Search results for: active learning approach

Authors: Naser Alajmi, Ali Alobaidly, Mubarak Alhajri, Salem Salamah, Muhammad Alsubaie

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Iterative Learning Control (ILC) known to be a controlling tool to overcome periodic disturbances for repetitive systems. This technique is required to let the error signal tends to zero as the number of operation increases. The learning process that lies within this context is strongly dependent on the initial input which if selected properly tends to let the learning process be more effective compared to the case where a system starts from blind. ILC uses previous recorded execution data to update the following execution/trial input such that a reference trajectory is followed to a high accuracy. Error convergence in ILC is generally highly dependent on the input applied to a plant for trial $1$, thus a good choice of initial starting input signal would make learning faster and as a consequence the error tends to zero faster as well. In the work presented within, an upper limit based on the Singular Values Principle (SV) is derived for the initial input signal applied at trial $1$ such that the system follow the reference in less number of trials without responding aggressively or exceeding the working envelope where a system is required to move within in a robot arm, for example. Simulation results presented illustrate the theory introduced within this paper.

Keywords: initial input, iterative learning control, maximum input, singular values

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Authors: Payal Maheshwari, Maheaswari Brindavan

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The present study aimed at studying the level of perceived self-efficacy of children with learning disability and their mother’s perception about the efficacy of the child and the relationship between the two. The study further aimed at finding out the relationship between the level of perceived self-efficacy of children with learning disability and their academic achievement and their mother’s perception about the Efficacy of the child and child’s Academic Achievement. The sample comprised of 80 respondents (40 children with learning disability and their mothers). Children with learning disability as their primary condition, belonging to middle or upper middle class, living with both the parents, residing in Mumbai and their mothers were selected. Purposive or judgmental and snowball sampling technique was used to select the sample for the present study. Proformas in the form of questionnaires were used to obtain the background information of the children with learning disability and their mother’s. A self-constructed Mother’s Perceived Efficacy of their Child Assessment Scale was used to measure mothers perceived level of efficacy of their child with learning disability. Self-constructed Child’s Perceived Self-Efficacy Assessment Scale was used to measure the level of child’s perceived self-efficacy. Academic scores of the child were collected from the child’s parents or teachers and were converted into percentage. The data were analyzed quantitatively using frequencies, mean and standard deviation. Correlations were computed to ascertain the relationships between the different variables. The findings revealed that majority of the mother’s perceived efficacy about their child with learning disability was above average as well as majority of the children with learning disability also perceived themselves as having above average level of self-efficacy. Further in the domains of self-regulated learning and emotional self-efficacy majority of the mothers perceived their child as having average or below average efficacy, 50% of the children also perceived their self-efficacy in the two domains at average or below average level. A significant (r=.322, p < .05) weak correlation (Spearman’s rho) was found between mother’s perceived efficacy about their child, and child’s perceived self-efficacy and a significant (r=.377, p < .01) weak correlation (Pearson Correlation) was also found between mother’s perceived efficacy about their child and child’s academic achievement. Significant weak positive correlation was found between child’s perceived self-efficacy and academic achievement (r=.332, p < .05). Based on the findings, the study discussed the need for intervention program for children in non-academic skills like self-regulation and emotional competence.

Keywords: learning disability, perceived self efficacy, academic achievement, mothers, children

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Authors: Fu Lin

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We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a prespecified number of line outages that lead to the maximum interruption of power generation and load at the transmission level, subject to the active power-flow model, the load and generation capacity of the buses, and the phase-angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power-flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove the convergence of our algorithm to a critical point, and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.

Keywords: load shedding, power system, proximal alternating linearization method, vulnerability analysis

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Khan Zeenat, S. B. Dandegaonkar

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Dyslexia is a neurological disorder which affects the reading and writing ability of children. A sample of 72 dyslexic children (36 from English medium and 36 from Marathi medium schools) of class V from English and Marathi medium schools were selected. The Experimental method was used to study the effect of Remedial Teaching Programme on the Learning outcome of Dyslexic students. The findings showed that there is a Positive effect of remedial teaching programme on the Learning outcome of English and Marathi medium students.

Keywords: remedial teaching, Dyslexic students, learning outcome, neurological

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Authors: C. Villamizar, M. Serrato

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In the pursuit of athletic performance, the role of physical training which is determined by a number of charges or taxes on physiological stress and musculoskeletal systems of the human body generated by the intensity and duration is fundamental. Given the physical demands of these activities both training and competitive must take into account the optimal relationship with a straining process recovery post favoring the process of overcompensation which aims to facilitate the return and rising energy potential and protein synthesis also of different tissues. Allowing muscle function returns to baseline or pre-exercise states. If this recovery process is not performed or is not allowed in a proper way, will result in an increased state of fatigue. Active recovery, is one of the strategies implemented in the sport for a return to pre-exercise physiological states. However, there are some adverse assumptions regarding the negative effects, as is the possibility of increasing the degradation of muscle glycogen and thus delaying the synthesis thereof. For them, it is necessary to investigate what would be the effects generated application made at different times after the effort. The aim of this study was to determine the effects of active recovery post effort made at three different times: immediately, at 12 and 24 hours on biochemical markers creatine kinase in youth soccer player’s categories. A randomized controlled trial with allocation to three groups was performed: A. active recovery immediately after the effort; B. active recovery performed at 12 hours after the effort; C. active recovery made at 24 hours after the effort. This study included 27 subjects belonging to a Colombian soccer team of the second division. Vital signs, weight, height, BMI, the percentage of muscle mass, fat mass percentage, personal medical history, and family were valued. The velocity, explosive force and Creatin Kinase (CK) in blood were tested before and after interventions. SAFT 90 protocol (Soccer Field specific Aerobic Test) was applied to participants for generating fatigue. CK samples were taken one hour before the application of the fatigue test, one hour after the fatigue protocol and 48 of the initial CK sample. Mean age was 18.5 ± 1.1 years old. Improvements in jumping and speed recovery the 3 groups (p < 0.05), but no statistically significant differences between groups was observed after recuperation. In all participants, there was a significant increment of CK when applied SAFT 90 in all the groups (median 103.1-111.1). The CK measurement after 48 hours reflects a recovery in all groups, however the group C, a decline below baseline levels of -55.5 (-96.3 /-20.4) which is a significant find. Other research has shown that CK does not return quickly to their baseline, but our study shows that active recovery favors the clearance of CK and also to perform recovery 24 hours after the effort generates higher clearance of this biomarker.

Keywords: active recuperation, creatine phosphokinase, post training, young soccer players

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Authors: Thomas Q. Pan, Anika Basu, