Search results for: CSRF detection extension

Authors: Kamel Belammi, Houria Fatrim

Abstract:

imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

Procedia PDF Downloads 527

Authors: Austin Ikechukwu Gbasouzor, Sam Nna Omenyi, Sabuj Malli

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This study is an extension of the previous work done with ARS-680 Environmental Chamber. Drying is a complex operation that demands much energy and time. Drying is essentially important for preservation of ginger rhizome. Drying of ginger was modeled, and then the effective diffusion coefficient and activation energy where determined. For this purpose, the experiments were done at six levels of varied temperature ranging from (10, 20, 30, 40, 50, 60°C). The average effective diffusion coefficient for their studies samples for temperature range of 40°C to 70°C was 4.48 x10-10m²/s, 4.96 x10-10m²/s, and 5.31 x10-10m²/s for 0.8, 1.5 and 3m/s drying air velocity respectively. These values closely agreed with the values of effective diffusion coefficients obtained in these studies for the variously treated ginger rhizomes and test conducted.

Keywords: activation energy, diffusion coefficients, drying model, drying time, ginger rhizomes, moisture ratio, thin layer

Procedia PDF Downloads 160

Authors: Huajuan Shi

Abstract:

Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

Procedia PDF Downloads 85

Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

Procedia PDF Downloads 266

Authors: Katalin Juhasz-Dora

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Several studies have shown that physical environments, milieu, atmosphere and service space play a significant role in the consumer experience. In the case of themed servicescapes, different tangible assets (design, decoration, facilities, amenities), intangible assets (service, activities) contribute to the luxury guest experience. This current study summarizes the literature related to the guest experience in the case of luxury hotels. Based on the results of a case study, additional dimensions of guest experience are explored. The research findings contribute to the extension of literature by providing a conceptual framework in specific themed luxury hotels. Understanding the elements of themed servicescape and dimensions of guest experience play a significant role in consumer behavior. Implications for management and future research directions are presented.

Keywords: atmosphere, guest experience, luxury service, themed hotel

Procedia PDF Downloads 160

Authors: Olivier Chaigneau, Damianos Oikonomidis, Marie-Cecile Delmas

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GAMBAS (Galileo Advanced features for the Maritime domain: Breakthrough Applications for Safety and security) is a project funded by the European Space Program Agency (EUSPA) aiming at identifying the search-and-rescue and ship security alert system needs for maritime users (including operators and fishing stakeholders) and developing operational concepts to answer these needs. The general objective of the GAMBAS project is to support the deployment of Galileo exclusive features in the maritime domain in order to improve safety and security at sea, detection of illegal activities and associated surveillance means, resilience to natural and human-induced emergency situations, and develop, integrate, demonstrate, standardize and disseminate these new associated capabilities. The project aims to demonstrate: improvement of the SAR (Search And Rescue) and SSAS (Ship Security Alert System) detection and response to maritime distress through the integration of new features into the beacon for SSAS in terms of cost optimization, user-friendly aspects, integration of Galileo and OS NMA (Open Service Navigation Message Authentication) reception for improved authenticated localization performance and reliability, and at sea triggering capabilities, optimization of the responsiveness of RCCs (Rescue Co-ordination Centre) towards the distress situations affecting vessels, the adaptation of the MCCs (Mission Control Center) and MEOLUT (Medium Earth Orbit Local User Terminal) to the data distribution of SSAS alerts.

Keywords: Galileo new advanced features, maritime, safety, security

Procedia PDF Downloads 90

Authors: V. Mantey, N. Findlay, I. Maddox

Abstract:

The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

Procedia PDF Downloads 166

Authors: Huajuan Shi

Abstract:

Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

Procedia PDF Downloads 61

Authors: Rafsan Al Mamun, Md. Motaharul Islam, Rabana Tajrin, Nabiha Noor, Shafinaz Qader

Abstract:

