Emerging Research in Computing, Information, Communication and Applications

Social media has become very popular communication tool among internet users in the recent years. A large unstructured data is available for analysis on the social web. The data available on these sites have redundancies as users are free to enter the data according to their knowledge and interest. This data needs to be normalized before doing any analysis due to the presence of various redundancies in it. In this paper, LinkedIn data is extracted by using LinkedIn API and normalized by removing redundancies. Further, data is also normalized according to locations of LinkedIn connections using geo coordinates provided by Microsoft Bing. Then, clustering of this normalized data set is done according to job title, company names and geographic locations using Greedy, Hierarchical and K-Means clustering algorithms and clusters are visualized to have a better insight into them.

The fast growth, along with the all encompassing presence, of the World Wide Web has given an unprecedented opportunity for organizations to maintain a strong online presence through a website catering to the requirements of varied users in an effective and efficient manner. In order to arrive at an optimal web site, relevant criteria need to be considered for selecting a set of web objects, from amongst a large number of web objects, which should be displayed on a web site. This being a combinatorial optimization problem would require simultaneous optimization of multiple relevant objectives based on relevant and key criteria for a given web site. In this paper, the multi-criteria web site optimization (

MCWSO

) problem, comprising of three criteria namely, download time, visualization score and product association level of web objects, has been addressed as a tri-objective optimization problem and solved using the vector evaluated genetic algorithm (

VEGA

). Experimental results show that the

VEGA

based

MCWSO

algorithm, in comparison to the

GA

based

MCWSO

algorithm, is able to select comparatively better web object sequences for a web site.

In today’s global world every organization or enterprise, storing their essential data in terms of Databases and shares their data to authenticated users on the web through some security mechanisms. But now-a-days security is a big issue because of various types of database attacks. SQL injection is one type of such an attacks that inoculate a destructive SQL code to misuse database of an application. In this paper, we did experimental analysis on web-database applications as well as purely database applications and presented prevention guidelines of SQL injection attacks.

With the ever growing amounts of data there is good motive to believe that smart data analysis will become more pervasive and an obligatory component in the progress of industry. Social Media act as a source between the company and the customers. People being at their work place can get to know the current status, discussions done by people on any trends and even he/she can express their opinions and views as per their interests through blogs, forums, emails etc. Twitter being a popular micro blog allows people to chat about their words with public in form of short texts (140 characters). Researchers of NLP and DM are attracted towards the sentiment analysis from past few years due to its many tricky research problems and purpose. In this paper, a novel Machine Learning Approach is used to classify the twitter dataset. The working of the Algorithm is explained with Sample set taken from twitter. If we have very little information i.e. the training sample, Algorithm defined may not give accurate estimation/probability of any object belonging to particular class as we have no other information to obtain a better estimation. This estimate will be reasonable if a training sample is very large and properly chosen. Thus in this paper Classification helps us to find the belongingness of an instance to a class to find the collective opinions of the users of twitter.

In this paper, the design of five port structures using power dividers, hybrid coupler and phase shifter was done using Advance Design Systems. The design termination considered as 50 ohm and the microstip based methodology is followed. The performance analysis of the design is carried out with s-parameter simulation and the corresponding return loss and mismatch loss are obtained. The results obtained are in greater coherence with the theoretical observations.

Orthogonal variable spreading factor (OVSF) codes are with various spreading factors that are used to allocate the variable data rates to users with different bandwidth requirements in 3G wideband code division multiple access (W-CDMA) networks. This paper investigates genetic algorithm based approach for optimization of code blocking and dynamic assignment of OVSF code. The ability of the genetic algorithm used in MSSGA structure build over the improvised crossover, mutation operators and the selection operator includes an age factor criteria that overrides the dominance and diploidy structure for early convergence of the result. The simulation results show that the MSSGA has improved spectral efficiency and reduced code blocking probability to result in high efficiency of code usage and to increase the capacity of the system.

Augmented Reality (AR) technology is considered to be an important emerging technology used in education today. One potentially key use of AR in education is to teach socio-scientific issues (SSI), topics that inure students towards social conscience and critical thinking. This work uses multiple markers and virtual buttons that interact with each other, creating a life-like visual spectacle. Learning about issues such as global warming by using AR technology, students will have an increased sense of experiencing immersion, immediacy, and presence, thereby enhancing their learning as well as likely improving their ability to make better informed decisions about considerations of such issues. Another advantage of AR is that it is a low cost technology, making it advantageous for educators to adapt to their classrooms. Also in this work we compare the effectiveness of AR versus ordinary video by polling a group of students to assess the content understandability, effectiveness and interaction of both the delivery methods.

