Search results for: attribute detection
1666 Relation between Significance of Attribute Set and Single Attribute
Authors: Xiuqin Ma, Norrozila Binti Sulaiman, Hongwu Qin
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In the research field of Rough Set, few papers concern the significance of attribute set. However, there is important relation between the significance of single attribute and that of attribute set, which should not be ignored. In this paper, we draw conclusions by case analysis that (1) the attribute set including single attributes with high significance is certainly significant, while, (2)the attribute set which consists of single attributes with low significance possibly has high significance. We validate the conclusions on discernibility matrix and the results demonstrate the contribution of our conclusions.
Keywords: relation, attribute set, single attribute, rough set, significance
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17381665 Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification
Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman
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In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26951664 Dynamic Attribute Dependencies in Relational Attribute Grammars
Authors: K. Barbar, M. Dehayni, A. Awada, M. Smaili
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Considering the theory of attribute grammars, we use logical formulas instead of traditional functional semantic rules. Following the decoration of a derivation tree, a suitable algorithm should maintain the consistency of the formulas together with the evaluation of the attributes. This may be a Prolog-like resolution, but this paper examines a somewhat different strategy, based on production specialization, local consistency and propagation: given a derivation tree, it is interactively decorated, i.e. incrementally checked and evaluated. The non-directed dependencies are dynamically directed during attribute evaluation.Keywords: Input/Output attribute grammars, local consistency, logical programming, propagation, relational attribute grammars.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14581663 Design and Implementation of an Image Based System to Enhance the Security of ATM
Authors: Seyed Nima Tayarani Bathaie
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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.
Keywords: Face detection algorithm, Haar features, Security of ATM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21081662 A Design-Based Cohesion Metric for Object-Oriented Classes
Authors: Jehad Al Dallal
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Class cohesion is an important object-oriented software quality attribute. It indicates how much the members in a class are related. Assessing the class cohesion and improving the class quality accordingly during the object-oriented design phase allows for cheaper management of the later phases. In this paper, the notion of distance between pairs of methods and pairs of attribute types in a class is introduced and used as a basis for introducing a novel class cohesion metric. The metric considers the methodmethod, attribute-attribute, and attribute-method direct interactions. It is shown that the metric gives more sensitive values than other well-known design-based class cohesion metrics.Keywords: Object-oriented software quality, object-orienteddesign, class cohesion.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22671661 Computing Entropy for Ortholog Detection
Authors: Hsing-Kuo Pao, John Case
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Biological sequences from different species are called or-thologs if they evolved from a sequence of a common ancestor species and they have the same biological function. Approximations of Kolmogorov complexity or entropy of biological sequences are already well known to be useful in extracting similarity information between such sequences -in the interest, for example, of ortholog detection. As is well known, the exact Kolmogorov complexity is not algorithmically computable. In prac-tice one can approximate it by computable compression methods. How-ever, such compression methods do not provide a good approximation to Kolmogorov complexity for short sequences. Herein is suggested a new ap-proach to overcome the problem that compression approximations may notwork well on short sequences. This approach is inspired by new, conditional computations of Kolmogorov entropy. A main contribution of the empir-ical work described shows the new set of entropy-based machine learning attributes provides good separation between positive (ortholog) and nega-tive (non-ortholog) data - better than with good, previously known alter-natives (which do not employ some means to handle short sequences well).Also empirically compared are the new entropy based attribute set and a number of other, more standard similarity attributes sets commonly used in genomic analysis. The various similarity attributes are evaluated by cross validation, through boosted decision tree induction C5.0, and by Receiver Operating Characteristic (ROC) analysis. The results point to the conclu-sion: the new, entropy based attribute set by itself is not the one giving the best prediction; however, it is the best attribute set for use in improving the other, standard attribute sets when conjoined with them.
