Search results for: zernike moment feature descriptor

Authors: Tauqir A. Moughal, Fusheng Yu, Abeer Mazher

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Land-cover and land-use change information are important because of their practical uses in various applications, including deforestation, damage assessment, disasters monitoring, urban expansion, planning, and land management. Therefore, developing change detection methods for remote sensing images is an important ongoing research agenda. However, detection of change through optical remote sensing images is not a trivial task due to many factors including the vagueness between the boundaries of changed and unchanged regions and spatial dependence of the pixels to its neighborhood. In this paper, we propose a binary change detection technique for bi-temporal optical remote sensing images. As in most of the optical remote sensing images, the transition between the two clusters (change and no change) is overlapping and the existing methods are incapable of providing the accurate cluster boundaries. In this regard, a methodology has been proposed which uses the fuzzy c-means clustering to tackle the problem of vagueness in the changed and unchanged class by formulating the soft boundaries between them. Furthermore, in order to exploit the neighborhood information of the pixels, the input patterns are generated corresponding to each pixel from bi-temporal images using 3×3, 5×5 and 7×7 window. The between images and within image spatial dependence of the pixels to its neighborhood is quantified by using Pearson product moment correlation and Moran’s I statistics, respectively. The proposed technique consists of two phases. At first, between images and within image spatial correlation is calculated to utilize the information that the pixels at different locations may not be independent. Second, fuzzy c-means technique is used to produce two clusters from input feature by not only taking care of vagueness between the changed and unchanged class but also by exploiting the spatial correlation of the pixels. To show the effectiveness of the proposed technique, experiments are conducted on multispectral and bi-temporal remote sensing images. A subset (2100×1212 pixels) of a pan-sharpened, bi-temporal Landsat 5 thematic mapper optical image of Los Angeles, California, is used in this study which shows a long period of the forest fire continued from July until October 2009. Early forest fire and later forest fire optical remote sensing images were acquired on July 5, 2009 and October 25, 2009, respectively. The proposed technique is used to detect the fire (which causes change on earth’s surface) and compared with the existing K-means clustering technique. Experimental results showed that proposed technique performs better than the already existing technique. The proposed technique can be easily extendable for optical hyperspectral images and is suitable for many practical applications.

Keywords: burned area, change detection, correlation, fuzzy clustering, optical remote sensing

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Authors: Peifei Zhu, Zisheng Li, Yasuki Kakishita, Mayumi Suzuki, Tomoaki Chono

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Segmentation of left ventricle (LV) from cardiac ultrasound images provides a quantitative functional analysis of the heart to diagnose disease. Active Shape Model (ASM) is a widely used approach for LV segmentation but suffers from the drawback that initialization of the shape model is not sufficiently close to the target, especially when dealing with abnormal shapes in disease. In this work, a two-step framework is proposed to improve the accuracy and speed of the model-based segmentation. Firstly, a robust and efficient detector based on Hough forest is proposed to localize cardiac feature points, and such points are used to predict the initial fitting of the LV shape model. Secondly, to achieve more accurate and detailed segmentation, ASM is applied to further fit the LV shape model to the cardiac ultrasound image. The performance of the proposed method is evaluated on a dataset of 800 cardiac ultrasound images that are mostly of abnormal shapes. The proposed method is compared to several combinations of ASM and existing initialization methods. The experiment results demonstrate that the accuracy of feature point detection for initialization was improved by 40% compared to the existing methods. Moreover, the proposed method significantly reduces the number of necessary ASM fitting loops, thus speeding up the whole segmentation process. Therefore, the proposed method is able to achieve more accurate and efficient segmentation results and is applicable to unusual shapes of heart with cardiac diseases, such as left atrial enlargement.

