Search results for: students with learning disabilities

Authors: Zhuang Hou, Xiaolei Cao

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The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.

Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program

Procedia PDF Downloads 139

Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

Procedia PDF Downloads 81

Authors: Nishaben Dholakiya, Anirban Roy, Ranjan Dey

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The unprecedented rise in global temperatures has triggered complex changes in marine ecosystems, necessitating a deeper understanding of seawater's thermophysical properties by experimentally measuring ultrasonic velocity and density at varying temperatures and salinity. This study investigates the critical relationship between temperature variations and molecular-level interactions in Arabian Sea surface waters, specifically focusing on internal pressure (π) and cohesion energy density (CED) as key indicators of ecosystem disruption. Our experimental findings reveal that elevated temperatures significantly reduce internal pressure, weakening the intermolecular forces that maintain seawater's structural integrity. This reduction in π correlates directly with decreased habitat stability for marine organisms, particularly affecting pressure-sensitive species and their physiological processes. Similarly, the observed decline in cohesion energy density at higher temperatures indicates a fundamental shift in water molecule organization, impacting the dissolution and distribution of vital nutrients and gases. These molecular-level changes cascade through the ecosystem, affecting everything from planktonic organisms to complex food webs. By employing advanced machine learning techniques, including Stacked Ensemble Machine Learning (SEML) and AdaBoost (AB), we developed highly accurate predictive models (>99% accuracy) for these thermophysical parameters. The results provide crucial insights into the mechanistic relationship between climate warming and marine ecosystem degradation, offering valuable data for environmental policymaking and conservation strategies. The novelty of this research serves as no such thermodynamic investigation has been conducted before in literature, whereas this research establishes a quantitative framework for understanding how molecular-level changes in seawater properties directly influence marine ecosystem stability, emphasizing the urgent need for climate change mitigation efforts.

Keywords: thermophysical properties, Arabian Sea, internal pressure, cohesion energy density, machine learning

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Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

Procedia PDF Downloads 206

Authors: Tzipi Eshet

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The importance of environmental education as part of a national strategy to promote the environment is recognized around the world. Lecturers at colleges of teacher education have considerable responsibility, directly and indirectly, for the environmental literacy of students who will end up teaching in the school system. This study examined whether lecturers in colleges of teacher education and teacher training in Israel, are able and willing to develop among the students, environmental literacy. Capability and readiness is assessed by evaluating the level of environmental literacy dimensions that include knowledge on environmental issues, positions related to the environmental agenda and "green" patterns of behavior in everyday life. The survey included 230 lecturers from 22 state colleges coming from various sectors (secular, religious, and Arab), from different academic fields and different personal backgrounds. Firstly, the results show that the higher the commitment to environmental issues, the lower the satisfaction with the current situation. In general, the respondents show positive environmental attitudes in all categories examined, they feel that they can personally influence responsible environmental behavior of others and are able to internalize environmental education in schools and colleges; they also report positive environmental behavior. There are no significant differences between teachers of different background characteristics when it comes to behavior patterns that generate personal income funds (e.g. returning bottles for deposit). Women show a more responsible environmental behavior than men. Jewish lecturers, in most categories, show more responsible behavior than Druze and Arab lecturers; however, when referring to positions, Arabs and Druze have a better sense in their ability to influence the environmental agenda. The Knowledge test, which included 15 questions, was mostly based on basic environmental issues. The average score was adequate - 83.6. Science lecturers' environmental literacy is higher than the other lecturers significantly. The larger the environmental knowledge base is, they are more environmental in their attitudes, and they feel more responsible toward the environment. It can be concluded from the research findings, that knowledge is a fundamental basis for developing environmental literacy. Environmental knowledge has a positive effect on the development of environmental commitment that is reflected in attitudes and behavior. This conclusion is probably also true of the general public. Hence, there is a great importance to the expansion of knowledge among the general public and teacher educators in particular on environmental. From the open questions in the survey, it is evident that most of the lecturers are interested in the subject and understand the need to integrate environmental issues in the colleges, either directly by teaching courses on the environment or indirectly by integrating environmental issues in different professions as well as asking the students to set an example (such as, avoid unnecessary printing, keeping the environment clean). The curriculum at colleges should include a variety of options for the development and enhancement of environmental literacy of student teachers, but first there must be a focus on bringing their teachers to a high literacy level so they can meet the difficult and important task they face.

