Search results for: object detection with multimodal inputs

Authors: Olais A. Ingrid, Peraza G. Leydi, Estrella C. Damaris

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Breast cancer is the most common among women worldwide; the different treatments can bring sequels that directly affect the quality of life, such as lymphedema. The objective was to determine if there is presence of lymphedema secondary to breast cancer in Yucatecan women. It was an observational, analytical, cross-sectional study, 92 women were included who met the following criteria: women with surgical treatment for unilateral: breast cancer, aged between 25 and 65 years old, minimum 6 weeks after unilateral breast surgery and have completed any type of chemotherapy or adjuvant radiotherapy treatment for breast cancer. The evaluation was through indirect measurement volume by circometry to determine the presence of lymphedema. 23% of women had lymphedema grade I. It related to the presence of some of the symptoms like stiffness, swelling, decreased range of motion and feeling of heaviness in the arm of the operated side of the breast. It is important to determine the presence of lymphedema to perform physical therapy treatment.

Keywords: breast cancer, lymphedema, physical therapy, Yucatan

Procedia PDF Downloads 342

Authors: María-Dolores Afonso-Suárez, Nayra Pumar-Carreras, Juan Ruiz-Alzola

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This research addresses the use of an e-Learning creation methodology for learning objects. Throughout the process, indicators are being gathered, to determine if it responds to the main objectives of an engineering discipline. These parameters will also indicate if it is necessary to review the creation cycle and readjust any phase. Within the project developed for this study, apart from the use of structured methods, there has been a central objective: the establishment of a learning atmosphere. A place where all the professionals involved are able to collaborate, plan, solve problems and determine guides to follow in order to develop creative and innovative solutions. It has been outlined as a blended learning program with an assessment plan that proposes face to face lessons, coaching, collaboration, multimedia and web based learning objects as well as support resources. The project has been drawn as a long term task, the pilot teaching actions designed provide the preliminary results object of study. This methodology is been used in the creation of learning content for the African countries of Senegal, Mauritania and Cape Verde. It has been developed within the framework of the MACbioIDi, an Interreg European project for the International cooperation and development. The educational area of this project is focused in the training and advice of professionals of the medicine as well as engineers in the use of applications of medical imaging technology, specifically the 3DSlicer application and the Open Anatomy Browser.

Keywords: teaching contents engineering, e-learning, blended learning, international cooperation, 3dslicer, open anatomy browser

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Authors: Amira Dhokar, Lobna Hlaoua, Lotfi Ben Romdhane

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Tweet contextualization (TC) is a new issue that aims to answer questions of the form 'What is this tweet about?' The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain the most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context.

Keywords: bipartite graphs, readability, summarization, tweet contextualization

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Authors: Dona Grace Jeyaseeli

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Water quality conditions in a pond are controlled by both natural processes and human influences. Natural factors such as the source of the pond water and the types of rock and soil in the pond watershed will influence some water quality characteristics. These factors are difficult to control but usually cause few problems. Instead, most serious water quality problems originate from land uses or other activities near or in the pond. The effects of these activities can often be minimized through proper management and early detection of problems through testing. In the present study a survey of three ponds in Coimbatore city, Tamilnadu, India were analyzed and found that water quality problems in their ponds, ranging from muddy water to fish kills. Unfortunately, most pond owners have never tested their ponds, and water quality problems are usually only detected after they cause a problem. Hence the present study discusses some common water quality parameters that may cause problems in ponds and how to detect through testing for better management of pond.

