Search results for: HR artificial intelligence
808 Characterization of Leakage Current on the Surface of Porcelain Insulator under Contaminated Conditions
Authors: Hocine Terrab , Abdelhafid Bayadi, Adel Kara, Ayman El-Hag
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Insulator flashover under polluted conditions has been a serious threat on the reliability of power systems. It is known that the flashover process is mainly affected by the environmental conditions such as; the pollution level and humidity. Those are the essential parameters influencing the wetting process. This paper presents an investigation of the characteristics of leakage current (LC) developed on the surface of porcelain insulator at contaminated conditions under AC voltage. The study is done in an artificial fog chamber and the LC is characterized for different stages; dry, wetted and presence of discharge activities. Time-frequency and spectral analysis are adopted to calculate the evolution of LC characteristics with various stages prior to flashover occurrence. The preliminary results could be used in analysing the LC to develop more effective diagnosis of early signs of dry band arcing as an indication for insulation washing.Keywords: flashover, harmonic components, leakage current, phase angle, statistical analysis
Procedia PDF Downloads 419807 Empirical and Indian Automotive Equity Portfolio Decision Support
Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu
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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis
Procedia PDF Downloads 473806 Establishment of a Classifier Model for Early Prediction of Acute Delirium in Adult Intensive Care Unit Using Machine Learning
Authors: Pei Yi Lin
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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model
Procedia PDF Downloads 59805 Addressing Primary Care Clinician Burnout in a Value Based Care Setting During the COVID-19 Pandemic
Authors: Robert E. Kenney, Efrain Antunez, Samuel Nodal, Ameer Malik, Richard B. Aguilar
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Physician burnout has gained much attention during the COVID pandemic. After-hours workload, HCC coding, HEDIS metrics, and clinical documentation negatively impact career satisfaction. These and other influences have increased the rate of physicians leaving the workforce. In addition, roughly 1% of the entire physician workforce will be retiring earlier than expected based on pre-pandemic trends. The two Medical Specialties with the highest rates of burnout are Family Medicine and Primary Care. With a predicted shortage of primary care physicians looming, the need to address physician burnout is crucial. Commonly reported issues leading to clinician burnout are clerical documentation requirements, increased time working on Electronic Health Records (EHR) after hours, and a decrease in work-life balance. Clinicians experiencing burnout with physical and emotional exhaustion are at an increased likelihood of providing lower quality and less efficient patient care. This may include a lack of suitable clinical documentation, medication reconciliation, clinical assessment, and treatment plans. While the annual baseline turnover rates of physicians hover around 6-7%, the COVID pandemic profoundly disrupted the delivery of healthcare. A report found that 43% of physicians switched jobs during the initial two years of the COVID pandemic (2020 and 2021), tripling the expected average annual rate to 21.5 %/yr. During this same time, an average of 4% and 1.5% of physicians retired or left the workforce for a non-clinical career, respectively. The report notes that 35.2% made career changes for a better work-life balance and another 35% reported the reason as being unhappy with their administration’s response to the pandemic. A physician-led primary care-focused health organization, Cano Health (CH), based out of Florida, sought to preemptively address this problem by implementing several supportive measures. Working with >120 clinics and >280 PCPs from Miami to Tampa and Orlando, managing nearly 120,000 Medicare Advantage lives, CH implemented a number of changes to assist with the clinician’s workload. Supportive services such as after hour and home visits by APRNs, in-clinic care managers, and patient educators were implemented. In 2021, assistive Artificial Intelligence Software (AIS) was integrated into the EHR platform. This AIS converts free text within PDF files into a usable (copy-paste) format facilitating documentation. The software also systematically and chronologically organizes clinical data, including labs, medical records, consultations, diagnostic images, medications, etc., into an easy-to-use organ system or chronic disease state format. This reduced the excess time and documentation burden required to meet payor and CMS guidelines. A clinician Documentation Support team was employed to improve the billing/coding performance. The effects of these newly designed workflow interventions were measured via analysis of clinician turnover from CH’s hiring and termination reporting software. CH’s annualized average clinician turnover rate in 2020 and 2021 were 17.7% and 12.6%, respectively. This represents a 30% relative reduction in turnover rate compared to the reported national average of 21.5%. Retirement rates during both years were 0.1%, demonstrating a relative reduction of >95% compared to the national average (4%). This model successfully promoted the retention of clinicians in a Value-Based Care setting.Keywords: clinician burnout, COVID-19, value-based care, burnout, clinician retirement
