Information Systems Design and Intelligent Applications

Microbial redox reactions are mediated by a diverse set of sulfur-oxidising bacteria. These redox reactions are important to maintain the environmental sulfur balance. The sulfur oxidation reactions are performed by sulfur-oxidizing gene cluster called the

sox

operon comprising of genes

soxEFCDYZAXBH

. However, the mechanistic details of sulfur oxidation process by

Hydrogenophilus thermoluteolus

are yet to be determined. In this study, the three-dimensional structures of SoxY and SoxZ proteins were constructed by homology modeling. Protein-protein docking generated SoxY–Z complex. Responsible amino acid residues for the protein interactions were identified after molecular dynamics simulation of SoxY–Z complex. The best binding mode of thiosulfate with SoxY–Z complex was identified through their molecular docking. Current study thereby, provides a rational frame-work to discern molecular mechanism and biophysical characterization of sulfur-oxidation process.

Alpha-dystroglycan (α-DG), a cell surface receptor links extracellular matrix with cellular cytoskeleton. Its post translational modification is carried out with number of glycosyltransferases, depending on cell types to make the ligand specific mature α-DG receptor protein. However, T192M mutation in α-DG has been found to cause hypo-glycosylation of the protein disabling its Laminin binding form and thereby triggering the onset of a limb girdle muscular dystrophy affecting early childhood. Here for the first time we exploit the effect of this mutation in protein conformational stability. We have found that this mutation leads to significant changes in secondary structure of the protein as well as in the accessible surface area. All these changes also hamper the crucial disulfide bond that is required to maintain the globular fold at the N terminus of α-DG. This molecular insight will therefore be useful for developing new therapeutic approaches to overcome the disease state.

Dissimilatory Sulfite Reductase (dSiR) is the main redox enzyme system utilized in sulfur metabolism in both sulfur oxidizing and reducing prokaryotes. Anoxygenic phototrophic bacteria

Allochromatium vinosum

produces elemental sulfur during sulfur cycle which is ultimately oxidized sulfate by DSR operon.

Allochromatium vinosum

encodes

dsr

AB reverse dSiR that oxidizes thiosulfate or elemental sulfur. DsrAB is a α

2

β

2

hetero-tetrameric complex. In our present study, we first reported the three dimensional structure of DsrAB protein complex from

Allochromatium vinosum

and we also predicted the protein-protein interactions between DsrA and DsrB proteins. DsrAB is a major redox enzyme complex required in both sulfur oxidation and reduction processes so this structure function relationship investigation will help in researches to predict the biochemical mechanism of sulfur-oxidation. The importance of the study lies in the fact that sulfur metabolism pathways are used in waste remediation and bio-hydrogen production. This is the most important aspect of our analysis.

In order to survive under temperature stress conditions, bacterial cells come up with different biochemical mechanisms like production of chaperones; a class of proteins that maintain the proper folding of the other necessary proteins for survival. Chaperones mostly function as complexes. A member of such a family in

E.coli

is IbpAB protein complex. This protein complex is known to bind a protease called Lon. However, till date the modes of binding between these proteins are still obscure. In the present work, we employed molecular modeling and simulation techniques to analyze the pattern of interactions between these proteins. We observed that under cold and heat shock conditions, the interactions between IbpA and IbpB proteins increase whereas at physiological temperature the interactions decrease which allows Lon to promote better binding and degradation at physiological temperature. So far this is the first report to predict the interactions scheme between these proteins.

Interactive individualized learning technologies and applications are probably one of the most significant innovations in the age of information revolution. However, Gender-based differences have not been considered as major variable in teaching-learning. Motivated by the gender differences this article reports the findings of the study that analyzed students’ gender differences in learning using interactive computer based instructional technology compared with traditional lecture based method of learning of Secondary students. The sample consisted of 120 students from different schools of West-Bengal (Eastern part of India). Each student completed a GIS (general information schedule), computer proficiency test, a prior knowledge test and post-test on geography. The finding of MANOVA result shows significant gender differences in different learning methods. This study also investigated gender differences in various learning objectives (factual, conceptual, and rules and principles knowledge). The MANOVA result shows significant difference in identification test which measured factual knowledge where female student performed better than male. In comprehension test which measured rules and principal, male student performed better than females. However, no significant difference were found in male and female student and they performed equally well in terminology test which measured the conceptual knowledge.

Protein-protein interactions (PPI) play pivotal roles in many biological processes like hormone-receptor binding. Their disruption leads to generation of inherited diseases. Therefore prediction of PPI is a challenging task. Machine learning has been found to be an appropriate tool for predicting PPI. Machine learning features generated from a set of protein hetero-complex structures were found to be a good predictor of PPIs. These machine learning features were used as training examples to develop Support Vector Machines (SVM) and Random Forests (RF) based PPI prediction tools. Among the important features the sequence based features related to sequence conservations and structure based features like solvent accessibility were found to have the maximum predictive capability as measured by their Area Under the Receiver Operating Characteristics (ROC) curves (AUC value). The RF based predictor was found to be a better performer than the SVM based predictor for this training set.

Heat shock response in

Escherichia coli

is mainly controlled by the alternative transcription factor σ32. This response leads to an up-regulation of heat shock proteins including chaperones and proteases. The activity and stability of σ32 is in turn altered through mutual interactions with these heat shock proteins. The work reported here mainly deals with the docking of σ32 with the chaperone GroEL and protease FtsH. The findings of the above studies together with the σ32—DnaK docking study reported earlier suggest that the binding of σ32 with GroEL and DnaK at normal temperature is stronger compared to those at high temperature. With rise in temperature σ32 adopts an open conformation and this probably favors binding with FtsH and renders it for degradation by FtsH.

The crystal and molecular structures of two nematogenic compounds 4′-(3, 4-difluoro-phenyl)-4-propyl-bicyclohexyl(3ccp-ff) and 4′-(3,4,5-trifluoro-phenyl)-4-propyl-bicyclohexyl(3ccp-fff) have been determined by direct methods using single crystal X-ray diffraction data. The compounds (C

21

H

30

F

2

) and (C

21

H

29

F

3

) crystallize in the monoclinic system with the space group P2

1

/n and Z = 4 and in the triclinic system with the space group

$$ {\text{P}}\overline{1} $$

P

1

¯

and Z = 2 respectively. The fluorine atoms are in the plane of phenyl ring. Both the cyclohexyl groups are found to be in chair conformation. Parallel imbricated mode of packing of the molecules are found in the crystalline state which is precursor to the nematic phase. Several van der Waals interactions are observed between the neighbouring molecules. Results of crystal structure have been compared with that obtained from molecular modelling.

This paper proposes two ways to improve the compression ratio achieved by Fractal image compression. During subdivision of the input image into range and domain blocks, Adaptive Quadtree Partitioning is used. During the final save, the parameters of the affine transforms of the fractal compressed images are losslessly compressed using two methods—once by Modified Region Based Huffman (MRBH) coding, and another time by its variant, MRBHM. The proposed techniques offer much better compression ratios most of the time, keeping PSNRs unchanged. However, compression time of the proposed techniques are significantly more than their counterparts.

In this paper, a new method based on empirical mode decomposition (EMD) for discrimination of focal and non-focal electroencephalogram (EEG) signals is proposed. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The IMFs obtained by EMD of EEG signals are plotted in a 3-D phase space diagram using phase space reconstruction (PSR). The average mean of Euclidean distances (AMED) and average standard deviation of Euclidean distances (ASED) are computed from 3-D phase space diagram. These features has been used as a feature in order to discriminate focal and non-focal EEG signals. The AMED and ASED measurement of IMFs has provided better discrimination performance. The class discriminating ability of these features are quantified using Kruskal-Wallis statistical test.