Keywords: water quality, pond, test, problem

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Giovanni Cuciniello

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The introduction of tilt-rotor aircraft into the existing civilian air transportation system will provide beneficial effects due to tilt-rotor capability to combine the characteristics of a helicopter and a fixed-wing aircraft into one vehicle. The disposability of reliable tilt-rotor simulation models supports the development of such vehicle. Indeed, simulation models are required to design automatic control systems that increase safety, reduce pilot's workload and stress, and ensure the optimal aircraft configuration with respect to flight envelope limits, especially during the most critical flight phases such as conversion from helicopter to aircraft mode and vice versa. This article presents a process to build a simplified tilt-rotor simulation model, derived from the analysis of flight data. The model aims to reproduce the complex dynamics of tilt-rotor during the in-flight conversion phase. It uses a set of scheduled linear transfer functions to relate the autopilot reference inputs to the most relevant rigid body state variables. The model also computes information about the rotor flapping dynamics, which are useful to evaluate the aircraft control margin in terms of rotor collective and cyclic commands. The rotor flapping model is derived through a mixed theoretical-empirical approach, which includes physical analytical equations (applicable to helicopter configuration) and parametric corrective functions. The latter are introduced to best fit the actual rotor behavior and balance the differences existing between helicopter and tilt-rotor during flight. Time-domain system identification from flight data is exploited to optimize the model structure and to estimate the model parameters. The presented model-building process was applied to simulated flight data of the ERICA Tilt-Rotor, generated by using a high fidelity simulation model implemented in FlightLab environment. The validation of the obtained model was very satisfying, confirming the validity of the proposed approach.

Keywords: flapping dynamics, flight dynamics, system identification, tilt-rotor modeling and simulation

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Authors: Sebai Amal, Massuel Sylvain

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Groundwater is the key element of the development of most of the semi-arid areas where water resources are increasingly scarce due to an irregularity of precipitation, on the one hand, and an increasing demand on the other hand. This is the case of the watershed of the Central Tunisia Merguellil, object of the present study, which focuses on an implementation of an underground flows hydrodynamic model to understand the recharge processes of the Kairouan’s plain groundwater by aquifers boundary through the hydraulic threshold of El Haouareb. The construction of a conceptual geological 3D model by the Hydro GeoBuilder software has led to a definition of the aquifers geometry in the studied area thanks to the data acquired by the analysis of geologic sections of drilling and piezometers crossed shells partially or in full. Overall analyses of the piezometric Chronicles of different piezometers located at the level of the dam indicate that the influence of the dam is felt especially in the aquifer carbonate which confirms that the dynamics of this aquifer are highly correlated to the dam’s dynamic. Groundwater maps, high and low-water dam, show a flow that moves towards the threshold of El Haouareb to the discharge of the waters of Ain El Beidha discharge towards the plain of Kairouan. Software FEFLOW 5.2 steady hydrodynamic modeling to simulate the hydraulic threshold at the level of the dam El Haouareb in a satisfactory manner. However, the sensitivity study to the different parameters shows equivalence problems and a fix to calibrate the limestones’ permeability. This work could be improved by refining the timing steady and amending the representation of limestones in the model.

Keywords: Hydrodynamic modeling, lithological modeling, hydraulic, semi-arid, merguellil, central Tunisia

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Authors: Ines Elouedi, Atef Hammouda

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This paper presents a new fast version of the generalized Multi-Directional Radon Transform method. The new method uses the inverse Fast Fourier Transform to lead to a faster Generalized Radon projections. We prove in this paper that the fast algorithm leads to almost the same results of the eldest one but with a considerable lower time computation cost. The projection end result of the fast method is a parameterized Radon space where a high valued pixel allows the detection of a curve from the original image. The proposed fast inversion algorithm leads to an exact reconstruction of the initial image from the Radon space. We show examples of the impact of this algorithm on the pattern recognition domain.

Keywords: fast generalized multi-directional Radon transform, curve, exact reconstruction, pattern recognition

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Christopher Schwarz, Katrin Riegler, Erwin Zinser

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The management of outsourcing relationships between IT service providers and their customers proofs to be a critical issue that has to be stipulated by means of Service Level Agreements (SLAs). Since service requirements differ from customer to customer, SLA content and language structures vary largely, standardized SLA templates may not be used and an automated processing of SLA content is not possible. Hence, SLA management is usually a time-consuming and inefficient manual process. For overcoming these challenges, this paper presents an innovative and ITIL V3-conform approach for automated SLA design and management using controlled natural language in enterprise collaboration portals. The proposed novel concept is based on a self-developed controlled natural language that follows a subject-predicate-object approach to specify well-defined SLA content structures that act as templates for customized contracts and support automated SLA processing. The derived results eventually enable IT service providers to automate several SLA request, approval and negotiation processes by means of workflows and business rules within an enterprise collaboration portal. The illustrated prototypical realization gives evidence of the practical relevance in service-oriented scenarios as well as the high flexibility and adaptability of the presented model. Thus, the prototype enables the automated creation of well defined, customized SLA documents, providing a knowledge representation that is both human understandable and machine processable.

