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Healthcare evaluates clinical datasets regularly by specialist`s learning and action. In the clinical field, computer-supported with prediction system is used in the healthcare department. Data mining approach provides innovation and strategy to replace voluminous information into useful data for achieving a decision. By utilizing information mining systems it needs less investment for the forecast of the sickness with more accuracy and precision. This paper evaluates various classifiers and algorithms are used for the expectation of cardiovascular illness.

WEKA tool, Data Mining techniques, Heart disease prediction, Computer Aided Support System

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[12] N. I. Patel and H. R. Patel, "A Survey on Prediction of Disease with Data Mining," International Journal of Computer Sciences and Engineering, vol. VII, no. 2, pp. 289-293, 2019.
[13] P. Singh, S. Singh and G. S. P. Jain, "Effective heart disease prediction system using data mining techniques," International Journal of Nanomedicine, 2018.
[14] V. Chaurasia and S. Pal, "Data Mining Approach to Detect Heart Disease," International Journal of Advanced Computer Science and Information Technology, 2014.
[15] J. Vijayashree and N. C. SrimanNarayanaIyengar, "Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review," International Journal of Bio-Science and Bio-Technology, vol. VIII, no. IV, pp. 139-148, 2016.
[16] V. Krishnaiah, G. Narsimha and N. S. Chandra, "Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review," International Journal of Computer Applications, vol. 136, no. 2, pp. 43-51, 2016.
[17] R. Thakur , B. Gauri, D. Ashwini and . P. Shinde, "Heart Attack Prediction System Using Data Mining," International Journal of Innovative Research in Computer and Communication Engineering, vol. IV, no. 8, pp. 15582-15585, 2016.
[18] A. Hazra, S. K. Mandal, A. Gupta, A. Mukherjee and A. Mukherjee, "Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques:A Review," Advances in Computational Sciences and Technology, vol. X, no. 7, pp. 2137-2160, 2017.
[19] P. S. C. Reddy, P. Palagi and Jaya, "HEART DISEASE PREDICTION USING ANN ALGORITHM IN DATA MINING," International Journal of Computer Science and Mobile Computing, vol. VI, no. 4, pp. 168-172, 2017.
[20] K. T K, "Heart Disease Prediction System using Data Mining Method," International Journal of Engineering Trends and Technology, vol. 47, no. 6, pp. 361-363, 2017.
[21] E. S. Kajal, "Prediction of Heart Disease using Data Mining Techniques," International Journal of Advance Research , Ideas and Innovations in Technology, vol. II, no. 3, pp. 1-7, 2016.
[22] A. Kaur and J. Arora, "HEART DISEASE PREDICTION USING DATA MINING TECHNIQUES: A SURVEY," International Journal of Advanced Research in Computer Science, vol. XI, no. 2, pp. 569-572, 2018.
[23] M. Raihan, S. Mondal, a. More, P. K. Boni and M. O. F. Sagor, "Smartphone Based Heart Attack Risk Prediction System with Statistical Analysis and Data Mining Approaches," Advances in Science, Technology and Engineering Systems Journal, vol. II, no. 3, pp. 1815-1822, 2017.
[24] K. Gomathi and D. Shanmugapriyaa, "Heart Disease Prediction Using Data Mining Classification," International Journal for Research in Applied Science & Engineering Technology, vol. IV, no. II, pp. 60-63, 2016.
[25] A. Hazra, S. K. Mandal, A. Gupta, A. Mukherjee and A. Mukherjee, "Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques:," Advances in Computational Sciences and Technology, vol. X, no. 7, pp. 2138-2158, 2017.

Akansha Jain, Manish Ahirwar, Rajeev Pandey, "A Review on Intutive Prediction Of Heart Disease Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.109-113, 2019.