Integration of Micro Electro Mechanical System (MEMS) with integrated optics is known as Micro-Opto-Electro-Mechanical-System (MEOMS). There is a wide development happening in the Lab-On-a-Chip fabrication industry for bio-medical application. In this paper we demonstrate simulation and modeling microfluidic channel embedded in a dielectric planar waveguide. As a fluid constituent the core of the waveguide is made up of glass and cladding consists of air. The model designed can be used as Refractive Index based sensor. In this simulation velocity is kept constant throughout the microfluidic channel. The pressure at the inlet is higher compared to the outlet.

Technology in the classroom is changing the way educators teach. When a serious game is combined with a pedagogical learning strategy the probability of learning and synthesizing new concepts can become much greater. In this work we take the concept of learning about one’s carbon footprint and combine it with the step by step process of Bloom’s Taxonomy, a proven and well-established learning method, and incorporate it into a serious game. The objective is to demonstrate that learning in this way will result in higher learning motivation and a better understanding of the subject matter. In the game the player needs to reduce their carbon footprint in an office building. Unlike in a traditional classroom where the student is only a receiver of knowledge, in this serious game the student has a more direct experience of the subject, eliciting good decision making and improving subject comprehension.

Security is an essential factor in wireless ad hoc network to have safety in transmitting data packets between two wireless sensor nodes. The nodes posses a unique characteristics and it leads to consequential challenges to security design. Comparing to other wireless networks WSN has more security problem; this may be due to its nature of broadcasting messages, resources and their environment. One of the traditional and main attacks of WSNs is Black Hole Attack.

In the business world, the term “startup” is frequently ringing the bell with the high frequency of young ventures. The main dilemma of startups is the unsuccessful management of the unique risks that have to be confronted in the present world of competition and technology. This research work tried to bring out a game based methodology to enhance enough real-world experience among entrepreneurs as well as management students to handle risks and challenges in the field. The game will provide experience to the player to overcome challenges like market problems, running out of cash, poor management, and product problems which can be resolved by a proper strategic approach in the entrepreneurship world. The proposed serious game works on the life cycle of a new software enterprise where the entrepreneur moves from the planning stage to secured financial stage, laying down the basic business structure, and initiates the operations ensuring the increment in confidence level of the player.

Machine learning techniques are useful for solving different problems in many applications. Different machine learning techniques are available for assessment and forecasting of data. For illustration, this paper is focused alight on four machine learning techniques that are Weka, Tanagra, R software and MATLAB for showing different views to analyze and forecast the data. Weka is the most effective machine learning technique for regression and classification problems. Tanagra, the data mining tool, which is a supervised learning technique and also suitable for statistical analysis, classification and clustering problems. R software is a flexible programming accent for statistical computing and graphical settings. Finally, MATLAB is exclusive for technical computing representing the data in 2D, 3D and it is very effective tool for predictive analysis.

In this paper, the correlation among the various performance parameters of Inter satellite optical wireless communication (ISOWC) systems has been scrutinized for achieving a highly proficient system for satellite communication covering a distance of 10,000 km. This work further investigates the performance of IS-OWC link using a combination of loop control and Erbium doped fibre amplifiers. Besides this, an improved system for high data rate and long distance transmission using the Optisystem Software has also been modelled and analyzed.

Voice Over IP applications are becoming popular due to proliferation of smart phones. Many people are using Internet on these devices. To support millions of VOIP users and increase QoS (Quality of Service) VOIP installations employs large number of SIP Proxy servers to route calls. To achieve high throughput and minimum response times usually a load balancer is used to dispatch calls to SIP proxy servers. Overload control in SIP is a well known problem in SIP proxy servers. Local overload control, hop-by-hop and end-to-end overload control methods are used in SIP networks. Stand-alone overload control method monitors the processor and memory resources of the proxy server where overload control is implemented. It does not consider call-rates to avoid the overload in SIP server. In this paper we developed a novel rate-based overload control method to detect and control overload in SIP servers. We have implemented the algorithm by modifying leading open source Kamailio SIP proxy server. We have evaluated our algorithm by experimental test setup and found results are outperforming the standard standalone overload control algorithms in terms of throughput and response time.

In this paper, an algorithm is proposed for mining frequent maximal itemsets. Discovering frequent itemsets is the key process in association rule mining. One of the major drawbacks of traditional algorithms is that they work only for items with single frequency per transaction. Proposed algorithm works with multiple frequency of an item per transaction. Proposed algorithm scans base database only once. The proposed algorithm took lesser time to find candidate itemsets. It uses directed graph for candidate itemsets generation. We conducted experiments on two datasets, Mushroom and Chess. Experimental results showed that proposed algorithm can quickly discover maximal frequent itemsets and effectively mine potential association rules.

