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The importance for optimization arises from the flexibleness provided by modern user interfaces to databases. With the widespread applications of Database Management Systems, users have to deal with an enormous amount of data. Therefore, it is necessary to store this data in such a way that it is retrieved from the information within the quickest possible manner to satisfy the request from a user. Databases are most helpful in representing information in an organized manner. It provides the user with the flexibility to acquire correct, reliable and timely data for effective decision making process. Thus, the importance of database systems is increasing day by day. At the same time, the complexity in queries is also increasing day by day which makes the problem of determining the best query optimization technique. Query optimization in databases continues to be an important issue in various fields for a long period of time. To reduce the execution cost, we need to reformulate the complex query with computationally equivalent and more efficient one. Now in this paper, we analyse and compare the performance of various query processing techniques like Aggregate function based approach, Reduced function based approach, Bitmap Index based approach, Filtered Bitmap Index based approach to process queries with set predicate.

query optimization, Aggregate function based approach, Reduced function based approach, Bitmap Index based approach, Filtered Bitmap Index based approach, set predicates

[1] J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ, “A scalable continuous query system for internet databases”, Published in Proc. SIGMOD, pages 379–390, 2000.
[2] Martin Arlitt and Tai Jin, “A Workload Characterization Study of the 1998 World Cup Web Site”, IEEE Network, vol. 14, no. 3, pp. 30-37, May/June 2000.
[3] J. Albrecht, W. Hümmer, W. Lehner, L. Schlesinger, “Query Optimization By Using Derivability In a Data Warehouse Environment”, Published in the Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP, DOLAP -2000, pages 49-56.
[4] Y. Ioannidis, “The History of Histograms(abridged)”, Published in theProceedings of the 29th VLDB Conference, 2003.
[5] R. Fagin, A. Lotem, and M. Naor, “Optimal Aggregation Algorithms for Middleware”, Published in Computer and System Sciences, vol. 66, no. 4, pp. 614-656, 2003.
[6] Panos Kalnis, Dimitris Papadias, "Multi-query optimization for on-line analytical processing", Published in Information Systems, Volume-27,Issue 5, July 2003.
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[8] Alaa Aljanaby, Emad Abuelrub, Jordan and Mohammed Odeh, “A Survey of Distributed Query Optimization”, published in The International Arab Journal of Information Technology, Vol. 2, No. 1, January 2005.
[9] C. Olston, B. Reed, U. Srivastava, R. Kumar and A. Tomkins, “Pig Latin: A Not-so-Foreign Language for Data Processing”, Proc. ACM SIGMOD International Conference Management of Data, pp. 1099-1110, 2008.
[10] Chatziantoniou, D. and E. Tzortzakakis, "Asset Queries: A Declarative Alternative to Mapreduce", Published in ACM SIGMOD Record, 38(2): 35-41, June 2009.
[11] Pawan Meena, Arun Jhapate & Parmalik Kumar, "Framework for Query Optimization", published in the International Journal of Computer Science and Information Security, Vol. 9, No. 10, October 2011.
[12] Hui Zhao, Shuqiang Yang, Zhikun Chen, Songcang Jin, Hong Yin and Long Li, ”MapReduce model-based optimization of range queries”, Published in 2012, 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).
[13] Swathi Kurunji, Tingjian Ge, Benyuan Liu, Cindy X. Chen, "Communication Cost Optimization for Cloud Data Warehouse Queries”, Published in the Proceedings of the IEEE 4th International Conference on Cloud Computing Technology and Science 2012.
[14] Davide Martinenghi and Marco Tagliasacchi, ” Cost-Aware Rank Join with Random and Sorted Access”, Published in the IEEE Transactions On Knowledge And Data Engineering, VOL. 24, NO. 12, DECEMBER 2012.
[15] P. Arpitha, "Query Optimization In Data Warehouse", Published in the International Journal of Engineering Research & Technology, Volume 2, Issue 8, 2013.
[16] Chengkai Li, Bin He, Ning Yan, M. Safiullah ”Set Predicates in SQL: Enabling Set-Level Comparisons for Dynamically Formed Groups”, IEEE Transactions on Knowledge and Data Engineering , Vol. 26, No. 2, FEBRYARY 2014.
[17] J. Rajurkar, T. Khan, "A System for Query Processing and Optimization in SQL for Set Predicates using Compressed Bitmap Index", International Journal for Scientific Research & Development Vol. 3, Issue 02, 2015.
[18] A.Regita Thangam and S.John Peter, “An Extensive Survey on Various Query Optimization Techniques” Published in the International Journal of Computer Science and Mobile Computing, Volume-5, Issue- 8, August 2016.
[19] Tejy Johnson and S.K. Srivatsa, "Multi Level Relational Mapping Algorithm Based Dependency Rule Generation for Query Optimization", Published in the American-Eurasian Journal of Scientific Research, vol. 11, no. 2, pp. 72-78, 2016.
[20] Rhia Mariam George and A. Ronalad Doni, "Query Processing and Optimization Using Set Predicates", Published in the American-Eurasian Journal of Scientific Research, vol. 11, no. 5, pp. 390-397, 2016.
[21] A.Regita Thangam and S.John Peter, “Efficient Processing and Optimization of Queries with Set Predicates using Filtered Bitmap Index” Published in the International Journal of Computer Sciences and Engineering, Volume-5, Issue-11, Nov 2017.
[22] A.Regita Thangam and S.John Peter, “Efficient Processing of Queries with Set Predicates using Reduced Function based Approach”, published in the Proceedings of the International Conference on Recent Trends in Multi-Disciplinary Research, pp.11, Dec 2018.