A new method based on Hilbert-Huang transform (HHT) is proposed for discrimination of electromyogram (EMG) signals. The HHT consists of two steps namely empirical mode decomposition (EMD) and the Hilbert transform (HT). The EMD decomposes EMG signal into set of narrow-band intrinsic mode functions (IMFs) and the Hilbert transformation of these IMFs provide analytic IMFs. The bandwidth due to amplitude modulation (

$$ B_{AM} $$

B

A

M

) and bandwidth due to frequency modulation (

$$ B_{FM} $$

B

F

M

) estimation of analytic IMFs have been used as a feature for discrimination of myopathy, neuropathy, and healthy EMG signals. The bandwidth features are very effective to discriminate myopathy, neuropathy, and healthy EMG signals. The experimental results show the effectiveness of the proposed method for discrimination of myopathy, neuropathy, and healthy EMG signals.

The proline rich formin homolog 1 (FH1) region of mouse formin FMN1 was initially reported to bind to WW domains and mediate its interaction with formin binding protein 4 (FNBP4) via the WW domain of FNBP4. However further structural, biochemical and functional details about this interaction have never been reported. The nature of the study that first reported this interaction, along with lack of further information, later created doubt about the possibility of this interaction under cellular environment. In this context, this computational study confirms the binding of mouse formin FMN1 FH1 with the 1st WW domain of FNBP4. Combined with our previous reports, this study concludes that only the 1st WW domain of FNBP4 is able to mediate its interaction with formins FH1 regions and its binding is stronger to the PPXXPP motif compared to the PPXP or PPXPP motifs, all of which are found in formin FH1 region.

Microbial redox reactions of inorganic sulphur compounds play vital role for recycling of sulphur to maintain environmental sulphur balance. These important reactions are carried out by enzyme system encoded by

sox operon

. Central player of sulphur oxidation process is SoxY–Z protein complex. Thermophilic beta-proteobacterium-

Hydrogenophilus thermoluteolus

, oxidizes sulphur compounds including thiosulfate with the help of proteins encoded by

sox

operon. Protein SoxF having sulfide dehydrogenase activity has the ability to reactivate the inactivated SoxY–Z complex. Till date no structural details are available for SoxF protein from

H. thermoluteolus

. In present work, homology modeling has been used to build 3D structures of SoxY, SoxZ and SoxF of

H. thermoluteolus

. 3D structure of SoxY–Z–F complex was obtained by ClusPro2.0. Amino acid residues responsible for protein-protein interaction were identified. Interactions in SoxY–Z–F were found to be mediated through hydrogen bonding. Probable biophysical mechanism of the interactions of SoxF with SoxY–Z complex has been identified.

Arylalkylamine N-acetyltransferase (AANAT) or Serotonin N-acetyltransferase is one of the key enzymes for the synthesis of melatonin in humans. In the process, the regular binding partner of this AANAT protein is 14-3-3 protein. A mutation of A129T in the AANAT protein leads to decreased functionality of the AANAT protein. So far the mechanistic details of this loss of binding have not been elucidated. In the present work, we tried to utilize structural bioinformatic approach to understand the differences in pattern of bindings between AANAT wild type and mutant forms. We used molecular mechanics calculations to describe the interactions of wild type and mutant AANAT proteins with its binding partner 14-3-3 proteins. So far, this is the first report that characterizes the difference in binding between the two forms of the protein. Therefore, the results from this study may be useful for the development of drugs in patients having impaired melatonin synthesis.

In recent years the Internet learners can freely absorb new knowledge without restrictions on time or place. At present, most E-Learning environment architectures use single computers or servers as their structural foundations. Traditional E-Learning systems have major drawback because of their limitations in scalability, availability, distribution of computing power and huge storage systems, as well as sharing information between users. In this context, the use of Grid technology reveals its utility and availability, as scalable, flexible coordinated and secure resource sharing among geographically distributed individuals or institutions, in the perspective of E-Learning system. The major focus of this paper is to design and develop different types of E-Learning services in Computational Grid Environment. This proposed system is able to handle large multimedia learning materials using the features of Grid Technology.

During the last two decades it has been observed that there has been substantial advancement in the domain of E-Learning systems. This may be attributed to development in the field on networking and communication. However most of these systems lack face to face interaction between the learner and the tutor and hence are unable to pinpoint the learner deficiencies. In this context, several researchers have used Concept Maps for identifying student learning barriers. However a major drawback of Concept Map approach is that all concepts are given equal degree of importance from the learner point of view. In this work a Weighted Concept Map based approach is proposed to provide remedial learning guidance to the learners. The system works by generating automated Concept Map and assigning weights to the concepts based on their importance for computation of best remedial learning path. The approach has been tested with a set of middle school students for identifying their learning deficiencies in science courses and the results found to be satisfactory.

The recent tendency for human recognition is biometrics. In today’s world, fingerprints, due to its stability and uniqueness, is the most widely used biometrics. Though it is the most ancient as well as most widely researched biometric technology, a 100 % accurate fingerprinting system is still a myth. Here we are proposing a new method for fingerprint recognition system, namely divide and conquer method, where a fingerprint is to be cropped and merged with the database fingerprint and a matching will confirm the verification/identification. Here frequency domain enhancement is used for pre-processing the image, and soft computing tool SOM is used for classification of fingerprints. The final matching is the most used feature of fingerprints, the minutia based.

Image denoising is among the most fundamental problems in image processing. A large range of methods covering various fields of mathematics are available for denoising an image. The initial denoising models are derived from energy minimization using nonlinear partial differential equations (PDEs). The filtering based models have also been used for quite a long time where the denoising is done by smoothing operators. The most successful among them was the very recently developed nonlocal means method proposed by Buades, Coll and Morel in 2005. Though the method is very accurate in removing noise, it is very slow and hence quite impractical. In 2008, Gilboa and Osher extended some known PDE and variational techniques in image processing to the nonlocal framework. The motivation behind this was to make any point interact with any other point in the image. Using nonlocal PDE operators, they proposed the nonlocal total variation method for Gaussian noise. In this paper, we develop a nonlinear PDE based accelerated diffusion speckle denoising model. For faster convergence, we use the Split Bregman scheme to find the solution to this new model. The new model shows more accurate results than the existing speckle denoising model. It is also faster than the original nonlocal means method.

Image thresholding is a reputed image segmentation process, extensively used to attain a binary image from a grey scale image. In this article, a bi-level and multi-level image segmentation approach is proposed for grey scale images using Bat Algorithm (BA). In this work, two novel Objective Functions (OF) are considered to obtain the optimal threshold values. The proposed segmentation process is demonstrated using six standard grey scale test images. The performance of the proposed OF-based segmentation procedure is validated using the traditional Otsu’s between-class variance. The performance assessment between the proposed and existing OF is measured using well-known parameters, such as objective value, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Matrix (SSIM) and CPU time. Results of this study show that the proposed OF provides a better objective value, PSNR and SSIM, whereas the existing OF offers faster convergence with a relatively lower CPU time.

