Search results for: fault detection and classification

Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

Procedia PDF Downloads 72

Authors: Asma A. Alsufyani, Maram A. Alharthi, Maha J. Althobaiti, Manal S. Alharthi, Huda Rizq

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Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user.

Keywords: corpus creation, feature extraction, machine learning, short text classification, social media, support vector machine, Twitter

Procedia PDF Downloads 155

Authors: Mehdi Moradi Sarmeidani, Peyman Keyhani, Hasan Momtaz

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Chlamydophila psittaci is a avian pathogen that may cause respiratory disorders in humans. Conjunctival and cloacal swabs from 54 captive psittacine birds presented at veterinary clinics were collected to determine the prevalence of C. psittaci in domestic birds in Isfahan. Samples were collected during 2014 from a total of 10 different species of parrots, with African gray(33), Cockatiel lutino(3), Cockatiel gray(2), Cockatiel cinnamon(1), Pearl cockatiel(6), Timneh African grey(1), Ringneck parakeet(2), Melopsittacus undulatus(1), Alexander parakeet(2), Green Parakeet(3) being the most representative species sampled. C. psittaci was detected in 27 (50%) birds using molecular detection (PCR) method. The detection of this bacterium in captive psittacine birds shows that there is a potential risk for human whom has a direct contact and there is a possibility of infecting other birds.

Keywords: chlamydophila psittaci, psittacine birds, PCR, Isfahan

Procedia PDF Downloads 371

Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

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The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

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Authors: Oladejo Olagoke Peter, Adagunodo T. A., Ogunkoya C. O.

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The research investigated the arrangement of some geological features under Ilesa employing aeromagnetic data. The obtained data was subjected to various data filtering and processing techniques, which are Total Horizontal Derivative (THD), Depth Continuation and Analytical Signal Amplitude using Geosoft Oasis Montaj 6.4.2 software. The Reduced to the Equator –Total Magnetic Intensity (TRE-TMI) outcomes reveal significant magnetic anomalies, with high magnitude (55.1 to 155 nT) predominantly at the Northwest half of the area. Intermediate magnetic susceptibility, ranging between 6.0 to 55.1 nT, dominates the eastern part, separated by depressions and uplifts. The southern part of the area exhibits a magnetic field of low intensity, ranging from -76.6 to 6.0 nT. The lineaments exhibit varying lengths ranging from 2.5 and 16.0 km. Analyzing the Rose Diagram and the analytical signal amplitude indicates structural styles mainly of E-W and NE-SW orientations, particularly evident in the western, SW and NE regions with an amplitude of 0.0318nT/m. The identified faults in the area demonstrate orientations of NNW-SSE, NNE-SSW and WNW-ESE, situated at depths ranging from 500 to 750 m. Considering the divergence magnetic susceptibility, structural style or orientation of the lineaments, identified fault and their depth, these lithological features could serve as a valuable foundation for assessing ground motion, particularly in the presence of sufficient seismic energy.

Keywords: lineament, aeromagnetic, anomaly, fault, magnetic

Procedia PDF Downloads 76

Authors: M. Kastek, M. Kowalski, M. Szustakowski, H. Polakowski, T. Sosnowski

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Development of highly efficient security systems is one of the most urgent topics for science and engineering. There are many kinds of threats and many methods of prevention. It is very important to detect a threat as early as possible in order to neutralize it. One of the very challenging problems is detection of dangerous objects hidden under human’s clothing. This problem is particularly important for safety of airport passengers. In order to develop methods and algorithms to detect hidden objects it is necessary to determine the thermal signatures of such objects of interest. The laboratory measurements were conducted to determine the thermal signatures of dangerous tools hidden under various clothes in different ambient conditions. Cameras used for measurements were working in spectral range 0.6-12.5 μm An infrared imaging Fourier transform spectroradiometer was also used, working in spectral range 7.7-11.7 μm. Analysis of registered thermograms and hyperspectral datacubes has yielded the thermal signatures for two types of guns, two types of knives and home-made explosive bombs. The determined thermal signatures will be used in the development of method and algorithms of image analysis implemented in proposed monitoring systems.