Keywords: hough forest, active shape model, segmentation, cardiac left ventricle

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Tomas Macsotay

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If monument removal and monument effacement may call to mind a Nietzschean proposal for vitalist disregard of conventional morality, it remains the case that it is often only by a willingness to go “beyond good and evil” in inherited monument politics that truthful, be it unexpected aspects of our co-existence with monuments can finally start to rise into fuller consciousness. A series of urgent questions press themselves in the panorama created by the affirmative idea that we can, as a community, make crucial decisions with regard to monumental preservation or discontinuation. Memorials are not the core concern for decolonial and racial dignity movements like Black Lives Matter (BLM), which have repeatedly shown they regard these actions as a welcome, albeit complementary, part of a reckoning with a past of racial violence and injustice, slavery, and colonial subaltern existence. As such, the iconoclastic issue of “rights and prohibitions of images” only tangentially touches on a cultural movement that seems rather question dominant ideas of history, pertinence, and the long life of the class, gender, and racial conflict through ossified memorial cultures. In the recent monument insurrection, we face a rare case of a new negotiation of rights of existence for this particular tract of material culture. This engenders a debate on how and why we accord rights to objects in public dominion ― indeed, how such rights impinge upon the rights of subjects who inhabit the public sphere. Incidentally, the possibility of taking away from monuments such imagined or adjoined rights has made it possible to tease open a sphere of emotionality that could not be expressed in patrimonial thinking: the reality of atmospheres as settings, often dependent on pseudo-objects and half-conscious situations, that situate individuals involuntarily in a pathic aesthetics. In this way, the unique moment we now witness ― full of the possibility of going “beyond good and evil” of monument preservation ― starts to look more like a moment of involuntary awaking: an awakening to the encrypted gaze of the monument and the enigma that the same monument or memorial site can carry day-to-day habits of life for some bystanders, while racialized and disenfranchised communities experience discomfort and erosion of subjective life in the same sites.

Keywords: monument, memorial, atmosphere, racial justice, decolonialism

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Authors: Ashutosh Saxena, Kumar Pallav, Ramji Dwivedi

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The present study estimates the seismicity parameter 'b' and maximum possible magnitude of an earthquake (Mmax) for Gujarat region with three well-established methods viz. Kijiko parametric model (KP), Kijiko-Sellevol-Bayern (KSB) and Tapered Gutenberg-Richter (TGR), as a combined seismic source regime. The earthquake catalogue is prepared for a period of 1330 to 2013 in the region Latitudes 20o N to 250 N and Longitudinally extending from 680 to 750 E for earthquake moment magnitude (Mw) ≥4.0. The ’a’ and 'b' value estimated for the region as 4.68 and 0.58. Further, Mmax estimated as 8.54 (± 0.29), 8.69 (± 0.48), and 8.12 with KP, KSB, and TGR, respectively.

Keywords: Mmax, seismicity parameter, Gujarat, Tapered Gutenberg-Richter

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Authors: M. A. Lahmer, K. Guergouri

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The ferromagnetic properties of carbon-doped ZnO (ZnO:CO) and hydrogenated carbon-doped ZnO (ZnO:CO+H) are investigated using the density functional tight binding (DFTB) method. Our results reveal that CO-doped ZnO is a ferromagnetic material with a magnetic moment of 1.3 μB per carbon atom. The presence of hydrogen in the material in the form of CO-H complex decreases the total magnetism of the material without suppressing ferromagnetism. However, the system in this case becomes quickly antiferromagnetic when the C-C separation distance was increased.

Keywords: ZnO, carbon, hydrogen, ferromagnetism, density functional tight binding

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

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Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Stacy Jarvis

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A silent culmination is a musical technique that creates or increases tension in a piece of music. It is a type of cadence in which the music gradually builds to a climax but suddenly stops without any resolution. This technique can create suspense and anticipation in the listener as they wait to find out what will happen next. It can also draw attention to a particular element of the music, such as a particular instrument or vocal line. Silent culminations can evoke a sense of mystery or ambiguity by not resolving the tension created. This technique has been used by composers of all musical genres, from classical to jazz, as well as in film scores. Silent culminations can also make a piece of music more dynamic and exciting. Verdi’s Aida is a classic example of the use of silent culminations to create tension and suspense. Throughout the opera, Verdi uses a technique of gradually building to a climax, only to abruptly stop without any resolution. This technique brings out the story's drama and intensity and creates anticipation for the climactic moments. For example, at the end of the second act, Verdi reaches a crescendo of tension as Aida and Radamès swear their undying love for one another, only to stop with a moment of silence. This technique also helps to draw attention to the important moments in the story, such as the duets between Aida and Radamès. By stopping the music just before it resolves, Verdi can create an atmosphere of anticipation and suspense that carries through to the opera's end. Silent culminations are used greatly in Aida and are integral to Verdi’s dramatic style. In his symphonic poem Mazeppa, Tchaikovsky uses silent culminations to emphasize the piece's drama and powerful emotions. The piece begins with a gentle introduction but quickly builds to a powerful climax. Throughout the piece, Tchaikovsky uses silent culminations to create tension and suspense, drawing the listener in and heightening the intensity of the music 2. The most dramatic moment of the piece comes when the music builds to a frantic climax and then suddenly cuts out, leaving the listener hanging in anticipation of what will happen next. This technique creates an intense atmosphere and leaves the listener eager to hear what comes next. In addition, the use of silent culminations helps to emphasize the strong emotions of the piece, such as fear, horror, and despair. By not resolving the tension with a resolution, the listener is left with a feeling of uneasiness and uncertainty that helps to convey the story of Mazeppa’s tragic fate.