In an attempt to enrich the lives of billions of people by providing proper information, security and a way of communicating with others, the need for efficient and improved satellites is constantly growing. Thus, there is an increasing demand for better error detection and correction (EDAC) schemes, which are capable of protecting the data onboard the satellites. The paper is aimed towards detecting and correcting such errors using a special algorithm called the Hamming Code, which uses the concept of parity and parity bits to prevent single-bit errors onboard a satellite in Low Earth Orbit. This paper focuses on the study of Low Earth Orbit satellites and the process of generating the Hamming Code matrix to be used for EDAC using computer programs. The most effective version of Hamming Code generated was the Hamming (16, 11, 4) version using MATLAB, and the paper compares this particular scheme with other EDAC mechanisms, including other versions of Hamming Codes and Cyclic Redundancy Check (CRC), and the limitations of this scheme. This particular version of the Hamming Code guarantees single-bit error corrections as well as double-bit error detections. Furthermore, this version of Hamming Code has proved to be fast with a checking time of 5.669 nanoseconds, that has a relatively higher code rate and lower bit overhead compared to the other versions and can detect a greater percentage of errors per length of code than other EDAC schemes with similar capabilities. In conclusion, with the proper implementation of the system, it is quite possible to ensure a relatively uncorrupted satellite storage system.

Keywords: bit-flips, Hamming code, low earth orbit, parity bits, satellite, single error upset

Procedia PDF Downloads 125

Authors: Donghyun Lee, Cam Nguyen

Abstract:

Multi-band systems offer a great deal of benefit in modern communication and radar systems. In particular, multi-band antenna-array radar systems with their extended frequency diversity provide numerous advantages in detection, identification, locating and tracking a wide range of targets, including enhanced detection coverage, accurate target location, reduced survey time and cost, increased resolution, improved reliability and target information. An accurate calibration is a critical issue in antenna array systems. The amplitude and phase errors in multi-band and multi-polarization antenna array transceivers result in inaccurate target detection, deteriorated resolution and reduced reliability. Furthermore, the digital beam former without the RF domain phase-shifting is less immune to unfiltered interference signals, which can lead to receiver saturation in array systems. Therefore, implementing integrated front-end architecture, which can support calibration function with low insertion and filtering function from the farthest end of an array transceiver is of great interest. We report a dual K/Ka-band T/R/Calibration switch module with quasi-elliptic dual-bandpass filtering function implementing a Q-enhanced metamaterial transmission line. A unique dual-band frequency response is incorporated in the reception and calibration path of the proposed switch module utilizing the composite right/left-handed meta material transmission line coupled with a Colpitts-style negative generation circuit. The fabricated fully integrated T/R/Calibration switch module in 0.18-μm BiCMOS technology exhibits insertion loss of 4.9-12.3 dB and isolation of more than 45 dB in the reception, transmission and calibration mode of operation. In the reception and calibration mode, the dual-band frequency response centered at 24.5 and 35 GHz exhibits out-of-band rejection of more than 30 dB compared to the pass bands below 10.5 GHz and above 59.5 GHz. The rejection between the pass bands reaches more than 50 dB. In all modes of operation, the IP1-dB is between 4 and 11 dBm. Acknowledgement: This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: microwaves, millimeter waves, T/R switch, wireless communications, wireless communications

Procedia PDF Downloads 156

Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization

Procedia PDF Downloads 128

Authors: Vigen Arakelian

Abstract:

This paper deals with the study of devices for displacement of the mould base of blow-molding machines. The displacement of the mould in the studied case is carried out by a linear actuator, which ensures the descent of the mould base and by extension springs, which return the letter in the initial position. The aim of this paper is to study the inverse dynamics of the device for displacement of the mould base of blow-molding machines and to determine its optimum parameters for higher rate of production. In the other words, it is necessary to solve the inverse dynamic problem to find the equation of motion linking applied forces with displacements. This makes it possible to determine the stiffness coefficient of the spring to turn the mold base back to the initial position for a given time. The obtained results are illustrated by a numerical example. It is shown that applying a spring with stiffness returns the mould base of the blow molding machine into the initial position in 0.1 sec.

