Search results for: early detection test

Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.

Keywords: antenna array, signal detection, ToA, AoA estimation

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Authors: Mekni Sabrine, Nouira Mariem

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Background and aim: Allogeneic hematopoietic stem cell transplantation is a curative treatment for several hematological diseases; however, it has a non-negligible morbidity and mortality depending on several prognostic factors, including pre-transplant hyperferritinemia. The aim of our study was to estimate the impact of hyperferritinemia on survivals and on the occurrence of post-transplant complications. Methods: It was a longitudinal study conducted over 8 years and including all patients who had a first allograft. The impact of pretransplant hyperferritinemia (ferritinemia ≥1500) on survivals was studied using the Kaplan Meier method and the COX model for uni- and multivariate analysis. The Khi-deux test and binary logistic regression were used to study the association between pretransplant ferritinemia and post-transplant complications. Results: One hundred forty patients were included with an average age of 26.6 years and a sex ratio (M/F)=1.4. Hyperferritinemia was found in 33% of patients. It had no significant impact on either overall survival (p=0.9) or event -free survival (p=0.6). In multivariate analysis, only the type of disease was independently associated with overall survival (p=0.04) and event-free survival (p=0.002). For post-allograft complications: The occurrence of early documented infections was independently associated with pretransplant hyperferritinemia (p=0.02) and the presence of acute graft versus host disease( GVHD) (p<10-3). The occurrence of acute GVHD was associated with early documented infection (p=0.002) and Cytomegalovirus reactivation (p<10-3). The occurrence of chronic GVHD was associated with the presence of Cytomegalovirus reactivation (p=0.006) and graft source (p=0.009). Conclusion: Our study showed the significant impact of pre-transplant hyperferritinemia on the occurrence of early infections but not on survivals. Early and more accurate assessment iron overload by other tests such as liver magnetic resonance imaging with initiation of chelating treatment could prevent the occurrence of such complications after transplantation.

Keywords: allogeneic, transplants, ferritin, survival

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Authors: Nataša Todorović, Jovana Nikolov, Ivana Stojković, Milan Vraneš, Jovana Panić, Slobodan Gadžurić

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The detection of ²¹⁰Pb levels in aquatic environments evokes interest in various scientific studies. Its precise determination is important not only for the radiological assessment of drinking waters but also ²¹⁰Pb, and ²¹⁰Po distribution in the marine environment are significant for the assessment of the removal rates of particles from the ocean and particle fluxes during transport along the coast, as well as particulate organic carbon export in the upper ocean. Measurement techniques for ²¹⁰Pb determination, gamma spectrometry, alpha spectrometry, or liquid scintillation counting (LSC) are either time-consuming or demand expensive equipment or complicated chemical pre-treatments. However, one other possibility is to measure ²¹⁰Pb on an LS counter if it is in equilibrium with its progeny ²¹⁰Bi - through the Cherenkov counting method. It is unaffected by the chemical quenching and assumes easy sample preparation but has the drawback of lower counting efficiencies than standard LSC methods, typically from 10% up to 20%. The aim of the presented research in this paper is to investigate the possible increment of detection efficiency of Cherenkov counting during ²¹⁰Pb/²¹⁰Bi detection on an LS counter Quantulus 1220. Considering naturally low levels of ²¹⁰Pb in aqueous samples, the addition of ionic liquids to the counting vials with the analysed samples has the benefit of detection limit’s decrement during ²¹⁰Pb quantification. Our results demonstrated that ionic liquid, 1-butyl-3-methylimidazolium salicylate, is more efficient in Cherenkov counting efficiency increment than the previously explored 2-hydroxypropan-1-amminium salicylate. Consequently, the impact of a few other ionic liquids that were synthesized with the same cation group (1-butyl-3-methylimidazolium benzoate, 1-butyl-3-methylimidazolium 3-hydroxybenzoate, and 1-butyl-3-methylimidazolium 4-hydroxybenzoate) was explored in order to test their potential influence on Cherenkov counting efficiency. It was confirmed that, among the explored ones, only ionic liquids in the form of salicylates exhibit a wavelength shifting effect. Namely, the addition of small amounts (around 0.8 g) of 1-butyl-3-methylimidazolium salicylate increases the detection efficiency from 16% to >70%, consequently reducing the detection threshold by more than four times. Moreover, the addition of ionic liquids could find application in the quantification of other radionuclides besides ²¹⁰Pb/²¹⁰Bi via Cherenkov counting method.

