Search results for: machine learning in finance

Authors: Belkacem Laimouche

Abstract:

With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

Procedia PDF Downloads 95

Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

Abstract:

The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

Procedia PDF Downloads 621

Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

Abstract:

Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

Procedia PDF Downloads 118

Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

Abstract:

In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

Procedia PDF Downloads 293

Authors: Anifowose Oluwafemi Dele, Ngah Rohana, Hasni Abdulahi

Abstract:

The economic crisis, which resulted in university funding cuts with an astronomically devastating impact on teaching and research around the world. Sequel to this, Nigerian universities are in disarray due to insufficient government funding and are under pressure to discover new financial streams of Internally Generated Revenue (IGR) to disentangle finance-related teething problems and most tangible means of outsourcing finance inclusively for the creation of more entrepreneurial ventures through the possibilities of prudent IGR management. To the best of our knowledge, one way to address this still-unknown or underappreciated cog is through the strategic use of IGR and the outsourcing of financing for the launch of entrepreneurial ventures. As a result, it is critical to investigate and evaluate financial inclusion through prudently managed IGR to achieve greater financial inclusion for more long-term entrepreneurial ventures. Justifying the need to look inward and devise mechanisms for strong instruments internal fund raising and managing cash inflows to benefit university entrepreneurial ventures to increase the University's IGR for the benefit of the university and its stakeholders. The paper concludes that University Managers must fully accept the use of genuine means of boosting IGR through financial inclusion of in-house funds to aggressively established IGR boosting and the creation of entrepreneurial ventures that could serve as an alternative to inadequate government funding.

Keywords: government funding, university managers, financial inclusion, entrepreneurial venture

Procedia PDF Downloads 65

Authors: Robin Lok Wang Ma

Abstract:

The purpose of this paper is to investigate how engineering students’ motivation and interests are maintained in their journeys. In recent years, different pedagogies of teaching, including entrepreneurship, experiential and lifelong learning, as well as dream builder, etc., have been widely used for education purposes. University advocates hands-on practice, learning by experiencing and experimenting throughout different courses. Students are not limited to gaining knowledge via traditional lectures, laboratory demonstrations, tutorials, and so on. The capability to identify both complex problems and their corresponding solutions in daily life are one of the criteria/skill sets required for graduates to obtain their careers at professional organizations and companies. A project-based course, namely Mechatronic Design and Prototyping, was developed for students to design and build a physical prototype for solving existing problems in their daily lives, thereby encouraging them as an entrepreneur to explore further possibilities to commercialize their designed prototypes and launch them to the market. Feedbacks from students show that they are keen to propose their own ideas freely with guidance from the instructor instead of using either suggested or assigned topics. Proposed ideas of the prototypes reflect that if students’ interests are maintained, they acquire the knowledge and skills they need, including essential communication, logical thinking, and, more importantly, problem solving for their lifelong learning journey.

Keywords: problem solving, lifelong learning, entrepreneurship, engineering

Procedia PDF Downloads 82

Authors: Leila Najeh Bel'Kiry

Abstract:

Artificial intelligence (AI) brought about many language robots, with ChatGPT being the most sophisticated thanks to its human-like linguistic capabilities. This aspect raises the idea of using ChatGPT in learning foreign languages. Starting from the premise that positions ChatGPT as a mediator between the language and the leaner, functioning as a “ghost teacher" offering a peaceful and secure learning space, this study aims to explore the attitudes of Tunisian students of English towards ChatGPT as a “Foreign Language Teacher” . Forty-five students, in their third year of fundamental English at Tunisian universities and high institutes, completed a Likert scale questionnaire consisting of thirty-two items and covering various aspects of language (phonology, morphology, syntax, semantics, and pragmatics). A scale ranging from 'Strongly Disagree,' 'Disagree,' 'Undecided,' 'Agree,' to 'Strongly Agree.' is used to assess the attitudes of the participants towards the integration of ChaGPTin learning a foreign language. Results indicate generally positive attitudes towards the reliance on ChatGPT in learning foreign languages, particularly some compounds of language like syntax, phonology, and morphology. However, learners show insecurity towards ChatGPT when it comes to pragmatics and semantics, where the artificial model may fail when dealing with deeper contextual and nuanced language levels.

