variation (SWV). Finally, we merge the entire text region from the detection of Text and thus recognise the Detected Text in the scene image. We use public dataset, namely, the Street View Text dataset and some other language (Tamil, Hindi and Chinese) images to evaluate the performance of our(extensive segmentation) method. The experimental results are shown that our(extensive segmentation) method achieves excellent improvement in the detection of text though the images being blurred, low-resolution and small in size from the existing method(Yuenwang et al.). We also achieve considerable rate of recall with the executed images.

digitization, Exhaustive segmentation, stroke Width Variation

[1] Yuanwang Wei, Zhijiang Zhang, Wei Shen, Dan Zeng,, Mei Fang, Shifu Zhou, “Text detection in scene images based on exhaustive segmentation”,2017, Signal Processing: Image communication, 50, pg 1-8.
[2] L. Neumann, J. Matas, Real-time scene text localization and recognition, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Providence, RI USA, 2012, pp. 3538–3545.
[3] Lei Sun, QiangHuo, WeiJia, KaiChen, A robust approach for text detection from natural scene images, in: 2015 Pattern recognition, 48, pp. 2906-2920.
[4] Qixiang Ye, Qingming Huang, Wen Gao, Debin Zhao, Fast and Robust text detection in images and video frames, in :2005, Image and vision computing, 23, pp. 565 – 576.
[5] Juhua Liu, HaiSu, YaohuaYi, WenbinHu, Robust text detection via multi-degree of sharpening and blurring, in: 2016, Signal Processing, 124, pp. 259-265.
[6] B. Epshtein, E. Ofek, Y. Wexler, Detecting text in natural scenes with stroke width transform, in: 2010 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), IEEE, San Francisco, California, USA, 2010, pp. 2963–2970.
[7] T. Wang, D.J. Wu, A. Coates, A.Y. Ng, End-to-end text recognition with convolutional neural networks, in: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, Tsukuba, 2012, pp. 3304–3308.
[8] K. Wang, B. Babenko, S. Belongie, End-to-end scene text recognition, in: 2011 IEEE International Conference on Computer Vision (ICCV), IEEE, Barcelona, Spain, 2011, pp. 1457–1464.
[9] K.I. Kim, K. Jung, J.H. Kim, Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift
[10] algorithm, IEEE Trans. Pattern Anal. Mach. Intell. 25 (12) (2003) 1631–1639.
[11] J. Gllavata, R. Ewerth, B. Freisleben, Text detection in images based on unsupervised classification of high-frequency wavelet coefficients, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 1, IEEE, Cambridge UK, 2004, pp. 425–428.
[12] L. Neumann, J. Matas, Text localization in real-world images using efficiently pruned exhaustive search, in: 2011 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Beijing, China, 2011, pp. 687–691.
[13] T. Wang, D.J. Wu, A. Coates, A.Y. Ng, End-to-end text recognition with convolutional neural networks, in: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, Tsukuba, 2012, pp. 3304–3308.

S. Gopinathan, "The Enhancement of Character and Non-Character images using Extensive segmentation Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.8-13, 2018.

Now a day‘s Image Restoration plays an essential task, since it is one of the major components of image processing technique. Image Restoration is used to enrich the appearance of the Image. It is the process of recovering original image from degraded image, which also reduces and removes the degraded image was found using Point Spread Function (PSF).Degradation transpires in many forms namely Motion blur, Noise, Camera Misfocus. There are two types of Image restoration namely degradation model and restoration model. Degradation model includes different types of noise model and restoration model includes different types of Deconvolution algorithm. Deconvolution algorithm is divided into two parts namely Blind Image Deconvolution algorithm and Non-blind Image Deconvolution algorithm. Blind Image Deconvolution algorithm will not have knowledge about how image was degraded. Non-Blind Image Deconvolution algorithm will have knowledge about how image was degraded. In this paper, surveys on various image restoration techniques for recovering high resolution images are analysed.

Point Spread Function, Blind Deconvolution, Degradation, Non-Blind Deconvolution

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N. Krishnammal, G. Muthulakshmi, "A Survey on Recovering High Resolution images by Using Various Image Restoration Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.14-20, 2018.