Keywords: hyperspectral detection, nultispectral detection, image processing, monitoring systems

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

Procedia PDF Downloads 372

Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

Procedia PDF Downloads 274

Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Yanchun Zuo, Yingao Liu, Wei Liu, Le Yu, Run Huang, Lixin Guo

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Electric field detection is important in many application scenarios. The traditional strategy is measuring the electric field with a man walking around in the area under test. This strategy cannot provide a satisfactory measurement accuracy. To solve the mentioned problem, an autopilot measurement system is divided. A mini-car is produced, which can travel in the area under test according to respect to the program within the CPU. The electric field measurement platform (EFMP) carries a central computer, two horn antennas, and a vector network analyzer. The mini-car stop at the sampling points according to the preset. When the car stops, the EFMP probes the electric field and stores data on the hard disk. After all the sampling points are traversed, an electric field map can be plotted. The proposed system can give an accurate field distribution description of the chamber.

Keywords: autopilot mini-car measurement system, electric field detection, field map, static zone measurement

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Adhitya Mangala, Yobel

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Bittuang geothermal prospect area located at Tana Toraja district, South Sulawesi. The geothermal system of the area related to Karua Volcano eruption product. This area has surface manifestation such as fumarole, hot springs, sinter silica and mineral alteration. Those prove that there are hydrothermal activities in the subsurface. However, the project and development of the area have not implemented yet. One of the important elements in geothermal exploration is to determine upflow and outflow zone. This information very useful to identify the target for geothermal wells and development which it is a risky task. The methods used in this research were Mercury (Hg) anomalies in soil, soil and manifestation temperature distribution and fault fracture density from 93 km² research area. Hg anomalies performed to determine the distribution of hydrothermal alteration. Soil and manifestation temperature distribution were conducted to estimate heat distribution. Fault fracture density (FFD) useful to determine fracture intensity and trend from surface observation. Those deliver Hg anomaly map, soil and manifestation temperature map that combined overlayed to fault fracture density map and geological map. Then, the conceptual model made from north – south, and east – west cross section to delineate upflow and outflow zone in this area. The result shows that upflow zone located in northern – northeastern of the research area with the increase of elevation and decrease of Hg anomalies and soil temperature. The outflow zone located in southern - southeastern of the research area which characterized by chloride, chloride - bicarbonate geothermal fluid type, higher soil temperature, and Hg anomalies. The range of soil temperature distribution from 16 – 19 °C in upflow and 19 – 26.5 °C in the outflow. The range of Hg from 0 – 200 ppb in upflow and 200 – 520 ppb in the outflow. Structural control of the area show northwest – southeast trend. The boundary between upflow and outflow zone in 1550 – 1650 m elevation. This research delivers the conceptual model with innovative methods that useful to identify a target for geothermal wells, project, and development in Bittuang geothermal prospect area.

Keywords: Bittuang geothermal prospect area, Hg anomalies, soil temperature, upflow and outflow zone

Procedia PDF Downloads 326

Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Yanxue Shang, Jingbin Zeng

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Chlorobenzene homologues (CBHs) are a category of environmental pollutants that can not be ignored. They can stay in the environment for a long period and are potentially carcinogenic. The traditional degradation method of CBHs is dechlorination followed by sample preparation and analysis. This is not only time-consuming and laborious, but the detection and analysis processes are used in conjunction with large-scale instruments. Therefore, this can not achieve rapid and low-cost detection. Compared with traditional sensing methods, colorimetric sensing is simpler and more convenient. In recent years, chromaticity sensors based on fluorescence have attracted more and more attention. Compared with sensing methods based on changes in fluorescence intensity, changes in color gradients are easier to recognize by the naked eye. Accordingly, this work proposes to use single drop microextraction (SDME) technology to solve the above problems. After the dechlorination reaction was completed, the organic droplet extracts Cl⁻ and realizes fluorescence colorimetric sensing at the same time. This method was integrated sample processing and visual in-situ detection, simplifying the detection process. As a fluorescence colorimetric sensor material, CsPbBr₃ was encapsulated in MOF-5 to construct CsPbBr₃@MOF-5 fluorescence colorimetric composite. Then the fluorescence colorimetric sensor was constructed by dispersing the composite in SDME organic droplets. When the Br⁻ in CsPbBr₃ exchanges with Cl⁻ produced by the dechlorination reactions, it is converted into CsPbCl₃. The fluorescence color of the single droplet of SDME will change from green to blue emission, thereby realizing visual observation. Therein, SDME can enhance the concentration and enrichment of Cl⁻ and instead of sample pretreatment. The fluorescence color change of CsPbBr₃@MOF-5 can replace the detection process of large-scale instruments to achieve real-time rapid detection. Due to the absorption ability of MOF-5, it can not only improve the stability of CsPbBr₃, but induce the adsorption of Cl⁻. Simultaneously, accelerate the exchange of Br- and Cl⁻ in CsPbBr₃ and the detection process of Cl⁻. The absorption process was verified by density functional theory (DFT) calculations. This method exhibits exceptional linearity for Cl⁻ in the range of 10⁻² - 10⁻⁶ M (10000 μM - 1 μM) with a limit of detection of 10⁻⁷ M. Whereafter, the dechlorination reactions of different kinds of CBHs were also carried out with this method, and all had satisfactory detection ability. Also verified the accuracy by gas chromatography (GC), and it was found that the SDME we developed in this work had high credibility. In summary, the in-situ visualization method of dechlorination reaction detection was a combination of sample processing and fluorescence colorimetric sensing. Thus, the strategy researched herein represents a promising method for the visual detection of dechlorination reactions and can be extended for applications in environments, chemical industries, and foods.

