Search results for: college student learning experience

Authors: Ali Alshahrani, Adel Almaai, Saad Garni

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Aim of the study: To explore duration and determinants of consultation time at a family medicine center. Methodology: This study was conducted at the Family Medicine Center in Ahad Rafidah City, at the southwestern part of Saudi Arabia. It was conducted on the working days of March 2013. Trained nurses helped in filling in the checklist. A total of 459 patients were included. A checklist was designed and used in this study. It included patient’s age, sex, diagnosis, type of visit, referral and its type, psychological problems and additional work-up. In addition, number of daily bookings, physician`s experience and consultation time. Results: More than half of patients (58.39%) had less than 10 minutes’ consultation (Mean+SD: 12.73+9.22 minutes). Patients treated by physicians with shortest experience (i.e., ≤5 years) had the longest consultation time while those who were treated with physicians with the longest experience (i.e., > 10 years) had the shortest consultation time (13.94±10.99 versus 10.79±7.28, p=0.011). Regarding patients’ diagnosis, those with chronic diseases had the longest consultation time (p<0.001). Patients who did not need referral had significantly shorter consultation time compared with those who had routine or urgent referral (11.91±8.42,14.60±9.03 and 22.42±14.81 minutes, respectively, p<0.001). Patients with associated psychological problems needed significantly longer consultation time than those without associated psychological problems (20.06±13.32 versus 12.45±8.93, p<0.001). Conclusions: The average length of consultation time at Ahad Rafidah Family Medicine Center is approximately 13 minutes. Less-experienced physicians tend to spend longer consultation times with patients. Referred patients, those with psychological problems, those with chronic diseases tend to have longer consultation time. Recommendations: Family physicians should be encouraged to keep their optimal consultation time. Booking an adequate number of patients per shift would allow the family physician to provide enough consultation time for each patient.

Keywords: consultation, quality, medicine, clinics

Procedia PDF Downloads 269

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

Procedia PDF Downloads 127

Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

Procedia PDF Downloads 191

Authors: A/Prof Marjan Khajehei

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In the last century, the role of fathers in the birth has changed exponentially. Before the 1970s, the principal view was that birth was a female business and not a man’s place. Changing cultural and professional attitudes around the emotional bond between a man and a woman, family structure and the more proactive involved role of men in the family have encouraged fathers’ attendance at birth. There is evidence that fathers’ support can make birthing less traumatic for some women and can make couples closer. This has made some clinicians to believe the fathers should be more involved throughout the birth process. Some clinicians even go further and ask the fathers to watch the medical procedures, such as inserting vaginal speculum, forceps or vacuum, episiotomy and stitches. Although birth can unfold like a beautiful picture captured by birth photographers, with fathers massaging women’s backs by candle light and the miraculous moment of birth, it can be overshadowed by less attractive images of cervical mucous, emptying bowels and the invasive medical procedures. What happens in the birth room and the fathers’ reaction to the graphic experience of birthing can be unpredictable. Despite the fact that most men are absolutely thrilled to be in the delivery room, for some men, a very intimate body part can become completely desexualised, and they can experience psychological and sexual scarring. They see someone they cherish dramatically sliced open and can then associate their partners with a disturbing scene, and it can dramatically affect their relationships. While most women want the expectant fathers by their side for this life-changing event, not all of them may be happy for their partners to watch the perineum to be cut or stitched or when large blades of forceps are inserted inside the vagina. Anecdotal reports have shown that consent is not sought from the labouring women as to whether they want their partners to watch these procedures. The majority of research1, 2, 3 focuses on men’s and women’s retrospective attitudes towards their birth experience. However, the effect of witnessing invasive procedures during childbirth on a man's attraction to his partner, while she is most vulnerable, and also an increased risk of post-traumatic stress disorder in fathers have not been widely investigated. There is a lack of sufficient research investigating whether women need to be asked for their consent before inviting their partners to closely watch medical procedures during childbirth. Future research is required to provide a basis for better awareness and involve the consumers to understanding the men’s and women’s experience and their expectations for labour and birth.

