Search results for: machine learning in finance

Authors: Huie-Wen Lin

Abstract:

This study integrated various teaching factors -based on the idea of a flipped classroom- in a financial management course. The study’s aim was to establish an effective teaching implementation strategy and evaluation mechanism with respect to learning outcomes, which can serve as a reference for the future modification of teaching methods. This study implemented a teaching method in five stages and estimated the learning efficiencies of 22 students (in the teaching scenario and over two semesters). Subsequently, data envelopment analysis (DEA) was used to compare, for each student, between the learning efficiencies before and after participation in the flipped classroom -in the first and second semesters, respectively- to identify the crucial external factors influencing learning efficiency. According to the results, the average overall student learning efficiency increased from 0.901 in the first semester to 0.967 in the second semester, which demonstrate that the flipped classroom approach can improve teaching effectiveness and learning outcomes. The results also revealed a difference in learning efficiency between male and female students.

Keywords: data envelopment analysis, flipped classroom, learning outcome, teaching and learning

Procedia PDF Downloads 136

Authors: Shayan Mohajer Hamidi

Abstract:

Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

Procedia PDF Downloads 51

Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

Abstract:

Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

Procedia PDF Downloads 157

Authors: Sahil Kumar, Surinder Pal, Rahul Kapoor

Abstract:

This paper is focused on to define the basic difference between hot and cold chamber high pressure die casting process which is not fully defined in a research before paper which we have studied. The pressure die casting is basically defined into two types (1) Hot chamber Die Casting (2) Cold chamber Die Casting. Cold chamber die casting is used for casting alloys that require high pressure and have a high melting temperature, such as brass, aluminum, magnesium, copper based alloys and other high melting point nonferrous alloys. Hot chamber die casting is suitable for casting zinc, tin, lead, and low melting point alloys. In hot chamber die casting machine, the molten metal is an integral pan of the machine. It mainly consists of hot chamber and gooseneck type metal container made of cast iron. This machine is mainly used for low melting alloys and alloys of metals like zinc, lead etc. Metals and alloys having a high melting point and those which are having an affinity for iron cannot be cast by this machine, which could otherwise attack the shot sleeve and damage the machine.

Keywords: hot chamber die casting, cold chamber die casting, metals and alloys, casting technology

Procedia PDF Downloads 599

Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 330

Authors: Ng Siew Keow, Cheah Seeh Lee

Abstract:

Students’ strong engagement in learning increases their motivation and satisfaction to learn, be resilient to combat academic stress. Engagement in learning is even crucial in the open distance learning (ODL) setting, where the adult students are learning remotely, lessons and learning materials are mostly delivered via online platforms. This study aimed to explore the relationship between learning engagement and perceived academic stress levels of adult students who enrolled in ODL learning mode. In this descriptive correlation study during the 2021-2022 academic years, 101 adult students from Wawasan Open University, Malaysia (WOU) were recruited through convenient sampling. The adult students’ online learning engagement levels and perceived academic stress levels were identified through the self-report Online Student Engagement Scale (OSE) and the Perception of Academic Stress Scale (PASS). The Pearson correlation coefficient test revealed a significant positive relationship between online student engagement and perceived academic stress (r= 0.316, p<0.01). The higher scores on PASS indicated lower levels of perceived academic stress. The findings of the study supported the assumption of the importance of engagement in learning in promoting psychological well-being as well as sustainability in online learning in the open distance learning context.

Keywords: student engagement, academic stress, open distance learning, online learning

Procedia PDF Downloads 134

Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

Abstract:

The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

Procedia PDF Downloads 61

Authors: Reza Khani, Ziba Hosseini

Abstract:

Ever since the importance of language learning strategy instruction (LLS) has been distinguished, there has been growing interest on how to teach LLS in language learning classrooms. So thus this study attempted to implement language strategy instruction based on CALLA approach for Iranian EFL learners in a real classroom setting. The study was testing the hypothesis that strategy instruction result in improved linguistic strategy of students. The participant of the study were 240 EFL learners who received language learning instruction for four months. The data collected using Oxford strategy inventory for language learning. The results indicated the instruction had statistically significant effect on language strategy use of intervention group who received instruction.

Keywords: CALLA, language learning strategy, language learning strategy instruction, Iranian EFL language strategy

Procedia PDF Downloads 547

Authors: Yessica Nataliani, Miin-Shen Yang

Abstract:

Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices.

