IJARAI Volume 4 Issue 4

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Semantic Image Retrieval: An Ontology Based Approach

Abstract: Images / Videos are major source of content on the internet and the content is increasing rapidly due to the advancement in this area. Image analysis and retrieval is one of the active research field and researchers from the last decade have proposed many efficient approaches for the same. Semantic technologies like ontology offers promising approach to image retrieval as it tries to map the low level image features to high level ontology concepts. In this paper, we have proposed Semantic Image Retrieval: An Ontology based Approach which uses domain specific ontology for image retrieval relevant to the user query. The user can give concept / keyword as text input or can input the image itself. Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose. Mammals domain is used as a test case and its ontology is developed. The proposed system is trained on Mammals dataset and tested on large number of test cases related to this domain. Experimental results show the efficiency / accuracy of the proposed system and support the implementation of the same.

Author 1: Umar Manzoor
Author 2: Mohammed A. Balubaid
Author 3: Bassam Zafar
Author 4: Hafsa Umar
Author 5: M. Shoaib Khan

Keywords: Image Retrieval; Ontology; Semantic Image; Image Understanding; Semantic Retrieval

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Paper 2: Military Robotics: Latest Trends and Spatial Grasp Solutions

Abstract: A review of some latest achievements in the area of military robotics is given, with main demands to management of advanced unmanned systems formulated. The developed Spatial Grasp Technology, SGT, capable of satisfying these demands will be briefed. Directly operating with physical, virtual, and executive spaces, as well as their combinations, SGT uses high-level holistic mission scenarios that self-navigate and cover the whole systems in a super-virus mode. This brings top operations, data, decision logic, and overall command and control to the distributed resources at run time, providing flexibility, ubiquity, and capability of self-recovery in solving complex problems, especially those requiring quick reaction on unpredictable situations. Exemplary scenarios of tasking and managing robotic collectives at different conceptual levels in a special language will be presented. SGT can effectively support gradual transition to automated up to fully robotic systems under the unified command and control.

Author 1: Peter Simon Sapaty

Keywords: military robots; unmanned systems; Spatial Grasp Technology; holistic scenarios; self-navigation; collective behavior; self-recovery

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Paper 3: New Cluster Validation with Input-Output Causality for Context-Based Gk Fuzzy Clustering

Abstract: In this paper, a cluster validity concept from an unsupervised to a supervised manner is presented. Most cluster validity criterions were established in an unsupervised manner, although many clustering methods performed in supervised and semi-supervised environments that used context information and performance results of the model. Context-based clustering methods can divide the input spaces using context-clustering information that generates an output space through an input-output causality. Furthermore, these methods generate and use the context membership function and partition matrix information. Additionally, supervised clustering learning can obtain superior performance results for clustering, such as in classification accuracy, and prediction error. A cluster validity concept that deals with the characteristics of cluster validities and performance results in a supervised manner is considered. To show the extended possibilities of the proposed concept, it demonstrates three simulations and results in a supervised manner and analyzes the characteristics.

Author 1: Keun-Chang Kwak

Keywords: Cluster Validation; Fuzzy clustering; Gustafson-Kessel clustering; Fuzzy covariance; Context based clustering; Input-output causality

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Paper 4: A Method of Multi-License Plate Location in Road Bayonet Image

Abstract: To solve the problem of multi-license plate location in road bayonet image, a novel approach was presented, which utilized plate’s color features, geometry characteristics and gray feature. Firstly, the RGB color image was converted to HSV color model and calculates the distance according to the plate’s color information in the color space. Secondly, the license plate candidate regions were segmented by binary and morphological processing. Finally, based on the plate’s geometry characteristics and gray feature, the license plate regions were segmented by and validated. In a certain degree, the method wasn’t limited the plate’s type, size, number, the location of the car and the background in the picture. It was tested using the road bayonet image.(Abstract)

Author 1: Ying Qian
Author 2: Zhi Li

Keywords: multi-license plate location; color features; geometry characteristics; gray feature

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Paper 5: Accurate Topological Measures for Rough Sets

Abstract: Data granulation is considered a good tool of decision making in various types of real life applications. The basic ideas of data granulation have appeared in many fields, such as interval analysis, quantization, rough set theory, Dempster-Shafer theory of belief functions, divide and conquer, cluster analysis, machine learning, databases, information retrieval, and many others. Some new topological tools for data granulation using rough set approximations are initiated. Moreover, some topological measures of data granulation in topological information systems are defined. Topological generalizations using dß -open sets and their applications of information granulation are developed.

Author 1: A. S. Salama

Keywords: component; Knowledge Granulation; Topological Spaces; Rough Sets; Rough Approximations; Data Mining; Decision Making

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Paper 6: Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Abstract: Lung nodules are potential manifestations of lung cancer, and their early detection facilitates early treatment and improves patient’s chances for survival. For this reason, CAD systems for lung cancer have been proposed in several studies. All these works involved mainly three steps to detect the pulmonary nodule: preprocessing, segmentation of the lung and classification of the nodule candidates. This paper overviews the current state-of-the-art regarding all the approaches and techniques that have been investigated in the literature. It also provides a comparison of the performance of the existing approaches.

