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power design is one of the most significant challenges to maximize battery life time in portable devices and to save the energy during system process. Discrete Cosine Transform (DCT) is widely used in image and video compression process. Here in this paper, we review on low power Discrete Cosine Transform architecture by using varies methods. Discrete Cosine Transform (DCT) is most popular method used today in video compression systems. A number of algorithms have been proposed for implementation of the DCT. Loeffler (1989) has specified a new class of 1D-DCT using only 29 additions and 11 multiplications. To implement such an algorithm, one or more than one multipliers have to be integrated. This process requires a high occupation of silicon area. Arithmetic distribution is generally used for such algorithms. The coding for reconfigurable 8-point Discrete Cosine Transform (DCT) has been done using VHDL, under Xilinx platform. KeywordsCosine Transform (DCT), Inverse Discrete Cosine Transform (IDCT), Very High Speed Integrated Circuit Hardware Description Language (VHDL)

Conventional cryptography is a method of transforming message into an unreadable format called as cipher text. This cipher text can easily be shared over the network without fear but anyone can get message easily by applying key on cipher text. Sharing and delivering secret images over the network is also known as a visual secret sharing (VSS) scheme. However main drawback of VSS scheme is that it is experience from high transmission risk due to the shares are like noise. As the shares are like noise which in turn lead to cause the attackers attention. To solve this problem mechanism is developed called Natural image based visual secret sharing (NVSS). This paper is motivated by need of novel mechanism to transfer the secret images over network. In proposed work for regeneration of secret image a generated noisy share, natural images and seed (needed for random functions used) are required at the receiving end. Though generated share is noisy and its contents are not recognizable, overall structure may be visible in some cases. So data hiding is should be performed on generated share to hide that structure (texture) in noisy share.

A Wireless Networks are more accessible to different types of attack than wired Network. One such attack is Wormhole Attack, in which traffic is forwarded and replayed from one location to another through the Wormhole tunnel without negotiating any cryptographic techniques over the network. Thus, it is challenging to defend against this attack. In this paper we review WSN concept and Wormhole Attack. Then we discuss classification of wormhole Attack and also mention few of the initiatives to detect the Wormhole Attack.

Due to rapid growth of internet, online shopping for products is an essential part of everyone’s daily life most of the time credit card is used to pay online for products. It is an easy way to shopping, people can get your desired product on your computer screen or on smart phone. For online purchase use of credit card increases dramatically but still there is some loop holes in system of online shopping which causes online frauds or credit card frauds. There are many techniques like ANIDS Distributed Data mining WSDL, Parallel granular neural networks, Hidden Markov Model, Clustering and Bayes classification, Cost model are used. These techniques use data mining to detect frauds by the use of datasets like KDD99, KDD cup, NSL KDD. In this paper a study on different techniques and evaluate each technique on the best parameters.

over Internet protocol (VoIP), which is a communication protocol existing over a network. The IP network can makes it possible for users to make telephone calls using the VoIP technology. Use of VoIP and Internet telephony has increased significantly in the recent years. These new phone services are based on the transmission of voice over packet switched IP networks. VoIP can be realized on any data network that uses IP, like the Internet, Intranets and Local Area Networks (LAN). VoIP customers use their Internet connection to connect to the Internet as well as to make phone calls. VoIP is the real-time transfer of voice signals using the Internet Protocol (IP) over the Internet or a private network. In simpler terms, your voice is converted to digital signal by VoIP that travels over the internet. The key factors that entice enterprises to switch to VoIP are its flexibility and cost efficiency. Some security problems may arise due to the widespread deployment of VoIP. Voice over IP (VoIP) has the potential to provide interactive communication services like video and voice conferencing. VoIP helps to transfer data which are difficult to transfer over circuit-switched wired and wireless networks.

