Search results for: diagnostic accuracy

Authors: Oyebade K. Oyedotun

Abstract:

The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

Procedia PDF Downloads 331

Authors: Falntina Ahmad Alata, Natheer Abu Obeid

Abstract:

This paper is a research report of an experimental study on a proposed typology system to diagnose and evaluate the affordances of varying architectural environments. The study focused on architectural environments which have been developed with a shift in their use of adaptive reuse. The novelty in the newly developed environments was tested in terms of human responsiveness and interaction using a variety of selected cases. The study is a follow-up on previous research by the same authors, in which a typology of 16 categories of environmental affordances was developed and introduced. The current study introduced other new categories, which together with the previous ones establish what could be considered a basic language of affordance typology. The experiment was conducted on ten architectural environments while adopting two processes: 1. Diagnostic process, in which the environments were interpreted in terms of their affordances using the previously developed affordance typology, 2. The evaluation process, in which the diagnosed environments were evaluated using measures of emotional experience and architectural evaluation criteria of beauty, economy and function. The experimental study demonstrated that the typology system was capable of diagnosing different environments in terms of their affordances. It also introduced new categories of human interaction: “multiple affordances,” “conflict affordances,” and “mix affordances.” The different possible combinations and mixtures of categories demonstrated to be capable of producing huge numbers of other newly developed categories. This research is an attempt to draw a roadmap for designers to diagnose and evaluate the affordances within different architectural environments. It is hoped to provide future guidance for developing the best possible adaptive reuse according to the best affordance category within their proposed designs.

Keywords: affordance theory, affordance categories, architectural environments, architectural evaluation criteria, adaptive reuse environment, emotional experience, shift in use environment

Procedia PDF Downloads 191

Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

Abstract:

Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

Procedia PDF Downloads 153

Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

Abstract:

Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

Procedia PDF Downloads 176

Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

Abstract:

This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

Procedia PDF Downloads 51

Authors: William Cheng, Rini Sekartini

Abstract:

Background: Sleep is essential for children because it provides enhancement for the neural system activities that give physiologic effects for the body to support growth and development. One of the modifiable factors that relates with sleep is nutrition, which includes nutritional status, iron intake, and magnesium intake. Nutritional status represents the balance between nutritional intake and expenditure, while iron and magnesium are micronutrients that are related to sleep regulation. The aim of this study is to identify prevalence of sleep disturbance among Indonesian children and to evaluate its relation with aspect to nutrition. Methods : A cross-sectional study involving children aged 5 to 7-years-old in an urban primary health care between 2012 and 2013 was carried out. Related data includes anthropometric status, iron intake, and magnesium intake. Iron and magnesium intake was obtained by 24-hours food recall procedure. Sleep Disturbance Scale for Children (SDSC) was used as the diagnostic tool for sleep disturbance, with score under 39 indicating presence of problem. Results: Out of 128 school-aged children included in this study, 28 (23,1%) of them were found to have sleep disturbance. The majority of children had good nutritional status, with only 15,7% that were severely underweight or underweight, and 12,4% that were identified as stunted. On the contrary, 99 children (81,8%) were identified to have inadequate magnesium intake and 56 children (46,3%) with inadequate iron intake. Our analysis showed there was no significant relation between all of the nutritional status indicators and sleep disturbance (p>0,05%). Moreover, inadequate iron and magnesium intake also failed to prove significant relation with sleep disturbance in this population. Conclusion: Almost fourth of school-aged children in Indonesia were found to have sleep disturbance and further study are needed to overcome this problem. According to our finding, there is no correlation between nutritional status, iron intake, magnesium intake, and sleep disturbance.

Keywords: iron intake, magnesium intake, nutritional status, school-aged children, sleep disturbance

Procedia PDF Downloads 465

Authors: Zhongjie Yu, Hancheng Yu

Abstract:

In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles.

