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Cloud computing is a technology that provides efficient services to the users over internet. Users stores volumes of data in cloud which is rendered as data as a service (DaaS) on demand and charged as per usage. Text mining is a technology that is used to retrieve data from a massive set of database. Cloud uses Text mining to retrieve data efficiently from various cloud data centres. Text classification is a technique used for discovering classes of indefinite data. Prior to applying any mining technique, trivial features should be filtered. Feature selection is capable of improving learning process, lesser computational complexity, organizes better general models, and decreasing required storage. We analyses towards effectiveness of the clustering based feature selection method. This paper is to analysis on different techniques used for feature selection. Further survey on Feature selection and Feature extraction technique has been extract the features from the documents, which results in single and multi-label document classification. Based on the extracted features the survey is done on multiple-feature based projective nonnegative matrix factorization technique to cluster the documents.

Data mining, Feature selection, Text mining, Filtering, factorization

[1] Lin Yue, Wanli Zuo , TaoPeng , YingWang, Xuming Han A fuzzy document clustering approach based on domain-specified ontology, “ Data & Knowledge Engineering”, 100 (2015) 148-166.
[2] Malik Tahir Hassana, Asim Karim, Jeong-Bae Kim, Moongu Jeon CDIM: Document Clustering by Discrimination Information Maximization, ”Information Sciences”, 316 (2015) 87–106.
[3] Charlotte Laclau, Mohamed Nadif "Hard and fuzzy diagonal co-clustering for document-term partitioning "Neurocomputing”, 193 (2016) 133–147
[4] Vıctor Mijangos, Gerardo Sierra, Azucena Montes Sentence level matrix representation for document spectral clustering "Pattern Recognition Letters, Elsevier”, 20 November 2016.
[5] Mei Lua, Xiang-Jun Zhao, Li Zhang, Fan-Zhang Li, Semi-supervised concept factorization for document clustering, “Information Sciences”, 331 (2016) 86–98.
[6] Tingting Wei, Yonghe Lu, Huiyou Chang, Qiang Zhou, Xianyu Bao, A semantic approach for text clustering using WordNet and lexical chains, “Expert Systems with Applications”, 42 (2015) 2264–2275.
[7] Sourav Dutta, Gerhard Weikum, Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment, “Transactions of the Association for Computational Linguistics”, 3(2015)15–28.
[8] Yong-Il Kim, Yoo-Kang Jiand Sun Park, Big Text Data Clustering using Class Labels and Semantic Feature Based on Hadoop of Cloud Computing, “International Journal of Software Engineering and Its Applications”, 8(2014),1-10.

Balavinothini, Gnanambigai, "Survey on Feature Selection Techniques towards Text Mining in Cloud", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.101-105, 2019.

Liar`s dominating set is one that identifies an intruder`s location even if one device in the neighborhood of the intruder vertex becomes faulty, that is, any one device in the neighborhood of the intruder vertex can misidentify any vertex in its closed neighborhood as the location of the intruder. The liar’s domination number is the minimum cardinality of a liar’s dominating set. In this paper, we determine the liar’s domination number for sun graphs, sun let graphs, line graphs of sun let graphs and wheel graphs.

Domination, Liar’s domination, Sun graphs, Sun let graphs, Wheel graphs

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[15] Ponraj, Raja and Narayanan, S. Sathish, “Difference cordiality of some snake graphs”, Journal of applied mathematics and informatics, Vol. 32, pp. 377-387, 2014.
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[18] P. J. Slater, “Liar’s Domination”, Networks, Vol. 54, pp. 70-74, 2009.
[19] A. M. Teguia, A. P. Godbole, “Sierpinski Gasket Graphs and Some of their Properties”, Australasian Journal of Combinatorics, Vol. 35, pp. 181-192, 2006.
[20] J. Vernold Vivin and M. Venkatachalam, “On b-chromatic number of Sun let graph and wheel graph families”, Journal of the Egyptian Mathematical Society, Vol. 23, pp. 215-218, 2014.

A. S. Shanthi, Diana Grace Thomas, "Liar’s Domination in Sun Graphs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.106-108, 2019.

Human emotions are mental states of feelings that are exposed unconsciously and followed by physical changes in their facial muscles which entail the expressions on the face. Certain emotions commonly expressed by human are happiness, sadness, anger, fear, disgust, surprise, and neutral. For a non-verbal communication, facial expression plays a vital role since it appears because of inner core feelings of a person that reflects on the faces. For the automatic recognition of facial emotions, many methods are used such as Artificial Neural networks, Neuro-fuzzy, Wavelet transformation, etc. However, the existing methods take more time for data classification, low accuracy in the optimization process and high level of error rate. To overcome these concerns, this paper depicts an amphibious operation of Multi Support Vector Machine (SVM) with the Convolutional Neural Networks (CNN). Initially, the characteristics of the pre-processed face image are efficiently extracted by using Local Binary Pattern (LBP), Principal Component Analysis (PCA) and Gray Level Occurrence Matrix (GLCM). In this model, CNN works as a trainable feature extractor, and Multi-SVM performs as a recognizer. The proposed system`s performance is analyzed with various human faces using the MATLAB tool. The results prove that the proposed method surpasses the earlier methods regarding high accuracy with low computation time and low error rate.

