Search results for: EFL learning/ teaching

Authors: Mardianto

Abstract:

Education and learning are now grown rapidly. Synergy of techonology especially instructional technology in the learning activities are very big influence on the effectiveness of learning and creativity to achieve optimal results. But on the other hand there is a education value that is difficult to be articulated through character-forming technology such as honesty, discipline, hard work, heroism, and so forth. Learning strategy and storytelling from the past until today is still an option for teachers to convey the message of character values. With the material was loaded from the local culture (stories folklore), the combination of learning objectives (build character child) strategy, and traditional methods (storytelling and story), and the preservation of local culture (dig tale folklore) is critical to maintaining the nation's culture. In the context of maintaining the nation's culture, then since the age of the child at the level of government elementary school a necessity. Globalization, the internet and technology sometimes feel can displace the role of the teacher in the learning activities. To the oral tradition is a mainstay of storytelling should be maintained and preserved. This research was conducted at the elementary school in the city of Medan, North Sumatra Indonesia, with a random sampling technique, the 27 class teachers were respondents who were randomly assigned to the Madrasah Ibtdaiyah (Islamic Elementary School) both public and private. Research conducted at the beginning of 2014 refers to a curriculum that is being transformed in the environment ministry Republic Religion Indonesia. The results of this study indicate that; the declining skills of teachers to develop storytelling this can be seen from; 74.07% of teachers have never attended a special training storytelling, 85.19% no longer nasakah new stories, only 22.22% are teachers who incorporate methods of stories in the learning plan. Most teachers are no longer concerned with storytelling, among those experiencing difficulty in developing methods because the story; 66.67% of children are more interested in children's cartoons like Bobo boy, Angrybirds and others, 59.26 children prefer other activities than listening to a story. The teachers hope, folklore books should be preserved, storytelling training should be provided by the government through the ministry of religion, race or competition of storytelling should be scheduled, writing a new script-based populist storytelling should be provided immediately. The teachers’ hope certainly not excessive, by realizing the story method becomes articulation as the efforts of child character development based populist, therefore the local knowledge can be a strong fortress facing society in the era of progress as at present, and future.

Keywords: story telling, local wisdom, education, technology development

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Authors: Jareeluk Ratanaphan

Abstract:

The purposed of this research were; 1. To survey the teacher’s needs on knowledge about special education management for special needs student 2. Development of distance training packages for teacher on special education management for special needs student 3. to study the effects of using the packages on trainee’s achievement 4. to study the effects of using the packages on trainee’s opinion on the distance training packages. The design of the experiment was research and development. The research sample for survey were 86 teachers, and 22 teachers for study the effects of using the packages on achievement and opinion. The research instrument comprised: 1) training packages on special education management for special needs student 2) achievement test 3) questionnaire. Mean, percentage, standard deviation, t-test and content analysis were used for data analysis. The findings of the research were as follows: 1. The teacher’s needs on knowledge about teaching for a learner with learning disability, mental retardation, autism, physical and health impairment and research in special education. 2. The package composed of special education management for special needs student document and manual of distance training packages. The document consisted by the name of packages, the explanation for the educator, content’s structure, concept, objectives, content and activities. Manual of distance training packages consisted by the explanation about a document, objectives, explanation about using the package, training schedule, and evaluation. The efficiency of packages was established at 79.50/81.35. 3. The results of using the packages were the posttest average scores of trainee’s achievement were higher than the pretest. 4. The trainee’s opinion on the package was at the highest level.

Keywords: distance training package, teacher, learner with special needs

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Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

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Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

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Authors: Nick Woodier, Iain Moppett