Keywords: design, mechanisms, dynamics, blow-molding machines

Procedia PDF Downloads 148

Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

Abstract:

This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

Procedia PDF Downloads 290

Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

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Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

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Authors: Waleed K. Saib, Abdulsalam M. Alhawsawi, Essam Banoqitah

Abstract:

The idea of this research is to design a radiation spectrometer using LSO scintillation detector coupled to a C series of SiPM (silicon photomultiplier). The device can be used to detects gamma and X-ray radiation. This device is also designed to estimates the activity of the source contamination. The SiPM will detect light in the visible range above the threshold and read them as counts. Three gamma sources were used for these experiments Cs-137, Am-241 and Co-60 with various activities. These sources are applied for four experiments operating the SiPM as a spectrometer, energy resolution, pile-up set and efficiency. The SiPM is connected to a MCA to perform as a spectrometer. Cerium doped Lutetium Silicate (Lu₂SiO₅) with light yield 26000 photons/Mev coupled with the SiPM. As a result, all the main features of the Cs-137, Am-241 and Co-60 are identified in MCA. The experiment shows how photon energy and probability of interaction are inversely related. Total attenuation reduces as photon energy increases. An analytical calculation was made to obtain the FWHM resolution for each gamma source. The FWHM resolution for Am-241 (59 keV) is 28.75 %, for Cs-137 (662 keV) is 7.85 %, for Co-60 (1173 keV) is 4.46 % and for Co-60 (1332 keV) is 3.70%. Moreover, the experiment shows that the dead time and counts number decreased when the pile-up rejection was disabled and the FWHM decreased when the pile-up was enabled. The efficiencies were calculated at four different distances from the detector 2, 4, 8 and 16 cm. The detection efficiency was observed to declined exponentially with increasing distance from the detector face. Conclusively, the SiPM board operated with an LSO scintillator crystal as a spectrometer. The SiPM energy resolution for the three gamma sources used was a decent comparison to other PMTs.

Keywords: PMT, radiation, radiation detection, scintillation detectors, silicon photomultiplier, spectrometer

Procedia PDF Downloads 152

Authors: Hong Yu, Ion Matei

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Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

Procedia PDF Downloads 188

Authors: Rajendra N. Ghosh, Paramjeet Singh, Niranjan Khandelwal, Sameer Vyas, Pratibha Singhi, Naveen Sankhyan

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Background: Tuberculoma and neurocysticercosis (NCC) are two most common intracranial infections in developing country. They often simulate on neuroimaging and in absence of typical imaging features cause significant diagnostic dilemmas. Differentiation is extremely important to avoid empirical exposure to antitubercular medications or nonspecific treatment causing disease progression. Purpose: Better characterization and differentiation of CNS tuberculoma and NCC by using morphological and multiple advanced functional MRI. Material and Methods: Total fifty untreated patients (20 tuberculoma and 30 NCC) were evaluated by using conventional and advanced sequences like CISS, SWI, DWI, DTI, Magnetization transfer (MT), T2Relaxometry (T2R), Perfusion and Spectroscopy. rCBV,ADC,FA,T2R,MTR values and metabolite ratios were calculated from lesion and normal parenchyma. Diagnosis was confirmed by typical biochemical, histopathological and imaging features. Results: CISS was most useful sequence for scolex detection (90% on CISS vs 73% on routine sequences). SWI showed higher scolex detection ability. Mean values of ADC, FA,T2R from core and rCBV from wall of lesion were significantly different in tuberculoma and NCC (P < 0.05). Mean values of rCBV, ADC, T2R and FA for tuberculoma and NCC were (3.36 vs1.3), (1.09x10⁻³vs 1.4x10⁻³), (0.13 x10⁻³ vs 0.09 x10⁻³) and (88.65 ms vs 272.3 ms) respectively. Tuberculomas showed high lipid peak, more choline and lower creatinine with Ch/Cr ratio > 1. T2R value was most significant parameter for differentiation. Cut off values for each significant parameters have proposed. Conclusion: Quantitative MRI in combination with conventional sequences can better characterize and differentiate similar appearing tuberculoma and NCC and may be incorporated in routine protocol which may avoid brain biopsy and empirical therapy.