Keywords: artificial language model, attitudes, foreign language learning, ChatGPT, linguistic capabilities, Tunisian English language learners

Procedia PDF Downloads 47

Authors: Tarandeep Kaur

Abstract:

Generation Y and Z nursing students have a much higher need for technology-based stimulation than previous generations, as they may find traditional methods of education boring and disinterested. These generations prefer experiential learning and the use of advanced technology for enhanced learning. Therefore, nursing educators must acquire knowledge to make better use of technology and technological tools for instruction. Millennials and generation are digital natives, optimistic, assertive, want engagement, instant feedback, and collaborative approach. The integration of technology and the efficacy of its use can be challenging for nursing educators. The SAMR (substitution, augmentation, modification, and redefinition) model designed and developed by Dr. Ruben Puentedura can help nursing educators to engage their students in different levels of technology integration for effective learning. Nursing educators should understand that technology use in the classroom must be purposeful. The influx of technology in nursing education is ever-changing; therefore, nursing educators have to constantly enhance and develop technical skills to keep up with the emerging technology in the schools as well as hospitals. In the Saskatchewan Collaborative Bachelor of Nursing (SCBSCN) program at Saskatchewan polytechnic, we use technology at various levels using the SAMR model in our program, including low and high-fidelity simulation labs. We are also exploring futuristic options of using virtual reality and gaming in our classrooms as an innovative way to motivate, increase critical thinking, create active learning, provide immediate feedback, improve student retention and create collaboration.

Keywords: generations, nursing, SAMR, technology

Procedia PDF Downloads 98

Authors: Sayed Hadi Sadeghi

Abstract:

This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.

Keywords: support services, e-Network practice, Australian universities, United States universities

Procedia PDF Downloads 154

Authors: Ihuarulam A. Ikenna

Abstract:

In the past few decades, the field of chemistry education has grown tremendously and researches indicated that after traditional chemistry instruction students often lacked deep conceptual understanding and failed to integrate their ideas into coherent conceptual framework. For several concepts in chemistry, students at all levels have demonstrated difficulty in changing their initial perceptions. Their perceptions are most often wrong and do not agree with correct scientific concepts. This study explored the effectiveness of intervention discussion sections for a college general chemistry course designed to apply research on students preconceptions, knowledge integration and student explanation. Three interventions discussions lasting three hours on bond energy and spontaneity were done tested and intervention (treatment) students’ performances were compared with that of control group which did not use the experimental pedagogy. Results indicated that this instruction which was capable of identifying students' misconceptions, initial conceptions and integrating those ideas into class discussion led to enhanced conceptual understanding and better achievement for the experimental group.

Keywords: remedying, students’ misconceptions, learning, intervention discussion, learning model

Procedia PDF Downloads 402

Authors: Nyong Princely Awazi, Gadinga Walter Forje, Barnabas Neba Nfornkah, Ndzifon Jude Kimengsi

Abstract:

Ecotourism is documented as a sustainable measure of bridging conservation goals and livelihood sustenance around protected areas, due to its ability of not just providing alternative livelihood, but also in providing the necessary resources that can help finance conservation initiatives. In Cameroon, all ecotourism activities around national parks are aimed at generating revenue through the conservation service while providing sustainable livelihood options to the local population. There exists an information lacuna regarding the contribution of ecotourism finances to conservation efforts in the country. This study was aimed at establishing the contribution of ecotourism finances to conservation initiatives in and around the Campo Ma’an National Park (CMNP). Data were collected through the administering of 120 structured questionnaires to ecotourism actors and 15 key/expert interviews with tourism and conservation actors in the Campo Ma’an landscape. Chi-square test, Spearman’s rank correlation and regressions were used for data analysis. The study revealed that the main sources of ecotourism financing to the park service are through entrance fees, cameras and vehicle fees paid by tourists as well as ecotourism project financing through NGOs. Calculations from the tourism register of the park showed that the park was able to raise as much as 1,576,000 FCFA (US$ 3,152) annually. It was further established that ecotourism revenue has not greatly supported conservation, with 54% of respondents perceiving ecotourism not contributing to biodiversity conservation. Chi Square test results highlighted poor ecotourism governance, low level of ecotourism development, corruption from park management staff, obsolete nature of the current finance law on the management of protected area revenue as key factors hindering ecotourism financing in conservation. For ecotourism financing to contribute to biodiversity conservation in the CMNP and in Cameroon’s protected areas, the government needs to revise the finance law on the management of revenue generated from protected areas, improve park governance to fight corruption and enhance transparency, invest in the development and marketing of the Campo Ma’an national park as a tourism destination in the country.

