Search results for: perceptual learning
3620 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 533619 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 2423618 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 2863617 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 1223616 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 2843615 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 1053614 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 153613 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 783612 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 3473611 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 3473610 Clustering and Modelling Electricity Conductors from 3D Point Clouds in Complex Real-World Environments
Authors: Rahul Paul, Peter Mctaggart, Luke Skinner
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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry
Procedia PDF Downloads 923609 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 953608 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 3603607 Online Early Childhood Monitoring and Evaluation of Systems in Underprivileged Communities: Tracking Growth and Progress in Young Children's Ability Levels
Authors: Lauren Kathryn Stretch
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A study was conducted in the underprivileged setting of Nelson Mandela Bay, South Africa in order to monitor the progress of learners whose teachers receive training through the Early Inspiration Training Programme. Through tracking children’s growth & development, the effectiveness of the practitioner-training programme, which focuses on empowering women from underprivileged communities in South Africa, was analyzed. The aim was to identify impact & reach and to assess the effectiveness of this intervention programme through identifying impact on children’s growth and development. A Pre- and Post-Test was administered on about 850 young children in Pre-Grade R and Grade R classes in order to understand children’s ability level & the growth that would be evident as a result of effective teacher training. A pre-test evaluated the level of each child’s abilities, including physical-motor development, language, and speech development, cognitive development including visual perceptual skills, social-emotional development & play development. This was followed by a random selection of the classes of children into experimental and control groups. The experimental group’s teachers (practitioners) received 8-months of training & intervention, as well as mentorship & support. After the 8-month training programme, children from the experimental & control groups underwent post-assessment. The results indicate that the impact of effective practitioner training and enhancing a deep understanding of stimulation on young children, that this understanding is implemented in the classroom, highlighting the areas of growth & development in the children whose teachers received additional training & support, as compared to those who did not receive additional training. Monitoring & Evaluation systems not only track children’s ability levels, but also have a core focus on reporting systems, mentorship and providing ongoing support. As a result of the study, an Online Application (for Apple or Android Devices) was developed which is used to track children’s growth via age-appropriate assessments. The data is then statistically analysed to provide direction for relevant & impactful intervention. The App also focuses on effective reporting strategies, structures, and implementation to support organizations working with young children & maximize on outcomes.Keywords: early childhood development, developmental child assessments, online application, monitoring and evaluating online
Procedia PDF Downloads 1893606 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 543605 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 2923604 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 2953603 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 4303602 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 6133601 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 2483600 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 3073599 User Experience in Relation to Eye Tracking Behaviour in VR Gallery
Authors: Veslava Osinska, Adam Szalach, Dominik Piotrowski
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Contemporary VR technologies allow users to explore virtual 3D spaces where they can work, socialize, learn, and play. User's interaction with GUI and the pictures displayed implicate perceptual and also cognitive processes which can be monitored due to neuroadaptive technologies. These modalities provide valuable information about the users' intentions, situational interpretations, and emotional states, to adapt an application or interface accordingly. Virtual galleries outfitted by specialized assets have been designed using the Unity engine BITSCOPE project in the frame of CHIST-ERA IV program. Users interaction with gallery objects implies the questions about his/her visual interests in art works and styles. Moreover, an attention, curiosity, and other emotional states are possible to be monitored and analyzed. Natural gaze behavior data and eye position were recorded by built-in eye-tracking module within HTC Vive headset gogle for VR. Eye gaze results are grouped due to various users’ behavior schemes and the appropriate perpetual-cognitive styles are recognized. Parallelly usability tests and surveys were adapted to identify the basic features of a user-centered interface for the virtual environments across most of the timeline of the project. A total of sixty participants were selected from the distinct faculties of University and secondary schools. Users’ primary knowledge about art and was evaluated during pretest and this way the level of art sensitivity was described. Data were collected during two months. Each participant gave written informed consent before participation. In data analysis reducing the high-dimensional data into a relatively low-dimensional subspace ta non linear algorithms were used such as multidimensional scaling and novel technique technique t-Stochastic Neighbor Embedding. This way it can classify digital art objects by multi modal time characteristics of eye tracking measures and reveal signatures describing selected artworks. Current research establishes the optimal place on aesthetic-utility scale because contemporary interfaces of most applications require to be designed in both functional and aesthetical ways. The study concerns also an analysis of visual experience for subsamples of visitors, differentiated, e.g., in terms of frequency of museum visits, cultural interests. Eye tracking data may also show how to better allocate artefacts and paintings or increase their visibility when possible.Keywords: eye tracking, VR, UX, visual art, virtual gallery, visual communication