Image segmentation is the most critical function in image analysis and processing. Results of segmentation fundamentally affect all subsequent image analysis processes such as representation and description of objects, measurement of features, and even higher-level tasks such as classification of objects. Image segmentation is therefore the most essential and crucial process for facilitating the delineation, characterization and visualization of regions of interest in any medical image. The radiologist`s manual segmentation of the medical image is not just a tedious and time-consuming technique, also not very accurate, especially with the increasing medical imaging modalities and the unmanageable quantity of medical images that need to be examined. It is therefore necessary to review current image segmentation methodologies using automated algorithms that are accurate and require as little user interaction as possible, especially for medical images. In the segmentation process, it is necessary to delineate and extract the anatomical structure or region of interest so that it can be viewed individually. In this paper, we are projecting the important place of image segmentation in decision-making information extraction and deliberating upon current techniques which are used in medical imaging and discussing about various advancements in this research field.

Medical Imaging, Segmentation, Watershed Transform (WT), Expectation Maximization (EM), Level Set Method (LSM), Genetic Algorithms (GA), Artificial Neural Networks (ANN)

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Aarish Shafi Dar, Devanand Padha, "Medical Image Segmentation: A Review of Recent Techniques, Advancements and a Comprehensive Comparison," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.114-124, 2019.

Association rule mining is an exceptionally imperative and important part of data mining. It will be used to Figure the fascinating designs from transaction databases. Apriori calculation will be a standout amongst those practically established calculations from claiming association rules, yet all the it need the bottleneck Previously, effectiveness. In this article, we suggested a prefixed-itemset-based information structure to generate frequent itemset, with those assistance of the structure we figured out how to enhance the effectiveness of the traditional Apriori calculation.

Apriori, Improved Apriori, Frequent itemset, Support, Candidate itemset, Time consuming

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S. Sahu, R.S. Bisht, "Improved Apriori Algorithm For Association Rules Using Pattern Matching," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.125-128, 2019.

Big data is the term representing any collection of datasets so large and complex which is difficult to process using traditional data processing applications. The challenges comprise of analysis, capture, search, sharing, storage, transfer, visualization, and privacy violations. Big data is a set of techniques and technologies that need new forms of integration to uncover large hidden values from large datasets which is diverse, complex, and of a massive scale. Big data environment is used to acquire, organize and analyze a variety of data. The main objective of this paper is to give a general perspective of big data analytics, its process, tools and techniques used. There is an immense need for the construction of algorithms to handle Big Data. Many algorithms are defined in the analysis of large data set. A review of various techniques and algorithms are also discussed in this paper. The massive volume of both structured and unstructured data which is so large, it is difficult to gather and analyze for getting the required solution. It is better to have some tools which help in processing the complex data sets. This paper is also focused on various tools available to extract required data from big data.

Big data, Data extraction, Data cleansing, Decision making, Visualization, Predictive analysis

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P. Pandeeswary, M. Janaki, "A General Perspective of Big Data Analytics: Algorithms, Tools and Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.129-137, 2019.

Handwritten recognition is for all time a pioneering area of research in the field of pattern recognition and image processing and there is a huge demand for optical character recognition (OCR) on handwritten documents. Most of these systems work for Arabic, roman, Japanese and Chinese characters, but not as much of research on Indian languages, though there are 11 main scripts in India. This article provides a comprehensive survey of recent developments in popular Indian scripts for handwriting recognition by comparing the feature selection techniques, classifiers and the recognition accuracy for each technique. Finally, some future research directions on offline handwritten recognition techniques are discussed.

handwritten recognition, optical character recognition, feature selection, pattern recognition, image processing

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P. Sujatha, D. Lalitha Bhaskari, "A Survey on Offline Handwritten Text Recognition of Popular Indian Scripts," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.138-149, 2019.

Parallel computing enables us to utilize hardware resources efficiently and to solve computationally intensive problems by dividing them into sub-problem using a shared-memory approach and solving them simultaneously. Emerging technologies are based on parallel computing as it involves complex simulations of real-world situations which are extremely computation-intensive and time-taking as well. Parallel programming is gaining significance due to the limitations of the hardware. Researchers are trying to enhance memory and bus speed to match the processor`s speed. Generating parallel code requires skill and a particular technique of parallelization. There are several parallelization techniques amongst which one needs to be shrewdly chosen for a particular task and architecture. A brief survey of existing parallelization procedures is provided through this paper. New hybrid techniques are required to be developed combining technical and architectural benefits two or more parallel models. A thorough revision of traditional parallelization techniques is required to derive new techniques.