As death rate due to heart diseases is increasing significantly, prediction of heart disease with required accuracy becomes a critical issue in health care industry. Data mining and machine learning algorithms, more specifically classification algorithm plays an important role in prediction. Still the accuracy of prediction is influenced by the evolving size of data, nature or format of data and velocity of data. Keeping these factors in mind, Big data based model has been proposed after a careful investigation on existing analytical algorithms. In this paper, some of the existing literature related to the prediction of heart diseases using data mining is presented. Inferences are drawn to find out the essential attributes to be considered for prediction. A study is carried out to find which algorithm will work better for prediction of heart diseases. With inference drawn, an approach is proposed based on Hadoop and MapReduce programming paradigm. It is proposed to employ Support Vector Machine(SVM) in parallel fashion in order to improve the accuracy of prediction. The overview of proposed model is presented.

Bid data in prediction, classification techniques in heart disease prediction, parallel SVM

[1] Himanshu Sharma,M A Rizvi,”Prediction of Heart Disease using Machine Learning Algorithms:A survey”,International Journal of Recent and Innovation Trends in Computing and Communication”, Volume:5,Issue 8,pp.99-104
[2] Himanshu Sharma,M A Rizvi,”Prediction of Heart Disease using Machine Learning Algorithms:A survey”,International Journal of Recent and Innovation Trends in Computing and Communication”, Volume:5,Issue 8,pp.99-104
[3] V. Manikantan, S.Latha, ”Predicting the Analysis of Heart Disease Symptoms using Medicinal Data Mining Methods, conference paper,2013,pp.5-10
[4] T.Revathi,S.Jeevitha,”Comparitive study on Heart Disease Prediction System using Data Mining Techniques”, International Journal of Science and Research,2013,pp. 2120-2123
[5] Pediredla Praveen Kumar,Sunita a Yadwad V V D L Tejaswi, ”Prediction of Heart Disease using Hadoop Mapreduce”, International Journal of Computer Application (2250-1797) Volume 6– No.6, November – December 2016. Pp.1-8
[6] Shamsher Bahadur Patel,Pramod Kumar Yadav,Dr.D.P.Shukla,”Predict the Diagnosis of Heart Disease Patients using classification Mining Techniques”,Journal of Agricultural and Verterinary Science,Volume 4,Issue 2,2013,pp.61-64.
[7] Nidhi Bhatla,Kiran Jyoti,”An Analysis of Heart Disease Prediction Using Different Data Mining Techniques”,International Journal Of Engineering Research & Technology,Volume1, Issue 8,Oct-2012,pp.1-4
[8] Miss.Chaitrali.s,Dangare,Dr.Mrs.Sulabha S.Apte,”A Data Mining approach for prediction of heart disease using Neural Networks”,International Journal of Computer Engineering & Technology,Volume 3, October 2012,pp.30-40
[9] K.Thenmozhi,”Heart Disease Prediction using Classification with Different Decision Tree Techniques”,International Journal of Engineering Research and General Science ,Volume 2,Issue 6,October 2014,pp.6-11.
[10] V. Subha and M.Revathi, D.Murugan,” Comparative Analysis of Support Vector Machine Ensembles for the Heart disease prediction”, International Journal of Computer Science & Communication Networks,Vol 5(6),386 – 390,December 2015,pp.386-390.
[11] Megha Shahi,Er.Rupinder Kaur Guram,”Heart Disease Prediction System Using Data Mining Techniques –A Review”, International Journal of Technology and Computation, Volume 3, Issue 4, April 2017,pp.73-77.
[12] Shalet K.S, V.Sabarinathan, V.Sugumaran, V.J.Sarath Kumar,” Diagnosis of Heart disease using Decision Tree and SVM Classifier”, International Journal of Applied Engineering Research,ISSN 0973-4562 Vol.10 No.68(2015),pp.598-602.
[13] Dr.Siddaraju,Sowmya.c l,Rashmi k,Rahul M,”Efficient Analysis of Big Data using Map Reduce Framework”,International Journal of Recent Development in Engineering and Technology, Volume2,Issue 6,June 2014, pp.64-68
[14] https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

R. Sharmila, S. Chellammal , "A conceptual method to enhance the prediction of heart diseases using big data Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.21-25, 2018.