Convolutional Neural Networks, Face recognition, Local Binary Pattern, Principal Component Analysis

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M.Regina, M.S. Josephine, V. Jeyabalraja, "Multi-Featured Extraction and Convolution Neural Network Based SVM for Automatic Facial Emotion Recognition", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.109-117, 2019.

The main aim of this work is the classification of Tamil palm leaf manuscript segmented characters using Machine Learning approach. For the segmentation of characters, first the images of the palm leaf manuscripts were allowed to preprocessing which includes filtering and thresholding. After the preprocessing stage, the preprocessed images were allowed for character segmentation using contour based bounding box segmentation. Then the segmented Tamil palm leaf manuscript characters were labelled with different classes for classification. To classify the characters Adaptive Backpropagation Neural Network (ABPN) with Shannon activation function was used with Nguyen Widrow weight initialization. For neural network, normally we use random initialization to generate the weights. Rather than random initialization here Nguyen-Widrow weight initialization technique was implemented. For comparison ABPN with Shannon activation function (method 1) and ABPN with Shannon activation function using Nguyen-Widrow initialization was used, from this ABPN with Shannon activation function using Nguyen-Widrow gives 96% of accuracy for Tamil palm leaf character classification.

ABPN, Bounding box, Convex hull, Contour, Shannon, Machine Learning

[1] Amit Choudhary, Rahul Rishi, Savita Ahlawat, “Off-Line Handwritten Character Recognition using Features Extracted from Binarization Techniques”, AARSI Procedia, , Vol.4, pp.306-312, 2013.
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[17] Yi-Chou Wu, Fei-Yin, Cheng-Lin Liu, “Improving Handwritten Chinese Text Recognition using Neural Network Language Models and Convolutional Neural Network Shape Models”, Pattern Recognition, Vol.65,pp.251-264, 2017.

Poornima Devi. M, M. Sornam, "Contour based Character Segmentation and Nguyen-Widrow Weight Generation for Classification of Tamil Palm Leaf Script Characters - Machine Learning Approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.118-123, 2019.

In the recent years, data mining has been employed in the medical field for extracting and manipulating information, and aids within the higher process. There is a growing want for the medical establishments to be extra suggested and knowledgeable concerning the diseases and to understand the risk factors before diagnosis. Predicting the results of a process with a high level of accuracy is a difficult task. In this study we took the advantage of the data mining models to predict the Type – 2 Diabetes mellitus. The benchmark dataset, “Pima Indian Diabetes” dataset is used for this study. The main objective of this study is to propose the extensive data pre-processing such as imputation of missing values and a feature engineering technique namely ‘Principal Component Analysis’ are used to transform the dataset into a compressed form. Ensemble or classifier combination method called boosting method such as Gradient boosting machine and Random Forest are used. The most downside that’s attempting to be resolved isn’t solely to extend the accuracy however additionally to retain all the information in the data set while not removing the missing data. The missing data are imputed by a method called ‘predictive mean matching’. The results show that the ensemble learners, once used alongside PCA attained 100% accuracy of prediction. Moreover, it ensures that no missing information must be removed and might be imputed to confirm the data quality is enough. As a result, the model is shown to be helpful for the real time prediction of Diabetes Mellitus.

Ensembles,Gradient boosting machine,Random Forest,Principal Component Analysis

[1] Ndisang, Joseph Fomusi, Alfredo Vannacci, and Sharad Rastogi. "Insulin Resistance, Type 1 and Type 2 Diabetes, and Related Complications 2017." Journal of diabetes research2017 ,2017
[2] Anjana, R.M., Deepa, M., Pradeepa, R., Mahanta, J., Narain, K., Das, H.K., Adhikari, P., Rao, P.V., Saboo, B., Kumar, A. and Bhansali, A., 2017. Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR–INDIAB population-based cross-sectional study. The Lancet Diabetes & Endocrinology,Vol. 5, Issue . 8, pp.585-596.
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M.Sornam, M.Meharunnisa, "Type 2 diabetes mellitus prediction model based on ensemble boosting method with Principal Component Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.124-130, 2019.

A new method for classifying fruits using image processing technique is proposed in this paper. The data set used had 70 apple images and 70 banana images for training and 25 images of apple and 25 images of bananas for testing. RGB image was first converted to HSI image. Then by using Otsu’s thresholding method region of interest was segmented by taking into account only the HUE component image of the HSI image. Later, after background subtraction, a total of 36 statistical and texture features were extracted with the help of the coefficients obtained by applying wavelet transformation on the segmented image using Haar filter. Extracted features were given as inputs to a SVM classifier to classify the test images as apples and bananas. As KNN classification method did not give 100% accuracy while classification SVM classification method was used. 140 sample images of apples and bananas were used for training and 25 images of banana and 25 images of apples were used for testing the proposed algorithm. The proposed algorithm gave 100% accuracy rate.