Data Storage Security is matter of concern especially in the field of Cloud Computing where large amount of data is being stored on daily basis over the internet. But the real world is facing some or other challenging issues regarding the same. An ubiquitous and an on-demand access with an interoperable network of your various computing resources and sharing necessary information is the need of this Internet dependent Earth, which has led the generation of Cloud and the virtual world related to it. As the number of users and data over the net are getting increased second by second there is a need of system or method which could hold this with all its privacy, integrity and confidentiality. But the vulnerabilities over Cloud data are weakening the expected progress of the field. Many complex encryption and decryption techniques are being introduced but the new risks get generated. The organisation at every level is concerned with privacy of the data and various attacks which don’t enable them to use the Cloud’s benefits and its susceptibility over the data holds the technology to grow. Some trustworthy and dependable Cloud storage will be a good option with the specific authentication as well as proper agreements and auditing protocols defines earlier itself will motivate the people to be secure and tension free while storing the data over the Cloud. General Terms Cloud Computing, Data storage security.

Steganography is the process to hide a secret message such that it is very difficult to detect the presence of secret message presence. On other way, the existence of secret message is hidden. A covert channel may refer to the actual medium that is used to communicate the information such as a message, image as well as file. This project uses steganography within the source address fields of Internet Protocol Version 6 (IPv6) packets to create a covert channel through which clandestine messages can be passed from one party to another. A fully functional computer program was designed and written that transparently embeds messages into the source address fields of packets and across IPv6 networks, embedded messages are by these packets. This demonstrates the covert channel possibility within a default Internet protocol. For a malicious purpose such as stealing encryption passwords as well as keys or other secrets from remote hosts in a sequence not easily detectable this channel is used, but it may be used for a noble cause such as secretly passing messages under the watchful eyes of an oppressive regime. The covert channel demonstration in itself the overall information security of society is increased by bringing awareness to the existence of such a steganographic medium.

A survey of various link analysis and clustering algorithms such as Page Rank, Hyperlink-Induced Topic Search, Weighted Page Rank based on Visit of Links K-Means, Fuzzy K-Means.Ranking algorithms illustrated, Weighted Page Rank is more efficient than Hyperlink-induced Topic Search Whereas clustering algorithms has described Fuzzy Soft, Rough K-Means is a mixture of Rough K-Means and fuzzy softest and provide efficient results than Rough K-Means approach and K-means. The literature survey shows how these algorithms are used for link-analysis and extracts the information, including contents and images from web pages efficiently.In new algorithm Weighted Page Content Rank user can get relevant and important pages easily as it employs web structure mining and web content mining. A webpage ranking analysis can be apply on the scenario where the searching and interaction with the numerous web data is required, so in order to provide effective result the technique can be used.

In this paper, an algorithm based on the frequency domain feature extraction method is discussed for the detection of currency. This method efficiently utilizes the local spatial features in a currency image to recognize it. The entire system is pre-processed for the optimal and efficient implementation of two dimensional discrete wavelet transform (2D DWT) which is used to develop a currency recognition system. A set of coefficient statistical moments are then extracted from the approximate efficient matrix. The extracted features can be used for recognition, classification and retrieval of currency notes. The classification result will facilitate the recognition of fake currency mainly using serial number extraction by implementing OCR. It is found that the proposed method gives superior results. Keywordsextraction, classification, discrete wavelet transform, textural feature, currency recognition, OCR, fake currency, security thread, serial number, RBI marks.

In today’s Era, message transmissions over the internet have protection problems of the digital data. Hence, protection of secret messages during transmission becomes a difficult subject. Digital watermarking is provide protection of digital information or identifying information against illegitimate exploitations and allocations. watermarking is a technology to guarantee and make possible data certification, security and copyright defense of the information. The intend of watermarking is to consist of hidden data in multimedia information to ensure security examination. It would be then probable to progress the surrounded message, even if the information was distorted by one or more non-dangerous attacks. In this paper, we present the various types of watermarking techniques and application region where water making technique required. Also a survey on the some new work is done in image watermarking field.