Keywords: group technology, cell formation, spectral clustering, grouping efficiency

Procedia PDF Downloads 385

Authors: A. Hassannia, S. Ramezani

Abstract:

The technology of superconductivity has developed in many power system devices such as transmission cable, transformer, current limiter, motor and generator. Superconducting wires can carry high density current without loss, which is the capability that is used to design the compact, lightweight and more efficient electrical machines. Superconducting motors have found applications in marine and air propulsion systems as well as superconducting generators are considered in low power hydraulic and wind generators. This paper presents a rim-driven superconducting synchronous generator for hydraulic power plant. The rim-driven concept improves the performance of hydro turbine. Furthermore, high magnetic field that is produced by superconducting windings allows replacing the rotor core. As a consequent, the volume and weight of the machine is decreased significantly. In this paper, a 1 MW coreless rim-driven superconducting synchronous generator is designed. Main performance characteristics of the proposed machine are then evaluated using finite elements method and compared to an ordinary similar size synchronous generator.

Keywords: coreless machine, electrical machine design, hydraulic generator, rim-driven machine, superconducting generator

Procedia PDF Downloads 154

Authors: Jamaludin Jamaludin, Suparji Suparji, Lilik Anifah, I. Gusti Putu Asto Buditjahjanto, Eppy Yundra

Abstract:

Learning media is an important and main component in the learning process. By using currently available media, cadets still have difficulty understanding how the piston engine works, so they are not able to apply these concepts appropriately. This study aims to examine the development of interactive media for piston engine courses in order to improve student learning outcomes. The research method used is a literature study of several articles, journals and proceedings of interactive media development results from 2010-2020. The results showed that the development of interactive media is needed to support the learning process and influence the cognitive abilities of students. With this interactive media, learning outcomes can be improved and the learning process can be effective.

Keywords: interactive media, learning outcomes, learning process, literature study

Procedia PDF Downloads 132

Authors: Jeanetta D. Sims, Chaunda L. Scott, Hung-Lin Lai, Sarah Neese, Atoya Sims, Angelia Barrera-Medina

Abstract:

Given the call for increased transformative learning experiences and the demand for academia to prepare students to enter workforce diversity careers, this study explores the landscape of workforce diversity learning in the United States. Using a multi-disciplinary syllabi browsing process and a content analysis method, the most prevalent instructional activities being used in workforce-diversity related courses in the United States are identified. In addition, the instructional activities are evaluated based on transformative learning tenants.

Keywords: workforce diversity, workforce diversity learning, transformative learning, diversity education, U. S. workforce diversity, workforce diversity assignments

Procedia PDF Downloads 484

Authors: Yi-Hsiang Pan, Ching-Hsiang Chen, Wei-Ting Hsu

Abstract:

The purpose of this study was to compare the learning effects of the sports education model (SEM) to those of the traditional teaching model (TTM) in physical education classes in terms of students learning motivation, action control, learning strategies, and learning performance. A quasi-experimental design was utilized in this study, and participants included two physical educators and four classes with a total of 94 students in grades 5 and 6 of elementary schools. Two classes implemented the SEM (n=47, male=24, female=23; age=11.89, SD=0.78) and two classes implemented the TTM (n=47, male=25, female=22, age=11.77; SD=0.66). Data were collected from these participants using a self-report questionnaire (including a learning motivation scale, action control scale, and learning strategy scale) and a game performance assessment instrument, and multivariate analysis of covariance was used to conduct statistical analysis. The findings of the study revealed that the SEM was significantly better than the TTM in promoting students learning motivation, action control, learning strategies, and game performance. It was concluded that the SEM could promote the mechanics of students self-regulated learning process, and thereby improve students movement performance.

Keywords: self-regulated learning theory, learning process, curriculum model, physical education

Procedia PDF Downloads 323

Authors: Leena Ahmad Khaleel Alfarani, Maggie McPherson, Neil Morris

Abstract:

Over the last decade, m-learning has been widely accepted and utilized by many western universities. However, Saudi universities face many challenges in utilizing such technology, a central one being to encourage teachers to use such technology. Although there are several factors that affect faculty members’ participation in the adoption of m-learning, this paper focuses merely on two factors, the usefulness and ease of using m-learning. A sample of 279 faculty members in one Saudi university has responded to the online survey. The results of the study have revealed that there is a statistically significant relationship (at the 0.05 level) between both usefulness and ease of using m-learning factors and the intention of teachers to use m-learning currently and in the future.