Keywords: colleges of teacher education, environmental literacy, environmental education, teacher's teachers

Procedia PDF Downloads 287

Authors: Nurah Saleh Alfares

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An aim of TS in EFL is to help learners to understand how they learn, which could help them in using the target language with other learners in language classrooms, and in their social life. The early researchers have criticised the system of teaching methods in EFL applied in Saudi schools, as they claim that it does not produce students who are highly proficient in English. Some of them suggested that enhancing learners’ TS would help to improve the learners’ proficiency of using the EFL. The textbook in Saudi schools is the central material for teachers to follow in the EFL classroom. Thus, this study is investigating the main issues that could promote TS in Saudi EFL: the textbook and the teachers. The purposes of the study are: to find out the extent to which the tasks in the textbook have the potential to support teachers in promoting TS; to discover insights into the nature of classroom activities that teachers use to encourage TS from the textbook and to explore the teachers’ views on the role of the textbook in promoting TS in the English language. These aims will improve understanding of the connection between the potential of the textbook content and the participants’ theoretical knowledge and their teaching practice. The investigation employed research techniques including the following: (1) analysis of the textbook; (2) questionnaire for EFL teachers; (3) observation for EFL classroom; (4) interviews with EFL teachers. Analysis of the third intermediate grade textbook has been undertaken, and six EFL teachers from five intermediate schools were involved in the study. Data analysis revealed that 36.71 % of the tasks in the textbook could have the potential to promote TS, and 63.29 % of the tasks in the textbook could not have the potential to promote TS. Therefore, the result of the textbook analysis showed that the majority of the tasks do not have the potential to help teachers to promote TS. Although not all teachers of the observed lessons displayed behaviour helpful to promote TS, teachers, who presented potential TS tasks in their lesson encouraged learners’ interaction and students’ engagement more than teachers who presented tasks that did not have the potential to promote TS. Therefore, the result of the teachers’ data showed that having a textbook that has the potential to promote TS is not enough to develop teaching TS in Saudi EFL since teachers’ behaviour could make the task more or less productive.

Keywords: English as a Foreign Language, metacognitive skills, textbook, thinking skills

Procedia PDF Downloads 130

Authors: Marcia Figueira

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Context: Positivist transformations in agriculture were responsible for top-down – often coercive – mechanisms of uniformed modernization that weathered local diversities and agency. New development pathways need to now shift according to comprehensive integrations of knowledge - scientific, indigenous, and local, and to be sustained on political interventions, bottom-up change, and social learning if climate goals are to be met – both in mitigation and adaptation. Objectives The objectives of this research are to understand how social-ecological and socio-technical systems characterisation can be nested to bridge scientific research/knowledge into a local context and knowledge system; and, with it, stem sustainable innovation. Methods To do so, we conducted a scoping review to explore theoretical and empirical works linked to Ostrom’s Social-Ecological Systems framework and Geels’ multi-level perspective of socio-technical systems transformations in the context of agriculture. Results As a result, we were able to identify key variables and connections to 1- understand the rules in use and the community attributes influencing resource management; and 2- how they are and have been shaped and shaping systems innovations. Conclusion Based on these results, we discuss how to leverage action research for mutual learning toward a replicable but highly place-based agriculture transformation frame.

Keywords: agriculture systems innovations, social-ecological systems, socio-technical systems, action research

Procedia PDF Downloads 97

Authors: Shan Zhang, Peter Suechting

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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.

Keywords: environmental economics, machine learning, recycling, international trade

Procedia PDF Downloads 175

Authors: Felicia Titilayo Olanrewaju

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Folktales are a subclass of folklores which are verbally told and passed down from one generation to another, from the elderly ones to their children, usually at moonlight. These tales are heavily laden with moral lessons of what should be done and what not within the society. Though these are oftentimes heavily embellished yet are related to guide, guard, train, and dishing out moral attributes and mores worthwhile for ethical progression of the young minds within our traditional settings. With the rapid advancement of technological know-how, the existence of most of these moral-inclined stories becomes questionable; hence this study appraised the influences of these traditional storytellings have in the upgrading of moral learning of ethical behavioral traits acceptable among the Yoruba people. Oral interviews couples with recording gadgets were used to collate both sample parents' and children’s responses within a particular community in Owo (ute) local government area of Owo Ondo State, Nigeria. Findings reveal that diverse tales told at moonlight periods have an untold impact on the speedy growth of the children intellectually than the modern happenings around them. These telltale stories become powerful aids in learning goodly traits and eschewing bad manners. It is recommended that folk stories be told within the household among the family after hard labour in the evenings as this would help develop human relationships and brings about a strong sense of community bindings.