Keywords: Verdi, Tchaikovsky, opera, culmination

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Authors: K. A. Adepoju, O. I. Shittu, A. U. Chukwu

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In this paper, a new four-parameter generalized version of the Fisher Snedecor distribution called Beta- F distribution is introduced. The comprehensive account of the statistical properties of the new distributions was considered. Formal expressions for the cumulative density function, moments, moment generating function and maximum likelihood estimation, as well as its Fisher information, were obtained. The flexibility of this distribution as well as its robustness using cancer remission time data was demonstrated. The new distribution can be used in most applications where the assumption underlying the use of other lifetime distributions is violated.

Keywords: fisher-snedecor distribution, beta-f distribution, outlier, maximum likelihood method

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Guilherme Gomes, Jose Lebre de Freitas

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The rules of Civil Procedure that are defined in the Portuguese Civil Procedure Code of 2013 particularly their articles 552 to 626- represent the model that the legislator thought that would be more suitable for national civil litigation, from the moment the action is brought by the plaintiff to the moment when the sentence is issued. However, procedural legalism is no longer a reality in the Portuguese Civil Procedural Law. According to the article 547 of the code of 2013, the civil judge has a duty to adopt the procedure that better suits the circumstances of the case, whether or not it is the one defined by law. The main goal of our paper is to answer the question whether the formal adequation imposed by this article diminishes the importance of the Portuguese rules of Civil Procedure and their daily application by national civil judges. We will start by explaining the appearance of the abovementioned rules in the Civil Procedure Code of 2013. Then we will analyse, using specific examples that were obtained by the books we read, how the legal procedure defined in the abovementioned code does not suit the circumstances of some specific cases and is totally inefficient in some situations. After that, we will, by using the data obtained in the practical research that we are conducting in the Portuguese civil courts within the scope of our Ph.D. thesis (until now, we have been able to consult 150 civil lawsuits), verify whether and how judges and parties make the procedure more efficient and effective in the case sub judice. In the scope of our research, we have already reached some preliminary findings: 1) despite the fact that the legal procedure does not suit the circumstances of some civil lawsuits, there are only two situations of frequent use of formal adequation (the judge allowing the plaintiff to respond to the procedural exceptions deduced in the written defense and the exemption from prior hearing for the judges who never summon it), 2) the other aspects of procedural adequation (anticipation of the production of expert evidence, waiving of oral argument at the final hearing, written allegations, dismissal of the dispatch on the controversial facts and the examination of witnesses at the domicile of one of the lawyers) are still little used and 3) formal adequation tends to happen by initiative of the judge, as plaintiffs and defendants are afraid of celebrating procedural agreements in most situations. In short, we can say that, in the Portuguese legal order of the 21st century, the flexibility of the legal procedure, as it is defined in the law and applied by procedural subjects, does not affect the importance of the rules of Civil Procedure of the code of 2013.

Keywords: casuistic adequation, civil procedure code of 2013, procedural subjects, rules of civil procedure

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Authors: Serkan Caliskan

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The aim of this study is to understand the spin-resolved properties of ZnO armchair and zigzag nanoribbons. The spin polarization can be induced by either geometry of the nanoribbons or ferromagnetic electrodes. Hence, spin-dependent behavior is revealed in these nanostructures in the absence of external magnetic field. Both electronic structure and magnetic properties of the nanoribbons are analyzed, employing first-principles calculations through Density Functional Theory. The relevant properties using the spin-dependent band structure, conductance, transmission, density of states and magnetic moment are elucidated. These results can be utilized to describe the nanoscale structures and stimulate the experimental works.

