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Accepted Papers

  • Evaluation on Techniques Available for Trusted Third Party on Cloud
    Sonia Arora and Pawan Luthra, SBSSTC Firozepur, India
    Cloud Computing provides various services to customers over network on a rent basis which can be vary according to customer requirements. As these services are driven by Third Party Provider who are the owner of these infrastructure. Due to fast development of cloud applications and large storage of data on cloud, concern of security and privacy also increases with it. This paper focus on storing the data on cloud using Trusted Third Party Auditor and mention various challenges faced by it also compare different techniques available to enhance security on cloud storage.
  • Hybrid Flow Shop Scheduling Using Hybrid ACO Cuckoo Algorithm to Minimize Makespan
    M.Omkumar and M.Ravianandan, Anna University, India
    Scheduling of jobs in manufacturing environment is often NP Hard [4].Multi stage Hybrid flowshop with a number of unequal parallel machines choices per stage makes it a further NP Hard to solve. Improved Hybrid ACO Cuckoo Algorithm proposed in this paper attempts to apply the algorithm to solve one such Hybrid flowshop problem. The performance of the algorithm was benchmarked against available Hybrid Algorithms to solve Hybrid flowshop. The outcome of the algorithm outperforms the performance of some of the Hybrid algorithm currently available.
  • A new approach in pattern matching: Codon Detection in DNA and RNA using Hash Function (CDDRHF)
    Paramita Basak Upama, Jarin Tasnim Khan, Zeba Yasmin, Farah Zemim and Nazmus Sakib, Ahsanullah University of Science and Technology, Bangladesh
    Detection of different known patterns in any newly found sequence is one of the most important works in the field of Bioinformatics. Given a specific pattern, a long string has to be searched for finding that pattern. That means pattern matching is used for checking the sequence of tokens and the match has to be exact, obviously. These patterns usually have the form of either sequences or tree structures. In DNA and RNA sequences, this type of pattern searching is often needed to detect codons (both “met” and “stop” sequences) in it. This paper presents the concept and implementation of a new algorithm “codon detection in DNA and RNA using hash function (CDDRHF)” for detection of codons in long DNA and RNA sequences, using the concepts of hash function along with metaheuristic approaches. Then the gene between “met” and “stop” codons will easily be extracted to be used for proper alignment. This new algorithm works so fast that any DNA or RNA sequence of any length can be used to perfectly search for codons within seconds.
  • Evaluation, Scope and Study of ‘Wavelet Transform’ In Image Processing
    Mohammad Mohsen Ahmadinejad, University of Kerla, India
    A wavelet is a localized function of mean zero. Wavelet transforms often incorporate a pyramidal representation of the result. We will also see examples later of cases where a set of successively smoother versions of an image are not down sampled. Wavelet transforms are computationally efficient, and part of the reason for this is that the scaling or wavelet function used is often of compact support, i.e. defined on a limited and finite domain. Wavelets also usually allow exact reconstitution of the original data. A sufficient condition for this in the case of the continuous wavelet transform is that the wavelet coefficients, which allow reconstitution, are of zero mean. Wavelet functions are often wave-like but clipped to a finite domain, which is why they are so named. In Recent years, many studies have been made on wavelets. An excellent overview of what wavelets have brought to the fields as diverse as biomedical applications, wireless communications, computer graphics or turbulence.
  • Data Synchronization on Android Clients
    Ashish Kedia and Anusha Prakash, NIT Karnataka, India
    Past decade has witnessed meteoric advances in the field of mobile computing owing to the development of affordable hardware technologies as well as user-friendly software platforms. Android, the platform marketed by Google has boomed in sales over the past few years making it one of the major mobile platforms in the market. The steady growth of wireless information and communication technology in convergence with rise in the penetration of Internet has led to the evolution of a wide range of mobile applications like news, multi-player games, social networking, messaging, etc. that need to access remote data. For the optimal functioning of all these applications an efficient synchronization mechanism is vital. However smartphones have limited computational resources, power restrictions and intermittent Internet connections which pose a challenge for smooth synchronization. This paper proposes a two-way data synchronization mechanism between multiple Android clients and a central server to address these challenges. We employ a batching logic to ensure efficient data transfer in poor network environments and a server-side conflict resolution mechanism to reduce overhead on the clients, which ensures optimal processing and battery power consumption by the clients.
