Search results for: fall detection
3730 Refined Edge Detection Network
Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni
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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone
Procedia PDF Downloads 963729 Induction Machine Bearing Failure Detection Using Advanced Signal Processing Methods
Authors: Abdelghani Chahmi
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This article examines the detection and localization of faults in electrical systems, particularly those using asynchronous machines. First, the process of failure will be characterized, relevant symptoms will be defined and based on those processes and symptoms, a model of those malfunctions will be obtained. Second, the development of the diagnosis of the machine will be shown. As studies of malfunctions in electrical systems could only rely on a small amount of experimental data, it has been essential to provide ourselves with simulation tools which allowed us to characterize the faulty behavior. Fault detection uses signal processing techniques in known operating phases.Keywords: induction motor, modeling, bearing damage, airgap eccentricity, torque variation
Procedia PDF Downloads 1343728 Howard Mold Count of Tomato Pulp Commercialized in the State of São Paulo, Brazil
Authors: M. B. Atui, A. M. Silva, M. A. M. Marciano, M. I. Fioravanti, V. A. Franco, L. B. Chasin, A. R. Ferreira, M. D. Nogueira
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Fungi attack large amount of fruits and those who have suffered an injury on the surface are more susceptible to the growth, as they have pectinolytic enzymes that destroy the edible portion forming an amorphous and soft dough. The spores can reach the plant by the wind, rain and insects and fruit may have on its surface, besides the contaminants from the fruit trees, land and water, forming a flora composed mainly of yeasts and molds. Other contamination can occur for the equipment used to harvest, for the use of boxes and contaminated water to the fruit washing, for storage in dirty places. The hyphae in tomato products indicate the use of raw materials contaminated or unsuitable hygiene conditions during processing. Although fungi are inactivated in heat processing step, its hyphae remain in the final product and search for detection and quantification is an indicator of the quality of raw material. Howard Method count of fungi mycelia in industrialized pulps evaluates the amount of decayed fruits existing in raw material. The Brazilian legislation governing processed and packaged products set the limit of 40% of positive fields in tomato pulps. The aim of this study was to evaluate the quality of the tomato pulp sold in greater São Paulo, through a monitoring during the four seasons of the year. All over 2010, 110 samples have been examined; 21 were taking in spring, 31 in summer, 31 in fall and 27 in winter, all from different lots and trademarks. Samples have been picked up in several stores located in the city of São Paulo. Howard method was used, recommended by the AOAC, 19th ed, 2011 16:19:02 technique - method 965.41. Hundred percent of the samples contained fungi mycelia. The count average of fungi mycelia per season was 23%, 28%, 8,2% and 9,9% in spring, summer, fall and winter, respectively. Regarding the spring samples of the 21 samples analyzed, 14.3% were off-limits proposed by the legislation. As for the samples of the fall and winter, all were in accordance with the legislation and the average of mycelial filament count has not exceeded 20%, which can be explained by the low temperatures during this time of the year. The acquired samples in the summer and spring showed high percentage of fungal mycelium in the final product, related to the high temperatures in these seasons. Considering that the limit of 40% of positive fields is accepted for the Brazilian Legislation (RDC nº 14/2014), 3 spring samples (14%) and 6 summer samples (19%) will be over this limit and subject to law penalties. According to gathered data, 82% of manufacturers of this product manage to keep acceptable levels of fungi mycelia in their product. In conclusion, only 9.2% samples were for the limits established by Resolution RDC. 14/2014, showing that the limit of 40% is feasible and can be used by these segment industries. The result of the filament count mycelial by Howard method is an important tool in the microscopic analysis since it measures the quality of raw material used in the production of tomato products.Keywords: fungi, howard, method, tomato, pulps
Procedia PDF Downloads 3723727 Fused Structure and Texture (FST) Features for Improved Pedestrian Detection
Authors: Hussin K. Ragb, Vijayan K. Asari
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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor
Procedia PDF Downloads 4793726 Hazardous Vegetation Detection in Right-Of-Way Power Transmission Lines in Brazil Using Unmanned Aerial Vehicle and Light Detection and Ranging
Authors: Mauricio George Miguel Jardini, Jose Antonio Jardini