Keywords: first principles, spin polarized transport, ZnO device, ZnO nanoribbons

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Authors: Caroline Nnokwe, Iheanyi Eneremadu

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Teachers and principals are vital and integral parts of the educational system. For educational objectives to be met, the role of teachers and the functions of the principals are not to be overlooked. However, the inability of teachers and principals to carry out their roles effectively has impacted the outcome of the students’ performance. The study, therefore, examined teachers’ roles and principal’s administrative functions as correlates of effective academic performance of public secondary school students in Imo state, Nigeria. Four research questions and two hypotheses guided the study. The study adopted a correlation research design. The sample size was 5,438 respondents via the Yaro-Yamane technique, which consists of 175 teachers, 13 principals and 5,250 students using the proportional stratified random sampling technique. The instruments for data collection were a researcher-made questionnaire titled Teachers’ Role/Principals’ Administrative Functions Questionnaire (TRPAFQ) with a Cronbach Alpha coefficient of .82 and student's internal results obtained from the school authorities. Data collected were analyzed using the Pearson product-moment correlation coefficient and simple linear regression. Research questions were answered using Pearson Product Moment Correlation statistics, while the hypotheses were tested at 0.05 level of significance using regression analysis. The findings of the study showed that the educational qualification of teachers, organizing, and planning correlated student’s academic performance to a great extent, while availability and proper use of instructional materials by teachers correlated the academic performance of students to a very high extent. The findings also revealed that there is a significant relationship between teachers’ role, principals’ administrative functions and student’s academic performance of public secondary schools in Imo State, The study recommended among others that there is the need for government, through the ministry of education, and education authorities to adequately staff their supervisory department in order to carry out proper supervision of secondary school teachers, and also provide adequate instructional materials to ensure greater academic performance among secondary school students of Imo state, Nigeria.

Keywords: instructional materials, principals’ administrative functions, students’ academic performance, teacher role

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Authors: Salem Alghamdi

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Genotypes collected from farmers at a different region of Saudi Arabia as well as from Egyptian cultivar and a new line from Yamen. Seeds of these genotypes were grown in Dirab Agriculture Research Station, (Middle Region) and Al-Ahsa Palms and Dates Research Center (East region), during summer of 2015. Field experiments were laid out in randomized complete block design on the first week of June with three replications. Each experiment plot contained 6 rows 3m in length. Inter- and intra-row spacing was 60 and 25cm, respectively. Seed yield and its components were estimated in addition to qualitative characters on cowpea plants grown only in Dirab using cowpea descriptor from IPGRI, 1982. Seeds for chemical composite and antioxidant contents were analyzed. Highly significant differences were detected between genotypes in both locations and the combined of two locations for seed yield and its components. Mean data clearly show exceeded determine genotypes in seed yield while indeterminate genotypes had higher biological yield that divided cowpea genotypes to two main groups 1- forage genotypes (KSU-CO98, KSU-CO99, KSU-CO100, and KSU-CO104) that were taller and produce higher branches, biological yield and these are suitable to feed on haulm 2- food genotypes (KSU-CO101, KSU-CO102, and KSU-CO103) that produce higher seed yield with lower haulm and also these genotypes characters by high seed index and light seed color. Highly significant differences were recorded for locations in all studied characters except the number of branches, seed index, and biological yield, however, the interaction of genotype x location was significant only for plant height, the number of pods and seed yield per plant.

