Search results for: predictive accuracy

Authors: Skyler Kim

Abstract:

An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

Procedia PDF Downloads 187

Authors: Shrabani Ghosh

Abstract:

Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

Procedia PDF Downloads 244

Authors: Sigal Gat-Lazer, Ronny Geva, Dan Ramon, Eitan Gur, Daniel Stein

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Introduction: Eating disorders (ED) are common psychopathologies which involve distorted body image and eating disturbances. Binge-purge eating disorders (B/P ED) are characterized by repetitive events of binge eating followed by purges. Patients with B/P ED behavior may be seen as impulsive especially when relate to food stimulation and affective conditions. The current study included innovative modification of the day-night task targeted to assess inhibitory control among patients with B/P ED. Methods: This prospective study included 50 patients with B/P ED during acute phase of illness (T1) upon their admission to specialized ED department in tertiary center. 34 patients repeated the study towards discharge to ambulatory care (T2). Treatment effect was evaluated by BMI and emotional questionnaires regarding depression and anxiety by the Beck Depression Inventory and State Trait Anxiety Inventory questionnaires. Control group included 36 healthy controls with matched demographic parameters who performed both T1 and T2 assessments. The current modification is based on the emotional day-night task (EDNT) which involves five emotional stimulation added to the sun and moon pictures presented to participants. In the current study, we designed the food-emotional modification day night task (F-EDNT) food stimulations of egg and banana which resemble the sun and moon, respectively, in five emotional states (angry, sad, happy, scrambled and neutral). During this computerized task, participants were instructed to push on “day” bottom in response to moon and banana stimulations and on “night” bottom when sun and egg were presented. Accuracy (A) and reaction time (RT) were evaluated and compared between EDNT and F-EDNT as a reflection of participants’ inhibitory control. Results: Patients with B/P ED had significantly improved BMI, depression and anxiety scores on T2 compared to T1 (all p<0.001). Task performance was similar among patients and controls in the EDNT without significant A or RT differences in both T1 and T2. On F-EDNT during T1, B/P ED patients had significantly reduced accuracy in 4/5 emotional stimulation compared to controls: angry (73±25% vs. 84±15%, respectively), sad (69±25% vs. 80±18%, respectively), happy (73±24% vs. 82±18%, respectively) and scrambled (74±24% vs. 84±13%, respectively, all p<0.05). Additionally, patients’ RT to food stimuli was significantly faster compared to neutral ones, in both cry and neutral emotional stimulations (356±146 vs. 400±141 and 378±124 vs. 412±116 msec, respectively, p<0.05). These significant differences between groups as a function of stimulus type were diminished on T2. Conclusion: Having to process food related content, in particular in emotional context seems to be impaired in patients with B/P ED during the acute phase of their illness and elicits greater impulsivity. Innovative modification using such procedures seem to be sensitive to patients’ illness phase and thus may be implemented during screening and follow up through the clinical management of these patients.

Keywords: binge purge eating disorders, day night task modification, eating disorders, food related stimulations

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Authors: Prateep Wajeetongratana

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This research aims to study the audit quality that affected to the reputation of the certified public accountants in Thailand. The researcher defined the population for this research as a group of the certified public accountants in Thailand who are the member of the federation of accounting professions under the royal patronage of his majesty the king also disclose their information .The total sampling size is 325. The results showed the audit quality factor has influence to the reputation of the certified public accountants in Thailand by accuracy auditing, objectiveness auditing and clearness auditing .These factors show by y1 = 1.381 + .372x1.1 + .309x1.2 + .305x1.3 can be describe as professional standard strictly factor (Y.1.1) and the new clients raised from word of mount of old clients regularly factor (Y.1.2) by regression coefficient (R2) as.242, this shows that such variables could predict the audit quality variable as 24.2 percent.

