Best Paper Award

Pedestrian re-identification technology has become the current research focus due to its wide range of applications. This study conducted cross dataset pedestrian re-identification to solve the problem that the single dataset’s difficulty for simulating the actual situation and its poor generalization ability. Deep learning has made remarkable achievements in the fields of machine learning recently, so the deep learning technology is integrated into cross datasets pedestrian re-identification system. Here we improved the three-layer convolutional neural network (CNN) structure proposed by Yang Hu in Asia Conference on Computer Vision (ACCV), 2014. The Batch Normalization (BN) layer has been added to reduce the over-fitting degree during training period and the adjusted cosine similarity algorithm is used for pedestrian feature match to solve the defect of cosine similarity algorithm. Finally we implemented the entire cross dataset pedestrian re-identification system and got the experimental results. The Shinpuhkan2014dataset was chosen as training set. We compared the training results before and after adding BN layer and found that test accuracy increased, test loss decreased and over-fitting phenomenon eased. The VIPeR and i_LIDS datasets were chosen as test sets. We evaluated the effects on VIPeR and i_LIDS based on the CNN model that training on Shinpuhkan2014dataset. The cumulative matching rate rank5 increased by 1.7% on VIPeR dataset compared with the current level, the rank10 and rank20 also increased. And the cumulative matching rate rank1 increased by 1.8% on i_LIDS dataset compared with the current level, the rank5 and rank10 also increased.

Cross dataset, Convolutional neural network, Batch normalization, Adjusted cosine similarity

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Hongmei Xie，Yanggang Zhou, Qiang Liu, "Deep Learning Feature Representation Applied to Cross Dataset Pedestrian Re-identification", International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.1-11, 2018.

In this paper, the differential equation of motion of the classical Helmholtz-Duffing oscillator, Van der Pol, Duffing oscillator and Duffing-Van der Pol oscillator equations have been solved analytically with the help of a new integral transform named Aboodh transform and homotopy perturbation method. By recasting the governing equations as nonlinear eigenvalue problems, we have obtained the excellent approximate analytical solution of the displacement and the relation between amplitude and angular frequency. We have also compared our results with exact numerical results graphically for few cases. Here, we have also demonstrated the sophistication and simplicity of this technique.

Aboodh Transform, Homotopy Perturbation Method, Helmholtz-Duffing Oscillator, Van der Pol, Duffing Oscillator, Duffing-Van der Pol Oscillator, Approximate Analytical Solution

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P.K. Bera, S.K. Das, P. Bera, "Applications of the Aboodh Transform and the Homotopy Perturbation Method to the Nonlinear Oscillators", International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.1-10, 2018.

This paper proposes a novel approach for sentiment analysis. The growing importance of sentiment analysis commensurate with the use of social media such as reviews, forum discussions, blogs, microblogs like Twitter, and other social networks. We require efficient and higher accuracy algorithms in sentiment polarity classification as well as sentiment strength detection. In comparison to pure vocabulary based system, deep learning algorithms show significantly higher performance. The goal of this research is to modify a Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU) by introducing stochastic depth in a hidden layer and comparing it with baseline Naïve Bayes, vanilla RNN and GRU-RNN models. To improve our results, we also incorporated Active Learning with Uncertainty Sampling approach. Movie review dataset from Rotten Tomatoes was used, the dataset includes 215,154 fine grained labelled phrases in addition to 11,855 full sentences. We performed pre-processing on the data and used an embedding matrix with pre-trained word vectors as features for training our model. These word vectors were generated using character level n-grams with fasttext on Wikipedia data.

Fasttext, Recurrent Neural Network, Gated Recurrent Unit, Active Learning

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Tulsi Jain, Kushagra Agarwal, Ronil Pancholia, "Sentiment Analysis Based on a Deep Stochastic Network and Active Learning", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.1-6, 2017.

The most popular open source distributed computing framework called Hadoop was designed by Doug Cutting and his team, which involves thousands of nodes to process and analyze huge amounts of data called Big Data. The major core components of Hadoop are HDFS (Hadoop Distributed File System) and MapReduce. This framework is the most popular and powerful for store, manage and process Big Data applications. But drawback with this tool related to stability and performance issues for small file applications in storage, manage and processing the data. Existing approaches deals with small files problem are Hadoop archives and SequenceFile. However, existing approaches doesn’t give an optimized performance to solve small files problems on Hadoop. In order to improve the performance in storing, managing and processing small files on Hadoop, we proposed an approach for Hadoop MapReduce framework to handle the small files applications. Experimental result shows that proposed framework optimizes the performance of Hadoop in handling of massive small files as compared to existing approaches.

Hadoop, Hadoop Distributed File System (HDFS), MapReduce, Hadoop Archives, Sequence File, Small Files

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Guru Prasad M.S., Nagesh H.R., Swathi Prabhu, "An Efficient Approach to Optimize the Performance of Massive Small Files in Hadoop MapReduce Framework", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.112-120, 2017.

A new Quantum Inspired Evolutionary Computational Technique (QIECT) is reported in this work. It is applied to a set of standard test bench problems and a few structural engineering design problems. The algorithm is a hybrid of quantum inspired evolution and real coded Genetic evolutionary simulated annealing strategies. It generates initial parents randomly and improves them using quantum rotation gate. Subsequently, Simulated Annealing (SA) is utilized in Genetic Algorithm (GA) for the selection process for child generation. The convergence of the successive generations is continuous and progresses towards the global optimum. Efficiency and effectiveness of the algorithm are demonstrated by solving a few unconstrained Benchmark Test functions, which are well-known numerical optimization problems. The algorithm is applied on engineering optimization problems like spring design, pressure vessel design and gear train design. The results compare favorably with other state of art algorithms, reported in the literature. The application of proposed heuristic technique in mechanical engineering design is a step towards agility in design.

