Search results for: snoring detection

Authors: Nougrara Zineb

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The unique structure of the blood vessels in the retina has been used for biometric identification. The retina blood vessel pattern is a unique pattern in each individual and it is almost impossible to forge that pattern in a false individual. The retina biometrics’ advantages include high distinctiveness, universality, and stability overtime of the blood vessel pattern. Once the creases have been extracted from the images, a registration stage is necessary, since the position of the retinal vessel structure could change between acquisitions due to the movements of the eye. Image registration consists of following steps: Feature detection, feature matching, transform model estimation and image resembling and transformation. In this paper, we present an algorithm of registration; it is based on the characterization of retinal feature points. For experiments, retinal images from the DRIVE database have been tested. The proposed methodology achieves good results for registration in general.

Keywords: fovea, optic disc, registration, retinal images

Procedia PDF Downloads 247

Authors: Gulten Yilmaz, A. Beril Tugrul, Mustafa Demir, Dogan Yasar, Bayram Demir, Bulent Buyuk

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A Positron Emission Tomography (PET) is a radioisotope imaging technique that illustrates the organs and the metabolisms of the human body. This technique is based on the simultaneous detection of 511 keV annihilation photons, annihilated as a result of electrons annihilating positrons that radiate from positron-emitting radioisotopes that enter biological active molecules in the body. This study was conducted on ten patients in an effort to conduct patient-related experimental studies. Dosage monitoring for the bladder, which was the organ that received the highest dose during PET applications, was conducted for 24 hours. Assessment based on measuring urination activities after injecting patients was also a part of this study. The MIRD method was used to conduct dosage calculations for results obtained from experimental studies. Results obtained experimentally and theoretically were assessed comparatively.

Keywords: PET/CT, TLD, MIRD, dose measurement, patient doses

Procedia PDF Downloads 503

Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

Procedia PDF Downloads 47

Authors: Andriana Mkrtchyan, Vahe Khlghatyan

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The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

Procedia PDF Downloads 46

Authors: Aran Abd Al Rahman

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Joubert syndrome regards as a congenital cerebellar ataxia caused by autosomal recessive carried on X chromosome. The disease diagnosed by brain imaging—the so-called molar tooth sign. Neurological signs were present from the neonatal period and include hypotonia progressing to ataxia, global developmental delay, ocular motor apraxia, and breathing dysregulation. These signs are variably associated with multiorgan involvement, mainly of the retina, kidneys, skeleton, and liver. 30 causative genes have been identified so far, all of which encode for proteins of the primary cilium or its apparatus, The purpose of our project was to detect the mutant gene (INPP5E gene) which cause Joubert syndrome. There were many methods used for diagnosis such as MRI and CT- scan and molecular diagnosis by doing ARMS PCR for detection of mutant gene that we were used in this research project. In this research for individual family which reported, the two children with parents, the two children were affected and were carrier.

Keywords: Joubert syndrome, genetic disease, Kurdistan region, Sulaimani

Procedia PDF Downloads 119

Authors: Yahia. Kourd, N. Guersi D. Lefebvre

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In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor.

Keywords: faults diagnosis, neural networks, multi-models, squirrel-cage induction motor

Procedia PDF Downloads 610

Authors: Dulal Bhaumik, Fei Jie, Runa Bhaumik, Bikas Sinha

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Human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and autism. fMRI has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose mixed-effects models together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities in whole brain studies. Results are illustrated with a large data set known as Autism Brain Imaging Data Exchange or ABIDE which includes 361 subjects from 8 medical centers. We believe that our findings have addressed adequately the small sample inference problem, and thus are more reliable for therapeutic target for intervention. In addition, our result can be used for early detection of subjects who are at high risk of developing neurological disorders.