Keywords: advanced functional MRI, differentiation, neurcysticercosis, tuberculoma

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Authors: Sahar Khedr, Dina Sayed, Ayman Hanafy

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Arabic is one of the most ancient and critical languages in the world. It has over than 250 million Arabic native speakers and more than twenty countries having Arabic as one of its official languages. In the past decade, we have witnessed a rapid evolution in smart devices, social network and technology sector which led to the need to provide tools and libraries that properly tackle the Arabic language in different domains. Stemming is one of the most crucial linguistic fundamentals. It is used in many applications especially in information extraction and text mining fields. The motivation behind this work is to enhance the Arabic light stemmer to serve the data mining industry and leverage it in an open source community. The presented implementation works on enhancing the Arabic light stemmer by utilizing and enhancing an algorithm that provides an extension for a new set of rules and patterns accompanied by adjusted procedure. This study has proven a significant enhancement for better search accuracy with an average 10% improvement in comparison with previous works.

Keywords: Arabic data mining, Arabic Information extraction, Arabic Light stemmer, Arabic stemmer

Procedia PDF Downloads 302

Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

Abstract:

Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

Abstract:

Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

Procedia PDF Downloads 80

Authors: Guillermo E. Quintero, Edwin G. Perez, Oriel Sanchez, Christian Espinosa-Bustos, Denis Fuentealba, Margarita E. Aliaga

Abstract:

Bisulfite ion (HSO3-) has been used as a preservative in food, drinks, and medication. However, it is well-known that HSO3- can cause health problems like asthma and allergic reactions in people. Due to the above, the development of analytical methods for detecting this ion has gained great interest. In line with the above, the current use of colorimetric and/or fluorescent probes as a detection technique has acquired great relevance due to their high sensitivity and accuracy. In this context, 2-quinolinone derivatives have been found to possess promising activity as antiviral agents, sensitizers in solar cells, antifungals, antioxidants, and sensors. In particular, 7-(diethylamino)-2-quinolinone derivatives have attracted attention in recent years since their suitable photophysical properties become promising fluorescent probes. In Addition, there is evidence that photophysical properties and reactivity can be affected by the study medium, such as micellar media. Based on the above background, 7-(diethylamino)-2-quinolinone derivatives based chalcone will be able to be incorporated into a cationic micellar environment (Cetyltrimethylammonium bromide, CTAB). Furthermore, the supramolecular control induced by the micellar environment will increase the reactivity of these derivatives towards nucleophilic analytes such as HSO3- (Michael-type addition reaction), leading to the generation of new colorimetric and/or fluorescent probes. In the present study, two derivatives of 7-(diethylamino)-2-quinolinone based chalcone DQD1-2 were synthesized according to the method reported by the literature. These derivatives were structurally characterized by 1H, 13C NMR, and HRMS-ESI. In addition, UV-VIS and fluorescence studies determined absorption bands near 450 nm, emission bands near 600 nm, fluorescence quantum yields near 0.01, and fluorescence lifetimes of 5 ps. In line with the foregoing, these photophysical properties aforementioned were improved in the presence of a cationic micellar medium using CTAB thanks to the formation of adducts presenting association constants of the order of 2,5x105 M-1, increasing the quantum yields to 0.12 and the fluorescence lifetimes corresponding to two lifetimes near to 120 and 400 ps for DQD1 and DQD2. Besides, thanks to the presence of the micellar medium, the reactivity of these derivatives with nucleophilic analytes, such as HSO3-, was increased. This was achieved through kinetic studies, which demonstrated an increase in the bimolecular rate constants in the presence of a micellar medium. Finally, probe DQD1 was chosen as the best sensor since it was assessed to detect HSO3- with excellent results.