  • High Frequency Trading by Transient Profit Peak Classification using Machine Learning on Real-Time Data Streams
    Vishnu Srivastava, University of Waterloo, Canada
    Predicting the movement of stock prices has been a major area of implementation of Machine Learning algorithms but due to large data processing and limited computing capability, most of this has been limited to long term predictions. Caching feature i.e. storing temporary feature vector in the memory or expanding classifier such as random forest by adding new trees with new incoming data can provides an alternative but is not sufficient to account for large volatility in the markets. This paper presents a transient profit peak classification algorithm combined with a classifier expansion mechanism to take advantage of the volatility in the stock markets by clas- sifying a data point as a buying point (BP), selling point (SP) or as a do-nothing point (DN).
  • Reversible Data Hiding Using BAT Optimization for Encrypted Images
    Gazal Betab and Rama Rani, DAV University, India
    Due to the development of latest technologies in communication and computer networks, exchange of images between places has become a usual practice these days. Text fusion in images is an important technology for image processing. We have lots of information related to the reports and need lots of space to store and the proper position and name which relates that image with that data. Reversible data hiding is a method to embed extra message into some unsatisfactory spread media, for example, medical or military images with a reversible way so that the original content can be accurately restored. Generally data hiding is utilized for communication which is secret. In this paper, a technique is proposed for reversible data hiding in encrypted images. An algorithm is proposed which will first find out the area of interest and after that noisy pixel. BAT algorithm is used to find the coordinates of the noisy pixels and will embed text data on it and after that rest of the data will be fed into the border area of the images.
  • Enhancement of OLSR Protocol in FANET for Improving Dynamic Routing
    Anchal and Ranjeet Kaur, DAV University, India
    FANETs refers to Flying Ad hoc Networks comprising of small flying UAVs (Unmanned Aerial Vehicles) in a network. One of the most important problems of multi UAVs in a network is data transfer. As soon as the distance between the nodes increases, loss in data packets occurs. Both in Single hop and Multi hop communication, loss in data packets occurs when the nodes reach out of their ranges [16]. In this paper, we have used an efficient technique of data transfer between the UAVs and the nodes without any loss in data packets and less energy consumption. In the proposed work, the OLSR protocol is optimized. The major achieved objective is it can find the optimized route from source to destination as per its location and maintain the QoS. With the help of this various open challenges are identified and the best possible ways of data transfer are achieved with less energy consumption.
  • Detection and Isolation of Sybil Attack in VANET using AODV Protocol
    Mandeep Kaur Saggi and Ranjeet Kaur, DAV University, India
    VANET is a Composition of vehicles and road-side units which send and receive information of current traffic situation without the need of any infrastructure. In this paper a novel technique has been proposed to detect and isolate Sybil attack on vehicles resulting in proficiency of network. It will work in twophases. In first phase RSU registers the nodes by identifying their credentials offered by them. If they are successfully verified, second phase starts & it allots identification to vehicles and thus, RSU gathers information from neighboring nodes & define speed limit to them. Malicious vehicles can slightly degrade the performance of network, so there is need to detect those misbehaving vehicles to isolate them from the network. A multiple identity generated by Sybil attack is very harmful for the network & can be misused to flood the wrong information over network. Simulation results show that the proposed detection model is able to increase the possibilities of detection and reduce the percentage of Sybil attack.
  • Multicasting in VANET using Prediction Based AODV Routing Protocol
    Anshu Joshi and Ranjeet Kaur, DAV University, India
    Vehicular Ad-Hoc Networks connects highly mobile and self configured vehicles. The topology is dynamic in nature due to which the connection lifetime is limited and leads to delay and network congestion problems. The I-AODV (Infrastructure based AODV) is routing protocol that felicitates communication among vehicles through RSU’s and is broadcasting in nature. The paper focuses on prediction based multicasting which aids in reducing delay and increases performance metrics. Broadcasting technique does not utilizes network resources which makes the network inefficient thus reduces throughput of the network, applying multicasting solves the purpose of proper utilization of resources as well as prediction technique helps in improving localization overhead. This technique improves the network performance metrics such as end-end delay, throughput and packet overhead.
  • Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
    Anupam Ghosh1, Mainak Talukdar2 and Uttam Roy3, 1IIT-Bombay, India, 2Cognizant Technology Solution, India and 3Jadavpur University, India
    Each and every biological function in living organismhappens as a result of protein-protein interactions. The diseases are no exception to this. Identifying one or more proteins for a particular disease and then designing a suitable chemical compound known as drug to destroy that protein(s) has been an interesting topic of research in bio-informatics. In previous methods drugs were designed using only seven chemical components and were represented as a fixed-length tree. But in reality, a drug contains many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug cannot be determined before designing the drug. In the present work, a PSO based methodology has been proposed to find out a suitable drug for a particular disease so that the drug-protein interaction becomes stable. In the proposed algorithm, the drug is represented as a variable length tree and essential functional groups are arranged in different positions of that drug. Finally the structure of the drug is obtained and its docking energy is minimized simultaneously. Also the orientation of chemical groups in the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well inside the active site of target protein also. Here several intermolecular forces have been consideredfor accuracy of the docking energy. Results show that PSO performs better than the earlier methods.