Procedia PDF Downloads 74804 Artificial Neural Networks with Decision Trees for Diagnosis Issues
Authors: Y. Kourd, D. Lefebvre, N. Guersi
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This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.Keywords: neural networks, decision trees, diagnosis, behaviors
Procedia PDF Downloads 488803 Spatial Audio Player Using Musical Genre Classification
Authors: Jun-Yong Lee, Hyoung-Gook Kim
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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing
Procedia PDF Downloads 421802 Effects of Coastal Structure Construction on Ecosystem
Authors: Afshin Jahangirzadeh, Shatirah Akib, Keyvan Kimiaei, Hossein Basser
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Coastal defense structures were built to protect part of shore from beach erosion and flooding by sea water. Effects of coastal defense structures can be negative or positive. Some of the effects are beneficial in socioeconomic aspect, but environment matters should be given more concerns because it can bring bad consequences to the earth landscape and make the ecosystem be unbalanced. This study concerns on the negative impacts as they are dominant. Coastal structures can extremely impact the shoreline configuration. Artificial structures can influence sediment transport, split the coastal space, etc. This can result in habitats loss and lead to noise and visual disturbance of birds. There are two types of coastal defense structures, hard coastal structure and soft coastal structure. Both coastal structures have their own impacts. The impacts are induced during the construction, maintaining, and operation of the structures.Keywords: ecosystem, environmental impact, hard coastal structures, soft coastal structures
Procedia PDF Downloads 474801 Heat Transfer and Friction Factor Study for Triangular Duct Solar Air Heater Having Discrete V-Shaped Ribs
Authors: Varun Goel
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Solar energy is a good option among renewable energy resources due to its easy availability and abundance. The simplest and most efficient way to utilize solar energy is to convert it into thermal energy and this can be done with the help of solar collectors. The thermal performance of such collectors is poor due to less heat transfer from the collector surface to air. In this work, experimental investigations of single pass solar air heater having triangular duct and provided with roughness element on the underside of the absorber plate. V-shaped ribs are used for investigation having three different values of relative roughness pitch (p/e) ranges from 4-16 for a fixed value of angle of attack (α), relative roughness height (e/Dh) and a relative gap distance (d/x) values are 60°, 0.044 and 0.60 respectively. Result shows that considerable augmentation in heat transfer has been obtained by providing roughness.Keywords: artificial roughness, solar air heater, triangular duct, V-shaped ribs
Procedia PDF Downloads 443800 A Combination of Independent Component Analysis, Relative Wavelet Energy and Support Vector Machine for Mental State Classification
Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui
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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.Keywords: EEG, ICA, SVM, wavelet
Procedia PDF Downloads 377799 Application of Adaptive Neural Network Algorithms for Determination of Salt Composition of Waters Using Laser Spectroscopy
Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko
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In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.Keywords: inverse problems, multi-component solutions, neural networks, Raman spectroscopy
Procedia PDF Downloads 519798 Machine Learning Based Digitalization of Validated Traditional Cognitive Tests and Their Integration to Multi-User Digital Support System for Alzheimer’s Patients
Authors: Ramazan Bakir, Gizem Kayar
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It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification
Procedia PDF Downloads 128797 LED Lighting Interviews and Assessment in Forest Machines
Authors: Rauno Pääkkönen, Fabriziomaria Gobba, Leena Korpinen
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The objective of the study is to assess the implementation of LED lighting into forest machine work in the dark. In addition, the paper includes a wide variety of important and relevant safety and health parameters. In modern, computerized work in the cab of forest machines, artificial illumination is a demanding task when performing duties, such as the visual inspections of wood and computer calculations. We interviewed entrepreneurs and gathered the following as the most pertinent themes: (1) safety, (2) practical problems, and (3) work with LED lighting. The most important comments were in regards to the practical problems of LED lighting. We found indications of technical problems in implementing LED lighting, like snow and dirt on the surfaces of lamps that dim the emission of light. Moreover, service work in the dark forest is dangerous and increases the risks of on-site accidents. We also concluded that the amount of blue light to the eyes should be assessed, especially, when the drivers are working in a semi-dark cab.Keywords: forest machines, health, LED, safety
Procedia PDF Downloads 418796 The Role of Context in Interpreting Emotional Body Language in Robots
Authors: Jekaterina Novikova, Leon Watts