Keywords: Cameroon, Campo Ma’an National Park, conservation, ecotourism, ecotourism financing

Procedia PDF Downloads 100

Authors: Mamochana, A. Ramatea, Fumane, P. Khanare

Abstract:

Enabling the learning environment is a significant and adaptive technique necessary to navigate learners’ educational challenges. However, research has indicated that quality provision of education in the environments that are enabling, especially to learners with visual impairments (LVIs, hereafter) in rural schools, remain an ongoing challenge globally. Hence, LVIs often have a lower level of academic performance as compared to their peers. To balance this gap and fulfill learners'fundamentalhuman rights¬ of receiving an equal quality education, appropriate measures and structures that make enabling learning environment a better place to learn must be better understood. This paper, therefore, intends to find possible means that rural schools of Lesotho can employ to make the learning environment for LVIs enabling. The present study aims to determine suitable assets that can be drawn to make the learning environment for LVIs enabling. The study is also informed by the transformative paradigm and situated within a qualitative research approach. Data were generated through focus group discussions with twelve teachers who were purposefully selected from two rural primary schools in Lesotho. The generated data were then analyzed thematically using Braun and Clarke's six-phase framework. The findings of the study indicated that participating teachers do have an understanding that rural schools boast of assets (existing and hidden) that have a positive influence in responding to the special educational needs of LVIs. However, the participants also admitted that although their schools boast of assets, they still experience limited knowledge about the use of the existing assets and thus, realized a need for improved collaboration, involvement of the existing assets, and enhancement of academic resources to make LVIs’ learning environment enabling. The findings of this study highlight the significance of the effective use of assets. Additionally, coincides with literature that shows recognizing and tapping into the existing assets enable learning for LVIs. In conclusion, the participants in the current study indicated that for LVIs’ learning environment to be enabling, there has to be sufficient use of the existing assets. The researchers, therefore, recommend that the appropriate use of assets is good, but may not be sufficient if the existing assets are not adequately managed. Hence,VILs experience a vicious cycle of vulnerability. It was thus, recommended that adequate use of assets and teachers' engagement as active assets should always be considered to make the learning environment a better place for LVIs to learan in the future

Keywords: assets, enabling learning environment, rural schools, learners with visual impairments

Procedia PDF Downloads 89

Authors: Adnan Z. Mkhelif

Abstract:

Grammatical collocations (GCs) are word combinations containing a preposition or a grammatical structure, such as an infinitive (e.g. smile at, interested in, easy to learn, etc.). Such collocations tend to be difficult for Iraqi EFL university students (IUS) to master. To help address this problem, it is important to identify the factors causing it. This study aims at investigating the effects of L2 proficiency, frequency of GCs and their transparency on IUSs’ productive knowledge of GCs. The study involves 112 undergraduate participants with different proficiency levels, learning English in formal contexts in Iraq. The data collection instruments include (but not limited to) a productive knowledge test (designed by the researcher using the British National Corpus (BNC)), as well as the grammar part of the Oxford Placement Test (OPT). The study findings have shown that all the above-mentioned factors have significant effects on IUSs’ productive knowledge of GCs. In addition to establishing evidence of which factors of L2 learning might be relevant to learning GCs, it is hoped that the findings of the present study will contribute to more effective methods of teaching that can better address and help overcome the problems IUSs encounter in learning GCs. The study is thus hoped to have significant theoretical and pedagogical implications for researchers, syllabus designers as well as teachers of English as a foreign/second language.