Keywords: audit quality, certified public accountants in Thailand, reputation

Procedia PDF Downloads 259

Authors: Aminu Abdullahi Isyaku

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Semi-arid Bornu basin in northeast Nigeria is characterised with flat topography, thick cover sediments and lack of continuous bedrock outcrops discernible for field geology. This paper presents the methodology for the characterisation of neotectonic surface structures and surface lithology in the north-eastern Bornu basin in northeast Nigeria as an alternative approach to field geological mapping using free multispectral Landsat 7 ETM+, SRTM DEM and ASAR Earth Observation datasets. Spectral lithological mapping herein developed utilised spectral discrimination of the surface features identified on Landsat 7 ETM+ images to infer on the lithology using four steps including; computations of band combination images; band ratio images; supervised image classification and inferences of the lithological compositions. Two complementary approaches to lineament mapping are carried out in this study involving manual digitization and automatic lineament extraction to validate the structural lineaments extracted from the Landsat 7 ETM+ image mosaic covering the study. A comparison between the mapped surface lineaments and lineament zones show good geospatial correlation and identified the predominant NE-SW and NW-SE structural trends in the basin. Topographic profiles across different parts of the Bama Beach Ridge palaeoshorelines in the basin appear to show different elevations across the feature. It is determined that most of the drainage systems in the northeastern Bornu basin are structurally controlled with drainage lines terminating against the paleo-lake border and emptying into the Lake Chad mainly arising from the extensive topographic high-stand Bama Beach Ridge palaeoshoreline.

Keywords: Bornu Basin, lineaments, spectral lithology, tectonics

Procedia PDF Downloads 139

Authors: Rafik Djemili, Hocine Bourouba, M. C. Amara Korba

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In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods.

Keywords: brain-computer interface, motor imagery, electroencephalogram, linear discriminant analysis, support vector machine

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Authors: Wenjing Ding, Weiwei Shan, Zijuan, Wang, Chao He

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Liquid spray heat exchanger is the critical equipment of temperature regulating system by gaseous nitrogen which realizes the environment temperature in the range of -180 ℃~+180 ℃. Liquid nitrogen is atomized into smaller liquid drops through liquid nitrogen sprayer and then contacts with gaseous nitrogen to be cooled. By adjusting the pressure of liquid nitrogen and gaseous nitrogen, the flowrate of liquid nitrogen is changed to realize the required outlet temperature of heat exchanger. The temperature accuracy of shrouds is ±1 ℃. Liquid nitrogen spray heat exchanger is simulated by CATIA, and the numerical simulation is performed by FLUENT. The comparison between the tests and numerical simulation is conducted. Moreover, the results help to improve the design of liquid nitrogen spray heat exchanger.

Keywords: liquid nitrogen spray, temperature regulating system, heat exchanger, numerical simulation

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Authors: Razi Khalafi

Abstract:

Myocardial Infarction (MI) or acute Myocardial Infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analysing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behaviour of these signals were checked. Results show this methodology can forecast the ECG and accordingly heart attack with high accuracy.

Keywords: heart attack, ECG, random walk, correlation dimension, forecasting

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Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.

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Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.

Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud

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Authors: Yinwei Lin, Cha'o-Kuang Chen

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In this article, the Laplace Adomian transform method (LADM) and double decomposition method (DDM) are used to solve the annular hyperbolic profile fins with variable thermal conductivity. As the thermal conductivity parameter ε is relatively large, the numerical solution using DDM become incorrect. Moreover, when the terms of DDM are more than seven, the numerical solution using DDM is very complicated. However, the present method can be easily calculated as terms are over seven and has more precisely numerical solutions. As the thermal conductivity parameter ε is relatively large, LADM also has better accuracy than DDM.

Keywords: fins, thermal conductivity, Laplace transform, Adomian, nonlinear

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Authors: Anastasia Papachristou, Anastasia Ntikoudi, Vasileios Saridakis