K-means clustering technique works as a greedy algorithm for partition the n-samples into k-clusters so as to reduce the sum of the squared distances to the centroids. A very familiar task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are more related among them than they are to the others. Kmeans clustering is a method of grouping items into k groups. In this work, an attempt has been made to study the importance of clustering techniques on hand g-indices, which are prominent markers of scientific excellence in the fields of publishing papers in various national and international journals. From the analysis, it is evidenced that k-means clustering algorithm has successfully partitioned the set of 18 observations into 3 clusters.

Web data mining is an emerging research area where mining data is an important task and various algorithms has been proposed in order to solve the various issues related to the web mining in existing dataset. This paper focuses the concept of data mining and FP-Growth algorithm. As for FPGrowth algorithm, the effectiveness is limited by internal memory size because mining process is on the base of large tree-form data structure. This Research work concentrates on web usage mining and in particular focuses on discovering the web usage patterns of web sites from the server log files. This paper finds the procedure to work with the proposed technique which can be possible to remove the drawback of limitation of the existed technique in the web mining area. The various web usages mining technique can further work on various scientific area, medical area and social media application to approach for the research and security related area. A detail and pattern growth technique can help in getting more data and further on using line up algorithm we can illustrate the data states presentation effectively.

plays a vital role in research area in the field of data mining. Clustering is a process of partitioning a set of data in a meaningful sub classes called clusters. It helps users to understand the natural grouping or cluster from the data set. It is unsupervised classification that means it has no predefined classes. This paper presents a study of various partitioning techniques of clustering algorithms and their relative study by reflecting their advantages individually. Applications of cluster analysis are Economic Science, Document classification, Pattern Recognition, Image Processing, text mining. No single algorithm is efficient enough to crack problems from different fields. Hence, in this study some algorithms are presented which can be used according to one's requirement. In this paper, various well known partitioning based methods - k-means, k-medoids and Clarans - are compared. The study given here explores the behaviour of these three methods.

his paper develops a trustable software agent framework to simulate the possible situations of trust between agents and propose novel solutions that could be provided by software agent technology. The technology is applied to the security and trust process to overcome the serious issues that could be faced by using software agent technology. This study demonstrates the use of autonomous software agents to secure the Examination Paper Preparation and Moderation Process (EPMP) domain and apply security and trust mechanisms on the agents that access and perform various domain tasks. Three levels of trust are designed, which correspond to the EPMP domain's groups of people who are the Examination Committee, Moderator, and Lecturer. Each level has its own trust requirements that should be fulfilled by the agent in order to access the level, otherwise the agent is blocked and access to the level is denied. This work implements the proposed framework for the domain and observes the results of the security and trust mechanisms. It then compares the performance before and after the implementation to see the effectiveness of the framework.

Steganography is the skill of hiding the existence of data in other transmission medium to attain secret communication. It does not restore cryptography but quite boost the security using its abstruse features. In this paper we have surveyed on a Steganography and cryptography techniques which provide highly secure skin tone data hiding. Biometric characteristic used to apply steganography is skin tone region of images. Here important data is implanted within skin region of image which will give an outstanding secure location for data hiding. For this skin tone detection is need to be performed. Different steps of data hiding can be applied by cropping an image interactively. Cropping of an image improved security than hiding data without cropping the whole image, so cropped region works as a key at decoding region. Cryptography algorithm is used to convert the secret messages to an unreadable form before embedding; which provides a strong backbone for data security. This survey paper focuses on illuminating the technique to secure data or message with authenticity and non repudiation. So with this object oriented steganogaphy we track skin tone objects in image with the higher security and satisfactory PSNR .Modern steganography’s goal is to keep its mere presence undetectable.