In rhizosphere Plant Growth Promoting Rhizobacteria (PGPR) produce N-acyl-l-homoserine lactones (AHL) as the quorum-sensing (QS) signals. AHLs can act as trans-kingdom signalling molecules between plants and rhizobacteria and that can regulate plant growth and development. The plant-beneficial PGPR

Burkholderia phytofirmans

PsJN promotes growth in

Arabidopsis thaliana

by producing 3-oxo-dodecanoyl homoserine lactone (oxo-C14-HSL) from their quorum-sensing (QS) system. In bacteria, QS system functions by binding AHL to the LuxR-family of sensor/regulator proteins through their response regulator receiver domain. It has been hypothesized that by using similar response domain, Arabidopsis response regulator 10 (ARR10) proteins may act as binding site for 3-oxo-dodecanoyl homoserine lactone. ARRs are involved in cytokinin signalling pathways and thus these types of lactones can regulate growth in Arabidopsis. We prove the binding of oxo-C14-HSL with ARR10 by using molecular docking technique and analysing the docking result.

Remote login authentication is very important to access resources from remote computers to control the illegal access. In this paper, we propose a remote login authentication scheme based on row vector theorems. In our scheme we use one way function to secure our message. In this scheme the system does not need to reconstruct any term of existing key table when a new user is inserted into a system. A legal user can freely choose and change his password using smart card. Our scheme is secure against different attacks like replay attack, forged login attack, password guessing attack.

A two stage encryption algorithm based on DNA sequence has proposed in this paper. In the first stage an encryption of plain text is done by generating a random key. The plain text is again encrypted to produce the cipher text in the second stage. Moreover, this encryption algorithm is based on a symmetric key cryptography system, where we provide a shared key to encrypt as well as decrypt the intended message. To encrypt the original key two stages are maintained and sending it over a separate secure channel other than the channel through which we are transferring the cipher text. A numerical study confirms that the proposed algorithm is reliable, secure, scalable, and robust for transmitting message.

Most of the text localization techniques are sensitive to text color, size, font and background clutter. They simply exploit the general segmentation rules or the prior knowledge about the text shape/size. As, inherently, a text consists of strokes of different sizes and orientations, so the concept of Stroke Filter is much more effective, particularly where text segmentation is taken into consideration. The problem with traditional stroke filter lies in its fixed width and is capable of segmenting strokes of predefined width. The proposed method uses adaptive stroke filter which can localize text regions, having varying stroke width, within camera captured images. The method is verified by experiment on a database containing 600 images.

This paper presents the dynamic equations of a three link manipulator which can be used as the fingers of a robotic hand. The main idea is to develop the mathematical representations of a three link finger using Newton-Euler analysis of inverse arm model of free body motion. The purpose of this study is to establish the relationship between the torque at each joint actuator and the angular position and angular velocity and angular acceleration of each link member.

Speed of system, adaptability in runtime, short system response time, tolerance for faults, low latency in packet delivery, effective utilisation of on chip hardware resources and bandwidth with desired functionality execution are essential for real time applications. Analysis of competitive reliable reconfigurable architectures in distinct applications like image processing and performance up gradation in Discrete Cosine Transform (DCT), System on Chip (SoC), Network on Chip (NoC), Digital Signal Processor (DSP), Field Programmable Gate Array (FPGA) is done. The analysis described for reconfigurable architectures like Synthesis and Partitioning for Adaptive Reconfigurable Computing System (SPARCS), Course-Grained Reconfigurable Architectures (CGRAs), Multi Processors System on chip (MPSoC), North-last-weave algorithm, Sequential Minimal Optimization (SMO) algorithm have efficacy in achieving above mentioned desires of a real time electronic system.

This paper presents an acceleration based gesture recognition approach with wearable MEMS tri-axial accelerometer. In the application model, we have introduced frame based lookup table for gesture recognition. In accelerometer based gesture recognition concept, sensor data calibration plays an important aspect owing to their erroneous output due to zero-G error. In this work six-point based calibration of the sensor data is presented. The calibrated acceleration data so obtained from the sensor is represented in the form of frame-based signifier, to extract discriminative gesture information. It is observed that this procedure is always advantageous over conventional video image processing based gesture recognition that uses cameras and bulky computational algorithms. Thus, this accelerometer based gesture recognition not only reduces the hardware complexity but also minimizes the consumption of power by associated circuitry. Finally, this study helps us to develop a real time implementation of wearable gesture recognition device.

E-Commerce recommender systems are vulnerable to different types of profile-injection attacks where a number of fake user profiles are inserted into the system to influence the recommendations made to the users. In this paper, we have used two proximity-based outlier detection strategies in identifying fake user profiles inserted into the recommender system by the attacker. The first strategy that has been used in detecting attack profiles is a k-Nearest Neighbor based algorithm. The second strategy used is a clustering-based algorithm in generating outlier score of each user profile in the system database. Three attack models namely random attack, average attack and bandwagon attack model have been considered for our analysis. Performance of the k-Nearest Neighbor-based and clustering-based outlier detection strategies have been analyzed for different attack percentages and different filler percentages of the attack profiles.

Multiplication is the fundamental operation in mathematics as well as in the field of engineering. Multiplier is the core part of Digital Signal Processor. In this paper a new approach of multiplier design using the Vedic mathematics has been proposed. The procedure of multiplication using Vedic mathematics is very simple, easy and time saving. The design approach of multiplier by using the popular sutra “Ekanyunena Purvena” of Vedic mathematics is very new and novel. This procedure is successfully presented in this paper. The simple algorithm, flow chart, mathematical expression etc. helps the design to understand easily. The core architecture of the multiplier has been also discussed in this paper. The method discussed in this paper is very helpful for some special type of multiplication, like where the multiplicand is an integer and the multiplier is 9 or array of 9 (for example 9 or 99 or 999 or so on). The less complexity is the most important advantage of this digital design.

The present paper undertakes to study the effect of imperfect production process on supply chain model under volume flexible manufacturing system. Manufacturing system produced stochastically imperfect items. Demand is taken to be continuous stochastic in nature under budget and shortage constraints. Holding cost is taken to be variable. Production rate is a decision variable. Production cost is dependent on production rate. A numerical example is provided to illustrate the optimality of the model.

Script identification from a given document image has some important applicability in many computer applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific Optical Character Recognition (OCR) engine in any multilingual environment. In this paper, we propose a texture based approach for text line-level script identification of six handwritten scripts

namely

,

Bangla

,

Devnagari

,

Malayalam

,

Tamil

,

Telugu

and

Roman

. A set of 80 features based on Gray Level Co-occurrence Matrix (GLCM) has been designed for the present work. Multi Layer Perceptron (MLP) is found to be the best classifier among a set of popular multiple classifiers which is then extensively tested by tuning different parameters. Finally, an accuracy of 95.67 % has been achieved on a dataset of 600 text lines using 3-fold cross validation with epoch size 1,500 of MLP classifier.

Most of the digital colour cameras are designed with

single colour sensor

CCD/CMOS masked with an array of colour filters called

colour filter array

(CFA). This CFA allows only a single colour to be captured at every pixel. The Demosaicking is the process of estimating the missing colours samples, called

Mosaics

so as to reconstruct the original Scene in true colours. The performance of demosaicking algorithm is utmost importance to how good a digital camera can perform. A lot of algorithms have been developed. In this paper, the comparative performance of SA Adaptive, SA Universal, AF Demosaic, DDFAPD, MN Demosaic, and AP Demosaic algorithms are presented.

In this paper a region based image retrieval scheme has been proposed based on integration of color, texture and shape features using local binary patterns (LBP). The color and texture features are extracted using LBP histograms of quantized color image and gray level images respectively. For improving the discrimination power of LBP, threshold computed using both centre pixel and its neighbors is used. Finally, shape features are computed using the binary edge map obtained using Sobel edge detector from each block. All three features are combined to make a single completed binary region descriptor (CBRD) represented in the LBP way. To support region based retrieval a more effective region code based scheme is employed. The spatial relative locations of objects are also considered to increase the retrieval accuracy.