Keywords: birth, childbirth, father, labour, men, women

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Célia Barreto Carvalho, Carolina da Motta, José Pinto-Gouveia, Ermelindo Bernardo Peixoto

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Auditory hallucinations among the most invalidating and distressing experiences reported by patients diagnosed with schizophrenia, leading to feelings of powerlessness and helplessness towards their illness. In more severe cases, these auditory hallucinations can take the form of commanding voices, which are often related to high suicidality rates in these patients. Several authors propose that the meanings attributed to the hallucinatory experience, rather than characteristics like form and content, can be determinant in patients’ reactions to hallucinatory activity, particularly in the case of voice-hearing experiences. In this study, 48 patients diagnosed with paranoid schizophrenia presenting auditory hallucinations were studied. Multiple regression analyses were computed to study the influence of several developmental aspects, such as family and social dynamics, bullying, depression, and socio-cognitive variables on the auditory hallucinations, on patients’ attributions and relationships with their voices, and on the resulting invalidation of hallucinatory experience. Overall, results showed how relationships with voices can mirror several aspects of interpersonal relationship with others, and how self-schemas, depression and actual social relationships help shaping the voice-hearing experience. Early experiences of victimization and submission help predict the attributions of omnipotence of the voices, and increased hostility from parents seems to increase the malevolence of the voices, suggesting that socio-cognitive factors can significantly contribute to the etiology and maintenance of auditory hallucinations. The understanding of the characteristics of auditory hallucinations and the relationships patients established with their voices can allow the development of more promising therapeutic interventions that can be more effective in decreasing invalidation caused by this devastating mental illness.

Keywords: auditory hallucination, beliefs, life events, schizophrenia

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 358

Authors: Sonia Alberti

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The Regional Conference to Restructure Psychiatric Care in Latin America, convened by the Pan American Health Organization (PAHO) in 1990, oriented the Brazilian Federal Act in 2001 that stipulated the psychiatric reform which requires deinstitutionalization and community-based treatment. Since then, the 15 years’ experience of different working teams in mental health led an academic working group – supervisors from personal practices, professors and researchers – to discuss certain clinical issues, as well as supervisions, and to organize colloquia in different cities as a methodology. These colloquia count on the participation of different working teams from the cities in which they are held, with team members with different levels of educational degrees and prior experiences, in order to increase dialogue right where it does not always appear to be possible. The principal aim of these colloquia is to gain interlocution between practitioners and academics. Working with the theory of case constructions, this methodology revealed itself helpful in unfolding new solutions. The paper also observes that there is not always harmony between what the psychiatric reform demands and clinical ethics.

Keywords: mental health, supervision, clinical cases, Brazilian experience

Procedia PDF Downloads 252

Authors: Budi Annisa Sidi

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Present-day Indonesia maintains a narrative of a culturally plural but unified nation. At the same time, multicultural policies extend different degrees of recognition, accommodation, toleration and even discrimination towards different socio-cultural groups. In conjunction with different ethnographic landscapes across regions in Indonesia, this approach leads to a varied experience and understanding of national identity and multiculturalism among people. As a result, governments seeking to maintain national unity while practicing multiculturalism have to juggle different expectations. This situation is examined through the microcosms of university students using questionnaires followed up by focus group discussions and personal interviews. A comparison between university students across four different provinces in Indonesia (Aceh, Jakarta, West Java and the Moluccas) highlights the influence of one’s surroundings on their perception of multiculturalism. Students in the more heterogeneous areas generally show more acceptance towards diversity compared to students in primarily homogenous areas who have little actual experience in dealing with diversity. Regardless of their environment, students claim to have positive feelings and a strong sense of attachment to Indonesia but hold different ideas of what constitutes an ideal Indonesian national identity.

