Search results for: fault detection and classification

Authors: Hossein Alimohammadi, Mohsen Amirmojahedi, Mehrdad Rowhani

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Soil properties are used by geotechnical engineers to evaluate and analyze site conditions for designing purposes. Although basic soil classification tests are easy to perform and provide useful information to determine the properties of soils, it may take time to get the result and add some costs to the projects. Standard Penetration Test (SPT) provides an opportunity to evaluate soil parameters without performing laboratory tests. In addition to its simplicity and cheapness, the results become available immediately. This research provides a guideline on the application of the SPT test method, reliability of adapting the SPT test results in evaluating soil physical and mechanical properties such as Atterberg limits, shear strength, and compressive strength compressibility parameters. A total of 70 boreholes were investigated in this study by taking soil samples between depths of 1.2 to 15.25 meters. The project site was located in Morrow County, Ohio. A regression-based formula was proposed based on Tobit regression with a stepwise variable selection analysis conducted between SPT and other typical soil properties obtained from soil tests. The results of the research illustrated that the shear strength and physical properties of the soil affect the SPT number. The proposed correlation can help engineers to use SPT test results in their design with higher accuracy.

Keywords: standard penetration test, soil properties, soil classification, regression method

Procedia PDF Downloads 175

Authors: Stephen Rathinaraj Benjamin, Flavio Colmati Junior, Maria Izabel Florindo Guedes, Rosa Amalia Fireman Dutra

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A new enzymatic electrochemical biosensor based on multiwall carbon nanotubes and cerium oxide nanoparticles for the detection of rutin has been developed. The cerium oxide nanoparticles /HRP/ multiwall carbon nanotubes/ carbon paste electrode (HRP/ CeO2/MWCNTs/CPE) was prepared by ensuing addition of MWCNTs and HRP on the CPE, followed by the mixing with cerium oxide nanoparticles. Surface physical characteristics of the modified electrode and the electrochemical properties of the composite were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), cylic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). The HRP/ CeO2/MWCNTs/CPE showed good selectivity, stability and reproducibility, which was further applied to detect rutin tablet and capsule samples with satisfactory results.

Keywords: cerium dioxide nanoparticles, horseradish peroxidase, multiwall carbon nanotubes, rutin

Procedia PDF Downloads 381

Authors: Tesfaye Alamirew, Delele Worku, Solomon W. Fanta, Nigus Gabbiye

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Recently graphen based nanocomposites are become an emerging research areas for fabrication of enzymatic biosensors due to their property of large surface area, conductivity and biocompatibility. This review summarizes recent research reports of graphen based nanocomposites for the fabrication of glucose and ethanol enzymatic biosensors. The newly fabricated enzyme free microwave treated nitrogen doped graphen (MN-d-GR) had provided highest sensitivity towards glucose and GCE/rGO/AuNPs/ADH composite had provided far highest sensitivity towards ethanol compared to other reported graphen based nanocomposites. The MWCNT/GO/GOx and GCE/ErGO/PTH/ADH nanocomposites had also enhanced wide linear range for glucose and ethanol detection respectively. Generally, graphen based nanocomposite enzymatic biosensors had fast direct electron transfer rate, highest sensitivity and wide linear detection ranges during glucose and ethanol sensing.

Keywords: glucose, ethanol, enzymatic biosensor, graphen, nanocomposite

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Authors: Naime Boudemagh, Zoheir Hammoudi

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The parameters estimation of the K-distribution is an essential part in radar detection. In fact, presence of interfering targets in reference cells causes a decrease in detection performances. In such situation, the estimate of the shape and the scale parameters are far from the actual values. In the order to avoid interfering targets, we propose an Automatic Censoring (AC) algorithm of radar interfering targets in K-distribution. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The homogeneous population is then used to estimate the unknown parameters by the classical Method of Moment (MOM). The AC algorithm does not need any prior information about the clutter parameters nor does it require both the number and the position of interfering targets. The accuracy of the estimation parameters obtained by this algorithm are validated and compared to various actual values of the shape parameter, using Monte Carlo simulations, this latter show that the probability of censing in multiple target situations are in good agreement.