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Background: It is estimated that one in ten patients admitted to hospital will suffer an adverse event in their care. While the majority of these will result in low harm, patients are being significantly harmed by the processes meant to help them. Healthcare, therefore, seeks to make improvements in patient safety by taking learning from other industries that are perceived to be more mature in their management of safety events. Of particular interest to healthcare are ‘near misses,’ those events that almost happened but for an intervention. Healthcare does not have any guidance as to how best to manage and learn from near misses to reduce the chances of harm to patients. The authors, as part of a larger study of near-miss management in healthcare, sought to learn from the UK nuclear sector to develop principles for how healthcare can identify, report, and learn from near misses to improve patient safety. The nuclear sector was chosen as an exemplar due to its status as an ultra-safe industry. Methods: A Grounded Theory (GT) methodology, augmented by a scoping review, was used. Data collection included interviews, scenario discussion, field notes, and the literature. The review protocol is accessible online. The GT aimed to develop theories about how nuclear manages near misses with a focus on defining them and clarifying how best to support reporting and analysis to extract learning. Near misses related to radiation release or exposure were focused on. Results: Eightnuclear interviews contributed to the GT across nuclear power, decommissioning, weapons, and propulsion. The scoping review identified 83 articles across a range of safety-critical industries, with only six focused on nuclear. The GT identified that nuclear has a particular focus on precursors and low-level events, with regulation supporting their management. Exploration of definitions led to the recognition of the importance of several interventions in a sequence of events, but that do not solely rely on humans as these cannot be assumed to be robust barriers. Regarding reporting and analysis, no consistent methods were identified, but for learning, the role of operating experience learning groups was identified as an exemplar. The safety culture across nuclear, however, was heard to vary, which undermined reporting of near misses and other safety events. Some parts of the industry described that their focus on near misses is new and that despite potential risks existing, progress to mitigate hazards is slow. Conclusions: Healthcare often sees ‘nuclear,’ as well as other ultra-safe industries such as ‘aviation,’ as homogenous. However, the findings here suggest significant differences in safety culture and maturity across various parts of the nuclear sector. Healthcare can take learning from some aspects of management of near misses in nuclear, such as how they are defined and how learning is shared through operating experience networks. However, healthcare also needs to recognise that variability exists across industries, and comparably, it may be more mature in some areas of safety.

Keywords: culture, definitions, near miss, nuclear safety, patient safety

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Authors: Kehinde, Taye Adetutu

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The fact that Nigeria as a nation is faced by myriads of problems associated with religious crises and political insecurity is no news, the spoken statement and actions of most political giant were the major cause of this unrest. The 'unlearnt' youth within the regions has encompassed the situation. This scenario is further compounded by multilingual nature of the country as it is estimated that there exists amount 400 indigenous languages in Nigeria. It is an indisputable fact that english language which has assumed the status of an official language in Nigeria, given its status has a language of power and captivity by a few with no privilege to attend school. However, educating people in their indigenous language; crises can be averted through the proper orientation and mass literacy campaign, especially for the timid illiterate one, so as to live in unity, peace, tranquillity, and harmony as indivisible nation. In investigating the problem in this study with an emphasis on three major Nigerian language (Yoruba, Igbo and Hausa), participants observations and survey questionnaire were administered to about one hundred and twenty (120) respondents who were randomly selected throughout the three major ethnic groups in Nigeria. Findings from this study reveals that teaching and learning of cognitive words and information are more effective in ones mother tongue and helps in stimulating new ideas and changes. This paper was able to explore and critically examine the current state of affairs in Nigeria and proffer possible solutions to the prevailing situations by identifying how indigenous languages and linguistics can be used to ameliorate the present political and religious crisis for Nigeria, thus providing a proper recommendation to achieve meaningful stability and coexistence within a nation.

Keywords: multilingualism, political crisis, religious, Nigeria

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Authors: Eleanor F. Willard

Abstract:

The attrition rate on distance learning courses can be high. This research examines how online students often react when faced with poor results. Using q methodology, it was found that the emotional response level and the type of social support sought by students were key influences on their attitude to failure. As educational and psychological researchers, we are adept at measuring learning and achievement, but examining attitudes towards barriers to learning are not so well researched. The distance learning student has differing needs from onsite learners and, as the attrition rate is notoriously high in the online student population, examining learners’ attitude towards adversity and barriers is important. Self-report measures such as questionnaires are useful in terms of ascertaining levels of constructs such as resilience and academic confidence. Interviewing, too, can gain in depth detail of the opinions of such a population, but only in individuals. The aim of this research was to ascertain what the feelings and attitudes of online students were when faced with a setback. This was achieved using q methodology due to its use of both quantitative and qualitative methodology and its suitability for exploratory research. The emphasis with this methodology is the attitudes, not the individuals. The work was focused upon a population of distance learning students who attended a school on site for one week as part of their studies. They were engaged in a psychology masters conversion course and, as such, were graduate students. The Q sort had 30 items taken from the Academic Resilience Scale (ARS-30). The scale items represent three constructs; perseverance, reflecting (including adaptive help-seeking) and negative affect. These are widely acknowledged as being relevant concepts underpinning psychological resilience. The q sort was conducted with 19 students in total. This is done by participants arranging statement cards regarding how similar to themselves they believe each statement to be. This was done after reading a vignette describing an experience of academic failure. Commonalities and differences between the sorts from all participants are then analyzed in terms of correlations and response patterns. Following data collection, the participants' responses were initially analyzed and the key perspectives (factors) to emerge were labelled ‘persevering individuals’ and ‘emotional networkers’. The differences between the two perspectives centre around the level of emotion felt when faced with barriers and the extent that students enlist the help of others inside and outside of the university. The dominant factor to emerge from the sorts of ‘persevering individuals’ demonstrated that many distance learners are tenacious. However, for other students, the level of emotional and social support is pivotal in helping them complete their studies when facing adversity. This was demonstrated by the ‘emotional networkers’ perspective. This research forms a starting point for further work on engaging and retaining online students at university and can potentially provide insight into how universities can lower attrition rates on distance learning courses.

Keywords: academic resilience, distance learning, online learning, q methodology

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Authors: Regina Rahimi, Delores Liston

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The advent of COVID-19 has heightened awareness of the need for social emotional learning (SEL) throughout all educational contexts. Given this, schools (most often p12 settings) have begun to embrace practices for addressing social-emotional learning. While there is a growing body of research and literature on common practices of SEL, there is no ‘standard’ for its implementation. Our work proposed here recognizes there is no universal approach for addressing SEL and rather, seeks to explore how SEL can be approached in and through diverse contexts. We assert that left unrecognized and unaddressed by teachers, issues with social and emotional well-being profoundly negatively affect students’ academic performance and exacerbate teacher stress. They contribute to negative student-teacher relationships, poor classroom management outcomes, and compromised academic outcomes. Therefore, teachers and administrators have increasingly turned to developing pedagogical and classroom practices that support the social and emotional dimensions of students. Substantive quantitative evidence indicates professional development training to improve awareness and foster positive teacher-student relationships can provide a protective function for psycho-social outcomes and a promotive factor for improved learning outcomes for students. Our work aims to add to the growing body of literature on improving student well-being by providing a unique examination of SEL through a lens of diverse contexts. Methodology: This presentation hopes to present findings from an edited volume that will seek to highlight works that examine SEL practices in a variety of academic settings. The studies contained within the work represent varied forms of qualitative research. Conclusion: This work provides examples of SEL in higher education/postsecondary settings, a variety of P12 academic settings (public; private; rural, urban; charter, etc.), and international contexts. This work demonstrates the variety of ways educational institutions and educators have used SEL to address the needs of students, providing examples for others to adapt to their own diverse contexts. This presentation will bring together exemplar models of SEL in diverse practice settings.

Keywords: social emotional learning, teachers, classrooms, diversity

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Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

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Authors: Osman Okumuş

Abstract:

The pandemic has significantly affected every aspect of life. Especially in recenttimes, as a result of this effect, we have come closer to technology. Distance education has taken the place of formal education rather than supporting formal education. Thiscreatednewexperiencesforbothteachersandstudents. This research focused on revealing the experiences of the same students in distance and formal education, especially in history lessons. In the study, which was designed as a case study, 20 students were interviewed through a semi-structured interview form prepared by the researcher. The results show that both learning environments provide students with important experiences. However, despite the fact that the students developed their digital competencies and experienced different learning environments, they focused on formal education in the name of socialization.