Keywords: Cowpea, genotypes, antioxidant contents, yield

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Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Hongjian Jia

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A finite-length cylindrical shell with a spherical cap is a typical engineering approximation model of actual underwater targets. The research on the omni-directional acoustic scattering characteristics of this target model can provide a favorable basis for the detection and identification of actual underwater targets. The elastic resonance characteristics of the target are the results of the comprehensive effect of the target length, shell-thickness ratio and materials. Under the conditions of different materials and geometric dimensions, the coincidence resonance characteristics of the target have obvious differences. Aiming at this problem, this paper obtains the omni-directional acoustic scattering field of the underwater hemispherical cylindrical shell by numerical calculation and studies the influence of target geometric parameters (length, shell-thickness ratio) and material parameters on the coincidence resonance characteristics of the target in turn. The study found that the formant interval is not a stable value and changes with the incident angle. Among them, the formant interval is less affected by the target length and shell-thickness ratio and is significantly affected by the material properties, which is an effective feature for classifying and identifying targets of different materials. The quadratic polynomial is utilized to fully fit the change relationship between the formant interval and the angle. The results show that the three fitting coefficients of the stainless steel and aluminum targets are significantly different, which can be used as an effective feature parameter to characterize the target materials.

Keywords: hemispherical cylindrical shell;, fine echo characteristics;, geometric and material parameters;, formant interval

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

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Authors: Wajih Abbassi, Zouhaier Ben Khelifa

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The present research points towards the empirical validation of three options valuation models, the ad-hoc Black-Scholes model as proposed by Berkowitz (2001), the constant elasticity of variance model of Cox and Ross (1976) and the Kou jump-diffusion model (2002). Our empirical analysis has been conducted on a sample of 26,974 options written on three indexes, the S&P 500, Nasdaq 100 and the Russell 2000 that were negotiated during the year 2007 just before the sub-prime crisis. We start by presenting the theoretical foundations of the models of interest. Then we use the technique of trust-region-reflective algorithm to estimate the structural parameters of these models from cross-section of option prices. The empirical analysis shows the superiority of the Kou jump-diffusion model. This superiority arises from the ability of this model to portray the behavior of market participants and to be closest to the true distribution that characterizes the evolution of these indices. Indeed the double-exponential distribution covers three interesting properties that are: the leptokurtic feature, the memory less property and the psychological aspect of market participants. Numerous empirical studies have shown that markets tend to have both overreaction and under reaction over good and bad news respectively. Despite of these advantages there are not many empirical studies based on this model partly because probability distribution and option valuation formula are rather complicated. This paper is the first to have used the technique of nonlinear curve-fitting through the trust-region-reflective algorithm and cross-section options to estimate the structural parameters of the Kou jump-diffusion model.

Keywords: jump-diffusion process, Kou model, Leptokurtic feature, trust-region-reflective algorithm, US index options

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Authors: Yulan Wu

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The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

Procedia PDF Downloads 391

Authors: Gong Zhilin, Jing Yang, Jian Yin

Abstract:

The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 131

Authors: Andrea Leon, J. M. Florez, P. Vargas

Abstract:

The effect of hydrogen adsorption on the magnetic properties of fcc Ni has been calculated using the linear-muffin-tin-orbital formalism and using the local-density approximation for the exchange y correlation. The calculations for the ground state show that the sequential addition of hydrogen atoms is found to monotonically reduce the total magnetic moment of the Ni fcc structure, as a result of changes in the exchange-splitting parameter and in the Fermi energy. In order to physically explain the effect of magnetization reduction as the Hydrogen concentration increases, we propose a Stoner impurity model to describe the influence of H impurity on the magnetic properties of Nickel.

Keywords: electronic structure, magnetic properties, Nickel hydride, stoner model

Procedia PDF Downloads 459

Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

Abstract:

Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: autonomous vehicles, deformable part model, dpm, pedestrian detection, real time

Procedia PDF Downloads 281

Authors: K. N. Dinesh Babu, P. K. Gargava

Abstract:

Energization of a transformer results in sudden flow of current which is an effect of core magnetization. This current will be dominated by the presence of second harmonic, which in turn is used to segregate fault and inrush current, thus guaranteeing proper operation of the relay. This additional security in the relay sometimes obstructs or delays differential protection in a specific scenario, when the 2^nd harmonic content was present during a genuine fault. This kind of scenario can result in isolation of the transformer by Buchholz and pressure release valve (PRV) protection, which is acted when fault creates more damage in transformer. Such delays involve a huge impact on the insulation failure, and chances of repairing or rectifying fault of problem at site become very dismal. Sometimes this delay can cause fire in the transformer, and this situation becomes havoc for a sub-station. Such occurrences have been observed in field also when differential relay operation was delayed by 10-15 ms by second harmonic blocking in some specific conditions. These incidences have led to the need for an alternative solution to eradicate such unwarranted delay in operation in future. Modern numerical relay, called as intelligent electronic device (IED), is embedded with advanced protection features which permit higher flexibility and better provisions for tuning of protection logic and settings. Such flexibility in transformer protection IEDs, enables incorporation of alternative methods such as dynamic switching of second harmonic feature for blocking the differential protection with additional security. The analysis and precautionary measures carried out in this case, have been simulated and discussed in this paper to ensure that similar solutions can be adopted to inhibit analogous issues in future.