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The safety and health care needs of refugees have become an increasingly important issue all over the world especially during last few decades. Wars are the primary reason for refugees to leave their countries. Moreover, refugees are frequently exposed to a variety of stressors such as socioeconomic disadvantages, poverty, changes in family structure and functioning, losing social support, difficulty to access education, living in very crowded places, experiencing racism and isolation. This systematic review included research studies published between 2007-2017 from the search databases Medline, Scopus, Cinahl and PubMed, with keywords 'war survivors', 'war trauma', 'psychiatric disorders', 'refugees'. In order to meet the purpose of the systematic review, further research for complementary studies was conducted into the literature references of the research articles included in this study that would meet the criteria. Overall, 14 studies were reviewed and evaluated. The majority of them demonstrated that the most common psychiatric disorders observed among war refugees are post-traumatic stress disorder (PTSD), depression, anxiety and multiple somatic complaints. Moreover, significant relationship was shown between the number of traumatic events experienced by the refugees and sociodemographic features such as gender, age and previous family history of any psychological disorder. War violence is highly traumatic, causing multiple, long-term negative outcomes such as the aforementioned psychiatric disorders. The number of the studies reviewed in this systematic review is not representative of the problem and its significance. The need for care of the survivors and their families is vital. Further research is necessary in order to clarify the role of predictive factors in the development and maintenance of post-traumatic stress and the rest psychiatric disorders following war trauma. In conclusion, it is necessary to have large multicenter studies in the future in order to be able to draw reliable conclusions about the effects of war.

Keywords: psychiatric disorders, refugees, war survivors, war trauma

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: Ayobami Solomon Popoola

Abstract:

Yam is majorly a carbohydrate food grown in most parts of the world. It could be boiled, fried or roasted for consumption in a variety of ways. Blanching is an established heat pre-treatment given to fruits and vegetables prior to further processing such as dehydration, canning, freezing etc. Losses of soluble solids during blanching has been a great problem because a reasonable quantity of the water-soluble nutrients are inevitably leached into the blanching water. Without blanching, the high residual levels of reducing sugars after extended storage produce a dark, bitter-tasting product because of the Maillard reactions of reducing sugars at frying temperature. Measurement and prediction of such losses are necessary for economic efficiency in production and to establish the level of effluent treatment of the blanching water. This paper aims at resolving this problem by investigating the effects of cube size and temperature on the rate of diffusional losses of reducing sugars and total sugars during hot water blanching of water-yam. The study was carried out using four temperature levels (65, 70, 80 and 90 °C) and two cubes sizes (0.02 m³ and 0.03 m³) at 4 times intervals (5, 10, 15 and 20 mins) respectively. Obtained data were fitted into Fick’s non-steady equation from which diffusion coefficients (Da) were obtained. The Da values were subsequently fitted into Arrhenius plot to obtain activation energies (Ea-values) for diffusional losses. The diffusion co-efficient were independent of cube size and time but highly temperature dependent. The diffusion coefficients were ≥ 1.0 ×10⁻⁹ m²s⁻¹ for reducing sugars and ≥ 5.0 × 10⁻⁹ m²s⁻¹ for total sugars. The Ea values ranged between 68.2 to 73.9 KJmol⁻¹ and 7.2 to 14.30 KJmol⁻¹ for reducing sugars and total sugars losses respectively. Predictive equations for estimating amount of reducing sugars and total sugars with blanching time of water-yam at various temperatures were also presented. The equation could be valuable in process design and optimization. However, amount of other soluble solids that might have leached into the water along with reducing and total sugars during blanching was not investigated in the study.

Keywords: blanching, kinetics, sugar losses, water yam

Procedia PDF Downloads 165

Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Marcin Sowa

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The paper discusses the subinterval-based numerical method for fractional derivative computations. It is now referred to by its acronym – SubIval. The basis of the method is briefly recalled. The ability of the method to be applied in time stepping solvers is discussed. The possibility of implementing a time step size adaptive solver is also mentioned. The solver is tested on a transient circuit example. In order to display the accuracy of the solver – the results have been compared with those obtained by means of a semi-analytical method called gcdAlpha. The time step size adaptive solver applying SubIval has been proven to be very accurate as the results are very close to the referential solution. The solver is currently able to solve FDE (fractional differential equations) with various derivative orders for each equation and any type of source time functions.

