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June 2017

Volume 10 | Number 2

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Articles under review

I-v Fuzzy Shortest Path in a Multigraph

Siddhartha Sankar Biswas

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In this research paper the author introduces the notion of i-v fuzzy multigraph. The classical Dijkstra’s algorithmic rule to search out the shortest path in graphs isn't applicable to fuzzy graph theory. Consequently the author proposes a brand new algorithmic rule referred to as by IVF-Dijkstra's algorithmic rule with the philosophy of the classical Dijkstra's formula to unravel the SPP in an i-v fuzzy multigraph

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Software Upgradation Model Based on Agile Methodology

Manvender Singh Rathore* and Deepa V. Jose

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Agile software development work on twelve principle for software development which implies requirements and solutions evolved through the combined teamwork of disciplined and interdisciplinary teams. The objective of this paper is to connect agile methodology with Version Control System for more efficient and effective utilization of resources. In this proposed model based on agile methodology, the Version Control System plays a vital role to make work done faster as compared to SCRUM. This paper compares various existing agile methodologies. The efficiency of the proposed model is proved through comparative analysis with existing agile methods and using ANOVA mathematical model. Bitbucket as Version Control System is used as web based hosting service and the proposed model is compared by maintaining similar sprints in SCRUM and VSprint model. VCS and previous SRS documents are the important components of this proposed model which helps in increasing the work speed at different phases of software development which the existing models does not consider.

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Automatic Feedback Generation in Software Performance Engineering: A Review

Javaid Iqbal and Syed Abrar Ul Haq

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Automation in generation of architectural feedback from performance indexes like probability distributions, mean values and variances has been of interest to the researchers from last decade. It is well established that due to the complexity in interpreting the performance indices obtained from performance analysis of software architecture and short time to the market, an automated approach is vital for acceptance of architecture based software performance engineering approach by software industry. In last decade some work has beendone in this direction. Aim of this paper is to explore the existing research in the field, which will be valuable for researchers looking forward to contributing to this research.

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Enhanced Content Based Double Encryption Algorithm Using Symmetric Key Cryptography

Junestarfield Lyngdoh Kynshi and Deepa V Jose

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This paper aims to solve the problems of the existing technique of the content based double encryption algorithm using symmetric key cryptography. Simple binary addition, folding method and logical XOR operation are used to encrypt the content of a plaintext as well as the secret key.This algorithm helps to achieve the secure transfer of data throught the network. It solved the problems of the existing algorithm and provides a better solution. The plaintext are encrypted using the above methods and produce a cipher text. The secret key is encrypted and shared through secure network and without knowing the secret key it is difficult to decipher the text. As per expected, enhanced encryption algorithm gives better result than the existing encryption algorithm.

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Classification of Human Organ Using Image Processing

Sindhu and V. Vaidhehi

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The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.

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Breast Cancer Detection using Image Processing Techniques

Prannoy Giri and K. Saravanakumar 

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Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it’s infant state.  The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary  or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

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Detection and Isolation of Zombie Attack under Cloud Environment

Sunil Kumar and Maninder Singh

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Network security, data security and several other security types such as the computer security collectively compose the word “Cloud Security”. Cloud computing posses a new challenge because traditional security mechanism is being followed are insufficient to safeguard the cloud resources. Cloud computing can easily be targeted by the attackers. A group of malicious users or illegitimate users can attack on system which may lead to denial the services of legitimate users. Such kinds of attacks are performed by the malicious (zombie) attackers. The zombie attack will degrade the network performance to large extend. Traditional techniques are not easily capable to detect the zombie attacker in the cloud network. So in this paper we have proposed a technique which is the enhancement of the mutual authentication scheme in order to detect and isolate zombie attack for the efficient performance of the network.