  • An adaptive modified super-twisting sliding mode controller: Applied to the active suspension system
    Jagat Jyoti Rath1, Md. Afroz Akhtar2 and Kalyana C. Veluvolu1, 1Kyungpook National Univeristy, Korea and 2Central Mechanical Engineering Research Institute, India
    A robust higher order sliding mode algorithm combining the merits of the modified super-twisting algorithm and the adaptive super-twisting algorithm has been proposed for a class of nonlinear uncertain systems in this article. For a class of linearly growing perturbations whose upper bounds are not known, the convergence of the sliding dynamics in finite time is proven. To illustrate the effectiveness of the proposed approach, an adaptive robust controller based on the proposed algorithm is developed for the nonlinear active suspension system faced with perturbations from the road surface. Simulation results provided demonstrate the effectiveness of the proposed approach.
  • Local Binary Pattern based Face Recognition for Automotive Security
    Shailaja Patil, R C Patel Institute of Technology, India
    In this paper we present an approach for face recognition under varying poses, illumination variation and facial expressions. Illumination variation, Pose variation and facial expressions are the main challenges among the various factors that severely affects the performance of the face recognition. The main aim of this paper is to calculate and evaluate the performance of combination of Independent Component Analysis and local binary pattern approach for different face databases that contains number of images with illumination variation, varying poses and facial expressions. 3 images per subject are used for training purpose and remaining images for testing or recognition purpose. Uniform Local Binary Pattern is used for extracting the features. These extracted features reduced by independent component analysis and Euclidean distance is used for matching. We have calculated FRR and TSR parameter which gives accuracy of given method. Finally comparing the results for different face databases.
  • Computation of Transfer Function of DSP modules using MATLAB for Hardware Implementation
    Dhan Raj and R.K. Sharma, National Institute of Technology-Kurukshetra, India
    Main objective of this paper to present a algorithm for computing transfer function that can be used to develop such DSP device on Development tools that works on digital data like FPGA and MATLAB. Motivation for this paper comes from the requirements of such modules in my academic project. Then I realized a need to develop a way that can apply on many DSP devices for hardware implementation on FPGA. For the verification of such design technique we are taking the example of Band Pass Filter and Discrete Wavelet Transform. In this paper first we discuss on how such devices works actually on analog signals. Our next step will be conversion of such analog logics to digital logic. Then we will implement Band Pass Filter and Discrete Wavelet Transform and verify result obtained from the logic using MATLAB.
  • Implementing Dynamic Gesture Recognition Using SIFT
    Vaibhavi Gandhi, Akshay Khond, Sanket Raut, Vaishali Thakur and Shabnam Shaikh, AISSMS College of Engineering, India
    Human Computer Interaction(HCI) is the field of interest in recent years. Use of conventional devices to interact with computer such as mouse and keyboards are now an obsolete. Computer vision helps us in interacting with computer by using various gestures, recognise them and use for different purposes. We are thus proposing a system in which we use gestures to interact with computer and perform many different activities for various applications and also focus on video processing activities. These dynamic gestures are detected at run time by the camera and the corresponding operation is performed. It has numerous applications in fields such as robotics, computer applications, hands free computing etc.
  • Task Scheduling in Computational Grid using NSGA II
    Dinesh Prasad Sahu1, Karan Singh1 and Shiv Prakash2, 1Jawaharlal Nehru University, India and 2Indian Institute of Technology-New Delhi, India
    Computational Grid (CG) offers an extensive distributed platform for bizarre end computationally exhaustive applications. CG Scheduling is often done by executing the submitted tasks on the nodes of the CG so that some quality of service parameter is optimized. Energy consumption of the computational nodes is significant parameters that define in term of Dynamic Voltage Scaling (DVS) Model. In this paper, we have explored makespan and the energy consumption of the computational nodes of the CG for the task execution and tried to optimize it. Since the scheduling problem is NP-Hard, so a meta-heuristics based techniques are often applied to solve this. We have proposed a NSGA II for this purpose. The performance estimation of the proposed Energy Aware NSGA II (EANSGA II) has been done by writing program in Java and Integrating with gridsim. The simulation results evaluated the performance of the all proposed model and the results of all proposed model is compared with existing model Min-Min and Min-Max procedure which proves effectiveness of the model.