Web Based Applications are data intensive. In addition to web content and structure, they collect huge amount of data in the form of User interactions with the web, forming Web Log Repository. Application of data mining techniques over the Web Log Repository to extract useful knowledge is referred to as Web Usage Mining. Web Usage Mining consists of three phases—Web Log Preprocessing, Knowledge Discovery and Pattern Analysis. In this paper, an efficient implementation for Web Log Pre-processing using Dimensionality Reduction Techniques and Combined Methodologies is presented.

Evolutionary computing algorithms play a great role in solving real time optimization problems. One of the evolutionary computing algorithm is Particle Swarm Optimization algorithm (

PSO

). The aim of this paper is to propose a model to improve the performance of

PSO

algorithm. Hybrid models of Particle Swarm Optimization (

PSO

) algorithm and Differential Evolution (

DE

) has already proved to be one of the better approaches for solving real world complex, dynamic and multimodal optimization problems. But these models hybridize

PSO

and

DE

to form a new serial algorithm. In these serial hybridization models, we are losing the originality of both

DE

and

PSO

algorithms since the structure of both the algorithms is being modified to get the hybridized

PSO

and

DE

algorithm. In this paper, we develop a model for

PSO

in distributed environment with improved performance in terms of speed and accuracy. The proposed model is a hybridized distributed mixing of

DE

and

PSO

(

dm

-

DEPSO

) which improves the performance of

PSO

algorithm. In this model, algorithms are implemented in a cluster environment to perform co-operative co-evolution. Better solutions are migrated from one node to another in the cluster environment. Co-operative co-evolving model shows better performance in terms of speed and accuracy. The algorithm is applied to a set of eight benchmarking functions and their performance are compared by mean of objective function values, standard deviation of objective function values, success rate, probability of convergence and quality measure.

We propose the significance in use of multiple databases for the purpose of authentication using the palm-print of the hand for increased speed. The technique is used for authentication using the acquired image of the hand. The hand could be held in any pose hence providing pose independence. We use Appearance analysis for feature extraction. This technique also suggests hand independence for biometric authentication. It also provides a robust technique for detecting the extremities using a neighborhood scan method.

The applications of using serious games as a teaching tool are vast. One of the fields of knowledge that is now being implemented in serious games is Disaster Relief Management. Serious games use a variety of multimedia and strategies that allow the learner to participate in a graphic simulation of a disaster. This work gamifies the topic of flood risk management. Floods are a unique type of disaster in that they have an element of predictability. This predictability can be utilized as a gaming element involving time limits to reduce the amount of destruction and loss of life due to flooding. Of the many benefits of using games for educational purposes, it can help people who live in rural areas who may have limited education comprehend this complex material in a meaningful way. A learning approach called Kolb’s Learning Model is used to convey the material. It familiarizes people with the different responses and terminologies of the hazard while achieving high concentration and interest from the learner. After developing this game, the effectiveness of this gaming method is analyzed by a comparative study of text book learning and this serious game.

Mobile ad hoc network (MANET) is a collection of mobile node which acts as both host and router at the same time during communication. Each node in MANET usually runs on battery and capable of communicating to other without fixed base station. Since node has limited transmission range, limited bandwidth and limited battery, obtaining optimal path with less overhead is a challenging task. In this paper, we proposed Signal Strength based Petal Ant on-demand routing (SS_PAR) algorithm which is based on swarm intelligence technique and the signal strength of mobile node. The proposed SS_PAR reduces overhead by analyzing signal strength inside the petal and optimize the node that participated during route discovery. We compared our routing algorithm with SARA and simulation results shows that SS_PAR performance is better than SARA.

This paper proposes a novel framework to combine two emerging technologies—wireless sensor networks and cloud computing. One of the major limitations of any wireless distributed system is energy conservation. The proposed framework tackles the energy problem in wireless sensor networks by combining it with cloud. The proposed framework aims to shift the processing and computational overhead of the wireless sensor network towards the cloud, by utilizing the cloud for routing and providing a platform to carry out complex processes such as clustering. The worst case of the proposed framework is compared with the best case of existing scenario. The results prove the furtherance of the proposed framework over the existing frameworks by reducing the energy consumption to 63.7 % and in-directly enhancing the network lifetime.

Integrate-And-Fire model is one of the latest neuron model. A biologically plausible Tonic Non-linear Integrate-And-Fire neuron model (TIFN) is proposed in this paper. The complete solution of the proposed model is derived and used for the construction of aggregation function in Multi-layer perceptron model, which is named as Tonic Single Neuron model (TSN). This modified neuron model is used for verification of online signature data. The comparison study of the Tonic Neuron model and the well-known Single Integrate-And-Fire neuron model (SIFN) are presented. It is observed that the proposed model perform well in terms of classification accuracy. Moreover, It is found that inclusion of biological phenomenon in an artificial neural network makes efficient for biometric authentication.