Keywords: mobile learning, diffusion of innovation theory, technology acceptance, faculty adoption

Procedia PDF Downloads 523

Authors: Panita Wannapiroon, Noppadon Phumeechanya, Sitthichai Laisema

Abstract:

This study is the research and development which is intended to: 1) design the ubiquitous cloud learning management system and: 2) assess the suitability of the design of the ubiquitous cloud learning management system. Its methods are divided into 2 phases. Phase 1 is the design of the ubiquitous cloud learning management system, phase 2 is the assessment of the suitability of the design the samples used in this study are work done by 25 professionals in the field of Ubiquitous cloud learning management systems and information and communication technology in education selected using the purposive sampling method. Data analyzed by arithmetic mean and standard deviation. The results showed that the ubiquitous cloud learning management system consists of 2 main components which are: 1) the ubiquitous cloud learning management system server (u-Cloud LMS Server) including: cloud repository, cloud information resources, social cloud network, cloud context awareness, cloud communication, cloud collaborative tools, and: 2) the mobile client. The result of the system suitability assessment from the professionals is in the highest range.

Keywords: learning management system, cloud computing, ubiquitous learning, ubiquitous learning management system

Procedia PDF Downloads 503

Authors: Theoktisti Makridou, Konstantinos Tsaprailis, George Arvanitakis, Charalampos Kontoes

Abstract:

In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance.

Keywords: mosquito abundance, supervised machine learning, culex pipiens, spatial sampling, west nile virus, earth observation data

Procedia PDF Downloads 126

Authors: Badiossadat Hassanpour, Reza Sirjani, Nangkuala Utaberta

Abstract:

The avant-garde educational systems are striving to find a life long learning methods. Different fields and majors have test variety of proposed models, and found their difficulties and strengths. Architecture as a critical stage of education due to its characteristics which are learning by doing and critique based education and evaluation is out of this study procedure. Learning contracts is a new alternative form of evaluation of students’ achievements, while it acts as agreement about learning goals. Obtained results from studies in different fields which confirm its positive impact on students' learning in those fields and positively affected students' motivation and confidence in meeting their own learning needs, prompted us to implement this model in architecture design studio. In this implemented contract to the studio, students were asked to use the existing possibility of contract to have self assessment and examine their professional development to identify whether they are deficient or they would like to develop more expertise. The evidences of this research as well indicate that students feel positive about the learning contract and see it accommodating their individual learning needs.

Keywords: contract (LC), architecture design studio, education, student-centered learning

Procedia PDF Downloads 424

Authors: Chalakorn Chitsaart, Suchada Rianmora, Mann Rattana-Areeyagon, Wutichai Namjaiprasert

Abstract:

G-code is the main factor in computer numerical control (CNC) machine for controlling the tool-paths and generating the profile of the object’s features. For obtaining high surface accuracy of the surface finish, non-stop operation is required for CNC machine. Recently, to design a new product, the strategy that concerns about a change that has low impact on business and does not consume lot of resources has been introduced. Cost and time for designing minor changes can be reduced since the traditional geometric details of the existing models are applied. In order to support this strategy as the alternative channel for machining operation, this research proposes the automatic generating codes for CNC milling operation. Using this technique can assist the manufacturer to easily change the size and the geometric shape of the product during the operation where the time spent for setting up or processing the machine are reduced. The algorithm implemented on MATLAB platform is developed by analyzing and evaluating the geometric information of the part. Codes are created rapidly to control the operations of the machine. Comparing to the codes obtained from CAM, this developed algorithm can shortly generate and simulate the cutting profile of the part.

Keywords: geometric shapes, milling operation, minor changes, CNC Machine, G-code, cutting parameters

Procedia PDF Downloads 334

Authors: Cristina Galván-Fernández

Abstract:

Students differentiate the different contexts of their lives as well as employment, hobbies or studies. In higher education is needed to transfer the experiential knowledge to theory and viceversa. However, is difficult to achieve than students use their personal experiences and social readings for get the learning evidences. In an experience with 178 education students from Chile and Spain we have used an e-portfolio system and a methodology for 4 years with the aims of help them to: 1) self-regulate their learning process and 2) use social networks and professional experiences for make the learning evidences. These two objectives have been controlled by interviews to the same students in different moments and two questionnaires. The results of this study show that students recognize the ownership of their learning and progress in planning and reflection of their own learning.

