Search results for: computer assisted learning
5105 Exploring Students’ Voices in Lecturers’ Teaching and Learning Developmental Trajectory
Authors: Khashane Stephen Malatji, Makwalete Johanna Malatji
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Student evaluation of teaching (SET) is the common way of assessing teaching quality at universities and tracing the professional growth of lecturers. The aim of this study was to investigate the role played by student evaluation in the teaching and learning agenda at one South African University. The researchers used a qualitative approach and a case study research design. With regards to data collection, document analysis was used. Evaluation reports were reviewed to monitor the growth of lecturers who were evaluated during the academic years 2020 and 2021 in one faculty. The results of the study reveal that student evaluation remains the most relevant tool to inform the teaching agenda at a university. Lecturers who were evaluated were found to grow academically. All lecturers evaluated during 2020 have shown great improvement when evaluated repeatedly during 2021. Therefore, it can be concluded that student evaluation helps to improve the pedagogical and professional proficiency of lecturers. The study therefore, recommends that lecturers conduct an evaluation for each module they teach every semester or annually in case of year modules. The study also recommends that lecturers attend to all areas that draw negative comments from students in order to improve.Keywords: students’ voices, teaching agenda, evaluation, feedback, responses
Procedia PDF Downloads 865104 AI for Efficient Geothermal Exploration and Utilization
Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson
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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal
Procedia PDF Downloads 395103 Deep Learning for Image Correction in Sparse-View Computed Tomography
Authors: Shubham Gogri, Lucia Florescu
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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net
Procedia PDF Downloads 1495102 Unfolding Simulations with the Use of Socratic Questioning Increases Critical Thinking in Nursing Students
Authors: Martha Hough RN
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Background: New nursing graduates lack the critical thinking skills required to provide safe nursing care. Critical thinking is essential in providing safe, competent, and skillful nursing interventions. Educational institutions must provide a curriculum that improves nursing students' critical thinking abilities. In addition, the recent pandemic resulted in nursing students who previously received in-person clinical but now most clinical has been converted to remote learning, increasing the use of simulations. Unfolding medium and high-fidelity simulations and Socratic questioning are used in many simulations debriefing sessions. Methodology: Google Scholar was researched with the keywords: critical thinking of nursing students with unfolding simulation, which resulted in 22,000 articles; three were used. A second search was implemented with critical thinking of nursing students Socratic questioning, which resulted in two articles being used. Conclusion: Unfolding simulations increase nursing students' critical thinking, especially during the briefing (pre-briefing and debriefing) phases, where most learning occurs. In addition, the use of Socratic questions during the briefing phases motivates other questions, helps the student analyze and critique their thinking, and assists educators in probing students' thinking, which further increases critical thinking.Keywords: briefing, critical thinking, Socratic thinking, unfolding simulations
Procedia PDF Downloads 1745101 An Experimental Machine Learning Analysis on Adaptive Thermal Comfort and Energy Management in Hospitals
Authors: Ibrahim Khan, Waqas Khalid
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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning
Procedia PDF Downloads 555100 Application of GeoGebra into Teaching and Learning of Linear and Quadratic Equations amongst Senior Secondary School Students in Fagge Local Government Area of Kano State, Nigeria
Authors: Musa Auwal Mamman, S. G. Isa
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This study was carried out in order to investigate the effectiveness of GeoGebra software in teaching and learning of linear and quadratic equations amongst senior secondary school students in Fagge Local Government Area, Kano State–Nigeria. Five research items were raised in objectives, research questions and hypotheses respectively. A random sampling method was used in selecting 398 students from a population of 2098 of SS2 students. The experimental group was taught using the GeoGebra software while the control group was taught using the conventional teaching method. The instrument used for the study was the mathematics performance test (MPT) which was administered at the beginning and at the end of the study. The results of the study revealed that students taught with GeoGebra software (experimental group) performed better than students taught with traditional teaching method. The t- test was used to analyze the data obtained from the study.Keywords: GeoGebra Software, mathematics performance, random sampling, mathematics teaching