Keywords: bisulfite detection, cationic micelle, colorimetric probes, quinolinone derivatives

Procedia PDF Downloads 87

Authors: P. Selva, B. Lorraina, J. Alexis, A. Seror, A. Longuet, C. Mary, F. Denard

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Over the past four decades, the fatigue behavior of nickel-based alloys has been widely studied. However, in recent years, significant advances in the fabrication process leading to grain size reduction have been made in order to improve fatigue properties of aircraft turbine discs. Indeed, a change in particle size affects the initiation mode of fatigue cracks as well as the fatigue life of the material. The present study aims to investigate the fatigue behavior of a newly developed nickel-based superalloy under biaxial-planar loading. Low Cycle Fatigue (LCF) tests are performed at different stress ratios so as to study the influence of the multiaxial stress state on the fatigue life of the material. Full-field displacement and strain measurements as well as crack initiation detection are obtained using Digital Image Correlation (DIC) techniques. The aim of this presentation is first to provide an in-depth description of both the experimental set-up and protocol: the multiaxial testing machine, the specific design of the cruciform specimen and performances of the DIC code are introduced. Second, results for sixteen specimens related to different load ratios are presented. Crack detection, strain amplitude and number of cycles to crack initiation vs. triaxial stress ratio for each loading case are given. Third, from fractographic investigations by scanning electron microscopy it is found that the mechanism of fatigue crack initiation does not depend on the triaxial stress ratio and that most fatigue cracks initiate from subsurface carbides.

Keywords: cruciform specimen, multiaxial fatigue, nickel-based superalloy

Procedia PDF Downloads 288

Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

Procedia PDF Downloads 494

Authors: Yansong Chen

Abstract:

In the age of media convergence, new media technology is constantly impacting, changing, and even reshaping the limits of Art. From the technological ontology of the new media art, the concept of interaction design has always been dominated by I/O (Input/Output) systems through the ages, which ignores the content of systems and kills the aura of art. Light, as a fusion media, basically comes from the extension of some human feelings and can be the content of the input or the effect of output. In this paper, firstly, on the basis of literature review, the interaction characteristics research was conducted on light. Secondly, starting from discourse patterns of people and machines, people and people, people, and imagining things, we propose three light modes: object-oriented interaction, Immersion interaction, Tele-Presence interaction. Finally, this paper explains how to regain the aura of art through light elements in new media art and understand multiple levels of 'Interaction design'. In addition, the new media art, especially the light-based interaction art, enriches the language patterns and motivates emerging art forms to be more widespread and popular, which achieves its aesthetics growth.

Keywords: new media art, interaction design, light art, immersion

Procedia PDF Downloads 230

Authors: Carla M. Machado, André A. Silva, Armando Bastos, Telmo G. Santos, J. Pamies Teixeira

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Advanced high-strength steels (AHSS) are increasingly being used in automotive components. The use of AHSS sheets plays an important role in reducing weight, as well as increasing the resistance to impact in vehicle components. However, the large-scale use of these sheets becomes more difficult due to the limitations during the forming process. Such limitations are due to the elastically driven change of shape of a metal sheet during unloading and following forming, known as the springback effect. As the magnitude of the springback tends to increase with the strength of the material, it is among the most worrisome problems in the use of AHSS steels. The prediction of strain hardening, especially under non-proportional loading conditions, is very limited due to the lack of constitutive models and mainly due to very limited experimental tests. It is very clear from the literature that in experimental terms there is not much work to evaluate deformation behavior under real conditions, which implies a very limited and scarce development of mathematical models for these conditions. The Bauschinger effect is also fundamental to the difference between kinematic and isotropic hardening models used to predict springback in sheet metal forming. It is of major importance to deepen the phenomenological knowledge of the mechanical and microstructural behavior of the materials, in order to be able to reproduce with high fidelity the behavior of extension of the materials by means of computational simulation. For this, a multi phenomenological analysis and characterization are necessary to understand the various aspects involved in plastic deformation, namely the stress-strain relations and also the variations of electrical conductivity and magnetic permeability associated with the metallurgical changes due to plastic deformation. Aiming a complete mechanical-microstructural characterization, uniaxial tensile tests involving successive cycles of loading and unloading were performed, as well as biaxial tests such as the Erichsen test. Also, nondestructive evaluation comprising eddy currents to verify microstructural changes due to plastic deformation and ultrasonic tests to evaluate the local variations of thickness were made. The material parameters for the stable yield function and the monotonic strain hardening were obtained using uniaxial tension tests in different material directions and balanced biaxial tests. Both the decrease of the modulus of elasticity and Bauschinger effect were determined through the load-unload tensile tests. By means of the eddy currents tests, it was possible to verify changes in the magnetic permeability of the material according to the different plastically deformed areas. The ultrasonic tests were an important aid to quantify the local plastic extension. With these data, it is possible to parameterize the different models of kinematic hardening to better approximate the results obtained by simulation with the experimental results, which are fundamental for the springback prediction of the stamped parts.