Due to the extensive use and increase in the number of published web services, clustering and automatic tagging of web services to facilitate efficient discovery of web services is crucial. Discovering composite services has gained importance as there is a need for integrating web services to meet complex service requirements. In this regard, we propose a system for clustering services based on features extracted from their WSDL documents for generating service tags and then the cluster tags. Also, based on the service requirements specified by the requester, our system can identify and generate potential composite service templates. These are basically the subgraphs of the service dependency graph generated by considering only relevant services determined by matching cluster tags and service tags with the request tokens. It was seen that the search domain for service composition was significantly reduced by clustering and tagging and the system obtained meaningful and encouraging results.

We present an experimental work on the denoising of mammogram with Poisson noise. Reviewing the literature, it is found that the denoising performance of the multiresolution tools like wavelet, contourlet and curvelet implemented on mammogram with Poisson noise is unique. The first part of the investigation deals with the confirmation of this exceptional performance with our result. The later half implements the recently developed denoising approach called the Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) to the Poisson noise corrupted mammogram with an objective to improve the peak signal to noise ratio (PSNR) further. The PURE-LET successfully removes Poisson noise better than the traditional mathematical transforms already mentioned. The computation time and PSNR are also evaluated in the perspective of the cycle spinning technique. This validates the applicability and efficiency of the novel denoising strategy in the field of digital mammography.

Designing of user interface has special importance in e-learning systems, as certain amount of users’ time is spent in attempting to learn the systems’ user-interface. A number of e-learning courses have been developed over the years; however only a small number of courses had a user interface design that is suitable in the interest of the users and their preferences. Thus, many of the existing e-learning contents did not meet the expectation of the users’ requirement. In this proposed work we try to design a systematic user interface framework for e-learning systems that will allow developers to create an effective user interface design for an e-learning system. This is proposed, based on results of studies in cognitive science, computer science, instructional design, graphics design and various other aspects of pedagogy. This will help designers to make decisions for creating e-learning courses with better user interface.

Differential Power Analysis (DPA) is a statistical approach to analyze the power consumption of a cryptographic system to break its security infrastructure. It has challenged the vulnerability of most of the cryptographic techniques like DES, AES, RSA etc. With DPA, attackers passively collect the power traces of the system and then make a comparative analysis with some hypothetical power traces. The analysis result having high value reveals the secret key used. This kind of attack has been explored by many researchers and has proposed techniques to make such attacks highly efficient. In this paper we present a detail on DPA along with the models and types for such attack. We also present some of the recent attack techniques as well as countermeasures on DPA.

The aim of this paper is to present an evaluation approach based on multi-attribute group decision making (MAGDM) technique for helping the health-care department of a country to review the over-all health-system of a state over time. In the process of decision making, experts provide their opinions linguistically regarding the alternatives depending on a finite set of interrelated attributes. Subsequently suitable aggregation method is applied to determine the overall performance value to make a final decision. Finally, we present health-system evaluation of a state for the time periods, namely,

$$ \{ t_{1} ,t_{2} , \ldots ,t_{n} \} $$

{

t

1

,

t

2

,

…

,

t

n

}

in order to judge whether the over-all health system of the state improves over time or not.

Image Quality Assessment (IQA) is a very difficult task, yet highly important characteristic for evaluation of the image quality. Widely popular IQA techniques, belonging to objective fidelity, like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) or subjective fidelity which corresponds to the human visual system (HVS), like, Universal Quality Index (UQI), Structural SIMilarity (SSIM), Feature SIMilarity (FSIM), Feature SIMilarity for color images (FSIMc), Gradient Magnitude Similarity (GSM) have been discussed in this paper. Also quality measured on basis of degradation model and Noise Quality Measure (NQM) has been discussed. Experiments have been conducted on IVC database available online at

http://www.irccyn.ec-nantes.fr/ivcdb/

and verified from the CSIQ database and LAR database available online at

http://vision.okstate.edu/?loc=csiq

and

http://www.irccyn.ec-nantes.fr/~autrusse/Databases/LAR/

. On the basis of the obtained values judgements about the image distortion and hence the optimum image quality metric has been decided. It has been found from all the experiments conducted that FSIM is the best metric for the JPEG, JPEG2000, blur and LAR whereas UQI failed to give better results for all except JPEG2000.

Digital image encryption can be done either by using the cryptography techniques or chaotic maps. Here, we have given focus to the chaotic maps due to the simplicity as compared to the cryptography techniques. In this paper, we have presented the color image encryption using different 2D chaotic maps, such as 2D Logistic map, Henon map, Baker nap, Arnold cat map and Cross chaos map. The efficiency of these image encryption techniques is verified by the differential attack. NPCR (number of pixels change rate) and UACI (unified average changing intensity) have been chosen as the evaluation parameters.

Today’s digitized world are completely dependent on softwares and the cost of programming errors for these softwares are increasing on a daily basis. C is one of the most popular programming language and has been widely used in developing safety critical softwares, embedded systems, etc. In spite of the fact that the area of formally verifying a code is quite rich, but still not much research has gone in the direction of automatically extracting the model from a code. SPIN model (Holzmann in SPIN model checker, the primer and reference manual. Addison Wesley, Boston, 2003, [

1

]) checker is one of the world’s most popular model checkers, and arguably one of the world’s most powerful tool for detecting software defects in concurrent system designs. However, SPIN is incapable of checking C programs directly, rather it accepts a specification language called Promela. In this paper, we provide an architectural overview of automatically extracting Promela model from programs written in C. The proposed architecture also provides scope for abstraction to handle state space exploration problem. In fact, we propose unified solutions for

data hiding

and

data narrowing

, the two most important techniques of program abstraction based on Generalized Program Graph (Debnath in A new abstraction for the study of module interaction, pp. 181–189, 1991, [

12

]).

The image registration is an indispensable process in remote sensing image processing. The remote sensing registration data is the process of aligning one image to a second image of the same scene that is acquired at the same or at different times by the different or the same sensors. This paper proposes an optimization approach for remote sensing image registration. The approach is proposed for determining pairs of corresponding points between the images, the approach based on the implementation of particle swarm optimization (PSO) used as a function optimizer and mutual information (MI) is used as a similarity measure. The first, Landmarks were chosen manually and used thin plate spline (TPS) to provide a geometric representation for the relative locations of corresponding landmarks. Secondly, MI was used as a cost function to determine the degree of similarity between two images. Finally, PSO was used to improve the correspondence between the landmarks and to maximize MI function.

The process of enzymes classification and prediction is Inevitability process to specify the functions of whole the proteins enzymatic class, due to the protein enzymatic play vital role in our life, path-ways and determine this role of enzyme experimentally consume more time and cost. Then propose and develop the computational approach to contribute to solve this problem is the reasonable and acceptable idea. Here we propose and develop a model to classify the enzymes based on their sequence alignment to compute the pair wise alignment between any two sequences namely, local and global alignment using different score matrices, BLOSUM30 and BLOSUM62 (default score matrix), through calculate the pair wise alignment between testing sequence and each sequence in training sequences. The results we have obtained were accept-able to some extent compared to previous studies that we surveyed.