Procedia PDF Downloads 2435099 Play in College: Shifting Perspectives and Creative Problem-Based Play
Authors: Agni Stylianou-Georgiou, Eliza Pitri
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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.Keywords: creative problem-based play, educational games, flexible thinking, tertiary education
Procedia PDF Downloads 2865098 Liver and Liver Lesion Segmentation From Abdominal CT Scans
Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid
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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithmKeywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm
Procedia PDF Downloads 4435097 A Question of Ethics and Faith
Authors: Madhavi-Priya Singh, Liam Lowe, Farouk Arnaout, Ludmilla Pillay, Giordan Perez, Luke Mischker, Steve Costa
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An Emergency Department consultant identified the failure of medical students to complete the task of clerking a patient in its entirety. As six medical students on our first clinical placement, we recognised our own failure and endeavoured to examine why this failure was consistent among all medical students that had been given this task, despite our best motivations as adult learner. Our aim is to understand and investigate the elements which impeded our ability to learn and perform as medical students in the clinical environment, with reference to the prescribed task. We also aim to generate a discussion around the delivery of medical education with potential solutions to these barriers. Six medical students gathered together to have a comprehensive reflective discussion to identify possible factors leading to the failure of the task. First, we thoroughly analysed the delivery of the instructions with reference to the literature to identify potential flaws. We then examined personal, social, ethical, and cultural factors which may have impacted our ability to complete the task in its entirety. Through collation of our shared experiences, with support from discussion in the field of medical education and ethics, we identified two major areas that impacted our ability to complete the set task. First, we experienced an ethical conflict where we believed the inconvenience and potential harm inflicted on patients did not justify the positive impact the patient interaction would have on our medical learning. Second, we identified a lack of confidence stemming from multiple factors, including the conflict between preclinical and clinical learning, perceptions of perfectionism in the culture of medicine, and the influence of upward social comparison. After discussions, we found that the various factors we identified exacerbated the fears and doubts we already had about our own abilities and that of the medical education system. This doubt led us to avoid completing certain aspects of the tasks that were prescribed and further reinforced our vulnerability and perceived incompetence. Exploration of philosophical theories identified the importance of the role of doubt in education. We propose the need for further discussion around incorporating both pedagogic and andragogic teaching styles in clinical medical education and the acceptance of doubt as a driver of our learning. Doubt will continue to permeate our thoughts and actions no matter what. The moral or psychological distress that arises from this is the key motivating factor for our avoidance of tasks. If we accept this doubt and education embraces this doubt, it will no longer linger in the shadows as a negative and restrictive emotion but fuel a brighter dialogue and positive learning experience, ultimately assisting us in achieving our full potential.Keywords: medical education, clinical education, andragogy, pedagogy
Procedia PDF Downloads 1225096 Ensemble Methods in Machine Learning: An Algorithmic Approach to Derive Distinctive Behaviors of Criminal Activity Applied to the Poaching Domain
Authors: Zachary Blanks, Solomon Sonya
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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection
Procedia PDF Downloads 2855095 Performance Enrichment of Deep Feed Forward Neural Network and Deep Belief Neural Networks for Fault Detection of Automobile Gearbox Using Vibration Signal
Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan
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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal
Procedia PDF Downloads 1055094 Integrating Wound Location Data with Deep Learning for Improved Wound Classification
Authors: Mouli Banga, Chaya Ravindra
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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.Keywords: wound classification, MobileNetV2, ResNet50, multimodel
Procedia PDF Downloads 175093 Feature Engineering Based Detection of Buffer Overflow Vulnerability in Source Code Using Deep Neural Networks
Authors: Mst Shapna Akter, Hossain Shahriar