amount information of moving objects on road network is being collected with the help of various recent technologies. The tracking of these moving objects on road networks is becoming important because of it's application in various areas. Classification has been used for classifying various kinds of data sets like graph, text documents. However, there is a lack of study on data like trajectories on road networks. Data mining techniques, especially, sequential pattern mining can be used to extract frequent spatio-temporal patterns. Again it needs to confine the length of sequential patterns to ensure high efficiency. After extracting frequent sequential patterns in trajectories, classification can be applied to classify patterns which provide useful information in applications such as city and transportation planning, road construction, design, and maintenance, marketing sector. In this paper, whole pattern matching query concept is adopted after the classification to find total traffic volume on given trajectory edge. At the same time, user can find number of vehicles moving in one as well as in both directions on that particular trajectory. Keywordssequential patterns, frequent pattern based classification, location based services, pattern matching.

is an important aspect for all living things. It proves to be a lifesaving component in case of emergency requirement. There are number of web based blood banks which are available for communication between blood bank and hospitals. None of the online blood bank offers the direct contact between donor and blood bank. This is the major drawback of the existing system. Existing systems are time consuming; require more man power and it is costly. This paper introduces comparison between existing system and improved system. The new idea will improve the existing system and it will move from conventional desktop system to mobile system. This paper introduces new features of improved system over existing system in many aspects. In this paper firstly we are going to explain merits and demerits of existing system. Also this paper will provide brief introduction about improved system and its features and Scope.

Paper, pens, and ballot-boxes, appropriate procedures were used in traditional voting schemes to reassure voters that the result of the tally is correct [1]. Most viable approach to address voter coercion and vote selling in Internet voting is still the basic concept [4]. Its unrealistic computational requirements of the quadratic-time tallying procedure.is one of the main open issues [4]. In this paper, they examine authorization of votes which is the main cause of the issue and the most recent proposals to perform this step in linear time will be summarized. Based on anonymity sets we explain the key underlying concepts of these proposals and introduce a new protocol. However at the negative side, high transparency will generally make it easier for voters to reveal how they voted [1]. Relevant information is published so that the voter may verify that her vote is included in the final tally and that accepted votes have been cast using proper voting material. With the purpose of internet voting protocol it is practically impossible for vote buyers or coercers to elicit the voters' behavior. The efficiency is compared efficiency with existing work under equal degrees of coercion-resistance using an appropriate measure. Internet voting enjoys wide interest from both researches as well as practices [2]. The main purpose of the new protocol is to move computational complexity introduced in recent works from the voter side to the tallying authority side.

Data mining is used to extract interested pattern or knowledge from large amount of data using many data mining technique. However it may also display sensitive information about individuals compromising the individual right to privacy When a collection of data is split among various parties. Now Each and Every party would wants to keep its sensitive information private during the mining process. Privacy preserving data mining is to develop data mining method without increases the risk of misuse of data. The main aim of privacy preserving data mining is to find the global mining results by preserving the individual sites private data/information. The various methods such as randomization, perturbation, heuristic and cryptography techniques. To Find privacy pre serving association rule mining in horizontally and vertically partitioned databases. In this paper, the analysis of different methods for PPARM is performed and their results are compared. Horizontally Partitioned databases, algorithm that combines advantage of both RSA public key cryptosystem and Homomorphic encryption scheme and algorithm that uses Paillier cryptosystem to compute global supports are used. This paper reviews the wide methods used for mining association rules over horizontally distributed dataset while preserving privacy.

of Data is the extraction of hidden prognosticative information from large databases or set of data, is a strong new technology with great prospective to help companies focus on the most important information in their data warehouses. Query optimization is a purpose of many relational database management systems. The query optimizer experiments to dictate the most efficient way to implement a given query by examining the possible query plans. In this paper, the different techniques is given for optimizing query using schema based and materialized views in data warehouse namely- view adaptation & synchronization, view selection and view maintenance. Keywordsoptimization, materialized view