In biometric-based security systems, generation of random biometric keys is a significant means to realize secure, reliable and robust data confidentiality. Here, a novel method for stream cipher design is proposed using biometric fingerprint template. The biometric feature vector is extracted from user’s fingerprint in discrete wavelet transform domain. The feature vector is shuffled and inter-mixed with chaotic sequence to eliminate similar patterns, if any. The whole method of enciphering and deciphering is under the control of user’s secret key and fingerprint template. The method is apt enough to yield great encryption effect and can resist cryptographic attacks. The method is tested on digital images to investigate its encryption performance. The measures like histogram distribution, pixels auto-correlation, entropy, plain-image sensitivity are assessed and compared with recent proposals to demonstrate effectiveness, suitability and consistency of proposed method.

To preserve the image features, in this paper we proposed a novel Partial Differential equation model which is based on linear diffusion model, Total-Variation (TV) denoising model and adaptive Perona-Malik (PM) model. This model is constructed by assign a weight parameter, in order to adjust the size of diffusion coefficient. We analyze the performance of the proposed PDE model and demonstrate that our algorithm competes favorably with state of the-art approaches in terms of producing better denoising results.

Fingerprint segmentation, an important step in Automatic Fingerprint Identification System (AFIS) helps reduce the time of subsequent processing. It aims at separating the foreground region from the background. This paper presents a pixel-wise segmentation scheme based on mathematical moment which provides a proper discrimination of the pixel intensities. A global threshold value is estimated from a set of local blocks with higher standard deviation. The relative local threshold values are derived subsequently to decide whether a pixel belongs to the foreground or background. Finally, morphological filtering is employed as post-processing step to identify the entire foreground region. The proposed method has been implemented and tested on a set of fingerprint images and the experimental results visually establish the effectiveness of the method. Besides, a comparison with the existing methods is presented to verify the accuracy of the proposed algorithm.

Case-based reasoning as a concept covers almost a lot of technologies and techniques including knowledge management, artificial intelligence, machine learning techniques as well as database technology. The usage of all these technologies can easily aid in early detection of breast cancer as well as help other decision makers take the right decision on time and all the times. Of the main hot topics nowadays concerning executive managers and decision makers is measuring the similarity between objects. For better performance most organizations are in need on semantic similarity and similarity measures. This article presents mathematically different distance metrics used for measuring the binary similarity between quantitative data within cases. The case study represents a quantitative data of breast cancer patients within Faculty of medicine Cairo University. The experimental results show that the squared chord distance yields better with a 96.76 % without normalization that correlate more closely with human assessments compared to other distance measures used in this study.

Permutative Cipher Technique (PCT), a session based symmetric key cryptographic technique was proposed by the authors in February 2008 [

1

]. In this paper, PCT is implemented on CBC mode of operation with variable block length (VCBC mode) where initialization vector IV is of variable length. The comparative study of the technique PCT with and without VCBC mode has done on twenty files each of four different file types (.dll, .doc, .exe and .txt) with file sizes varying from 1 kB to 6.3 MB (approx.). Results are generated for comparison of PCT with and without VCBC mode with respect to encryption and decryption times, character frequencies, Chi-square values, Avalanche and Strict Avalanche values, Bit Independence values.

In this research effort, we propose a novel mesh smoothing algorithm using Cubic B-Splines. The basic fact that the coordinates of neighboring vertices of a mesh vary insignificantly is the underlying idea of this paper. The mesh reconstruction is performed by replacing the corrupt vertices which are responsible for the rugged topology of the mesh by interpolating the original noise-free vertices. The final reconstructed meshes were quantitatively measured using Signal to Noise ratio. The results were impressive both in quantitative and visual terms.

This article proposes a scheme for automatic extraction of text from scene images. First, we apply a color image segmentation algorithm to a scene image. To improve the color image segmentation performance, we incorporate local binary pattern (LBP) and business features within it. Local Binary Pattern (LBP) operator is a texture descriptor for grayscale images. On the other hand, the business feature describes the variation in intensity. The segmentation procedure separates out certain homogenous connected components from the image. We next inspect these connected components in order to identify possible text components. Here, we define a number of shape based features that distinguish between text and non-text connected components. Our experiments are based on the ICDAR 2011 Born Digital data set. The experimental results are satisfactory.

Human beings can easily categorize a person’s age group from a facial image where as this ability has not been promising in the computer vision community. To address this problem very important area of research, the present paper proposes a novel scheme of age classification system using features derived from co-occurrence parameters using Rank based Edge Texture Unit (RETU). The Co-occurrence Matrix (CM) on RETU characterizes the relationship between the neighboring edge values, while preserving local information. The novelty of the proposed RETU is it classifies the age of human into seven categories i.e. in the age groups of 1–10, 11–20, 21–30, 31–40, 41–50, 51–60, and greater than 60. The TU of the proposed RETU ranges from 0 to 17 and thus reduces overall complexity in evaluating features from CM. The co-occurrence features extracted from the RETU provide complete facial image information for age classification purpose. The RETU reduces each 3 × 3 sub image into 2 × 2 sub image while preserving the texture features and thus reduces the overall dimensionality of the image.

This paper presents analysis of Devanagari characters for writer identification. Being originated from Brahmic script, Devanagari is the most popular script in India. It is used by over 400 million people around the world. Application of writer identification of Devanagari handwritten characters covers a vast area such as The Questioned Document Examination (QDE) is an area of the Forensic Science with the main purpose to answer questions related to questioned document (authenticity, authorship and others). Signature verification in banking, in Graphology (study of handwriting) a theory or practice for inferring a person’s character, disposition, and attitudes from their handwriting. Here we collect 5 copies of handwritten characters to nullify intra-writing variation, from 50 different people mainly students. After preprocessing and character extraction, 64-dimensional feature is computed based on gradient of the images. Some manual processing is required because some noises are too difficult to remove automatically as they are much closer to the characters. We have used LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters. We have done the writer identification with all the characters and obtained 99.12 % accuracy for LIBLINEAR with all writers. Features collected from this work can be used in the next level to identify writers from their cursive writing.

Machine Translation (MT) systems do not have real-world knowledge or contextual awareness. MT errors are possible at any level: lexical, grammatical, syntactic, etc., MT systems give 10–70 % accurate output, so human post-editing(HPE) is required for final output. But HPE is very expensive and slow, if we can filter out good translations out of all translations, those can make correct via miner edits then our HPE would be fast and less expensive. We can estimate good quality of a sentence using language model (LM). There are different LMs available. We showed in our experiment that Kneser-Ney smoothing LM is the right choice for measuring MT-Engine-output’s quality for the post-editing.

In this paper, an improved mutation operator in GA is proposed to solve multiple sequence alignment problems. The pair wise alignment method is used to generate a child population using the mutation operator. The performances have been tested on a number of bench mark datasets and the results are compared with some of the existing methods available in literature. The experimental results shows that proposed method achieved better solutions than the others for most of the cases.

Visual speech information plays an important role in

automatic speech recognition

(

ASR

), but the problem of visual speech decoding remained open in pose variation. Face detection proposed by ‘

Viola

-

Jones

’ based on image statistic is most popular, but the accuracy of the method is not enough to detect facial features in multipose scenario. In this paper we compared and proposed advanced skin color detection method for automatic isolation of region-of-interest based on induced and non-induced lip color over ‘Viola-Jones’ algorithm for multi-pose audio visual speech recognition system. The ‘

Viola

-

Jones

’ algorithm was widely used for detection of face components (eyes, nose and mouth) and offers accurate face detection for full frontal visual stream but it’s performance dramatically degrades for non-frontal poses whereas the efficiency of our proposed system for induced lip-color based isolation scheme is 100 % each and ROI isolation for non-induced lip color is 100, 92.67 and 93.4 % each applicable for full front, 45° and side pose profile respectively.