Keywords: parameters estimation, method of moments, automatic censoring, K distribution

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Authors: Ananta Kumar Das, Sujit Kumar Chakrabarti

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Architectural tactics such as heartbeat, ping-echo, encapsulate, encrypt data are techniques that are used to achieve quality attributes of a system. Detecting architectural tactics has several benefits: it can aid system comprehension (e.g., legacy systems) and in the estimation of quality attributes such as safety, security, maintainability, etc. Architectural tactics are typically spread over the source code and are implicit. For large codebases, manual detection is often not feasible. Therefore, there is a need for automated methods of detection of architectural tactics. This paper presents a formalization of the heartbeat architectural tactic and a program analytic approach to detect this tactic in source code. The experiment of the proposed method is done on a set of Java applications. The outcome of the experiment strongly suggests that the method compares well with a manual approach in terms of its sensitivity and specificity, and far supersedes a manual exercise in terms of its scalability.

Keywords: software architecture, architectural tactics, detecting architectural tactics, program analysis, AST, alias analysis

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Authors: Anil P. Dewani, Alok S. Tripathi, Anil V. Chandewar

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Present manuscript reports the development and validation of a quantitative high performance liquid chromatography method for the pharmacokinetic evaluation of Glimepiride (GLM) and Ilaprazole (ILA) in rat plasma. The plasma samples were involved with Solid phase extraction process (SPE). The analytes were resolved on a Phenomenex C18 column (4.6 mm× 250 mm; 5 µm particle size) using a isocratic elution mode comprising methanol:water (80:20 % v/v) with pH of water modified to 3 using Formic acid, the total run time was 10 min at 225 nm as common wavelength, the flow rate throughout was 1ml/min. The method was validated over the concentration range from 10 to 600 ng/mL for GLM and ILA, in rat plasma. Metformin (MET) was used as Internal Standard. Validation data demonstrated the method to be selective, sensitive, accurate and precise. The limit of detection was 1.54 and 4.08 and limit of quantification was 5.15 and 13.62 for GLM and ILA respectively, the method demonstrated excellent linearity with correlation coefficients (r2) 0.999. The intra and inter-day precision (RSD%) values were < 2.0% for both ILA and GLM. The method was successfully applied in pharmacokinetic studies followed by oral administration in rats.

Keywords: pharmacokinetics, glimepiride, ilaprazole, HPLC, SPE

Procedia PDF Downloads 357

Authors: Bo Ram Choi, Ji Su Kim, Juyeon Cho, Hyukjin Lee

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Food contaminated by bacteria (E.coli), causes food poisoning, which occurs to many patients worldwide annually. We have introduced an application of rolling circle amplification (RCA) as a versatile biosensor and developed a diagnostic platform composed of capillary tube and microbeads for rapid and easy detection of Escherichia coli (E. coli). When specific mRNA of E.coli is extracted from cell lysis, rolling circle amplification (RCA) of DNA template can be achieved and can be visualized by beads aggregation in capillary tube. In contrast, if there is no bacterial pathogen in sample, no beads aggregation can be seen. This assay is possible to detect visually target gene without specific equipment. It is likely to the development of a genetic kit for point of care testing (POCT) that can detect target gene using microbeads.

Keywords: rolling circle amplification (RCA), Escherichia coli (E. coli), point of care testing (POCT), beads aggregation, capillary tube

Procedia PDF Downloads 351

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

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

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

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: B. Gonzalez-Yebra, H. Barajas, P. Palomares, M. Hernandez, O. Torres, M. Ayala, A. L. González, G. Vazquez-Ortiz, M. L. Guzman