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Transmission power utilities participate with kilometers of circuits, many with particularities in terms of vegetation growth. To control these rights-of-way, maintenance teams perform ground, and air inspections, and the identification method is subjective (indirect). On a ground inspection, when identifying an irregularity, for example, high vegetation threatening contact with the conductor cable, pruning or suppression is performed immediately. In an aerial inspection, the suppression team is mobilized to the identified point. This work investigates the use of 3D modeling of a transmission line segment using RGB (red, blue, and green) images and LiDAR (Light Detection and Ranging) sensor data. Both sensors are coupled to unmanned aerial vehicle. The goal is the accurate and timely detection of vegetation along the right-of-way that can cause shutdowns.Keywords: 3D modeling, LiDAR, right-of-way, transmission lines, vegetation
Procedia PDF Downloads 1253725 Eliminating Injury in the Work Place and Realizing Vision Zero Using Accident Investigation and Analysis as Method: A Case Study
Authors: Ramesh Kumar Behera, Md. Izhar Hassan
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Accident investigation and analysis are useful to identify deficiencies in plant, process, and management practices and formulate preventive strategies for injury elimination. In India and other parts of the world, industrial accidents are investigated to know the causes and also to fulfill legal compliances. However, findings of investigation are seldom used appropriately to strengthen Occupational Safety and Health (OSH) in expected lines. The mineral rich state of Odisha in eastern coast of India; known as a hub for Iron and Steel industries, witnessed frequent accidents during 2005-2009. This article based on study of 982 fatal ‘factory-accidents’ occurred in Odisha during the period 2001-2016, discusses the ‘turnaround-story’ resulting in reduction of fatal accident from 122 in 2009 to 45 in 2016. This paper examines various factors causing incidents; accident pattern in steel and chemical sector; role of climate and harsh weather conditions on accident causation. Software such as R, SQL, MS-Excel and Tableau were used for analysis of data. It is found that maximum fatality is caused due to ‘fall from height’ (24%); steel industries are relatively more accident prone; harsh weather conditions of summer increase chances of accident by 20%. Further, the study suggests that enforcement of partial work-restriction around lunch time during peak summer, screening and training of employees reduce accidents due to fall from height. The study indicates that learning from accident investigation and analysis can be used as a method to reduce work related accidents in the journey towards ‘Vision Zero’.Keywords: accident investigation and analysis, fatal accidents in India, fall from height, vision zero
Procedia PDF Downloads 1483724 Liver Tumor Detection by Classification through FD Enhancement of CT Image
Authors: N. Ghatwary, A. Ahmed, H. Jalab
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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.
Procedia PDF Downloads 3533723 Multitemporal Satellite Images for Agriculture Change Detection in Al Jouf Region, Saudi Arabia
Authors: Ali A. Aldosari
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Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region.Keywords: spatial analysis, geographical information system, change detection
Procedia PDF Downloads 3973722 Fourier Transform and Machine Learning Techniques for Fault Detection and Diagnosis of Induction Motors
Authors: Duc V. Nguyen
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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.Keywords: fault detection, FFT, induction motor, predictive maintenance
Procedia PDF Downloads 1583721 Tailoring Polythiophene Nanocomposites with MnS/CoS Nanoparticles for Enhanced Surface-Enhanced Raman Spectroscopy (SERS) Detection of Mercury Ions in Water
Authors: Temesgen Geremew
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The excessive emission of heavy metal ions from industrial processes poses a serious threat to both the environment and human health. This study presents a distinct approach utilizing (PTh-MnS/CoS NPs) for the highly selective and sensitive detection of Hg²⁺ ions in water. Such detection is crucial for safeguarding human health, protecting the environment, and accurately assessing toxicity. The fabrication method employs a simple and efficient chemical precipitation technique, harmoniously combining polythiophene, MnS, and CoS NPs to create highly active substrates for SERS. The MnS@Hg²⁺ exhibits a distinct Raman shift at 1666 cm⁻¹, enabling specific identification and demonstrating the highest responsiveness among the studied semiconductor substrates with a detection limit of only 1 nM. This investigation demonstrates reliable and practical SERS detection for Hg²⁺ ions. Relative standard deviation (RSD) ranged from 0.49% to 9.8%, and recovery rates varied from 96% to 102%, indicating selective adsorption of Hg²⁺ ions on the synthesized substrate. Furthermore, this research led to the development of a remarkable set of substrates, including (MnS, CoS, MnS/CoS, and PTh-MnS/CoS) nanoparticles were created right there on SiO₂/Si substrate, all exhibiting sensitive, robust, and selective SERS for Hg²⁺ ion detection. These platforms effectively monitor Hg²⁺ concentrations in real environmental samples.Keywords: surface-enhanced raman spectroscopy (SERS), sensor, mercury ions, nanoparticles, and polythiophene.