Media is one of the foremost roles in human daily life activity. Multimedia is the integration of multiple forms of media, which includes text, image, audio, and video. Most of the people are always working with their Personal Digital Assistant (PDA) that provides computing, information storage and retrieval capabilities for personal or business use. Images and videos engage more space than other kinds of data on their PDA or electronic device. There are many kinds of videos available in day to day life, so we need an efficient tool to classify the videos with sky-scraping accuracy. The main goal of video classification is to help the people to find video of their interest. In this paper we study multi dimensional view of video classification methods and techniques, compare them and also conclude with opinion for further research.

Video Classification

[1] Darin Brezeale and Diane J. Cook, ―”Automatic Video Classification: A Survey of Literature”, IEEE Transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 38, no. 3, may 2008.
[2] Jiajun Sun, Jing Wang, Ting-chun Yeh, - “Video Understanding: From Video Classification to Captioning”, 2017.
[3] Mittal C. Darji1, Dipti Mathpal2, - “ A Review of Video Classification Techniques”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 06 | June -2017.
[4] Nirav Bhatt, - “A survey on video classification techniques”, International Journal of Emerging Technologies and Innovative Research, March 2015, Volume 2, Issue 3.
[5] M.Ramesh, Dr. K. Mahesh, - “Significance of various Video Classification Techniques and Methods: A Retrospective”, International Journal of Pure and Applied Mathematics, Volume 118 No. 8 2018, 523-526.
[6] Hansang Kim, Youngbae Kim, Jae-Young Sim, and Chang-Su Kim, ―”Spatiotemporal Saliency Detection for Video Sequences Based on Random Walk With Restart”, IEEE transactions on image processing, vol. 24, no. 8, august 2015.
[7] Reza Fuad Rachmadi†∗ , Keiichi Uchimura†, and Gou Koutaki, ―”Video classification using compacted dataset based on selected keyframe”, Proceedings of the International Conference,2016 IEEE Region.
[8] Shankar, K., K. Mahesh, and K. Kuppusamy. ―”Analyzing Image Quality via Color Spaces.‖ image. In 1.1 (2014).
[9] Shankar, K., K. Mahesh, and K. Kuppusamy. "Video Segmentation on 2D Images with 3D Effect." International Journal of Computer Applications 43.8 (2012): 1-4.
[10] Darin Brezeale and Diane J. Cook, ―”Automatic Video Classification: A Survey of Literature‖”, IEEE Transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 38, no. 3, may 2008

M. Ramesh, K. Mahesh, "Multidimentional View of Automatic Video Classification : An Elucidation", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.26-29, 2018.

The bridge game is one of the most generally known card games comprising many mesmerizing aspects, such as bidding, playing and winning the trick including estimation of human hand strength. The harmonizing input data based on the human knowledge of the game to improvement the quality of tricks. The bridge game classification under a game of imperfect information is to be equally well-defined. The decision made on any stage of the game is simply based on the assessment that was made on the immediate preceding stage. The intelligent game of bridge incompleteness of information, the real spirit of the card game in proceeding further deals of the game are taking into many forms especially during the distribution of cards for the next deal. The cascade correlation neural network architecture with supervised learning implemented in resilient back - propagation algorithm to train data and therefore to test data it is together along with the bamberger point count method and work point count methods.

Cascade-correlation neural network, Resilient back-propagation algorithm, Bridge game, Double dummy bridge problem, Bamberger point count method, Work point count method

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Dharmalingam Muthusamy, "Supervised Learning Architecture for Solving Double Dummy Bridge Problem", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.30-37, 2018.

