Search results for: method detection limit

Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

Procedia PDF Downloads 82

Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

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Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

Procedia PDF Downloads 164

Authors: Retno Supriyanti, Best Leader Nababan, Yogi Ramadhani, Wahyu Siswandari

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Blood cell morphology is an important parameter in a hematology test. Currently, in developing countries, a lot of hematology is done manually, either by physicians or laboratory staff. According to the limitation of the human eye, examination based on manual method will result in a lower precision and accuracy. In addition, the hematology test by manual will further complicate the diagnosis in some areas that do not have competent medical personnel. This research aims to develop a simple tool in the detection of blood cell morphology-based computer. In this paper, we focus on the detection of the outer contour of leukocytes. The results show that the system that we developed is promising for detecting blood cell morphology automatically. It is expected, by implementing this method, the problem of accuracy, precision and limitations of the medical staff can be solved.

Keywords: morphology operation, developing countries, hematology test, limitation of medical personnel

Procedia PDF Downloads 339

Authors: Shalini Menon, K. Girish Kumar

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Norepinephrine (NE) is one of the naturally occurring catecholamines which act both as a neurotransmitter and a hormone. Catecholamine levels are used for the diagnosis and regulation of phaeochromocytoma, a neuroendocrine tumor of the adrenal medulla. The development of simple, rapid and cost-effective sensors for NE still remains a great challenge. Herein, a dual-channel sensor has been developed for the determination of NE. A mixture of AgNO₃, NaOH, NH₃.H₂O and cetrimonium bromide in appropriate concentrations was taken as the working solution. To the thoroughly vortexed mixture, an appropriate volume of NE solution was added. After a particular time, the fluorescence and absorbance were measured. Fluorescence measurements were made by exciting at a wavelength of 400 nm. A dual-channel optical sensor has been developed for the colorimetric as well as the fluorimetric determination of NE. Metal enhanced fluorescence property of nanoparticles forms the basis of the fluorimetric detection of this assay, whereas the appearance of brown color in the presence of NE leads to colorimetric detection. Wide linear ranges and sub-micromolar detection limits were obtained using both the techniques. Moreover, the colorimetric approach was applied for the determination of NE in synthetic blood serum and the results obtained were compared with the classic high-performance liquid chromatography (HPLC) method. Recoveries between 97% and 104% were obtained using the proposed method. Based on five replicate measurements, relative standard deviation (RSD) for NE determination in the examined synthetic blood serum was found to be 2.3%. This indicates the reliability of the proposed sensor for real sample analysis.

Keywords: norepinephrine, colorimetry, fluorescence, silver nanoparticles

Procedia PDF Downloads 113

Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

Procedia PDF Downloads 231

Authors: V. Makis, L. Jafari

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In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart.

Keywords: Bayesian u-chart, economic design, optimal stopping, semi-Markov decision process, statistical process control

Procedia PDF Downloads 573

Authors: Yu-Kuei Hsu, Yan-Gu Lin

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A facile and simple fabrication of copper(II) oxide (CuO) nanosheet on copper foil as nanoelectrode for H2O2 sensing application was proposed in this study. The spontaneous formation of CuO nanosheets by immersing the copper foil into 0.1 M NaOH aqueous solution for 48 hrs was carried out at room temperature. The sheet-like morphology with several ten nanometers in thickness and ~500 nm in width was observed by SEM. Those nanosheets were confirmed the monoclinic-phase CuO by the structural analysis of XRD and Raman spectra. The directly grown CuO nanosheets film is mechanically stable and offers an excellent electrochemical sensing platform. The CuO nanosheets electrode shows excellent electrocatalytic response to H2O2 with significantly lower overpotentials for its oxidation and reduction and also exhibits a fast response and high sensitivity for the amperometric detection of H2O2. The novel spontaneously grown CuO nanosheets electrode is readily applicable to other analytes and has great potential applications in the electrochemical detection.

Keywords: CuO, nanosheets, H2O2 detection, Cu foil

Procedia PDF Downloads 289

Authors: Liang Liu, Xiaopeng Luo

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In this paper, we propose an accelerated stochastic gradient method with momentum. The momentum term is the weighted average of generated gradients, and the weights decay inverse proportionally with the iteration times. Stochastic gradient descent with momentum (SGDM) uses weights that decay exponentially with the iteration times to generate the momentum term. Using exponential decay weights, variants of SGDM with inexplicable and complicated formats have been proposed to achieve better performance. However, the momentum update rules of our method are as simple as that of SGDM. We provide theoretical convergence analyses, which show both the exponential decay weights and our inverse proportional decay weights can limit the variance of the parameter moving directly to a region. Experimental results show that our method works well with many practical problems and outperforms SGDM.

