Volume-6 , Issue-11 , Nov - 2018 Go Back

Data mining is used to bring out the unknown information from known large data set. In data mining Association Rule Mining (ARM) is a technique which discovers the frequent relation between the patterns by using the terms support and confidence. Apriori, Partition, Border and Incremental algorithms are some of the algorithms in ARM. In this work microarray dataset for psychological disorders is extracted from GEO data base, applied Apriori algorithm, implemented using R tool and recognized the relationship between the diseases in psychological disorder.

Association Rule Mining, Apriori, Microarray dataset, Psychological Disorder, Occurrences

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K. Mohan Kumar, S. Devi, "Computational Study on Association Rule Mining Using Microarray Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.299-303, 2018.

The recent developments in underwater video monitoring system makes automatic object detection and object tracking a significant and challenging task. In such processing, the method involves preprocessing, feature extraction, object classification, object detection and tracking. Detecting moving objects from the underwater video has many potential applications for Remotely Operated Vehicles (ROVs) or Autonomous Underwater Vehicles (AUVs), such as tracking fish, recognizing underwater objects etc. Underwater object recognition is a cumbersome due to the change in water structure, seasonal, climatic changes, temperature variation and further degraded by a poor non-uniform source of artificial light. Diverse approaches using image processing and pattern recognition have been proposed by numerous scientists and marine engineers to tackle these problems using methods such as neural network, contour matching, and statistical analysis. In this article, we provide a comprehensive overview of different methods and techniques of object detection and object tracking in general and underwater scenario in particular. We have been successful in highlighting the several key features and aspects of underwater object detection and tracking which will take the work in this domain further.

Underwater image enhancement, Object Detection, Tracking, Recognition and Machine Learning

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Girish Gaude, Samarth Borkar, "Comprehensive Survey on Underwater Object Detection and Tracking," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.304-313, 2018.

Cloud computing provides unlimited on-demand resources and services through remote servers based on pay-per-use model. It includes Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS). Cloud computing facilitates efficient utilization of computing resources in large-scale cloud data centers. Day-by-day, increasing usage of cloud computing services leads to increasing energy consumption and operational cost. Moreover, it produces high amount of Co2, causing huge environmental damage. Heavy usage of cloud data centers has also become a problem to sacrifice system performance and Quality of Services (QoS). In order to overcome these problems, an efficient job-scheduling algorithm is required to reduce energy consumption and execution time without diminishing performance of the system. Apart from this, a green cloud data center plays a significant role in cloud computing to reduce Co2 emissions. Energy-efficient heuristics model is used to find an optimal solution for executing jobs of varying sizes and timings. In this paper, using Dynamic Voltage Frequency Scaling (DVFS), we introduce Energy-Efficient Job Scheduling (EEJS) algorithm to green cloud data centers. Our proposed algorithm is compared to Energy-Conscious Scheduling algorithm (ECS) and Green Energy-Efficient Scheduling algorithm (Green-EES). Experimental results are evaluated using CloudSim 3.0.3 toolkit and simulation results are validated in low-, medium-, and high-workload conditions. Compared to other two algorithms, EEJS demonstrates reduced energy consumption and execution time without violating Service Level Agreements (SLA).

Cloud Computing, Job Scheduling, Heuristics Model, DVFS, Energy Consumption, SLA Violation

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[2] Ali Vafamehr, Mohammad E. Khodayar, Energy-aware cloud computing, The Electricity Journal, Vol. 31, No. 2, pp. 40-49, March 2018.
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K. Sutha, G. M. Kadhar Nawaz, "Energy-Efficient Heuristics Job Scheduling Algorithm using DVFS Technique for Green Cloud Data Centers," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.314-322, 2018.

To improve the accuracy in segmenting the brain tumor from Magnetic resonance images (MR), pre-processing of raw MR images plays significant importance, is required for proper visualization and detection of the tumor part, to increase the quality of the image affected by the noise. The paper is mainly focused on skull removal and denoising techniques. Inclusion of skull may lead to misclassification of tumour tissues and might increase the time complexity. In this study T1, T1contrast, T2, Flair MR images is analysed in axial, sagittal and coronal planes. Mathematical morphology operation with histogram thresholding is performed to remove the skull region. Denoising the images with various filters and evaluation of filters in terms of mean square error (MSE), signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) are considered for the study. The Algorithm developed provides better results for skull stripping and high PSNR is obtained for wiener filter reducing the Gaussian noise by preserving the edges.

Denoising, MR Image, Morphology, , MSE, PSNR, Skull removal

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Akshath M J, H S Sheshadri, "Denoising of skull stripped brain tumor MR images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.323-329, 2018.

Due to the changing scenario of the 21st century and because of the rapid advancement in the information technology, the role of university librarian has become very vital and significant. Automation and digitization of the libraries have become an essential factor for achieving the objectives of the users of the present and future generations. During the past decade we have seen many changes around the world in the library and information field. Various libraries and information centers are managed to improve their efficiency, capability, services and productivity by using the latest technologies. This study has been presented with the types of library management software packages being used in the Central University of Jammu and Shri Mata Vaishno Devi University (SMVDU) - Katra. The main purpose of the study is to provide some basic idea to librarians which help them in selecting the suitable library management software package for their respective libraries.

