Second National Conference On Frontiers of Computer Science and Computation Technologies NCFCSCT-2013



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A Human Iris Recognition Using Fuzzy Matching Technique[ ]


The Iris recognition based biometric identification has gradually attracted the attention of researchers due to its richness in amount of features. Iris recognition contains annular region between the sclera and the pupil of the human eye. In this region, there exists an extraordinary texture including many prominent features, on which the recognition is mainly relied. The proposed system uses the iris segmentation canny edge detection and Hough transform method can be applied to detect the inner and outer boundaries of an iris to segment the iris region.For feature extraction Log-Gabor filters are adopted to detect the local feature points from the segmented iris image. After feature extraction the matching algorithm, which is based on the possibilistic fuzzy matching technique, is used to compares two sets of feature points by considering not only the local features but also the relative positions to all the other points. The proposed schemes have been tested using iris images from CASIA IrisV3Interval Iris Image Database.

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A Stochastic Analysis on Message, Time and Energy Complexity on Wireless Sensor Networks[ ]


Recently considerable contributions have been made in the area of wireless sensor networks (WSN) because they can monitor the environment in a more efficient and convenient way. These are increasingly deployed for applications such as wildlife habitat monitoring, forest fire prevention, battlefield, emergency relief, environment monitoring and military surveillance. Due to its wide-range potential applications sensor networks has recently emerged as a premier research topic. In many sensor network applications, often the ultimate goal is to collect sensing data from all sensors to certain sink nodes and the sensed data is aggregated and transmitted to the sinks for analysis. Typically, an aggregate (or summarized) value is computed at the data sink by applying the corresponding aggregate function, e.g., MAX, COUNT, AVERAGE or MEDIAN to the collected data. It is envisioned that the sink node issues queries regarding the data collected by some target sensors, and the sensors collaboratively generate an accurate or approximate response. Researchers have designed several energy-efficient algorithms for computing aggregates using the tree-based approach. Thus, in-network data aggregation [1] becomes an important technique in WSN and has been well studied in recent years. In this paper, we study three different data processing, operations, namely data collection, data aggregation, and data selection. For each problem, we will study its complexity and present efficient algorithms to solve it. The main contributions of this paper are as follows: We design algorithms for data collection, data aggregation and data selection whose time complexity and message complexity are within constant factors of the optimum. Thus, the method achieves the best trade-offs among the time complexity, message complexity, and energy complexity. The analysis and simulation results show that the proposed algorithm out performs other aggregation scheduling algorithms.

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An Analytical Approach for Algotrading[ ]


Algorithmic trading, also called automated trading, black-box trading or algo trading, is the use of computers for entering trading orders, with an algorithm deciding on aspects of the order such as the timing, price and quantity of the order, or in many cases initiating the order without human intervention. Expert and intelligent systems provide the mechanics for both reacting to and affecting a financial market that is now significantly faster and operating across multiple time zones and markets. This paper presents a rule based trading approach for algo trading by incorporating the concepts of Elliott waves. The study consists of three phases. In the first phase, we select a technical indicator. In the second phase, we define the rules for conducting trade using the selected technical indicator. In the third phase we analyse the trend using Elliot waves. The study is aimed at adoption, deployment and development of an algorithmic trading system.

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Assessment of critical behavior for MI, PCI and CABG events[ ]


Coronary Heart Disease (CHD) is one of the main causes of death in and around countries. Several studies with different technologies have been made in diagnosis and treatment of CHD, which includes association rules, logistic regression, fuzzy modeling, and neural network. The existing techniques are confined to small datasets that are specific to one particular disease and this knowledge mined is not indispensible for classification of risk factors for the CHD events. The implemented methodology uses C4.5 decision tree algorithm for identification of CHD related risk factors for the events that includes Myocardial Infarction, Percutaneous Coronary Intervention, and Coronary Artery bypass graft surgery based on five different splitting criteria that includes Information Gain, Gini Index, Likelihood Ratio Chi-Squared Statistics, Gain Ratio, and Distance Measure. Using performance measures, correctly classified values have been found for each splitting criteria's and accuracy is calculated. The criterion which has highest accuracy is distance measure and it is used for classification of risk factors and CHD diagnosis. The implemented methodology, C4.5 decision tree algorithm gives high classification accuracy compared to the aforementioned existing techniques

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Automatic Ascii Art Conversion of Binary Images using NNF and Steganography[ ]


In this application, I have presented a novel application of NMF related methods to the task of automatic ASCII art conversion, where I fit a binary image to a basis constructed from monospace font glyphs using a winner-takes-all assignment.I presented some examples, and demonstrated that when compared to a standard pseudo inverse approach, non-negative constraints minimize the black space of the ASCII art image, producing better defined curves. Furthermore, I propose the use of the divergence cost function for this task, as it provides an element of control over the final ASCII art representation.In the computer world, there is a constant struggle to keep secret information secret, private information private, and when profits are involved, protect the copyrights of data. To accomplish these increasingly difficult tasks, new methods based on the principals of steganography are being developed and used.