Keywords: liquid scintillation counting, ionic liquids, Cherenkov counting, ²¹⁰PB/²¹⁰BI in water

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Authors: M. F. Abreu, A. I. Aguiar, R. Gaio, R. Duarte

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Background: Prison establishments constitute high-risk environments for the transmission and spread of tuberculosis (TB), given their epidemiological context and the difficulty of implementing preventive and control measures. Guidelines for control and prevention of tuberculosis in prisons have been described as incomplete and heterogeneous internationally, due to several identified obstacles, for example scarcity of human resources and funding of prisoner health services. In Portugal, a protocol was created in 2014 with the aim to define and standardize procedures of detection and prevention of tuberculosis within prisons. Objective: The main objective of this study was to identify and describe barriers to tuberculosis detection in prisons of Porto and Lisbon districts in Portugal. Methods: A cross-sectional study was conducted from 2ⁿᵈ January 2018 till 30ᵗʰ June 2018. Semi-structured questionnaires were applied to health care professionals working in the prisons of the districts of Porto (n=6) and Lisbon (n=8). As inclusion criteria we considered having work experience in the area of tuberculosis (either in diagnosis, treatment, or follow up). The questionnaires were self-administered, in paper format. Descriptive analyses of the questionnaire variables were made using frequencies and median. Afterwards, a hierarchical agglomerative clusters analysis was performed. After obtaining the clusters, the chi-square test was applied to study the association between the variables collected and the clusters. The level of significance considered was 0.05. Results: From the total of 186 health professionals, 139 met the criteria of inclusion and 82 health professionals were interviewed (62,2% of participation). Most were female, nurses, with a median age of 34 years, with term employment contract. From the cluster analysis, two groups were identified with different characteristics and behaviors for the procedures of this protocol. Statistically significant results were found in: elements of cluster 1 (78% of the total participants) work in prisons for a longer time (p=0.003), 45,3% work > 4 years while 50% of the elements of cluster 2 work for less than a year, and more frequently answered they know and apply the procedures of the protocol (p=0.000). Both clusters answered frequently the need of having theoretical-practical training for TB (p=0.000), especially in the areas of diagnosis, treatment and prevention and that there is scarcity of funding to prisoner health services (p=0.000). Regarding procedures for TB screening (periodic and contact screening) and procedures for transferring a prisoner with this disease, cluster 1 also answered more frequently to perform them (p=0.000). They also referred that the material/equipment for TB screening is accessible and available (p=0.000). From this clusters we identified as barriers scarcity of human resources, the need to theoretical-practical training for tuberculosis, inexperience in working in health services prisons and limited knowledge of protocol procedures. Conclusions: The barriers found in this study are the same described internationally. This protocol is mostly being applied in portuguese prisons. The study also showed the need to invest in human and material resources. This investigation bridged gaps in knowledge that could help prison health services optimize the care provided for early detection and adherence of prisoners to treatment of tuberculosis.

Keywords: barriers, health care professionals, prisons, protocol, tuberculosis

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Authors: Nagmani Lnu

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Often, Quality Engineering refers to testing the applications that either have a User Interface (UI) or an Application Programming Interface (API). We often find mature test practices, standards, and automation regarding UI or API testing. However, another kind is present in almost all types of industries that deal with data in bulk and often get handled through something called a Batch Application. This is primarily an offline application companies develop to process large data sets that often deal with multiple business rules. The challenge gets more prominent when we try to automate batch testing. This paper describes the approaches taken to test a Batch application from a Financial Industry to test the payment settlement process (a critical use case in all kinds of FinTech companies), resulting in 100% test automation in Test Creation and Test execution. One can follow this approach for any other batch use cases to achieve a higher efficiency in their testing process.

Keywords: batch testing, batch test automation, batch test strategy, payments testing, payments settlement testing

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Authors: Sunanta Klibthong

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In view of the Thailand government policy creating increasing awareness of opportunity for children with special needs, the number of children with disabilities enrolled in kindergartens in Thailand has increased. This study explores early childhood teachers’ experiences and concerns of teaching children with disabilities in inclusive classrooms. The population of the study was private early childhood teachers who teach in inclusive classrooms in Thailand. Quantitative data obtained through a questionnaire were supplemented by early childhood teachers’ interviews to identify key experiences and concerns of the teachers when teaching children with and without disabilities in the same classrooms. The results of this study indicated that many teachers face challenges including lack of professional development opportunities, difficulty identifying the needs of all children and how to use effective strategies to support inclusive practices in their classrooms. Teachers also expressed concern about parents’ lack of willingness to accept children without disabilities studying together with those with disabilities in the same classrooms. Findings from this study can inform program support for parents and professional support needs of teachers in the provision of high-quality inclusive programs for all students.