Keywords: ABIDE, autism spectrum disorder, fMRI, mixed-effects model

Procedia PDF Downloads 263

Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have played an indispensable role in controlling illness and death associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global healthcare problem. Many antibiotics had lost their effectiveness since the beginning of the antibiotic era because many bacteria have adapted defenses against these antibiotics. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing require the isolation of the pathogen from a clinical specimen by culturing on the appropriate media (this culturing stage lasts 24 h-first culturing). Then, chosen colonies are grown on media containing antibiotic(s), using micro-diffusion discs (second culturing time is also 24 h) in order to determine its bacterial susceptibility. Other methods, genotyping methods, E-test and automated methods were also developed for testing antimicrobial susceptibility. Most of these methods are expensive and time-consuming. Fourier transform infrared (FTIR) microscopy is rapid, safe, effective and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria; nonetheless, its true potential in routine clinical diagnosis has not yet been established. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The UTI E.coli bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 700 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 90% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E.coli, FTIR, multivariate analysis, susceptibility, UTI

Procedia PDF Downloads 154

Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

Procedia PDF Downloads 435

Authors: Illyas Isa, Mohamad Idris Saidin, Mustaffa Ahmad, Norhayati Hashim

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A highly selective and sensitive electrode for determination of trace amounts of Cu (II) using square wave anodic stripping voltammetry (SWASV) was proposed. The electrode was made of the paste of multiwall carbon nanotubes (MWCNT) and 2,6–diacetylpyridine-di-(1R)–(-)–fenchone diazine tetracarbonylmolybdenum(0) at 100:5 (w/w). Under optimal conditions the electrode showed a linear relationship with concentration in the range of 1.0 × 10–10 to 1.0 × 10– 6 M Cu (II) and limit of detection 8.0 × 10–11 M Cu (II). The relative standard deviation (n = 5) of response to 1.0 × 10–6 M Cu(II) was 0.036. The interferences of cations such as Ni(II), Mg(II), Cd(II), Co(II), Hg(II), and Zn(II) (in 10 and 100-folds concentration) are negligible except from Pb (II). Electrochemical impedance spectroscopy (EIS) showed that the charge transfer at the electrode-solution interface was favorable. Result of analysis of Cu(II) in several water samples agreed well with those obtained by inductively coupled plasma-optical emission spectrometry (ICP-OES). The proposed electrode was then recommended as an alternative to spectroscopic technique in analyzing Cu (II).

Keywords: chemically modified electrode, Cu(II), Square wave anodic stripping voltammetry, tetracarbonylmolybdenum(0)

Procedia PDF Downloads 244

Authors: Jinming Ma, Tianbing Xia, Janusz Getta

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This paper presents the application of fuzzy set theory to implement of mobile advertisement anti-fraud systems. Mobile anti-fraud is a method aiming to identify mobile advertisement fraudsters. One of the main problems of mobile anti-fraud is the lack of evidence to prove a user to be a fraudster. In this paper, we implement an application by using fuzzy set theory to demonstrate how to detect cheaters. The advantage of our method is that the hardship in detecting fraudsters in small data samples has been avoided. We achieved this by giving each user a suspicious degree showing how likely the user is cheating and decide whether a group of users (like all users of a certain APP) together to be fraudsters according to the average suspicious degree. This makes the process more accurate as the data of a single user is too small to be predictable.

Keywords: mobile internet, advertisement, anti-fraud, fuzzy set theory

Procedia PDF Downloads 151

Authors: P. Subbuthai, S. Muruganand

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Diabetic Retinopathy is one of the complicated diseases and it is caused by the changes in the blood vessels of the retina. Extraction of retina image through Fundus camera sometimes produced poor contrast and noises. Because of this noise, detection of blood vessels in the retina is very complicated. So preprocessing is needed, in this paper, a novel algorithm is implemented to remove the noisy pixel in the retina image. The proposed algorithm is Extended Median Filter and it is applied to the green channel of the retina because green channel vessels are brighter than the background. Proposed extended median filter is compared with the existing standard median filter by performance metrics such as PSNR, MSE and RMSE. Experimental results show that the proposed Extended Median Filter algorithm gives a better result than the existing standard median filter in terms of noise suppression and detail preservation.

Keywords: fundus retina image, diabetic retinopathy, median filter, microaneurysms, exudates

Procedia PDF Downloads 320

Authors: Bency Jacob, S.B. Waykar

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Degradations in historical document images appear due to aging of the documents. It is very difficult to understand and retrieve text from badly degraded documents as there is variation between the document foreground and background. Thresholding of such document images either result in broken characters or detection of false texts. Numerous algorithms exist that can separate text and background efficiently in the textual regions of the document; but portions of background are mistaken as text in areas that hardly contain any text. This paper presents a way to overcome these problems by a robust binarization technique that recovers the text from a severely degraded document images and thereby increases the accuracy of optical character recognition systems. The proposed document recovery algorithm efficiently removes degradations from document images. Here we are using the ostus method ,local thresholding and global thresholding and after the binarization training and recognizing the characters in the degraded documents.