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Leukemia arises by molecular alterations of the normal hematopoietic stem cell (HSC) transforming it into a leukemic stem cell (LSC) with high cell proliferation, self-renewal, and cell differentiation. Chronic myeloid leukemia (CML) originates from an LSC-leading to elevated proliferation of myeloid cells and acute lymphoblastic leukemia (ALL) originates from an LSC development leading to elevated proliferation of lymphoid cells. In both cases, LSC can be identified by multicolor flow cytometry using several antibodies. However, to date, LSC levels in peripheral blood (PB) are not established well enough in ALL and CML patients. On the other hand, the detection of the minimal residue disease (MRD) in leukemia is mainly based on the identification of the mRNA bcr-abl gene in CML patients and some other genes in ALL patients. There is no a properly biomarker to detect MDR in both types of leukemia. The objective of this study was to determine mRNA bcr-abl and the percentage of LSC in peripheral blood of patients with CML and ALL and identify a possible association between the amount of LSC in PB and clinical data. We included in this study 19 patients with Leukemia. A PB sample was collected per patient and leukocytes were obtained by Ficoll gradient. The immunophenotype for LSC CD34+ was done by flow cytometry analysis with CD33, CD2, CD14, CD16, CD64, HLA-DR, CD13, CD15, CD19, CD10, CD20, CD34, CD38, CD71, CD90, CD117, CD123 monoclonal antibodies. In addition, to identify the presence of the mRNA bcr-abl by RT-PCR, the RNA was isolated using TRIZOL reagent. Molecular (presence of mRNA bcr-abl and LSC CD34+) and clinical results were analyzed with descriptive statistics and a multiple regression analysis was performed to determine statistically significant association. In total, 19 patients (8 patients with ALL and 11 patients with CML) were analyzed, 9 patients with de novo leukemia (ALL = 6 and CML = 3) and 10 under treatment (ALL = 5 and CML = 5). The overall frequency of mRNA bcr-abl was 31% (6/19), and it was negative in ALL patients and positive in 80% in CML patients. On the other hand, LSC was determined in 16/19 leukemia patients (%LSC= 0.02-17.3). The Novo patients had higher percentage of LSC (0.26 to 17.3%) than patients under treatment (0 to 5.93%). The amount of LSC was significantly associated with the amount of LSC were: absence of treatment, the absence of splenomegaly, and a lower number of leukocytes, negative association for the clinical variables age, sex, blasts, and mRNA bcr-abl. In conclusion, patients with de novo leukemia had a higher percentage of circulating LSC than patients under treatment, and it was associated with clinical parameters as lack of treatment, absence of splenomegaly and a lower number of leukocytes. The mRNA bcr-abl detection was only possible in the series of patients with CML, and molecular detection of LSC could be identified in the peripheral blood of all leukemia patients, we believe the identification of circulating LSC may be used as biomarker for the detection of the MRD in leukemia patients.

Keywords: stem cells, leukemia, biomarkers, flow cytometry

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Authors: Evgen Prokhorov, Gabriel Luna-Barcenas

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The polysaccharide chitosan (CS) is an attractive biopolymer for the stabilization of several nanoparticles in acidic aqueous media. This is due in part to the presence of abundant primary NH2 and OH groups which may lead to steric or chemical stabilization. Applications of most CS nanocomposites are based upon the interaction of high surface area nanoparticles (NPs) with different substance. Therefore, agglomeration of NPs leads to decreasing effective surface area such that it may decrease the efficiency of nanocomposites. The aim of this work is to measure nanocomposite’s electrical conductivity phenomena that will allow one to formulate optimal concentrations of conductivity NPs in CS-based nanocomposites. Additionally, by comparing the efficiency of such nanocomposites, one can guide applications in the biomedical (antibacterial properties and tissue regeneration) and sensor fields (detection of copper and nitrate ions in aqueous solutions). It was shown that the best antibacterial (CS-AgNPs, CS-AgNPs-carbon nanotubes) and would healing properties (CS-AuNPs) are observed in nanocomposites with concentrations of NPs near the percolation threshold. In this regard, the best detection limit in potentiometric and impedimetric sensors for detection of copper ions (using CS-AuNPs membrane) and nitrate ions (using CS-clay membrane) in aqueous solutions have been observed for membranes with concentrations of NPs near percolation threshold. It is well known that at the percolation concentration of NPs an abrupt increasing of conductivity is observed due to the presence of physical contacts between NPs; above this concentration, agglomeration of NPs takes place such that a decrease in the effective surface and performance of nanocomposite appear. The obtained relationship between electrical percolation threshold and performance of polymer nanocomposites with conductivity NPs is important for the design and optimization of polymer-based nanocomposites for different applications.