Keywords: the concern, early childhood, experience, inclusive education, Thailand

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Authors: Abdul Aleem

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The study aimed to establish age-related normative values for motor skill assessments in Pakistani children aged 15 to 20 years, utilize two motor coordination tests: the Test of Gross Motor Development (TGMD) and the Korper Coordinations test for kinder. In the present study, BMI, age and gender were correlated with the motor performance of the children. The study developed the preliminary normative data for the Test for Gross Motor Development TGMD-2 and KTK test for primary school children. There was a positive correlation between age and TGMD-2 and KTK test scores. All participants performed 100% on the locomotor subset of TGMD-2 test and boys showed better motor proficiency than girls on scores of TGMD-2 test. Moreover, there was a negative correlation between the KTK test score and the BMI of participants.

Keywords: motor competence, Korper coordinations, normative values, developmental coordination disorder

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Youn-Sung Kim, Mi-Sung Kim, Jae-Kun Lee

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This paper deals with the accelerated life cycle test method of the motor for home appliances that demand high reliability. Life Cycle of parts in home appliances also should be 10 years because life cycle of the home appliances such as washing machine, refrigerator, TV is at least 10 years. In case of washing machine, the life cycle test method of motor is advanced for 3000 cycle test (1cycle = 2hours). However, 3000 cycle test incurs loss for the time and cost. Objectives of this study are to reduce the life cycle test time and the number of test samples, which could be realized by using acceleration factor for the test time and reduction factor for the number of sample.

Keywords: accelerated life cycle test, motor reliability test, motor for washing machine, BLDC motor

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Authors: Kittivate Boonyopakorn

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The purposes of this study were to find the English competence of the public health volunteers and to develop the use of their English. The samples for the study were 41 public health village volunteers at Ban Prasukchai, in Thailand. The findings showed that the sum of all scores for the pre-test was 452, while the score for the post-test was 1,080. Therefore, the results of the experiment confirm that the post-test scores (1,080) significantly are higher than the pre-test (452). The mean score (N=41) for the pre-test was 11.02 while the mean score (N=41) for the post-test was 18.10. The standard deviation for the pre-test was 2.734; however, for the post-test it was 1.934. In addition to the experts-evaluated research tools, the results of the evaluation for the structured interviews (IOC) were 1 in value. The evaluation of congruence for the content with learning objectives (IOC) were 0.66 to 1.00 in value. The evaluation of congruence for the pre and post-test with learning objectives (IOC) are 1 in value.

Keywords: finding the English competency, developing public health, village volunteers

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Authors: Afifah Nur Aini, Elsa Mega Suryani, Betty Lukiaty

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Drymoglossum piloselloides or more commonly known as sisik naga fern is a member of Polipodiaceae Family that is abundant and widely distributed in nature. That being said, there hasn’t been many studies reporting about the benefits of this fern. The aim of this study was to find out the active compounds and antioxidant activity of water fraction extract of sisik naga leaves. The study will be able to optimize the use of this fern in the future. In this study, phytochemical test was done qualitatively by using Mayer, Dragendorff and Wagner reagent for alkaloid test; FeCl3 for phenolic test; Shinoda test for flavonoid; Liebermann-Burchard test for triterprnoid and Forth test for saponin. Antioxidant activity test was done by using 20D spectronic spectrophotometer to determine the percentage of DPPH free radical inhibition. The results showed that water fraction extract of sisik naga leaves contain phenolic and IC50 = 5.44 μg/ml. This means that sisik naga leaves can be used as an antioxidant.

Keywords: antioxidant activity test, dpph, phytochemical test, drymoglossum piloselloides

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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Authors: Theophilus O. Odunlami, Hasan Haroglu, Nader Saleh-Matter

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The challenges facing the construction industry are often worsened by the compounded nature of projects coupled with the complexity of key stakeholders involved at different stages of the project. Projects are planned to achieve outlined benefits in line with the business case; however, a lack of effective management of key stakeholders can result in unrealistic delivery aspirations, unnecessary re-works, and overruns. The aim of this study is to examine the early stages of a project lifecycle and investigate the stakeholder management and involvement processes and their impact on the successful delivery of the project. The research engaged with conventional construction organisations and project personnel and stakeholders on diverse projects, using a research strategy to analyse existing project case studies, narrative enquiries, interviews, and surveys using a combined qualitative, quantitative, and mixed method of analysis. Research findings have shown that the involvement of stakeholders at different levels during the early stages has pronounced effects on project delivery; it helps to forge synergy and promotes a clear understanding of individual responsibilities, strengths, and weaknesses. This has often fostered a positive sense of productive collaboration right through the early stages of the project. These research findings intend to contribute to the development of a process framework for stakeholder and project team involvement in the early stages of a project. This framework will align with the selection criteria for stakeholders, contractors, and resources, ultimately contributing to the successful completion of projects. The primary question addressed in this study is stakeholder involvement and management of the early stages of a building project life cycle impacts project delivery. Findings showed that early-stage stakeholder involvement and collaboration between project teams and contractors significantly contribute to project success. However, a strong and healthy communication strategy would be required to maintain the flow of value-added ideas among stakeholders at the early stages to benefit the project at the execution stage.