Software, Automation, Libsys, Koha, Etc

[1] Sanjay, Kataria. “Emerging Technologies and changing dimensions of Libraries and information services” KBD publications, New Delhi 2010.
[2] A.P. Khan & Others. Automated Library Data Tracking System By Smartphone. International Journal of Computer Sciences and Engineering. Vol.6, Issue. 4, pp. 379-382.2018
[3] Vikash Kumar Shukla & Others. “Android Based Management System for Library”, International Journal of Computer Sciences and Engineering. Vol.4, Issue. 4, pp. 102-105, 2016
[4] www.shodhganga.inflibnet.ac.in
[5] www.doaj.org
[6] www.emeraldinsight.com
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[8] www.scholar.google.co.in

Sudesh Kumar, Sangita Gupta, "Library Management Software Packages: A Case Study of Central University of Jammu and SMVDU-Katra.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.330-335, 2018.

In this paper we are implementing parametric classifier Linear and quadratics using fisher linear discriminant for find the misclassification rate using cross validation, useful in recognizing the degraded devnagari script scan document.Dimensionality reduction is the process of transforming input data into a lower dimensional space where a more efficient classifier can be built are divided in two groups: Feature extraction, which map input data using linear transformation i.e. a transformation matrix and feature selection, which performs the mapping by selecting a subset of the original features.Feature extraction methods are supported by fisher’s linear discriminant function.Feature selection is use to choose an optimal subset according to some criterion of cardinality m among the d input features. In feature ranking each Feature is evaluated individually according to the chosen criterion, and the values are then sorted the m features with the best value of the criterion are retained for classification. Also we focus on learning machine stages which consists of two stages: dimensionality reduction and classification.

Linear, Quadratic, Fisher Linear Discriminant, Cross validation, Feature Extraction, Dimensionality

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Sushilkumar N. Holambe, Ulhas B Shinde, "Recognise the Degraded Devnagari Script by Dimensionality Reduction Linear and quadratic Classifiers using Fisher Linear Discriminant," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.336-340, 2018.

The spatio-temporal modeling of a social system play an important role in forecasting the future trend of that system. In this paper, we present an approach to model the past crime behavior for future crime prediction. The study considered major crime event from Haryana state and used clustering approach to predict future crime trend. The analysis results obtained on ‘R’ tool for the past few years are found inconformity with that of real time trend, which envisage the success of our model proposed in this paper.

crime location, criminal, crime prediction, clustering

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Romika Yadav, Savita Kumari Sheoran, "Analysis of Criminal Behavior through Clustering Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.341-344, 2018.

Folding plays imperative role in the cross validation studies of machine learning based models. The folding divides the original sample into training and test sets, which evaluate performance of the machine learning based models and present scenarios for optimising the efficacy of such models. The present study discusses about the computational approaches applied for preparing training and test sets at different folds from 12S rRNA molecular marker sequence dataset of fish and application of these sets to estimate the performance of the proposed models based on machine learning techniques viz. Random Forest and Support Vector Machine. Additionally, the study presents the comparative accounts on efficacies of these models estimated at different folding. The findings from the study showed that folding has linear relationship with the efficacy of the model. The model with random forest was found better for solving the classification problems of the molecular marker sequence data. This study provides understanding on utility of the folding level in increasing the efficacy of the machine learning based methods and suggests for suitable machine learning method for solving the multiclass problem data especially where the identification using the molecular markers sequence data is involved.

Machine learning method, Random forest, Support vector machine, Folding level, 12S rRNA, Cross validation

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Rameshwar Pati, Ajey Kumar Pathak,, Navita Srivastava, "Cross Validation Of Supervised Machine Learning Models Based On Random Forest and Support Vector Machine Techniques for 12S rRNA Molecular Marker: Implementation, Comparison and Utility," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.345-349, 2018.

In this paper, a hybrid GA-LCDRC model is proposed to address multiclass functional MRI classification problem. KNN based genetic algorithm is used as the feature selector and linear collaborative discriminant regression classifier (LCDRC) is used as the classifier. The effectiveness and usefulness of this model is assessed based on its classification specificity, sensitivity and accuracy. This approach is tested to Haxby’s 2001 functional MRI dataset with eight different classes. The result indicates that the proposed hybrid model can be used for multiclass cognitive state classification.

fMRI, multiclass, linear collaborative discriminant regression classifier (LCDRC), genetic algorithm

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K. O. Gupta, P. N. Chatur, "Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.350-353, 2018.

Face recognition is a type of biometric identification method, it is challenging and one of the active research area in the field of Computer Vision. Variations in face image is due to changes in expression, presence of occlusion, geographical variations and illumination along with aging are some of the challenges to face recognition technique. In this paper, we attempt to solve illumination challenge for face recognition. Here, we propose a novel symbolic face recognition technique using Logarithm Gradient Histogram (LGH). Experimental results are carried out on standard benchmark databases like Extended YaleB, ORL. The performance of the proposed face recognition technique turns out to be 94.35 to 100% for the mentioned databases.

Face recognition, Illumination invariant feature, Logarithm Gradient Histogram (LGH)

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Yogish Naik G.R., Arun Kumar H.D., Prabhakar C.J., "Face Recognition using Symbolic Data with Texture Features," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.354-358, 2018.