Proceedings of the First International Conference on Intelligent Computing and Communication

Protein-Protein Interactions (PPI) have a huge impact on several biological processes viz. enzyme substrate interaction, cell signaling to name a few. It is also well documented that altering PPI can also lead to onset of diseases. We have studied one circadian rhythm sleep disorder with genetic correlation viz. Familial Advanced Sleep Phase Syndrom (FASPS). In the present work, utilizing structural bioinformatics approach we tried to elucidate the differences in pattern of bindings between two different core “clock genes” products viz. human Period protein (hPER2) and human Casein Kinase I delta (hCKId), both in wild type and disease-causing mutated variants. Molecular mechanics calculations have also been used to describe the interactions of wild type and mutant proteins. The results from this study may be useful for the development of newer drugs in patients having impaired circadian rhythm.

Recently, different image analysis methods are used for human body parts. But the internal pectoral muscle segmentation of important body parts in a automatic way is widely used. This is also vital for multi modal image registration. Previously, breast MRI image analysis by automatic pectoral muscle segmentation is studied. In this paper, we introduce a comparative framework of probabilistic atlas segmentation method for breast with brain, chest, heart and liver MRI. For breast, brain, heart and liver and chest segmentation, the obtained DSC values are 0.76 ± 0.12, 0.71 ± 0.15, 0.66 ± 0.08, 0.77 ± 0.12 and 0.72 ± 0.13 respectively. The total overlap values for each case are 0.76 ± 0.12, 0.76 ± 0.15, 0.71 ± 0.08, 0.70 ± 0.12 and 0.70 ± 0.13 respectively.

Diabetes is a major problem affecting millions of people today and if left unchecked can create enormous implication on the health of the population. Among the various noninvasive methods of detection, breath analysis presents an easier, more accurate and viable method in providing comprehensive clinical care for the disease. This paper examines the concentration of acetone levels in breath for monitoring blood glucose levels and thus predicting diabetes. The analysis uses the support vector mechanism to classify the response to healthy and diabetic samples. For the analysis ten subject samples of acetone levels are taken into consideration and are classified according to three labels which are healthy, type 1 diabetic and type 2 diabetic.

Retinal G-protein receptor; rhodopsin upon light-activation, gets phosphorylated, experiences conformational shift and interacts with G-protein; transducin. To completely obstruct the signal transduction visual protein; arrestin binds consecutively to disrupt the cationic channels of plasma membrane. Experimented binding assays documents the protein interactions but hitherto computational investigation was undone. This probe aims at the computational study of conformational alterations in rhodopsin upon sequential interactions, accompanied by variations in its surface electrostatic potential and net solvent accessible area. 3D structures of human transducin, arrestin and rhodopsin were analyzed. Residual participation from the optimized and simulated trio-complex (rhodopsin-transducin-arrestin) disclosed that predominantly positively charged amino-acid residues; Arg474, Arg412, Arg229, Arg13, Lys15 and Lys408 from rhodopsin participated with transducin and arrestin forming 9 ionic interactions. Rhodopsin was perceived to interact in a gradual firmer pattern with its partner proteins. This study presents a novel viewpoint into the computational disclosure for participation of concerned visual proteins.

Seismocardiography is a new, low cost and non-invasive method for measurement of local vibrations in the sternum due to cardiac activity. Signals recorded using this procedure are termed as seismocardiogram or SCG signals. Analysis of SCG signals provides information about the functionality of the cardiovascular system. Performing an automatic diagnosis using SCG signals involves the use of signal processing, feature extraction and learning machines. However for such methods to yield reliable results, the digitally acquired SCG signals should be accurately denoised and free from artifacts. In this paper, we evaluate the performance of state of the art algorithms in denoising these signals. In our work, clean SCG signals were corrupted with additive white Gaussian noise and the signals were further denoised. Denoising using wavelet transforms, empirical mode decomposition, adaptive filters and morphological techniques has been considered in our work. Standard metrics: mean squared error (MSE), mean absolute error (MAE), signal to noise ratio (SNR), peak signal to noise ratio (PSNR), cross correlation (xcorr) and CPU consumption time have been computed to assess the performance the aforementioned techniques. From our study it is concluded that wavelet thresholding yields the best denoising and is hence the most suitable method for enhancement of real world SCG signals.

We present a methodology, common subcluster mining, to explore gene expression data for possible biomarkers of lung cancer. Subclusters refer to the peaks formed through superimposition of clusters obtained from expression data of normal samples. Application of the method on the corresponding data sets from diseased samples extracts the genes that undergo high fold changes. The potential candidate genes are examined on the datasets of Stage I through stage IV of the disease. Few genes emerge as indicative molecular markers of lung cancer.

Gathering personal information of individuals’, in return to provide different personalized services, continues to grow. The adaptability and flexibility of cloud that allows mobility of data access and multiple ownerships provide a favorable platform for users and service providers to adapt Cloud services for storing and accessing personal data. However data flow from one level to another service level of cloud may cause data loss or leakage and put the privacy of individuals at risk without them being aware of it. Ensuring privacy of information on Cloud, presents a major challenge to be tackled by future researchers. This paper aims at providing an overall picture of cloud privacy and security at its different level of architecture and discusses the proposed solutions. It will further provide detailed analyses of the various adopted techniques. We will also discuss Security-SLA as a security protection mechanism for cloud users. Further we will try to highlight the areas which can be further researched and make cloud a more secure place to store data.