Keywords: chitosan, conductivity nanoparticles, percolation threshold, polymer nanocomposites

Procedia PDF Downloads 201

Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Liqin Zhang, Liang Yan

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This paper addresses the speed synchronization control problem for a network-based multi-motor system from the perspective of cluster consensus theory. Each motor is considered as a single agent connected through fixed and undirected network. This paper presents an improved control protocol from three aspects. First, for the purpose of improving both tracking and synchronization performance, this paper presents a distributed leader-following method. The improved control protocol takes the importance of each motor’s speed into consideration, and all motors are divided into different groups according to speed weights. Specifically, by using control parameters optimization, the synchronization error and tracking error can be regulated and decoupled to some extent. The simulation results demonstrate the effectiveness and superiority of the proposed strategy. In practical engineering, the simplified models are unrealistic, such as single-integrator and double-integrator. And previous algorithms require the acceleration information of the leader available to all followers if the leader has a varying velocity, which is also difficult to realize. Therefore, the method focuses on an observer-based variable structure algorithm for consensus tracking, which gets rid of the leader acceleration. The presented scheme optimizes synchronization performance, as well as provides satisfactory robustness. What’s more, the existing algorithms can obtain a stable synchronous system; however, the obtained stable system may encounter some disturbances that may destroy the synchronization. Focus on this challenging technological problem, a state-dependent-switching approach is introduced. In the presence of unmeasured angular speed and unknown failures, this paper investigates a distributed fault-tolerant consensus tracking algorithm for a group non-identical motors. The failures are modeled by nonlinear functions, and the sliding mode observer is designed to estimate the angular speed and nonlinear failures. The convergence and stability of the given multi-motor system are proved. Simulation results have shown that all followers asymptotically converge to a consistent state when one follower fails to follow the virtual leader during a large enough disturbance, which illustrates the good performance of synchronization control accuracy.

Keywords: consensus control, distributed follow, fault-tolerant control, multi-motor system, speed synchronization

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Authors: Eva Savy, Anthony Ruiz

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In extrusion blow molding, there is great variability in product quality due to the sensitivity of the machine settings. These variations lead to unnecessary rejects and loss of time. Yet production control is a major challenge for companies in this sector to remain competitive within their market. Current quality control methods only apply to finished products (vision control, leak test...). It has been shown that material melt temperature, blowing pressure, and ambient temperature have a significant impact on the variability of product quality. Since blowing is a key step in the process, we have studied this parameter in this paper. The objective is to determine if airflow analysis allows the identification of quality problems before the full completion of the manufacturing process. We conducted tests to determine if it was possible to identify a leakage defect and an obstructed defect, two common defects on products. The results showed that it was possible to identify a leakage defect by airflow analysis.

Keywords: extrusion blow molding, signal, sensor, defects, detection

Procedia PDF Downloads 135

Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Rupesh K. Mishra, Gaëlle Catanante, Akhtar Hayat, Jean-Louis Marty

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Ochratoxins (OTA) are secondary metabolites present in a wide variety of food stuff. They are dangerous by-products mainly produced by several species of storage fungi including the Aspergillus and Penicillium genera. OTA is known to have nephrotoxic, immunotoxic, teratogenic and carcinogenic effects. Thus, needs a special attention for a highly sensitive and selective detection system that can quantify these organic toxins in various matrices such as cocoa beans. This work presents a folding based aptasensors by employing an aptamer conjugated redox probe (methylene blue) specifically designed for OTA. The aptamers were covalently attached to the screen printed carbon electrodes using diazonium grafting. Upon sensing the OTA, it binds with the immobilized aptamer on the electrode surface, which induces the conformational changes of the aptamer, consequently increased in the signal. This conformational change of the aptamer before and after biosensing of target OTA could produce the distinguishable electrochemical signal. The obtained limit of detection was 0.01 ng/ml for OTA samples with recovery of up to 88% in contaminated cocoa samples.