RGB, HSI, Region of interest, Wavelet domain, Haar filter, SVM classification

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[2] Tarale, Ketki, and Anil Bavaskar. "Fruit Detection Using Image Processing Technique." National Conference on Advances in Engineering and Applied Science (NCAEAS). 2017,(3)2: 178-183
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[5] Zawbaa, Hossam M., Maryam Hazman, Mona Abbass, and Aboul Ella Hassanien. "Automatic fruit classification using random forest algorithm." In Hybrid Intelligent Systems (HIS), 2014 14th International Conference on, pp. 164-168. IEEE, 2014.
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[9] Gongal, A., M. Karkee, and S. Amatya. "Apple fruit size estimation using a 3D machine vision system." Information Processing in Agriculture 5, no. 4 (2018): 498-503.
[10] Wang, Weizu, Zhou Yang, Huazhong Lu, and Han Fu. "Mechanical damage caused by fruit-to-fruit impact of litchis." IFAC- PapersOnLine 51, no. 17 (2018): 532-535.
[11] Momin, M. A., et al. "Geometry-based mass grading of mango fruits using image processing." Information Processing in Agriculture, 4.2 (2017): 150-160.

PL.Chithra, M.Henila, "Fruits Classification Using Image Processing Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.131-135, 2019.

Deep feature representation is more effective to perform classification of renal ultrasound images. Increases in distance of the features would suppresses the classification accuracy, conventional methods for categorization of renal diseases using medical ultrasound have the lack of accuracy due to restricted way of feature extraction. The main objective of this work is to classify the different renal diseases using ultrasound brightness mode images. Inception is derived with multiple convolutions and down sampling of input image elements in order to produce the deep features for classification. The projection of average pooling with convolution layer makes exacts reduction of unwanted invariants on the input image. The activation function rectified linear units are used for fast computation of the network architecture. The performance metrics for the classification of renal diseases have analyzed using confusion matrix. Inception produces better results than traditional convolution networks. The performance accuracy for the classification of renal diseases are given by 87.43%.

Confusion matrix, Deep learning, Inception, Rectified linear units, Renal diseases, Ultrasound B-mode

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R. Vasanthselvakumar, M. Balasubramanian, S. Sathiya, "Automatic Renal Defect Classification Using Inception", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.136-139, 2019.

This paper aims to present face recognition based on Principal Component Analysis (PCA) and Modular Principal Component Analysis (MPCA) approach. The PCA based face recognition method is not very effective under the conditions of varying poses and expressions rather than the proposed MPCA method. In the MPCA method the original face image was partitioned into tiny sub-images and then PCA technique is applied for each sub-image. Since a few of the normal facial features of an individual do not differ even when the pose and expression may differ, the proposed method manages these variations and takes only a few numbers of principal components for matching the faces for similarity. The proposed method improves the recognition rates with less number of principal components when compared with the conventional PCA method. This present system is tested with two standard face databases and results are presented.

Eigen faces; Euclidean Distance; Face Recognition; MPCA; Principal Component Analysis

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S.P. Sundarsingh, C.D. Daniel Dharamaraj, "Face Recognition System using Modular Principal Component Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.140-145, 2019.

In todays scenario the organization of textual information has become a necessity due to the availability of various digital information. The purpose of Text Mining is to process unstructured (textual) information, extract meaningful numeric indices from the text, and, thus, makes the information contained in the text accessible to the various data mining (statistical and machine learning) algorithm. Information Extraction is the technique of automatically extracting information from unstructured and/or semi-structured machine-readable documents. An Information Extraction system target a specific topic or domain based on the user’s interest and searches for information that has more reliance to the domain. Information Extraction tools make it possible to pull information from text document, database, websites or multiple sources. Information Extraction depends on named entity recognition, a sub-tool used to find targets information to extract. This paper presents the review of various Information Extraction techniques such as Supervised, Unsupervised and Semi-supervised Information Extraction and its application.

Text Mining, Information Extraction, Machine Learning, Supervised, Unsupervised, Semi-supervised

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[17] Jinxiu Chen, Donghong Ji, Chew Lim Tan, Zhengyu Niu, “Relation Extraction Using Label Propagation Based Semi-Supervised Learning”.
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R.Jayanthi, D.Nirmala, "Information Extraction from Unstructured Documents", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.146-151, 2019.

Traffic monitoring is important in every country to cope up with the increasing population. Tracking and vehicle monitoring is always a challenging task as they are used for surveillance control and traffic planning. In earlier method, the detection of vehicles is classified using Artificial Neural Network with Histograms of Oriented Gradients. The major challenge due to advent of computer is to choose appropriate algorithms for real time dataset. Therefore, the entire work in this study is carried out by using Python with OpenCV method. A vehicle detection and classification algorithm that works in real time is proposed in this work. Further, the heavy vehicles detection is classified using the Support Vector Machine with a new set of features, Histograms of Oriented Gradients. The results show that the proposed method with Support Vector Machine training parameters is better than earlier method.

Vehicle detection, Classification, Python, OpenCV, Support Vector Machine, Histograms of Oriented Gradients, Traffic Surveillance

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V.Sowmya, R.Radha, "Heavy-Vehicle Detection Using SVM and HOG Features", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.152-155, 2019.