The fuzzy C-means (FCM) algorithm has got significant importance compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by some auxiliary variables and spatial information in the membership functions. By combining these two aspects, we are able to solve the problems of sensitivity to noisy data and inhomogeneity. The experimental results on several simulated and real-patient MRI brain images show that the csFCM method has superior performance on image segmentation than the FCM algorithm and some other FCM-based clustering algorithms.

The guard zone computation problem claims utmost importance in VLSI layout design, where the circuit components (or the functional units/modules or groups/blocks of different sub-circuits) that may be viewed as a set of polygonal regions on a two-dimensional plane, are not supposed to be placed much closer to each other in order to avoid electrical (parasitic) effects among them. Each (group of) circuit component(s)

C

i

is associated with a parameter

δ

i

such that a minimum clearance zone of width

δ

i

is to be maintained around

C

i

. Beyond this, it has huge significance in the field of robotic motion planning, geographical information system, automatic monitoring of metal cutting tools, and design of any embedded systems. If the guard zonal regions overlap, we have to remove the overlapped regions in order to compute the resultant outer guard zone (sometimes inner guard zones are also an issue to be considered). In this paper, we have developed an algorithm to compute the guard zone of a simple polygon as well as to exclude the overlapped regions among the guard zonal segments (if any) in O(

n

log

n

) time, where

n

is the number of vertices of the given simple polygon.

Digital Microfluidic Biochip (DMFB) is revolutionizing many areas of Microelectronics, Biochemistry, and Biomedical sciences. It is also known as ‘Lab-on-a-Chip’ for its popularity as an alternative for laboratory experiments. The mixing of samples and reagents for a biological or chemical lab-on-a-chip is important, yet difficult, in microfluidic operations. As volume is of the order of sub-nanoliter, the mixing of liquids is hindered by laminar flow conditions. The fixed geometric parameters of the mixer and the type of movement of the coalesced droplet control droplet mixing time. In this paper, we study the effects of varying mixer sizes and propose of mixing strategies applicable to some Polymerase Chain Reaction (PCR) where a series of mixing is required. A design of mixer has been proposed including its pin assignment where reduction of time of mixing is a key challenging issue to enhance the performance of the chip in terms of throughput.

Stress analysis in wireless environment plays a vital role in everyday life. Monitoring of mental state with significant physiological changes is important, which can help to recognize the matter of anxiety. Particularly it is more important in wireless environment. GSR sensor is one of the various methods to detect the stress at a particular time in different position with various activities. In this paper, it has been considered three different activities like; normal, tension, and physical exercise with laying, sitting and standing activities and send the information in wireless environment. It has been observed that, GSR sensed data are varies in respect to contact surface area with body, environment and activities. Further the data can be sent through wireless mode to the destination point for analysis.

Dynamic metrics are the class of software metrics that capture the dynamic behavior of a software system. This paper gives a brief overview of various dynamic metrics proposed till date for object oriented software. Further, it provides review results based on four aspects viz. metric types, metric validation, metric data collection approach, and software quality attributes addressed by the existing dynamic metrics.

The volume and variety of data in biology is increasing at an exponential velocity. Every week new proteins are getting sequenced and novel structures are being discovered. With the advent of hitherto unknown diseases, it has become imperative that vaccines and drugs be designed as fast as possible. This is causing an immense surge of information which is becoming increasing difficult to process due to limited computational resources. Thus the need of the hour is to harness technologies, like Big Data, which will help distribute computations over a group of nodes and hasten the process of data analysis. In this paper we have explored some techniques to dispense the job of data analysis to several computers which could work in parallel and reach a solution faster.

India like any other knowledge economy depends strongly upon the development in education sector. Quality of its education significantly defines its academic capital and status of human resource. Research findings have often pointed out to the deficiency of technology enabled quality education in India. The authors have tried out to identify the key issues to this disappointing scenario of ICT-enabled education in India. In this quest, it has been found that innovative use of ICT would help overcome different barriers like geographical, socio-economic and cultural constraints to a good extent. Implementing ICT in education primarily involves massive drives of digital literacy, development of requisite infrastructure, and devising of special measures to incorporate different ICT tools. ICT-enabled education would not only improve the quality of education but also redefine the traditional mode of learning to an interactive, efficient and interesting one. To review the scenario of ICT implementation in education and familiarity of students in the digital environment of education, a survey has been conducted over a group of students in India. The reports pointed out to the intense lack of ICT-enabled teaching methodologies. The authors have worked on proposing solutions for an inclusive development of the education standard and technique using ICT as a potential tool. Massive Open Online Courses (MOOCs) are also discussed in this perspective which has emerged as one of the most promising methods of catering Higher Education in an open and online fashion. In this connection, the authors have viewed MOOCs as a way to promote a Ubiquitous Learning environment in India which holds the potential in promulgating education to everyone and everywhere. Overall a vivid vision has been portrayed to sheath the existing education system in India with a technology cover which would not only improve the teaching-learning experience but also would show the way to ubiquitous learning in India, ensuring quality education for all.

In recent times, many algorithms have been applied on hyper spectral images to extract useful patterns and graph kernel method (Shervashidze et al., in International conference on artificial intelligence and statistics, pp. 488–495, 2009 [

1

]) is one of them. This paper proposes a method of applying graphlet kernel to extract obstacles from small land holdings and comparing them with existing templates so as to get the required field features. On these extracted field images NDVI (Jalili et al., in Nation-wide prediction of drought conditions in Iran based on remote sensing data, IEEE, p. 1, 2013 [

2

]) and TCI calculations can be performed in between the crop harvesting stages for example “kharif” in India. This technique is helpful for land covers that are having various obstacles that can cause hindrances in the parametric calculations. NDVI calculations are very common in other countries as they have large holdings and many papers have proved NDVI as an important parameter for vegetation calculation. Graph based kernel function helps in analyzing the small land cover vegetation index with NDVI calculations.

In this paper a technique for automatic segmentation of spoken word signals is presented for identifying letters for transcription into textual form. Signal patterns for each letter present in different words have been used for the purpose. Voice signals are obtained by taking pronunciations of 1,000 words available in the standard dictionary. After collecting the signals, pre-processing is performed to reduce the noise taking a heuristically determined threshold value. Then the signals are segmented based on Amplitude Variation (AV) in different portions of the signal, each corresponding to an alphabet in that particular word. Signal Peak Value (SPV) is the feature used for recognizing the letters. Accuracy of the method is estimated using Bagging, Bayes Net, J48, Naive Bayes, PART and SVM classifiers available in Weka. The best and the average classification accuracies obtained in this method are 95.15 % (given by J48 classifier) and 86.92 %, respectively, which are quite acceptable.

In every electrical process there is an amount of energy conversion. At the time of conversion, some of the energy is exhale at different parts of the system, as loss, in the form of heat. As losses in the process increased, the generation of heat in the system also increased. So, due to overheating, at different parts of the system, after a certain point of temperature, the process may not work properly. In some responsive process, the malfunctioning is more perilous than discontinuing of process. Using thermal sensors, the process can be scheduled from overheating and malfunctioning, by switching off the system. A group of sensors organised in a logical manner, can pause the process automatically. A group of four sensors used for the prototype model, to work together in different parts of the system, for automatic protection of the process as well as to conserve energy.