Today’s era of cloud computing and everlasting demands for real-time analysis of the storage data on cloud, it is essential for IT industries to have cognizance about the storage performance. Cloud is elastic computing model where users can hire computing and on demand storage resources from a remote infrastructure and its popularity depends on low cost and on demand availability. Simultaneous execution of huge number of data-intensive applications on the public cloud call for a huge amount of storage in order to access the persistent data leads to degradation of overall system performance. IT personnel have to be assisted with storage performance measurement for prediction of best storage need. Input/Output Operations Per Second (IOPS) calculation helps to determine the amount of I/O’s storage to run. This IOPS calculation is incorporated in cloud environment to alleviate Linux based application delivery.

In recent years, there is tremendous demand of cutting-edge cloud-based applications in many of the industries. We have proposed in the paper a shared disk cloud database architecture as the basis on which an intelligent data storage management system can be developed for enriching cloud-based web applications. Important features of this proposed architecture are single copied data consistency, dynamic load balancing and high benchmark performance. Based on the software layer, an intelligent data management system for popularizing the concept of SaaS has been pointed out suggesting a cost-effective solution for popularizing the cloud environment.

The application partitioning is the process of the breaking the application processes in the smaller processes for the easy execution and to enable the offloading capabilities of the process. In the proposed model, the process cost evaluation has been calculated in the form of the execution time, from where the threshold is calculated for the offloading decision. At first, the proposed model evaluates the number of instructions followed by the sequencing on the basis of the latter. The proposed model then compute the time cost for every process and make the decision on the basis of the threshold calculating. The experimental results have shown the effectiveness of the proposed model.

Today, Cloud Computing has paved the way for enormous computing and storage. Cloud servers are third party systems which could be rented on demand basis and paid on usage basis. More and more users are adopting cloud based applications but the only factory that hinders its development is security issue. Users have a fear of trusting a third party system like cloud and they show reluctance to outsource their sensitive information to cloud. Encryption seems to be a direct solution but it limits the computability on data. Hence encryption schemes should be chosen based on the application they need to implement. In this article we study the basic encryption schemes which are widely used in cloud scenario. We compare these schemes in terms of their computational complexity, security, performance etc.

With the evolution of ICT in different business domain, it has become essential to almost all the IT project stakeholders to move applications into cloud for saving IT cost and ensuring sustainability of IT solutions for future. On this fact, it is already proven that cloud implementation has given ample opportunity to reduce overall IT cost of the organization. In cloud implementation, Project stakeholders would have to bear only cost which is project owners ought to pay according to uses of IT resources on pay-per-use cost model. But, in the cloud implementation journey, the most impacting and challenging activity is to do migration of existing application to cloud platform smoothly without interruption of existing application’s user’s experiences and on-going business activities. In this study, authors’ main focus is to propose conceptual framework for assisting to do migration of IT projects in cloud platform. This generic framework will mostly cover End to End processes which require migrating Microsoft based technology applications those are mostly developed in Dot Net and SQL server technology and deployed in Internet Information Server (IIS). The holistic objective of the proposed framework is to facilitate business to provide faster, more reliable, robust and cost effective migration process. This study also has taken in scope to develop very simple proof of concept (POC) to do some sort of validity of the proposed idea.

Cloud computing has opened up a whole new vista wherein it is possible to segregate and compartmentalize the process of constructing infrastructure with the intent of providing certain services to end users from the very business itself. In this paper, we have introduced a communication network that is aimed at augmenting network performance in situations where a company needs to operate a branch through means other than wired or mobile networks. Our proposed model utilizes a mix of VSAT and cable technologies that has been designed to improve efficiency of a cloud data center. The introduction of VSAT connectivity also decreases chances of a network breach as the communication conduit in such is not generally shared. Though, VSAT technology continues to lag behind other forms of communication, its ability to bypass complex routing can be harnessed to improve energy efficiency and network performance while bringing down carbon emission rates.

A message in computer networks is often divided into frames/packets for various reasons. For a store-and-forward network, a suitable packet-size can drastically reduce delay. In this paper, I have investigated the impact of packet size on delivery time. I have shown that delay is a non-linear function of (i) number of hops the packet traverses, (ii) message size and (ii) the number the message is divided into packets. Since, I can’t customize the former two; the last one can suitably be chosen to minimize delay. I found an optimal number of packets that minimizes the delay. Analytical and simulation results show the correctness of the proposed scheme.

Interpretation of medical document requires descriptors to define semantically meaningful relations but due to the ever changing demands in healthcare environment such information sources can be highly dynamic. In these situations the most challenging problem is frequent ontology search keeping with user’s interest. To manage this problem efficiently the paper suggests an ontology model using context aware properties of the system to facilitate the search process and allow dynamic ontology modification. The proposed method has been evaluated on Cancer datasets collected from publicly accessible sites and the results confirm its superiority over well known semantic similarity measures.

For the electronic business applications, web services are usually considered as the design models. Here, our aim is to design an efficient model to deal with both the distributed applications and cooperative applications. In both the cases when it comes to implementation of web services in respective applications, the consumer (developer) has to put an effort to manually provide the reference of the respective web service through a specific set of steps depending upon the target IDE. But what if we have a technique to perform the above mentioned approach in a dynamic and generic fashion without manually adding the web reference for any web service. In this paper, we will represent an efficient approach for interacting with any web service irrespective of its syntax (WSDL) and semantics without adding its web reference. Through this approach the consumer of the web service can access the respective web service dynamically by just mentioning its URL in his/her code and through a little object oriented methodology. Our approach is based on accessing the particular web service by automatic generation of proxy class, delegation, dynamic data type handling through reflections API and producing the desired output in a generic fashion.

An optical bus that connects two nodes of a WDM network and allows multiple communications between those two nodes is called light trail. Failure of a link within a light trail can bring down the communication to halt. In this paper, a link based protection scheme is proposed to solve both single and multiple link failure of light trails in a WDM mesh topology. This scheme is applicable to both static and dynamic light trail.