  • Review: Automatic Semantic Image Annotation
    Shereen A.Hussein1, Howida Youssry2 and Aliaa A.A.Youssif3, 1Future University, Egypt, 2Misr University for Science & Technology, Egypt and 3Helwan University, Egypt
    There are many approaches for automatic annotation in digital images. Nowadays digital photography is a common technology for capturing and archiving images because of the digital cameras and storage devices reasonable price. As amount of the digital images increase, the problem of annotating a specific image becomes critical issue. Automated image annotation is creating a model capable of assigning terms to an image in order to describe its content. It consists of number of techniques that aim to find the correlation between words and image features such as color, shape, and texture to provide correct annotation words to images which provides an alternative to the time consuming work of manual image annotation. This paper aims to cover the different techniques of automating the process of image annotation as an intermediate step in image retrieval process.
  • Object detection and recognition using ORB and SIFT
    Harshwardhan Pawar, Kiran Darekar, Pratik Paliwal, Pratik Darak, S.A.Tiwaskar, Vishwakarma Institute of Information Technology, India
    Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. There are different feature descriptors available for detection and matching. In this paper, we have studied about BRIEF(Binary Robust Independent Elementry Feature), called ORB, and SIFT(Scale Invarient Fetaure Transform).And also done comparison of this two technique on different parameters with different classes of images.
  • Denoising of DT-MR Images with an Iterative PCA
    Sreelakshmi Priya U and Jyothisha. J. Nair, Amrita School of Engineering
    Nowadays most of the clinical applications uses Magnetic Resonance Images (MRI) for diagnosing neurological abnormalities. During MR image acquisition the emitted energy is converted to image by using some mathematical models, and this may cause addition of noise. Therefore we need to denoise the image. Currently most of the clinical application uses Diffusion Tensor-MR Images for tracking neural fibres by extracting features from the images. Noise in DT-MR Images make fibre tracking and disease diagnosing tougher. So our work aims to denoise the Diffusion Tensor MR images with better visual quality. In this paper, we propose a denoising technique that uses Structural Similarity Index Matrix (SSIM) for grouping similar patches and performs Iterative Principal Component Analysis on each group. By performing the weighted average on Principal Component, we have obtained the denoised DT-MR Image. For getting better visual quality of the denoised images we employ Iterative Principal component Analysis technique.
  • Extraction of relevant dataset for support vector machine training: A Comparison
    Adeena K D and Remya R, Amrita Vishwa Vidyapeetham, India
    Support Vector Machine (SVM) is a popular machine learning technique for classification. SVM is computationally infeasible with large dataset due to its large training time. In this paper we compare three different methods for training time reduction of SVM. Different combination of Decision Tree (DT), Fisher Linear Discriminant (FLD), QR Decomposition (QRD) and Modified Fisher Linear Discriminant (MFLD) makes reduced dataset for SVM training. Experimental results indicates that SVM with QRD and MFLD have good classification accuracy with significantly smaller training time.
  • Enhancing QOS by Adapting Data Flow Rates in Wireless Networks Using Hierarchical Docition
    Simi S and Sruthi Ann Varghese, Amrita University, India
    Wireless network finds application in military environments, emergency, rescue operations and medical monitoring due to its self-configuring nature. As the availability of resources such as processing power, buffer capacity and energy are limited in wireless networks; it is required to devise efficient algorithm for packet forwarding. Due to the dynamic nature of the wireless environment, the traditional packet forwarding strategies cannot guarantee good network performance every time. This paper proposes a method for data flow rate adaptation in wireless network to improve quality of service in the network. Each node in the network learns the environment using reinforcement learning approach and selects appropriate neighbors for packet forwarding. In order to improve the learning capacity of nodes, the hierarchical docition technique is employed. Docition applied to each layer of network, which selects a set of special nodes which has more information about the environment and share this information with less informative nodes. The algorithm is tested in a geographical routing protocol and the results indicate improved network performance.
  • Disrupted Structural Connectivity Using DTI Tractography In Epilepsy
    Geetha M and Suchithra S Pillay, Amrita Vishwa Vidyapeetham, India
    Human thoughts and emotions are communicated between different brain regions through pathways comprising of white matter tracts. Diffusion Tensor Imaging (DTI) is a newly developed Magnetic Resonance Imaging (MRI) technique to locate the white matter lesions which cannot be found on other types of clinical MRI. Fiber tracking using streamline tractography approaches has a limitation that it could not detect crossing or branching fibers. This limitation is overcome in Fast Marching technique of tractography where branching bers are detected correctly but it takes more time than streamline tracking technique. For tracking fiber pathways in a noninvasive way, we propose an approach which utilizes the advantages of both tracking techniques: Fiber Assignment by Continuous Tracking and Fast Marching, to give a better and accurate tracking of fiber pathways as given by Fast Marching tracking technique and in less time as given by Fiber Assignment by Continuous tracking.