Keywords: binarization, denoising, global thresholding, local thresholding, thresholding

Procedia PDF Downloads 323

Authors: Omar S. Sharaf

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The mitochondrial 12s rRNA (mt-12s rDNA) gene for pig-specific was developed to detect material from pork species in different products collected from Jordanian market. The amplification PCR products of 359 bp and 531 bp were successfully amplified from the cyt b gene of pig the amplification product using mt-12S rDNA gene were successfully produced a single band with a molecular size of 456 bp. In the present work, the PCR amplification of mtDNA of cytochrome b has been shown as a suitable tool for rapid detection of pig DNA. 100 samples from different dairy, gelatin and chocolate based products and 50 samples from baby food formula were collected and tested to a presence of any pig derivatives. It was found that 10% of chocolate based products, 12% of gelatin and 56% from dairy products and 5.2% from baby food formula showed single band from mt-12S rDNA gene.

Keywords: halal food, baby infant formula, chocolate based products, PCR, Jordan

Procedia PDF Downloads 515

Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

Procedia PDF Downloads 370

Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

Procedia PDF Downloads 189

Authors: Josef Slapal

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Digital images are approximations of real ones and, therefore, to be able to study them, we need the digital plane Z2 to be equipped with a convenient structure that behaves analogously to the Euclidean topology on the real plane. In particular, it is required that such a structure allows for a digital analogue of the Jordan curve theorem. We introduce certain adjacency graphs on the digital plane and prove digital Jordan curves for them thus showing that the graphs provide convenient structures on Z2 for the study and processing of digital images. Further convenient structures including the wellknown Khalimsky and Marcus-Wyse adjacency graphs may be obtained as quotients of the graphs introduced. Since digital Jordan curves represent borders of objects in digital images, the adjacency graphs discussed may be used as background structures on the digital plane for solving the problems of digital image processing that are closely related to borders like border detection, contour filling, pattern recognition, thinning, etc.

Keywords: digital plane, adjacency graph, Jordan curve, quotient adjacency

Procedia PDF Downloads 354

Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

Procedia PDF Downloads 117

Authors: Ewelina Grabowska, Martyna Marchelek

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Among the various semiconductors, nanosized TiO2 has been widely studied due to its high photosensitivity, low cost, low toxicity, and good chemical and thermal stability. However, there are two main drawbacks to the practical application of pure TiO2 films. One is that TiO2 can be induced only by ultraviolet (UV) light due to its intrinsic wide bandgap (3.2 eV for anatase and 3.0 eV for rutile), which limits its practical efficiency for solar energy utilization since UV light makes up only 4-5% of the solar spectrum. The other is that a high electron-hole recombination rate will reduce the photoelectric conversion efficiency of TiO2. In order to overcome the above drawbacks and modify the electronic structure of TiO2, some semiconductors (eg. CdS, ZnO, PbS, Cu2O, Bi2S3, and CdSe) have been used to prepare coupled TiO2 composites, for improving their charge separation efficiency and extending the photoresponse into the visible region. It has been proved that the fabrication of p-n heterostructures by combining n-type TiO2 with p-type semiconductors is an effective way to improve the photoelectric conversion efficiency of TiO2. SrTiO3 is a good candidate for coupling TiO2 and improving the photocatalytic performance of the photocatalyst because its conduction band edge is more negative than TiO2. Due to the potential differences between the band edges of these two semiconductors, the photogenerated electrons transfer from the conduction band of SrTiO3 to that of TiO2. Conversely, the photogenerated electrons transfer from the conduction band of SrTiO3 to that of TiO2. Then the photogenerated charge carriers can be efficiently separated by these processes, resulting in the enhancement of the photocatalytic property in the photocatalyst. Additionally, one of the methods for improving photocatalyst performance is addition of nanoparticles containing one or two noble metals (Pt, Au, Ag and Pd) deposited on semiconductor surface. The mechanisms were proposed as (1) the surface plasmon resonance of noble metal particles is excited by visible light, facilitating the excitation of the surface electron and interfacial electron transfer (2) some energy levels can be produced in the band gap of TiO2 by the dispersion of noble metal nanoparticles in the TiO2 matrix; (3) noble metal nanoparticles deposited on TiO2 act as electron traps, enhancing the electron–hole separation. In view of this, we recently obtained series of TiO2@SrTiO3 and SrTiO3 photocatalysts loaded with noble metal NPs. using photodeposition method. The M- TiO2@SrTiO3 and M-SrTiO3 photocatalysts (M= Rh, Rt, Pt) were studied for photodegradation of phenol in aqueous phase under UV-Vis and visible irradiation. Moreover, in the second part of our research hydroxyl radical formations were investigated. Fluorescence of irradiated coumarin solution was used as a method of ˙OH radical detection. Coumarin readily reacts with generated hydroxyl radicals forming hydroxycoumarins. Although the major hydroxylation product is 5-hydroxycoumarin, only 7-hydroxyproduct of coumarin hydroxylation emits fluorescent light. Thus, this method was used only for hydroxyl radical detection, but not for determining concentration of hydroxyl radicals.