In this paper, we try to get the interest of users of certain location based on tweets on certain topics (like entertainment, politics, sports, technology, business, etc.). This study helps us in recommending things to users more accurately. We first analyze the sentiments of tweets and then classify the tweets according to topics. In this way, we are able to get the topic in which users are positively and negatively interested. We have done this experiment on 1500 Indian users and 800 USA users.

Twitter is a social networking site that allows a large number of users to communicate with each other. Twitter allows users to share their views on different topics ranging from day to day life to what is going in society. Event detection in twitter is the process of detecting popular events using messages generated by the users. Event detection is difficult in twitter as compared to other media because the message known as tweets is only allowed to be less than 140 characters. Moreover the tweets are noisy because there may be personal messages by the user also. The focus of this paper is to find top k popular events from tweets using keywords contained in the tweets. This paper also classified the popular events into different categories.

Mobile malwares have been rising in scale as Android operating system enabled smart phones are getting popularity around the world. To fight against this outburst of Android malwares, different static and dynamic malware detection methods have been proposed. One of the popular methods of static detection technique is permission based detection of malwares through AndroidManifest.xml file using machine learning classifiers. However, the comparison of different machine learning classifiers on different data sets has not been fully cultivated by existing literatures. In this work we propose a framework which extracts the permission features of manifest files, generates feature vectors and uses different machine learning classifiers of a Data Mining Tool, Weka to classify android applications. We evaluate our method on a set of total 170 applications (100 benign, 70 malwares) and results show that highest TPR rate is 96.70 % while accuracy is up to 77.13 % and highest F1 score is 0.8583.

Today’s world has seen a massive explosion in various kinds of data having some unique characteristics such as high-dimensionality and heterogeneity. The need of automated data driven techniques has become a necessity to extract useful information from this huge and diverse data sets. Data mining is an important step in the process of knowledge discovery in databases (KDD) and focuses on discovering hidden information in data that go beyond simple analysis. Traditional data mining methods are often found inefficient and unsuitable in analyzing today’s data sets due to their heterogeneity, massive size and high-dimensionality. So, the need of parallelization of traditional data mining algorithms has almost become inevitable but challenging considering available hardware and software solutions. The main objective of this paper is to look at the need and limitations of parallelization of data mining algorithms and finding ways to achieve the best. In this comparative study, we took a look at different parallel computer architectures, well proven parallelization methods, and programming language of choice.

Suffix tree is a fundamental data structure in the area of combinatorial pattern matching. It has many elegant applications in almost all areas of data mining. This is an efficient data structure for finding solutions in these areas but occupying good amount of space is the major disadvantage of it. Optimizing this data structure has been an active area of research ever since this data structure has been introduced. Presenting major works on optimization of suffix tree is the matter of this article. Optimization in terms of space required to store the suffix tree or time complexity associated with the construction of the tree or performing operation like searching on the tree are major attraction for researcher over the years. In this article, we have presented different forms of this data structure and comparison between them have been studied. A comparative study on different algorithms of these data structures which turns out to be optimized versions of suffix tree in terms of space and time or both required to construct the tree or the time required to perform a search operation on the tree have been presented.

From search engines to e-commerce websites and online video channels to smartphone applications, most of the internet applications use advertising as one of their primary source of revenue generation. Malvertising is the act of distributing malicious software to users via advertisements on websites. The major causes of malvertisement are the presence of hundreds of third party advertising solutions and the improper verification of ads at the publisher’s site. Moreover, smartly tailored advertisements are placed which exploit a browser’s bugs and vulnerabilities to infect user with malicious software. In this paper, we highlight loopholes in the currently applied advertising policies and the vulnerabilities that are exploited to attack customers by serving malicious ads on user applications. The major contribution of the authors is a framework developed to identify malicious advertisements at the publishers’ end. It is based on two types of analyses. The first type of analysis involves static analysis of the advertisement’s source code. The other type is the behavioral analysis of the advertisements done in a secure sandboxed environment to detect any malicious activity. We extracted a total of 9 features from 15,000 advertisements and classified it using a trained one class SVM classifier. Our result shows that 53 % of the suspicious ads contain dubious iFrames while 69 % of them perform redirections followed by drive by download 18 % with very low false positive and false negative rates.

Building a classifier using association rules for classification task is a supervised data mining technique called Associative Classification (AC). Experiments show that AC has higher degree of classification accuracy than traditional approaches. The learning methodology used in most of the AC algorithms is apriori based. Thus, these algorithms inherit some of the Apriori’s deficiencies like multiple scans of dataset and accumulative increase of number of rules. Closed itemset based approach is a solution to the above mentioned drawbacks. Here, we proposed a closed itemset based associative classifier (ACFIST) to generate the class association rules (CARs) along with biclusters. In this paper, we have also focused on generating lossless and condensed set of rules as it is based on closed concept. Experiments done on benchmark datasets to show the amount of result it is generating.

Big data analytics is a process of examining large set of data and extracting only the useful information out of it for further research. Data that is generated these days doesn’t follow any particular structure. Data can be structured, un-structured or semi-structured. Data can be in form of text, image, video, live streams etc. It is a challenging job to handle such data for data mining, web mining and text mining or environmental research works. In this paper we have discussed about the step by step process of big analytics and the relevant challenges while applying on real life events. Also this paper provides a comparative study on the popular data mining algorithms which are generally used for big analytics.