Keywords: numerical method, SubIval, fractional calculus, numerical solver, circuit analysis

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Authors: Tahisa M. Pedroso, Hérida R. N. Salgado

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The microbiological turbidimetric assay allows the determination of potency of the drug, by measuring the turbidity (absorbance), caused by inhibition of microorganisms by ertapenem sodium. Ertapenem sodium (ERTM), a synthetic antimicrobial agent of the class of carbapenems, shows action against Gram-negative, Gram-positive, aerobic and anaerobic microorganisms. Turbidimetric assays are described in the literature for some antibiotics, but this method is not described for ertapenem. The objective of the present study was to develop and validate a simple, sensitive, precise and accurate microbiological assay by turbidimetry to quantify ertapenem sodium injectable as an alternative to the physicochemical methods described in the literature. Several preliminary tests were performed to choose the following parameters: Staphylococcus aureus ATCC 25923, IAL 1851, 8 % of inoculum, BHI culture medium, and aqueous solution of ertapenem sodium. 10.0 mL of sterile BHI culture medium were distributed in 20 tubes. 0.2 mL of solutions (standard and test), were added in tube, respectively S1, S2 and S3, and T1, T2 and T3, 0.8 mL of culture medium inoculated were transferred to each tube, according parallel lines 3 x 3 test. The tubes were incubated in shaker Marconi MA 420 at a temperature of 35.0 °C ± 2.0 °C for 4 hours. After this period, the growth of microorganisms was inhibited by addition of 0.5 mL of 12% formaldehyde solution in each tube. The absorbance was determined in Quimis Q-798DRM spectrophotometer at a wavelength of 530 nm. An analytical curve was constructed to obtain the equation of the line by the least-squares method and the linearity and parallelism was detected by ANOVA. The specificity of the method was proven by comparing the response obtained for the standard and the finished product. The precision was checked by testing the determination of ertapenem sodium in three days. The accuracy was determined by recovery test. The robustness was determined by comparing the results obtained by varying wavelength, brand of culture medium and volume of culture medium in the tubes. Statistical analysis showed that there is no deviation from linearity in the analytical curves of standard and test samples. The correlation coefficients were 0.9996 and 0.9998 for the standard and test samples, respectively. The specificity was confirmed by comparing the absorbance of the reference substance and test samples. The values obtained for intraday, interday and between analyst precision were 1.25%; 0.26%, 0.15% respectively. The amount of ertapenem sodium present in the samples analyzed, 99.87%, is consistent. The accuracy was proven by the recovery test, with value of 98.20%. The parameters varied did not affect the analysis of ertapenem sodium, confirming the robustness of this method. The turbidimetric assay is more versatile, faster and easier to apply than agar diffusion assay. The method is simple, rapid and accurate and can be used in routine analysis of quality control of formulations containing ertapenem sodium.

Keywords: ertapenem sodium, turbidimetric assay, quality control, validation

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Authors: Kholod Hajo

Abstract:

In this study COMSOL Multiphysics was used to design lateral flow biosensors (LFBs) which provide advantages in low cost, simplicity, rapidity, stability and portability thus making LFBs popular in biomedical, agriculture, food and environmental sciences. This study was focused on simulation model of biosensor based on gold nanoparticles (GNPs) designed using software package (COMSOL Multiphysics), the magnitude of the laminar velocity field in the flow cell, concentration distribution in the analyte stream and surface coverage of adsorbed species and average fractional surface coverage of adsorbed analyte were discussed from the model and couples of suggestion was given in order to functionalize GNPs and to increase the accuracy of the biosensor design, all above were obtained acceptable results.

Keywords: model, gold nanoparticles, biosensor, COMSOL Multiphysics

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Authors: Jian-Jun Shu

Abstract:

The statistical properties of CRMs are explored by estimating similar-word set occurrence distribution. It is observed that CRMs tend to have a fat-tail distribution for similar-word set occurrence. Thus, the fat-tail test with two fatness coefficients is proposed to distinguish CRMs from non-CRMs, especially from exons. For the first fatness coefficient, the separation accuracy between CRMs and exons is increased as compared with the existing content-based CRM prediction method – fluffy-tail test. For the second fatness coefficient, the computing time is reduced as compared with fluffy-tail test, making it very suitable for long sequences and large data-base analysis in the post-genome time. Moreover, these indexes may be used to predict the CRMs which have not yet been observed experimentally. This can serve as a valuable filtering process for experiment.