Keywords: composites TiO2, SrTiO3, photocatalysis, phenol degradation

Procedia PDF Downloads 202

Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

Procedia PDF Downloads 409

Authors: Gupta. Ashok, V. talakokula, S. bhalla

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Rebar corrosion is one of the main causes of damage and premature failure of the reinforced concrete (RC) structures worldwide, causing enormous costs for inspection, maintenance, restoration and replacement. Therefore, early detection of corrosion and timely remedial action on the affected portion can facilitate an optimum utilization of the structure, imparting longevity to it. The recent advent of the electro-mechanical impedance (EMI) technique using piezo sensors (PZT) for structural health monitoring (SHM) has provided a new paradigm to the maintenance engineers to diagnose the onset of the damage at the incipient stage itself. This paper presents a model based approach for corrosion assessment based on the equivalent parameters extracted from the impedance spectrum of concrete-rebar system using the EMI technique via the PZT sensors.

Keywords: impedance, electro-mechanical, stiffness, mass, damping, equivalent parameters

Procedia PDF Downloads 522

Authors: Reji Thankachan, Varsha PS

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Both image capturing devices and human visual systems are nonlinear. Hence nonlinear filtering methods outperforms its linear counterpart in many applications. Linear methods are unable to remove impulsive noise in images by preserving its edges and fine details. In addition, linear algorithms are unable to remove signal dependent or multiplicative noise in images. This paper presents an approach to denoise and smoothen the Bipolar impulse noised images and videos using improved Kuwahara filter. It involves a 2 stage algorithm which includes a noise detection followed by filtering. Numerous simulation demonstrate that proposed method outperforms the existing method by eliminating the painting like flattening effect along the local feature direction while preserving edge with improvement in PSNR and MSE.

Keywords: bipolar impulse noise, Kuwahara, PSNR MSE, PDF

Procedia PDF Downloads 480

Authors: Chaima Babi, Said Gadri

Abstract:

The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

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Authors: Teguh Sugiarto

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Background: The research aims to explore if there is fraud in a financial statement, use the Act stated that Benford's distribution all digits must compare the number will follow the trend of lower number. Research methods: This research uses all the analysis number being in Benford's law. After receiving the results of the analysis of all the digits, the author makes a distinction between implementation using the scale above and below 5%, the rate of occurrence of difference. With the number which have differences in the range of 5%, then can do the follow-up and the detection of the onset of fraud against the financial statements. The findings: From the research that has been done can be drawn the conclusion that the average of all numbers appear in the financial statements, and compare the rates of occurrence of numbers according to the characteristics of Benford's law. About the existence of errors and fraud in the financial statements of PT medco Energy Tbk did not occur. Conclusions: The study concludes that Benford's law can serve as indicator tool in detecting the possibility of in financial statements to case studies of PT Medco Energy Tbk for the fiscal year 2000-2010.