A. Regita Thangam, S.John Peter, "A Comparative Study of Query Processing and Optimization Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.1-5, 2019.

Object recognition is emerging technology to detect and classify objects based in their characteristics. Fruit it is also a domain of object recognition and it is still a complicated task due to the various properties of numerous types of fruits. Different fruits have different shapes, sizes, color, textures and other properties. Tangerines and Madarin oranges have the same characteristics such as color, texture size, etc. Multi-feature extraction methods are based on supervised machine learning algorithms and image processing mechanisms. These algorithms are used to find a better fruit classification. Firstly, we pre-process the training sample of fruits images. The preprocessing is included a separating foreground and background, scaling and cropping and it reduce the dimension. So the processing is fast then, we extract features the fruit’s image, which includes color, texture and shape of the fruit image. Extracted features are then fitted into the neural classifier machine learning algorithm. This paper is obtained the results from the test sample is cross validated using machine learning network. The output obtained will give us the fruit that it has acknowledged.

Classification, Feature Extraction, Neural Classifier, Object Recognition Fruit Classification

[1]. Aasima Rafiq et al, “Application of Computer Vision System in Food Processing - A Review”, Journal of Engineering Research and Applications, Volume 3, Issue 6, Nov-Dec 2013.
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K. Vanitha, G. Heran Chellam, "Recognition of Fruits Using Neural Classification Methods", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.6-9, 2019.

In recent years, Educational Data Mining has developed into a research realm. All educational institutions are striving hard to prove themselves as the best to attract the student community. Academic performance of the students plays a vital role in determining the status of the institution. So, all the institutions strive hard to know their wards in advance and improve their performance to stand out among their competitors. Unfortunately most the information in the academic institutions are hidden and need to be extracted out. Data mining is a well known technique for bringing out the hidden potential of the institutions. For mining, the data need to be transformed and reduced for better performance. This model is mainly focused on finding out the significance of the relationship between the derived influencing factors.

Educational Data Mining, Academic Performance, Higher Education, Prediction, Contingency table, Chi-square