Keywords: compression, decision tree, entropy, ortholog, ROC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18251660 Invariant Characters of Tolerance Class and Reduction under Homomorphism in IIS
Authors: Chen Wu, Lijuan Wang
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Some invariant properties of incomplete information systems homomorphism are studied in this paper. Demand conditions of tolerance class, attribute reduction, indispensable attribute and dispensable attribute being invariant under homomorphism in incomplete information system are revealed and discussed. The existing condition of endohomomorphism on an incomplete information system is also explored. It establishes some theoretical foundations for further investigations on incomplete information systems in rough set theory, like in information systems.
Keywords: Attribute reduction, homomorphism, incomplete information system, rough set, tolerance relation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7441659 Fuzzy Decision Making via Multiple Attribute
Authors: Behnaz Zohouri, Mahdi Zowghiand, Mohsen haghighi
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In this paper, a method for decision making in fuzzy environment is presented.A new subjective and objective integrated approach is introduced that used to assign weight attributes in fuzzy multiple attribute decision making (FMADM) problems and alternatives and fmally ranked by proposed method.
Keywords: Multiple Attribute Decision Making, Triangular fuzzy numbers, ranking index, Fuzzy Entropy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14721658 Studies on Properties of Knowledge Dependency and Reduction Algorithm in Tolerance Rough Set Model
Authors: Chen Wu, Lijuan Wang
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Relation between tolerance class and indispensable attribute and knowledge dependency in rough set model with tolerance relation is explored. After giving definitions and concepts of knowledge dependency and knowledge dependency degree for incomplete information system in tolerance rough set model by distinguishing decision attribute containing missing attribute value or not, the result of maintaining reflectivity, transitivity, augmentation, decomposition law and merge law for complete knowledge dependency is proved. Knowledge dependency degrees (not complete knowledge dependency degrees) only satisfy some laws after transitivity, augmentation and decomposition operations. An algorithm to solve attribute reduction in an incomplete decision table is designed. The correctness is checked by an example.Keywords: Incomplete information system, rough set, tolerance relation, knowledge dependence, attribute reduction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7271657 An Attribute Based Access Control Model with POL Module for Dynamically Granting and Revoking Authorizations
Authors: Gang Liu, Huimin Song, Can Wang, Runnan Zhang, Lu Fang
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Currently, resource sharing and system security are critical issues. This paper proposes a POL module composed of PRIV ILEGE attribute (PA), obligation and log which improves attribute based access control (ABAC) model in dynamically granting authorizations and revoking authorizations. The following describes the new model termed PABAC in terms of the POL module structure, attribute definitions, policy formulation and authorization architecture, which demonstrate the advantages of it. The POL module addresses the problems which are not predicted before and not described by access control policy. It can be one of the subject attributes or resource attributes according to the practical application, which enhances the flexibility of the model compared with ABAC. A scenario that illustrates how this model is applied to the real world is provided.Keywords: Access control, attribute based access control, granting authorizations, privilege, revoking authorizations, system security.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10571656 Heterogeneous Attribute Reduction in Noisy System based on a Generalized Neighborhood Rough Sets Model
Authors: Siyuan Jing, Kun She
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Neighborhood Rough Sets (NRS) has been proven to be an efficient tool for heterogeneous attribute reduction. However, most of researches are focused on dealing with complete and noiseless data. Factually, most of the information systems are noisy, namely, filled with incomplete data and inconsistent data. In this paper, we introduce a generalized neighborhood rough sets model, called VPTNRS, to deal with the problem of heterogeneous attribute reduction in noisy system. We generalize classical NRS model with tolerance neighborhood relation and the probabilistic theory. Furthermore, we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous attributes and construct a forward greedy algorithm for attribute reduction based on it. Experimental results show that the model is efficient to deal with noisy data.Keywords: attribute reduction, incomplete data, inconsistent data, tolerance neighborhood relation, rough sets
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15871655 Attribute Selection Methods Comparison for Classification of Diffuse Large B-Cell Lymphoma
Authors: Helyane Bronoski Borges, Júlio Cesar Nievola
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The most important subtype of non-Hodgkin-s lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately 40% of the patients suffering from it respond well to therapy, whereas the remainder needs a more aggressive treatment, in order to better their chances of survival. Data Mining techniques have helped to identify the class of the lymphoma in an efficient manner. Despite that, thousands of genes should be processed to obtain the results. This paper presents a comparison of the use of various attribute selection methods aiming to reduce the number of genes to be searched, looking for a more effective procedure as a whole.Keywords: Attribute selection, data mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14161654 A Family of Entropies on Interval-valued Intuitionistic Fuzzy Sets and Their Applications in Multiple Attribute Decision Making
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The entropy of intuitionistic fuzzy sets is used to indicate the degree of fuzziness of an interval-valued intuitionistic fuzzy set(IvIFS). In this paper, we deal with the entropies of IvIFS. Firstly, we propose a family of entropies on IvIFS with a parameter λ ∈ [0, 1], which generalize two entropy measures defined independently by Zhang and Wei, for IvIFS, and then we prove that the new entropy is an increasing function with respect to the parameter λ. Furthermore, a new multiple attribute decision making (MADM) method using entropy-based attribute weights is proposed to deal with the decision making situations where the alternatives on attributes are expressed by IvIFS and the attribute weights information is unknown. Finally, a numerical example is given to illustrate the applications of the proposed method.