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Cluster Based Ant Colony Optimization Routing For Vehicular Ad Hoc Networks[ ]


Vehicular Ad hoc Networks (VANET) are offset of Mobile Ad hoc Networks (MANET) made by vehicles communicating with each other. The special characteristic of VANET is high speed of nodes, so the time period of network is very small. Performing routing operation in such high dynamic network environment is difficult process. This work proposes cluster based Ant Colony Optimization routing in VANET scenario. In flat architecture each node bear equal responsibility, so that routing is performed in deployed cluster architecture. In first phase deploy a cluster architecture followed by Ant Colony Optimization routing procedures that results in a DYMO variant protocol called as ACO-DYMO routing protocol, that perform well in dynamic network, has it performance evaluated in urban scenario. The obtained results suggest that making use of Ant Colony optimization Procedures in Cluster architecture is more suitable for Vehicular Ad hoc Networks.

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Decentralized Time Slot Allocation Protocol for mobile Wireless Sensor Networks[ ]


In Data-Intensive Applications, Wireless Sensor networks are used to transmit the data's that has been generated within an applica-tion. While transmission, the network load is generated where there would be excessive packet collisions causing packet losses and retransmission. In order to address this issue, we introduce a Decentralized Time Slot Scheduling access scheme that reduces high data loss in the networks and also handles some mobility. Our approach minimizes transmission collisions by assigning time slot scheduling in virtual grids employed.

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Diagnosis of Mental Handicap in School Children using Machine Learning Techniques Support Vector Machines and Artificial Neural Network[ ]


Mental Handicap is a neurological disorder that affects the brain's ability to receive process, store and respond to information. A mental handicap can cause a child to have trouble in learning certain skills. In India around 13% to 14% of all school children suffer from mental handicap. There is no cure for mental handicap. Many children in India from the age of 6 years to 16 years and 11 months now have some form of mental handicap. Mental handicap can be diagnosed using psychometric intelligence scale test that is WISC IV (Wechsler Intelligent Scale Test for children) test. The information gained from evaluation helps the parents and school teachers provide a good learning environment for these students. Machine learning techniques are used for diagnosis. Machine learning is a subfield of artificial intelligence (AI) concerned with the development of algorithms and techniques that allow computers to learn. This paper proposes two classification methods based on SVM with two kernels and ANN for the diagnosis of mental handicap and compares the accuracy and sensitivity.

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Multi-SVM For Enhancing Image Search[ ]


Searching of relevant images from a large dataset for the given query image is a challenging task in computer vision systems. To overcome the problems such as time complexity and computational complexity a new methodology is proposed that will classify the whole dataset and also the given query image .By doing so we can reduce the similarity searching time and retrieval performance achieved by the classifier named Multi-SVM. Similarity measures are taken for query image against the whole database by using KNN. The images which are similar to the query image are retrieved from the database and displayed to the user. The images are categorized based on the extracted texture and color moment features. Finally compute precision and recall measures for CBIR performance analysis.

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Path Diversity in Stateless Multicast Protocol for Adhoc Networks[ ]


In this paper we have developed a Stateless receiver based multicast protocol that uses a list of multicast members 'addresses embedded in packet headers so that individual nodes do not have to maintain state information hence enabling receivers to decide the most efficient way to forward the multicast traffic. Multicast routing protocols depend on a priori creation where the state information is maintained by the individual nodes in the network. The maintenance of state information adds a lot of memory overhead and adds a large amount of communication between the nodes in dynamic networks with bursty traffic. The Receiver-Based Multicast uses the geographic locations of the nodes so that the need for costly state maintenance is removed and making it suitable for dynamic networks. A path diversity scheme is implemented for the networks where there two channels between two nodes and when the first channel process is a failure channel one will send information to channel two and hence data transmission will occur through channel two and hence the overall delay is reduced.