Spam in emails has become a major issue. Spam messages consume space, network bandwidth and are of no use to the receiver. It is very difficult to filter spam as spammers try to tackle the processes carried out by the filtering mechanism. Various classification algorithms are used to classify a mail as spam or non-spam (ham). The present paper compares and discusses the effectiveness of four machine learning classification algorithms, belonging to different categories (Probabilistic, Decision Tree, Vector Machines and Lazy Algorithms) on the basis of various performance measures, using WEKA, a data mining tool to analyze different algorithms. Enron dataset is taken in a processed form from Athens University of Economics and Business and it is found that J48 and BayesNet algorithms perform better than SVM.

CSCR is a context sensitive ranking approach which combines both linguistic and statistical approach to exploit the contextual information around a set of concepts to rank them. It represents each concept as a very high dimensional vector of context terms gathered from its surrounding. This paper discusses a probabilistic approach to assign weight to those terms in the vector and there by rank a set concept. The objective of this work is to bring the semantically related concepts closer so that the result can be used for application where there is a need to consider semantic closeness between concepts e.g. incremental ontology learning. The effectiveness of CSCR is evaluated with a set of 300 concepts extracted using frequency based approach by C Finder algorithm.

Mobile Cloud Computing (MCC) extends cloud computing with the advantages of mobility and wireless networks to create a new infrastructure where cloud takes over mobile devices’ responsibilities of executing tasks and storing enormous amounts of data. Through offloading, all the major data processing work takes place in the cloud instead of the mobile devices. The main aim of MCC is to achieve a rich user experience by enabling wide range of mobile devices to execute rich mobile applications. Scheduling of tasks require minimum completion time, better performance, effective utilization of resources and quick response time for which cloud uses virtualization concept. For task allocation, cloud provides virtual machines which are scalable but scheduling them while efficiently utilizing the idle service capacities of the mobile devices are still remains major problem. Likewise, there are other issues faced in MCC such as insufficient resource, low connectivity and limited energy due to which utilizing its full capability is a challenge. The existing application scheduling algorithms in MCC do not take each task’s profit or the overall energy consumption of mobile devices into consideration. Also it cannot increase the profit of the system, which is an import target for scheduling the tasks in commercial mobile cloud environment. In this paper, E-MACS (Energy-aware Mobile Application Consolidation and Scheduling) algorithm is proposed to make the mobile devices contribute their computing and sensing capabilities to attain efficient scheduling of application in hybrid cloud model. The consolidation of application minimizes the overall energy consumption in cloudlet. The proposed system minimizes the response latency, cost of application migration and it improves quality of service like throughout and scalability among resources using load balancing techniques by mobile cloud computing.

Few common devices currently used in classrooms for collaboration include computers, projectors, interactive whiteboards and multi-touch devices. Such devices are often expensive and not easily acquirable to all. We propose a low-cost multi-touch collaborative surface that can be created using a simple and inexpensive gaming device, Nintendo’s Wiimote. Wiimote and hand-made IR-LED pen was combined to setup the multi-touch surface. IR sensor in Wiimote was programmed to accurately track the IR signal from the IR-LED pen. Our system can be used to create a multi-touch surface anytime-anywhere and thus overcoming the mobility constraints that exist with current systems. Proposed system was found to be effective for collaborative classroom activities through the closed-group experiments conducted at multiple workshops on certain digital-media based classroom activities. Through the proposed system we have created a pathway to bring in gaming devices to classroom to make learning fun and more engaging with less complex setup and at an optimized cost.

Data retrieval plays a critical role in the progress of the technology in the present day technological scenario with huge databases. Content Based Image Retrieval (CBIR) is one of the popular image retrieval techniques which find its application in varied fields including medical image analysis, historical research, military applications etc. Teleconferencing is gaining widespread acceptance in the field of medical analysis. One of the methods for testing through the above method is to use images of the fundus of the eye. Although there are CBIR algorithms that are accurate, their implementation on PC based systems suffer from long execution time. The paper proposes to accelerate the algorithm through its implementation in a mixed hardware/software platform. The first towards this process is implementation through a Verilog HDL code which can be used on a VLSI system. The extraction of features from these images may indicate the presence of infirmities namely exudates that are determined and its possible implementation through Verilog HDL is addressed in this paper. An improvement in terms of effective implementation was observed using the standard DRIVE database. The algorithm has been implemented and optimized on Xilinx ISE Design Suite version 14.2 and simulated on Modelsim simulator version 10.1d.