Keywords: Indonesia, multiculturalism, national identity, nationalism

Procedia PDF Downloads 215

Authors: Fatemeh Jannesarvatan, Ghazaal Parastooei, Jimmy frerejan, Saedeh Mokhtari, Peter Van Rosmalen

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Medical and dental education increasingly emphasizes the acquisition, integration, and coordination of complex knowledge, skills, and attitudes that can be applied in practical situations. Instructional design approaches have focused on using real-life tasks in order to facilitate complex learning in both real and simulated environments. The Four component instructional design (4C/ID) model has become a useful guideline for designing instructional materials that improve learning transfer, especially in health profession education. The objective of this study was to apply the 4C/ID model in the creation of virtual patients (VPs) that dental students can use to practice their clinical management and clinical reasoning skills. The study first explored the context and concept of complication factors and common errors for novices and how they can affect the design of a virtual patient program. The study then selected key dental information and considered the content needs of dental students. The design of virtual patients was based on the 4C/ID model's fundamental principles, which included: Designing learning tasks that reflect real patient scenarios and applying different levels of task complexity to challenge students to apply their knowledge and skills in different contexts. Creating varied learning materials that support students during the VP program and are closely integrated with the learning tasks and students' curricula. Cognitive feedback was provided at different levels of the program. Providing procedural information where students followed a step-by-step process from history taking to writing a comprehensive treatment plan. Four virtual patients were designed using the 4C/ID model's principles, and an experimental design was used to test the effectiveness of the principles in achieving the intended educational outcomes. The 4C/ID model provides an effective framework for designing engaging and successful virtual patients that support the transfer of knowledge and skills for dental students. However, there are some challenges and pitfalls that instructional designers should take into account when developing these educational tools.

Keywords: 4C/ID model, virtual patients, education, dental, instructional design

Procedia PDF Downloads 63

Authors: Muhammed Kök & Adem Kantar

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Learning a foreign language can be affected by various factors such as age, aptitude, motivation, L2 disposition, etc. Among these factors, masculine gender roles stress (MGRS) that male learners possess is the least touched area that has been examined so far.MGRS can be defined as the traditional male role stress when the male learners feel the masculinity threat against their traditionally adopted masculinity norms. Traditional masculine norms include toughness, accuracy, completeness, and faultlessness. From this perspective, these norms are diametrically opposed to the language learning process since learning a language, by its nature, involves stages such as making mistakes and errors, not recalling words, pronouncing sounds incorrectly, creating wrong sentences, etc. Considering the potential impact of MGRS on the language learning process, the main purpose of this study is to investigate the mediating role of MGRS on the relationship between the EFL learners’ self-disclosure and English class anxiety. Data were collected from Turkish EFL learners (N=282) who study different majors in various state universities across Turkey. Data were analyzed by means of the Bootstraping method using the SPSS Process Macro plugin. The findings show that the indirect effect of self-disclosure level on the English Class Anxiety via MGRS was significant. We conclude that one of the reasons why Turkish EFL learners have English class anxiety might be the pressure that they feel because of their traditional gender role stress.

Keywords: masculine, gender role stress, english class anxiety, self-disclosure, masculinity norms

Procedia PDF Downloads 84

Authors: Elizabeth Grier, Meg Gemmill, Mary Martin, Leora Reiter, Herman Tang, Alexandra Donaldson, Isis Lunsky, Mia Wu