Kinesthetic, Auditory, Reading / Writing Preference Learning and Visual Learning are the 4 categories of learning types as per the widely used Fleming VAK / VARK model. Visual Learning represents concepts visually. By representing the information visually students are able to concentrate on meaning, acknowledge and group similar ideas easily making better use of visual memory. Visual learning improves student performance in the areas of Retention, Reading Comprehension, Student Achievement and Critical Learning skills. this system focuses on semi-automatically converting textual learning resources related to important world events into visual information. Few examples include Terrorist Activities in India, 26/11 Mumbai bomb blasts and Earth Quakes in India. This systems scrapes the unstructured text from textual learning resources, applies natural language processing to extract named entities in the text, uses location api's and innovative visualizations (e.g. Google Maps) to visualize the world event information in an intuitive manner. Human intervention is needed in case of incomplete information in the learning resource and verification of generated information. This system makes it easy to identify human intervention need via data health reports. General Terms Fleming VAK / VARK model, Visual Learning Data mining, NLP(Natural Language Processing), visualization, cloud.

The importance of automatic methods has increased greatly for paper currency recognition due to the development of modern banking services like automated teller machine (ATM) and automatic goods seller machines. For a machine, it is a challenge to recognize paper currency whether it is of 10, 20, 50, 100, 500 or 1000. Various method or algorithm has been applied to overcome this challenge and in most of systems neural network is used. For this, LBP algorithm is proposed in this paper. The proposed method has advantages of simplicity and high speed. The experimental results show that this method has a high recognition rate. The recognition ratio can achieve 100% in case of good quality images.

Large volume of queries over large volume of users allows search engine to build methods for generating query suggestion for input query. Query recommendation methods are powerful technique to generate related queries or alternate queries as a query suggestion for original query which is given by user in the search engine first time. In this review paper we discussed about query recommendation methods and comparison between different methods how the query suggestion is given by these methods for the input query by user in search engine. Some query recommendation method do not covers the unseen and rare queries but some of them covers these queries by some additional feature added such as generalize the query token of input query by a suitable place holder from hierarchy tool wordnet3.0 or wekepedia by yago mapping. These generalization techniques are also discussed in this paper.

In the last few years, many image processing techniques have been presented in order to perform different brain tumor detection tasks. These cover Content-Based Retrieval Technique, Component Labelling Algorithm, Fuzzy C-Mean Algorithm. There is fast growth of image processing available in last few years; image segmentation have work together to get extraction of meaningful data into useful information. Image processing is used to finding applicable and efficient information from data available. With this survey paper we focus on literature survey of segmentation based image processing model as contain efficient performance.

In the medical field, images, and especially digital images, are produced in ever increasing quantities and used for diagnostics and therapy. Content based access to medical images for supporting clinical decision making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into Picture Archiving and Communication Systems (PACS) have been created. This article gives an overview of available literature in the field of content based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content based image retrieval and the technologies used. Section 2 describes the basic algorithm used in the implemented systems. Section 3 describes various methods of implementing CBIR. New research directions are being defied that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. Content-Based Image Retrieval (CBIR) has been a topic of research interest for nearly a decade. Approaches to date use image features for describing content. A survey of the literature shows that progress has been limited to prototype systems that make gross assumptions and approximations. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org

CONFLICT OF INTEREST: NONE DECLARED Defect tracking systems play an important role in the software development organizations as they can store historical information about defects. There are many research in defect tracking models and systems to enhance their capabilities to be more specifically tracking, and were adopted with new technology. Furthermore, there are different studies in classifying bugs in a step by step method to have clear perception and applicable method in detecting such bugs. This paper shows a new proposed defect tracking model for the purpose of classifying the inserted defects reports in a step by step method for more enhancement of the software quality.