Keywords: chlorobenzene homologues, colorimetric sensor, metal halide perovskite, metal-organic frameworks, single drop microextraction

Procedia PDF Downloads 143

Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Suveen Kumar

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Oral cancer (OC) is the sixth most death causing cancer in world which includes tumour of lips, floor of the mouth, tongue, palate, cheeks, sinuses, throat, etc. Conventionally, the techniques used for OC detection are toluidine blue staining, biopsy, liquid-based cytology, visual attachments, etc., however these are limited by their highly invasive nature, low sensitivity, time consumption, sophisticated instrument handling, sample processing and high cost. Therefore, we developed biosensing chips for non-invasive detection of OC via CYFRA-21-1 biomarker. CYFRA-21-1 (molecular weight: 40 kDa) is secreted in saliva of OC patients which is a non-invasive biological fluid with a cut-off value of 3.8 ng mL-1, above which the subjects will be suffering from oral cancer. Therefore, in first work, 3-aminopropyl triethoxy silane (APTES) functionalized zirconia (ZrO2) nanoparticles (APTES/nZrO2) were used to successfully detect CYFRA-21-1 in a linear detection range (LDR) of 2-16 ng mL-1 with sensitivity of 2.2 µA mL ng-1. Successively, APTES/nZrO2-RGO was employed to prevent agglomeration of ZrO2 by providing high surface area reduced graphene oxide (RGO) support and much wider LDR (2-22 ng mL-1) was obtained with remarkable limit of detection (LOD) as 0.12 ng mL-1. Further, APTES/nY2O3/ITO platform was used for oral cancer bioseneor development. The developed biosensor (BSA/anti-CYFRA-21-1/APTES/nY2O3/ITO) have wider LDR (0.01-50 ng mL-1) with remarkable limit of detection (LOD) as 0.01 ng mL-1. To improve the sensitivity of the biosensing platform, nanocomposite of yattria stabilized nanostructured zirconia-reduced graphene oxide (nYZR) based biosensor has been developed. The developed biosensing chip having ability to detect CYFRA-21-1 biomolecules in the range of 0.01-50 ng mL-1, LOD of 7.2 pg mL-1 with sensitivity of 200 µA mL ng-1. Further, the applicability of the fabricated biosensing chips were also checked through real sample (saliva) analysis of OC patients and the obtained results showed good correlation with the standard protein detection enzyme linked immunosorbent assay (ELISA) technique.

Keywords: non-invasive, oral cancer, nanomaterials, biosensor, biochip

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Michael Zimba

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A duplicated image region may be subjected to a number of attacks such as noise addition, compression, reflection, rotation, and scaling with the intention of either merely mating it to its targeted neighborhood or preventing its detection. In this paper, we present an effective and robust method of detecting duplicated regions inclusive of those affected by the various attacks. In order to reduce the dimension of the image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of a suspicious image. However, unlike most existing copy move image forgery (CMIF) detection algorithms operating in the DWT domain which extract only the low frequency sub-band of the DWT of the suspicious image thereby leaving valuable information in the other three sub-bands, the proposed algorithm simultaneously extracts features from all the four sub-bands. The extracted features are not only more accurate representation of image regions but also robust to additive noise, JPEG compression, and affine transformation. Furthermore, principal component analysis-eigenvalue decomposition, PCA-EVD, is applied to reduce the dimension of the features. The extracted features are then sorted using the more computationally efficient Radix Sort algorithm. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. The proposed algorithm is not only fast but also more robust to attacks compared to the related CMIF detection algorithms. The experimental results show high detection rates.