Keywords: ochratoxin A, cocoa, DNA aptamer, labelled probe

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Janos Juhasz, Sandor Pongor, Balazs Ligeti

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Next-generation sequencing, especially whole genome shotgun sequencing, is becoming a common approach to gain insight into the microbiomes in a culture-independent way, even in clinical practice. It does not only give us information about the species composition of an environmental sample but opens the possibility to detect antimicrobial resistance and novel, or currently unknown, pathogens. Accurately and reliably detecting the microbial strains is a challenging task. Here we present a sensitive approach for detecting pathogens in metagenomics samples with special regard to detecting novel variants of known pathogens. We have developed a pipeline that uses fast, short read aligner programs (i.e., Bowtie2/BWA) and comprehensive nucleotide databases. Taxonomic binning is based on the lowest common ancestor (LCA) principle; each read is assigned to a taxon, covering the most significantly hit taxa. This approach helps in balancing between sensitivity and running time. The program was tested both on experimental and synthetic data. The results implicate that our method performs as good as the state-of-the-art BLAST-based ones, furthermore, in some cases, it even proves to be better, while running two orders magnitude faster. It is sensitive and capable of identifying taxa being present only in small abundance. Moreover, it needs two orders of magnitude less reads to complete the identification than MetaPhLan2 does. We analyzed an experimental anthrax dataset (B. anthracis strain BA104). The majority of the reads (96.50%) was classified as Bacillus anthracis, a small portion, 1.2%, was classified as other species from the Bacillus genus. We demonstrate that the evaluation of high-throughput sequencing data is feasible in a reasonable time with good classification accuracy.

Keywords: metagenomics, taxonomy binning, pathogens, microbiome, B. anthracis

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Mustapha Majid

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Gone were the days when technicians/mechanics will have to spend too much time trying to identify a mechanical fault and rectify the problem. Now the emphasis is on the use of Automobile diagnosing Equipment through the use of computers and special software. An investigation conducted at Tamale Metropolis and Accra in the Northern and Greater Accra regions of Ghana, respectively. Methodology for data gathering were; questionnaires, physical observation, interviews, and newspaper. The study revealed that majority of mechanics lack computer skills which can enable them use diagnosis tools such as Exhaust Gas Analyzer, Scan Tools, Electronic Wheel Balancing machine, etc.

Keywords: diagnosing, local garages and modern garages, lack of knowledge of diagnosing posing an existential threat, training of local mechanics

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Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Nahla Elsherbiny, Ahmed Ahmed, Hamada Mohammed, Mohamed Ali

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Background: The enterococci may be considered as opportunistic agents particularly in immunocompromised patients. It is one of the top three pathogens causing many healthcare associated infections (HAIs). Resistance to several commonly used antimicrobial agents is a remarkable characteristic of most species which may carry various genes contributing to virulence. Objectives: to determine the prevalence of enterococci species in different intensive care units (ICUs) causing health care-associated infections (HAIs), intestinal carriage and environmental contamination. Also, to study the antimicrobial susceptibility pattern of the isolates with special reference to vancomycin resistance. In addition to phenotypic and genotypic detection of gelatinase, cytolysin and biofilm formation among isolates. Patients and Methods: This study was carried out in the infection control laboratory at Assiut University Hospitals over a period of one year. Clinical samples were collected from 285 patients with various (HAIs) acquired after admission to different ICUs. Rectal swabs were taken from 14 cases for detection of enterococci carriage. In addition, 1377 environmental samples were collected from the surroundings of the patients. Identification was done by conventional bacteriological methods and confirmed by analytical profile index (API). Antimicrobial sensitivity testing was performed by Kirby Bauer disc diffusion method and detection of vancomycin resistance was done by agar screen method. For the isolates, phenotypic detection of cytolysin, gelatinase production and detection of biofilm by tube method, Congo red method and microtiter plate. We performed polymerase chain reaction (PCR) for detection of some virulence genes (gelE, cylA, vanA, vanB and esp). Results: Enterococci caused 10.5% of the HAIs. Respiratory tract infection was the predominant type (86.7%). The commonest species were E.gallinarum (36.7%), E.casseliflavus (30%), E.faecalis (30%), and E.durans (3.4 %). Vancomycin resistance was detected in a total of 40% (12/30) of those isolates. The risk factors associated with acquiring vancomycin resistant enterococci (VRE) were immune suppression (P= 0.031) and artificial feeding (P= 0.008). For the rectal swabs, enterococci species were detected in 71.4% of samples with the predominance of E. casseliflavus (50%). Most of the isolates were vancomycin resistant (70%). Out of a total 1377 environmental samples, 577 (42%) samples were contaminated with different microorganisms. Enterococci were detected in 1.7% (10/577) of total contaminated samples, 50% of which were vancomycin resistant. All isolates were resistant to penicillin, ampicillin, oxacillin, ciprofloxacin, amikacin, erythromycin, clindamycin and trimethoprim-sulfamethaxazole. For the remaining antibiotics, variable percentages of resistance were reported. Cytolysin and gelatinase were detected phenotypically in 16% and 48 % of the isolates respectively. The microtiter plate method showed the highest percentages of detection of biofilm among all isolated species (100%). The studied virulence genes gelE, esp, vanA and vanB were detected in 62%, 12%, 2% and 12% respectively, while cylA gene was not detected in any isolates. Conclusions: A significant percentage of enterococci was isolated from patients and environments in the ICUs. Many virulence factors were detected phenotypically and genotypically among isolates. The high percentage of resistance, coupled with the risk of cross transmission to other patients make enterococci infections a significant infection control issue in hospitals.