Keywords: statistical approach, transcription factor binding sites, cis-regulatory modules, DNA sequences

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Authors: Danilo F. Rodrigues, Hérida Regina N. Salgado

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Introduction: The emergence of resistant microorganisms to a large number of clinically approved antimicrobials has been increasing, which restrict the options for the treatment of bacterial infections. As a strategy, drugs with high antimicrobial activities are in evidence. Stands out a class of antimicrobial, the cephalosporins, having as fourth generation cefepime (CEF) a semi-synthetic product which has activity against various Gram-positive bacteria (e.g. oxacillin resistant Staphylococcus aureus) and Gram-negative (e.g. Pseudomonas aeruginosa) aerobic. There are few studies in the literature regarding the development of microbiological methodologies for the analysis of this antimicrobial, so researches in this area are highly relevant to optimize the analysis of this drug in the industry and ensure the quality of the marketed product. The development of microbiological methods for the analysis of antimicrobials has gained strength in recent years and has been highlighted in relation to physicochemical methods, especially because they make possible to determine the bioactivity of the drug against a microorganism. In this context, the aim of this work was the development and validation of a microbiological method for quantitative analysis of CEF in powder lyophilized for injectable solution by turbidimetric assay. Method: For performing the method, Staphylococcus aureus ATCC 6538 IAL 2082 was used as the test microorganism and the culture medium chosen was the Casoy broth. The test was performed using temperature control (35.0 °C ± 2.0 °C) and incubated for 4 hours in shaker. The readings of the results were made at a wavelength of 530 nm through a spectrophotometer. The turbidimetric microbiological method was validated by determining the following parameters: linearity, precision (repeatability and intermediate precision), accuracy and robustness, according to ICH guidelines. Results and discussion: Among the parameters evaluated for method validation, the linearity showed results suitable for both statistical analyses as the correlation coefficients (r) that went 0.9990 for CEF reference standard and 0.9997 for CEF sample. The precision presented the following values 1.86% (intraday), 0.84% (interday) and 0.71% (between analyst). The accuracy of the method has been proven through the recovery test where the mean value obtained was 99.92%. The robustness was verified by the parameters changing volume of culture medium, brand of culture medium, incubation time in shaker and wavelength. The potency of CEF present in the samples of lyophilized powder for injectable solution was 102.46%. Conclusion: The turbidimetric microbiological method proposed for quantification of CEF in lyophilized powder for solution for injectable showed being fast, linear, precise, accurate and robust, being in accordance with all the requirements, which can be used in routine analysis of quality control in the pharmaceutical industry as an option for microbiological analysis.

Keywords: cefepime hydrochloride, quality control, turbidimetric assay, validation

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: A. A. Hussen, I. N. Jleta

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Micro-electromechanical system (MEMS) accelerometers and gyroscopes are suitable for the inertial navigation system (INS) of many applications due to the low price, small dimensions and light weight. The main disadvantage in a comparison with classic sensors is a worse long term stability. The estimation accuracy is mostly affected by the time-dependent growth of inertial sensor errors, especially the stochastic errors. In order to eliminate negative effect of these random errors, they must be accurately modeled. Where the key is the successful implementation that depends on how well the noise statistics of the inertial sensors is selected. In this paper, the Allan variance technique will be used in modeling the stochastic errors of the inertial sensors. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data.

Keywords: Allan variance, accelerometer, gyroscope, stochastic errors

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Authors: Eric Fang, Robert Chen, Ghim Song Chia, Bien Soo Tan