Keywords: corpus linguistics, frequency, grammatical collocations, L2 vocabulary learning, productive knowledge, proficiency, transparency

Procedia PDF Downloads 236

Authors: Frederik Calsius, Mirela Popa, Alexia Briassouli

Abstract:

In the field of machine learning, it is common practice to use benchmark datasets to prove the working of a method. The domain of action recognition in videos often uses datasets like Kinet-ics, Something-Something, UCF-101 and HMDB-51 to report results. Considering the properties of the datasets, there are no datasets that focus solely on very short clips (2 to 3 seconds), and on highly-similar fine-grained actions within one specific domain. This paper researches how current state-of-the-art action recognition methods perform on a dataset that consists of highly similar, fine-grained actions. To do so, a dataset of skateboarding tricks was created. The performed analysis highlights both benefits and limitations of state-of-the-art methods, while proposing future research directions in the activity recognition domain. The conducted research shows that the best results are obtained by fusing RGB data with OpenPose data for the Temporal Shift Module.

Keywords: activity recognition, fused deep representations, fine-grained dataset, temporal modeling

Procedia PDF Downloads 215

Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

Abstract:

Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

Procedia PDF Downloads 76

Authors: Tigist Girma Kidane, Yalew Dessalegn Asfaw

Abstract:

Abstract: The current investigation aims to measure the longitudinal and transversal three-point bending tests of bamboo fibres polypropylene composites (BFPPCs) for the application of the automobile industry. Research has not been done on the properties of Ethiopian bamboo fibres for the utilization of composite development. The samples of bamboo plants have been harvested in 3–groups of age, 2–harvesting seasons, and 3–regions of bamboo species. Roll milling machine used for the extraction of bamboo fibres which has been developed by the authors. Chemical constituents measured using gravimetric methods. Unidirectional bamboo fibres prepreg has been produced using PP and hot press machine, then BFPPCs were produced using 6 layers of prepregs at automatic hot press machine. Age, harvesting month, and bamboo species have a statistically significant effect on the longitudinal and transverse flexural strength (FS), modulus of elasticity (MOE), and failure strain at α = 0.05 as evaluated by one-way ANOVA. 2–yrs old of BFPPCs have the highest FS and MOE, whereas November has the highest value of flexural properties. The highest to the lowest FS and MOE of BFPPCs has measured in Injibara, Mekaneselam, and Kombolcha, respectively. The transverse 3-point bending test has a lower FS and MOE compared to the longitudinal direction. The chemical constituents of Injibara, Mekaneselam, and Kombolcha have the highest to the lowest, respectively. 2-years old of bamboo fibres has the highest chemical constituent. The chemical constituents improved the flexural properties. Bamboo fibres in Ethiopia can be relevant for composite development, which has been applied in the area of requiring higher flexural properties.

Keywords: age, bamboo species, flexural properties, harvesting season, polypropylene

Procedia PDF Downloads 39

Authors: B. Chandra

Abstract:

In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

Procedia PDF Downloads 310

Authors: I. Tiahi

Abstract:

The aim of the current paper is to explore educational practices in contemporary Algeria. Researches explain such practices bear traditional approach and the overlooks modern teaching methods such as mobile learning. That is why the research output of examining student engagement in respect of mobile learning was obtained from the following objectives: (1) To evaluate the current practice of English language teaching within Algerian higher education institutions, (2) To explore how social constructivism theory and m-learning help students’ engagement in the classroom and (3) To explore the feasibility and acceptability of m-learning amongst institutional leaders. The methodology underpins a case study and action research. For the case study, the researcher engaged with 6 teachers, 4 institutional leaders, and 30 students subjected for semi-structured interviews and classroom observations to explore the current teaching methods for English as a foreign language. For the action research, the researcher applied an intervention course to investigate the possibility and implications for future implementation of mobile learning in higher education institutions. The results were deployed using thematic analysis. The research outcome showed that the disengagement of students in English language learning has many aspects. As seen from the interviews from the teachers, the researcher found that they do not have enough resources except for using ppt for some teacher. According to them, the teaching method they are using is mostly communicative and competency-based approach. Teachers informed that students are disengaged because they have psychological barriers. In classroom setting, the students are conscious about social approval from the peer, and thus if they are to face negative reinforcement which would damage their image, it is seen as a preventive mechanism to be scared of committing mistakes. This was also very reflective in this finding. A lot of other arguments can be given for this claim; however, in Algerian setting, it is usual practice where teachers do not provide positive reinforcement which is open up students for possible learning. Thus, in order to overcome such a psychological barrier, proper measures can be taken. On a conclusive remark, it is evident that teachers, students, and institutional leaders provided positive feedback for using mobile learning. It is not only motivating but also engaging in learning processes. Apps such as Kahoot, Padlet and Slido were well received and thus can be taken further to examine its higher impact in Algerian context. Thus, in the future, it will be important to implement m-learning effectively in higher education to transform the current traditional practices into modern, innovative and active learning. Persuasion for this change for stakeholder may be challenging; however, its long-term benefits can be reflective from the current research paper.