Feature extraction is one of the most important step in image pattern recognition. Some sources of difficulty are the presence of irrelevant information and the relativity of a feature set to a particular application. Feature extraction and description are essential components of various computer vision applications. The concept of feature extraction and description refers to the process of identifying points in an image (interested points) that can be used to describe the image’s contents. The One major goal of feature extraction is to increase the accuracy of learned models by compactly extracting prominent features from the input data, while also possibly removing noise and redundancy from the input. Additional objectives include low-dimensional representations for data imagining and compression for the purpose of reducing data storage requirements as well as increasing training and implication speed. The aim of this paper is to report an descriptive study of most popular feature extraction methods PCA and LDA which are generally used in pattern recognition and the role of PCA and LDA in gait feature extraction.

Feature extraction, PCA, LDA, Gait Feature Extraction

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[7] Pranjit Das, Sarat Saharia, Human Gait Recognition Based on Principal Component Analysis, International Journal of Computer Sciences and Engineering, Volume-4, Special Issue-7, Dec 2016.
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[13] T. Dacheng, L. Xuelong, W. Xindong, and S. J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 1700-1715, 2007.
[14] Y. Mu and D. Tao, "Biologically inspired feature manifold for gait recognition," Neurocomputing, vol. 73, pp. 895-902,2010.
[15] R. Hu, W. Shen, and H. Wang, "Recursive spatio temporal subspace learning for gait recognition," Neuro computing,vol. 73, pp. 1892-1899, 2010.
[16] E. Zhang, Y. Zhao, and W. Xiong, "Active energy imageplus 2DLPP for gait recognition," Signal Processing, vol.90, pp. 2295-2302, 2010.
[17] G. Baofeng and M. S. Nixon, "Gait Feature Subset Selectionby Mutual Information," IEEE Transactions on Systems,Man and Cybernetics, Part A: Systems and Humans vol. 39, pp. 36-46, 2009.
[18] G. Baofeng and M. S. Nixon, "Gait Feature Subset Selection by Mutual Information," in First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007, pp. 1-6.
[19] Jinyan Chen Jiansheng Li, Average Gait Differential Image Based Human Recognition, Scientific World Journal. 2014. 27.
[20] Yang J, Zhang D, Frangi AF, Yang J-Y. Two-dimensional PCA: a new approach to appearance based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004;26(1):131–137.
[21] Worapan Kusakunniran, Qiang Wu, Hongdong Li, Jian Zhang, Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image, IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009
[22] Deng Cai, Xiao fei He, Jia wei Han.“Training Linear Discriminant Analysis in Linear Time,” IEEE International Conference on Data Engineering, 2008(24):pp. 209-217.
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K. Annbuselvi, N. Santhi, S. Sivakumar, "Role of Feature Extraction Techniques : PCA and LDA for Appearance Based Gait Recognition", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.38-44, 2018.

— Reinforcement Learning is a combination of supervised learning and unsupervised learning, the two main streams of Machine Learning .It has many applications in Artificial Intelligence arena. Multi Armed Bandits problem, a classical Reinforcement Learning task employs exploration and exploitation tradeoff. Efficient Bandit Algorithms for solving Bandit problem proides solutions for various problems from Dynamic pricing to online multi class prediction. This research article analyses the elements of Reinforcement learning, mathematical formulation of multi armed Bandits problem and attempts to present a naive RL algorithm for N-Queens problem for an instance of N=4 and concludes with applications of Reinforcement Learning .

Reinforcement Learning, multiarmed Bandit problem, ϵ- Greedy algorithm

[1] Alpaydm, Introduction to Machine Learning, MIT Press, 2010.
[2] Dai Shi, Exploring Bandit algorithms for Automatic content selection, Barcelona, 2014.
[3] Ellis Horrowitz, Fundamentals of Computer Algorithms, Galgottia Publictions,1996
[4] Kakade et al, Efficient Bandit Algorithms for online multiclass prediction, Conference Proceedings, University of Pennysylvnia,2008.
[5] Nahum Shimkin, Reinforcement Learning –Basic algorithms, Learning in Complex Systems Lecture notes, Spring 2011.
[6] Prathamesh, Multi armed bandit approach for Dynamic pricing, M.Tech Dissertation, Iit, Mumbai, 2015
[7] Sutton and Barto, Reinforcement Learning – an introduction, The MIT Press, England, 2017.
[8] Szepesvari, Algorithms for Reinforcement Learning, Synthesis lectures on Artificial Intelligence and machine learning, 2009.
[9] http://cs.mcgill.ca/~dprecup/courses /AI/Lectures/ai-lecture13.pdf
[10] www.medium.com/machinelearning for humans .html

R. Raja Rajeswari, A. Pethalakshmi, "Optimally Facing the uncertainty : A brief survey on Reinforcement Learning", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.45-48, 2018.