[1] Jai Ruby & K. David, “A study model on the impact of various indicators in the performance of students in higher education“, IJRET International Journal of Research in Engineering and Technology, Vol. 3, Issue 5, May-2014, pp.750-755.
[2] Han. J & Kamber. M, “Data mining concepts and techniques”, San Francisco, USA, Morgan Kaufmann, 2001.
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[4] Daiho Uhm, Sunghae Jun and Seung-Joo Lee,“A Classification Method Using Data Reduction”, International Journal of Fuzzy Logic and Intelligent Systems, Vol. 12, no. 1, March 2012, pp. 1-5, pISSN 1598-2645
[5] J. H. Friedman, "On Bias, Variance, 0/1-loss, and the Curse of Dimensionality," Data Mining and Knowledge Discovery Vol. 1, pp. 55-77, 1997.
[6] Cynthia Fraser,” Association between Two Categorical Variables: Contingency Analysis with Chi Square. In: Business Statistics for Competitive Advantage with Excel 2007”, Springer, New York, NY
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[8] https://en.wikipedia.org/wiki/Contingency_table
[9] Anne F. Maben, 2005, Chi-square test adapted from Statistics for the Social Sciences.
[10] McHugh, Mary L. “The chi-square test of independence” Biochemia medica, Vol. 23(2), 2013, Pages 143-149.
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Jai Ruby, K. David, "Significance of Influencing Factors’ Relationship in Education Domain", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.10-15, 2019.

This paper proposes a set of algorithms which work for the better detection and classification of Brain Tumor. The MRI image based Brain Tumor analysis would efficiently deal with classification process for Brain Tumor analysis. There are three stages namely Feature Extraction, Feature Reduction and Classification. Feature Extraction and Feature Reduction using for two algorithms. There are Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA). The Features Extracted are Mean, Standard deviation, Kurtosis, Skewness, Entropy, Contrast, Variance, Smoothness, Correlation and Energy. The result is then given to Support Vector Machine (SVM) for tumor classification as Benign or Malignant.

Brain Tumor, Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), Support Vector Machine (SVM), Magnetic Resonance image (MRI).

[1] N. Varuna Shree T. N. R. Kumar “Detection and classification of brain tumor MRI images with using Discrete Wavelet Transform and probabilistic neural network”. Published online: 8 January 2018
[2] M.Rama Krishna, Fahmeeda, G.Daizy Florence and K.Sravani, “Brain Tumor Image Segmentation Based On Discrete Wavelet Transform and Support Vector Machine”, International Journal for Modern Trends in Science and Technology, Vol. 03, Special Issue 02, 2017, pp. 12-18.
[3] 1P. Kumar and 2B. Vijayakumar “Brain Tumor MRI Image of the process of Segmentation of Classification Using by Principle Analysis Component (PCA) and RBF Kernel Based Support Vector Machine (SVM)” Middle-East Journal of Scientific Research 23 (9): 2106-2116, 2015 ISS 1990-9233 © IDOSI Publications, 2015

G. Mahalakshmi, G. Heren Chellam, "Segmentation and Classification of Brain Tumor MRI Images Using Support Vector Machine", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.16-20, 2019.

Human identification is one of the most noticeable disciplines in forensic medicine. To the area of Forensic Anthropology, identification task is performed by reviewing the skeletal remains. Over the past few decades, anthropologists have paid their attention on improving those techniques that allow a more precise identification. Hence, Forensic Identification has become a very active research area & Skull identification has been emerging as a vital field in this discipline. Skull Identification is drawing wide attention and been applied in huge number of forensic domains, ranging from the identification of victims of the Indian Ocean tsunami, Uttarakhand disaster to the recognition of terrorists. At present skull identification research points mainly on two categories. One is craniofacial superimposition and other is craniofacial reconstruction. In Craniofacial superimposition a photograph of a missing person is compared with a skull found to determine its identity. On the other hand, craniofacial reconstruction is concerned about getting a visual outlook of an individual where only skull and bone are remaining. These research topics are reviewed in this article.

Craniofacial Superimposition, Skull Outlay, 3 D Modeling, Craniofacial Reconstruction