Shared Memory; Parallel programming; Parallelization techniques

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Vijay Kumar, Alka Singh, "Techniques of Parallelization : A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.150-153, 2019.

Verification gives a way to check the authenticity of a client attempting to get to any classified or delicate data. The requirement for ensuring secure information facilitated on the web has been rising exponentially as associations are moving their applications on the web. Static techniques for validation can`t totally ensure the validity of a client. This has prompted the advancement of multifaceted validation frameworks. Risk-based validation; a type of multifaceted verification adjusts as per the risk profile of the clients. This paper advances the plan of risk motor incorporated with the framework to inspect the client`s past login records and produce an appropriate example utilizing AI calculations to figure the risk dimension of the client. The risk level further chooses the confirmation technique that the client will be tested with. In this manner the versatile verification model aides in giving a more elevated amount of security to its clients.

User Metadata, Risk Metadata, Authentication System

[1] Kumar Abhishek, SahanaRoshan, Prabhat Kumar and Rajeev Ranjan. "A comprehensive study on Multifactor Authentication Schemes". Advances in Computing and Information Technology, 177, pp. 561-568,2013.
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Bharat Sharma, Sidharth Singh, "Risk-Based Authentication using Autoencoders," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.155-160, 2019.

Today’s businesses are interested in secure data and their applications can be accessible from anywhere using any device. It is able to achieve using cloud technology, but there are implicit challenges to making it realism. What can enterprise businesses do to reap the benefits of cloud technology while ensuring a secure environment for sensitive information? Recognizing those challenges is the first process to finding solutions that work. Increasingly many companies plan to move their local data management systems to the cloud and store and manage their product details on cloud servers. An accompanying issue is how to protect the security of the commercially confidential data, while preserve the ability to search the data. In this paper we are analysis of different securities scheme for encryption of item information and also for data search scheme in cloud computing.

Cloud Computing, Cloud Security, Security issues, Information Security

[1] YING-SI ZHAO “Secure and Efficient Product Information Retrieval in Cloud Computing” Received February 10, 2018, accepted March 11, 2018, date of publication March 19, 2018, date of current version April 4, 2018
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Nitin Kumar Sahu, Anuj Kumar Pal, "A Review on Analysis of TPA model for Secure Information Retrieval in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.161-164, 2019.

In this paper, the Acoustic Vowel Parameters which is based upon the Dialect Classification for Punjabi Speech is provided. The information from the formant’s dynamics F1, F2, and F3 was analyzed and further used in the process. The sound was evaluated with open source software PRAAT for the acoustic assessment. Multilingual speakers having age between twenty to thirty years has been selected for recording from Malwai and Doaba dialects of Punjab. The data of the total 20 people from Doaba region and 20 from Malwai region in which 7 females and 13 males has been taken for each dialect. In the proposed work MATLAB platform is used. First all the training dataset for the Doabi and the Malwai sound files were collected. The total training set has 140 sound files and the testing file has 19 sound files. Various parameters were analyzed in the training process. These parameters are Duration of the Sound file, Pitch, and Formants (F1, F2, and F3). The Formants (F1, F2, and F3) values were analyzed through PRAAT also. The formants are evaluated using the LPC method in MATLAB. The classifier used in the work was LDA and has classified the input sound file as per Doabi and Malwai sound file. The overall accuracy achieved in the system is 94.44%.

Acoustic, Punjabi, Formants, classification, training, testing, LDA (Linear Discriminant Analysis), and MATLAB

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H. Kaur, M. K. Gill, "Acoustic Vowel Parameters Based Dialect Classification for Punjabi Speech," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.165-175, 2019.

The fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation.In this paper, the author present the basic theory and applications of ICA, and our recent work focuses on separation of source signal and artefact removal using Independent Component Analysis.

Ballistocardiogram ,Component, ICA,mixing,unmixing matrix

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Manjula B.M, Prashantha H.S, Goutham M.A, "Independnt Component Analysis for Separation and Artifact Removal of Ballistocardiogram Signal," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.176-180, 2019.