This paper focuses on the role of python in dramatic increase in productivity and high-level of code reuse in computational science. The salient features of python make it an ideal language for scientific computing exposing the shortcomings of legacy languages and prototyping platforms. Python provides a rich collection of built-in data types such as strings, lists, dictionaries; dynamic typing and dynamic binding, modules, classes, exceptions handling, automatic memory management, multiprocessing, parallel computing capabilities. Python can also be used as a glue language to wrap around existing static compiled code to obtain optimum performance. The uptrend of adopting python as a general purpose language along with its vast collection of scientific libraries are also reviewed in this paper, which ensures the long term presence of python and its growing user base in the foreseeable future. General Terms Computational Science. Keywords

This paper introduces Amharic text chunker using conditional random fields. To get the optimal feature set of the chunker; the researchers’ conduct different experiments using different scenarios until a promising result obtained. In this study different sentences are collected from Amharic grammar books, new articles, magazines and news of Walta Information Center (WIC) for the training and testing datasets. Thus, these datasets were analyzed and tagged manually and used as a corpus for our model training and testing. The entire datasets were chunk tagged manually for the training dataset and approved by linguistic professionals. For the identification of the boundary of the phrases IOB2 chunk specification is selected and used in this study. The result of all experiments is reported with the maximum overall accuracy off 97.26%, with a window size of two on both sides, with their corresponding POS tag of each token and the worst performance achieved is 84.57%, with only the window size of one word on both the left and right sides.

The adoption of Bring Your Own Device (BYOD), also known as Bring Your Own Technology (BYOT), Bring Your Own Phone (BYOP), or Bring Your Own Personal Computer (BYOPC), is a policy which allows people access to privileged resources, information and services available on the private computer network of an organization using their own personal computer devices. BYOD, since its emergence in 2009, courtesy of Intel, is now a common practice in many organizations. Academic institutions that attempt to implement BYOD, can derive many benefits as well as many risks to its network infrastructure, largely security-based. This paper presents an assessment of a WLAN network which has been deployed for a campus-wide data centric e-learning platform at Kwame Nkrumah University of Science and Technology (KNUST) towards the overall objective of achieving a barrier free internet access to enhance the teaching and learning process at the university. The paper subsequently evaluates the WLAN infrastructure, its accompanying BYOD set-up, and associated likely security risks and threats, and recommends appropriate solutions.

Academic organizations are supposed to seek certain balance between the concerns of security, stability and availability of shared computer labs to achieve the course learning objectives. Some academic activities in computer science courses require students to control and configure some laboratory devices. Students in advanced courses may use some risky applications for some learning activities. It may affect the stability, availability and security of the working nature in the shared lab and networks. Virtualization Platform has the ability to overcome these concerns to achieve learning objectives. Virtualization technologies allow students to access and control the computer settings as well as the working environment in advanced applications without affecting the operational availability of computer labs throughout the college day. Virtualization enables computer security teachers to train their students on many hands-on activities without exposing or causing damages to computer labs. This paper presents the usage of virtualization technology in such environment, the opportunities and benefits of using this technology in computer security oriented courses. The paper also discusses the use of the VMware workstation pro as a sandbox for risky lab activities in computer security courses. Here the Desktop Virtualization offers great opportunities to teach advanced skills in computer security courses. Also, this paper outlines the challenges and limitations of virtualization along with some recommendations to address these limitations.

The fifth-generation (5G) network supports many systems such as reliable communication in potential applications that require maximum security. Advancement in Software-Defined Networking (SDN) is growing with the emerging network architectures targeted from many servers with the various types of Distributed Denial of Service (DDoS) attackers. When malicious users send DDoS attacks, the SDN based 5G networks face security problems and challenges. Despite the security solutions for preventing DDoS attacks in SDN, securing the SDN controller is one of the challenging problems. The purpose of this research is to analyze the suitable machine learning (ML) for securing the SDN controller targeted by DDoS attacks. This paper proposes a security scheme that includes the ML algorithm, adaptive bandwidth mechanism, and dynamic threshold technique. Therefore, the main focus is on the mitigation scheme of DDoS attacks considered in SDN controller through the ML trained model. In this scheme, the proposed approach uses the best ML as a method for finding security solutions that enhance the security of the SDN controller and network performance. In this method, the Extreme Gradient Boosting (XGBoost) and other ML algorithms were used, which not only enhance the accuracy of the security solutions but also improve the overall network performance.