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In the emerging world of human-robot interaction, people and robots will interact socially in real-world situations. This paper presents the results of an experimental study probing the interaction between situational context and emotional body language in robots. 34 people rated video clips of robots performing expressive behaviours in different situational contexts both for emotional expressivity on Valence-Arousal-Dominance dimensions and by selecting a specific emotional term from a list of suggestions. Results showed that a contextual information enhanced a recognition of emotional body language of a robot, although it did not override emotional signals provided by robot expressions. Results are discussed in terms of design guidelines on how an emotional body language of a robot can be used by roboticists developing social robots.Keywords: social robotics, non-verbal communication, situational context, artificial emotions, body language
Procedia PDF Downloads 281795 Verification of the Supercavitation Phenomena: Investigation of the Cavity Parameters and Drag Coefficients for Different Types of Cavitator
Authors: Sezer Kefeli, Sertaç Arslan
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Supercavitation is a pressure dependent process which gives opportunity to eliminate the wetted surface effects on the underwater vehicle due to the differences of viscosity and velocity effects between liquid (freestream) and gas phase. Cavitation process occurs depending on rapid pressure drop or temperature rising in liquid phase. In this paper, pressure based cavitation is investigated due to the fact that is encountered in the underwater world, generally. Basically, this vapor-filled pressure based cavities are unstable and harmful for any underwater vehicle because these cavities (bubbles or voids) lead to intense shock waves while collapsing. On the other hand, supercavitation is a desired and stabilized phenomena than general pressure based cavitation. Supercavitation phenomena offers the idea of minimizing form drag, and thus supercavitating vehicles are revived. When proper circumstances are set up, which are either increasing the operating speed of the underwater vehicle or decreasing the pressure difference between free stream and artificial pressure, the continuity of the supercavitation is obtainable. There are 2 types of supercavitation to obtain stable and continuous supercavitation, and these are called as natural and artificial supercavitation. In order to generate natural supercavitation, various mechanical structures are discovered, which are called as cavitators. In literature, a lot of cavitator types are studied either experimentally or numerically on a CFD platforms with intent to observe natural supercavitation since the 1900s. In this paper, firstly, experimental results are obtained, and trend lines are generated based on supercavitation parameters in terms of cavitation number (), form drag coefficientC_D, dimensionless cavity diameter (d_m/d_c), and length (L_c/d_c). After that, natural cavitation verification studies are carried out for disk and cone shape cavitators. In addition, supercavitation parameters are numerically analyzed at different operating conditions, and CFD results are fitted into trend lines of experimental results. The aims of this paper are to generate one generally accepted drag coefficient equation for disk and cone cavitators at different cavitator half angle and investigation of the supercavitation parameters with respect to cavitation number. Moreover, 165 CFD analysis are performed at different cavitation numbers on FLUENT version 21R2. Five different cavitator types are modeled on SCDM with respect tocavitator’s half angles. After that, CFD database is generated depending on numerical results, and new trend lines are generated based on supercavitation parameters. These trend lines are compared with experimental results. Finally, the generally accepted drag coefficient equation and equations of supercavitation parameters are generated.Keywords: cavity envelope, CFD, high speed underwater vehicles, supercavitation, supercavitating flows, supercavitation parameters, drag reduction, viscous force elimination, natural cavitation verification
Procedia PDF Downloads 124794 Seismic Behavior of a Jumbo Container Crane in the Low Seismicity Zone Using Time-History Analyses
Authors: Huy Q. Tran, Bac V. Nguyen, Choonghyun Kang, Jungwon Huh
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Jumbo container crane is an important part of port structures that needs to be designed properly, even when the port locates in low seismicity zone such as in Korea. In this paper, 30 artificial ground motions derived from the elastic response spectra of Korean Building Code (2005) are used for time history analysis. It is found that the uplift might not occur in this analysis when the crane locates in the low seismic zone. Therefore, a selection of a pinned or a gap element for base supporting has not much effect on the determination of the total base shear. The relationships between the total base shear and peak ground acceleration (PGA) and the relationships between the portal drift and the PGA are proposed in this study.Keywords: jumbo container crane, portal drift, time history analysis, total base shear
Procedia PDF Downloads 181793 Optimizing Approach for Sifting Process to Solve a Common Type of Empirical Mode Decomposition Mode Mixing
Authors: Saad Al-Baddai, Karema Al-Subari, Elmar Lang, Bernd Ludwig
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Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.Keywords: empirical mode decomposition (EMD), mode mixing, sifting process, over-sifting
Procedia PDF Downloads 385792 Enhancing Value of Dam Dredged Sediments as a Component of a Self Compacting Concrete