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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.Keywords: cyber security, vulnerability detection, neural networks, feature extraction
Procedia PDF Downloads 805092 Bounded Rational Heterogeneous Agents in Artificial Stock Markets: Literature Review and Research Direction
Authors: Talal Alsulaiman, Khaldoun Khashanah
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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks
Procedia PDF Downloads 3485091 Education for Sustainability Using PBL on an Engineering Course at the National University of Colombia
Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez
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This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.Keywords: sustainability, project-based learning, engineering education, higher education for sustainability
Procedia PDF Downloads 3475090 The Art and Science of Trauma-Informed Psychotherapy: Guidelines for Inter-Disciplinary Clinicians
Authors: Daphne Alroy-Thiberge
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Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care
Procedia PDF Downloads 955089 Improving the Students’ Writing Skill by Using Brainstorming Technique
Authors: M. Z. Abdul Rofiq Badril Rizal
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This research is aimed to know the improvement of students’ English writing skill by using brainstorming technique. The technique used in writing is able to help the students’ difficulties in generating ideas and to lead the students to arrange the ideas well as well as to focus on the topic developed in writing. The research method used is classroom action research. The data sources of the research are an English teacher who acts as an observer and the students of class X.MIA5 consist of 35 students. The test result and observation are collected as the data in this research. Based on the research result in cycle one, the percentage of students who reach minimum accomplishment criteria (MAC) is 76.31%. It shows that the cycle must be continued to cycle two because the aim of the research has not accomplished, all of the students’ scores have not reached MAC yet. After continuing the research to cycle two and the weaknesses are improved, the process of teaching and learning runs better. At the test which is conducted in the end of learning process in cycle two, all of the students reach the minimum score and above 76 based on the minimum accomplishment criteria. It means the research has been successful and the percentage of students who reach minimum accomplishment criteria is 100%. Therefore, the writer concludes that brainstorming technique is able to improve the students’ English writing skill at the tenth grade of SMAN 2 Jember.Keywords: brainstorming technique, improving, writing skill, knowledge and innovation engineering
Procedia PDF Downloads 3605088 Autism Spectrum Disorder Classification Algorithm Using Multimodal Data Based on Graph Convolutional Network
Authors: Yuntao Liu, Lei Wang, Haoran Xia
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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability
Procedia PDF Downloads 565087 A Shared Space: A Pioneering Approach to Interprofessional Education in New Zealand
Authors: Maria L. Ulloa, Ruth M. Crawford, Stephanie Kelly, Joey Domdom
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In recent decades health and social service delivery have become more collaborative and interdisciplinary. Emerging trends suggest the need for an integrative and interprofessional approach to meet the challenges faced by professionals navigating the complexities of health and social service practice environments. Terms such as multidisciplinary practice, interprofessional collaboration, interprofessional education and transprofessional practice have become the common language used across a range of social services and health providers in western democratic systems. In Aotearoa New Zealand, one example of an interprofessional collaborative approach to curriculum design and delivery in health and social service is the development of an innovative Masters of Professional Practice programme. This qualification is the result of a strategic partnership between two tertiary institutions – Whitireia New Zealand (NZ) and the Wellington Institute of Technology (Weltec) in Wellington. The Master of Professional Practice programme was designed and delivered from the perspective of a collaborative, interprofessional and relational approach. Teachers and students in the programme come from a diverse range of cultural, professional and personal backgrounds and are engaged in courses using a blended learning approach that incorporates the values and pedagogies of interprofessional education. Students are actively engaged in professional practice while undertaking the programme. This presentation describes the themes of exploratory qualitative formative observations of engagement in class and online, student assessments, student research projects, as well as qualitative interviews with the programme teaching staff. These formative findings reveal the development of critical practice skills around the common themes of the programme: research and evidence based practice, education, leadership, working with diversity and advancing critical reflection of professional identities and interprofessional practice. This presentation will provide evidence of enhanced learning experiences in higher education and learning in multi-disciplinary contexts.Keywords: diversity, exploratory research, interprofessional education, professional identity