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Assessment of Accuracy Enhancement of Back Propagation Algorithm by Training the Model using Deep Learning

Baby Kahkeshan and Syed Imtiyaz Hassan

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Deep learning is a branch of machine learning which is recently gaining a lot of attention due to its efficiency in solving a number of AI problems. The aim of this research is to assess the accuracy enhancement by using deep learning in back propagation algorithm. For this purpose, two techniques has been used. In the first technique, simple back propagation algorithm is used and the designed model is tested for accuracy. In the second technique, the model is first trained using deep learning via deep belief nets to make it learn and improve its parameters values and then back propagation is used over it. The advantage of softmax function is used in both the methods. Both the methods have been tested over images of handwritten digits and accuracy is then calculated. It has been observed that there is a significant increase in the accuracy of the model if we apply deep learning for training purpose.

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Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

Suruchi Chawla

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The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need.  Trust based web page recommendations retrieve search results according to the user’s information need.  In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.

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An Automated Testing Model using Test Driven Development Approach

C.P. Patidar1 and Arun Dev Dongre2

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Today we live in the era of software and web applications. Software is used in every minor and major field. In defense, medical, education, research, government, administration and much other field software became a necessary part. Software also brings transparency in the systems. Software also makes people’s life easy and comfortable. Software testing is a very important part of any software development process. Software testing requires approximately forty percent budget of any software development process. Like in an automobile industry every vehicle is tested before it goes to the customer. Also in software testing it is must to test the software before deployment. Because if software deployed without testing then user will face the bug and user will be unhappy with the software. In this paper we compare manual and automated testing and proposed an automated testing model with test driven development (TDD).

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An Optimal Resource Provisioning Algorithm for Cloud Computing Environment

Shivangi Nigam and Abhishek Bajpai

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Resource Provisioning in a Cloud Computing Environment ensures flexible and dynamic access of the cloud resources to the end users. The Multi-Objective Decision Making approach considers assigning priorities to the decision alternatives in the environment. Each alternative represents a cloud resource defined in terms of various characteristics termed as decision criteria. The provisioning objectives refer to the heterogeneous requirements of the cloud users. This research study proposes a Resource Interest Score Evaluation Optimal Resource Provisioning (RISE-ORP) algorithm which uses Analytical Hierarchy Process (AHP) and Ant Colony Optimization (ACO) as a unified MOMD approach to design an optimal resource provisioning system. It uses AHP as a method to rank the cloud resources for provisioning. The ACO is used to examine the cloud resources for which resource traits best satisfy the provisioning. The performance of this approach is analyzed using CloudSim. The experimental results show that our approach offers improvement in the performance of previously used AHP approach for resource provisioning.

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Comparative study on Classification of Digital Images

H. B. Basanth Kumar

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Digital images are widespread today. The use of digital images is classified into natural images and computer graphic images. Discrimination of natural images and computer graphic (CG) images are used in the applications which include flower classification, indexing of images, video classification and many more. With the rapid growth in the image rendering technology, the user can produce very high realistic computer graphic images using sophisticated graphics software packages. Due to high realism in CG images, it is very difficult for the user to distinguish it from natural images by a naked eye. This paper presents comparative study of the existing schemes used to classify digital images.

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Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier

Aziz Makandar and Anita Patrot

 

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Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level  decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising. 

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A New Distributed Intrusion Detection System in Computer Network: An Approach to Detect Malicious Intrusion Threats at Initial Stage

parveen Sadotra and Chandrakant Sharma

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Internet is a blessing for human community modern days and use of network is indispensable in present time. Use of networks and internet has also brought large numbers of security threats to our database and information systems.  There are so many intrusion attacks on public and private networks. Main objective of this research work to study about problem associated with intrusion in network system and analyzes the use of intrusion Detection systems. Scrutinize the use of various IDS and develop a new IDS which should be most effective and easy to use also cost effective for users. So, we will be presenting our newly developed application based IDS which is to be suitable way to detect threat in the network system which can be cost effective and easy to use also it should have instantaneous alert system to notify intrusion to security professionals.