Data clustering is inevitable in today’s era of data deluge. k-Means is a popular partition based clustering technique. However, with the increase in size and complexity of data, it is no longer suitable. There is an urgent need to shift towards parallel algorithms. We present a MapReduce based k-Means clustering, which is scalable and fault tolerant. The major advantage of our proposed work is that it dynamically determines the number of clusters, unlike k-Means where the final number of clusters has to be specified. MapReduce jobs are iteration sensitive as multiple read and write to the file system increase the cost as well as computation time. The algorithm proposed is not iterative one, it reads the data from and writes the output back to the file system once. We show that the proposed algorithm performs better than an Improved MapReduce based k-Means clustering algorithm.

This paper addresses a critical issue of selection of supplier occurred in supply chain of a manufacturing company. As there are lot more criteria present for decision making of suitable supplier selection among many, it becomes more challenging task for any company to make as this decision is entangled with company’s profit and time. So, to address this problem, this paper proposes a multi-criteria decision making (MCDM) method using Decision Making Trial and Evaluation Laboratory (DEMATEL) based on Analytic Network Process (ANP), i.e., DANP, with fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) to judiciously select suppliers based on important criteria and to point out interrelationships among dimensions and criteria in SCM by Network Relationship Map (NRM) for this company. Furthermore, the ranking is supported by sensitivity analysis.

Over the last few decades there is considerable increase in number of students both in Traditional as well as Online education. Especially students are showing utmost interest to learn from advanced online systems (Moretti in EDM, 2014, [1]) such as Intelligent Tutoring systems, Massive Open Online Courses (MOOC) and Virtual learning environments. These all systems are generating huge amount of Data. Now it is crucial to handle Big Data in Education. If Big Data in Education is handled properly by applying Big Data Analytics Techniques and Tools then some Intelligent Patterns can be retrieved which helps to improve Education process. In this paper Hadoop Framework (White in The definitive guide, O’Reily Media, 2009, [2]) is used to handle and process data. Analytics is applied by taking Resumes Data which are the most useful and commonly available Educational Data for Analysis and various valid Skill Inferences are drawn. Further Performance Analysis for Experiments is done by comparing Nondistributed Environment and Distributed Hadoop cluster by increasing the number of nodes. Stepwise experimentation is provided in Appendix with screenshots.

In the face of stiff global competition and fast changing technology, the sustainability of the software companies undoubtedly depends on their contented and committed intellectual capital. To attract and retain best talents from the market, the industry emphasizes on strategic and innovative HR practices. As the driving force of the industry is its’ workforce, maintenance and management of skills and potentialities of the individual employees’ and working teams are considered as the instrument to retain talents in the organizations and also to continue to provide quality service to the customers. Performance management hence plays a central role in these organizations. Continuous monitoring and management of individual competences therefore always be in focus for the HR department. The paper investigates the performance assessment process and its’ implication on employee engagement mediated though an HRD culture conducive of growth and innovation in some IT/ITes/BPO companies of West Bengal.

This paper presents an in-depth analysis of NMOS capacitances across various technology nodes and device parameters which are extracted for different operating regions namely accumulation, cutoff, saturation and triode, while keeping the aspect ratio same for each transistor. Since MOS capacitances are the key parameters for estimating process development, material selection and device modeling, this paper enlists their variation with gate-to-source voltage (V_GS) while keeping drain-to-source voltage (V_DS) constant. This paper also aims to present the impact of capacitance variation on device performance that includes operating speed, power consumption, delay product and so on. The simulations results have been extensively verified using HSPICE simulator @ various technology nodes.

This paper primarily focuses on the power dissipation of cross coupled CMOS dynamic latches and also takes the technology scalability of the design into account. Mainly 3 topologies namely the Cascade Voltage Switch Logic (CVSL), Dynamic Single Transistor Clocked (DSTC) and Dynamic Ratio Insensitive (DRIS) have been investigated. A comparative study is provided which validates the suitability of the above latches for high-speed low power applications. Further, a brief account regarding the use of these latches for the design of high speed edge triggered flip-flops is also provided. The simulations results have been extensively verified on SPICE simulator using TSMC’s industry standard 180 nm technology model parameters and the technology scalability is tested with 22 nm predictive technology model developed by Nanoscale Integration and Modeling (NIMO) Group of Arizona State University (ASU).

This paper presents an in-depth analysis of the propagation delay of dynamic CMOS latches and its variability when subjected to process, voltage and temperature (PVT) variations. Three basic topologies namely the cascade voltage switch logic (CVSL), dynamic single transistor clocked (DSTC) and dynamic ratio insensitive (DRIS) have been investigated for robustness and switching characteristics. The extensive analysis provides well-defined guidelines for selection of variation-aware CMOS latches used in digital logic design. All simulations have been performed on 180 nm TSMC industry standard technology node using SPICE circuit simulator.

Quantum Cellular Automata (QCA) is one of the latest upcoming technology. In the present extension we have proposed a configuration of full adder utilizing two reversible half adders. However the proposed design contains only majority voter gate and inverter gate. Moreover the dissipation energy, incidence energy and effective area have been evaluated and reported also. To the best of our knowledge such a design in the present form has not been reported in literature as yet.

In the last few years, the demand of data outsourcing in the cloud has enormously increased. However, there are some significant barriers to cloud computing adoption. One of the most significant barriers of outsourcing data in the cloud computing is its security issues especially when sensitive data such as personal profile, financial records, medical reports, etc. are outsourced on cloud servers. Therefore, strong security measures are essential to protect data within the cloud. Recently, secret sharing schemes have been applied to protect sensitive data in cloud storage. In this paper, we propose a (t, n) multi secret sharing scheme (MSSS) for secure data outsourcing in the cloud. In the proposed scheme, t secrets are divided into n shares, which are distributed to n cloud servers (one share per server) and a trusted user can recover all secrets by combining shares from at least t servers. The analysis shows that the proposed scheme can provide secure and efficient data outsourcing system in the cloud.