Keywords: concentric circle detection, gradient, contour, edge pre-classification, RANSAC

Procedia PDF Downloads 129

Authors: Awan A. Zaima, Tanvieer Ayesha, Mirshahi Shahsoltan, Pocard Marc, Mirshahi Massoud

Abstract:

Brain-derived neurotrophic factor (BDNF) is described as a factor helping to support the survival of existing neurons by involving the growth and differentiation of new neurons and synapses. Cancer diagnosis impacts the mental health, and in consequences, depression arise eventually hinders recovery and disrupts the quality of life and surviving chances of patients. The focus of this study is to hint upon a prospective biomarker as a promising diagnostic tool for an early indicator/predictor of depression prevalence in cancer patients for better care and treatment options. The study aims to analyze peripheral biomarkers from neuro immune axis (BDNF, IL21 as a NK cell activator) using co-relation approach. Samples were obtained from random non cancer candidates and advanced peritoneum carcinomatosis patients with 25% pseudomyxoma, 21% Colon cancer,19% stomach cancer, 10% ovarian cancer, 8% appendices cancer, and 10% other area of peritoneum cancer patients. Both groups of the study were categorized by gender and age, with a range of 18 to 86 years old. Biomarkers were analyzed in collected plasma by performing multiplex sandwich ELISA system. Data were subjected to statistical analysis for the assessment of the correlation. Our results demonstrate that BNDF and IL 21 down regulated significantly in patient groupas compared to non-cancer candidates (ratio of patients/normalis 2.57 for BNDF and 1.32 for IL21). This preliminary investigation suggested that the neuro immune biomarkers are down regulated in carcinomatosis patients and can be associated with cancer expansion and cancer genesis. Further studies on larger cohort are necessary to validate this hypothesis.

Keywords: biomarkers, depression, peritoneum carcinoma, BNDF, IL21

Procedia PDF Downloads 113

Authors: Fatima Akhmedova, Simon Liao

Abstract:

One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

Procedia PDF Downloads 374

Authors: Christopher Spiewak, M. R. Islam, Mohammad Arifur Rahaman, Mohammad H. Rahman, Roger Smith, Maarouf Saad

Abstract:

For those who have lost the ability to move their hand, going through repetitious motions with the assistance of a therapist is the main method of recovery. We have been developed a robotic assistive device to rehabilitate the hand motions in place of the traditional therapy. The developed assistive device (RAD-HR) is comprised of four degrees of freedom enabling basic movements, hand function, and assists in supporting the hand during rehabilitation. We used a nonlinear computed torque control technique to control the RAD-HR. The accuracy of the controller was evaluated in simulations (MATLAB/Simulink environment). To see the robustness of the controller external disturbance as modelling uncertainty (±10% of joint torques) were added in each joints.

Keywords: biorobotics, rehabilitation, robotic assistive device, exoskeleton, nonlinear control

Procedia PDF Downloads 478

Authors: Maassoumeh Bemani Naeini

Abstract:

Studies on interlanguage have long been engaged in describing the phenomenon of variation in SLA. Pursuing the same goal and particularly addressing the role of linguistic features, this study describes the use of Persian morphology in the interlanguage of two adult English-speaking learners of Persian L2. Taking the general approach of a combination of contrastive analysis, error analysis and interlanguage analysis, this study focuses on the identification and prediction of some possible instances of transfer from English L1 to Persian L2 across six elicitation tasks aiming to investigate whether any of contextual features may variably influence the learners’ order of morpheme accuracy in the areas of copula, possessives, articles, demonstratives, plural form, personal pronouns, and genitive cases.  Results describe the existence of task variation in the interlanguage system of Persian L2 learners.

Keywords: English L1, Interlanguage Analysis, Persian L2, SLA variation

Procedia PDF Downloads 315

Authors: Seyed Saeid Nabavi

Abstract:

Background: Many infants with intrauterine growth disorder are screened for TORCH infections. This action has no economic justification in terms of the imposed costs. In this regard, due to the research gap in this field, this study aimed to investigate the relationship between intrauterine growth disorder and TORCH infection in neonates referred to Milad hospital in 2019 and 2020. Materials and Methods: In this cross-sectional study, 41IUGR newborns were selected and evaluated based on diagnostic and clinical studies in Milad Hospital in 2019 and 2020. TORCH results found in IgG and IgM antibody titer assay were tested in mother and infant. Antibody titers of toxoplasmosis, rubella, cytomegalovirus, herpes, and syphilis were determined in cases, and other variables were compared. The collected data were entered in SPSS software 25 and analyzed at a significant level of 0.05 using the statistical tests of Kolmogorov–Smirnov, Shapiro–Wilk, chi-square, and Mann–Whitney. Results: Most of the IUGR infants studied were girls (68.3%), Gravida and Parity were reported to be 68.3% and 80%, respectively, in the study. Mean weight, APGAR score, and neonatal gestational age are reported as 1710.62±334.43 g, 7.71±1.47, and 35.7+ 1.98 weeks, respectively. Most of the newborns were born by cesarean section (92.7%). TORCH infection was reported in three patients, 7.3%. The mean gestational age of IUGR infants with TORCH infection was reported to be less than other babies with IUGR. Therefore, the mean gestational age of subjects with TORCH infection was 33±1.4 weeks and in others 35.94±1.91 weeks (p-value = 0.038). No significant relationship between TORCH infection and gender, gravidity, and parity of newborns was found (p-value > 0.05). Conclusion: TORCH infection was reported in 3 patients( 7.3%). No significant relationship between TORCH infection and gender, gravidity, and parity of newborns was found. p-value > 0.05