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Background: Individuals with Cerebral Palsy (CP) and/or IDD face numerous physical and mental health challenges, including difficulty accessing effective palliative care. The aim of this study is to assess the knowledge and comfort of healthcare providers in providing community-based palliative care for patients with Cerebral Palsy (CP) and severe to profound Intellectual and Developmental Disabilities (IDD). Methods: This study includes a mixed methods approach obtaining both quantitative and qualitative data. Quantitative data from palliative care practitioners was obtained through an online survey assessing comfort in symptom management, grief assessment, and goals of care discussion. This survey was distributed to physicians and allied health practitioners across Canada through the College of Family Physicians of Canada Member Interest Groups for Palliative Care and for IDD. Survey results guided the development of a semi-structured interview template, which was used to conduct a focus group on the same topic. Participants were four palliative care providers (3 physicians and one spiritual care practitioner). The focus group transcript is currently undergoing thematic analysis using NVivo 12 software. Results: 57 palliative care practitioners completed the survey. 87% of participants indicated they have provided palliative care services for persons with CP and/or IDD. Findings suggest practitioners are somewhat confident in identifying specific physical symptoms (dyspnea, pressure ulcers) but less confident in identifying physical/emotional pain, addressing grief, and prognosticating life expectancy in this population. 54% of responses indicated they had little/no training on palliating those with CP or IDD, and 45% somewhat or strongly disagree members of their profession can manage symptoms for this population. Focus group analysis is underway, and results will be available at the time of the poster presentation. Conclusion: Persons with CP and IDD are more likely to experience severe health inequities when accessing palliative care. Results of this study suggest further education is needed for palliative care professionals to address the barriers and challenges in providing palliative care to this patient population.

Keywords: palliative care, symptom management, health equity, community healthcare, intellectual and developmental disabilities

Procedia PDF Downloads 115

Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

Procedia PDF Downloads 169

Authors: Mutiara Amalina Khairisa, Purnama Arafah, Rahayu Listiana Ramli

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In recent years, so many cases of divorce increasingly occur. Betrayal in form of infidelity, less communication one another, economically problems, selfishness of two sides, intervening parents from both sides which frequently occurs in Asia, especially in Indonesia, the differences of both principles and beliefs, “Sense of Romantism” depletion, role confict, a large difference in the purpose of marriage,and sex satisfaction are expected as the primary factors of the causes of divorce. Every couple of marriage wants to reach happy life in their family but severe problems brought about by either of those main factors come as a reasonable cause of failure marriage. The purpose of this study is to find out how marital adjustment and supporting factors in ensuring the success of that previous marital adjusment are inseparable two things assumed as a framework can affect the success in marriage becoming a resolution to reduce the desires to divorce. Those two inseparable things are able to become an aspect of learning from the success of the different ethnics marriage to keep holding on wholeness.

Keywords: marital adjustment, marital sustainability, learning process, successful ethnicity differences marriage, basical cultural values

Procedia PDF Downloads 414

Authors: Abdullah Nurus Salam Khan, Farhana Karim, Mohiuddin Ahsanul Kabir Chowdhury, S. Masum Billah, Nabila Zaka, Alexander Manu, Shams El Arifeen

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Globally, pre-eclampsia (PE) and ante-partum haemorrhage (APH) are two major causes of maternal mortality. Prompt identification and management of these conditions depend on competency of the birth attendants. Since these conditions are infrequent to be observed, clinical vignette based assessment could identify the extent of health worker’s competence in managing emergency obstetric care (EmOC). During June-August 2016, competence of 39 medical officers (MO) and 95 nurses working in obstetric ward of 15 government health facilities (3 district hospital, 12 sub-district hospital) was measured using clinical vignettes on PE and APH. The vignettes resulted in three outcome measures: total vignette scores, scores for diagnosis component, and scores for management component. T-test was conducted to compare mean vignette scores and linear regression was conducted to measure the strength and association of vignette scores with different cadres of health workers, facility’s readiness for EmOC and average annual utilization of normal deliveries after adjusting for type of health facility, health workers’ work experience, training status on managing maternal complication. For each of the seven component of EmOC items (administration of injectable antibiotics, oxytocic and anticonvulsant; manual removal of retained placenta, retained products of conception; blood transfusion and caesarean delivery), if any was practised in the facility within last 6 months, a point was added and cumulative EmOC readiness score (range: 0-7) was generated for each facility. The yearly utilization of delivery cases were identified by taking the average of all normal deliveries conducted during three years (2013-2015) preceding the survey. About 31% of MO and all nurses were female. Mean ( ± sd) age of the nurses were higher than the MO (40.0 ± 6.9 vs. 32.2 ± 6.1 years) and also longer mean( ± sd) working experience (8.9 ± 7.9 vs. 1.9 ± 3.9 years). About 80% health workers received any training on managing maternal complication, however, only 7% received any refresher’s training within last 12 months. The overall vignette score was 8.8 (range: 0-19), which was significantly higher among MO than nurses (10.7 vs. 8.1, p < 0.001) and the score was not associated with health facility types, training status and years of experience of the providers. Vignette score for management component (range: 0-9) increased with higher annual average number of deliveries in their respective working facility (adjusted β-coefficient 0.16, CI 0.03-0.28, p=0.01) and increased with each unit increase in EmOC readiness score (adjusted β-coefficient 0.44, CI 0.04-0.8, p=0.03). The diagnosis component of vignette score was not associated with any of the factors except it was higher among the MO than the nurses (adjusted β-coefficient 1.2, CI 0.13-2.18, p=0.03). Lack of competence in diagnosing and managing obstetric complication by the nurses than the MO is of concern especially when majority of normal deliveries are conducted by the nurses. Better EmOC preparedness of the facility and higher utilization of normal deliveries resulted in higher vignette score for the management component; implying the impact of experiential learning through higher case management. Focus should be given on improving the facility readiness for EmOC and providing the health workers periodic refresher’s training to make them more competent in managing obstetric cases.