Procedia PDF Downloads 2935086 Successful Cesarean Delivery with Veno-Venous Extracorporeal Membrane Oxygenation Support in a Pregnant Woman with Severe Acute Respiratory Distress Syndrome and Heart Failure Complicated by a Rare Condition of Pre-B Cell Acute Lymphoblastic Leukemia in P
Authors: Kristel Dame Bañez Sumagaysay, Marie Victoria Cruz-javier
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The current subject is a case of a 21 year-old woman at 29 1/7 weeks of gestation with Pre-B cell Acute Lymphoblastic Leukemia who was admitted to the coronary care unit (CCU) of the St. Luke’s Medical Center-Global City for Severe Acute Respiratory Distress Syndrome (ARDS) secondary to hospital-acquired pneumonia secondary to pneumocystis jiroveci; central line-associated bloodstream infection (E. aerogenes). She presented with chronic hypoxemia caused by Pulmonary edema, probably secondary to heart failure secondary to cardiomyopathy chemotherapy-induced. Due to worsening feto-maternal status, extracorporeal membrane oxygenation (ECMO) for respiratory support was instituted, and an elective cesarean section was done due to multiple maternal factors and deteriorating health status under total intravenous anesthesia assisted by veno-venous extracorporeal membrane oxygenation. She delivered a live preterm newborn male, APGAR Score: 1, 0, 0, birth weight 985 grams, birth length: 40.5cm, small for gestational age.Keywords: extracorporeal membrane oxygenation, pre-b cell acute lymphoblastic leukemia, severe acute respiratory distress syndrome, ethical dilemmas
Procedia PDF Downloads 705085 Design and Implementation of Machine Learning Model for Short-Term Energy Forecasting in Smart Home Management System
Authors: R. Ramesh, K. K. Shivaraman
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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management
Procedia PDF Downloads 2975084 Learning Chinese Suprasegmentals for a Better Communicative Performance
Authors: Qi Wang
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Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)
Procedia PDF Downloads 4305083 Service Information Integration Platform as Decision Making Tools for the Service Industry Supply Chain-Indonesia Service Integration Project
Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra
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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.Keywords: service industry, customer service, machine learning, decision making, information platform
Procedia PDF Downloads 6135082 Prediction of Survival Rate after Gastrointestinal Surgery Based on The New Japanese Association for Acute Medicine (JAAM Score) With Neural Network Classification Method
Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi
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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)
Procedia PDF Downloads 2505081 The Role of Asset Recovery in Combatting Organized Crime
Authors: Tamas Bezsenyi, Noemi Katona
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Fighting Human Trafficking is a highly important issue worldwide that states need to deal with in international politics. In the EU combatting human trafficking is emphasized in international policy making and also in the work of international law enforcement, thus in the work of the EUROPOL. While the EU Directive against Human Trafficking prescribes how states should fight this transnational crime and also how victims should be assisted, the EUROPOL focuses on the effective cooperation between national law enforcement agencies. However, despite the aims of the common fight, human trafficking is regulated differently in the punitive law of various nation states. This deeply defines the work and possibilities of national law enforcement organizations. Among the manifold differences in this paper, we focus on the role of regulating asset recovery. We highlight that money, and the regulation and practice how the law enforcement deals with income gained from criminal activities, play essential role in combatting human trafficking. While doing research on the investigation of transnational human trafficking by the Hungarian Law Enforcement Agencies, we have found that the unfortunate regulation of asset recovery determines the lower effectiveness of eliminating criminal organizations. While i.e. in the Netherlands confiscation of property takes place in an early stage of the criminal procedure, in Hungary it can be conducted only if money laundering is also assumed. Our presentation builds on the comparison of criminal procedures which we analyse based on criminal files and interviews with coworkers of the National Bureau of Investigation.Keywords: human trafficking, law enforcement, asset recovery, organized crime