[1] M.Chitra Devi, M.Pushpa Rani, “Recognizing Human by Matching Between Skull and Face Shape: A Survey”, International Journal of Engineering Research & Technology (IJERT), Volume 3, Issue 28, Special Issue 2015.
[2] Fuqing Duan, Yanchao Yang, Yan Li, Yun Tian, Ke Lu, Zhongke Wu,and Mingquan Zhou, “Skull Identification via Correlation Measure Between Skull and Face Shape”, IEEE Transactions On Information Forensics And Security, Vol. 9, No. 8, August 2014.
[3] S. Damas et al., “Forensic identification by computer-aided craniofacial superimposition: A survey”, ACM Comput. Surv., vol. 43, no. 4, pp. 1–27,2011.
[4] W. A. Aulsebrook, M. Y. ˙I¸scan, J. H. Slabbert, and P. Becker, “Superimpositionand reconstruction in forensic facial identification: A survey”, Forensic Sci. Int., vol. 75, nos. 2–3, pp. 101–120, 1995.
[5] C. Wilkinson, Forensic Facial Reconstruction. Cambridge, U.K.:Cambridge Univ. Press, 2004.
[6] P. Claes, D. Vandermeulen, S. De Greef, G. Willems, J. G. Clement, and P. Suetens, “Computerized craniofacial reconstruction: Conceptual framework and review”, Forensic Sci. Int., vol. 201, nos. 1–3, pp. 138–145, 2010
[7] M. Yoshino, “Craniofacial superimposition”, in Craniofacial Identification, C. Wilkinson and C. Rynn, Eds. Cambridge, U.K.: Cambridge Univ. Press, 2012, pp. 238–253.
[8] Bastiaan, R.J., Dalitz, G.D. and Woodward, C. (1986) Video superimposition of skulls and photographic portraits—a new aid to identification. J Forensic Sci, 31(4): p. 1373-9
[9] Austin, D. (1999) Video superimposition at the CA Pound Laboratory 1987 to 1992. J Forensic Sci, 44(4): p. 695-9
[10] M. I. Huete, O. Ib´a˜nez, C. Wilkinson, and T. Kahana. Past, present, and future of craniofacial superimposition: Literature and international surveys. Legal Medicine, 17:267–278, 2015.
[11] S. Damas, C. Wilkinson, T. Kahana, E. Veselovskaya, A. Abramov, R. Jankauskas, P.T. Jayaprakash, E. Ruiz, F. Navarro, M.I. Huete, E.Cunha, F. Cavalli, J. Clement, P. Leston, F. Molinero, T. Briers, F.Viegas, K. Imaizumi, D. Humpire, and O. Ib´a˜nez. Study on the performance of different craniofacial superimposition approaches (ii):best practices proposal. Forensic Science International, 257:504–508,2015.
[12] L. Ballerini, O. Cordón, J. Santamaria, S. Damas, I. Aleman, and M. Botella, “Craniofacial superimposition in forensic identification using genetic algorithms”, in Proc. 3rd Int. Symp. Inf. Assurance Security, 2007, pp. 429–434.
[13] O. Ibáñez, O. Cordón, S. Damas, and J. Santamaría, “Modeling the skull–face overlay uncertainty using fuzzy sets,” IEEE Trans. Fuzzy Syst., vol. 19, no. 5, pp. 946–959, Oct. 2011.
[14] Maya de Buhan and Chiara Nardoni ,”A facial reconstruction method based on new mesh deformation techniques” FORENSIC SCIENCES RESEARCH, 2018
[15] Claes P, Vandermeulen D, De Greef S, et al. Computerized craniofacial reconstruction: conceptual framework and review. Forensic Sci Int. 2010; 201:138–45.
[16] [P Vanezis, Application of 3-D computer graphics for facial reconstruction and comparison with sculpting techniques. Forensic Sci. Int. 42, 69–84 (1989)
[17] P Vanezis, M Vanezis, G McCombe, T Niblet, Facial reconstruction using 3-D computer graphics. Forensic Sci. Int. 108, 81–95 (2000)
[18] R Evenhouse, M Rasmussen, L Sadler, Computer-aided forensic facial reconstruction. J. Biocommun. 19, 22–28 (1992)
[19] AW Shahrom, P Vanezis, RC Chapman, A Gonzales, C Blenkinsop, ML Rossi, Techniques in facial identification: computer-aided facial reconstruction using laser scanner and video superimposition. Int. J. Legal Med. 108, 194–200 (1996)
[20] AJ Tyrell, MP Evison, AT Chamberlain, MA Green, Forensic three- imensional facial reconstruction: historical review and contemporary developments. J. Forensic Sci. 42, 653–661 (1997)
[21] MW Jones, in Proceedings of the Sixth International Vision Modelling and Visualisation Conference. Facial Reconstruction Using Volumetric Data, (Stuttgart, Germany, 2001), pp. 135–150
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[24] Y Pei, H Zha, Z Yuan, in The Third International Conference on Image and Graphics (ICIG’04). Tissue map based craniofacial reconstruction and facial deformation using rbf network, (Hong Kong, China, 2004), pp. 398–401
[25] P Tu, R Hartley, W Lorensen, M Allyassin, R Gupta, L Heier, in Computer-graphic Facial Reconstruction, ed. by JG Clement and MK Marks. Face reconstruction using flesh deformation modes (Academic Press, New York, 2005), pp. 145–162
[26] Q Deng, M Zhou, W Shui, Z Wu, Y Ji, R Bai, A novel skull registration based on global and local deformations for craniofacial reconstruction. Forensic Sci. Int. 208, 95–102 (2011)
[27] WD Turner, REB Brown, TP Kelliher, PH Tu, MA Taister, KWP Miller, A novel method of automated skull registration for forensic facial approximation. Forensic Sci. Int. 154, 149–158 (2005)
[28] P Tu, R Book, X Liu, N Krahnstoever, C Adrian, P Williams, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07). Automatic face recognition from skeletal remains, (Minneapolis, Minnesota, USA, 2007), pp. 1–7
[29] D Vandermeulen, P Claes, D Loeckx, S De Greef, G Willems, P Suetens, Computerized craniofacial reconstruction using CT-derived implicit surface representations. Forensic Sci. Int. 159, S164–S174 (2006)
[30] Y Pei, H Zha, Z Yuan, in The Third International Conference on Image and Graphics (ICIG’04). Tissue map based craniofacial reconstruction and facial deformation using rbf network, (Hong Kong, China, 2004), pp. 398–401