Author 1: H. Mahersia
Author 2: M. Zaroug
Author 3: L. Gabralla

Keywords: Classification; Computed Tomography; Lung cancer; Nodules; Segmentation

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Paper 7: Analysis of Security Protocols using Finite-State Machines

Abstract: This paper demonstrates a comprehensive analysis method using formal methods such as finite-state machine. First, we describe the modified version of our new protocol and briefly explain the encrypt-then-authenticate mechanism, which is regarded as more a secure mechanism than the one used in our protocol. Then, we use a finite-state verification to study the behaviour of each machine created for each phase of the protocol and examine their behaviours together. Modelling with finite-state machines shows that the modified protocol can function correctly and behave properly even with invalid input or time delay.

Author 1: Dania Aljeaid
Author 2: Xiaoqi Ma
Author 3: Caroline Langensiepen

Keywords: identity-based cryptosystem; cryptographic protocols; finite-state machine

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Paper 8: A Semantic-Aware Data Management System for Seismic Engineering Research Projects and Experiments

Abstract: The invention of the Semantic Web and related technologies is fostering a computing paradigm that entails a shift from databases to Knowledge Bases (KBs). There the core is the ontology that plays a main role in enabling reasoning power that can make implicit facts explicit; in order to produce better results for users. In addition, KB-based systems provide mechanisms to manage information and semantics thereof, that can make systems semantically interoperable and as such can exchange and share data between them. In order to overcome the interoperability issues and to exploit the benefits offered by state of the art technologies, we moved to KB-based system. This paper presents the development of an earthquake engineering ontology with a focus on research project management and experiments. The developed ontology was validated by domain experts, published in RDF and integrated into WordNet. Data originating from scientific experiments such as cyclic and pseudo dynamic tests were also published in RDF. We exploited the power of Semantic Web technologies, namely Jena, Virtuoso and VirtGraph tools in order to publish, storage and manage RDF data, respectively. Finally, a system was developed with the full integration of ontology, experimental data and tools, to evaluate the effectiveness of the KB-based approach; it yielded favorable outcomes.

Author 1: Md. Rashedul Hasan
Author 2: Feroz Farazi
Author 3: Oreste Bursi
Author 4: Md. Shahin Reza
Author 5: Ernesto D’Avanzo

Keywords: Ontology; Knowledge Base; Earthquake Engineering; Semantic Web; Virtuoso

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Paper 9: Using Mining Predict Relationships on the Social Media Network: Facebook (FB)

Abstract: The objective of this paper is to study on the most famous social networking site Facebook and other online social media networks (OSMNs) based on the notion of relationship or friendship. This paper discussed the methodology which can used to conduct the analysis of the social network Facebook (FB) and also define the framework of the Web Mining platform. Lastly, various technological challenges were explored which were lying under the task of extracting information from FB and discuss in detail the about crawling agent functionality.

Author 1: Dr. Mamta Madan
Author 2: Meenu Chopra

Keywords: Online Social Media Networks (OSMNs); Facebook (FB); Data Mining; Crawling Process; Protocol

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Paper 10: Software Requirements Management

Abstract: Requirements are defined as the desired set of characteristics of a product or a service. In the world of software development, it is estimated that more than half of the failures are attributed towards poor requirements management. This means that although the software functions correctly, it is not what the client requested. Modern software requirements management methodologies are available to reduce the occur-rence of such incidents. This paper performs a review on the available literature in the area while tabulating possible methods of managing requirements. It also highlights the benefits of following a proper guideline for the requirements management task. With the introduction of specific software tools for the requirements management task, better software products are now been developed with lesser resources.

Author 1: Ali Altalbe

Keywords: Software; Software Requirements; Software Devel-opment; Software Management

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Paper 11: Density Based Support Vector Machines for Classification

Abstract: Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used classification algorithm is very sensitive to these outliers and lacks the ability to discard them. Many research results prove this sensitivity which is a weak point for SVM. Different approaches are proposed to reduce the effect of outliers but no method is suitable for all types of data sets. In this paper, the new method of Density Based SVM (DBSVM) is introduced. Population Density is the basic concept which is used in this method for both linear and non-linear SVM to detect outliers. Experiments on artificial data sets, real high-dimensional benchmark data sets of Liver disorder and Heart disease, and data sets of new and fatigued banknotes’ acoustic signals can prove the efficiency of this method on noisy data classification and the better generalization that it can provide compared to the standard SVM.

Author 1: Zahra Nazari
Author 2: Dongshik Kang

Keywords: SVM; Density Based SVM; Classification; Pattern Recognition; Outlier removal

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