Authors: N. Belas, O. Belaribi, S. Aggoun, K. Bendani, N. Bouhamou, A. Mebrouki
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This experimental work is a part of a long research on the valorization of the dam dredged sediments issued from Fergoug Dam (Mascara-West Algeria). These sediments have to be subjected to thermal treatment to become reactive with the cement and thus to obtain an artificial pozzolana. It is therefore a question of developing the calcined mud as substitutable material in part to the cement used in the composition of self compacting concrete. The objective of the present work is to highlight its influence on the behavior of self compacting concrete compared to that of the natural pozzolana and this, in fresh and hardened states. The study is being conducted on three SCC, the first using 20% in volume of natural pozzolana, the second with 20 % of calcined mud and the third for the sake of comparison is made with cement only. The first results showed the possibility of obtaining SCC with calcined mud complying with the AFGC recommendations having a good mechanical behavior which makes interesting its development as construction materials.Keywords: dam, fresh state, hardened state mud, sediments, self compacting concrete, valorization
Procedia PDF Downloads 503791 Critical Review of Web Content Mining Extraction Mechanisms
Authors: Rabia Bashir, Sajjad Akbar
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There is an inevitable demand of web mining due to rapid increase of huge information on the Internet, but the striking variety of web structures has made required content retrieval a difficult task. To counter this issue, Web Content Mining (WCM) emerges as a potential candidate which extracts and integrates suitable resources of data to users. In past few years, research has been done on several extraction techniques for WCM i.e. agent-based, template-based, assumption-based, statistic-based, wrapper-based and machine learning. However, it is still unclear that either these approaches are efficiently tackling the significant challenges of WCM or not. To answer this question, this paper identifies these challenges such as language independency, structure flexibility, performance, automation, dynamicity, redundancy handling, intelligence, relevant content retrieval, and privacy. Further, mapping of these challenges is done with existing extraction mechanisms which helps to adopt the most suitable WCM approach, given some conditions and characteristics at hand.Keywords: content mining challenges, web content mining, web content extraction approaches, web information retrieval
Procedia PDF Downloads 539790 A Thorough Analysis of the Literature on the Airport Service Quality and Patron Satisfaction
Authors: Mohammed Saad Alanazi
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Satisfaction of travelers with services provided in the airports is a sign of competitiveness and the corporate image of the airport. This study conducted a systematic literature review of recent studies published after 2017 regarding the factors that positively influence travelers’ satisfaction and encourage them to report positive reviews online. This study found variations among the studies found. They used several research methodologies, and datasets and focused on different airports, yet, they commonly categorized airport services into seven categories that should receive high intention because their qualities were found increasing review rate and positivity. It was found that studies targeting travelers’ satisfaction and intention of revisiting tended to use primary sources of data (survey); meanwhile, studies concerned positivity and negativity of comments towards airport services often used online reviews provided by travelers.Keywords: business Intelligence, airport service quality, passenger satisfaction, thorough analysis
Procedia PDF Downloads 67789 Medical Advances in Diagnosing Neurological and Genetic Disorders
Authors: Simon B. N. Thompson
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Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning
Procedia PDF Downloads 380788 Parental Investment in Education: A Pathway for the Children's Access to Quality Education
Authors: Tukur Husaini Nahuche
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The parent resources play a vital role in the life of the offspring. It help give children basic necessities of life like food, clothing, and housing. In a like manner financial assets allow parents to move into neighborhood with more affluent school systems, to pay school bills, purchase expensive technologies like personal computer, save money for tutoring books, magazines, journals, Newspapers etc. Making of proper provision in the home environment conducive for learning after school hours and creation of other outdoor activities for them are what necessitate in enhancing and accelerating children’s learning opportunities. Indeed, this paper intends to discuss parental investment in education, parent income resources, parental education, occupation, and income as relatively influencing children’s access to quality education. With the hope that families would provide equal opportunities for children irrespective of their sex, intelligence, subject choice,etc.Keywords: parental investment, children's access, quality education
Procedia PDF Downloads 544787 Wireless Sensor Anomaly Detection Using Soft Computing
Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh
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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.Keywords: IDS, Machine learning, WSN, ZigBee technology
Procedia PDF Downloads 536786 Precipitation and Age Hardening in Al-Mg-Si-(Cu) Alloys for Automotive Body Sheet