C. Kamali, D. Murugan, "Skull Identification in Forensic Science- A Literature Review", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.21-25, 2019.

Haze is a main degradation of outdoor images, weakening both colors and contrasts due to atmospheric phenomena. Dehazed images mean sustaining low bitrates in the transmission pipeline. .In this paper to remove haze from a single input image combination of dark channel and bright channel method were used. In a dark channel method , the non-sky patches, at least one color channel has very low intensity at some pixels or, the minimum intensity in such a patch should has a very low value. A kind of statistics of outdoor haze-free images is a dark channel prior method. Then estimate the bright channel to control the amount of brightness enhancement and combine both dark channel and bright channel method to remove haze. The Noise estimation can be measured using MSE (Mean Square Error), RMSE (Root Mean Square Error), BER (Bit Error Rate), PSNR (Peak Signal-to-Noise Ratio) and MAE (Median Angular Error). Experimental result shows that the proposed method can provide the better restored result than the existing methods.

Image Dehazing, Dark Channel prior, Contrast Enhancement

[1] “FPGA implementation of haze removal algorithm for image processing” by Ghorpade P. V1, Dr. Shah S. K2
[2]https://www. quora.com/What-is-dark-channel-prior-in-image-processing.
[3] Rachel Yuen, Chad Van De Hey, and Jake Trotman ” Fast Single Image Haze Removal Using Dark Channel Prior and Bilateral Filters”.
[4]https://www.semanticscholar.org/paper/Image-Enhancement-Using-Bright-Channel-Prior-Sun-Guo/33df832f332fd667449327b3be21f54e11adcb38
[5]”An Investigation of Dehazing Effects on Images and Video Coding” by IEEE transactions on processing
[6]”Improving Air Light Estimation Algorithm by using fuzzy and Dark Channel with Large Haze Gradients” by International journal of computer applications
[7] “Removal of haze and analysis of dehazing effects on image using median filters”Sivagowri .R, Suhashini .L, M.Dhineshiya, C.S.Dhevisri, J.Sindhukavi
[8]R. Tan, “Visibility in Bad Weather from a Single Image,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2008.
[9] S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” Int.J. Comput. Vis., vol. 48, no. 3, pp. 233–254, Jul./Aug. 2008.
[10] T. L. Ji, M. K. Sundareshan, and H. Roehrig, “Adaptive image contrast enhancement based on human visual properties,” IEEE Trans. Med.