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Major challenges of Mobile Adhoc Networks

Dharmesh Bhatt* and Bijendra Agrawal

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In the unknown environment of network the security the major issue for safe communication between the nodes. In such networks a lot of active and passive attacks are done by the attackers in groups in the data packets and routing messages. Mobile Adhoc networks are such type of networks which does not have any centralized authentication and the nodes communicate with each other. In MANET security plays a vital role for safe communication. The focus is given on security of Mobile Adhoc Networks. MANET is open network which is available for the malicious and trustworthy users to communicate. The MANET has to design a robust solution which deals with such type of malicious attackers so that a health network transmission takes place between trustworthy nodes. The Mobile Adhoc Networks are the network which works in isolation along with the wired infrastructure. This elasticity of MANET is its biggest strength but it is also a big vulnerability for security issues. In paper we will discuss the active and passive attacks including black hole attacks, spoofing, warm hole, flooding and traffic monitoring, traffic analysis and eavesdropping respectively.

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A Review on Electronic Dictionary and Machine Translation System Developed in North-East India

Saiful Islamand Bipul Syam Purkayastha

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Electronic Dictionary and Machine Translation system are both the most important language learning tools to achieve the knowledge about the known and unknown natural languages. The natural languages are the most important aspect in human life for communication. Therefore, these two tools are very important and frequently used in human daily life. The Electronic Dictionary (E-dictionary) and Machine Translation (MT) systems are specially very helpful for students, research scholars, teachers, travellers and businessman. The E-dictionary and MT are very important applications and research tasks in  Natural Language Processing (NLP). The demand of research task in E-dictionary and MT system are growing in the world as well as in India. North-East (NE)  is a very popular and multilingual region of India. Even then, a small number of E-dictionary and MT system have been developed for NE languages. Through this paper,  we want to elaborate about the importance, approaches and features of E-dictionary and MT system. This paper also tries to review about the existing E-dictionary and MT system which are developed for NE languages in NE India.

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Plastic Money Security Issues in India

Reena Hoodaand  Deepak Dhaka2

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The benefit of plastic money is its accessibility and acceptability with lightness than cash in wallet plus ready notion with the major problem of fraudsters who may exploit the rights of the genuine stakeholders. At present, the computing resources, communication technology, availability of the cards in competitive market are escalating India to be placed at the growing edge of popularity of plastic money. People after demonetization are intensely employing the technology; moreover it’s a step towards the digitalization of India. With increasing population of the smart phone users, people are more aware about the cautions needed to pursue while using P-cards. However as compare to the developed nations, in a land of villages- India, the problem become more perilous as there are petite infrastructural facility & literacy, high percentage of less skilled population, old customs and non-reach of the government facilities, spoiling the p-money applicability & security and digital India campaign.. Therefore, besides the plastic money rewards and contributions to the digitalization, present paper contributed towards a range of security issues in addition to technique to make money protected and reliable.

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Hybrid Intrusion Detection System for Private Cloud & Public Cloud

Riddhi Gaur and Uma Kumari

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Internet based applications and data storage services can be easily acquired by the end users by the permission of Cloud computing. Providing security to the cloud computing environment has become important issue with the increased demand of cloud computing. Other than the traditional security methods, additional methods like control access, confidentiality, firewalls and user authentication are required in order to provide security to the cloud computing environment. One of the needful components in terms of cloud security is Intrusion Detection System (IDS). To detect various attacks on cloud, Intrusion Detection System (IDS) is the most commonly used mechanism. This paper discusses about the intrusion detection and different intrusion detection techniques namely anomaly based techniques and signature based techniques.