Keywords: automated processing, controlled natural language, knowledge representation, information technology outsourcing, service level management

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Authors: Eugenija Strazdiene, Violeta Bytautaite, Daivute Krisciuniene

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The object of the investigation is the suit for chemical protection, which totally covers human body together with breathing apparatus, breathing mask and helmet (JSC Ansell Protective Solutions Lithuania). The end users of such clothing are the members of rescue team – firefighters. During the presentation, the results of 3D scanning with stationary Human Solutions scanner and portable Artec Eva scanner will be compared on the basis of the efficiency of scanning procedure and scanning accuracy. Also, the possibilities to exporting scanned bodies into specialized CAD systems for suit design development and material consumption calculation will be analyzed. The necessity to understand and to implement corresponding clothing material properties during 3D visualization of garment on CAD systems will be presented. During the presentation, the outcomes of the project ‘Smart and Safe Work Wear Clothing SWW’ will be discussed. The project is carried out under the Interreg Baltic Sea Region Program as 2014-2020 European territorial cooperation objective. Thematic priority is Capacity for Innovation. The main goal of the project is to improve competitiveness and to increase business possibilities for work wear enterprises in the Baltic Sea Region. The project focuses on mass customization of products for various end users. It engages textile and clothing manufacturing technology researchers, work wear producers, end users, as well as national textile and clothing branch organizations in Finland, Lithuania, Latvia, Estonia and Poland.

Keywords: CAD systems, mass customization, 3D scanning, safe work wear

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Authors: Mohammad Zainie Bin Hassan

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Background: Erectile dysfunction (ED) is a complication commonly seen among men with diabetes which can be assessed based upon International Index of Erectile Function (IIEF-5) questionnaire. Creatinine level is a blood test that indicates kidney functionality. Object: To evaluate the association between ED, determined by the IIEF-5scores and Creatinine level in diabetic men attending Temerloh Health Clinic, Pahang, Malaysia.Hence, to identify raising Creatinine level related with ED or not. Methods: All married diabetic patients will be investigated face to face after consented for answering the IIEF-5 questionnaire. Creatinine level will be taken by using standard method.Patients with no sexual partner, refuse to answer the questionnaire, cancer, stroke, heart disease and language barrier will be excluded.Data obtained from IIEF-5 score and Creatinine level will be analyzed by using Pearson correlation. All statistical value determined by p=0.05. ED will be categorized accordingly to IIEF-5 scores: no ED (22-25), mild (17-21), moderate (12-16), severe (8-11) and very severe (1-7). Results: A total of 450 patients were investigated with 385 patients were included (85.6% respondant rate) and 65 patients were excluded in this study with age range from 29 to 85 years old. 7% had no ED, 28% mild ED, 34% moderate ED, 16% severe ED and 15% had very severe ED. There was a significant negative correlation between Creatinine level and IIEF-5 scores (r=-0.218, p <0.001). This result implicated that poor kidney function which indicated by high Creatinine level associated significantly with erectile dysfunction. 93% had ED with a different range of severity which triggers for appropriate aggressive ED management among diabetics. Conclusion: The high level of Creatinine is associated with erectile dysfunction among diabetics in Temerloh Health Clinic.

Keywords: correlation, creatinine level, erectile dysfunction, ED, diabetes

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Authors: Zhang Yuqi

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Service facilities are the boosters for the cultivation and development of innovative functions in innovative cluster areas. At the same time, reasonable service facilities planning can better link the internal functional blocks. This paper takes Zhejiang University Zijin Co-Maker Town as the research object, based on the combination of network data mining and field research and verification, combined with the needs of its internal innovative groups. It studies the distribution characteristics and existing problems of service facilities and then proposes a targeted planning suggestion. The main conclusions are as follows: (1) From the perspective of view, the town is rich in general life-supporting services, but lacking of provision targeted and distinctive service facilities for innovative groups; (2) From the perspective of scale structure, small-scale street shops are the main business form, lack of large-scale service center; (3) From the perspective of spatial structure, service facilities layout of each functional block is too fragile to fit the characteristics of 2aggregation- distribution' of innovation and entrepreneurial activities; (4) The goal of optimizing service facilities planning should be guided for fostering function of innovation and entrepreneurship and meet the actual needs of the innovation and entrepreneurial groups.