Keywords: antimicrobial resistance, enterococci, ICUs, virulence factors

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Sneha Leslie

Abstract:

The current situation in the cyber world is that crimes performed by Botnets are increasing and the masterminds (botmaster) are not detectable easily. The botmaster in the botnet compromises the legitimate host machines in the network and make them bots or zombies to initiate the cyber-attacks. This paper will focus on the live detection of the botmaster in the network by using the strong framework 'metasploit', when distributed denial of service (DDOS) attack is performed by the botnet. The affected victim machine will be continuously monitoring its incoming packets. Once the victim machine gets to know about the excessive count of packets from any IP, that particular IP is noted and details of the noted systems are gathered. Using the vulnerabilities present in the zombie machines (already compromised by botmaster), the victim machine will compromise them. By gaining access to the compromised systems, applications are run remotely. By analyzing the incoming packets of the zombies, the victim comes to know the address of the botmaster. This is an effective and a simple system where no specific features of communication protocol are considered.

Keywords: bonet, DDoS attack, network security, detection system, metasploit framework

Procedia PDF Downloads 246

Authors: Jeron Maheswaran, Sudath C. Siriwardane

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In fatigue analysis, the structural detail of tubular joint has taken great attention among engineers. The DNV-RP-C203 is covering this topic quite well for simple and clear joint cases. For complex joint and geometry, where joint classification isn’t available and limitation on validity range of non-dimensional geometric parameters, the challenges become a fact among engineers. The classification of joint is important to carry out through the fatigue analysis. These joint configurations are identified by the connectivity and the load distribution of tubular joints. To overcome these problems to some extent, this paper compare the fatigue life of tubular joints in offshore jacket according to the stress concentration factors (SCF) in DNV-RP-C203 and finite element method employed Abaqus/CAE. The paper presents the geometric details, material properties and considered load history of the jacket structure. Describe the global structural analysis and identification of critical tubular joints for fatigue life estimation. Hence fatigue life is determined based on the guidelines provided by design codes. Fatigue analysis of tubular joints is conducted using finite element employed Abaqus/CAE [4] as next major step. Finally, obtained SCFs and fatigue lives are compared and their significances are discussed.

Keywords: fatigue life, stress-concentration factor, finite element analysis, offshore jacket structure

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Authors: Lotfi O. Gargab, Ruichong R. Zhang

Abstract:

A wave-based framework is presented for modeling seismic motion in multistory buildings and using measured response for system identification which can be utilized to extract important information regarding structure integrity. With one pair of building response at two locations, a generalized model response is formulated based on wave propagation features and expressed as frequency and time response functions denoted, respectively, as GFRF and GIRF. In particular, GIRF is fundamental in tracking arrival times of impulsive wave motion initiated at response level which is dependent on local model properties. Matching model and measured-structure responses can help in identifying model parameters and infer building properties. To show the effectiveness of this approach, the Millikan Library in Pasadena, California is identified with recordings of the Yorba Linda earthquake of September 3, 2002.

Keywords: system identification, continuous-discrete mass modeling, damage detection, post-earthquake

Procedia PDF Downloads 365

Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 51