Wireless Sensor Network (WSN) has encapsulated the major attention from the research group owing to its potential features as well as some of its unsolved issues. From the various issues, an energy issue is particularly found to be a root cause of majority of the problems. In the past, there are various research attempts to enhance the network lifetime of WSN, but very few of the studies has proved fruitful. Moreover performing optimization of energy from routing as well as load-balancing perspectives are not witnessed any standard research attempts. Hence, this paper discusses about a framework that uses standard radio and energy model and performs energy optimization by introducing a novel routing agent. The routing agent is incorporated within aggregator node and base station to perform dynamic reconfiguration in case of energy depletion. Compared with standard LEACH algorithm, the proposed technique has better energy efficiency within optimal data aggregation duration.

Load balancing is the process of distributing tasks among different nodes in a network. The nodes may be either on different machines or nodes (virtual machines) on the same machine. Based on the availability of the nodes, processes from fully loaded node can be migrated from one node to another having less load known as process migration. Process migration can also be done in the virtualization environment by using a hypervisor called Xen hypervisor. This hypervisor safely multiplexes the hardware resources of the physical machine leading the resource allocation in the Virtual Machine (VM) to improve the utilization and performance. To optimize the task of balancing load among nodes, Genetic algorithm (GA) may be used for selecting the destination. GA is a search algorithm, based on natural genetics and principle of evolution and is been widely used in optimization with binary and continuous variables. With the adaptive crossover operation of GA and the searching heuristic and fitness function, lots of possible solutions are searched and the best one is selected as the destination for migration. GA is been proved to be effective in discovering the global optimum even in a very complex searching space.

Coordination and communication of multiple robots in an unknown environment is a great challenge to deal with. In this paper an approach for this particular problem is proposed, in which efficient hybrid architecture is created combining two different approaches for communication and coordination each. Multiple robots are treated as ad hoc mobile nodes and the phenomenon of swarm intelligence is used for them to coordinate, while they use Cluster Head Gateway Switch Routing (CGSR) protocol for communication. Various experiments such as goal reaching and collision avoidance are performed using this architecture. Using this particular framework an application of “detection of land mines in an uneven terrain” is worked upon. The swarm robots communicate and coordinate with each other and the environment to detect the land mines using the proposed architecture, and store its position in a table. This application thus has its focus on saving human lives while exploring the environment consisting of land mines, which has already been explored by robots and thus it becomes safe for them. The hybrid of two algorithms results in better performance of robots in an uneven terrain.

It is lucid that smart phones have become a must needed device in today’s world. With such large pool of people using smart phones ignorantly increases the danger of security extensively. Survey in the paper proves that most of the cyber attacks are possible due to ignorance and negligence of the user. Mistakes made by users like granting permissions to apps without understanding them and without knowing the source, avoiding security features available in the device and storing critical information without encrypting it are major cause of security breach. The paper also provides proactive and reactive solutions to Information security issues.

Foundation of any software system is its architecture. However, intended architecture may lose capability when the implementation does not conform to the designed architecture. In this paper, a set of methods and tools is presented to perform architecture validation for maintaining consistency between defined architecture and implementation. This paper compares three proposed tools on various parameters by assessing its capabilities to detect architectural violations (i.e., deviations between the intended architecture and the implemented architecture).

In the Indian Premier League (IPL), team owners build their cricket team by buying players in the IPL auction. Before the auction begins, the teams have the liberty to retain some of its previously auctioned players in the past IPL season. The rest of the players are available for selection via auction. Initially, all the owners of the teams have the same limited amount of funds to build their team. Naturally, the more players an owner retains, the lesser funds the owner would have to enter into the auction. Therefore, the decision of retaining players has to be perfect for an optimal selection of retaining players as well as selection of players in the auction. We analyze the requirement of the structure of the team, based on voids created due to the remaining players after the selective retaining process. For an optimal decision making in the auction, we define the size and type of voids clearly, which helps the owner select the best combination of players in the auction. Our proposed method attempts to ensure that the owner will be aware of his next steps clearly, he or she buys a player in the auction and direct their funds to buy specifically those players that will fill the voids in the team. We compute

Most Valuable Player (MVP)

by using player’s batting points, bowling points and player experience. After obtaining the MVP values, we classify the players by using decision tree approach. Further, we try to find out the players responsible for success of the team and how any two players tend to play in an IPL match.

Empowering the user to access databases using simple keyword search can be a relief for the user to learn SQL and to understand the schema. Keyword Search has also received a lot of attention in database as it is an effective technique to retrieve the query without knowing the underlying schema. Researches done in this area mostly deal with keyword search in single database then what for when the user is dealing with more than one database, to overcome this problem we are introducing an algorithm of SIL (Searching in Linked Databases). The other challenge in keyword processing is storing the keyword in the table i.e. via Inverted Index, so we introduce a novel technique to DeINIX (Density INverted IndeX) which reduce the memory storage space henceforth the pre-processing time is also reduced and the answer displayed will be First-10 answers. Our empirical result shows that this method answers queries more precisely, takes lesser time and memory space to retrieve a quality answer.