Keywords: folktales, folklores, impact, advancement, ethical progression

Procedia PDF Downloads 182

Authors: Anusha Edirisinghe

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Sexual Gender based violence is a most common social phenomenon in higher education institutions. It has become a hidden crime of the Universities. Masculinities norms and attitudes are more influential and serve as key drivers and risk for ragging and SGBV. This research will reveal that in Sri Lankan universities, SGBV takes from the violence and murder of women students, assault and battery coerced sex, sexual harassment including harassment via information technology. This study focus is to prevention of ragging and SGBV in University system. Main objective of this paper describes and critically analyses of plight of ragging and SGBV in higher education institutions and legal and national level policy implementation to prevent these crimes in society. This paper is with special reference to ragging case from University of Kelaniya 2016. University Grant commission introduced an Act for the prevention of Ragging and gender standing committee established in Sri Lanka in 2016. And each university has been involved in the prevention of SGBV and ragging in higher education institutions. Case study from first year female student, reported sexual harassment was reported to the police station in May in 2016. After this case, the university has been implementing emergency action plan, short term and long term action plan. Ragging and SGBV task force was established and online complaint center opened to all students and academic and non- academics. Under these circumstances student complained to SGBV and other harassment to the university. University security system was strong support with police and marshals, and vigilant committees including lecturers. After this case all universities start to several programmes to stop violence in university

Keywords: higher Education, ragging, sexual gender-based violence, Sri Lanka

Procedia PDF Downloads 384

Authors: S. R. Santhanam

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A gifted child is one who gives evidence of creativity, good memory, rapid learning. In mathematics, a teacher often comes across some gifted children and they exhibit the following characteristics: unusual alertness, enjoying solving problems, getting bored on repetitions, self-taught, going beyond what teacher taught, ask probing questions, connecting unconnected concepts, vivid imagination, readiness for research work, perseverance of a topic. There are two main areas of research carried out on them: 1)identifying gifted children, 2) interacting and channelizing them. A lack of appropriate recognition will lead the gifted child demotivated. One of the main findings is if proper attention and nourishment are not given then it leads a gifted child to become depressed, underachieving, fail to reach their full potential and sometimes develop negative attitude towards school and study. After identifying them, a mathematics teacher has to develop them into a fall fledged achiever. The responsibility of the teacher is enormous. The teacher has to be resourceful and patient. But interacting with them one finds a lot of surprises and awesomeness. The elementary algebraic identities like (a+b)(a-b)=a²-b², expansion of like (a+b)²(a-b)² and others are taught to students, of age group 13-15 in India. An average child will be satisfied with a single proof and immediate application of these identities. But a gifted child expects more from the teacher and at one stage after a little training will surpass the teacher also. In this short paper, the author shares his experience regarding teaching algebraic identities to gifted children. The following problem was given to a set of 10 gifted children of the specified age group: If a natural number ‘n’ to expressed as the sum of the two squares, will 2n also be expressed as the sum of two squares? An investigation has been done on what multiples of n satisfying the criterion. The attempts of the gifted children were consolidated and conclusion was drawn. A second problem was given to them as: can two natural numbers be found such that the difference of their square is 3? After a successful solution, more situations were analysed. As a third question, the finding of the sign of an algebraic expression in three variables was analysed. As an example: if a,b,c are real and unequal what will be sign of a²+4b²+9c²-4ab-12bc-6ca? Apart from an expression as a perfect square what other methods can be employed to prove an algebraic expression as positive negative or non negative has been analysed. Expressions like 4x²+2y²+13y²-2xy-4yz-6zx were given, and the children were asked to find the sign of the expression for all real values of x,y and z. In all investigations, only basic algebraic identities were used. As a next probe, a divisibility problem was initiated. When a,b,c are natural numbers such that a+b+c is at least 6, and if a+b+c is divisible by 6 then will 6 divide a³+b³+c³. The gifted children solved it in two different ways.