Keywords: Interval-valued intuitionistic fuzzy sets, intervalvalued intuitionistic fuzzy entropy, multiple attribute decision making
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16461653 Attribute Weighted Class Complexity: A New Metric for Measuring Cognitive Complexity of OO Systems
Authors: Dr. L. Arockiam, A. Aloysius
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In general, class complexity is measured based on any one of these factors such as Line of Codes (LOC), Functional points (FP), Number of Methods (NOM), Number of Attributes (NOA) and so on. There are several new techniques, methods and metrics with the different factors that are to be developed by the researchers for calculating the complexity of the class in Object Oriented (OO) software. Earlier, Arockiam et.al has proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC) which is an extension of Weighted Class Complexity which is proposed by Mishra et.al. EWCC is the sum of cognitive weights of attributes and methods of the class and that of the classes derived. In EWCC, a cognitive weight of each attribute is considered to be 1. The main problem in EWCC metric is that, every attribute holds the same value but in general, cognitive load in understanding the different types of attributes cannot be the same. So here, we are proposing a new metric namely Attribute Weighted Class Complexity (AWCC). In AWCC, the cognitive weights have to be assigned for the attributes which are derived from the effort needed to understand their data types. The proposed metric has been proved to be a better measure of complexity of class with attributes through the case studies and experimentsKeywords: Software Complexity, Attribute Weighted Class Complexity, Weighted Class Complexity, Data Type
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21171652 Some Investigations on Higher Mathematics Scores for Chinese University Student
Authors: Xun Ge, Jingju Qian
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To investigate some relations between higher mathe¬matics scores in Chinese graduate student entrance examination and calculus (resp. linear algebra, probability statistics) scores in subject's completion examination of Chinese university, we select 20 students as a sample, take higher mathematics score as a decision attribute and take calculus score, linear algebra score, probability statistics score as condition attributes. In this paper, we are based on rough-set theory (Rough-set theory is a logic-mathematical method proposed by Z. Pawlak. In recent years, this theory has been widely implemented in the many fields of natural science and societal science.) to investigate importance of condition attributes with respective to decision attribute and strength of condition attributes supporting decision attribute. Results of this investigation will be helpful for university students to raise higher mathematics scores in Chinese graduate student entrance examination.