Cognitive Radios are intelligent radios which possess awareness of their surroundings and bandwidth availability in the existing network. Parameters such as location, nearby transmitters, time of day are some special functionalities that Cognitive Radios dynamically tune for the available spectrum. Cognitive Radios use intelligence to discover if any part of the available spectrum is utilized or not. In wireless networks Denial of Service attacks, such as jamming causes significant performance degradation to the existing network and thus needs to be detected at an early stage. This becomes more important in a Cognitive wireless network employing Dynamic Spectrum Access, where it is simpler for the attackers to dispatch Denial of Service attacks. The proposed work shows the detection of Denial of Service attacks as an early detection mechanism that detects the abrupt changes in some parameters which result in comparatively.

With the increasing demands of educational knowledge, adoption of modern learning management system is found to gain a faster pace in modern educational system. However, it is also found that existing mechanism of digital library system pertaining to educational domain suffers from significant pitfalls that are detrimental for quality education system. Hence, the proposed paper introduces a framework termed as Interactive Digital Learning System (IDLS) which potentially addresses the flaws in existing Massive Open Online Courses (MOOC) by mechanizing a novel and simple component modelling schema. The paper discusses the design principles of IDLS and compared its outcome with existing MOOC, where the proposed IDLS is found to excel better compatibility with cloud based learning management system with respect to assessment quality, assessment technique, contents, interaction, feedback, IP security, and features.

A base station and multiple sensor nodes are two main components of the wireless sensor network. Main task of sensor nodes is to aggregate and forward the collected data to the base station. The main objective of wireless sensor network is to maximize the network lifetime and providing as low latency delay as possible. To seek to accomplish this, we are proposing a Vertical and Horizontal segregation based data dissemination protocol. This protocol considers mobile base station that helps in improving the network lifetime by collecting the data from each and every node by moving to provide high throughput. Mobile base station requires informing all the sensor nodes about its location. This consumes large energy. Our proposed protocol saves this energy by informing few sensor nodes, in place of informing all the sensor nodes.

Globally, the medical industry is presumably “information rich” and “knowledge poor”. KDD, i.e. knowledge discovery from data is hence, applied to extract interesting patterns from the dataset using different data mining techniques. This massive data available is essential for the extraction of useful information and generate relationships amongst the attributes. The aim of this paper is to compile, tabulate and analyze the different data mining techniques that have been implied and implemented in the recent years for Heart Disease Prediction. Each previous paper exhibits a set of strengths and limitations in terms of the data types used in the dataset, accuracy, ease of interpretation, reliability and generalization ability. This paper strives to bring out stark comparisons and put light to the pros and cons of each of the techniques. By far, the observations reveal that Neural Networks performed well as compared to Naive Bayes and Decision Tree considering appropriate conditions.

Realization of graphic sequences and finding the spanning tree of a graph are two popular problems of combinatorial optimization. A simple graph that realizes a given non-negative integer sequence is often termed as a realization of the given sequence. In this paper we have proposed a method for obtaining a spanning tree directly from a degree sequence by applying BFS and DFS algorithm separately, provided the degree sequence is graphic and non-regular. The proposed method is a two step process. First we apply an algorithm to check whether the input sequence is realizable through the construction of the adjacency matrix corresponding to the degree sequence. Then we apply the BFS and DFS algorithm separately to generate the spanning tree from it.

A new method is created secret fragment visible mosaic image for secure data communication. This mosaic image is created by composing small fragments of a given secret image and selected cover image. The cover image is arbitrarily selected and uses of this image to hiding of the secret image. Secret image and cover image is split into tiny fragments called tile image and target block respectively. Color variation process is used to hiding a tile image in a similar target block. Color transform algorithm is used for transforming color characteristic of each tile image to corresponding target blocks. The extraction of the secret image may be retrieved nearly lossless. If variable size of the secret image and cover image, then resizing the cover image. Customized metafile (CMF) is created to store the required information for retrieving the secret image. Hash value should be calculated to providing integrity of secret message. The implemented method is possible for big volume and different size of the secret image and cover image. The proposed method is feasible for secure and nearly lossless data embedding technique.

X-ray image is one of the prominent modality of medical imaging used in medical diagnosis. This may be corrupted with Gaussian noise due to thermal fluctuations during its acquisition. For reducing these noises a method is applied which combines the Anisotropic Diffusion filter with an edge preserved Wavelet Packet Transformation. Here the edges are detected in each sub-band using Sobel edge detection operator and preserved by excluding these edge coefficients during hard thresholding. This method has proposed a new technique to calculate a threshold value for each sub-band of Wavelet Packet. The quality metrics SNR, RMSE, SSIM, Precision, Accuracy and etc. are used to measure the performance of this method and shows that this approach is better as compared to other noise reduction methods like Adaptive Median Filter, simple Anisotropic Diffusion and simple Wavelet Packet Transformation.