Keywords: Bangladesh, emergency obstetric care, clinical vignette, competence of health workers

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Authors: Naima Mohammadi

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The present paper critically reports on the emergency of Iranian international students in a large public university in Italy. Although the most sizeable diaspora of Iranians dates back to the 1979 revolution, a huge wave of Iranian female students travelled abroad after the Iranian Green Movement (2009) due to the intensification of gender discrimination and Islamization. To explore the experience of Iranian female students at an Italian public university, two complementary methods were adopted: a focus group and individual interviews. Focus groups yield detailed collective conversations and provide researchers with an opportunity to observe the interaction between participants, rather than between participant and researcher, which generates data. Semi-structured interviews allow participants to share their stories in their own words and speak about personal experiences and opinions. Research participants were invited to participate through a public call in a Telegram group of Iranian students. Theoretical and purposive sampling was applied to select participants. All participants were assured that full anonymity would be ensured and they consented to take part in the research. A two-hour focus group was held in English with participants in the presence and some online. They were asked to share their motivations for studying in Italy and talk about their experiences both within and outside the university context. Each of these interviews lasted from 45 to 60 minutes and was mostly carried out online and in Farsi. The focus group consisted of 8 Iranian female post-graduate students. In analyzing the data a blended approach was adopted, with a combination of deductive and inductive coding. According to research findings, although 9/11 was the beginning of the West’s challenges against Muslims, the nuclear threats of Islamic regimes promoted the toughest international sanctions against Iranians as a nation across the world. Accordingly, carrying an Iranian identity contributes to social, political, and economic exclusion. Research findings show that geopolitical factors such as international sanctions and Islamophobia, and a lack of reciprocity in terms of recognition, have created a sense of stigmatization for veiled and unveiled Iranian female students who are the largest groups of ‘non-European Muslim international students’ enrolled in Italian universities. Participants addressed how their nationality has devalued their public image and negatively impacted their self-confidence and self-realization in academia. They highlighted the experience of an unwelcoming atmosphere by different groups of people and institutes, such as receiving marked students’ badges, rejected bank account requests, failed visa processes, secondary security screening selection, and hyper-visibility of veiled students. This study corroborates the need for institutions to pay attention to geopolitical factors and religious diversity in student recruitment and provide support mechanisms and access to basic rights. Accordingly, it is suggested that Higher Education Institutions (HEIs) have a social and moral responsibility towards the discrimination and both social and academic exclusion of Iranian students.