Procedia PDF Downloads 623720 Protein Remote Homology Detection and Fold Recognition by Combining Profiles with Kernel Methods
Authors: Bin Liu
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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)
Procedia PDF Downloads 1543719 Implementation of a Method of Crater Detection Using Principal Component Analysis in FPGA
Authors: Izuru Nomura, Tatsuya Takino, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata
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We propose a method of crater detection from the image of the lunar surface captured by the small space probe. We use the principal component analysis (PCA) to detect craters. Nevertheless, considering severe environment of the space, it is impossible to use generic computer in practice. Accordingly, we have to implement the method in FPGA. This paper compares FPGA and generic computer by the processing time of a method of crater detection using principal component analysis.Keywords: crater, PCA, eigenvector, strength value, FPGA, processing time
Procedia PDF Downloads 5443718 Early Detection of Damages in Railway Steel Truss Bridges from Measured Dynamic Responses
Authors: Dinesh Gundavaram
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This paper presents an investigation on bridge damage detection based on the dynamic responses estimated from a passing vehicle. A numerical simulation of steel truss bridge for railway was used in this investigation. The bridge response at different locations is measured using CSI-Bridge software. Several damage scenarios are considered including different locations and severities. The possibilities of dynamic properties of global modes in the identification of structural changes in truss bridges were discussed based on the results of measurement.Keywords: bridge, damage, dynamic responses, detection
Procedia PDF Downloads 2653717 Phishing Detection: Comparison between Uniform Resource Locator and Content-Based Detection
Authors: Nuur Ezaini Akmar Ismail, Norbazilah Rahim, Norul Huda Md Rasdi, Maslina Daud
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A web application is the most targeted by the attacker because the web application is accessible by the end users. It has become more advantageous to the attacker since not all the end users aware of what kind of sensitive data already leaked by them through the Internet especially via social network in shake on ‘sharing’. The attacker can use this information such as personal details, a favourite of artists, a favourite of actors or actress, music, politics, and medical records to customize phishing attack thus trick the user to click on malware-laced attachments. The Phishing attack is one of the most popular attacks for social engineering technique against web applications. There are several methods to detect phishing websites such as Blacklist/Whitelist based detection, heuristic-based, and visual similarity-based detection. This paper illustrated a comparison between the heuristic-based technique using features of a uniform resource locator (URL) and visual similarity-based detection techniques that compares the content of a suspected phishing page with the legitimate one in order to detect new phishing sites based on the paper reviewed from the past few years. The comparison focuses on three indicators which are false positive and negative, accuracy of the method, and time consumed to detect phishing website.Keywords: heuristic-based technique, phishing detection, social engineering and visual similarity-based technique
Procedia PDF Downloads 1683716 Training of Future Computer Science Teachers Based on Machine Learning Methods
Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova
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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.Keywords: algorithm, artificial intelligence, education, machine learning
Procedia PDF Downloads 683715 Colorimetric Detection of Ceftazdime through Azo Dye Formation on Polyethylenimine-Melamine Foam
Authors: Pajaree Donkhampa, Fuangfa Unob