Keywords: Algerian context, mobile learning, social constructivism, student engagement

Procedia PDF Downloads 122

Authors: Zhang Dongqing

Abstract:

Concern for students' mental health and the application of intelligent advanced technologies are driving changes in teaching models. The traditional teacher-centered classroom is beginning to transform into a student-centered smart interactive learning environment. Nowadays, smart classrooms are compatible with constructivist learning. This theory emphasizes the role of teachers in the teaching process as helpers and facilitators of knowledge construction, and students learn by interacting with them. The spatial design of classrooms is closely related to the teaching model and should also be developed in the direction of smart classroom design. The goal is to explore the impact of smart classroom layout on student-centered teaching environment and teacher-student interaction under the guidance of constructivist learning theory, by combining the design process and feedback analysis of the smart transformation project on the campus of Tongji University in Shanghai. During the research process, the theoretical basis of constructivist learning was consolidated through literature research and case analysis. The integration and visual field analysis of the traditional and transformed indoor floor plans were conducted using space syntax tools. Finally, questionnaire surveys and interviews were used to collect data. The main conclusions are as followed: flexible spatial layouts can promote students' learning effects and mental health; the interactivity of smart classroom layouts is different and needs to be combined with different teaching models; the public areas of teaching buildings can also improve the interactive learning atmosphere by adding discussion space. This article provides a data-based research basis for improving students' learning effects and mental health, and provides a reference for future smart classroom design.

Keywords: spatial layout, smart classroom, space syntax, renovation, educational environment

Procedia PDF Downloads 57

Authors: Achirawatt Tungsombatsanti, Toansakul Santiboon, Kamon Ponkham

Abstract:

Strategies of the STEM education was aimed to prepare of an interdisciplinary and applied approach for the instructional of science, technology, engineering, and mathematics in an integrated students for enhancing engagement of their science skills to the Problem-Based Learning (PBL) method in Borabu School with a sample consists of 80 students in 2 classes at the 12th grade level of their learning achievements on electromagnetic issue. Research administrations were to separate on two different instructional model groups, the 40-experimental group was designed with the STEM instructional experimenting preparation and induction in a 40-student class and the controlling group using the PBL was designed to students identify what they already know, what they need to know, and how and where to access new information that may lead to the resolution of the problem in other class. The learning environment perceptions were obtained using the 35-item Physics Laboratory Environment Inventory (PLEI). Students’ creating attitude skills’ sustainable development toward physics were assessed with the Test Of Physics-Related Attitude (TOPRA) The term scaling was applied to the attempts to measure the attitude objectively with the TOPRA was used to assess students’ perceptions of their science attitude toward physics. Comparisons between pretest and posttest techniques were assessed students’ learning achievements on each their outcomes from each instructional model, differently. The results of these findings revealed that the efficiency of the PLB and the STEM based on criteria indicate that are higher than the standard level of the 80/80. Statistically, significant of students’ learning achievements to their later outcomes on the controlling and experimental physics class groups with the PLB and the STEM instructional designs were differentiated between groups at the .05 level, evidently. Comparisons between the averages mean scores of students’ responses to their instructional activities in the STEM education method are higher than the average mean scores of the PLB model. Associations between students’ perceptions of their physics classes to their attitudes toward physics, the predictive efficiency R2 values indicate that 77%, and 83% of the variances in students’ attitudes for the PLEI and the TOPRA in physics environment classes were attributable to their perceptions of their physics PLB and the STEM instructional design classes, consequently. An important of these findings was contributed to student understanding of scientific concepts, attitudes, and skills as evidence with STEM instructional ought to higher responding than PBL educational teaching. Statistically significant between students’ learning achievements were differentiated of pre and post assessments which overall on two instructional models.