Age Invariant Face Recognition is an emerging research topic in Face Recognition Research Community has many practical applications such as in law enforcement, identifying criminals, passport renewal etc. Facial Aging has not received adequate attention compared to other sources of variations due to pose, lighting, and expression. This paper aims to give a detailed survey of age invariant face recognition. This review covers the techniques that attempt to solve the age invariant problems. This paper also discusses different techniques to extract features and textures of age invariant facial part. Existing problems in age invariant face recognition are covered and possible solutions are suggested in this review. Advantage and disadvantage of each methods and recognition accuracy have been discussed.

Age invariant, face recognition, aging database, Morph, FGNET and Epoch database.

[1] Zhifeng Li, Unsang Park, Anil K. Jain. “A Discriminative Model for Age Invariant Face Recognition”, IEEE transactions on information forensics and security, vol. 6, no. 3, September 2011.
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[6] Diana Sungatullina1, Jiwen Lu, Gang Wang, and Pierre Moulin ,“Multiview Discriminative Learning for Age-Invariant Face Recognition”,10th IEEE international conference,2013.
[7] Djamel bouchaffra, “Nonlinear topological component analysis: application to age-invariant face recognition”, IEEE transactions on neural networks and learning systems, vol. 26, 2015.
[8] A.Sindhuja`, S.Devi Mahalakshmi, Dr.K.Vijayalakshmi, “Age Invariant Face Recognition with Occlusion”, IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2012.
[9] Felix luefei-Xu, Khoa LUU, Marios Savvides1, Tien D. Bui, and Ching Y. Suen,, “Investigating Age Invariant Face Recognition Based on Periocular Biometrics” International joint conference, 2011.
[10] Junyong Si, Weiping Li, “Age-invariant face recognition using a feature progressing model”, 3rd IAPR Asian Conference on Pattern Recognition, 2015.
[11] Dihong Gong, Zhifeng Li, Dahua Lin, “Hidden Factor Analysis for Age Invariant Face Recognition”, IEEE International Conference on Computer Vision, 2013.
[12] Amal Seralkhatem Osman Ali, Vijanth Sagayan, Aamir Malik Saeed, Hassan Ameen, Azrina Aziz, “Age-invariant face recognition system using combined shape and texture features”, IET Biometrics,2014.
[13] Ravi Pal and Ajith kumar Gautam, “Age Invariant Face Recognition Using Multiclass SVM”, 3rd IAPR Asian Conference on Pattern Recognition, 2015.
[14] Tianyue Zheng, Weihong Deng, Jiani Hu, “Age Estimation Guided Convolutional Neural Network for AgeInvariant Face Recognition”,IEEE conference,,2017.
[15] Yandong Wen, Zhifeng Li , Yu Qiao, “Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition”,IEEE conference on 2017.
[16] Dihong Gong,Zhifeng Li and Dacheng Tao, “A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition”, IEEE conference on 2015.
[17] Chi Nhan Duong , Kha Gia Quach , Khoa Luu , T. Hoang Ngan Le and Marios Savvides, “Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition”, computer vision IEEE conference on 2017.
[18] Huiling Zhou, Kwok-Wai Wong, and Kin-Man Lam, “Feature-Aging for Age-Invariant Face Recognition”, Proceedings of APSIPA Annual Summit and Conference on 2015.
[19] Cui Meng, Jiwen Lu, and Yap-Peng Tan, “A Comparative Study of Age-Invariant Face Recognition with Different Feature Representations”, Int. Conf. Control, Automation, Robotics and Vision on 2010.
[20] Xiaonan Hou, Shouhong Ding, Lizhuang Ma, “Robust feature encoding for age-invariant face recognition”,Multimedia and Expo IEEE conference on 2016.
[21] Albert, A.M. and K. Ricanek, “The MORPH database: investigating the effects of adult craniofacial aging on automated face-recognition technology”, Forensic Sci. Commun., 10(2), 2008.
[22] FGNET, “The FG-NET Aging Database”, Retrieved form: /http: // www.fgnet.rsunit. Com, 2009.
[23] M. Parisa Beham and S. Md. Mansoor Roomi, “ INFACE and EPOCH Database: A Benchmark for Face Recognition in Uncontrolled Conditions”, Research Journal of Applied Sciences, Engineering and Technology 8(6): 795-802, 2014.