Keywords: history lessons, distance education, pandemic., formal education

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Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: R. Ajith, Kamal Bijlani

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Over the past few years, tremendous evolutions have happened in the educational discipline. Serious game, which is regarded as one of the most important inventions is being widely for learning purposes. Serious games can be used to negate the various drawbacks that the current evaluation and assessment methods have, like examination anxiety and the lack of proper feedback given to the learners. This paper proposes serious game as a tool for conducting evaluations and assessments. The examination anxiety faced by learners can be reduced, as they are provided with a game as an examination. The serious game also tracks learner’s actions, records them and provide feedback based on the predefined set of actions according to the course objectives. The appropriate feedback given to the learner will help in developmental activities in the learning process.

Keywords: serious games, evaluation, performance assessment, examination anxiety, performance feedback

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Sami M. Alshareef

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The rapid growth of smart grid technology has brought significant advancements to the power industry. However, with the increasing interconnectivity and reliance on information and communication technologies, smart grids have become vulnerable to cyber-attacks, posing significant threats to the reliable operation of power systems. Among the critical components of smart grids, the Automatic Generation Control (AGC) system plays a vital role in maintaining the balance between generation and load demand. Therefore, protecting the AGC system from cyber threats is of paramount importance to maintain grid stability and prevent disruptions. Traditional security measures often fall short in addressing sophisticated and evolving cyber threats, necessitating the exploration of innovative approaches. Machine learning, with its ability to analyze vast amounts of data and learn patterns, has emerged as a promising solution to enhance AGC system security. Therefore, this research proposal aims to address the challenges associated with detecting and mitigating cyber-attacks on AGC in smart grids by leveraging machine learning techniques on automatic generation control of two-area power systems. By utilizing historical data, the proposed system will learn the normal behavior patterns of AGC and identify deviations caused by cyber-attacks. Once an attack is detected, appropriate mitigation strategies will be employed to safeguard the AGC system. The outcomes of this research will provide power system operators and administrators with valuable insights into the vulnerabilities of AGC systems in smart grids and offer practical solutions to enhance their cyber resilience.

Keywords: machine learning, cyber-attacks, automatic generation control, smart grid

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Authors: Jeffrey M. Byford

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The conceptualized theory of decision making on historical events often does not conform to uniform beliefs among students. When presented the opportunity, many students have differing opinions and rationales associated with historical events and outcomes. The intent of this paper was to provide students with the economic, social and political dilemmas associated with the autonomy of East Berlin. Students ranked seven possible actions from the most to least acceptable. In addition, students were required to provide both positive and negative factors for each decision and relative ranking. Results from this activity suggested that while most students chose a financial action towards West Berlin, some students had trouble justifying their actions.

Keywords: content-centered learning, cold war, Berlin, decision-making

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Authors: M. Talebzadegan, S. Bina , I. Riazi

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The purpose of this paper is to present an active solar energy system to supply heating demands of the teaching staff dormitory of Islamic Azad University of Ramhormoz. The design takes into account the solar radiations and climate data of Ramhormoz town and is based on the daily warm water consumption for health demands of 450 residents of the dormitory, which is equal to 27000 lit of 50 C° water, and building heating requirements with an area of 3500 m² well-protected by heatproof materials. First, heating demands of the building were calculated, then a hybrid system made up of solar and fossil energies was developed and finally, the design was economically evaluated. Since there is only roof space for using 110 flat solar water heaters, the calculations were made to hybridize solar water heating system with heat pumping system in which solar energy contributes 67% of the heat generated. According to calculations, the Net Present Value “N.P.V.” of revenue stream exceeds “N.P.V.” of cash paid off in this project over three years, which makes economically quite promising. The return of investment and payback period of the project is 4 years. Also, the Internal Rate of Return (IRR) of the project was 25%, which exceeds bank rate of interest in Iran and emphasizes the desirability of the project.

Keywords: solar energy, heat demand, renewable, pollution

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Seedwell Sithole

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One of the persistent problems in accounting education is how to effectively support students’ learning. A promising technique to this issue is to investigate the extent that learning is determined by the design of instructional material. This study examines the academic performance of students using three instructional designs in financial accounting. Student’s performance scores and reported mental effort ratings were used to determine the instructional effectiveness. The findings of this study show that accounting students prefer graph and text designs that are integrated. The results suggest that spatially separated graph and text presentations in accounting should be reorganized to align with the requirements of human cognitive architecture.