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Objective and Rationale: Training of radiology residents hinges on practical, on-the-job training in all facets and modalities of diagnostic radiology. Although residency is structured to be comprehensive, clinical exposure depends on the case mix available locally and during the posting period. To supplement clinical training, there are several e-learning resources available to allow for greater exposure to radiological cases. The objective of this study was to survey residents and faculty on the usefulness of these e-learning resources. Methods: E-learning resources were shortlisted with input from radiology residents, Google search and online discussion groups, and screened by their purported focus. Twelve e-learning resources were found to meet the criteria. Both radiology residents and experienced radiology faculty were then surveyed electronically. The e-survey asked for ratings on breadth, depth, testing capability and user-friendliness for each resource, as well as for rankings for the top 3 resources. Statistical analysis was performed using SAS 9.4. Results: Seventeen residents and fifteen faculties completed an e-survey. Mean response rate was 54% ± 8% (Range: 14- 96%). Ratings and rankings were statistically identical between residents and faculty. On a 5-point rating scale, breadth was 3.68 ± 0.18, depth was 3.95 ± 0.14, testing capability was 2.64 ± 0.16 and user-friendliness was 3.39 ± 0.13. Top-ranked resources were STATdx (first), Radiopaedia (second) and Radiology Assistant (third). 9% of responders singled out R-ITI as potentially good but ‘prohibitively costly’. Statistically significant predictive factors for higher rankings are familiarity with the resource (p = 0.001) and user-friendliness (p = 0.006). Conclusion: A good e-learning system will complement on-the-job training with a broad case base, deep discussion and quality trainee evaluation. Based on our study on twelve e-learning resources, no single program fulfilled all requirements. The perception and use of radiology e-learning resources depended more on familiarity and user-friendliness than on content differences and testing capability.

Keywords: e-learning, medicine, radiology, survey

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Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

Abstract:

Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

Abstract:

For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

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Authors: Aymen Laadhari

Abstract:

We present in this paper a fully implicit finite element method tailored for the numerical modeling of inextensible fluidic membranes in a surrounding Newtonian fluid. We consider a highly simplified version of the Canham-Helfrich model for phospholipid membranes, in which the bending force and spontaneous curvature are disregarded. The coupled problem is formulated in a fully Eulerian framework and the membrane motion is tracked using the level set method. The resulting nonlinear problem is solved by a Newton-Raphson strategy, featuring a quadratic convergence behavior. A monolithic solver is implemented, and we report several numerical experiments aimed at model validation and illustrating the accuracy of the proposed method. We show that stability is maintained for significantly larger time steps with respect to an explicit decoupling method.

Keywords: finite element method, level set, Newton, membrane

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Authors: H. Naderpour, R. C. Barros, S. M. Khatami

Abstract:

Seismic excitation is naturally caused large horizontal relative displacements, which is able to provide collisions between two adjacent buildings due to insufficient separation distance and severe damages are occurred due to impact especially in tall buildings. In this paper, an impact is numerically simulated and two needed parameters are calculated, including impact force and energy absorption. In order to calculate mentioned parameters, mathematical study needs to model an unreal link element, which is logically assumed to be spring and dashpot to determine lateral displacement and damping ratio of impact. For the determination of dynamic response of impact, a new equation of motion is theoretically suggested to evaluate impact force and energy dissipation. In order to confirm the rendered equation, a series of parametric study are performed and the accuracy of formula is confirmed.

Keywords: pounding, impact, dissipated energy, coefficient of restitution

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Authors: Abdelkrim Belhaoua, Jean-Pierre Radoux

Abstract:

In France, the railway network is one of the main transport infrastructures and is the second largest European network. Therefore, railway inspection is an important task in railway maintenance to ensure safety for passengers using significant means in personal and technical facilities. Artificial vision has recently been applied to several railway applications due to its potential to improve the efficiency and accuracy when analyzing large databases of acquired images. In this paper, we present a vision system able to detect fastening elements based on artificial vision approach. This system acquires railway images using a CCD camera installed under a control carriage. These images are stitched together before having processed. Experimental results are presented to show that the proposed method is robust for detection fasteners in a complex environment.

Keywords: computer vision, image processing, railway inspection, image stitching, fastener recognition, neural network

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Authors: Oğuzhan Urhan

Abstract:

In this paper, an improved motion estimation (ME) approach based on weighted constrained one-bit transform is proposed for block-based ME employed in video encoders. Binary ME approaches utilize low bit-depth representation of the original image frames with a Boolean exclusive-OR based hardware efficient matching criterion to decrease computational burden of the ME stage. Weighted constrained one-bit transform (WC‑1BT) based approach improves the performance of conventional C-1BT based ME employing 2-bit depth constraint mask instead of a 1-bit depth mask. In this work, the range of constraint mask is further extended to increase ME performance of WC-1BT approach. Experiments reveal that the proposed method provides better ME accuracy compared existing similar ME methods in the literature.

Keywords: fast motion estimation; low-complexity motion estimation, video coding

Procedia PDF Downloads 316