H. Jebina, M. Parisa Beham, S. Md. Mansoor Roomi and R. Tamilselvi, "Performance Analysis of Age Invariant Face Recognition Methods", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.49-55, 2018.

Wireless systems have gained momentum in recent years due to ease, universality and cost reduction. In a wireless network the signal degradation is time variant based on environmental factors. As variations in signal quality and interference from external sources affect the network, it is necessary and important to adapt the rate to the channel conditions via a rate adaptation algorithm driven by changes in the signal quality. This research addresses signal integrity in a local area network and proposes a rate adaptation algorithm for 802.11 wireless networks based on signal quality and packet error rate. The relationship between signal strength and packet error rate is analyzed and a new rate adaptation algorithm for 802.11n is presented. The algorithm developed in this thesis effectively adapts the rate to minimize packet error rate while it maximizes throughput. Through experiments, it has been shown that the proposed algorithm achieves better throughput compared to state of the technology rate adaptation algorithms currently implemented in commercial 802.11n systems.

Wireless, 802.11, MAC

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[3] J. Kim, S. Kim, S. Choi, and D. Qiao, “CARA: Collision-aware rate adaptation for IEEE 802.11 WLANs,” in INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings, pp. 1–11, Apr. 2006.
[4] S. H. Y. Wong, H. Yang, S. Lu, and V. Bharghavan, “Robust rate adaptation for 802.11 wireless networks,” in MobiCom ’06: Proceedings of the 12th annual international conference on Mobile computing and networking, (New York, NY, USA), pp. 146–157, ACM, 2006.
[5] T. Joshi, D. Ahuja, D. Singh, and D. Agrawal, “Sara: Stochastic automata rate adaptation for ieee 802.11 networks,” Parallel and Distributed Systems, IEEE Transactions on, vol. 19, pp. 1579–1590, Nov. 2008.
[6] Shu-Ming Liu, Yu-Hong Lin, Wu-i Chou, Zan-Yu Chen, Tsung-Nan Lin, “Performance Evaluation of Rate Adaptation Algorithms in 802.11-based Mesh Networks,” GLOBECOM Workshops, 2009 IEEE, IEEE Transactions on, vol. 19, pp. 1–5, Nov. 2009.
[7] Ioannis Pefkianakis, Yun Hu, Starsky H.Y. Wong, Hao Yang, Songwu Lu, “MIMO Rate Adaptation in 802.11n Wireless Networks”, in MobiCom ’10: Proceedings of the sixteenth annual international conference on Mobile computing and networking, (New York, NY, USA), pp. 110-121, 2010.
[8] https://wireless.wiki.kernel.org/en/developers/documentation/mac80211/ratecontrol/minstrel - Minstrel Algorithm
[9] http://www.cacetech.com/documents/PPI_Header_format_1.0.1.pdf - PPI Header Format
[10] G. Holland, N. Vaidya, and P. Bahl. A rate-adaptive MAC protocol for multi-hop wireless networks. In Proceedings of ACM MOBICOM`01, Rome, Italy,2001.
[11] Liping Qian; Jinfang Zhou; Ying Qian; Kangsheng Chen; , "Improved Opportunistic Auto Rate Media Access Protocol for Multi-Rate Ad Hoc Networks," Wireless Communications, Networking and Mobile Computing, 2006. WiCOM 2006.International Conference on , vol., no., pp.1-4, 22-24 Sept. 2006S.

Mohan Kumar Seerangarajan, "A Signal Integrity Based Rate Adaptation Algorithm", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.56-64, 2018.