In this paper we proposed a new simple standard deviation based model update method for tracking the object of interest in the successive frame of the video. Each candidate object in the searching frame is first represented with a matrix of features and then transferred into the low dimensional covariance matrix for the purpose of matching the target object. Suitable candidate object is judged on the basis of the minimum Forster distance between the candidate and the reference object. A Model updation method is adopted in this proposed work to track longer trajectory when the object undergoes deformation in shape and size. This model update is carried out by updating each observation vector of the referenced model by adding the vector mean difference of standard deviation between the referenced object and the detected objects to the referenced model. A covariance matrix is then extracted from this updated feature image which can be used for the next set of future reference frame. In the proposed model also we use the Kalman filtering to effectively handle the background clutter and temporary occlusion. Simulation result shows the current method is robust for real time tracking.

Parts Of Speech (POS) tagging is one of the basic text processing tasks of Natural Language Processing (NLP). It is a great challenge to develop POS tagger for Indian Languages, especially Kannada due to its rich morphological and highly agglutinative nature. A Kannada POS tagger has been developed using Conditional Random Fields (CRFs), a supervised machine learning technique and it is discussed in this paper. The results presented are based on experiments conducted on a large corpus consisting of 80,000 words, where 64,000 is used for training and 16,000 is used for testing. These words are collected from Kannada Wikipedia and annotated with POS tags. The tagset from Technology Development for Indian Languages (TDIL) containing 36 tags are used to assign the POS. The n-gram CRF model gave a maximum accuracy of 92.94 %. This work is the extension of “Parts of Speech (POS) Tagger for Kannada Using Conditional Random Fields (CRFs).

In agriculture, Plant disease is one of the major congestion for increasing productivity and quality of food. False diagnosis of plant disease causes excessive use of pesticides which in turns affects the quality of crop. In this paper, we proposed algorithm for detection of Soybean disease and its Severity. The research paper focuses on classification and infected area estimation of Frogeye, Downy mildew and Bacterial Pustule disease of Soybean. In this proposed approach, Image enhancement technique for enhancing the image quality is used. Then k-means segmentation algorithm is applied to separate infected cluster from leaf. Neural Network is used to classify Frogeye, Downy mildew and Bacterial Pustule. Accuracy of 93.3 % is achieved for 30 images. After classification area estimation of infected area is performed.

Today’s civilization is inextricably involved with all sorts of data communication. For every organization and business bodies, handling large amount of data with proper confidentiality and security is an important task to do. To achieve the secure transfer of data through the network we have proposed a symmetric key cryptographic algorithm that provides security to the confidential information by encrypting this information twice. This algorithm comes up with the concept of secure key transfer as we use symmetric key concept. This algorithm incorporate binary addition, folding method, logical XOR operation with encryption key and generation of 2’s complement of a number represented in 8-bit binary equivalent value.

Context-aware Computing, considered as a part of ubiquitous computing, is an upcoming technology that has the potential to be used for improving one’s own health and providing personalized healthcare services. This paper discusses a randomized controlled trial conducted amongst 97 individuals, who were screened for stress and obesity. Out of these, 33 individuals (n = 33) were identified as suffering from both stress and obesity. With the fact that physical activity acts as a catalyst in reducing stress and obesity, the mobile application, ‘Let’s Exercise’ was designed to send context-aware alerts to the users. These alerts motivated and recommended these users to take up physical activity depending upon their operating environment. The 33 users were subject to a four-week observational period, after which a positive behavioral change was observed amongst these individuals. This was due to the increase in the level of physical activity in their daily routines after receiving the contextual alerts. Post the study, the users also showed strong confidence and willingness in the adoption of this technology.

Now a day, in cities so many people may lose their lives in accident due to driving the vehicles over speed or sometimes because of violation of the traffic rules. Currently in all cities there is a speed limit for vehicles in different locations. The vehicles should follow the rule and drive within these limits, if violates it may be penalized by the traffic officials. Present system is camera, radar based and human controlled interceptor. If multiple vehicles violate the rules simultaneously then current system is finding difficult to trap them and it leads more errors. However, once the vehicle crosses the official, once again the vehicle may cross the speed limits. The police official will never notice that, the person has violated the rules. In such cases, it is needed to track the vehicles throughout the range. This paper addresses the problem by giving a smart solution and novel approach to accomplish this task by designing, an efficient low cost embedded system. This will automatically generate the violation report and sends a penalty message to vehicle owner immediately. And it is fully automated and there is no need for the traffic officials to manually monitor the vehicles round the clock. The system is implemented using ARM processor LPC2129, ZigBee transceiver P20 module, microcontroller 89C51, GPS634R and GSM SIM300 modules.