Keywords: accounting, cognitive load, education, instructional preferences, students

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Eylem Tas

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This empirical study aims to explore the development of students' entrepreneurial skills through problem-based learning within the context of university-industry collaborations (UICs) in curriculum co-design and co-delivery (CDD). The research question guiding this study is: "How do problem-based learning and university-industry collaborations influence the development of students' entrepreneurial skills in the context of curriculum co-design and co-delivery?” To address this question, the study was conducted in a big city in Germany and involved interviews with stakeholders from various industries, including the private sector, government agencies (govt), and non-governmental organizations (NGOs). These stakeholders had established collaborative partnerships with the targeted university for projects encompassing entrepreneurial development aspects in CDD. The study sought to gain insights into the intricacies and subtleties of UIC dynamics and their impact on fostering entrepreneurial skills. Qualitative content analysis, based on Mayring's guidelines, was employed to analyze the interview transcriptions. Through an iterative process of manual coding, 442 codes were generated, resulting in two main sections: "the role of problem-based learning and UIC in fostering entrepreneurship" and "challenges and requirements of problem-based learning within UIC for systematical entrepreneurship development.” The chosen experimental approach of semi-structured interviews was justified by its capacity to provide in-depth perspectives and rich data from stakeholders with firsthand experience in UICs in CDD. By enlisting participants with diverse backgrounds, industries, and company sizes, the study ensured a comprehensive and heterogeneous sample, enhancing the credibility of the findings. The first section of the analysis delved into problem-based learning and entrepreneurial self-confidence to gain a deeper understanding of UIC dynamics from an industry standpoint. It explored factors influencing problem-based learning, alignment of students' learning styles and preferences with the experiential learning approach, specific activities and strategies, and the role of mentorship from industry professionals in fostering entrepreneurial self-confidence. The second section focused on various interactions within UICs, including communication, knowledge exchange, and collaboration. It identified key elements, patterns, and dynamics of interaction, highlighting challenges and limitations. Additionally, the section emphasized success stories and notable outcomes related to UICs' positive impact on students' entrepreneurial journeys. Overall, this research contributes valuable insights into the dynamics of UICs and their role in fostering students' entrepreneurial skills. UICs face challenges in communication and establishing a common language. Transparency, adaptability, and regular communication are vital for successful collaboration. Realistic expectation management and clearly defined frameworks are crucial. Responsible data handling requires data assurance and confidentiality agreements, emphasizing the importance of trust-based relationships when dealing with data sharing and handling issues. The identified key factors and challenges provide a foundation for universities and industrial partners to develop more effective UIC strategies for enhancing students' entrepreneurial capabilities and preparing them for success in today's digital age labor market. The study underscores the significance of collaborative learning and transparent communication in UICs for entrepreneurial development in CDD.

Keywords: collaborative learning, curriculum co-design and co-delivery, entrepreneurial skills, problem-based learning, university-industry collaborations

Procedia PDF Downloads 55

Authors: Diana Ruth Caga-Anan

Abstract:

Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

Procedia PDF Downloads 163

Authors: Yarhands Dissou Arthur, Samuel Asiedu Addo, Jonathan Annan

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Interest in subject specific is very essential in the quest to ensure effective teaching and learning. In building interest in subject specific areas requires certain factors and strategies well-spelled out.The factors such as the gender of the student, the type of basic school attended, the parent interest as well as the scare of the basic school mathematics teacher is very important to consider. The relational effect and the contribution these above mentioned variables on student have not been fully investigated and this paper address the effect of these factors on the student interest. In the attainment of this goal, the current paper addresses the effect of parent interest, the type of basic school attended, the scare by basic school mathematics teacher and its effect on student’s interest in mathematics. A cross sectional data collected from two hundred and sixty post-secondary school student were analyzed using descriptive and inferential statistical methods by aid of SPSS version 16. The study found that parent interest and value for mathematics significantly influenced students interest and joy in solving mathematical problems. Moreover, we also observed that the fear imposed by basic school mathematics teachers was found to significantly influence students’ interest. The study further found that the type of basic school attended and gender are factors that do not influence students’ interest in mathematics. In addition to concluding that a student’s interest is influenced by both parent interest and the fear of basic school mathematics teacher, the study also showed that the type of basic school attended and gender does not affect the students’ interest in mathematics.