Keywords: exponential decay rate weight, gradient descent, inverse proportional decay rate weight, momentum

Procedia PDF Downloads 164

Authors: Raja Chinnappan, Huda Alanazi, Shanmugam Easwaramoorthi, Tanveer Mir, Balamurugan Kanagasabai, Ahmed Yaqinuddin, Sandhanasamy Devanesan, Mohamad S. AlSalhi

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Serum albumin (SA) is a highly rich water-soluble protein in plasma. It is known to maintain the living organisms' health and help to maintain the proper liver function, kidney function, and plasma osmolality in the body. Low levels of serum albumin are an indication of liver failure and chronic hepatitis. Therefore, it is important to have a low-cost, accurate and rapid method. In this study, we designed a fluorescent probe, triphenylamine rhodanine-3-acetic acid (mRA), which triggers the fluorescence signal upon binding with serum albumin (SA). mRA is a bifunctional molecule with twisted intramolecular charge transfer (TICT)-induced emission characteristics. An aqueous solution of mRA has an insignificant fluorescence signal; however, when mRA binds to SA, it undergoes TICT and turns on the fluorescence emission. A SA dose-dependent fluorescence signal was performed, and the limit of detection was found to be less than ng/mL. The specific binding of SA was tested from the cross-reactivity study using similar structural or functional proteins.

Keywords: serum albumin, fluorescent sensing probe, liver diseases, twisted intramolecular charge transfer

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Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

Procedia PDF Downloads 477

Authors: Jiwon Lee, Chanho Jung, Si-Hwan Jang, Kyung-Ill Kim, Sanghyun Joo, Wook-Ho Son

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Since the advances in digital imaging technologies have led to development of high quality digital devices, there are a lot of illegal copies of copyrighted video content on the internet. Thus, we propose a high-quality (HQ) video watermarking scheme that can prevent these illegal copies from spreading out. The proposed scheme is applied spatial and temporal gradient methods to improve the fidelity and detection performance. Also, the scheme duplicates the watermark signal temporally to alleviate the signal reduction caused by geometric and signal-processing distortions. Experimental results show that the proposed scheme achieves better performance than previously proposed schemes and it has high fidelity. The proposed scheme can be used in broadcast monitoring or traitor tracking applications which need fast detection process to prevent illegally recorded video content from spreading out.

Keywords: editing prevention technique, gradient method, luminance change, video watermarking

Procedia PDF Downloads 457

Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

Procedia PDF Downloads 538

Authors: Desissa Yadeta Muleta

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Purely organic long-persistent luminescence materials (OLPLMs) have been developed as emerging organic materials due to their simple production process, low preparation cost and better biocompatibilities. Notably, OLPLMs with afterglow-time-tunable long-persistent luminescence (LPL) characteristics enable higher-level protection applications and have great prospects in biological applications. The realization of these advanced performances depends on our ability to gradually tune LPL duration under ambient conditions, however, the strategies to achieve this are few due to the lack of unambiguous mechanisms. Here, we propose a two-step strategy to gradually tune LPL duration of OLPLMs over a wide range of seconds in water and air, by using derivatives as the guest and introducing a third-party material into the host-immobilized host–guest doping system. Based on this strategy, we develop an analysis method for deoxyribonucleic acid (DNA) content detection without DNA separation in aqueous samples, which circumvents the influence of the chromophore, fluorophore and other interferents in vivo, enabling a certain degree of in situ detection that is difficult to achieve using today’s methods. This work will expedite the development of afterglow-time-tunable OLPLMs and expand new horizons for their applications in data protection, bio-detection, and bio-sensing

Keywords: deoxyribonucliec acid, long persistent luminescent materials, water, air

Procedia PDF Downloads 77

Authors: Shreyansh Adesara, Sneha Pandiya

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The mobile ad-hoc network (MANET) is an important and open area research for the examination and determination of the performance evolution. Temporary ordered routing algorithm (TORA) is adaptable and distributed MANET routing algorithm which is totally dependent on internet MANET Encapsulation protocol (IMEP) for the detection of the link and sensing of the link. If IMEP detect the wrong link failure then the network suffer from congestion and unnecessary route maintenance. Thus, the improvement in link detection method of TORA is introduced by various methods on IMEP by different perspective from different person. There are also different reactive routing protocols like AODV, TORA and DSR has been compared for the knowledge of the routing scenario for different parameter and using different model.