Keywords: early stages, project lifecycle, stakeholders, decision-making strategy, project framework

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: R. Nagarani

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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

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Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

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

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

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

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Authors: Jiangbo Li, Wenqian Huang

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Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest commercialization process. The automatic and nondestructive detection of infected fruits is still a challenge for the citrus industry. At present, the visual inspection of rotten citrus fruits is commonly performed by workers through the ultraviolet induction fluorescence technology or manual sorting in citrus packinghouses to remove fruit subject with fungal infection. However, the former entails a number of problems because exposing people to this kind of lighting is potentially hazardous to human health, and the latter is very inefficient. Orange is used as a research object. This study would focus on this problem and proposed an effective method based on Vis-NIR hyperspectral imaging in the wavelength range of 400-1000 nm with a spectroscopic resolution of 2.8 nm. In this work, three normalization approaches are applied prior to analysis to reduce the effect of sample curvature on spectral profiles, and it is found that mean normalization was the most effective pretreatment for decreasing spectral variability due to curvature. Then, principal component analysis (PCA) was applied to a dataset composing of average spectra from decayed and normal tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. In this case, it was observed that normal and decayed spectra were separable along the resultant first principal component (PC1) axis. Subsequently, five wavelengths (band) centered at 577, 702, 751, 808, and 923 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A multispectral combination image was generated based on five selected characteristic wavelength images. Based on the obtained multispectral combination image, the intensity slicing pseudocolor image processing method is used to generate a 2-D visual classification image that would enhance the contrast between normal and decayed tissue. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudocolor image coupled with a simple thresholding method. For the investigated 238 independent set samples including infected fruits infected by Penicillium digitatum and normal fruits, the total success rate is 100% and 97.5%, respectively, and, the proposed algorithm also used to identify the orange infected by penicillium italicum with a 100% identification accuracy, indicating that the proposed multispectral algorithm here is an effective method and it is potential to be applied in citrus industry.

Keywords: citrus fruit, early rotten, fungal infection, hyperspectral imaging

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Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud

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Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.

Keywords: android malware detection, machine learning, malware, malware analysis

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

Abstract:

In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Shanshan Zhu, Mohammad Nasim

Abstract:

Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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Authors: Shu-Ling Wang

Abstract:

Background: Multiple studies showed early skin-to-skin contact (SSC) with mothers was beneficial to newborns such as breastfeeding and maternal childcare. In cases of newborns unable to have early SSC with mothers, fathers’ involvement could let early SSC continue without interruption. However, few studies had explored the effects of early SSC by fathers in comparison to early SSC with mothers. Paternal involvement of early SSC should be equally important in term of childcare and breastfeeding. The purpose of this study was to evaluate the efficacy of early SSC by fathers in particular in their support of breastfeeding. Methods: A quasi-experimental design was employed by the study. One hundred and forty-four father-infant pairs had participated the study, in which infants were assigned either to SSC with their fathers (n = 72) or to routine care (n = 72) as the control group. The study was conducted at a regional hospital in northern Taiwan. Participants included parents of both vaginal delivery (VD) and caesarean section birth (CS) infants. To be eligible for inclusion, infants must be over 37-week gestational ages. Data were collected twice: as pretest upon admission and as posttest with online questionnaire during first, second, and third postpartum months. The questionnaire included items for Breastfeeding Social Support, methods of feeding, and the mother-infant 24-hour rooming-in rate. The efficacy of early SSC with fathers was evaluated using the generalized estimating equation (GEE) modeling. Research Result: The primary finding was that SSC with fathers had positive impact on fathers’ support of breastfeeding. Analysis of the online questionnaire indicated that early SSC with fathers improved the support of breastfeeding than the control group (VD: t = -4.98, p < .001; CS: t = -2.37, p = .02). Analysis of mother-infant 24-hour rooming-in rate showed that SSC with fathers after CS had a positive impact on the rooming-in rate (χ² = 5.79, p = .02); however, with VD the difference between early SSC with fathers and the control group was insignificant (χ² = .23, p = .63). Analysis of the rate of exclusive breastfeeding indicated that early SSC with fathers had a higher rate than the control group during first three postpartum months for both delivery methods (VD: χ² = 12.51, p < .001 on 1st postpartum month, χ² = 8.13, p < .05 on 2nd postpartum month, χ² = 4.43, p < .05 on 3rd postpartum month; CS: χ² = 6.92, p < .05 on 1st postpartum month, χ² = 7.41, p < .05 on 2nd postpartum month, χ² = 6.24, p < .05 on 3rd postpartum month). No significant difference was found on the rate of exclusive breastfeeding with both methods of delivery between two groups during hospitalization. (VD: χ² =2 .00, p = .16; CS: χ² = .73, p = .39). Conclusion: Implementing early SSC with fathers has many benefits to both parents. The result of this study showed increasing fathers’ support of breastfeeding. This encourages our nursing personnel to focus the needs of father during breastfeeding, therefore further enhancing the quality of parental care, the rate and duration of breastfeeding.