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ProcurePass: A User Authentication Protocol to Resist Password Stealing and Password reuse Attacks[ ]


The most popular form of user authentication is the text password, which is the most convenient and the simplest. Users mostly choose weak passwords and reuse the same password across different websites and thus, a domino effect. ie., when an adversary compromises one password, she exploits, gaining access to more websites. Also typing passwords into public computers(kiosks) suffers password thief threat, thereby the adversary can launch several password stealing attacks, such as phishing, keyloggers and malware. Therefore user's passwords tends to be stolen and compromised under different threats and vulnerabilities. A user authentication protocol named ProcurePass, which benefits a user's cellphone and short message service to prevent password stealing and password reuse attacks. ProcurePass adopts the one-time password strategy, which free users from having to remember or type any passwords into conventional public computers for authentication.

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Protecting Source-Location Privacy against Hotspot-Locating Attack in Wireless Sensor Networks using Partial Cloud-Based Scheme[ ]


The wireless sensor networks adversaries can make use of the traffic information's to locate the monitored objectsin platform as a ser-vice(Paas) , e.g., to hunt endangered animals or kill soldiers. In this paper, we first define a hotspot phenomenon that causes an obvious inconsistency in the network traffic pattern due to the large volume of packets originating from a small area. Second, we develop a realistic adversary model, assuming that the adversary can monitor the network traffic in multiple areas, rather than the entire network or only one area. Using this model, we introduce a novel attack called Hotspot-Locating where the adversary uses traffic analysistechniques to locate hotspots. Finally, we propose a cloud-based scheme for efficiently protecting source nodes' location privacy against Hotspot-Locating attack by creating a cloud with an irregular shape of fake traffic, to counteract the inconsistency in the traffic pattern and camouflage the source node in the nodes forming the cloud. To reduce the energy cost, clouds are active only during data transmission and the intersection of clouds creates a larger merged cloud, to reduce the number of fake packets and also boost privacy preservation. Simulation and analytical results demonstrate that our scheme can provide stronger privacy protection than routing-based schemes and requires much less energy than global-adversary-based schemes.

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Role of Attribute Selection in Classification Algorithms[ ]


Feature selection is the technique of removing irrelevant features and to reduce dimensionality of the feature. This paper proposed attribute selection of information gain attribute evaluator and ranker search method to selected attribute and each selected attribute is ranked based on the filter and wrapper method. Tree based J48 classifier is used with different test options namely 10 fold cross validation, use training set, supplied test set, and percentage split default of 66% are compared with all options and to generate best accuracy results; Labor dataset is implementing to test the above methods.

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SECURE ON-DEMAND ROUTING PROTOCOL USING FRIENDSHIP MECHANISM[ ]


A Mobile Ad-hoc Network (MANET) is a collection of wireless nodes that can be dynamically set anywhere and anytime without using any pre-existing network infrastructure. It is an autonomous system in which mobile host connected by of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. MANET can operate without fixed infrastructure and can survive rapid changes in the network topology. In this paper we have presented a trust based on-demand routing protocol using friendship mechanism. This mechanism is used to find the secure path to transfer data between nodes and evaluate their performance with respect to performance metrics like packet delivery ratio, End-to-End delay, Friendship Message activity. The main method for evaluating the performance of MANETs is simulation and we prefer Network Simulator (NS2).

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Semantic Web Services-A Survey[ ]


The technology where the meaning of the information and the service of the web is defined by making the web to understand and satisfies the request of the people is called Semantic Web Services. That is the idea of having data on the web defined and linked in a way that it can be used by machines not just for display purpose, but for automation, integration and reuse of data across various application .The idea of the semantic is raised to overcome the limitation of the Web services such as Average WWW searches examines only about 25% of potentially relevant sites and return a lot of unwanted information, Information on web is not suitable for software agent and Doubling of size. It is built on top of the Web Services extended with rich semantic representations along with capabilities for automatic reasoning developed in the field of artificial intelligence. This survey attempts to give an overview of the underlying concepts and technologies along with the categorization, selection and discovery of services based on semantic.

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Significant analysis of terms in bootstrapping ontology for web services[ ]


Ontology construction is important for semantic based web services.Identifying concepts and relationships for a specific given domain is one of the promising technique in ontology construction. Bootstrapping means, dynamically creating ontology for specific domain or web services. Bootstrapping ontology based on set of predefined textual sources such as web services, must address the problem of multiple, largely unrelated concepts. In this paper ontology bootstrapping process for web services is done based on identifying the related concepts. WSDL document contains large set of tokens. The tokens are may be closely related to respective WSDL file or sometimes tokens related with the less importance to the WSDL file. The proposed approach finds the related concept that have more significant for the domain.it uses result of two methods to find the related concept: significant analysis of terms using relevance abstraction identification method, and web context extraction using WordNet for the ontology evolution.

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