Keywords: IMEP, mobile ad-hoc network, protocol, TORA

Procedia PDF Downloads 442

Authors: Hanaa M. Abdelgawad, Mona Safar, Ayman M. Wahba

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Real-time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Therefore, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Canny edge detection is one of the common blocks in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream.

Keywords: high level synthesis, canny edge detection, hardware accelerators, computer vision

Procedia PDF Downloads 480

Authors: Benkouiten Imene, Chabli Ouerdia, Boutoutaou Hamid, Kadri Nesrine, Bouledroua Omar

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Algeria is actively enhancing gas productivity by augmenting the supply flow. However, this effort has led to increased internal pressure, posing a potential risk to the pipeline's integrity, particularly in the presence of corrosion defects. Sonatrach relies on a vast network of pipelines spanning 24,000 kilometers for the transportation of gas and oil. The aging of these pipelines raises the likelihood of corrosion both internally and externally, heightening the risk of ruptures. To address this issue, a comprehensive inspection is imperative, utilizing specialized scraping tools. These advanced tools furnish a detailed assessment of all pipeline defects. It is essential to recalculate the pressure parameters to safeguard the corroded pipeline's integrity while ensuring the continuity of production. In this context, Sonatrach employs symbolic pressure limit calculations, such as ASME B31G (2009) and the modified ASME B31G (2012). The aim of this study is to perform a comparative analysis of various limit pressure calculation methods documented in the literature, namely DNV RP F-101, SHELL, P-CORRC, NETTO, and CSA Z662. This comparative assessment will be based on a dataset comprising 329 burst tests published in the literature. Ultimately, we intend to introduce a novel approach grounded in the finite element method, employing ANSYS software.

Keywords: pipeline burst pressure, burst test, corrosion defect, corroded pipeline, finite element method

Procedia PDF Downloads 58

Authors: Dongwan Kang, Joohyung Oh, Chaetae Im

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Advancement of communication technologies and smart devices in the recent times is leading to changes into the integrated wired and wireless communication environments. Since early days, businesses had started introducing environments for mobile device application to their operations in order to improve productivity (efficiency) and the closed corporate environment gradually shifted to an open structure. Recently, individual user's interest in working environment using mobile devices has increased and a new corporate working environment under the concept of BYOD is drawing attention. BYOD (bring your own device) is a concept where individuals bring in and use their own devices in business activities. Through BYOD, businesses can anticipate improved productivity (efficiency) and also a reduction in the cost of purchasing devices. However, as a result of security threats caused by frequent loss and theft of personal devices and corporate data leaks due to low security, companies are reluctant about adopting BYOD system. In addition, without considerations to diverse devices and connection environments, there are limitations in detecting abnormal behaviors such as information leaks which use the existing network-based security equipment. This study suggests a method to detect abnormal behaviors according to individual behavioral patterns, rather than the existing signature-based malicious behavior detection and discusses applications of this method in BYOD environment.

Keywords: BYOD, security, anomaly behavior detection, security equipment, communication technologies

Procedia PDF Downloads 325

Authors: O. Tovide, N. Jahed, N. Mohammed, C. E. Sunday, H. R. Makelane, R. F. Ajayi, K. M. Molapo, A. Tsegaye, M. Masikini, S. Mailu, A. Baleg, T. Waryo, P. G. Baker, E. I. Iwuoha

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A graphenated–polyaniline (GR-PANI) nanocomposite sensor was constructed and used for the determination of anthracene. The direct electro-oxidation behavior of anthracene on the GR-PANI modified glassy carbon electrode (GCE) was used as the sensing principle. The results indicate thatthe response profile of the oxidation of anthracene on GR-PANI-modified GCE provides for the construction of sensor systems based onamperometric and potentiometric signal transductions. A dynamic linear range of 0.12- 100 µM anthracene and a detection limit of 0.044 µM anthracene were established for the sensor system.

Keywords: electrochemical sensors, environmental pollutants, graphenated-polymers, polyaromatic hydrocarbon

Procedia PDF Downloads 356

Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

Procedia PDF Downloads 306

Authors: Ahmad H. Abdelgwad, Ahmed A. Nashat

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Landmine detection is an important and yet challenging problem remains to be solved. Ground Penetrating Radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. The detection methodology of GPR depends mainly on the contrast of the dielectric properties of the searched target and its surrounding soil. This contrast produces a partial reflection of the electromagnetic pulses that are being transmitted into the soil and then being collected by the GPR.  One of the most critical hardware components for the performance of GPR is the antenna system. The current paper explores the design and simulation of a pyramidal horn antenna operating at L-band frequencies (1- 2 GHz) to detect a landmine. A prototype model of the GPR system setup is developed to simulate full wave analysis of the electromagnetic fields in different soil types. The contrast in the dielectric permittivity of the landmine and the sandy soil is the most important parameter to be considered for detecting the presence of landmine. L-band horn antenna is proved to be well-versed in the investigation of landmine detection.