A full adder is designed which in turn is used as the building block to design a BCD adder. We have also compared the design of two-dot one-electron QCA BCD adder with the existing four-dot two-electron QCA BCD adder variant. The analysis of the proposed design justifies its effectiveness, in respect of energy utilization, compactness and stability.

Software development has now days evolved into an extreme change that uses the best modules being run in various closed and open source software. The basic idea is to extract the best component so that it can be fitted into the ongoing software development process. For this reason reusable and best components are required. Merely selection of high cohesive components is not sufficient but we have to select the components which are having low coupling as well. In this proposal we have introduced the new coupling metric on a higher level that is at the package level.

The scientific experiments handle huge amount of data from various sources. The processing of data includes various computing stages along with their dependency pattern. The scientific workflow for data-intensiveness is used to model different processes. The scientific workflow paradigm integrates, structures and orchestrates services of heterogeneity and software design tools locally and globally to form scientific processes with complexity for enabling scientific discoveries. The Scientific Workflow Management System (SWfMS) deploys the scientific workflows for data-intensiveness by means of executing parallelism and the resources distributed in different infrastructures like grid and cloud. The community model is a data fusion methodology which is used to fuse data from various sources with multiplicity. The SWfMS for community model is used to describe the flow of data in various parts of the model and their corresponding working principle. This paper presents a data-intensive SWfMS for the community model.

A comparative study of the conventional fixed gain PI and Fuzzy Logic based adaptation mechanisms for estimating the stator resistance in a Model Reference Adaptive System (MRAS) based sensorless induction motor drive is investigated here. The rotor speed is estimated parallely by means of a PI control based adaptive mechanism and the electromagnetic torque is also estimated to add more resilience. By considering the external Load torque perturbation as a model perturbation on the estimated stator resistance, the effects of the same on the estimated parameters are observed. The superiority of the Fuzzy based stator resistance adaptation mechanism is observed through detailed simulation performed offline using Matlab/Simulink blocksets. Furthermore, a sensitivity analysis of the stator resistance estimate with respect to load torque is also done to verify the effectiveness of the above concept.

This paper deals with the development of a propulsion system for a robotic vehicle using a permanent magnet Brushless DC (BLDC) motor with sensorless commutated drive. The proposed vehicle has four BLDC motor driven wheels, each having separate sensorless drive circuitry but all controlled by a single supervisory controller. The vehicle is capable of performing angular and linear displacements, ruled by a distantly located operator. A drive/break by-wire technology is utilized for operation of the wheels. In this work, a real time system with sensorless commutation is designed and implemented that utilizes a three phase inverter, a microcontroller and a motor speed feedback as drive circuitry. A suitable cost effective algorithm has also been developed to generate an appropriate six transistor switching sequence to commute the BLDC motor. The characteristics of the implemented drive give satisfactory outputs over a wide range of controlled speed variation from 330 to 2440 rpm. The effectiveness of the system so designed is demonstrated through the real time experimental data.

Utilization of IT professionals in software firms is an important problem in which vagueness is a very common factor. Most of these problems are multiobjective in nature and contain a large number of binary decision variables. In this work, a mathematical model is developed for allocating different categories of IT professionals, according to the requirement of various consultancy projects, as a binary fuzzy multiobjective programming problem. The considered objectives are effort maximization at all phases for executing the projects, and overall cost minimization of the firm. A binary fuzzy goal programming technique is applied to find the solution of the problem. A case example is presented based on the data collected from a software firm located at the Electronic Complex of Salt Lake, Sector-V, Kolkata, India and the effectiveness of the technique is demonstrated.

In this paper an implementation of an Atmel AVR based Conference Presentation Timer (CPT) is discussed which will serve as an automatic time keeper that keeps the track of time during presentation in a conference or seminar and alarms the speaker accordingly. Usually the duration of conferences vary therefore a provision of adjusting the total time is kept as well as a facility for changing the alarm time is also kept. However this paper elaborates a prototype which has further scopes for improvement.

In this present paper, we have designed and developed a person identification system with biometrics using modified Radial Basis Function Network based multiple classifiers. Three different classifiers using the same Modified RBFN with Optimal Clustering Algorithm, separately identify fingerprint, iris and facial images and the individual conclusions are fused together with programming based boosting. The conclusions from individual classifiers as well as the super-classifier performing fusion of conclusions are fuzzy in nature. Holdout method with Fuzzy Confusion Matrix is used to compute different performance metrics like accuracy, precision, recall and F-score. The different performance metrics are quite satisfactory. Also the learning and performance evaluation time with Fuzzy Confusion Matrix is low and affordable.

In fact the concept of land varies with the time and person concern. With the increasing pressure of population on the earth, the scientists are compelled to think about the land afresh. In this context, the present study involves an scientific investigation in the upper catchment area of Kangsabati watershed situated in the western part of Purulia district, bounded by latitude 23° 05′N to 23° 30′N and longitude 86° E to 86° 20′E. Tributaries of Kangsabati River are the main drainage in the area. In spite of moderate average annual rainfall (1446.4 mm) groundwater recharge is inadequate due to lack of permeability in the country rock. Present research involves planning pertaining to land use in a chronically drought-hit and degraded land. The main objective of this investigation is to identify suitable potential zones within the fallow or waste land for pasture development which will ultimately support a livestock rearing livelihood to the local rural and tribal poor. The different thematic layers of the area involving slope, drainage, lineament, surface water bodies, hydro-geomorphology and land use or land cover have been generated using SOI topographical sheets (73 I/3, 73 I/4 and 73 I/7), IRS-IB and IRS-P6 LISSIVMX satellite data aided by field verification for ground truth. The layers are analyzed in GIS environment and it reveals the potential pasture zones which are suitable for grazing by local livestock community. Generated digital maps in GIS environment have revealed many suitable areas for grazing as well as live stock watering.