Keywords: advanced high strength steel, Bauschinger effect, sheet metal forming, springback

Procedia PDF Downloads 224

Authors: Jai Heui Kim, Sotheara Veng

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This paper studies the portfolio optimization problem for a pension fund under a hybrid model of stochastic volatility and constant elasticity of variance (CEV) using asymptotic analysis method. When the volatility component is fast mean-reverting, it is able to derive asymptotic approximations for the value function and the optimal strategy for general utility functions. Explicit solutions are given for the exponential and hyperbolic absolute risk aversion (HARA) utility functions. The study also shows that using the leading order optimal strategy results in the value function, not only up to the leading order, but also up to first order correction term. A practical strategy that does not depend on the unobservable volatility level is suggested. The result is an extension of the Merton's solution when stochastic volatility and elasticity of variance are considered simultaneously.

Keywords: asymptotic analysis, constant elasticity of variance, portfolio optimization, stochastic optimal control, stochastic volatility

Procedia PDF Downloads 294

Authors: Patricia B. Cruz, Catrina Jean G. Valenzuela, Analyn N. Yumang

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Nearly a hundred per million of the Filipino population is diagnosed with Chronic Kidney Disease (CKD). The early stage of CKD has no symptoms and can only be discovered once the patient undergoes urinalysis. Over the years, different methods were discovered and used for the quantification of the urinary albumin such as the immunochemical assays where most of these methods require large machinery that has a high cost in maintenance and resources, and a dipstick test which is yet to be proven and is still debated as a reliable method in detecting early stages of microalbuminuria. This research study involves the use of the liquid chromatography concept in microfluidic instruments with biosensor as a means of separation and detection respectively, and linear regression to quantify human urinary albumin. The researchers’ main objective was to create a miniature system that quantifies and detect patients’ urinary albumin while reducing the amount of volume used per five test samples. For this study, 30 urine samples of unknown albumin concentrations were tested using VITROS Analyzer and the microfluidic system for comparison. Based on the data shared by both methods, the actual vs. predicted regression were able to create a positive linear relationship with an R² of 0.9995 and a linear equation of y = 1.09x + 0.07, indicating that the predicted values and actual values are approximately equal. Furthermore, the microfluidic instrument uses 75% less in total volume – sample and reagents combined, compared to the VITROS Analyzer per five test samples.

Keywords: Chronic Kidney Disease, Linear Regression, Microfluidics, Urinary Albumin