Keywords: full wave analysis, ground penetrating radar, horn antenna design, landmine detection

Procedia PDF Downloads 221

Authors: Nimisha Elsa Koshy, Varun P. Gopi, V. I. Thajudin Ahamed

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The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space.

Keywords: contourlet transform, log gabor filter, ulcer, wireless capsule endoscopy

Procedia PDF Downloads 540

Authors: Mostafa Firoozabadi, Alireza Foroughi Nematollahi

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Wet mills are one of the most important equipment in the mining industries and any defect occurrence in them can stop the production line and it can make some irrecoverable damages to the system. Electromotors are the significant parts of a mill and their monitoring is a necessary process to prevent unwanted defects. The purpose of this study is to investigate the Electromotor bearing defects, theoretically and practically, using the vibration analysis method. When a defect happens in a bearing, it can be transferred to the other parts of the equipment like inner ring, outer ring, balls, and the bearing cage. The electromotor defects source can be electrical or mechanical. Sometimes, the electrical and mechanical defect frequencies are modulated and the bearing defect detection becomes difficult. In this paper, to detect the electromotor bearing defects, the electrical and mechanical defect frequencies are extracted firstly. Then, by calculating the bearing defect frequencies, and the spectrum and time signal analysis, the bearing defects are detected. In addition, the obtained frequency determines that the bearing level in which the defect has happened and by comparing this level to the standards it determines the bearing remaining lifetime. Finally, the defect length is calculated by theoretical equations to demonstrate that there is no need to replace the bearing. The results of the proposed method, which has been implemented on the wet mills in the Golgohar mining and industrial company in Iran, show that this method is capable of detecting the electromotor bearing defects accurately and on time.

Keywords: bearing defect length, defect frequency, electromotor defects, vibration analysis

Procedia PDF Downloads 502

Authors: Elham A. Kwildi, Yahia S. Abugnah, Nuri S. Madi

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This study was designed to evaluate the microbiological quality and safety of two types of salads prepared at a catering center affiliated with Libyan Airlines in Tripoli, Libya. Two hundred and twenty-one (221) samples (132 economy-class and 89 first- class) were used in this project which lasted for ten months. Biweekly, microbiological tests were performed which included total plate count (TPC) and total coliforms (TCF), in addition to enumeration and/or detection of some pathogenic bacteria mainly Escherichia coli, Staphylococcus aureus, Bacillus cereus, Salmonella sp, Listeria sp and Vibrio parahaemolyticus parahaemolyticus, By using conventional as well as compact dry methods. Results indicated that TPC of type 1 salad ranged between (<10 – 62 x 103 cfu/gm) and (<10 to 36 x103 cfu/g), while TCF were (<10 – 41 x 103 cfu/gm) and (< 10 to 66 x102 cfu/g) using both methods of detection respectively. On the other hand, TPC of type 2 salad were: (1 × 10 – 52 x 103) and (<10 – 55 x 103 cfu/gm) and in the range of (1 x10 to 45x103 cfu/g), and the (TCF) counts were between (< 10 to 55x103 cfu/g) and (< 10 to 34 x103 cfu/g) using the 1st and the 2nd methods of detection respectively. Also, the pathogens mentioned above were detected in both types of salads, but their levels varied according to the type of salad and the method of detection. The level of Staphylococcus aureus, for instance, was 17.4% using conventional method versus 14.4% using the compact dry method. Similarly, E. coli was 7.6% and 9.8%, while Salmonella sp. recorded the least percentage i.e. 3% and 3.8% with the two mentioned methods respectively. First class salads were also found to contain the same pathogens, but the level of E. coli was relatively higher in this case (14.6% and 16.9%) using conventional and compact dry methods respectively. The second rank came Staphylococcus aureus (13.5%) and (11.2%), followed by Salmonella (6.74%) and 6.70%). The least percentage was for Vibrio parahaemolyticus (4.9%) which was detected in the first class salads only. The other two pathogens Bacillus cereus and Listeria sp. were not detected in either one of the salads. Finally, it is worth mentioning that there was a significant decline in TPC and TCF counts in addition to the disappearance of pathogenic bacteria after the 6-7th month of the study which coincided with the first trial of the HACCP system at the center. The ups and downs in the counts along the early stages of the study reveal that there is a need for some important correction measures including more emphasis on training of the personnel in applying the HACCP system effectively.