Procedia PDF Downloads 343598 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 5713597 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 1653596 Automatic Adult Age Estimation Using Deep Learning of the ResNeXt Model Based on CT Reconstruction Images of the Costal Cartilage
Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng
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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning
Procedia PDF Downloads 663595 The Functions of Spatial Structure in Supporting Socialization in Urban Parks
Authors: Navid Nasrolah Mazandarani, Faezeh Mohammadi Tahrodi, Jr., Norshida Ujang, Richard Jan Pech
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Human evolution has designed us to be dependent on social and natural settings, but designed of our modern cities often ignore this fact. It is evident that high-rise buildings dominate most metropolitan city centers. As a result urban parks are very limited and in many cases are not socially responsive to our social needs in these urban ‘jungles’. This paper emphasizes the functions of urban morphology in supporting socialization in Lake Garden, one of the main urban parks in Kuala Lumpur, Malaysia. It discusses two relevant theories; first the concept of users’ experience coined by Kevin Lynch (1960) which states that way-finding is related to the process of forming mental maps of environmental surroundings. Second, the concept of social activity coined by Jan Gehl (1987) which holds that urban public spaces can be more attractive when they provide welcoming places in which people can walk around and spend time. Until recently, research on socio-spatial behavior mainly focused on social ties, place attachment and human well-being; with less focus on the spatial dimension of social behavior. This paper examines the socio-spatial behavior within the spatial structure of the urban park by exploring the relationship between way-finding and social activity. The urban structures defined by the paths and nodes were analyzed as the fundamental topological structure of space to understand their effects on the social engagement pattern. The study uses a photo questionnaire survey to inspect the spatial dimension in relation to the social activities within paths and nodes. To understand the legibility of the park, spatial cognition was evaluated using sketch maps produced by 30 participants who visited the park. The results of the sketch mapping indicated that a spatial image has a strong interrelation with socio-spatial behavior. Moreover, an integrated spatial structure of the park generated integrated use and social activity. It was found that people recognized and remembered the spaces where they engaged in social activities. They could experience the park more thoroughly, when they found their way continuously through an integrated park structure. Therefore, the benefits of both perceptual and social dimensions of planning and design happened simultaneously. The findings can assist urban planners and designers to redevelop urban parks by considering the social quality design that contributes to clear mental images of these places.Keywords: spatial structure, social activities, sketch map, urban park, way-finding
Procedia PDF Downloads 3023594 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
Procedia PDF Downloads 1643593 Using Deep Learning in Lyme Disease Diagnosis
Authors: Teja Koduru
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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash
Procedia PDF Downloads 2303592 The Role of Organizational Identity in Disaster Response, Recovery and Prevention: A Case Study of an Italian Multi-Utility Company
Authors: Shanshan Zhou, Massimo Battaglia
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Identity plays a critical role when an organization faces disasters. Individuals reflect on their working identities and identify themselves with the group and the organization, which facilitate collective sensemaking under crisis situations and enable coordinated actions to respond to and recover from disasters. In addition, an organization’s identity links it to its regional community, which fosters the mobilization of resources and contributes to rapid recovery. However, identity is also problematic for disaster prevention because of its persistence. An organization’s ego-defenses system prohibits the rethink of its identity and a rigid identity obstructs disaster prevention. This research aims to tackle the ‘problem’ of identity by study in-depth a case of an Italian multi–utility which experienced the 2012 Northern Italy earthquakes. Collecting data from 11 interviews with top managers and key players in the local community and archived materials, we find that the earthquakes triggered the rethink of the organization’s identity, which got reinforced afterward. This research highlighted the importance of identity in disaster response and recovery. More importantly, it explored the solution of overcoming the barrier of ego-defense that is to transform the organization into a learning organization which constantly rethinks its identity.Keywords: community identity, disaster, identity, organizational learning
Procedia PDF Downloads 7173591 Single Imputation for Audiograms
Authors: Sarah Beaver, Renee Bryce
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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.Keywords: machine learning, audiograms, data imputations, single imputations
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