S. Bhavani, R. Shenbagavalli, "Haze Removal on Image Using Dark Channel and Bright Channel Methods", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.26-31, 2019.

The most significant challenges in image processing and pattern recognition is image decomposition and restoration. Image Restoration is the operation of taking a noisy image and estimating the clean, original image. Corrupt image may come in many forms such as motion blur, noise and camera mis-focus. Restoration is a process of eliminating degraded noise and increases the quality of image. Image decomposition is to decompose an image into its component structures. When two image signals are considered, a combined image signal should contain the image structure of both these signals In this paper, the mural images are decomposed into cartoon component or geometrical part of blurred images and Texture component or small scale special pattern using Bilateral filter. In cartoon component augmented Lagrangian method has been used to fill the missing pixel. In texture component the blurring can be removed using median filter and conservative filter. Median filter is used to remove noise and conservative filter is used for smoothening the image. By using these filters, degraded mages can be restored successfully. The restoration efficiency can be measured with MSE (Mean Square error) and PSNR (Peak Signal to Noise Ratio) parameter. Various mural images have been analyzed and tested. The accuracy is comparatively better than the existing.

Image restoration, Cartoon component, Texture component, Image decomposition

[1] A.Deepika, K.Raja Sundari, R.Ravi,”Image Decomposition and Restoration for Blurred Images Using Filtering Techniques”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 3, March 2014
[2] Michal K. Ng, Xiaoming and Wenxing Zhang, ”Coupled Variational Image Decomposition and restoration Model for Blurred Cartoon-Plus-Texture Images With Missing Pixels”, IEEE Trans. Image Processing, vol. 22, No. 6, pp. 2233-2246,2013.
[3]. G. Karuna “Image Decomposition and Restoration Model for BlurredCartoon and Texture Images with Filtering Techniques” International Journal of Electronics, Electrical and Computational SystemIJEECSISSN 2348-117XVolume 6, Issue 11 November 2017
[4] www.google.com “mural images”, “Mean Square error” , “Peak Signal to Noise Ratio”,”median filter”,“conservative filter”,”Lagrangian method”
[5] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ball ester, Image Inpainting,in Proc. 27th Annu. Conf. Comput. Graph. Interact. Tech., 2000,pp. 417–424.
[6] M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, Simultaneous structure and texture image Inpainting, IEEE Trans. Image Process., vol. 12, no.8, pp. 882–889, Aug. 2003.
[7] J.-F. Cai, R. H. Chan, and Z. Shen, Simultaneous cartoon and textureInpainting, Inverse Prob. Imaging, vol. 4, no. 3, pp. 379–395

M. Rathika, R. Shenbagavalli, "Image Restoration of Damaged Mural images based on Image Decomposition", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.32-37, 2019.

Insomnia is one of the most dangerous sleepless disorders and can continue through the teenage years and lifelong of human being. This research work focuses on one of the major problems of this disorder happen in human brain called amygalada abnormality. The size and growth of the amygalada will decide the insomnia disorder. Various existing research works to extract the amygalada (Head and body) are surveyed in this thesis and an automatic diagnosis technique is proposed to extract the amygalada in MRI brain images. In the proposed method, Graph Cut Method is used to make it suitable for segmenting small, low contrast structure such as the amygalada to predicting the Insomnia Disorder. The results show accurate and very fast performances in external amygalada segmentation in a real data set.

Insomnia, Amygalada, Graph Cut, MRI, Segmentation

[1] http://www.optisom.com/introduction-to-insomnia/
[2] http://medical-dictionary.thefreedictionary.com/insomnia
[3] http://www.onlymyhealth.com/who-affected-insomnia-1316675052
[4] http://www.mayoclinic.org/diseases-conditions/insomnia/basics/causes/con-20024293
[5] Doan, H., Slabaugh, G.G., Unal, G.B. & Fang, T. (2006). Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts. Paper presented at the 2006 IEEE International Conference on Image Processing,, 08-10-2006 - 11-10-2006, Atlanta, USA.
[6] Igual L, Soliva JC, Gimeno AR, Escalera S, Vilarroya O, RadevaP: Automatic Internal Segmentation of Amygaladafor Diagnosis of Attention-Deficit/Hyperactivity Disorder. In Proceedingsof the International Conference on Image Analysis and Recognition, Lecture Notes in Computer Science, Springer-Verlag 2012.