Keywords: Iranian diaspora, female students, recognition theory, inclusive university

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Siphiwe P. Mandina, Tinashe Shamuyashe

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Tourism has become a topical issue amongst academics and practitioners due to its potential to contribute significantly towards an economy’s GDP. The problem underpinning this research is the fact that the major attraction, Victoria Falls, is being marketed in neighboring countries like South Africa, Botswana and Zambia with tour operators providing just day trips to the Victoria Falls. This has deprived Zimbabwe of income from tourism with tourists making day trips and actually not spending nights in Zimbabwe. This therefore calls for cutting edge marketing strategies that are superior to or inimitable by competing nations such as South Africa and Zambia. This study proposes a shift towards an experience management paradigm in the tourism sector. A qualitative research was adopted for this study, and findings of this study were generalized across different tourism contexts, therefore making the survey based research design more appropriate. The target population for this study is tourists visiting Zimbabwe over the period 2016 and ZTA visitor database acquired from the Department of Immigration will form the sampling frame for the purposes of this study.

Keywords: tourist experiences, Zimbabwe, tourist arrivals, competitiveness

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Authors: Saugata Bose, Ritambhra Korpal

Abstract:

The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.

Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

Abstract:

A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Bita Mashayekhi, Marjan Fayyazi, Parisa Sefati

Abstract:

Audit quality is a popular topic in accounting and auditing research because recent decades’ financial crises reduce the reliability of financial reports to public investors and cause significant doubt about the audit profession. Therefore, doing research to identify effective factors in improving audit quality is necessary for bringing back public investors’ trust to financial statements as well as audit reports. In this study, we explore the relationship between audit rotation and audit quality. For this purpose, we employ the Duff (2009) model of audit quality to measure audit quality and use a questionnaire survey of 27 audit service quality attributes. Our results show that there is a negative relationship between auditor’s rotation and audit quality as we consider the auditor’s reputation, capability, assurance, experience, and responsiveness as surrogates for audit quality. There is no evidence for verifying a same relationship when we use the auditor’s independence and expertise for measuring audit quality.

Keywords: audit quality, auditor’s rotation, reputation, capability, assurance, experience, responsiveness, independence, expertise

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Authors: Valentina Zhang

Abstract:

While facial expression is a complex and individualized behavior, all facial emotion recognition (FER) systems known to us rely on a single facial representation and are trained on universal data. We conjecture that: (i) different facial representations can provide different, sometimes complementing views of emotions; (ii) when employed collectively in a discussion group setting, they enable more accurate emotion reading which is highly desirable in autism care and other applications context sensitive to errors. In this paper, we first study FER using pixel-based DL vs semantics-based DL in the context of deepfake videos. Our experiment indicates that while the semantics-trained model performs better with articulated facial feature changes, the pixel-trained model outperforms on subtle or rare facial expressions. Armed with these findings, we have constructed an adaptive FER system learning from both types of models for dyadic or small interacting groups and further leveraging the synthesized group emotions as the ground truth for individualized FER training. Using a collection of group conversation videos, we demonstrate that FER accuracy and personalization can benefit from such an approach.

Keywords: neurodivergence care, facial emotion recognition, deep learning, ground truth for supervised learning

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Authors: Md. Abdul Karim, Syed Imran Ahmed, Pandora T. Hardtman

Abstract:

Informed consent is one of the basic human and ethical rights for childbearing women. It plays a central role in promoting informed decision making between patients and service providers during the labor process. It gives mothers rights to accept or reject any examination and/or procedure, increases the respect and dignity of the mother during pregnancy, delivery and postpartum care. To assess the practices of this right during normal vaginal delivery and immediate postpartum care in tertiary level hospital setting in Bangladesh, a quantitative study with cross-sectional design was conducted in Dhaka Medical College & Hospital (DMCH) and Sir Salimullah Medical College & Mitford Hospital (SSMCH) in Dhaka in November 2015. A prevalence-based sample size of 190 was calculated where prevalence, confidence interval and level of significance were at 9.7%, 98% and 5% respectively. The respondents were the mothers who gave normal vaginal childbirth within past 24 hours and received postpartum care there. They were selected through systematic random sampling technique and their face-to-face interview of 190 mothers was done using a structured questionnaire. Data were entered into the spreadsheet (MS Excel 2013 version) and descriptive analysis of findings was done. The result shows the complete absence of informed consent practices and mostly absence of consented care such as right to information, respect for choices of preferences for examination and/or procedure of childbearing women. Although 95% of the mothers were informed that they were being proceeded with normal vaginal delivery, their choice of preference was absent during the process. Only consent (not informed consent) was taken from 50%-72% mothers for examination (except breast examination ‘0%’) and 8%-83% for any procedures during postpartum care. Only one-ninth (11%) of the mothers could ask service providers regarding the services they received. No consent was taken from 3% of the mothers- neither in the labor process nor in postpartum care. This current practice doesn’t comply with the Respectful Maternity Care (RMC) Charter 2011. The issue is not even clarified in the current Standard Clinical Management Protocols of the country. So, improvement of the existing protocol and increased awareness are essential to address this right of child-bearing women and to practice it during normal vaginal delivery and postpartum care.

Keywords: informed consent, normal vaginal delivery, respectful maternity care, tertiary level hospital

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

Abstract:

Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Lim Lie Pin, Lin Hui Wen

Abstract:

Rural tourism has become popular in Taiwan, the villages based on agriculture including fishery have to follow the trend to develop the local economy and achieve the sustainable development of the rural areas. Through cultural festivals, the tourist could experience the benefit while promoting and stimulating the local development of rural tourism. Cigu is famous for salt history and abundant natural resources, such as lagoon, black-faced spoonbills and other fishery products. Digging clam has become the most special parent-child activities in Tainan and increasing awareness since it was initiated. Therefore, festival organizers and regional destination marketers need to identify visitors’ experiences attributes which lead to opportunities for industry professionals, community involvement to plan and organize regional festivals and their programmes for effective destination branding finding out more potential rural resources encouraging the local industry growth and sustainable development.

Keywords: rural tourism, cultural festival, destination branding, tourist experience, sustainable development

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Authors: Ntite Orji Kalu, Martina Kurowski

Abstract:

Until recently, the Czech Republic had an educational system dominated by indigenous people, who accounted for 95% of the school population. With the increasing influx of migrants and foreign students, especially from outside European Union, came a great disparity among the quality of learners and their learning needs and consideration for the challenges associated with being a minority and living within a foreign culture. This has prompted the research into ways of tailoring the educational system to meet the rising demand of learning styles and needs for the diverse learners in the Czech classrooms. Literature is reviewed regarding the various ways to accommodate the international students considering racial differences, focusing on theoretical approach and pedagogical principles. This study examines the compulsory educational system of the Czech Republic and the position and responsibility of the teacher in fostering a culturally sensitive and inclusive learning environment. Descriptive and content analysis is relied upon for this study. Recommendations are made for stakeholders to imbibe a more responsive environment that enhances the cultural and social integration of all learners.

Keywords: culturally responsive teaching, cultural competence, diversity, learners, inclusive education, Czech schools

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Authors: Albert Bou, Sebastian Dittert, Gianni de Fabritiis

Abstract:

Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To match the first requirement, the most popular RL libraries advocate for highly modular agent composability, which facilitates experimentation and development. To solve challenging environments within reasonable time frames, scaling RL to large sampling and computing resources has proved a successful strategy. However, this capability has been so far difficult to combine with modularity. In this work, we explore design choices to allow agent composability both at a local and distributed level of execution. We propose a versatile approach that allows the definition of RL agents at different scales through independent, reusable components. We demonstrate experimentally that our design choices allow us to reproduce classical benchmarks, explore multiple distributed architectures, and solve novel and complex environments while giving full control to the user in the agent definition and training scheme definition. We believe this work can provide useful insights to the next generation of RL libraries.

Keywords: deep reinforcement learning, Python, PyTorch, distributed training, modularity, library

Procedia PDF Downloads 60