Keywords: Rough set, higher mathematics scores, decision attribute, condition attribute.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10841651 Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory
Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi
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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19361650 A Combined Cipher Text Policy Attribute-Based Encryption and Timed-Release Encryption Method for Securing Medical Data in Cloud
Authors: G. Shruthi, Purohit Shrinivasacharya
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The biggest problem in cloud is securing an outsourcing data. A cloud environment cannot be considered to be trusted. It becomes more challenging when outsourced data sources are managed by multiple outsourcers with different access rights. Several methods have been proposed to protect data confidentiality against the cloud service provider to support fine-grained data access control. We propose a method with combined Cipher Text Policy Attribute-based Encryption (CP-ABE) and Timed-release encryption (TRE) secure method to control medical data storage in public cloud.Keywords: Attribute, encryption, security, trapdoor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7581649 Examining the Value of Attribute Scores for Author-Supplied Keyphrases in Automatic Keyphrase Extraction
Authors: Vicky Min-How Lim, Siew Fan Wong, Tong Ming Lim
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Automatic keyphrase extraction is useful in efficiently locating specific documents in online databases. While several techniques have been introduced over the years, improvement on accuracy rate is minimal. This research examines attribute scores for author-supplied keyphrases to better understand how the scores affect the accuracy rate of automatic keyphrase extraction. Five attributes are chosen for examination: Term Frequency, First Occurrence, Last Occurrence, Phrase Position in Sentences, and Term Cohesion Degree. The results show that First Occurrence is the most reliable attribute. Term Frequency, Last Occurrence and Term Cohesion Degree display a wide range of variation but are still usable with suggested tweaks. Only Phrase Position in Sentences shows a totally unpredictable pattern. The results imply that the commonly used ranking approach which directly extracts top ranked potential phrases from candidate keyphrase list as the keyphrases may not be reliable.Keywords: Accuracy, Attribute Score, Author-supplied keyphrases, Automatic keyphrase extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13501648 Anomaly Detection and Characterization to Classify Traffic Anomalies Case Study: TOT Public Company Limited Network
Authors: O. Siriporn, S. Benjawan
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This paper represents four unsupervised clustering algorithms namely sIB, RandomFlatClustering, FarthestFirst, and FilteredClusterer that previously works have not been used for network traffic classification. The methodology, the result, the products of the cluster and evaluation of these algorithms with efficiency of each algorithm from accuracy are shown. Otherwise, the efficiency of these algorithms considering form the time that it use to generate the cluster quickly and correctly. Our work study and test the best algorithm by using classify traffic anomaly in network traffic with different attribute that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and compare it to the previously used (K-Means). Our research will be use to develop anomaly detection system to more efficiency and more require in the future.
Keywords: Unsupervised, clustering, anomaly, machine learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21111647 Further Investigations on Higher Mathematics Scores for Chinese University Students
Authors: Xun Ge
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Recently, X. Ge and J. Qian investigated some relations between higher mathematics scores and calculus scores (resp. linear algebra scores, probability statistics scores) for Chinese university students. Based on rough-set theory, they established an information system S = (U,CuD,V, f). In this information system, higher mathematics score was taken as a decision attribute and calculus score, linear algebra score, probability statistics score were taken as condition attributes. They investigated importance of each condition attribute with respective to decision attribute and strength of each condition attribute supporting decision attribute. In this paper, we give further investigations for this issue. Based on the above information system S = (U, CU D, V, f), we analyze the decision rules between condition and decision granules. For each x E U, we obtain support (resp. strength, certainty factor, coverage factor) of the decision rule C —>x D, where C —>x D is the decision rule induced by x in S = (U, CU D, V, f). Results of this paper gives new analysis of on higher mathematics scores for Chinese university students, which can further lead Chinese university students to raise higher mathematics scores in Chinese graduate student entrance examination.
Keywords: Rough set, support, strength, certainty factor, coverage factor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13681646 Intelligent Recognition of Diabetes Disease via FCM Based Attribute Weighting
Authors: Kemal Polat
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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.
Keywords: Fuzzy C-means clustering, Fuzzy C-means clustering based attribute weighting, Pima Indians diabetes dataset, SVM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17611645 A Multi-Attribute Utility Model for Performance Evaluation of Sustainable Banking
Authors: Sonia Rebai, Mohamed Naceur Azaiez, Dhafer Saidane
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In this study, we develop a performance evaluation model based on a multi-attribute utility approach aiming at reaching the sustainable banking (SB) status. This model is built accounting for various banks’ stakeholders in a win-win paradigm. In addition, it offers the opportunity for adopting a global measure of performance as an indication of a bank’s sustainability degree. This measure is referred to as banking sustainability performance index (BSPI). This index may constitute a basis for ranking banks. Moreover, it may constitute a bridge between the assessment types of financial and extra-financial rating agencies. A real application is performed on three French banks.