The concept of Internet of Things requires the seamlessly connectivity of millions of heterogeneous devices. In today’s world, Implementation of IoT in Smart Homes is one of the major applications of Internet of Things. In this paper, we will discuss the different challenges and risk to implement IoT in Smart Homes of India. An Indian perspective is taken, because India is in very first stage for implementing the Smart Homes. This paper explains many challenges in Indian scenario like availability of Internet, Cost of devices, device maintenance issue etc. and proposes a framework of smart homes is also discussed here.

Almost all countries have imposed strict laws on the disclosure of Personally Identifiable Information(PII). However PII need to be published for many purposes like research. In such cases, we apply different types of methods like anonymization, encryption etc. This paper discuss about the different methods of anonymization of tabular microdata. The most popular method of data anonymization of tabular data is k-anonymity. However, it suffers from many attacks and hence l-diversity was proposed. The l-diversity anonymization also possessed various limitations and hence t-closeness was proposed. This paper summarize these anonymization techniques and their limitations.

the breast cancer has been become one of the main reasons of death in women especially in the developed countries, there have been done many research for breast cancer diagnosis. Although researchers have recently proposed many methods by using intelligent approaches for diseases diagnosis, a few of them fulfill the need of high accuracy. In this paper, the most popular swarm intelligence algorithms PSO, ICA, FA and IWO are applied to diagnosis the breast cancer. The experimental results show that swarm intelligence approach can be applied for breast cancer diagnosis with high accuracy. Moreover, FA can diagnose the breast cancer more accurate than other swarm intelligence methods compared in this paper. KeywordsIntelligence, Diseases diagnosis, Breast cancer

People looking to buy a new home tend to be more conservative with their budgets and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase. The goal of the paper is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. By analyzing previous market trends and price ranges, and also upcoming developments future prices will be predicted. The functioning of this paper involves a website which accepts customer’s specifications and then combines the application of multiple linear regression algorithm of data mining. This application will help customers to invest in an estate without approaching an agent. It also decreases the risk involved in the transaction.

is the most prominent & primary mode of Communication among human beings. Now-a-days Speech also has potential of being important mode of interaction with computers. This paper gives an overview of Automatic Speech Recognition System, Classification of Speech Recognition System and also includes overview of the steps followed for developing the Speech Recognition System in stages. This paper also helps in choosing the tool and technique along with their relative merits & demerits. A comparative study of different techniques is also included in this paper. KeywordsSpeech Recognition (ASR), ASR classification, Speech Analysis, Feature Extraction, Modelling Techniques, Language Modelling, Testing, ASR Tools.

Breast cancer is one of the ruinous diseases causing more number of deaths in women. Many efforts are being made to develop efficient techniques to identify the breast cancer at early stage. This paper implements weaver ant colony optimization (WACO) for classifying mammograms. The behavior of weaver ants' to bind the neurons between the layer to make the path to find the output of Neural Network(NN). The unique nest building behavior of Weaver ants' made a considerable remark, NN have proved to be one of the best methods for classification & WACO is used for optimized pathfinding. Each layer of NN is considered as one leaf and the leaves are divided into as many portions equal to the number of neurons,& let ants connect portion of leaf from one layer to another, makes the weight connection in the NN. The task is accomplished by using a NN with a back propagation technique (BPT). To determine the usefulness of WACO based Neural Network (NN)closed nests building behavior made us change the architecture of BPN. Weaver ants show indirect message between ants to find the shortest path between their nest and food sources. These ants ability is to construct nests by weaving together leaves using larval silk .Local Binary Pattern is a method which can be used extract the features for identifying the breast cancer abnormality by mammogram. Multilayer perceptron (MLP) and support vector machine for mammogram classification and mammogram images are preprocessed to reduce noise and detect the background of the mammogram to extract the required features. This paper implements WACO for classifying mammograms. Here we used MIAS database which describes the mammogram is in which stage benign or normal stage