Q&A forums offer users a platform to gain and showcase their knowledge and facilitate intellectual sharing among community members. An incentive based mechanism is devised as a means to encourage users to contribute to questions posed in the forum by allowing various users to Upvote and Downvote posts and answers. Though this provides the needed impetus to users to contribute quality content, oftentimes the reward mechanism is more biased towards questions on popular topics such as Java, html etc. Moreover simple questions—questions whose answer can be found in most textbooks or on the web—sometimes get a large number of votes due to a number of people finding the answer with the least amount of effort from their side. The downside of this is that quality questions—questions where the user has given thought to the topic, put in effort and which would require an effort from the answerer—is seldom answered or voted up. The investigations in this paper are oriented towards separating popularity from quality. We propose an iterative technique based on link analysis algorithms to separate the quality posts from the low quality ones even if the latter have a high number of upvotes. Such a scoring can be utilized for improving search and for highlighting good posts finding answers to which would enhance the knowledge level of the community as a whole. Experimental evaluations aligning the ranks produced with expert given ranks are promising. In two out of three cases the proposed approach had higher rank correlation with those of the experts as compared to ranks inferred through Stack Overflow assigned scores. Moreover examination of individual posts which refer to common questions illustrate that the proposed techniques have the ability to push down their scores even if Stack Overflow has assigned high scores.

Fractal based compression technique has a very good performance in terms of achieving a high compression ratio and producing a good quality of image/picture. Fractal coding technique is limited because of large number of computation is required for searching best possible domain blocks, number of comparisons and transformations applied on each domain blocks present in domain pool. This paper discusses a quadtree based Extended Weighted Finite Automata (EWFA) coding approach for individual frame coding i.e. similar to the intraframe coding. In this approach the quadtree is used to specify the address of each subimage or quadrants for creating a EWFA. We observed that the EWFA encoding process is also similar to fractal encoding process and the size of domain pool increases dynamically during the process of EWFA encoding. Experimentations are carried on standard databases like football, suzie, crew, highway, soccer, ice, and harbour sequence etc.

Transaction volume per day in Indian e-Governance projects are growing constantly due to popularity and acceptability of the e-Governance applications among common citizens. This constant growth of e-Governance data size is referring e-Governance projects as a potential candidate for doing BIG data analysis. BIG data technologies stack enable application stake holders to get faster analytical report from huge amount of historical data, even from unstructured database. Now-a-days, Most of the enterprise drivers have chosen BIG data technology to get the ability for dealing vast amount of data efficiently in order to bring significant business insight, which may lead to take better business decision. In e-Governance, this kind of analytical report could help to Government policy maker to take more corrective decision towards developing Indian society; thus technology could be used for the mankind of Indian Society. In this study, authors have proposed a framework for Indian e-Governance projects BIG data platform. Hadoop, PDW, SSRS, SSAS and SharePoint would be selected technology in the proposed framework to design the complete BIG data platform for Indian e-Governance.

The organizations are in a continuous search of a bias free and an integrated performance measurement and management system for their employees in order to attain business excellence and achieve organizational goals to sustain in the long run in the market coping with the competitive environment. The research study is conducted on some selected IT companies operating at West Bengal, to get a comparative view of the factors affecting the concept of faith among the employee about the performance appraisal system practiced in these organizations. The exploratory study also aimed to examine the comparative influence of these appraisal based factors on the concept of faith in the appraisal process among the employees of the IT companies’ understudy. The study found a significant difference between the influences of the factors on the concept of faith among the employees.

The paper based ticket system for collecting the bus fare has been found to be a source of major financial loss in India. It is difficult to assure the purchase of ticket by each and every passenger. Also, a paper ticket becomes useless to the passengers when the destination is reached. A large number of such tickets are wasted every day. This causes a tremendous increase in pollution due to the reckless felling of the trees for production of these tickets. Even the count of many unsold tickets per day is very high. In the era of technology, India must focus on inculcating an automated system for collecting bus fare. Hence, this paper proposes an automated card driven system for bus journeys in India. Different scenarios concerning the implementation of this system are discussed in this paper.

While providing several benefits of e-Learning, different organizers and the technology group are facing several challenges like configuration, security issues and standard framework etc. Researchers are also discussing many ways regarding security implementation issues or secured framework for e-Learning system. Considering Object Oriented Approach (OOA), Digital Right Management (DRM) and access control, Cryptographic Security Algorithm (CSA), we have provided an idea for designing the Secure e-Learning Framework (SeLF), which will be encouraging to implement for the next generation of learning system.

Use of Smartphone as mobile nodes in different communication infrastructures is excessively explored in recent years. Such smart phones can be considered as a good candidate for situations like Disaster Management, where there is no infrastructure available to support communication and connectivity among the group members is a prime objective. Disaster rescue operations are generally based on location intensive operations including neighboring nodes’ locations and their availability. The storage limitations of such devices ask for suitable strategies to store information efficiently. In this work, a method has been proposed that employs an extra layer of compression, while storing location data in the form of latitude-longitude (lat-long) pairs, to the HBase database. Location data in a mobile network is big-data, as continuous collection of such information adds numerous data inputs. By incurring a negligible overhead on the system in the form of small encoding and decoding time, the proposed method obtains almost 70 % compression ratio, even for thousands of input data. In this work Huffman lossless encoding scheme has been used.

This work describes a simple method to detect gestures revealing muscle and joint pain. The data is acquired using Kinect Sensor. For the purpose of feature extraction, the twenty joint coordinates are processed in three dimensional space. From each frame, 171 Euclidean distances are calculated and to reduce the dimension of the feature space, ReliefF algorithm is implemented. The classification stage is consists of fuzzy

k

-nearest neighbour classifier. The proposed method is employed to recognize 24 body gestures and yields a high recognition rate of 90.63 % which is comparatively higher than several other algorithms for young person gesture recognition works.

With the advancement of digital imaging technology in the medical domain an increased amount of sampled biological data is being generated. Clipping of volume data has become more and more important because it allows us to cut away selected parts of the volume and plays a crucial part in medical image understanding, computer assisted diagnosis and surgery simulations. We propose a method for volume cutting using deformable surfaces modeled with level sets. Using Boolean operations the method is extended to multi-object clipping. The overall computational cost is reduced by using a fast and computationally efficient narrow band level set algorithm. The proposed model has been used to extract arbitrary shapes from scanned volume data including some low contrast medical data with promising results.

Nowadays, social media is being used by most of the people around the world for expressing sentiments and opinions. Twitter is a very popular social networking website, where people can express their views. A user follows other users to see their updates. Following on the twitter signifies the interest of a user in others’ life and views. A huge number of contacts or followers are increasing globally. Follower counts on twitter users are very important; it shows that you are a renowned personality in society. It can be very useful for Coaching Institutes, Company Products, Colleges and Hospitals etc. A Proposed method is used to find the popularity of any given applications or organizations by finding the zonal followers for small regions. In this paper, a technique is proposed to check out “zonal followers (followers in a particular radius) for small regions” from total followers of an account, based on improved Density Based Clustering Algorithm. Information of zonal followers for small region can be very useful for many business to create their marketing strategies. Experimental results show the effectiveness of the proposed approach.

The present work is the detection and revocation of misbehaving vehicles in vehicular ad-hoc network. In the present work vehicles are within the coverage area of base stations and the base stations are within the coverage area of certifying authority. Each vehicle detects misbehaving vehicles from its neighbors, creates a certificate revocation list by mentioning the identification of the misbehaving vehicles and sends this list to its parent base station. Each base station creates a certificate revocation list after receiving the certificate revocation lists from the vehicles within its coverage area and sends it to the certifying authority. The certifying authority creates a final certificate revocation list after receiving the certificate revocation lists from the base stations within its coverage area and broadcasts it among the vehicles within its coverage area through base stations. The independent operations of vehicle, base station and certifying authority are divided into a few blocks for parallel execution which helps to reduce the time of detection and revocation of misbehaving vehicles from vehicular ad-hoc network. The performance of the proposed scheme outperforms the existing schemes.