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Enhancing the Classification Accuracy of Noisy Dataset By Fusing Correlation Based Feature Selection with K-Nearest Neighbour

Samir Kumar Singha  and Syed Imtiyaz Hassan*

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The performance of data mining and machine learning tasks can be significantly degraded due to the presence of noisy, irrelevant and high dimensional data containing large number of features. A large amount of real world data consist of noise or missing values. While collecting data, there may be  many irrelevant features that  are collected by the storage repositories. These redundant and irrelevant feature values distorts the classification principle  and  simultaneously increases calculations overhead and decreases the prediction ability of the classifier. The high-dimensionality of such datasets possesses major bottleneck  in the field of data mining, statistics, machine learning. Among several methods of dimensionality reduction,  attribute or feature selection technique is often used in dimensionality reduction. Since the k-NN algorithm is sensitive to irrelevant attributes therefore its performance degrades significantly when a dataset contains missing values or noisy data. However, this weakness of the k-NN algorithm can be minimized when combined with the other feature selection techniques. In this research we combine the Correlation based Feature Selection (CFS) with k-Nearest Neighbour (k-NN) Classification algorithm to find better result in classification when the dataset contains missing values or noisy data.  The reduced attribute set decreases the time required for classification. The research shows that when dimensionality reduction is done using CFS and classified with k-NN algorithm, dataset with nil or very less noise may have negative impact in the classification accuracy, when compared with classification accuracy of k-NN algorithm alone. When additional noise is introduced to these datasets, the performance of k-NN degrades significantly. When these noisy datasets are classified using CFS and k-NN together, the percentage in  classification accuracy is improved.

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A Novel Approach to Address Sensor Interoperability Using Gabor Filter

Neha Bhatia1, Himani2 and Chander Kant3

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Biometric authentication using fingerprint is one of the unique and reliable method of verification processes. Biometric System suffers a significant loss of performance when the sensor is changed during enrollment and authentication process. In this paper fingerprint sensor interoperability problem is addressed using Gabor filter and classifying images into good and poor quality. Gabor filters play an important role in many application areas for the enhancement of various types of fingerprint images. Gabor filters can remove noise, preserve the real ridges and valley structures, and it is used for fingerprint image enhancement. Experimental results on the FVC2004 databases show improvements of this approach.

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Cellular Automata Based Study of Spectral Signatures of Dal_Lake Infrared Imagery

Shahi Jahan Wani and Fasel Qadir

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Among all the water bodies in Jammu & Kashmir Dal Lake has a peculiar significance due to its location in the heart of the capital city Srinagar. Historical studies over last fifteen hundred years indicate a continuous squeezing of the Lake due to different natural and manmade interventions. Over this long period, the governance of the land has passed through various wise and ugly human plans besides some slow natural processes. The mathematical modelling of such a dynamics is not an easy task because of the many intervening variables and the difficulty which implies their measurements. On the other hand, during the last decades, the use of Cellular Automata (CA) techniques to simulate the behaviour of linear or non-linear systems is becoming of great interest. This fact is mainly due to the fact that this approach depends largely on local relations and a series of rules instead of precise mathematical formulae. The infrared (IR) satellite imagery can be helpful in identifying the different areas of interest using CA as a tool of image processing. The study will not only separate the areas of interest but also pave a way towards a comprehensive study of all the identified zones using spectral signatures received from the continuous IR imagery of both pre-monsoon and post-monsoon periods in future.

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A Review on IoT Security Issues and Countermeasures

J. Yashaswini

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Internet of things (IoT) is a system of connected physical objects that are accessible through an internet. The things in IoT is an object that assigned with an IP address and have the ability to collect and transfer the data over a network without manual intervention. As IOT does not need any human to machine interaction, it seems to be one of the largest waves of revolution as per the research going on, hence security is needed. The quick development of IOT has derived with the challenges in terms of security of things. This paper focus on the general security issues in IoT and measures used to overcome with those security issues.

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Iris Biometric Modality: A Review

Iqra Altaf Mattoo and Parul Agarwal*

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Biometric Recognition is the most suitable and informed identification method which is used in different fields due to its uniqueness of the countless behavioural and physiological traits like hand geometry, finger prints, iris recognition, face recognition, handwriting, voice recognition, etc. Iris recognition system is widely being used as it has inherently distinctive patterns that provide a robust method for the identification purpose. Different nations have already started to use biometric recognition system for the identification purposes including patient identification, border security, etc. In this review paper, different steps that are involved in Iris Recognition system are defined and evaluation of different Iris Recognition methods used by different researchers for each recognition step is done as well.

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