Constraint Optimization, Mechanical Engineering Design problems, Quantum Inspired Evolutionary Computational Technique, Unconstrained Optimization

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Astuti. V., K. Hansraj, A. Srivastava, "A Quantum Inspired Evolutionary Computational Technique with Applications to Structural Engineering Design", International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.20-33, 2017.

This paper presents an approach to automatic detection of liver tumor in CT images by using region-growing and Support Vector Machine (SVM) which is successfully classifies the liver cancer types such as hepatoma, hemangioma and carcinoma.The method rectifies the problem of manual segmentation and classification which is time consuming due to the variance in the characteristics of CT images.Our proposed method has been tested on a group of CT images obtained from hospitals in Kerala with a promising results both in liver and tumor segmentation. The average error rate and accuracy rate obtained from our proposed method is 0.02 and 0.9.

Region-growing,preprocessing,feature extraction,Segmentation, SVM Classifier.

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R. Sreeraj, G. Raju, "Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier", International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.26-29, 2016.

Synthetic Aperture Radar(SAR) image filtering has been of interest since its inception. A variety of denoising filters for SAR images have been proposed in the recent years, which are targeted at removing the speckle noise to increase the contrast of the image, and make it useful for further image interpretation and applications. Of late, Wavelet based SAR data denoising techniques have been gaining popularity due to its space-frequency localization capability and the capacity to analyse the data at different scales. In this paper, we have attempted to derive an optimal approach for wavelet based SAR image filtering based on the quality criteria which takes into account not only the radiometric quality but also the geometric quality using point target data of actual Corner Reflector. Different orders of Daubechies wavelet coefficients have been used in the DWT(Discrete Wavelet Transform) based approach. In this study all aspects of an image quality have been taken into consideration such as the geometric fidelity and the radiometric quality, and using a simple heuristic soft thresholding criteria, optimal basis has been arrived at.

SAR, speckle, denoising, Wavelet based denoising, thresholding, decomposition, mother wavelets, radiometric resolution, geometric resolution, corner reflector.

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A. Ray, B. Kartikeyan, S. Garg, "Towards Deriving an Optimal Approach for Denoising of RISAT-1 SAR Data Using Wavelet Transform", International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.33-46, 2016.

This study deals with the design of an hostel inmate - informatics system, which addresses the issues to discover the fact likeness to stay. By using Data Mining (DM) techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncovering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish the conceptual design of Data Warehouse by applying the multidimensional modeling paradigm. This paper presents a novel approach to integrating data mining model into multidimensional models in order to accomplish the conceptual design of Data Warehouse with Association Rules (AR). To this extent, the Association Rules for modeling in the conceptual level. The main advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Warehouse, instead of the traditional method of specifying Association Rules by considering only the final database implementation structures such as tables, rows or columns. In this way to show the benefits of our approach, implementation of specified Association Rules would be created on a commercial database management server.

SData Mining ; Data Warehousing; Multidimensional; Association rule

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N. Balajiraja, "Mining Association Rule of Frequent Itemsets Measures for an Educational Environment", International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.8-17, 2016.

In this work, a novel quaternary algebra has been proposed that can be used to implement an arbitrary quaternary logic function in more than one systematic ways. The proposed logic has evolved from and is closely related to the Boolean algebra for binary domain; yet it does not lack the benefits of a higher-radix system. It offers seamless integration of the binary logic functions and expressions through a set of transforms and allows any binary logic simplification technique to be applied in quaternary domain. Since physical realization of the operators defined in this logic has recently been reported, it has become very important to have a well-defined algebra that will facilitate the algebraic manipulation of the novel quaternary logic and aid in designing various complex logic circuits. Therefore, based on our earlier works, here we describe the complete algebraic representation of this logic for the first time. The efficacy of the logic has been shown by designing and comparing several common logic circuits with existing designs in both binary and quaternary domain.

Propositional Logic, Quaternary algebra, Quaternary Transformation, Sum-of-products

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Ifat Jahangir, Anindya Das, Masud Hasan, "Facile Algebraic Representation of a Novel Quaternary Logic", International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.1-15, 2016.

By increasing the use of distributed systems and increasing software attacks, software security is considered very important and treated as an active research area. Security is usually taken into account after design and implementation of the system, whereas like other quality attributes, it must be considered from the beginning of the process of building software, such as architectural design. Considering 1) the long-term effects of design stage decisions on final software product, 2) one of the important design decisions, is selection of suitable software architecture style, and 3) the quantitative impact of software architecture style on quality attributes, especially security, has not been investigated, the aim of this research is quantification of the impact of architectural styles on security quality attribute. This study aims at evaluating the software architectural styles from the viewpoint of the security quality attribute based on scenario-based evaluation method. In this study, by presenting security scenarios, the architectural styles are evaluated from the perspective of security. Then architectural styles are ranked based on the results of the evaluation and importance of scenarios using Analytical Hierarchy Process, in terms of supporting software security. The most important contribution of this paper is to propose an approach to select the software architecture style in which security attribute plays a major role.

Security; Scenario-based Evaluation; Software Architecture Styles

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Gholamreza Shahmohammadi, "Scenario-based Evaluation of Software Architecture Styles from the Security Viewpoint", International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.95-101, 2016.