Keywords: affine transformation, discrete wavelet transform, radix sort, SATS

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Authors: Temesgen Geremew

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This study investigates the evolution of polythiophene nanoparticles (PThNPs) as surface-enhanced Raman spectroscopy (SERS) sensors for Pb²⁺ detection in river water. We analyze the PThNPs' optical, structural, and morphological properties at different stages of their development to understand their SERS performance. Techniques like UV-Vis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) are employed for characterization. The SERS sensitivity towards Pb²⁺ is evaluated by monitoring the peak intensity of a specific Raman band upon increasing metal ion concentration. The study aims to elucidate the relationship between the PThNPs' characteristics and their SERS efficiency for Pb²⁺ detection, paving the way for optimizing their design and fabrication for improved sensing performance in real-world environmental monitoring applications.

Keywords: polythiophene, Pb2+, SERS, nanoparticles

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Authors: Ebenezer Obaloluwa Olaniyi

Abstract:

In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

Procedia PDF Downloads 398

Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

Abstract:

All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

Procedia PDF Downloads 235

Authors: Jianuo Sun, Jinghan Wang, Sirui Zhang, Chenhan Xu, Hongxia Hao, Hong Zhou

Abstract:

In recent years, the diversification and industrialization of drug-related crimes have posed significant threats to public health and safety globally. The widespread and increasingly younger demographics of drug users and the persistence of drug-impaired driving incidents underscore the urgency of this issue. Drug detection, a specialized forensic activity, is pivotal in identifying and analyzing substances involved in drug crimes. It relies on pharmacological and chemical knowledge and employs analytical chemistry and modern detection techniques. However, current drug detection methods are limited by their inability to perform semi-quantitative, real-time field analyses. They require extensive, complex laboratory-based preprocessing, expensive equipment, and specialized personnel and are hindered by long processing times. This study introduces an alternative approach using nucleic acid aptamers and Aggregation-Induced Emission (AIE) technology. Nucleic acid aptamers, selected artificially for their specific binding to target molecules and stable spatial structures, represent a new generation of biosensors following antibodies. Rapid advancements in AIE technology, particularly in tetraphenyl ethene-based luminous, offer simplicity in synthesis and versatility in modifications, making them ideal for fluorescence analysis. This work successfully synthesized, isolated, and purified an AIE molecule and constructed a probe comprising the AIE molecule, nucleic acid aptamers, and exonuclease for cocaine detection. The probe demonstrated significant relative fluorescence intensity changes and selectivity towards cocaine over other drugs. Using 4-Butoxytriethylammonium Bromide Tetraphenylethene (TPE-TTA) as the fluorescent probe, the aptamer as the recognition unit, and Exo I as an auxiliary, the system achieved rapid detection of cocaine within 5 mins in aqueous and urine, with detection limits of 1.0 and 5.0 µmol/L respectively. The probe-maintained stability and interference resistance in urine, enabling quantitative cocaine detection within a certain concentration range. This fluorescent sensor significantly reduces sample preprocessing time, offers a basis for rapid onsite cocaine detection, and promises potential for miniaturized testing setups.

Keywords: drug detection, aggregation-induced emission (AIE), nucleic acid aptamer, exonuclease, cocaine

Procedia PDF Downloads 62

Authors: Osama Mohamed Elrajubi, Idris El-Feghi, Mohamed Abu Baker Saghayer

Abstract:

This paper describes an identification of specific shapes within binary images using the morphological Hit-or-Miss Transform (HMT). Hit-or-Miss transform is a general binary morphological operation that can be used in searching of particular patterns of foreground and background pixels in an image. It is actually a basic operation of binary morphology since almost all other binary morphological operators are derived from it. The input of this method is a binary image and a structuring element (a template which will be searched in a binary image) while the output is another binary image. In this paper a modification of Hit-or-Miss transform has been proposed. The accuracy of algorithm is adjusted according to the similarity of the template and the sought template. The implementation of this method has been done by C language. The algorithm has been tested on several images and the results have shown that this new method can be used for similar shape detection.