Keywords: congenital infection, intrauterine growth restriction, TORCH infections, neonates

Procedia PDF Downloads 132

Authors: Jiahau Guo, Yihe Zhang

Abstract:

This paper proposes solutions for pricing options on stocks paying discrete dividends in markets with daily price limits. We first extend the intraday density function of Guo and Chang (2020) to a multi-day one and use the framework of Haug et al. (2003) to value European options on stocks paying discrete dividends. Next, we adopt the fast Fourier transform (FFT) to derive accurate and efficient formulae for American options and further employ the three-point Richardson extrapolation to accelerate the computation. Finally, the accuracy of our proposed methods is verified by simulations.

Keywords: daily price limit, discrete dividend, early exercise, fast Fourier transform, multi-day density function, Richardson extrapolation

Procedia PDF Downloads 162

Authors: Liu-Chao Qiu

Abstract:

The damage caused by surge waves generated in water bodies by flow-like landslides can be very high in terms of human lives and economic losses. The complicated phenomena occurred in this highly unsteady process are difficult to model because three interacting phases: air, water and sediment are involved. The problem therefore is challenging since the effects of non-Newtonian fluid describing the rheology of the flow-like landslides, multi-phase flow and free surface have to be included in the simulation. In this work, the commercial computational fluid dynamics (CFD) package FLUENT is used to model the surge waves due to flow-like landslides. The comparison between the numerical results and experimental data reported in the literature confirms the accuracy of the method.

Keywords: flow-like landslide, surge wave, VOF, non-Newtonian fluids, multi-phase flows, free surface flow

Procedia PDF Downloads 415

Authors: Bounoua Nadia, Rebizi Mohamed Nadjib

Abstract:

Chiral molecules in relation to particular biological roles are stereoselective. Enantiomers differ significantly in their biochemical responses in a biological environment. Despite the current advancement in drug discovery and pharmaceutical biotechnology, the chiral separation of some racemic mixtures continues to be one of the greatest challenges because the available techniques are too costly and time-consuming for the assessment of therapeutic drugs in the early stages of development worldwide. Various nanoparticles became one of the most investigated and explored nanotechnology-derived nanostructures, especially in chirality, where several studies are reported to improve the enantiomeric separation of different racemic mixtures. The production of surface-modified nanoparticles has contributed to these limitations in terms of sensitivity, accuracy, and enantioselectivity that can be optimized and therefore makes these surface-modified nanoparticles convenient for enantiomeric identification and separation.

Keywords: chirality, enantiomeric recognition, selectors, analysis, surface-modified nanoparticles

Procedia PDF Downloads 92

Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

Abstract:

China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping.

Keywords: GF-2 images, feature extraction-rectification, nearest neighbour object based classification, segmentation algorithms, neural network classification, multilayer perceptron

Procedia PDF Downloads 387

Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

Abstract:

Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

Procedia PDF Downloads 54

Authors: Authors: A. Lakhal, M. Bahalou, A. Khattabi

Abstract:

Introduction: Tuberculosis is one of the world's deadliest infectious diseases. In Morocco, a total of 30,636 cases of Tuberculosis, all forms combined, were reported in 2015, representing an incidence of 89 cases per 100,000 population. The number of deaths from tuberculosis (TB) was 656 cases. In the prefecture of Meknes, its incidence remains high compared to the national level. The objective of this work is to describe the epidemiological profile of tuberculosis in the prefecture of Meknes. Methods: It is a descriptive analysis of TB cases reported between 2016 and 2020 at the regional diagnostic center of tuberculosis and respiratory diseases. We performed analysis by using Microsoft Excel and EpiInfo 7. Results: Epidemiological data from 2016 to 2020 report a total of 4100 new cases of all forms of tuberculosis, with an average of 820 new cases per year. The median age is 32 years. There is a clear male predominance, on average 58% of cases are male and 42% female. The incidence rate of bacteriologically confirmed tuberculosis per 100,000 inhabitants has increased from 35 cases per 100,000 inhabitants in 2016 to 39.4 cases per 100,000 inhabitants in 2020. The confirmation rate for pulmonary tuberculosis decreased from 84% in 2016 to 75% in 2020. Pulmonary involvement predominates by an average of 46%, followed by lymph node involvement 29%and pleural involvement by an average of 10%. Digestive, osteoarticular, genitourinary, and meningeal involvement occurs in 8% of cases. Primary tuberculosis infection occurs in an average of 0.5% of cases. The proportion of HIV-TB co-infections was 2.8 in 2020. Conclusion: The incidence of tuberculosis in Meknes remains high compared to the national level. Thus, it is imperative to reinforce the earlier detection; improve the contact tracing, detection methods of cases for their confirmation and treatment, and to reduce the proportion of the lost to follow up as well.