Keywords: competences, e-portfolio, higher education, self-regulation

Procedia PDF Downloads 278

Authors: Rohit Mittal, Bright Keswani, Amit Mithal

Abstract:

This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

Procedia PDF Downloads 626

Authors: Damian Gordon, Paul Doyle, Anna Becevel, Tina Baloh

Abstract:

The goal of this research is to explore the evolution of the concept of “blended learning” over a twenty-year period, to see whether or not the conceptualization has remained consistent or if it has become either more specific or more general. To achieve this goal, the term “blended learning” (and variations) was searched for in various bibliographical repositories for each year 2003-2022 to locate a highly cited paper that is not behind a paywall, to locate unique definitions that would be freely available to all academics each year. Each of the twenty unique definitions is explored to identify how they categorize both the Classroom Component and the Computer Component of blended learning, as well as identify which discipline each definition originates from and which country it comes from to see if there are any significant geographical variations. Based on this analysis, trends that appear in the definitions are noted, as well as an overall interpretation of the notion of “Blended Learning.”

Keywords: blended learning, definitions of blended learning, e-learning, thematic searches

Procedia PDF Downloads 110

Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

Abstract:

Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

Procedia PDF Downloads 341

Authors: Muhammad Fathurridho

Abstract:

Grammar as a rule used by every language to be understood by everyone is always related to syntax and morphology. Arabic grammar is different with another languages’ grammars. It has more rules and difficulties. This paper aims to investigate and describe the English grammatical errors of machine translation systems in translating Arabic sentences, including declarative, exclamation, imperative, and interrogative sentences, specifically in year 2018 which can be supported with artificial intelligence’s role. The Arabic sample sentences which are divided into two; verbal and nominal sentence of several Arabic published texts will be examined as the source language samples. The translated sentences done by several popular online machine translation systems, including Google Translate, Microsoft Bing, Babylon, Facebook, Hellotalk, Worldlingo, Yandex Translate, and Tradukka Translate are the material objects of this research. Descriptive method that will be taken to finish this research will show the grammatical errors of English target language, and classify them. The conclusion of this paper has showed that the grammatical errors of machine translation results are varied and generally classified into morphological, syntactical, and semantic errors in all type of Arabic words (Noun, Verb, and Particle), and it will be one of the evaluations for machine translation’s providers to correct them in order to improve their understandable results.

Keywords: Arabic, Arabic-English translation, machine translation, grammatical errors

Procedia PDF Downloads 140

Authors: Kuliya Muhammed

Abstract:

This paper examines the potentials of online learning and the challenges to its adoption in Nigeria’s higher institutions of learning. The research would assist in tackling the challenges of online learning adoption and enlighten institutions on the numerous benefits of online learning in Nigeria. The researcher used survey method for the study and questionnaires were used to obtain the needed data from 230 respondents cut across 20 higher institutions in the country. The findings revealed that online learning has the prospect to boost access to learning tools, assist students’ to learn from the comfort of their offices or homes, reduce the cost of learning, and enable individuals to gain self-knowledge. The major challenges in the adoption of e-learning are poor Information and Communication Technology infrastructures, poor internet connectivity where available, lack of Information and Communication Technology background, problem of power supply, lack of commitment by institutions, poor maintenance of Information and Communication Technology tools, inadequate facilities, lack of government funding and fraud. Recommendations were also made at the end of the research work.

Keywords: electronic, ICT, institution, internet, learning, technology

Procedia PDF Downloads 365

Authors: Larry Audet

Abstract:

Creativity is suddenly and fortunately a new educational focus in the United Arab Emirates and around the world. Yet still today many leaders of creativity are not sure how to introduce it to their teachers. It is impossible to simultaneously introduce every aspect of creativity into a work climate and reach any degree of organizational coherence. The number of alternatives to explore is so great; the information teachers need to learn is so vast, that even an approximation to including every concept and theory of creativity into the school organization is hard to conceive. Effective leaders of creativity need evidence-based and practical guidance for introducing and stimulating creativity in others. Machine learning models reveal new findings from KEYS Survey© data about teacher perceptions of stimulants and barriers to their individual and collective creativity. Findings from predictive and causal models provide leaders with a rational for decision-making when introducing creativity into their organization. Leaders should focus on management practices first. Analyses reveal that creative outcomes are more likely to occur when teachers perceive supportive management practices: providing teachers with challenging work that calls for their best efforts; allowing freedom and autonomy in their practice of work; allowing teachers to form creative work-groups; and, recognizing them for their efforts. Once management practices are in place, leaders should focus their efforts on modeling risk-taking, providing optimal amounts of preparation time, and evaluating teachers fairly.