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Ceftazidime is an antibiotic drug commonly used to treat several human and veterinary infections. However, the presence of ceftazidime residues in the environment may induce microbial resistance and cause side effects to humans. Therefore, monitoring the level of ceftazidime in environmental resources is important. In this work, a melamine foam platform was proposed for simultaneous extraction and colorimetric detection of ceftazidime based on the azo dye formation on the surface. The melamine foam was chemically modified with polyethyleneimine (PEI) and characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Ceftazidime is a sample that was extracted on the PEI-modified melamine foam and further reacted with nitrite in an acidic medium to form an intermediate diazonium ion. The diazotized molecule underwent an azo coupling reaction with chromotropic acid to generate a red-colored compound. The material color changed from pale yellow to pink depending on the ceftazidime concentration. The photo of the obtained material was taken by a smartphone camera and the color intensity was determined by Image J software. The material fabrication and ceftazidime extraction and detection procedures were optimized. The detection of a sub-ppm level of ceftazidime was achieved without using a complex analytical instrument.Keywords: colorimetric detection, ceftazidime, melamine foam, extraction, azo dye
Procedia PDF Downloads 1633714 An Electrochemical DNA Biosensor Based on Oracet Blue as a Label for Detection of Helicobacter pylori
Authors: Saeedeh Hajihosseini, Zahra Aghili, Navid Nasirizadeh
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An innovative method of a DNA electrochemical biosensor based on Oracet Blue (OB) as an electroactive label and gold electrode (AuE) for detection of Helicobacter pylori, was offered. A single–stranded DNA probe with a thiol modification was covalently immobilized on the surface of the AuE by forming an Au–S bond. Differential pulse voltammetry (DPV) was used to monitor DNA hybridization by measuring the electrochemical signals of reduction of the OB binding to double– stranded DNA (ds–DNA). Our results showed that OB–based DNA biosensor has a decent potential for detection of single–base mismatch in target DNA. Selectivity of the proposed DNA biosensor was further confirmed in the presence of non–complementary and complementary DNA strands. Under optimum conditions, the electrochemical signal had a linear relationship with the concentration of the target DNA ranging from 0.3 nmol L-1 to 240.0 nmol L-1, and the detection limit was 0.17 nmol L-1, whit a promising reproducibility and repeatability.Keywords: DNA biosensor, oracet blue, Helicobacter pylori, electrode (AuE)
Procedia PDF Downloads 2613713 Comparative Analysis of Two Approaches to Joint Signal Detection, ToA and AoA Estimation in Multi-Element Antenna Arrays
Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev
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In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.Keywords: antenna array, signal detection, ToA, AoA estimation
Procedia PDF Downloads 4873712 Clustering Color Space, Time Interest Points for Moving Objects
Authors: Insaf Bellamine, Hamid Tairi
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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering
Procedia PDF Downloads 3733711 Timely Detection and Identification of Abnormalities for Process Monitoring
Authors: Hyun-Woo Cho
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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.Keywords: detection, monitoring, identification, measurement data, multivariate techniques
Procedia PDF Downloads 2323710 Analytical Solution for End Depth Ratio in Rectangular Channels
Authors: Abdulrahman Abdulrahman, Abir Abdulrahman
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Free over-fall is an instrument for measuring discharge in open channels by measuring end depth. A comprehensive researchers investigated theoretically and experimentally brink phenomenon with various approaches for different cross-sectional shapes. Anderson's method, based on Boussinq's approximation and energy approach was used to derive a pressure distribution factor at end depth. Applying the one-dimensional momentum equation and the principles of limit slope analysis, a relevant analytical solution may be derived for brink depth ratio (EDR) in prismatic rectangular channel. Also relationships between end depth ratio and slope ratio for a given non-dimensional normal or critical depth with upstream supercritical flow regime are presented. Simple indirect procedure is used to estimate the end depth discharge ratio (EDD) for subcritical and supercritical flow using measured end depth. The comparison of this analysis with all previous theoretical and experimental studies showed an excellent agreement.Keywords: analytical solution, brink depth, end depth, flow measurement, free over fall, hydraulics, rectangular channel