Keywords: algebraic identities, gifted children, Indian scenario, research

Procedia PDF Downloads 187

Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour

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Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.

Keywords: physical activity, machine learning, under 5s, disability, accelerometer

Procedia PDF Downloads 216

Authors: Igor A. Bessmertny, Bidru C. Enkomaryam

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Active involving patients and communities in health decisions can improve both people’s health and the healthcare system. Adopting artificial intelligence can lead to more accurate and complete patient record management. This review aims to identify the current state of researches conducted using artificial intelligence techniques to improve patient engagement and wellbeing, medical domains used in patient engagement context, and lastly, to assess opportunities and challenges for patient engagement in the wellness process. A search of peer-reviewed publications, reviews, conceptual analyses, white papers, author’s manuscripts and theses was undertaken. English language literature published in 2013– 2022 period and publications, report and guidelines of World Health Organization (WHO) were also assessed. About 281 papers were retrieved. Duplicate papers in the databases were removed. After application of the inclusion and exclusion criteria, 41 papers were included to the analysis. Patient counseling in preventing adverse drug events, in doctor-patient risk communication, surgical, drug development, mental healthcare, hypertension & diabetes, metabolic syndrome and non-communicable chronic diseases are implementation areas in healthcare where patient engagement can be implemented using artificial intelligence, particularly machine learning and deep learning techniques and tools. The five groups of factors that potentially affecting patient engagement in safety are related to: patient, health conditions, health care professionals, tasks and health care setting. Active involvement of patients and families can help accelerate the implementation of healthcare safety initiatives. In sub-Saharan Africa, using digital technologies like artificial intelligence in patient engagement context is low due to poor level of technological development and deployment. The opportunities and challenges available to implement patient engagement strategies vary greatly from country to country and from region to region. Thus, further investigation will be focused on methods and tools using the potential of artificial intelligence to support more simplified care that might be improve communication with patients and train health care professionals.

Keywords: artificial intelligence, patient engagement, machine learning, patient involvement

Procedia PDF Downloads 83

Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

Procedia PDF Downloads 146

Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

Procedia PDF Downloads 137

Authors: Kyung-Sook Bang

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Background: The health status of school-age children has a great influence on their growth and life-long health. The purposes of this study were to identify physical and mental health status of late school-age children in Seoul, South Korea and to investigate the related factors for their health. Methods: After gaining the approval from Institutional Review Board (IRB), a cross-sectional study was conducted with elementary students in grade 4 or 5. Questionnaires were distributed to eight elementary schools located different regions of Seoul in November, 2016, and 302 participants were finally included. From all participants, informed consents from the parents, and assents from children were received. Children's socioeconomic status, family functioning, peer relations, physical health symptoms, and depression were measured with self-reported questionnaires. Data were analyzed with descriptive statistics, t-test, Pearson’s correlations, and multiple regression. Results: Children's physical health symptoms and depression were not significantly different, and only their peer relations were significantly different according to their socioeconomic status (t=-3.93, p<.001). Depression showed significant positive correlation with physical health symptoms (r=.720, p<.001) and negative correlations with family functioning (r=-.428, p<.001) and peer relations (r=-.775, p<.001). The multiple regression model, which explained 73.5% of variance, showed peer relations (r2 =.604), physical health symptoms (r2 change=.125), and family functioning (r2 change=.005) as significant predictors for depression. Only the peer relations was significant predictor for their physical health symptoms and explained 50.6% of it. Conclusions: The peer relations was the most important factor in their physical and mental health at this age, and it can be affected by their socioeconomic status. Nursing interventions for promoting social relations and family functioning are required to improve children’s physical and mental health, especially for vulnerable population.

Keywords: child, depression, health, peer relation

Procedia PDF Downloads 235

Authors: Alisha Sinha, Laxmi Kant Sharma

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Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters.