Keywords: tuberculosis, epidemiological profile, meknes, morocco

Procedia PDF Downloads 156

Authors: Chae Hoon Park, Jong Kang Park, Jong Tae Kim

Abstract:

Electrical characteristics of capacitive touchscreen need to be accurately analyzed to result in better performance for multi-channel capacitance sensing. In this paper, we extracted the panel resistances and capacitances of the touchscreen by comparing measurement data and model data. By employing a lumped RC model for driver-to-receiver paths in touchscreen, we estimated resistance and capacitance values according to the physical lengths of channel paths which are proportional to the RC model. As a result, we obtained the model having 95.54% accuracy of the measurement data.

Keywords: electrical characteristics of capacitive touchscreen, iterative extraction, lumped RC model, physical lengths of channel paths

Procedia PDF Downloads 332

Authors: M. Abdo, Ahmed Elghandour, Khairy Eltahlawy, Mohamed Shalaby

Abstract:

A key development in the field of inertial sensors, fiber-optic gyroscopes (FOGs) are currently thought to be a competitive alternative to mechanical gyroscopes for inertial navigation and control applications. For the past few years, research and development efforts have been conducted all around the world using the FOG as a crucial sensor for high-accuracy inertial navigation systems. The main fundamentals of optical gyros were covered in this essay, followed by discussions of the main types of optical gyros—fiber optic gyroscopes and ring laser gyroscopes—and comparisons between them. We also discussed different types of fiber optic gyros, including interferometric, resonator, and brillion fiber optic gyroscopes.

Keywords: mechanical gyros, ring laser gyros, interferometric fiber optic gyros, resonator fiber optic gyros

Procedia PDF Downloads 82

Authors: Jing Niu, Jun X. Wang

Abstract:

Arterial stiffness predicts mortality and morbidity, independently of other cardiovascular risk factors. And it is a major risk factor for age-related morbidity and mortality. The non-invasive industry gold standard measurement system of arterial stiffness utilizes pulse wave velocity method. However, the desktop device is expensive and requires trained professional to operate. The main objective of this research is the proof of concept of the proposed non-invasive method which uses measurement of magnetic flux disturbance and electrocardiogram (ECG) signal for measuring arterial stiffness. The method could enable accurate and easy self-assessment of arterial stiffness at home, and to help doctors in research, diagnostic and prescription in hospitals and clinics. A platform for assessing arterial stiffness through acquisition and analysis of radial artery pulse waveform and ECG signal has been developed based on the proposed method. Radial artery pulse waveform is acquired using the magnetic based sensing technology, while ECG signal is acquired using two dry contact single arm ECG electrodes. The measurement only requires the participant to wear a wrist strap and an arm band. Participants were recruited for data collection using both the developed platform and the industry gold standard system. The results from both systems underwent correlation assessment analysis. A strong positive correlation between the results of the two systems is observed. This study presents the possibility of developing an accurate, easy to use and affordable measurement device for arterial stiffness assessment.

Keywords: arterial stiffness, electrocardiogram, pulse wave velocity, Magnetic Flux Disturbance

Procedia PDF Downloads 186

Authors: Patricia Kopetz

Abstract:

Too often our youngest children with disabilities receive diagnostic labels and accompanying treatment plans based upon perceptions that the children are of limited aptitude and/or ambition. However, children of varied-ability levels who are diagnosed with ‘multiple disabilities,’ can participate and excel in school-based instruction that aligns with their desires, interests, and fortitude – criteria components not foretold by scores on standardized assessments. The paper represents theoretical work in Special Education Innovative Instruction, and includes presenting research materials, some developed by the author herself. The majority of students with disabilities are now served in general education settings in the United States, embracing inclusive practices in our schools. ‘There is now a stronger call for special education to step up and improve efficiency, implement evidence-based practices, and provide greater accountability on key performance indicators that support successful academic and post-school outcomes for students with disabilities.’ For example, in the United States, the Office of Special Education Programs (OSEP) is focusing on results-driven indicators to improve outcomes for students with disabilities. School personnel are appreciating the implications of research-driven approaches for students diagnosed with multiple disabilities, and aim to align their practices toward such focus. The paper presented will provide updates on current theoretical principles and perspectives, and explore advancements in latest, evidence-based and results-driven instructional practices that can motivate children with multiple disabilities to advance their skills and engage in learning activities that as nonconventional, innovative, and proven successful.