R. Subhulakshmi, "A Novel Segmentation Technique to Extract Amygalada of Brain to Detect Insomnia Disorder Using Graph Cut Method", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.38-43, 2019.

Crop cultivation plays a necessary function in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reduce the manufacture rate. Plant diseases have curved into a problem as it can cause chief reduction in both quality and quantity of agricultural products. Automatic detection of plant diseases is a critical research topic as it may prove benefits in monitoring large fields of crops, and thus automatically discover the symptoms of diseases as soon as they appear on plant leaves. The proposed approach consists of four main steps, first the input image is converted using color transformation into RGB image and then as a second step the green pixels are masked and removed by segmentation process using specific threshold value, the texture features are extracted then passed through the classifier.

Feature Extraction, Segmentation, Transformation, Detection, Classifier

[1] Dheeb Al Bashish, Malik Braik, Sulieman Bani- Ahmad, “Detection and classification of leaf diseases using k-means based segmentation and neural network based classification”, Information Technology Journal, ISSN 1812-5638, pp.267-275, 2011.
[2] Rampf T., A. K. Mahlein, U. Steiner, E. C. Oerke,H.W.Dehne,L. Plumer, “Early detection and classification of plant diseases with Support Vector Machine based on Hyperspectral reflectance”, Computers and Electronics in Agriculture, Volume 74, Issue 1, ISSN 0168-1699, pp. 91-99, October2010.
[3] Hillnhuetter C., A. K. Mahlein, “Early detection and localization of sugar beet diseases: new approaches”, Gesunde Pfianzen 60 (4), pp. 143-149, 2008.
[4] Prasad Babu, Srinivasa Rao, “Leaves recognition using back-propagation neural network – advice for pest and disease controlon crops”, Technical report, department of Computer Science and Systems Engineering, Andhra University, 2010.
[5] Camargo A., Smith J. S., “An image processing based algorithm to automatically identify plant disease visual symptoms”, Biosystems Engineering, Vol. 102, Issue 1, pp. 9-21, 2008.
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[7] G. Anthonys, N. Wickramarachchi, “An Image Recognition System for Crop Disease Identification of Paddy fields in Sri Lanka,” Fourth International Conference on Industrial and Information Systems, ICIIS 2009, 28 - 31 December, 2009, Sri Lanka.
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S. Malini, T. Ratha Jeyalakshmi, "Detection of Unhealthy Region of Plant Leaves Using Texture Features", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.44-47, 2019.

Surveillance videos are proficient to detain a diversity of sensible anomalies. In this work, we advise to find out anomalies by comparing both normal and irregular videos. To remain on away from annotating the irregular segments or clips in training videos, which is very time overwhelming, we recommend to learn anomaly during the deep multiple case position framework by stage averaging weakly labeled direction videos, i.e. the training labels are at video level instead of clip-level. In our approach, we think normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and mechanically learn a deep anomaly location form that predicts high anomaly scores for anomalous video segments. Furthermore, we begin sparsity and temporal softness constraints in the ranking loss function to improved localize anomaly during training. We also set up a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 practical anomalies such as fighting, road accident, burglary, break-in, etc. as well as normal activities. This dataset can be used for two tasks. First, general irregularity detection considering all anomaly in one group and all normal activities in another group. Second, for recognizing each of 13 abnormal actions. Our investigational consequence clarify that our MIL performance for anomaly detection achieves significant development on anomaly detection act as compared to the state-of-the-art Techniques. We present the consequences of several current deep learning baselines on anomalous action recognition. The low detection presentation of these baselines finds that the dataset taken is very hard and opens extra opportunities for opportunity work.

Multiple instance learning, anomaly, dataset, surveillance video

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M. Petchiammal Baby, T. Ratha Jeyalakshmi, "Suspicious Activities and Anomaly Detection in Surveillance Video Using Multiple Instance Learning Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.48-53, 2019.