A compression technique for image is proposed that is SPIHT algorithm based. Encoding of wavelet transformed quantized image is done using variants of Huffman coding. Comparisons are made among the binary un-coded SPIHT, SPIHT with Huffman coding, the proposed technique and its modified variant which show that the proposed techniques offer better PSNRs maintaining the same compression rates. But, the encoding time of the proposed techniques are slightly high.

With the immense acceptance and adoption of personal identification using biometrics, exigency of a reliable, faster and less expensive authentication process has arrived. In this novel work, we have attempted to exhibit some crucial features of human retina which are sufficient to build a secured biometric template in considerable amount of time, ignoring the other hazardous factors which may lead to even authentication failure. Experimental results prove that this proposed work would be able to identify the distinct bifurcation points in retinal fundus image, avoiding the crossovers consciously as they are not anatomically stable and could change their locations as an effect of some diseases. This work will reduce the complexity of any authentication algorithm drastically by concentrating only on existing bifurcations, ignoring tedious calculations on every junction points.

The unexpected changes in the width of retinal blood vessels can be recognized as one of the significant characters during the proliferation of several diseases. Clinical studies verify that width of retinal blood vessels may undergo changes during diabetes, hypertension, Retinopathy of prematurity and Proliferative Diabetic Retinopathy. Optical vessel swelling or Papilledema and optical neuritis are prominent ocular diseases that contribute to changes in the width. In this approach, width of retinal blood vessels has been determined to benefit the identification of such diseases. Every retinal fundus images exhibits several unique bifurcation points. The bifurcation points ensure stability and are not subject to any change even when prone to diseases. Identifying these points, our algorithm attempts to find the ratio of the width of a parent vessel with its corresponding child vessels at the bifurcation points. Experimental results prove that this ratio lies in the range of 1.1–1.8. Abrupt changes in the ratio ensures the probability of existence of ocular diseases.

Handwritten Digit Recognition (HDR) has become one of the challenging areas of research in the field of document image processing during the last few decades. It has wide variety of applications including reading the amounts in cheque, mail sorting, reading aid for the blind and so on. In this paper, an attempt is made to recognize handwritten digits written in four different scripts namely, Bangla, Devanagari, Arabic and Telugu using Mojette transform. The Principal Component Analysis (PCA) is then applied for dimensionality reduction of the feature vector and also shortening the training time. Finally, a 48-element feature vector is tested on CMATERdb3 handwritten digit databases using multiple classifiers and an average overall accuracy of 98.17 % is achieved using Multi Layer Perceptron (MLP) classifier.

In this paper we present a survey of current research in Music Information Retrieval in North Indian Classical Music and describe all the characteristics of ragas used for classification. We then describe Bhatkhande’s classification scheme and show how it can simplify the classification process of 100 ragas to 10 categories. We also discuss the issues that need to be addressed and the similarities and differences between Hindustani classical music and Western Classical music. Current research efforts on Raga identification are also described.

This paper presents a neuro-fuzzy classification technique using electronic nose, electronic tongue and the fused response of electronic nose and electronic tongue for the evaluation of black tea quality. In the tea industries an automated, neutral and low cost instrumental system to determine the overall tea quality is in great requirement. A general fuzzy rule based and neural network model can produces accurate predictions. But both models have some weakness. In this pursuit, Pseudo outer-product based fuzzy neural, a kind of fuzzy neural network classifying system has been attempted to classify tea grades. Results show that above model can classify in a better way compared to other models.

In the present work, a new feature vector has been designed towards recognition of handwritten online Bangla basic characters. At first, Center of Gravity (CG) of a particular character sample is determined. After that a circle enclosing the character sample is drawn whose radius is estimated as the distance of farthest data pixel from that CG. From this circular region, a 136-element feature vector is generated considering both the global as well as local information of the character sample. The feature set has been tested with several well-known classifiers on 10,000 isolated Bangla basic characters. Finally, Support Vector Machine (SVM) has produced 98.26 % recognition accuracy.

Accuracy in handwritten character recognition system is a challenge in the area of pattern recognition because of a variety of writing styles. Eigenface is a method that has been widely used in face recognition systems. This method is proposed in the field of handwritten character recognition, in this paper. Here, Eigencharacters are created from a 2-D training set of images and weight vectors are generated. These weight vectors are used as feature vectors for classification. The classification is performed using Euclidean Distance, k-NN and SVM classifiers. Experimental results proved that the proposed Eigencharacter method using Euclidean distance produced good classification accuracy.

Development of an automated system for handwritten document analysis is being considered as an important research topic since last few decades. Digitized documents, either handwritten or printed, contain a mixture of text and non-text elements which need to be separated for designing a document layout analyzer or even an Optical Character Recognizer. In this paper, a technique is described to separate the text objects from the non-text objects present in a handwritten document image. For this purpose, a Rotation Invariant Local Binary Pattern (RILBP) based texture feature is used to represent the said components, at the feature space. Finally, the classification is carried out using an Artificial Neural Network based classifier called, Multi-layer Perceptron (MLP). The system provides an impressive result on a database comprising of 100 handwritten document images.