Authors: Tahar Abid, Haoues Ghouss, Abdelhamid Boubertakh
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This present work is focused on the hardening precipitation in two AlMgSi(Cu) automotive body sheets. The effect of pre-aging, aging treatment and 0.10 wt % copper addition on the hardening response was investigated using scanning calorimetry (DSC), transmission electron microscopy (TEM), and Vickers microhardness measurements (Hv). The results reveal the apparition of α-AlFeSi, α-AlFe(Mn)Si type precipitates frequently present and witch remain stable at high temperature in Al-Mg-Si alloys. Indeed, the hardening response in both sheets is certainly due to the predominance of very fine typical phases β' and β'' as rods and needles developed during aging with and without pre-aging. The effect of pre ageing just after homogenization and quenching is to correct the undesirable effect of aging at ambient temperature by making faster alloy hardening during artificial aging.The addition of 0.10 wt % copper has allowed to refine and to enhance the precipitation hardening after quenching.Keywords: AlMgSi alloys, precipitation, hardening, activation energy
Procedia PDF Downloads 82785 A Comparison of Image Data Representations for Local Stereo Matching
Authors: André Smith, Amr Abdel-Dayem
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The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.Keywords: colour data, local stereo matching, stereo correspondence, disparity map
Procedia PDF Downloads 361784 Mathematical Modeling for Diabetes Prediction: A Neuro-Fuzzy Approach
Authors: Vijay Kr. Yadav, Nilam Rathi
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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy
Procedia PDF Downloads 247783 New Approach for Load Modeling
Authors: Slim Chokri
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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression
Procedia PDF Downloads 428782 Modeling of System Availability and Bayesian Analysis of Bivariate Distribution
Authors: Muhammad Farooq, Ahtasham Gul
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To meet the desired standard, it is important to monitor and analyze different engineering processes to get desired output. The bivariate distributions got a lot of attention in recent years to describe the randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed using two independent models developed by the nesting approach to study the effect of each component on reliability for better understanding. Further, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution, like mean median and quantile function, are discussed. We use inverse Gamma prior to study its frequentist properties by conducting Monte Carlo Markov Chain (MCMC) sampling scheme.Keywords: reliability, system availability Weibull, inverse Lomax, Monte Carlo Markov Chain, Bayesian
Procedia PDF Downloads 65781 Numerical Simulation of Bio-Chemical Diffusion in Bone Scaffolds
Authors: Masoud Madadelahi, Amir Shamloo, Seyedeh Sara Salehi
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Previously, some materials like solid metals and their alloys have been used as implants in human’s body. In order to amend fixation of these artificial hard human tissues, some porous structures have been introduced. In this way, tissues in vicinity of the porous structure can be attached more easily to the inserted implant. In particular, the porous bone scaffolds are useful since they can deliver important biomolecules like growth factors and proteins. This study focuses on the properties of the degradable porous hard tissues using a three-dimensional numerical Finite Element Method (FEM). The most important studied properties of these structures are diffusivity flux and concentration of different species like glucose, oxygen, and lactate. The process of cells migration into the scaffold is considered as a diffusion process, and related parameters are studied for different values of production/consumption rates.Keywords: bone scaffolds, diffusivity, numerical simulation, tissue engineering
Procedia PDF Downloads 376780 Analytical Study of Data Mining Techniques for Software Quality Assurance
Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar
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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.Keywords: data mining, defect prediction, missing requirements, software quality
Procedia PDF Downloads 458779 Mapping of Potential Areas for Groundwater Storage in the Sais Plateau and Its Middle Atlas Borders, Morocco
Authors: Abdelghani Qadem, Zohair Qadem, Mohamed Lasri
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At the level of the Moroccan Sais Plateau, groundwater constitutes strategic natural resources for agricultural, industrial, and domestic use. Today, due to climate change and population growth, the pressure on groundwater has increased considerably. This contribution aims to delineate and map potential areas for groundwater storage in the area in question using GIS and remote sensing. The methodology adopted is based on the identification of the thematic layers used to assess the potential recharge of the aquifer. The mapping of potential areas for groundwater storage is developed through the method of modeling and weighted overlay using the spatial analysis tool on the Geographic Information System. The results obtained can be used for the planning of future artificial recharge projects in the study area in order to ensure the good sustainable use of this underground gift.Keywords: Morocco, climate change, groundwater, mapping, recharge
Procedia PDF Downloads 74