Keywords: hit-or-miss operator transform, HMT, binary morphological operation, shape detection, binary images processing

Procedia PDF Downloads 333

Authors: Nimet Yildirim Tirgil, Ahmed Busnaina, April Z. Gu

Abstract:

Environmental waters are monitored worldwide to protect people from infectious diseases primarily caused by enteric pathogens. All long, Escherichia coli (E. coli) is a good indicator for potential enteric pathogens in waters. Thus, a rapid and simple detection method for E. coli is very important to predict the pathogen contamination. In this study, to the best of our knowledge, as the first time we developed a rapid, direct and reusable SWCNTs (single walled carbon nanotubes) based biosensor system for sensitive and selective E. coli detection in water samples. We use a novel and newly developed flexible biosensor device which was fabricated by high-rate nanoscale offset printing process using directed assembly and transfer of SWCNTs. By simple directed assembly and non-covalent functionalization, aptamer (biorecognition element that specifically distinguish the E. coli O157:H7 strain from other pathogens) based SWCNTs biosensor system was designed and was further evaluated for environmental applications with simple and cost-effective steps. The two gold electrode terminals and SWCNTs-bridge between them allow continuous resistance response monitoring for the E. coli detection. The detection procedure is based on competitive mode detection. A known concentration of aptamer and E. coli cells were mixed and after a certain time filtered. The rest of free aptamers injected to the system. With hybridization of the free aptamers and their SWCNTs surface immobilized probe DNA (complementary-DNA for E. coli aptamer), we can monitor the resistance difference which is proportional to the amount of the E. coli. Thus, we can detect the E. coli without injecting it directly onto the sensing surface, and we could protect the electrode surface from the aggregation of target bacteria or other pollutants that may come from real wastewater samples. After optimization experiments, the linear detection range was determined from 2 cfu/ml to 10⁵ cfu/ml with higher than 0.98 R² value. The system was regenerated successfully with 5 % SDS solution over 100 times without any significant deterioration of the sensor performance. The developed system had high specificity towards E. coli (less than 20 % signal with other pathogens), and it could be applied to real water samples with 86 to 101 % recovery and 3 to 18 % cv values (n=3).

Keywords: aptamer, E. coli, environmental detection, nanobiosensor, SWCTs

Procedia PDF Downloads 197

Authors: Mohamad Syahrizal Ahmad, Illyas M. Isa

Abstract:

In this paper, a multiwall carbon nanotubes (MWCNT) paste electrode modified by a Zn layered hydroxide-ferulate (ZLH-F) was used for detection of hydroquinone (HQ). The morphology and characteristic of the ZLH-F/MWCNT were investigated by scanning electron microscope (SEM), transmission electron microscope (TEM) and square wave voltammetry (SWV). Under optimal conditions, the SWV response showed linear plot for HQ concentration in the range of 1.0×10⁻⁵ M – 1.0×10⁻³ M. The detection limit was found to be 5.7×10⁻⁶ M and correlation coefficient of 0.9957. The glucose, fructose, sucrose, bisphenol A, acetaminophen, lysine, NO₃⁻, Cl⁻ and SO₄²⁻ did not interfere the HQ response. This modified electrode can be used to determine HQ content in wastewater and cosmetic cream with range of recovery 97.8% - 103.0%.

Keywords: 1, 4-dihydroxybenzene, hydroquinone, multiwall carbon nanotubes, square wave voltammetry

Procedia PDF Downloads 229

Authors: Tamas Madl

Abstract:

Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

Procedia PDF Downloads 234

Authors: Pam Bitrus Marcus

Abstract:

Compounds in Berom, a Niger-Congo language that is spoken in parts of central Nigeria, have been understudied, and the semantics of N-V nominal compounds have not been sufficiently delineated. This study describes the lexical classification of compounds in Berom and, specifically, examines the semantics of nominal compounds with N-V constituents. The study relied on a data set of 200 compounds that were drawn from Bere Naha (a newsletter publication in Berom). Contrary to the nominalization process in defining the lexical class of compounds in languages, the study revealed that verbal and adjectival classes of compounds are also attested in Berom and N-V nominal compounds have an agentive or locative interpretation that is not solely determined by the meaning of the constituents of the compound but by the context of the usage.

Keywords: berom, berom compounds, nominal compound, N-V compounds

Procedia PDF Downloads 78

Authors: Hamidreza Fallah Lajimi, Elham Karami, Alireza Arab, Fatemeh Alinasab

Abstract:

Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with Four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering, business engineering

Procedia PDF Downloads 713