Keywords: learning instructional managements, problem-based learning, STEM education, method, enhancement, students learning achievements, science attitude, physics classes

Procedia PDF Downloads 219

Authors: Taha Benarbia

Abstract:

The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

Procedia PDF Downloads 60

Authors: Ali Asheibi

Abstract:

The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

Procedia PDF Downloads 233

Authors: Ramona-Niculina Jurcau, Ioana-Marieta Jurcau, Dong Hun Kwak, Nicolae-Alexandru Colceriu

Abstract:

Background: Academic environment led, in time, to the birth of some research subjects concluded with many publications. One of these issues is related to the learning stress. Thus far, the PubMed website displays an impressive number of papers related to the academic stress. Aims: Through this study, we aimed to evaluate the research concerning academic learning stress (ALS), by a retrospective analysis of PubMed publications. Methods: We evaluated the ALS, considering: a) different keywords as - ‘academic stress’ (AS), ‘academic stressors’ (ASs), ‘academic learning stress’ (ALS), ‘academic student stress’ (ASS), ‘academic stress college’ (ASC), ‘medical academic stress’ (MAS), ‘non-medical academic stress’ (NMAS), ‘student stress’ (SS), ‘nursing student stress’ (NS), ‘college student stress’ (CSS), ‘university student stress’ (USS), ‘medical student stress’ (MSS), ‘dental student stress’ (DSS), ‘non-medical student stress’ (NMSS), ‘learning students stress’ (LSS), ‘medical learning student stress’ (MLSS), ‘non-medical learning student stress’ (NMLSS); b) the year average for decades; c) some selection filters provided by PubMed website: Article types - Journal Article (JA), Clinical Trial (CT), Review (R); Species - Humans (H); Sex - Male (M) and Female (F); Ages - 13-18, 19-24, 19-44. Statistical evaluation was made on the basis of the Student test. Results: There were differences between keywords, referring to all filters. Nevertheless, for all keywords were noted the following: the majority of studies have indicated that subjects were humans; there were no important differences between the number of subjects M and F; the age of participants was mentioned only in some studies, predominating those with teenagers and subjects between 19-24 years. Conclusions: 1) PubMed publications document that concern for the research field of academic stress, lasts for 56 years and was materialized in more than 5.010 papers. 2) Number of publications in the field of academic stress varies depending on the selected keywords: those with a general framing (AS, ASs, ALS, ASS, SS, USS, LSS) are more numerous than those with a specific framing (ASC, MAS, NMAS, NS, CSS, MSS, DSS, NMSS, MLSS, NMLSS); those concerning the academic medical environment (MAS, NS, MSS, DSS, MLSS) prevailed compared to the non-medical environment (NMAS, NMSS, NMLSS). 3) Most of the publications are included at JA, of which a small percentage are CT and R. 4) Most of the academic stress studies were conducted with subjects both M and F, most aged under 19 years and between 19-24 years.

Keywords: academic stress, student stress, academic learning stress, medical student stress

Procedia PDF Downloads 548

Authors: Freddy Rodriguez Saza, Erika Sanabria, Jair Tibana

Abstract:

Currently, the important role of neurosciences in the professional training of the physical educator is known, highlighting in the teaching-learning process aspects such as the nervous structures involved in the adjustment of posture and movement, the neurophysiology of locomotion, the process of nerve impulse transmission, and the relationship between physical activity, learning, and cognition. The teaching-learning process of neurosciences is complex, due to the breadth of the contents, the diversity of teaching contexts required, and the demanding ability to relate concepts from different disciplines, necessary for the correct understanding of the function of the nervous system. This text presents the results of the application of a didactic environment based on the Allosteric Learning Model in morphophysiology students of the Faculty of Military Physical Education, Military School of Cadets of the Colombian Army (Bogotá, Colombia). The research focused then, on analyzing the change in the cognitive structure of the students on neurosciences. Methodology. [1] The predominant learning styles were identified. [2] Students' cognitive structure, core concepts, and threshold concepts were analyzed through the construction of Pathfinder Associative Networks. [3] Didactic Units in Neuroscience were designed to favor metacognition, the development of Executive Functions (working memory, cognitive flexibility, and inhibitory control) that led students to recognize their errors and conceptual distortions and to overcome them. [4] The Teacher's Professional Knowledge and the role of the assessment strategies applied were taken into account, taking into account the perspective of the Dynamizer, Obstacle, and Questioning axes. In conclusion, the study found that physical education students achieved significant learning in neuroscience, favored by the development of executive functions and by didactic environments oriented with the predominant learning styles and focused on increasing cognitive networks and overcoming difficulties, neuromyths and neurophobia.