Keywords: air travel, vegetable salads, foodborne outbreaks, Libya

Procedia PDF Downloads 328

Authors: Malika Kharouf, Aly Chkeir, Khac Tuan Huynh

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Detecting change points in time series data is a challenging problem, especially in scenarios where there is limited prior knowledge regarding the data’s distribution and the nature of the transitions. We present a method designed for detecting changes in the covariance structure of high-dimensional time series data, where the number of variables closely matches the data length. Our objective is to achieve unbiased test statistic estimation under the null hypothesis. We delve into the utilization of Random Matrix Theory to analyze the behavior of our test statistic within a high-dimensional context. Specifically, we illustrate that our test statistic converges pointwise to a normal distribution under the null hypothesis. To assess the effectiveness of our proposed approach, we conduct evaluations on a simulated dataset. Furthermore, we employ our method to examine changes aimed at detecting frailty in the elderly.

Keywords: change point detection, hypothesis tests, random matrix theory, frailty in elderly

Procedia PDF Downloads 56

Authors: Yanjie Zhu, André Jesus, Irwanda Laory

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Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

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Authors: Yushen Dai, Tao Deng, Miaoying Chen, Baoqin Huang, Yan Ji, Yongshen Feng, Shaofei Liu, Dongmei Zhong, Tao Zhang, Lifeng Zhang

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Background: Detection and diagnosis are prerequisites for early interventions in the care of children with Autism Spectrum Disorder (ASD). However, few studies have focused on this topic. Aim: This study aims to characterize the timing from symptom detection to intervention in children with ASD and to identify the potential predictors of early detection, diagnosis, and intervention. Methods and procedures: A cross-sectional survey was conducted with 314 parents of children with ASD in Guangzhou, China. Outcomes and Results: This study found that most children (76.24%) were diagnosed within one year after detection, and 25.8% of them did not receive the intervention after diagnosis. Predictors to ASD diagnosis included ASD-related symptoms identified at a younger age, more serious symptoms, and initial symptoms with abnormal development and sensory anomalies. ASD-related symptoms observed at an older age, initial symptoms with the social deficit, sensory anomalies, and without language impairment, parents as the primary caregivers, family with lower income and less social support utilization increased the odds of the time lag between detection and diagnosis. Children whose fathers had a lower level of education were less likely to receive the intervention. Conclusions and Implications: The study described the time for detection, diagnosis, and interventions of children with ASD. Findings suggest that the ASD-related symptoms, the timing at which symptoms first become a concern, primary caregivers’ roles, father’s educational level, and the family economic status should be considered when offering support to improve early detection, diagnosis, and intervention. Helping children and their families take full advantage of support is also important.

Keywords: autism spectrum disorder, child, detection, diagnosis, intervention, social support

Procedia PDF Downloads 92

Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

Procedia PDF Downloads 196

Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

Abstract:

The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

Procedia PDF Downloads 358

Authors: Yang Jian-Hong, Zhang Ren-Cheng, Huang Li

Abstract:

Arc fault is one of the main inducements of electric fires. Arc Fault Detection Device (AFDD) can detect arc fault effectively. Arc fault detections and unhooking standards are the keys to AFDD practical application. First, an arc fault continuous production system was developed, which could count the arc half wave number. Then, Combining with the UL1699 standard, ignition probability curve of cotton and unhooking time of various currents intensity were obtained by experiments. The combustion degree of arc fault could be expressed effectively by arc area. Experiments proved that electric fires would be misjudged or missed only using arc half wave number as AFDD unhooking basis. At last, Practical tests were carried out on the self-developed AFDD system. The result showed that actual AFDD unhooking time was the sum of arc half wave cycling number, Arc wave identification time and unhooking mechanical operation time And the first two shared shorter time. Unhooking time standard depended on the shortest mechanical operation time.

Keywords: arc fault detection device, arc area, arc half wave, unhooking time, arc fault

Procedia PDF Downloads 510

Authors: Pavel Psota, Vít Lédl, Jan Václavík, Roman Doleček, Pavel Mokrý, Petr Vojtíšek

Abstract:

This paper presents advanced time average digital holography by means of frequency shift and phase modulation. This technique can measure amplitudes of vibrations at ultimate dynamic range while the amplitude distribution evaluation is done independently in every pixel. The main focus of the paper is to gain insight into behavior of the method at low amplitudes of vibrations. In order to reach that, a set of experiments was performed. Results of the experiments together with novel noise suppression show the limit of the method to be below 0.1 nm.

Keywords: acusto-optical modulator, digital holography, low amplitudes, vibrometry

Procedia PDF Downloads 413