Extraction of words directly from handwritten document images is still a challenging problem in the development of a complete Optical Character Recognition (OCR) system. In this paper, a robust word extraction scheme is reported. Firstly, applying Harris corner point detection algorithm, key points are generated from the document images which are then clustered using well-known DBSCAN technique. Finally, the boundary of the text words present in the document images are estimated based on the convex hull drawn for each of the clustered key points. The proposed technique is tested on randomly selected 50 images from CMATERdb1database and the success rate is found to be 90.48 % which is equivalent to the state-of-the-art.

This paper presents a computer vision framework for detecting dominant boundary-points on an object’s contour through polygonal approximation of the shape without loss of its significant visual-interpretation. The proposed framework attempts to approximate a polygonal representation of the contour with each polygonal-side having a meaningful thickness to handle noisy curvatures with irregular bumps. The vertices of the polygon are extracted through a novel recursive strategy. The merit of such a scheme depends on how closely it can represent the shape with minimal number of vertices as dominant points without losing its inherent visual characteristics. As per our observation, the proposed framework seems to perform reasonably well in approximating the shape of an object with a small number of dominant points on the contour.

Synthetic Aperture Radar (SAR) is a coherent imagery tool used for extracting information in astronomy and meteorology. But these images are generally corrupted with a granular noise called speckle, making it difficult for extracting information. In this paper, the Anisotropic Diffusion (AD) filter is modified by incorporating the use of the Kuan filter for speckle removal. In the proposed work, the image is segmented into two regions based on the value of conduction function; the homogeneous region is processed using the Kuan filter and the other non-homogeneous region is processed with anisotropic diffusion. This modified AD filter provides better detection of weak edges and effective reconstruction of structural content with enhanced image restoration features. Further, based on the simulation results and via image quality metrics analysis; the proposed work is claimed better.

A dual image based reversible data hiding (RDH) method using pixel value difference (PVD) and exploiting modification direction (EMD) are introduced in this paper. First enlarge the cover image size and then select a pair of pixel for data embedding. We embed 4 bits secret message through pixel value difference and 3 bits secret message using embedding function of exploiting modification direction within a pixel pair. We obtain two modified pairs of pixel which contain 7 bits secret message. We then distribute these pixel pairs among dual stego images depending on a shared secret key (K). The recipient successfully obtain secret message and retrieve original image using same shared secret key. We compared our proposed method with other existing techniques and find out averagely good results with respective to payload.

Cheng et al. proposed an asymmetric image encryption scheme which is based on adaptively synchronised chaotic cellular neural network and unified chaotic systems in [Communication in Nonlinear Science and Numerical Simulation 18(10) 2825–2837 2013]. The cryptosystem was asymmetric in nature and the synchronization error converged quickly to zero. Numerical simulations for performance evaluation included synchronization effectiveness, cryptosystem robustness and statistical analyses like key space, key sensitivity and NPCR/UACI analyses, all with effective results. But, this paper demonstrates the cryptanalysis of Cheng et al. cryptosystem by exploiting inherent deficiencies of encryption algorithm like low robustness and poor plain-image sensitivity. It is done by mounting the proposed cryptographic CPA or KPA attack which leads to successful retrieval of original plaintext image. The simulated cryptanalysis shows that Cheng et al. cryptosystem is not suitable for practical utility in image security applications.

Image encryption is a process of conversion of original image into an unintelligible form, which is not understood by anyone except authorized parties after performing decryption operation on it with the help of a secret key. Image encryption is a challenging task due to its large amount of data and correlation among pixels restricts using traditional encryption algorithm. So, to obtain an efficient and robustness against security violation during image transmission chaotic based image encryption techniques are proven to be more suitable. These techniques are considered more effective due to low computational power, high sensitivity to initial conditions. In this paper, we discuss about chaos and their properties and we give a general overview of chaotic map based image encryption scheme and various phases in the process.

In our paper, we have proposed a new mechanism of E-voting using two layers of security using steganographic technique. The basic idea conveyed in our paper is simple, but novel. We have dealt with the Personal Identification Number (PIN) and the fingerprint for establishing uniqueness among the individual voters, in order to make a vote count. The techniques used here are the Least Significant Bit (LSB) embedding and the Minimal Impact Decimal Digit Embedding (MIDDE). If the steps are followed backwards, we retrieve the PIN and the fingerprint, which is impossible without prior knowledge of the embedding used. It has also been seen that our algorithm is foolproof against statistical attacks and malicious attempts of recovery.

An Intrusion Detection System (IDS) monitors the system events and examines the log files in order to detect the security problem. In this paper, we analyze the classification algorithms, especially Entropy based classification, Naïve classifier, and J48 using KDD-CUP’99 dataset to detect the different types of attacks. The KDD-Cup’99 dataset is a standard dataset for analysing these type of classification techniques. In KDD-CUP’99 dataset, each instance corresponds to either attack or normal connection. The KDD-Cup’99 dataset contains mainly four types of attack, namely, DOS, U2R, R2L, Probe and these four types of attacks also have subcategories attacks. In this paper, we carry out simulations on the KDD-Cup’99 dataset for all four types of attacks and their subcategories. The back, land, Neptune, pod, smurf, teardrop belong to DoS; the rootkit, Perl, loadmodule, buffer-overflow belong to U2R; the FTP-write, spy, phf, guess-passwd, imap, warezclient, warezmaster, multihop belong to R2L, and the Ipsweep, nmap, portsweep, satan belong to the probe. The simulation results show that the entropy based classification algorithm gives high detection rate and accuracy for normal instances over the J48 and Naïve Bayes classifiers.

Lossless image cryptography is always preferred over lossy image cryptography. In this approach the authors have proposed a very resilient and novel image encryption/decryption algorithm. Initially the image is first converted to frequency components and the encryption is performed on sub-bands and the encrypting algorithm is found to be very strong, reliable and strong. The encryption algorithm involves pixel breakup into two parts and reversing parts of the pixel. The results show a deviation of pixel between the images present in the original and encrypted domains. The decryption algorithm is exactly the encryption algorithm in reverse. The proposed algorithm is evaluated by standard measures and it is seen to be attack-resistant to well-known attacks.