Keywords: wildfire susceptibility mapping, LST, random forest, GEE, MODIS, climatic parameters

Procedia PDF Downloads 27

Authors: Anamika Devi

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Based on Vygotsky’s cultural, historical theory and Hedegaard’s concept of transition, this study aims to investigate to propose strategies to maintain digital wellbeing of children and parents during and post COVID pandemic. Due COVID 19 pandemic, children and families have been facing new challenges and sudden changes in their everyday life. While children are juggling to adjust themselves in new circumstance of onsite and online learning settings, parents are juggling with their work-life balance. A number of papers have identified that the COVID-19 pandemic has affected the lives of many families around the world in many ways, for example, the stress level of many parents increased, families faced financial difficulties, uncertainty impacted on long term effects on their emotional and social wellbeing. After searching and doing an intensive literature review from 2020 and 2021, this study has found some scholarly articles provided solution or strategies of reducing stress levels of parents and children in this unprecedented time. However, most of them are not underpinned by proper theoretical lens to ensure they validity and success. Therefore, this study has proposed strategies that are underpinned by theoretical lens to ensure their impact on children’s and parents' emotional wellbeing during and post COVID-19 era. The strategies will highlight on activities for positive coping strategies to the best use of family values and digital technologies.

Keywords: onsite and online learning, strategies, emotional wellbeing, tips, and strategies, COVID19

Procedia PDF Downloads 177

Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Louis Placido F. Lachica

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Freshman year in college, being a transition from high school to college, requires students to adjust by equipping themselves with competencies that will make them survive in college. This study conducted at in a state university in the Philippines aimed to determine the quality of school life and linguistic intelligence of 214 randomly selected college freshmen. Frequency counts and percentages were used to analyze quality of school life and linguistic intelligence. The chi-square test was utilized to determine significant relationship between quality of school life and linguistic intelligence and selected demographic variables. Results on quality of school life revealed that availability of religious books and paperbacks at home were significantly related to relationship with teachers. None of the selected demographic characteristics were significantly related to sense of achievement. Parents’ highest educational attainment was significantly related with opportunity at school. The availability of general references and song hits were significantly and highly significantly related to sense of identity which means that these promoted their sense of identity since their peers also preferred its availability. Type of high school graduated from was significantly related with students’ self-esteem. Graduates of public high schools have higher boosted self-esteem than those from private high schools. Both type of high school graduated from and reading materials available at home (religious books) had a highly significant relationship with linguistic intelligence. In addition, there was a significant relationship between time spent in reading per day and linguistic intelligence. There was a highly significant relationship between quality of school life in terms of relationship with teachers and sense of achievement with linguistic intelligence. Further, sense of identity and linguistic intelligence were significantly related.

Keywords: quality of school life, linguistic intelligence, college freshmen, state university

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Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Akhentoolove Corbin, Leta De Jonge, Charmaine De Jonge

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Introduction: Various writers have promoted the adoption of the 4th R in the education curricula (relationships, respect, reasoning, religion, computing, science, art, conflict management, music) and the 5th R (responsibility). They argue that the traditional 3 Rs are not adequate for the modern environment and the requirements for students to become functional citizens in society. In particular, the developing countries of the anglophone Caribbean (most of which are tiny islands) are susceptible to the dangers and complexities of climate change and global economic volatility. These proposed additions to the 3Rs do have some justification, but this research considers Resilience as even more important and relevant in a world that is faced with the negative prospects of climate change, poverty, discrimination, and economic volatility. It is argued that the foundation for resilient citizens, workers, and workplaces, must be built in the elementary and secondary/middle schools and then through the tertiary level, to achieve an outcome of more resilient students. Government, business, and society require widespread resilience to be capable of ‘bouncing back’ and be more adaptable, transformational, and sustainable. Methodology: The paper utilises a mixed-methods approach incorporating a questionnaire and interviews to determine participants’ opinions on the importance and relevance of resilience in the schools’ curricula and to government, business, and society. The target groups are as follows: educators at all levels, education administrators, members of the business sector, public sector, and 3rd sector. The research specifically targets the anglophone Caribbean developing countries (Barbados, Guyana, Jamaica, Trinidad, St. Lucia, and St Vincent, and the Grenadines). The research utilises SPSS for data analysis. Major Findings: The preliminary findings suggest that the majority of participants support the adoption of resilience as a 4th R in the curricula of the elementary, secondary/middle schools, and tertiary level in the anglophone Caribbean. The final results will allow the researchers to reveal more specific details on any variations among the islands in the sample andto engage in an in-depth discussion of the relevance and importance of resilience as the 4th R. Conclusion: Results seem to suggest that the education system should adopt the 4th R of resilience so that educators working in collaboration with the family and community/village can develop young citizens who are more resilient and capable of manifesting the behaviours and attitudes associated with ‘bouncing back,’ adaptability, transformation, and sustainability. These findings may be useful for education decision-makers and governments in these Caribbean islands, who have the authority and responsibility for the development of education policy, laws, and regulations.