Keywords: Multi-attribute utility theory, Performance, Sustainable banking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22051644 The Comprehensive Study Based on Ultrasonic and X-ray Visual Technology for GIS Equipment Detection
Authors: Wei Zhang, Hong Yu, Xian-ping Zhao, Da-da Wang, Fei Xue
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For lack of the visualization of the ultrasonic detection method of partial discharge (PD), the ultrasonic detection technology combined with the X-ray visual detection method (UXV) is proposed. The method can conduct qualitative analysis accurately and conduct reliable positioning diagnosis to the internal insulation defects of GIS, and while it could make up the blindness of the X-ray visual detection method and improve the detection rate. In this paper, an experimental model of GIS is used as the trial platform, a variety of insulation defects are set inside the GIS cavity. With the proposed method, the ultrasonic method is used to conduct the preliminary detection, and then the X-ray visual detection is used to locate and diagnose precisely. Therefore, the proposed UXV technology is feasible and practical.Keywords: GIS, ultrasonic, visual detection, X-ray
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17211643 Sensory Evaluation of the Selected Coffee Products Using Fuzzy Approach
Authors: M.A. Lazim, M. Suriani
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Knowing consumers' preferences and perceptions of the sensory evaluation of drink products are very significant to manufacturers and retailers alike. With no appropriate sensory analysis, there is a high risk of market disappointment. This paper aims to rank the selected coffee products and also to determine the best of quality attribute through sensory evaluation using fuzzy decision making model. Three products of coffee drinks were used for sensory evaluation. Data were collected from thirty judges at a hypermarket in Kuala Terengganu, Malaysia. The judges were asked to specify their sensory evaluation in linguistic terms of the quality attributes of colour, smell, taste and mouth feel for each product and also the weight of each quality attribute. Five fuzzy linguistic terms represent the quality attributes were introduced prior analysing. The judgment membership function and the weights were compared to rank the products and also to determine the best quality attribute. The product of Indoc was judged as the first in ranking and 'taste' as the best quality attribute. These implicate the importance of sensory evaluation in identifying consumers- preferences and also the competency of fuzzy approach in decision making.Keywords: fuzzy decision making, fuzzy linguistic, membership function, sensory evaluation,
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27791642 Adversarial Disentanglement Using Latent Classifier for Pose-Independent Representation
Authors: Hamed Alqahtani, Manolya Kavakli-Thorne
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The large pose discrepancy is one of the critical challenges in face recognition during video surveillance. Due to the entanglement of pose attributes with identity information, the conventional approaches for pose-independent representation lack in providing quality results in recognizing largely posed faces. In this paper, we propose a practical approach to disentangle the pose attribute from the identity information followed by synthesis of a face using a classifier network in latent space. The proposed approach employs a modified generative adversarial network framework consisting of an encoder-decoder structure embedded with a classifier in manifold space for carrying out factorization on the latent encoding. It can be further generalized to other face and non-face attributes for real-life video frames containing faces with significant attribute variations. Experimental results and comparison with state of the art in the field prove that the learned representation of the proposed approach synthesizes more compelling perceptual images through a combination of adversarial and classification losses.Keywords: Video surveillance, disentanglement, face detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6061641 Efficient Signal Detection Using QRD-M Based On Channel Condition in MIMO-OFDM System
Authors: Jae-Jeong Kim, Ki-Ro Kim, Hyoung-Kyu Song
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In this paper, we propose an efficient signal detector that switches M parameter of QRD-M detection scheme is proposed for MIMO-OFDM system. The proposed detection scheme calculates the threshold by 1-norm condition number and then switches M parameter of QRD-M detection scheme according to channel information. If channel condition is bad, the parameter M is set to high value to increase the accuracy of detection. If channel condition is good, the parameter M is set to low value to reduce complexity of detection. Therefore, the proposed detection scheme has better tradeoff between BER performance and complexity than the conventional detection scheme. The simulation result shows that the complexity of proposed detection scheme is lower than QRD-M detection scheme with similar BER performance.