This paper deals with a new approach to implement QR codes in door locks security. In this work an advanced security system is presented using Unique QR Identification (or UQID) code, which is specially designed to be used in door locks. The UQID system presented here is a new methodology implemented to provide security services to hotel rooms along with better hospitality to guests. A guest books a room online on the hotel website and immediately after booking a room a QR code is emailed to the guest. The guest can save this QR code in his phone/smart watch/tablet or any other device with a display. When visits the hotel he simply holds this QR code against the door computer which authenticates whether the right QR code has been presented by the guest and unlocks the door or keeps the door locked accordingly. The QR code sent to the guest at the time of registering for the hotel room is the QR code which is indeed the key to access the room for the guest.

Image Steganography is an immemorial technique of data hiding behind an Image known as vessel image, camouflaging the covert image from the outside world. In this paper a novel algorithm of image steganography has been proposed where two techniques are used—(i) Bit Plane Complexity Segmentation (BPCS) analysis and (ii) QR Decomposition of linear algebra to choose the region where the full secret message is embedded without exposing its existence.

In this paper, a scheme aimed at analyzing the confusion characteristics of block ciphers has been proposed. The scheme analyzes the S-Boxes which are the source of confusion in block cipher. The test results obtained from the application of the proposed scheme on DES S-Boxes as well as on sole AES S-Box has been listed in the paper. The proposed scheme subsequently could very well be a part of a good test suit aimed at comprehensively analyzing the cryptographic strength of block ciphers.

IEEE 802.15.4 compatible instruments are resource-limited; thereby cannot run conventional resource-hungry routing algorithms. Even though, ZigBee supplied a simple routing algorithm for tree networks, it cannot handle long linear networks typically set up in mines, roadside, agricultural field, mountains etc. his paper proposes a light-weight routing logic based on source routing that supports arbitrary long networks. The proposed algorithm is especially suitable for networks that look almost linear. The algorithm leverages the properties that a pseudo-linear network has limited branching. Experimental results show that this flexible mechanism exhibits excellent packet delivery performance.

Mobile wireless sensor networks (MWSN) are most popular for new generation of sensor networks. Now a days’ many applications such as health monitoring, environment monitoring or surveillance, the performance of MWSNs are much more versatile than WSNs. Some protocols from MANET such as AODV, DSR and GPSR are able to work on this environment. This manuscript has focused on two major issues. Firstly, formulating an energy-aware selective stable single-path routing protocol for MWSNs that is pertinent to the present challenges of the researchers. Secondly, presenting a germane architecture that can accumulate the data and synchronize with cloud database at periodic interval in order to predict about ominous situation. Coda of the manuscript presents the simulation result of our proposed algorithm and behavioural analysis of successful packet delivery from source to sink node with respect to node density and node mobility condition. Our proposed single-path selection methodology ameliorates the overall network lifetime in case of high network traffic.

In energy harvesting wireless sensor networks (EH-WSNs) sensor nodes are capable of harvesting energy from environmental sources. Usually, knowledge about the link quality improves the performance of WSNs. During data routing, selection of good quality links are important to maintain stable communication. Thus helps to reduce the unnecessary energy wastage. Obtaining the link state information is more challenging for EH-WSN as different nodes has different energy profiles and state of the node depends on several environmental conditions. In this paper, we have studied different factors affecting the link quality and model it using finite state Markov Model. Energy availability of the harvesting devices through real time traces is considered for modeling the network. This model can significantly provide relevant information which is very effective to improve the routing decisions as the next hop decision will be more accurate. The usefulness and validity of the proposed approach is illustrated through simulations for specific examples.

In the past few years network layer activities in wireless sensor networks gain enormous attention to improve network lifetime. Development of routing algorithms with energy efficacy is one of the most popular techniques to improve it. In this article, energy efficient and balanced route generation algorithm is proposed with considering both energy efficacy and energy balancing issues. Here, we consider the distance and residual energy of the nodes as energy efficiency parameters and energy is balanced by diverting the incoming traffic to other nodes having comparably lower incoming traffic. To develop the routing schedule, we have applied Genetic Algorithm which can quickly compute the routing schedule as per the current state of the network. It is observed that the performance of proposed algorithm is better than existing algorithm in terms of first node die and energy consumption in the network.

An energy efficient model in cellular networks is proposed in this paper to reduce the number of long range channels as well as the energy consumption of the mobile devices. The devices having high energy in terms of battery level are used to establish communication between the base station and the requesting mobile devices. The overall energy consumed by all devices is estimated if one such high energy device receives data from the base station and subsequently propagates the same to other mobile devices. The energy consumption is assessed for increasing number of bridging devices. The proposed approach reduces the number of long range channels used and the overall energy consumption of devices. The experimental results show the effectiveness of the proposed approach.

This paper illustrates how to solve some risky tasks in military and rescue operations. The task like the continuous firing of bullets, provide weapons to soldiers in the war field, rescue operations in flood, earthquakes etc., a robot needs proper guideline to perform the task. In this work these tasks are solved by Radio Frequency (RF) based communication, because command is transmitted by a letter. Hence encryption of the data is not required. And also it presents the efficient way of utilizing the power and replaces the soldiers by many swam like robots. The communication on this system takes place by using RF signals. The robot path is controlled by remote controlled RF signal or using a mobile phone if the mobile carrier service is available. The real time result parameter are comparing with the vision based robot path planning system.