Keywords: childhood special education, educational technology , innovative instruction, multiple disabilities

Procedia PDF Downloads 248

Authors: Engin Eyceyurt, Josko Zec

Abstract:

The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

Procedia PDF Downloads 155

Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

Procedia PDF Downloads 125

Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

Procedia PDF Downloads 168

Authors: Shih-Pin Chen, Shih-Syuan You

Abstract:

Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

Procedia PDF Downloads 435

Authors: Weizhi Zhong, Xiaoyi Lu, Lei Xu

Abstract:

In order to solve the problem of inaccurate synchronization for distributed multiple-input multiple-output (MIMO) system in multipath environment, a golay pair aided timing synchronization method is proposed in this paper. A new synchronous training sequence based on golay pair is designed. By utilizing the aperiodic auto-correlation complementary property of the new training sequence, the fine timing point is obtained at the receiver. Simulation results show that, compared with the tradition timing synchronization approaches, the proposed algorithm can provide high accuracy in synchronization, especially under multipath condition.

Keywords: distributed MIMO system, golay pair, multipath, synchronization

Procedia PDF Downloads 246

Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

Abstract:

Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

Procedia PDF Downloads 350

Authors: P. Tueku, P. Supnithi, R. Wongsathan

Abstract:

Heat-Assisted Magnetic Recording (HAMR) is one of the leading technologies identified to enable areal density beyond 1 Tb/in2 of magnetic recording systems. A key challenge to HAMR designing is accuracy of positioning, timing of the firing laser, power of the laser, thermo-magnetic head, head-disk interface and cooling system. We study the effect of HAMR parameters on transition center and transition width. The HAMR is model using Thermal Williams-Comstock (TWC) and microtrack model. The target and equalizer are designed by the minimum mean square error (MMSE). The result shows that the unit energy constraint outperforms other constraints.

Keywords: heat-assisted magnetic recording, thermal Williams-Comstock equation, microtrack model, equalizer

Procedia PDF Downloads 347

Authors: Predrag Kavaric, Vladimir L. Jubic, Maxim Cadenovic

Abstract:

The patient was transferred from his dentist to our tertiary medical institution. In anamnesis, we got information that his dental intervention was two years ago when he got dental implants but because of the coronavirus pandemic event, he didn’t finish the whole procedure. After two years, he decided that he will continue his work at his dentist, then his dentist noticed that there is no earlier inserted implant in the upper jaw on the right side. They do Panoramic X-ray and find that the implant is all in the maxillary sinus cavity. The flour of the maxilla was intact without any fistula on the place where the implant was inserted in the maxilla bone, After that initial diagnostic they sent the patient to maxillofacial surgery and otorhinolaryngology. We asked for a CT scan of paranasal sinuses, which confirmed the foreign body in the right maxillary sinus. The plan was that in general anesthesia we do FESS and try to find a foreign body in the maxillary sinus or in case of failure to do Caldwel Luc on that side. After preoperative preparation in GA, we do FESS. In inspection, we find small polyps and chronically changed mucosa of osteomeatal complex and right maxillary sinus. After removing polyps we did uncinectomy and medial maxillectomy. With Heuweiser Antrum grasping forceps after several attempts we managed to extract a foreign body from the bottom of the right maxillary sinus. On the first postoperative day we did detamponade, and then we discharge the patient from hospital. The Covid pandemic has contributed to the postponement of a large number of planned operations, which has resulted in various complications in the treatment of a number of patients. In this case, it happened that the implant was most likely rejected by the bone but in the direction of the maxillary sinus, which is not a common cause. On the other hand, the success was that less traumatic intervention was able to remove the foreign body from the maxillary sinus in which it was located. Since the sinus floor is free of bone defects, it can be continued relatively quickly with dental procedures.

Keywords: x-ray, surgery, maxillar sinus, complication, fees

Procedia PDF Downloads 145