Keywords: creativity, leadership, KEYS survey, teaching, work climate

Procedia PDF Downloads 146

Authors: Aqeel Zeid, Hasna Anees, Mohamed Adheeb, Mohamed Rifan, Kalpani Manathunga

Abstract:

To achieve constructive alignment in a higher education program, a clear set of learning outcomes must be defined. Traditional learning outcome definition techniques such as Bloom’s taxonomy are not written to be utilized by the student. This might be disadvantageous for students in student-centric learning settings where the students are expected to formulate their own learning strategies. To solve the problem, we propose the learning outcome relation and aggregation (LORA) model. To achieve alignment, we developed learning outcome, assessment, and resource authoring tools which help teachers to tag learning outcomes during creation. A pilot study was conducted with an expert panel consisting of experienced professionals in the education domain to evaluate whether the LORA model and tools present an improvement over the traditional methods. The panel unanimously agreed that the model and tools are beneficial and effective. Moreover, it helped them model learning outcomes in a more student centric and descriptive way.

Keywords: learning design, constructive alignment, Bloom’s taxonomy, learning outcome modelling

Procedia PDF Downloads 170

Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

Abstract:

This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

Procedia PDF Downloads 198

Authors: Maroonisha Maharajh

Abstract:

Based on the Black Swan and Disruptive Innovation Theories, this study proposes an educational game based learning model within the context of the traditional classroom learning environment. In the proposed model, the perceived e-learning component is decomposed into accessibility, perceived quality and perceived usability within the traditional rural classroom environment. A sample of 92 respondents took part in this study. The results suggest that users’ continuance intention is determined by both economic and grassroots internet accessibility, which in turn is jointly determined by perceived usefulness, information quality, service quality, system quality, perceived ease of use and cognitive absorption of learning.

Keywords: blended learning, flipped classroom, e-learning, gaming

Procedia PDF Downloads 234

Authors: Randa El Bedawy

Abstract:

Exchanging best practices in the scope of entrepreneurship education and the use of experiential learning approaches are growing lately at a very fast pace. Educators should be challenged to promote such a learning approach to bridge the gap between entrepreneurship students and the actual business work environment. The study aims to share best practices, experiences, and knowledge to support entrepreneurship education. The study is exploratory qualitative research based on a case study approach to demonstrate how experiential learning can be used for supporting learning effectiveness in entrepreneurship education through demonstrating a set of fourteen tasks that were used to engage practically the students who were studying a course of entrepreneurship at the American University in Cairo. The study sheds the light on the rational process of using experiential learning to endorse entrepreneurship education through the illustration of each task along with its learning outcomes. The study explores the benefits and obstacles that educators may face when implementing such an experiential approach. The results of the study confirm that developing an experiential learning approach based on constructing a set of well designed practical tasks that complement the overall intended learning outcomes has proven very effective for promoting the students’ learning of entrepreneurship education. However, good preparation for both educators and students is needed primarily to ensure the effective implementation of such an experiential learning approach.

Keywords: business education, entrepreneurship, entrepreneurship education, experiential learning

Procedia PDF Downloads 144

Authors: Samar Swaid

Abstract:

Today, corporates are moving toward the adoption of Design-Thinking techniques to develop products and services, putting their consumer as the heart of the development process. One of the leading companies in Design-Thinking, IDEO (Innovation, Design, Engineering Organization), defines Design-Thinking as an approach to problem-solving that relies on a set of multi-layered skills, processes, and mindsets that help people generate novel solutions to problems. Design thinking may result in new ideas, narratives, objects or systems. It is about redesigning systems, organizations, infrastructures, processes, and solutions in an innovative fashion based on the users' feedback. Tim Brown, president and CEO of IDEO, sees design thinking as a human-centered approach that draws from the designer's toolkit to integrate people's needs, innovative technologies, and business requirements. The application of design thinking has been witnessed to be the road to developing innovative applications, interactive systems, scientific software, healthcare application, and even to utilizing Design-Thinking to re-think business operations, as in the case of Airbnb. Recently, there has been a movement to apply design thinking to machine learning and artificial intelligence to ensure creating the "wow" effect on consumers. The Association of Computing Machinery task force on Data Science program states that" Data scientists should be able to implement and understand algorithms for data collection and analysis. They should understand the time and space considerations of algorithms. They should follow good design principles developing software, understanding the importance of those principles for testability and maintainability" However, this definition hides the user behind the machine who works on data preparation, algorithm selection and model interpretation. Thus, the Data Science program includes design thinking to ensure meeting the user demands, generating more usable machine learning tools, and developing ways of framing computational thinking. Here, describe the fundamentals of Design-Thinking and teaching modules for data science programs.

Keywords: data science, design thinking, AI, currculum, transformation

Procedia PDF Downloads 62