Procedia PDF Downloads 1733709 Evolving Digital Circuits for Early Stage Breast Cancer Detection Using Cartesian Genetic Programming
Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad
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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration
Procedia PDF Downloads 2113708 Refactoring Object Oriented Software through Community Detection Using Evolutionary Computation
Authors: R. Nagarani
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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.Keywords: community detection, complex network, genetic algorithm, package, refactoring
Procedia PDF Downloads 4123707 Using Deep Learning for the Detection of Faulty RJ45 Connectors on a Radio Base Station
Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner
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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.Keywords: radio base station, maintenance, classification, detection, deep learning, automation
Procedia PDF Downloads 1953706 Traffic Sign Recognition System Using Convolutional Neural NetworkDevineni
Authors: Devineni Vijay Bhaskar, Yendluri Raja
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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve
Procedia PDF Downloads 1133705 Medical Advances in Diagnosing Neurological and Genetic Disorders
Authors: Simon B. N. Thompson
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Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning
Procedia PDF Downloads 3843704 Semi-Supervised Outlier Detection Using a Generative and Adversary Framework
Authors: Jindong Gu, Matthias Schubert, Volker Tresp
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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning
Procedia PDF Downloads 1443703 AI-Powered Models for Real-Time Fraud Detection in Financial Transactions to Improve Financial Security
Authors: Shanshan Zhu, Mohammad Nasim
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Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection
Procedia PDF Downloads 163702 Electrochemical Anodic Oxidation Synthesis of TiO2 nanotube as Perspective Electrode for the Detection of Phenyl Hydrazine
Authors: Sadia Ameen, M. Nazim, Hyumg-Kee Seo, Hyung-Shik Shin
Abstract:
TiO2 nanotube (NT) arrays were grown on titanium (Ti) foil substrate by electrochemical anodic oxidation and utilized as working electrode to fabricate a highly sensitive and reproducible chemical sensor for the detection of harmful phenyl hydrazine chemical. The fabricated chemical sensor based on TiO2 NT arrays electrode exhibited high sensitivity of ~40.9 µA.mM-1.cm-2 and detection limit of ~0.22 µM with short response time (10s).Keywords: TiO2 NT, phenyl hydrazine, chemical sensor, sensitivity, electrocatalytic properties
Procedia PDF Downloads 4953701 Sensing Mechanism of Nano-Toxic Ions Using Quartz Crystal Microbalance
Authors: Chanho Park, Juneseok You, Kuewhan Jang, Sungsoo Na
Abstract:
Detection technique of nanotoxic materials is strongly imperative, because nano-toxic materials can harmfully influence human health and environment as their engineering applications are growing rapidly in recent years. In present work, we report the DNA immobilized quartz crystal microbalance (QCM) based sensor for detection of nano-toxic materials such as silver ions, Hg2+ etc. by using functionalization of quartz crystal with a target-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz crystal is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated fast and in situ detection of nanotoxic materials using quartz crystal microbalance. We report the label-free and highly sensitive detection of silver ion for present case, which is a typical nano-toxic material by using QCM and silver-specific DNA. The detection is based on the measurement of frequency shift of Quartz crystal from constitution of the cytosine-Ag+-cytosine binding. It is shown that the silver-specific DNA measured frequency shift by QCM enables the capturing of silver ions below 100pM. The results suggest that DNA-based detection opens a new avenue for the development of a practical water-testing sensor.Keywords: nano-toxic ions, quartz crystal microbalance, frequency shift, target-specific DNA
Procedia PDF Downloads 315