Keywords: Benford law, first digits, all digits number Benford law, financial statement

Procedia PDF Downloads 222

Authors: Francis Angelo A. Sta. Ana

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Heavy metal pollutants are a major environmental concern in built-up areas in the Philippines. It causes negative effects on aquatic organisms and human health. Heavy metals concentrations of chromium, mercury, lead, copper, arsenic, zinc, cadmium, and nickel were investigated in Sta. Maria river, in Laguna. A total of 16 sediment samples were collected from the river at four stations. Atomic absorption spectroscopy (AAS) was used for element detection. It is found that copper is associated with chromium based on statistical analysis using principal component analysis (PCA). Conduct of Sediment Quality Guideline (SQG) revealed that chromium has high toxicity due to values higher than Sediment Quality Guidelines Probable Effect Level (SQG’s PEL). Copper, Nickel, and Pb fall on average toxicity while others are below PEL and effect range low (ERL).

Keywords: heavy metals, pollutants, sediment quality guidelines, atomic absorption spectroscopy

Procedia PDF Downloads 119

Authors: Thang V. Kieu, Long P. Nguyen

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Kernelized Correlation Filter (KCF) based trackers have gained a lot of attention recently because of their accuracy and fast calculation speed. However, this algorithm is not robust in cases where the object is lost by a sudden change of direction, being obscured or going out of view. In order to improve KCF performance in long-term tracking, this paper proposes an anomaly detection method for target loss warning by analyzing the response map of each frame, and a classification algorithm for reliable target re-locating mechanism by using Random fern. Being tested with Visual Tracker Benchmark and Visual Object Tracking datasets, the experimental results indicated that the precision and success rate of the proposed algorithm were 2.92 and 2.61 times higher than that of the original KCF algorithm, respectively. Moreover, the proposed tracker handles occlusion better than many state-of-the-art long-term tracking methods while running at 60 frames per second.

Keywords: correlation filter, long-term tracking, random fern, real-time tracking

Procedia PDF Downloads 120

Authors: Avatharam Ganivada, Krishna Shah

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The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

Procedia PDF Downloads 55

Authors: Riccardo Angiuli, Michele Giannuzzi, Rodolfo Franchi, Gabriele Papadia

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Temperature knowledge in machining is fundamental to improve the numerical and FEM models used for the study of some critical process aspects, such as the behavior of the worked material and tool. The extreme conditions in which they operate make it impossible to use traditional measuring instruments; infrared thermography can be used as a valid measuring instrument for temperature measurement during metal cutting. In the study, a large experimental program on superduplex steel (ASTM A995 gr. 5A) cutting was carried out, the relevant cutting temperatures were measured by infrared thermography when certain cutting parameters changed, from traditional values to extreme ones. The values identified were used to calibrate a FEM model for the prediction of residual life of the tools. During the study, the problems related to the detection of cutting temperatures by infrared thermography were analyzed, and a dedicated procedure was developed that could be used during similar processing.

Keywords: machining, infrared thermography, FEM, temperature measurement

Procedia PDF Downloads 166

Authors: Ece Kök Yetimoğlu, Soner Çubuk, Neşe Taşci, M. Vezir Kahraman

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Lead(II) is one of the most toxic environmental pollutants in the world, due to its high toxicity and non-biodegradability. Lead exposure causes severe risks to human health such as central brain damages, convulsions, kidney damages, and even death. To determine lead(II) in environmental or biological samples, scientists use atomic absorption spectrometry (AAS), inductively coupled plasma mass spectrometry (ICPMS), fluorescence spectrometry and electrochemical techniques. Among these systems the fluorescence spectrometry and fluorescent chemical sensors have attracted considerable attention because of their good selectivity and high sensitivity. The fluorescent polymers usually contain covalently bonded fluorophores. In this study imidazole based UV cured polymeric film was prepared and designed to act as a fluorescence chemo sensor for lead (II) analysis. The optimum conditions such as influence of pH value and time on the fluorescence intensity of the sensor have also been investigated. The sensor was highly sensitive with a detection limit as low as 1.87 × 10−8 mol L-1 and it was successful in the determination of Pb(II) in water samples.

Keywords: fluorescence, lead(II), photopolymerization, polymeric sensor

Procedia PDF Downloads 649

Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

Abstract:

We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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