Procedia PDF Downloads 130

Authors: Supannika Klangphukhiew, Roongnapa Srichana, Rina Patramanon

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Cortisol has been used as a well-known commercial stress biomarker. A homeostasis response to psychological stress is indicated by an increased level of cortisol produced in hypothalamus-pituitary-adrenal (HPA) axis. Chronic psychological stress contributing to the high level of cortisol relates to several health problems. In this study, the cortisol biosensor was fabricated that mimicked the natural receptors. The artificial antibodies were prepared using molecular imprinted polymer technique that can imitate the performance of natural anti-cortisol antibody with high stability. Cortisol-molecular imprinted polymer (cortisol-MIP) was obtained using the multi-step swelling and polymerization protocol with cortisol as a target molecule combining methacrylic acid:acrylamide (2:1) with bisacryloyl-1,2-dihydroxy-1,2-ethylenediamine and ethylenedioxy-N-methylamphetamine as cross-linkers. Cortisol-MIP was integrated to the sensor. It was coated on the disposable screen-printed carbon electrode (SPCE) for portable electrochemical analysis. The physical properties of Cortisol-MIP were characterized by means of electron microscope techniques. The binding characteristics were evaluated via covalent patterns changing in FTIR spectra which were related to voltammetry response. The performance of cortisol-MIP modified SPCE was investigated in terms of detection range, high selectivity with a detection limit of 1.28 ng/ml. The disposable cortisol biosensor represented an application of MIP technique to recognize steroids according to their structures with feasibility and cost-effectiveness that can be developed to use in point-of-care.

Keywords: stress biomarker, cortisol, molecular imprinted polymer, screen-printed carbon electrode

Procedia PDF Downloads 267

Authors: Yagahira E. Castro-Sesquen, Robert H. Gilman, Carolina Mejia, Daniel E. Clark, Jeong Choi, Melissa J. Reimer-Mcatee, Rocio Castro, Jorge Flores, Edward Valencia-Ayala, Faustino Torrico, Ricardo Castillo-Neyra, Lance Liotta, Caryn Bern, Alessandra Luchini

Abstract:

Early diagnosis of reactivation of Chagas disease in HIV patients could be lifesaving; however, in Latin American the diagnosis is performed by detection of parasitemia by microscopy which lacks sensitivity. To evaluate if levels of T. cruzi antigens in urine determined by Chunap (Chagas urine nanoparticle test) are correlated with parasitemia levels in T. cruzi/HIV co-infected patients. T. cruzi antigens in urine of HIV patients (N=55: 31 T. cruzi infected and 24 T. cruzi serology negative) were concentrated using hydrogel particles and quantified by Western Blot and a calibration curve. The percentage of Chagas positive patients determined by Chunap compared to blood microscopy, qPCR, and ELISA was 100% (6/6), 95% (18/19) and 74% (23/31), respectively. Chunap specificity was 91.7%. Linear regression analysis demonstrated a direct relationship between parasitemia levels (determined by qPCR) and urine T. cruzi antigen concentrations (p<0.001). A cut-off of > 105 pg was chosen to determine patients with reactivation of Chagas disease (6/6). Urine antigen concentration was significantly higher among patients with CD4+ lymphocyte counts below 200/mL (p=0.045). Chunap shows potential for early detection of reactivation and with appropriate adaptation can be used for monitoring Chagas disease status in T. cruzi/HIV co-infected patients.

Keywords: antigenuria, Chagas disease, Chunap, nanoparticles, parasitemia, poly N-isopropylacrylamide (NIPAm)/trypan blue particles (polyNIPAm/TB), reactivation of Chagas disease.

Procedia PDF Downloads 371

Authors: A. M. Omoare, O. O. Sofowora, W. O. Oyediran

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The study was carried out to assess the factors affecting the sustainability of rice processing and marketing in Ogun State, Nigeria. Multi-stage sampling technique was used to select one hundred and twenty (120) respondents for the study. Descriptive statistics was used to describe the objectives while hypotheses were analyzed with Pearson Product Moment Correlation. The result showed that most (85%) of the respondents was less than 50 years old and had been in rice business for more than 6 years. The majority (66.67%) of the respondents got their capitals from cooperative societies. All (100%) the respondents used rice as household food security and source of income. However, efficient rice processing and marketing were affected by inadequate manpower capacity development and inputs. There was a positive and significant relationship between socio-economic characteristics and processing techniques (p < 0.05). It is hereby recommended that extension service providers should introduce improved rice processing systems to the rice millers traders in the study area.

Keywords: sustainability, rice processing, marketing, constraints, millers traders

Procedia PDF Downloads 387