study embodied in this paper, aims at making use of machine learning and computer vision algorithms in order to reliably identify the species of bacteria, from their microscopic images. The study has taken into consideration three of the most commonly occurring species of bacteria that are clinically important. The work shown further in this study can be extended to a larger number of bacteria species. The study makes use of the Speeded Up Robust Features or SURF algorithm for detecting image keypoints. The artificial intelligence classifier makes use of these keypoint vectors as its input variable. It is noteworthy that the images used are taken at the gram staining stage of bacterial identification, in order to minimize any biases in the dataset owing to a variance in staining technique, although the feature detector invariably grayscales the image prior to keypoint computation. The paper follows the study from the conception of the idea, to the formulating of the algorithm, the training and testing of the same.

Robotic Telepresence systems focus majorly on the concept of telecommunication and remote presence with independent mobility. Recently a number of Telepresence robots have been introduced in the market, each with some unique feature and show-off factor. While these systems promise good user experience, they are too expensive for the general public to use them. This paper proposes an innovative approach to build a commercial robotic Telepresence system called OATS. OATS stands for “Open Access Telepresence Systems”, which is an architecture primarily based on open source platforms like, WebRTC and MQTT. The uniqueness of the architecture is that, the Telepresence robot can use any mobile smartphone running an OATS client application, as the brain of the robot, reducing the need of expensive, onboard electronics and processing power. The smartphone connects to a robotic platform that enables the robot to move. Using this robot, a person can remotely interact using any smartphone or laptop. This OATS client connects to a server which provides services required for peer to peer videoconferencing and machine communication between the clients. A user can login to one of this Telepresence robot and pilot it in a remote area, from any part of the world. This architecture not only reduces the cost of the Telepresence robot, but the use of smartphone platform also opens up whole new possibilities for development of next generation of telepresence robots. In this paper, we briefly discuss the OATS architecture, its deployment strategies and its advantages over existing

Mining large data set is an important issue to deal with as data is growing as the field grows. Today, crime rate is a menace that each country faces. With the increase in crime rate the data is increasing and it is such a critical field that accuracy is important at the same time. This paper shows the comparison in the results between clustering and the classification. K means is used in clustering and in classification decision tree is used. The process of applying decision tree and clustering one after the other is used CDDT(clustered data of decision tree) in this paper. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.

As new devices and technologies are invented to access the internet, from computer desktops, laptops, mobile phones to smart TVs, there has been a great need to upgrade the techniques used in the field of website design, because these new devices come along with their own specific sizes and views. Although most devices & technologies try to be as compatible as possible with the common web design features, but there has been an absolute need for website designers to do a lit bit more to adapt to the fast growing race in internet devices and provide all their viewers with the best possible experience while accessing their websites. In this paper, different responsive website design techniques are presented that could adapt to different technologies and devices while at the same time focusing on cutting down the time and effort needed for a website designer or programmer to maintain and edit it. The code samples in this paper were implemented using the ASP.net programming language, but it can be implemented in other development languages and environments using the same concepts.

Many of the today‟s real time signal processing algorithm included multiplication as its processing heart. In case of signal and image processing, it mostly used functional unit. In this paper we are simulating different multiplication algorithm with their effective architecture. Also paper introducing new multiplication technique using barrel shifter which gives some sort of modification in previously described shift and add multiplication algorithm. Research targeting mainly four algorithms as Vedic vertical crosswise multiplication algorithm, Array multiplier, Shift and add multiplier, Wallace tree multiplier. Further work will carried comparative study of different multiplier with respect to some parameters like logical resources used, delay, power consumption and area. For implementation and parametric analysis, experimental setup uses sparten-3 XC3S400 FPGA as a hardware platform, VHDL coding language for hardware description. Xilinx ISEsimulation tool has many inbuilt compatible facility for parameter analysis like XPE for power analysis. Finally Paper comprises simulation results for 8-bit, 16-bits and 32-bits each of above mentioned multiplier.