Keywords: the cultivation of innovative function, Zhejiang University Zijin Co-Maker Town, service facilities, network data mining, space optimization advice

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Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Animesh Pan, Geoffrey D. Bothun

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With the development of nanotechnology, research in multifunctional delivery systems has a new pace and dimension. An incipient challenge is to design an all-in-one delivery system that can be used for multiple purposes, including tumor targeting therapy, radio-frequency (RF-), near-infrared (NIR-), light-, or pH-induced controlled release, photothermal therapy (PTT), photodynamic therapy (PDT), and medical diagnosis. In this regard, various inorganic nanoparticles (NPs) are known to show great potential as the 'functional components' because of their fascinating and tunable physicochemical properties and the possibility of multiple theranostic modalities from individual NPs. Magnetic, luminescent, and plasmonic properties are the three most extensively studied and, more importantly biomedically exploitable properties of inorganic NPs. Although successful attempts of combining any two of them above mentioned functionalities have been made, integrating them in one system has remained challenge. Keeping those in mind, controlled designs of complex colloidal nanoparticle system are one of the most significant challenges in nanoscience and nanotechnology. Therefore, systematic and planned studies providing better revelation are demanded. We report a multifunctional delivery platform-based liposome loaded with drug, iron-oxide magnetic nanoparticles (MNPs), and a gold shell on the surface of liposomes, were synthesized using a lipid with polyelectrolyte (layersomes) templating technique. MNPs and the anti-cancer drug doxorubicin (DOX) were co-encapsulated inside liposomes composed by zwitterionic phophatidylcholine and anionic phosphatidylglycerol using reverse phase evaporation (REV) method. The liposomes were coated with positively charge polyelectrolyte (poly-L-lysine) to enrich the interface with gold anion, exposed to a reducing agent to form a gold nanoshell, and then capped with thio-terminated polyethylene glycol (SH-PEG2000). The core-shell nanostructures were characterized by different techniques like; UV-Vis/NIR scanning spectrophotometer, dynamic light scattering (DLS), transmission electron microscope (TEM). This multifunctional system achieves a variety of functions, such as radiofrequency (RF)-triggered release, chemo-hyperthermia, and NIR laser-triggered for photothermal therapy. Herein, we highlight some of the remaining major design challenges in combination with preliminary studies assessing therapeutic objectives. We demonstrate an efficient loading and delivery system to significant cell death of human cancer cells (A549) with therapeutic capabilities. Coupled with RF and NIR excitation to the doxorubicin-loaded core-shell nanostructure helped in securing targeted and controlled drug release to the cancer cells. The present core-shell multifunctional system with their multimodal imaging and therapeutic capabilities would be eminent candidates for cancer theranostics.

Keywords: cancer thernostics, multifunctional nanostructure, photothermal therapy, radiofrequency targeting

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Zahra Khoshnood, Mehdi Kazaie, Sajedeh Neisi

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In 2013, 24 fish samples were taken from two fishery regions in the north of Persian Gulf near the Iranian coastal lines. The two flatfishes were Yellofin seabream (Acanthopagrus latus) and Longtail tuna (Thannus tonggol). We analyzed total Hg concentration of liver and muscle tissues by Mercury Analyzer (model LECO AMA 254). The average concentration of total Hg in edible Muscle tissue of deep-Flounder was measured in Bandar-Abbas and was found to be 18.92 and it was 10.19 µg.g-1 in Bandar-Lengeh. The corresponding values for Oriental sole were 8.47 and 0.08 µg.g-1. The average concentration of Hg in liver tissue of deep-Flounder, in Bandar-Abbas was 25.49 and that in Bandar-Lengeh was 12.52 µg.g-1.the values for Oriental sole were 11.88 and 3.2 µg.g-1 in Bandar-Abbas and Bandar-Lengeh, respectively.