Keywords: allosteric learning model, military physical education, neurosciences, pathfinder associative networks, teacher professional knowledge

Procedia PDF Downloads 216

Authors: Willem J. A. Louw

Abstract:

The wave of new advanced computing technology that has been developed during the recent past has significantly changed the way we communicate, collaborate and collect information. It has created a new technology environment and paradigm in which our children and students grow-up and this impacts on their learning. Research confirmed that Generation Y students have a preference for learning in the new technology environment. The challenge or question is: How do we adjust our teaching and learning to make the most of these changes. The complexity of effective and efficient teaching and learning must not be underestimated and changes must be preceded by proper objective research to prevent any haphazard developments that could do more harm than benefit. A blended learning approach has been used in the Forestry department for a few numbers of years including the use of electronic-peer assisted learning (e-pal) in a fixed-computer set-up within a learning management system environment. It was decided to extend the investigation and do some exploratory research by using a range of different Tablet devices. For this purpose, learning activities or assignments were designed to cover aspects of communication, collaboration and collection of information. The Moodle learning management system was used to present normal module information, to communicate with students and for feedback and data collection. Student feedback was collected by using an online questionnaire and informal discussions. The research project was implemented in 2013, 2014 and 2015 amongst first and third-year students doing a forestry three-year technical tertiary qualification in commercial plantation management. In general, more than 80% of the students alluded to that the device was very useful in their learning environment while the rest indicated that the devices were not very useful. More than ninety percent of the students acknowledged that they would like to continue using the devices for all of their modules whilst the rest alluded to functioning efficiently without the devices. Results indicated that information collection (access to resources) was rated the highest advantageous factor followed by communication and collaboration. The main general advantages of using Tablets were listed by the students as being mobility (portability), 24/7 access to learning material and information of any kind on a user friendly device in a Wi-Fi environment, fast computing process speeds, saving time, effort and airtime through skyping and e-mail, and use of various applications. Ownership of the device is a critical factor while the risk was identified as a major potential constraint. Significant differences were reported between the different types and quality of Tablets. The preferred types are those with a bigger screen and the ones with overall better functionality and quality features. Tablets significantly increase the collaboration, communication and information collection needs of the students. It does, however, not replace the need of a computer/laptop because of limited storage and computation capacity, small screen size and inefficient typing.

Keywords: tablets, teaching, blended learning, tablet quality

Procedia PDF Downloads 241

Authors: Mohamed Mokhtar, Mostafa F. Shaaban

Abstract:

Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

Procedia PDF Downloads 128

Authors: David Rafetseder, Walter Bauer, Florian Poltschak, Wolfgang Amrhein

Abstract:

A flat double-stator linear induction machine (DSLIM) with a short squirrel-cage mover is designed for high thrust force at moderate speed < 5m/s. The performance and motor parameters are determined on the basis of a 2D time-transient simulation with the finite element (FE) software Maxwell 2015. Design guidelines and transformation rules for space vector theory of the LIM are presented. Resulting thrust calculated by flux and current vectors is compared with the FE results showing good coherence and reduced noise. The parameters of the equivalent circuit model are obtained.

Keywords: equivalent circuit model, finite element model, linear induction motor, space vector theory

Procedia PDF Downloads 553

Authors: Nazatul Aini Abd Majid, Faridah Yunus, Haslina Arshad, Mohammad Farhan Mohammad Johari

Abstract:

Mobile technology, augmented reality, and game-based learning are some of the key learning technologies that can be fully optimized to promote pre-literacy skills. The problem is how to design an effective pre-literacy kit that utilizes some of the learning technologies. This paper presents a framework based on mobile augmented reality for the development of pre-literacy kit. This pre-literacy kit incorporates three main components which are contents, design, and tools. A prototype of a mobile app based on the three main components was developed for promoting pre-literacy. The results show that the children and teachers gave positive feedbacks after using the mobile app for the pre-literacy.