Keywords: differential protection, intelligent electronic device (IED), 2nd harmonic inhibit, inrush inhibit

Procedia PDF Downloads 300

Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

Abstract:

Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

Procedia PDF Downloads 73

Authors: Adrian Forca, Simeon Cainday III

Abstract:

The Electronic Payment Recording with Payment History Retrieval Module is developed intendedly for the College of Science and Technology. This system software innovates the manual process of recording the payments done in the department through the development of electronic payment recording system software shifting from the slow and time-consuming procedure to quick yet reliable and accurate way of recording payments because it immediately generates receipts for every transaction. As an added feature to its software process, generation of recorded payment report is integrated eliminating the manual reporting to a more easy and consolidated report. As an added feature to the system, all recorded payments of the students can be retrieved immediately making the system transparent and reliable payment recording software. Viewing the whole process, the system software will shift from the manual process to an organized software technology because the information will be stored in a logically correct and normalized database. Further, the software will be developed using the modern programming language and implement strict programming methods to validate all users accessing the system, evaluate all data passed into the system and information retrieved to ensure data accuracy and reliability. In addition, the system will identify the user and limit its access privilege to establish boundaries of the specific access to information allowed for the store, modify, and update making the information secure against unauthorized data manipulation. As a result, the System software will eliminate the manual procedure and replace with an innovative modern information technology resulting to the improvement of the whole process of payment recording fast, secure, accurate and reliable software innovations.

Keywords: collection, information system, manual procedure, payment

Procedia PDF Downloads 166

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

Abstract:

In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

Procedia PDF Downloads 133

Authors: Hee Yong Kang, Hyeon Ho Shin, Jung Cheol Yoo, Il Taek Lee, Sung Mo Yang

Abstract:

Lightweight technology using composites is being developed for vehicle seat structures, and its design must meet the safety requirements. According to the Federal Motor Vehicle Safety Standard (FMVSS) 207 seating systems test procedure, the back moment load is applied to the seat back frame structure for the safety evaluation of the vehicle seat. The seat back frame using the composites is divided into three parts: upper part frame, and left- and right-side frame parts following the manufacturing process. When a rear moment load is applied to the seat back frame, the side frame receives the bending load and the torsional load at the same time. This results in the largest loaded strength. Therefore, strength test of the component unit is required. In this study, a component test method based on the FMVSS 207 seating systems test procedure was proposed for the strength analysis of bending load and torsional load of the automotive Bulk Molding Compound (BMC) Seat Back Side Frame. Moreover, strength evaluation according to the carbon band reinforcement was performed. The back-side frame parts of the seat that are applied to the test were manufactured through BMC that is composed of vinyl ester Matrix and short carbon fiber. Then, two kinds of reinforced and non-reinforced parts of carbon band were formed through a high-temperature compression molding process. In addition, the structure that is applied to the component test was constructed by referring to the FMVSS 207. Then, the bending load and the torsional load were applied through the displacement control to perform the strength test for four load conditions. The results of each test are shown through the load-displacement curves of the specimen. The failure strength of the parts caused by the reinforcement of the carbon band was analyzed. Additionally, the fracture characteristics of the parts for four strength tests were evaluated, and the weakness structure of the back-side frame of the seat structure was confirmed according to the test conditions. Through the bending and torsional strength test methods, we confirmed the strength and fracture characteristics of BMC Seat Back Side Frame according to the carbon band reinforcement. And we proposed a method of testing the part strength of a seat back frame for vehicles that can meet the FMVSS 207.

Keywords: seat back frame, bending and torsional strength, BMC (Bulk Molding Compound), FMVSS 207 seating systems

Procedia PDF Downloads 210