Keywords: breastfeeding, skin-to-skin contact, support of breastfeeding, rooming-in

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Authors: Nicholas Mavrogiannis, Francesca Crivellari, Zachary Gagnon

Abstract:

Development of low-cost, rapid, sensitive and portable biosensing systems are important for the detection and prevention of disease in developing countries, biowarfare/antiterrorism applications, environmental monitoring, point-of-care diagnostic testing and for basic biological research. Currently, the most established commercially available and widespread assays for portable point of care detection and disease testing are paper-based dipstick and lateral flow test strips. These paper-based devices are often small, cheap and simple to operate. The last three decades in particular have seen an emergence in these assays in diagnostic settings for detection of pregnancy, HIV/AIDS, blood glucose, Influenza, urinary protein, cardiovascular disease, respiratory infections and blood chemistries. Such assays are widely available largely because they are inexpensive, lightweight, and portable, are simple to operate, and a few platforms are capable of multiplexed detection for a small number of sample targets. However, there is a critical need for sensitive, quantitative and multiplexed detection capabilities for point-of-care diagnostics and for the detection and prevention of disease in the developing world that cannot be satisfied by current state-of-the-art paper-based assays. For example, applications including the detection of cardiac and cancer biomarkers and biothreat applications require sensitive multiplexed detection of analytes in the nM and pM range, and cannot currently be satisfied with current inexpensive portable platforms due to their lack of sensitivity, quantitative capabilities and often unreliable performance. In this talk, inexpensive label-free biomolecular detection at liquid interfaces using a newly discovered electrokinetic phenomenon known as fluidic dielectrophoresis (fDEP) is demonstrated. The electrokinetic approach involves exploiting the electrical mismatches between two aqueous liquid streams forced to flow side-by-side in a microfluidic T-channel. In this system, one fluid stream is engineered to have a higher conductivity relative to its neighbor which has a higher permittivity. When a “low” frequency (< 1 MHz) alternating current (AC) electrical field is applied normal to this fluidic electrical interface the fluid stream with high conductivity displaces into the low conductive stream. Conversely, when a “high” frequency (20MHz) AC electric field is applied, the high permittivity stream deflects across the microfluidic channel. There is, however, a critical frequency sensitive to the electrical differences between each fluid phase – the fDEP crossover frequency – between these two events where no fluid deflection is observed, and the interface remains fixed when exposed to an external field. To perform biomolecular detection, two streams flow side-by-side in a microfluidic T-channel: one fluid stream with an analyte of choice and an adjacent stream with a specific receptor to the chosen target. The two fluid streams merge and the fDEP crossover frequency is measured at different axial positions down the resulting liquid

Keywords: biodetection, fluidic dielectrophoresis, interfacial polarization, liquid interface

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Authors: Sultana Razia

Abstract:

The purpose of this study was to investigate the effect of early therapeutic intervention on children with an autism spectrum disorder. Participants were 140 children with autism spectrum disorder from Autism Corner in a selected rehabilitation center of Bangladesh. This study included children who are at aged of 18-month to 36-month and who were taking occupational therapy and speech and language therapy from the autism center. They were primarily screened using M-CHAT; however, children with other physical disabilities or medical conditions were excluded. 3-months interventions of 6 sessions per week are a minimum of 45-minutes long per session, one to one interaction followed by parent-led structured home-based therapy were provided. The results indicated that early intensive therapeutic intervention improves understanding, social skills and sensory skills. It can be concluded that therapeutic early intervention has a positive effect on diminishing symptoms of Autism Spectrum Disorder.

Keywords: autism, m-chat, reciprocal social behavior, CRP

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Authors: Hitesh Tahbildar

Abstract:

The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

Procedia PDF Downloads 399