Keywords: framework, mobile technology, augmented reality, pre-literacy skills

Procedia PDF Downloads 576

Authors: Yi Li, Yinan Zheng, Ya Ke, Yungwing Ho

Abstract:

Motor learning is a process that frequently happens among humans and rodents, which is defined as the changes in the capability to perform a skill that is conformed to have a relatively permanent improvement through practice or experience. There are many ways to learn a behavior, among which is observational learning. Observational learning is the process of learning by watching the behaviors of others, for example, a child imitating parents, learning a new sport by watching the training videos or solving puzzles by watching the solutions. Many research explores observational learning in humans and primates. However, the neuronal mechanism of which, especially observational motor learning, was uncertain. It’s well accepted that mirror neurons are essential in the observational learning process. These neurons fire when the primate performs a goal-directed action and sees someone else demonstrating the same action, which suggests they have high firing activity both completing and watching the behavior. The mirror neurons are assumed to mediate imitation or play a critical and fundamental role in action understanding. They are distributed in many brain areas of primates, i.e., posterior parietal cortex (PPC), premotor cortex (M2), and primary motor cortex (M1) of the macaque brain. However, few researchers report the existence of mirror neurons in rodents. To verify the existence of mirror neurons and the possible role in motor learning in rodents, we performed customised string-pulling behavior combined with multiple behavior analysis methods, photometry, electrophysiology recording, c-fos staining and optogenetics in healthy mice. After five days of training, the demonstrator (demo) mice showed a significantly quicker response and shorter time to reach the string; fast, steady and accurate performance to pull down the string; and more precisely grasping the beads. During three days of observation, the mice showed more facial motions when the demo mice performed behaviors. On the first training day, the observer reduced the number of trials to find and pull the string. However, the time to find beads and pull down string were unchanged in the successful attempts on the first day and other training days, which indicated successful action understanding but failed motor learning through observation in mice. After observation, the post-hoc staining revealed that the c-fos expression was increased in the cognitive-related brain areas (medial prefrontal cortex) and motor cortices (M1, M2). In conclusion, this project indicated that the observation led to a better understanding of behaviors and activated the cognitive and motor-related brain areas, which suggested the possible existence of mirror neurons in these brain areas.

Keywords: observation, motor learning, string-pulling behavior, prefrontal cortex, motor cortex, cognitive

Procedia PDF Downloads 72

Authors: Khaldi Youcef

Abstract:

This investigation seeks to inquire into the effectiveness of using authentic videos for grammar teaching purposes. In this investigation, an English animated situation, Hercules, was used as a type of authentic multimedia to teach a particular grammatical structure, namely conditional sentences. This study also aims at investigating the EFL learners’ attitudes toward grammar learning after being exposed to such an authentic video. To reach that purpose, 56 EFL learners were required ultimately to respond to a questionnaire with an aim to reveal their attitudes towards grammar as a language entity and as a subject for being learned. Then, as a second stage of the investigation, the EFL learners were divided into a control group and an experimental group with 28 learners in each. The first group was taught grammar -conditional sentences- using a deductive-inductive approach, while the second group was exposed to an authentic video to learn conditional sentences. There was a post-lesson stage that included a questionnaire to be answered by learners of each group. The aim of this stage is to capture any change in learners' attitudes shown in the pre-lesson questionnaire. The findings of the first stage revealed learners' negative attitudes towards grammar learning. And the third stage results showed the effectiveness of authentic videos in entirely turning learners' attitudes toward grammar learning to be significantly positive. Also, the utility of authentic videos in highly motivating EFL learners can be deduced. The findings of this survey asserted the need for incorporation and integration of authentic videos in EFL classrooms as they resulted in rising effectively learners’ awareness of grammar and looking at it from a communicative perspective.

Keywords: multimedia, authentic videos, negative attitudes, grammar learning, EFL learners

Procedia PDF Downloads 86