Keywords: mercury, Acanthopagrus latus, Thannus tonggol, Persian Gulf

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Akbar Gharbali, Ali Abbasian Ardekani, Afshin Mohammadi

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Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules.

Keywords: ultrasound imaging, thyroid nodules, computer aided diagnosis, texture analysis, PCA, LDA, NDA

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Authors: Rizky Kertanegara

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Reception studies is one of the cultural studies lately evolved in the realm of communication science. This qualitative study was pioneered by Stuart Hall who initiated the dominant, negotiation, and opposition of audience reading to the text of the media. In its development, this reception studies is developed by Kim Christian Schroder become multidimensional reception studies. In this update, Schroder aware that there has been a bias between readings made by the informant with readings conducted by researchers over the informant. Therefore, he classifies the reception into two dimensions, namely the dimension of reading by informants and implications dimensions conducted by researcher. Using Schroder approach, these studies seek to describe the reception of adolescent girls, as research subjects, to the elements contained sexual openness in the music video Cinta Laura as the object of research. Researcher wanted to see how they interpret the values of Western culture based on the values of their culture as a teenager. Researchers used a descriptive qualitative research method by conducting in-depth interviews to the informants who comes from a religious school. The selection of informants was done by using purposeful sampling. Collaboration with the school, the researchers were able to select informants who could provide rich data related to the topic. The analysis showed that there is permissiveness informants in addressing sexual openness in the music video. In addition, informants from Catholic schools were more open than the informant derived from Islamic schools in accepting the values of sexual openness. This permisiveness is regarded as a form of self-actualization and gender equality.

Keywords: cultural studies, multidimensional reception model, sexual openness, youth audience

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Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

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Authors: Krzysztof Pomorski

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In the given work we present universal mathematical framework for modeling of cattle farm system that can set and validate various hypothesis that can be tested against experimental data. The presented work is preliminary but it is expected to be valid tool for future deeper analysis that can result in new class of prediction methods allowing early detection of cow dieseaes as well as cow performance. Therefore the presented work shall have its meaning in agriculture models and in machine learning as well. It also opens the possibilities for incorporation of certain class of biological models necessary in modeling of cow behavior and farm performance that might include the impact of environment on the farm system. Particular attention is paid to the model of coupled oscillators that it the basic building hypothesis that can construct the model showing certain periodic or quasiperiodic behavior.

Keywords: coupled ordinary differential equations, cattle farm system, numerical methods, stochastic differential equations

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies indicate that a large fronto-parieto-temporal network support nouns and their features, with some areas store semantic knowledge (visual, auditory, olfactory, gustatory,…), other areas store lexical representation and other areas are implicated in general semantic processing. However, it is not well understood how this fronto-parieto-temporal network can be modulated by different semantic tasks and different semantic relations between nouns. In this study, we combine a behavioral semantic network, functional MRI studies involving object’s related nouns and brain network studies to explain how different semantic tasks and different semantic relations between nouns can modulate the activity within the brain network of nouns and their features. We first describe how nouns and their features form a large scale brain network. For this end, we examine the connectivities between areas recruited during the processing of nouns to know which configurations of interaction areas are possible. We can thus identify if, for example, brain areas that store semantic knowledge communicate via functional/structural links with areas that store lexical representations. Second, we examine how this network is modulated by different semantic tasks involving nouns and finally, we examine how category specific activation may result from the semantic relations among nouns. The results indicate that brain network of nouns and their features is highly modulated and flexible by different semantic tasks and semantic relations. At the end, this study can be used as a guide to help neurosientifics to interpret the pattern of fMRI activations detected in the semantic processing of nouns. Specifically; this study can help to interpret the category specific activations observed extensively in a large number of neuroimaging studies and clinical studies.