1-Wire communication protocol was basically designed for short distance communication between very close by devices. This paper presents how the 1-wire protocol can be used for debugging an ARM based master Modem chipset through a slave device. The master and slave devices here are the Modem chipsets of mobile phones, of which master can be any new Modem in mobile platforms and the slave is another Modem chipset connected to the master using the single wire interface. For a new mobile chipset, getting a debugging environment is a desirable task which is presented in this paper. A minimum debugging environment and a simpler way of logging is desired. For this, a debugging environment is developed that would have a better capability of detecting the faults and errors and correct them in any new bring up Modem board for mobile phone platforms used in Intel. The general purpose input output pins are configured to work in the early boot stage of the phone. The whole implementation allows the slave to keep track of the master in the early boot process of the master. Since at this stage the operating system and the interrupts of the Modem would not be initialized, a slave is used to debug the master through a 1-wire communication protocol. A Digital Storage Oscilloscope is used to check the arrival of bits at the GPIO of the master in the early boot process and slave is checked for the same bits using an external debugger Trace32 and attention commands in Intel standard Tool for Hardware interaction. This implementation is used in Intel Mobile Communications for chipset bring up camp and for chipset validation process.

Since Hearing loss vary with frequency, the gain and compression ratios in the hearing aid must also vary with frequency. This is implemented by filtering the signal into different frequency bands and applying separate gain and compression to the signal in each band. Multirate signal processing involves the change of the sampling rate while the signal is in digital domain. The main idea of using multirate filter banks is the ability of the system to divide the signal of consideration into two or more signals or to compose two or more signals into single signal in frequency domain. The work reviews on a design technique for analysis and synthesis filter banks. The analysis bank is to divide the incoming signal into different bands and the synthesis bank to perfectly reconstruct the amplified version of original signal.

The widespread effects of digital media help people to explore the world more and get entertained with no effort. People became fond of these kind of sedentary life style. The increase in sedentary time and a decrease in physical activities has negative impacts on human health. Even though the addiction to video games has been exploited in exergames, to make people exercise and enjoy game challenges, the contribution is restricted only to physical wellness. This paper proposes creation and implementation of a game with the help of digital media in a virtual environment. The game is designed by collaborating ideas from neural linguistic programming and Stroop effect that can also be used to identify a person’s mental state, to improve concentration and to eliminate various phobias. The multiplayer game is played in a virtual environment created with Kinect sensor, to make the game more motivating and interactive.

With the Advent of technology and man’s interest in exploring underwater environment made him to develop new aquatic applications. Underwater Acoustic Networks are used to support these new applications. UAN’s are made up of sensor nodes, vehicles and other devices, for communication between them many network protocols are designed, after developing any new protocol that has to be validated in the ocean environment which is difficult and much costlier, so the next option is to rely on simulators. Simulator results may not be fully accurate. There are many simulators for wireless communication using radio waves but these can’t be used for underwater acoustic communication as they differ greatly. In this paper acoustic jprowler simulator is implemented using jprowler as the base simulator, at acoustic layer an improved channel model is used which considers few ocean dynamics and environmental parameters. Right communication can be achieved by appropriate Noise model, attenuation model at the acoustic layer and a mac protocol is designed using results from the physical layer. Simulation results analyzed and compared against the results from field experiment in Chesapeake Bay in USA.

Mobile Ad Hoc Networks (MANETs) are self-configuring collection of mobile nodes that are designed to function in an infrastructure-less environment. Since the lifetime of mobile nodes is limited by their battery capacity, energy efficiency of routing protocols is essential for sustenance of the network. Two algorithms namely, Energy Mean Value algorithm and Energy Efficient Maximum Lifetime Ad Hoc Routing (EEMLAR) algorithm have been proposed to improve the energy efficiency of Ad hoc On demand Distance Vector (AODV). The conventional AODV protocol has been simulated on QualNet for two different scenarios and the results have been compared.

With the rise of web applications, there has been a paradigm shift as web servers are no longer a technical requirement for any organization, but it is seen as an extension for business propositions. These web servers are designed with the basic function of hosting websites and applications such as gaming, data storage and enterprise applications. This paper attempts to investigate the various vulnerabilities of the web server and the path followed by the attacker to attack. The extensive survey has been done on the various methods of attacks which are an outcome of the loopholes in the design and architecture, deployment of the web server. If the components are designed and modeled taking into considerations the various vulnerabilities explained in the paper, the chances of attacks on the web server is minimized drastically. The paper also discusses the novel formula for the calculation of severity of attacks. This formula helps to assess the attack and is instrumental in the calculation of cost-benefit analysis.