Keywords: MIMO-OFDM, QRD-M, Channel condition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20211640 Moving Vehicles Detection Using Automatic Background Extraction
Authors: Saad M. Al-Garni, Adel A. Abdennour
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Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.
Keywords: Image processing, Automatic background extraction, Moving vehicle detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24231639 Evaluation of Attribute II Bt Sweet Corn Resistance and Reduced-Risk Insecticide Applications for Control of Corn Earworm
Authors: R. Weinzierl, R. Estes, N. Tinsley, M. Keshlaf
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The corn earworm, Helicoverpa zea Boddie, is a serious pest of corn. Larval feeding in ear tips destroys kernels and allows growth of fungi and production of mycotoxins. Infested sweet corn is not marketable. Development of improved transgenic hybrids expressing insecticidal toxins from Bacillus thuringiensis (Bt) may limit or prevent crop losses. The effectiveness of Attribute® II Bt resistance and applications of Voliam Xpress insecticide were evaluated for effectiveness in controlling corn earworm in plots near Urbana, IL, USA, in 2013. Where no insecticides were applied, ear infestations and kernel damage in Attribute® II ‘Protector’ plots were consistently lower (near zero) than in plots of the non-Bt isoline ‘Garrison.’ Multiple applications of Voliam Xpress significantly reduced the number of corn earworm larvae and kernel damage in the Garrison plots, but infestations and damage in these plots were greater than in Protectorplots that did not receive insecticide applications. Our results indicate that Attribute® II Bt resistance is more effective than multiple applications of an insecticide for preventing losses caused by corn earworm in sweet corn.
Keywords: Bacillus thuringiensis, Helicoverpa zea, insect pest management, transgenic sweet corn.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22211638 Attribute Based Comparison and Selection of Modular Self-Reconfigurable Robot Using Multiple Attribute Decision Making Approach
Authors: Manpreet Singh, V. P. Agrawal, Gurmanjot Singh Bhatti
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From the last decades, there is a significant technological advancement in the field of robotics, and a number of modular self-reconfigurable robots were introduced that can help in space exploration, bucket to stuff, search, and rescue operation during earthquake, etc. As there are numbers of self-reconfigurable robots, choosing the optimum one is always a concern for robot user since there is an increase in available features, facilities, complexity, etc. The objective of this research work is to present a multiple attribute decision making based methodology for coding, evaluation, comparison ranking and selection of modular self-reconfigurable robots using a technique for order preferences by similarity to ideal solution approach. However, 86 attributes that affect the structure and performance are identified. A database for modular self-reconfigurable robot on the basis of different pertinent attribute is generated. This database is very useful for the user, for selecting a robot that suits their operational needs. Two visual methods namely linear graph and spider chart are proposed for ranking of modular self-reconfigurable robots. Using five robots (Atron, Smores, Polybot, M-Tran 3, Superbot), an example is illustrated, and raking of the robots is successfully done, which shows that Smores is the best robot for the operational need illustrated, and this methodology is found to be very effective and simple to use.
Keywords: Self-reconfigurable robots, MADM, TOPSIS, morphogenesis, scalability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8881637 Detection and Classification of Power Quality Disturbances Using S-Transform and Wavelet Algorithm
Authors: Mohamed E. Salem Abozaed
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Detection and classification of power quality (PQ) disturbances is an important consideration to electrical utilities and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. S-transform algorithm and continuous wavelet transforms (CWT) are time-frequency algorithms, and both of them are powerful in detection and classification of PQ disturbances. This paper presents detection and classification of PQ disturbances using S-transform and CWT algorithms. The results of detection and classification, provides that S-transform is more accurate in detection and classification for most PQ disturbance than CWT algorithm, where as CWT algorithm more powerful in detection in some disturbances like notchingKeywords: CWT, Disturbances classification, Disturbances detection, Power quality, S-transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2598