Procedia PDF Downloads 2795080 Iranian Students’ and Teachers’ Perceptions of Effective Foreign Language Teaching
Authors: Mehrnoush Tajnia, Simin Sadeghi-Saeb
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Students and teachers have different perceptions of effectiveness of instruction. Comparing students’ and teachers’ beliefs and finding the mismatches between them can increase L2 students’ satisfaction. Few studies have taken into account the beliefs of both students and teachers on different aspects of pedagogy and the effect of learners’ level of education and contexts on effective foreign language teacher practices. Therefore, the present study was conducted to compare students’ and teachers’ perceptions on effective foreign language teaching. A sample of 303 learners and 54 instructors from different private language institutes and universities participated in the study. A questionnaire was developed to elicit participants’ beliefs on effective foreign language teaching and learning. The analysis of the results revealed that: a) there is significant difference between the students’ beliefs about effective teacher practices and teachers’ belief, b) Class level influences students’ perception of effective foreign language teacher, d) There is a significant difference of opinion between those learners who study foreign languages at university and those who study foreign language in private institutes with respect to effective teacher practices. The present paper concludes that finding the gap between students’ and teachers’ beliefs would help both of the groups to enhance their learning and teaching.Keywords: effective teacher, effective teaching, students’ beliefs, teachers’ beliefs
Procedia PDF Downloads 3085079 Utilising Sociodrama as Classroom Intervention to Develop Sensory Integration in Adolescents who Present with Mild Impaired Learning
Authors: Talita Veldsman, Elzette Fritz
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Many children attending special education present with sensory integration difficulties that hamper their learning and behaviour. These learners can benefit from therapeutic interventions as part of their classroom curriculum that can address sensory development and allow for holistic development to take place. A research study was conducted by utilizing socio-drama as a therapeutic intervention in the classroom in order to develop sensory integration skills. The use of socio-drama as therapeutic intervention proved to be a successful multi-disciplinary approach where education and psychology could build a bridge of growth and integration. The paper describes how socio-drama was used in the classroom and how these sessions were designed. The research followed a qualitative approach and involved six Afrikaans-speaking children attending special secondary school in the age group 12-14 years. Data collection included observations during the session, reflective art journals, semi-structured interviews with the teacher and informal interviews with the adolescents. The analysis found improved self-confidence, better social relationships, sensory awareness and self-regulation in the participants after a period of a year.Keywords: education, sensory integration, sociodrama, classroom intervention, psychology
Procedia PDF Downloads 5725078 Data Refinement Enhances The Accuracy of Short-Term Traffic Latency Prediction
Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong
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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.Keywords: data refinement, machine learning, mutual information, short-term latency prediction
Procedia PDF Downloads 1655077 Systematic Process for Constructing an Augmented Reality Display Platform
Authors: Cheng Chieh Hsu, Alfred Chen, Yu-Pin Ma, Meng-Jie Lin, Fu Pai Chiu, Yi-Yan Sie
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In this study, it is attempted to construct an augmented reality display platform (ARDP), and its objectives are two facets, i.e. 1) providing a creative display mode for museums/historical heritages and 2) providing a benchmark for human-computer interaction professionals to build an augmented reality display platform. A general augmented reality theory has been explored in the very beginning and afterwards a systematic process model is proposed. There are three major core tasks to be done for the platform, i.e. 1) constructing the physical interactive table, 2) designing the media, and 3) designing the media carrier. In order to describe how the platform manipulates, the authors have introduced Tainan Confucius Temple, a cultural heritage in Taiwan, as a case study. As a result, a systematic process with thirteen steps has been developed and it aims at providing a rational method for constructing the platform.Keywords: human-computer interaction, media, media carrier, augmented reality display platform
Procedia PDF Downloads 4085076 Analysis and Detection of Facial Expressions in Autism Spectrum Disorder People Using Machine Learning
Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor
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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH
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