Keywords: gender, mathematics interest, teacher interest, teacher interest, student interest

Procedia PDF Downloads 359

Authors: Peter Akayuure, Michael Johnson Nabie

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Similar to many colonized nations in the world, one indelible mark left by colonial masters after Ghana’s independence in 1957 has been the fact that many contexts used to teach statistics and probability concepts are often alien and do not resonate with the social domain of our indigenous Ghanaian child. This has seriously limited the understanding, discoveries, and applications of mathematics for national developments. With the recent curriculum demands of making the Ghanaian child mathematically literate, this qualitative study involved video recordings and mathematical analysis of play sessions of an indigenous girl game called Ampe with the aim to demystify the concepts in probability theorem, which is applied in mathematics related fields of study. The mathematical analysis shows that the game of Ampe, which is widely played by school girls in Ghana, is suitable for learning concepts of the probability theorems. It was also revealed that as a girl game, the use of Ampe provides good lessons to educators, textbook writers, and teachers to rethink about the selection of mathematics tasks and learning contexts that are sensitive to gender. As we undertake to transform teacher education and student learning, the use of indigenous games should be critically revisited.

Keywords: Ampe, mathematical analysis, probability theorem, Ghanaian girl game

Procedia PDF Downloads 364

Authors: Frazer G. Thompson

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Israel is a nation of cultural and historical diversity, yet the success factors for a modern Bedouin-Arab high-tech entrepreneur seem to be different from those of other Jewish-Israeli citizens. The purpose of this descriptive narrative case study is to explore how an Arab-Israeli all Negev-Bedouin technology company has succeeded in the Israeli high-tech industry by utilizing technology and engineering career opportunities available to Bedouin youth for ‘Sadel Tech,’ at Be’er-Sheva, the Negev, Israel. Methods: The strategy of inquiry seeks to explore real-life contextual understandings, multi-level perspectives, and the cultural influences of personal, community, educational, and entrepreneurial factors. The research methodology includes in-depth one-on-one interviews, focus group sessions, and overt observation to explore the meaning and understanding of the constructs toward determining the effect all or a few of the elements may have on the overall success factors of the company. Results: Study results indicate that the state-run educational system in Israel fails to adequately integrate important aspects of Bedouin culture into the learning environment. However, Bedouin entrepreneurs are finding ways to compensate for these inadequacies by utilizing non-traditional methods of teaching, learning, and doing business. Government incentives for Bedouin start-ups are also recognized as contributors. Employees of Sadel live and work in the Negev, the Gaza Strip, and the West Bank, further informing the study that the traditions of tribal etiquette continue to contribute to modern Bedouin-Arab business culture. Conclusion: Bedouin's business success in Israel is a multi-dimensional concept. While cultural acumen plays a prominent and unique role for both Arab-Israelis and Jewish-Israelis in economic and entrepreneurial pursuits, the marginalization of the Bedouin continues to contribute to the lack of educational and professional opportunities for Bedouin in Israel. Although recognized as important at the government level, programs necessary to implement the infrastructure required to support Bedouin entrepreneurship in Israel remain infantile. The Israeli Government is providing opportunities through grants and other incentives for Bedouin entrepreneurial start-ups, indicating that Israel has recognized the impact of this growing demographic. However, although many Bedouin graduates from University each year with advanced degrees, opportunities for Bedouin within the Israeli high-tech sector remain scarce.

Keywords: Bedouin education, Bedouin entrepreneur, economic anthropology, ethnic business opportunities, Israeli tech, Silicon Wadi

Procedia PDF Downloads 117

Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

Procedia PDF Downloads 333