Keywords: nouns, features, network, category specificity

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Authors: Lucia Perez, Maria Gaton y Santa Palella

Abstract:

Starting from the etymology of the word culture, the Latin term ‘colere’, whose meaning is to cultivate, we understand that the society that cultivates its knowledge is laying the foundations for new possibilities. In this sense, art and fashion contain the same attributes: concept, aesthetic principles, and refined techniques. Both play a crucial role, communication, and this implies a sense of community, relationship with tradition, and innovation. This is the mirror in which to contemplate, but also the space that helps to grow. This is the framework where our object of study opens up: the anthropological management or the mission management model applied to fashion exhibitions in museums and cultural institutions. For this purpose, a bibliographic review has been carried out with its subsequent analysis, a case study of three successful exhibitions: ‘Christian Dior: designer of dreams’, ‘Balenciaga and the Spanish painting’, and ‘China: Through the Looking Glass’. The methodology has been completed with interviews focused on the curators. Amongst the results obtained, it is worth highlighting the fundamental role of transcendent leadership, which, in addition to being results-oriented, must align the motivations of the collaborators with the mission. The anthropological management model conceives management as a service, and it is oriented to the interests of the staff and the public, in short, of the person; this is what enables the objectives of effectiveness, efficiency, and social value to be achieved; dimensions, all necessary for the proper development of the mission of the exhibitions. Fashion, understood as art, is at the service of culture, and therefore of the human being, which defines a transcendent mission. We conclude that the profile of an anthropological management model applied to fashion exhibitions in museums is the ideal one to achieve the purpose of these institutions.

Keywords: art, culture, fashion, anthropological model, fashion exhibitions

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Authors: Sajidullah Khan, Najeeb Ullah, Wang Yin Chai, Chai Soo See

Abstract:

In this paper, we propose an approach to remove impulse and Poisson noise from X-ray images. Many filters have been used for impulse noise removal from color and gray scale images with their own strengths and weaknesses but X-ray images contain Poisson noise and unfortunately there is no intelligent filter which can detect impulse and Poisson noise from X-ray images. Our proposed filter uses the upgraded layer discrimination approach to detect both Impulse and Poisson noise corrupted pixels in X-ray images and then restores only those detected pixels with a simple efficient and reliable one line equation. Our Proposed algorithms are very effective and much more efficient than all existing filters used only for Impulse noise removal. The proposed method uses a new powerful and efficient noise detection method to determine whether the pixel under observation is corrupted or noise free. Results from computer simulations are used to demonstrate pleasing performance of our proposed method.

Keywords: X-ray image de-noising, impulse noise, poisson noise, PRWF

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Authors: Launcelot McGrath

Abstract:

The Coronavirus disease 2019 (COVID-19) has spread rapidly around the world, resulting in high mortality rates and very large numbers of people requiring medical treatment in ICU. Management of patient hospitalisation is a critical aspect to control this disease and reduce chaos in the healthcare systems. Remote monitoring provides a solution to protect vulnerable and elderly high-risk patients. Continuous remote monitoring of oxygen saturation, respiratory rate, heart rate, and temperature, etc., provides medical systems with up-to-the-minute information about their patients' statuses. Remote monitoring also limits the spread of infection by reducing hospital overcrowding. This paper examines the potential of remote monitoring for Covid-19 to assist in the rapid identification of patients at risk, facilitate the detection of patient deterioration, and enable early interventions.

Keywords: remote monitoring, patient care, oxygen saturation, Covid-19, hospital management

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Authors: Mosab Alrashed, Wei Xu, Stephen Abineri, Yifan Zhao, Jörn Mehnen

Abstract:

Thermographic inspection is a relatively new technique for Non-Destructive Testing (NDT) which has been gathering increasing interest due to its relatively low cost hardware and extremely fast data acquisition properties. This technique is especially promising in the area of rapid automated damage detection and quantification. In collaboration with a major industry partner from the aerospace sector advanced thermography-based NDT software for impact damaged composites is introduced. The software is based on correlation analysis of time-temperature profiles in combination with an image enhancement process. The prototype software is aiming to a) better visualise the damages in a relatively easy-to-use way and b) automatically and quantitatively measure the properties of the degradation. Knowing that degradation properties play an important role in the identification of degradation types, tests and results on specimens which were artificially damaged have been performed and analyzed.

Keywords: NDT, correlation analysis, image processing, damage, inspection

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Authors: Seyed Mahdi Jameii

Abstract:

Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

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Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

Abstract:

The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

Procedia PDF Downloads 429