Keywords: education, resilient students, adaptable, transformational, resilient citizens, workplaces, government

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Authors: Qiuying Li

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It is a shared opinion that sustainable development requires a system discontinuity, meaning that radical changes in the way we produce and consume are needed. Within this framework there is an emerging understanding that an important contribution to this change can be directly linked to decisions taken in the design phase of products, services and systems. Design schools have therefore to be able to provide design students with a broad knowledge and effective Design for Sustainability tools, in order to enable a new generation of designers in playing an active role in reorienting our consumption and production patterns.

Keywords: design for sustainability, services, systems, materials, ecomaterials

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: Beatriz Mella Lira, Karla Yohannessen, Robin Hickman

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The aim of the paper is recognising inequalities through the self-assessment of health-related factors, in the context of daily mobilities in Santiago de Chile. Human capabilities will be used as the theoretical basis for the recognition and assessment of these factors regarding the functioning (what people are currently able to do) and capabilities (what people want to achieve and what is valuable for them), reflecting differences across social groups and among types of transport users. The self-assessment of health-related factors considers perceptions of stress, physical effort, proximity to other transport users, pollution, safety, and comfort. The types of transport users are classified as: private (cars, taxis, colectivos, motos), public (buses and metro) and active (bicycles and walking). The methodology follows a capability-based questionnaire, which was applied in different areas of Santiago de Chile, considering concepts extracted from the human capabilities list. The self-assessment of these health-related factors examines the context of peoples’ mobilities for performing their daily activities, considering socioeconomic differences as income, age, gender, disabilities, residence location and primary mode choice. The paper uses Relative Importance Index (RII) for weighting the relative influence or valuation of the factors. The respondents were asked to rate the importance of each factor on a scale from 1 to 5, in an ascending order of importance. The results suggest that these health-related factors impact not just the perceptions of users, but their well-being and their propensity for achieving their capabilities and the things they value in life. The paper is focused on the development of an applicable approach, measuring factors that should be included in transport project appraisal, as a more comprehensive and complementary method.

Keywords: active transport, health, human capabilities, Santiago de Chile, transport inequalities, transportation planning, urban planning

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Authors: Peter Hajnal

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The need to combine serious training in disciplinary/scholarly approaches to problems of general significance with an educational experience that engages students with these very same problems on a personal level is one of the key challenges facing modern liberal education in the West. The typical approach to synthesizing these two goals, one highly abstract, the other elusively practical, proceeds by invoking ideals traditionally associated with Enlightenment and 19th century “humanism”. These ideas are in turn rooted in an approach to reality codified by Cartesianism and the rise of modern science. Articulating this connection of the modern humanist tradition with Cartesianism allows for demonstrating how the central problem of modern liberal education is rooted in the strict separation of knowledge and personal experience inherent in the dualism of Descartes. The question about the shape of contemporary liberal education is, therefore, the same as asking whether an anti-Cartesian version of liberal education is possible at all. Although the formulation of a general answer to this question is a tall order (whether in abstract or practical terms), and might take different forms (nota bene in Eastern and Western contexts), a key inspiration may be provided by a certain shift of attitude towards the Cartesian conception of the relationship of knowledge and experience required by discussion based close-reading of works of visual art. Taking the work of Stanley Cavell as its central inspiration, my paper argues that this shift of attitude in question is best described as a form of “second naïveté”, and that it provides a useful model of conceptualizing in more concrete terms the appeal for such a “second naïveté” expressed in recent writings on the role of various disciplines in organizing learning by philosophers of such diverse backgrounds and interests as Hilary Putnam and Bruno Latour. The adoption of naïveté so identified as an educational ideal may be seen as a key instrument in thinking of the educational context as itself a medium of synthesis of the contemplative and the practical. Moreover, it is helpful in overcoming the bad dilemma of ideological vs. conservative approaches to liberal education, as well as in correcting a certain commonly held false view of the historical roots of liberal education in the Renaissance, which turns out to offer much more of a sui generis approach to practice rather than represent a mere precursor to the Cartesian conception.

Keywords: liberal arts, philosophy, education, Descartes, naivete

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Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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