Defects, voids and cracks are the most ineffective components of buildings and bridges, respectively. The steady state vibrations and involuntarily happenings like earth quake triggers events such as accident and incident. The presence of air gaps or voids caused by the shifting of components like steel rods accelerates towards such catastrophic failure. Infrared thermography based on remote sensing of radiation energy from concrete surface, is a non-contact technique allows the visualization of concrete surface temperature as two-dimensional thermo-grams. The paper proposes a robust technique of analysis of the defect region using infrared image and estimate the area, shape and location of defect along with its depth.

The efficient knowledge transformation with the concept class detection is an important challenge for the incremental learning system, where the student’s data is flowing continuously. The massive amount of raw student’s data in the education system can be transformed into the information and buried knowledge can be taken out of it for the purpose of offering good career choice to the students or for the purpose of detection of student’s performance. The algorithm proposed in the paper learn continuous raw data, transform previous knowledge to the current data without referring to the old data and able to efficiently accommodate new concept class detected by the system. The proposed system is applied to the student’s classification problem for detecting new students samples introduced to the system. In this paper four classifiers are used as a base classifier and for the updating of weight distribution randomly for efficient knowledge transformation and dynamically consult vote strategy is used for detection of new concept class. Simulation results over student’s data are used to validate the efficiency of the proposed method.

There are fewer practices for identification of risk factors in software development though everyone is aware of the impact of risks over project’s success parameters. To avoid risks or to identify risks, each team member needs to practice goal modeling at each phase of software development life cycle. It is not only project manager’s task to focus over risk occurrences but also it must be the duty of each team member to keep an eye on an assigned task and relative risk factors. As per an industrial need, “Sketch the Risk” tool is designed and developed in the most user-friendly way and hence, any team member from the project group can analyze given task from many views. The focus of development of “Sketch the Risk” tool is to identify risk events and avoid risk factors like project development delays, increase in the estimated cost, unnecessary rework, human resources utilization, etc. Unlike of existing approaches that are focusing over early stage to identify risk events, we proposed and developed new technique that takes care at each stage of software development life cycle. In essence, this paper presents identification goals and sub-goals in recurring manner just parallel to the spiral model of the software process.

Internet of Things has started contributing in all sectors, be it agriculture, transportation or healthcare. The industry is looking forward to unleash the potential of revolutionary technology called Internet of Things. Although IoT is making huge inroads in every sector, but efficient delivery of services still remain a concern. Also, Agile Methodologies are increasingly being adopted across the software development industry. This paper briefs the concept of Internet of Things, followed by exploring the prospects of modeling Agility in Internet of Things Architecture to provide for better delivery of services and improved Quality of Service. In this paper, we focus on the potential of making Internet of Things more agile and flexible. The Internet of Things can exploit an advancing market open to new and innovative services. Complex and ever-evolving business requirements demand adoption of agile services in Internet of Things applications. Further, this paper intends to leverage the benefits offered by Agile Services in the application fields of Internet of Things ranging from smart agriculture and intelligent transportation to smart healthcare and logistics optimisation.

In this paper a novel technique for authentication has been proposed in composite real and imaginary components of Z transform domain in higher region of convergence

$$ (r = 2) $$

(

r

=

2

)

. Three bits from authenticating image are embedded into real as well as imaginary components of second, third and fourth transform domain coefficients keeping the low frequency component as it is and is used for adjustment. The proposed method obtain a good security in terms of robustness and sensitivity of Z transform in higher magnitude. A comparison has been made with existing methods such as AINCDCT (Authentication of images through non convoluted DCT, IEEE, pp. 1–4, 2011 [3]), Luo’s Method (Inf Sci 181:308–328, 2011 [4]), SCDFT (IEEE Trans Inf Forensics Secur 3(1):16–28, 2008 [5]) which shows better performance and visual quality.

This paper addresses an idea to blend the quality ascribes of flash and no-flash image pairs; yielding a synthesized image, better in visual quality than the original ones. Bilateral filtering algorithm is employed in this work to subdue the effect of noise superimposed at the acquisition stage without fading of details. The proposed synthesis approach performs sub-band decomposition of denoised (flash and no-flash images processed via bilateral filter) images using wavelets followed by the synthesis of coefficients via max-max decision rule. Simulations are carried out on flash/no-flash image pairs contaminated with different levels of additive Gaussian noise and are evaluated using a no-reference image quality parameter. Significant improvement in quality of the synthesized image has been observed in comparison to original ones.

In this paper, a simulation optimization tool ASSOLVE is proposed for assignment problems. The proposed tool is able to simulate and compute the results for assignment models. It is developed in JavaScript and back-end simulation results are stored for analysis with the help of an effective algorithm. This manuscript proposes a user friendly tool for output performance measurements of classical models of assignment system. Results and analysis are described with the help of numerical example.

Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurological disorder seen in children. The 3d structure of the receptor proteins implicated in ADHD used in this work (i.e., ADRA2A, HTR1B, NET1, TPH2, NGF and SNAP25) and modeled by homology modeling. Homology modeling generated five templates for each receptor and the best model was determined by Ramachandran Plot analysis. Again, the best model for each receptor protein are screened with phyto-compounds and best ligand for receptor are ascertained.

Magnetic Resonance Images (MRI) are known to be corrupted by the additive Gaussian noise during the acquisition process. The presence of this noise affects the diagnosis as it tends to alter image details and pixel intensities. Conventional iterative denoising approaches fail to preserve the details and structures during MRI restoration. This paper proposes a Non-Local Averaging based MRI denoising algorithm to facilitate preservation of the finer structures. The proposed algorithm computes the weighted average of the similar pixels of the image within the local window. Method noise has been used as a measure for detail preservation which corresponds to the difference between original and the restored image. Simulation trials are performed on the image at differing levels of Gaussian noise which are then justified by method noise analysis and performance evaluation factors such as Peak Signal-Noise Ratio (

PSNR

) and Structural Similarity (

SSIM

). The proposed algorithm has demonstrated good performance, both in terms of visual quality as well as values of performance parameter.

Software testing suggests the quality of the software product. More the effective testing means high quality software product. In this paper we have identified and prioritized the parameters according to the perspective of different entities involved in software lifecycle. The prime objective of the paper is to perform software testing from the perspective of Kansei Engineering methodology with multi-objective optimization.

This paper is basically extension of our work in detection of diseases using data mining techniques. Here it shows that heart disease can be diagnose by using various data mining techniques and algorithms such as decision tree, split validation and apply model. Some Attributes like age, root canal treatment, smoking and diabetes predicts the probability of patients getting a heart disease. The proposed system that is the union of computer-based patient records with clinical decision support can lower down medical mistakes, unwanted practice variation and helps in improving patient safety and outcome.

An Artificial Neural Networks Model by using Wavelet Analysis for Speaker Recognition has been presented in this paper. The wavelet analysis was used to extract the features. These extracted features were trained using Artificial Neural Networks with popular Back Propagation Learning Algorithm. In this analysis of testing, the speakers speak out the same set of words, with these set words the features were extracted and fed into the training of the neural network. The neural network notifies the identity of the speaker. In order to test the system, the voice data of the speakers were recorded. The experiments were carried out by using 800 data sets of total 40 individual speakers. For each of these speakers, 20 speech signals were used for training. All these signals were used for training, validation and testing. This approach reveals that the overall performance of system is 95 %.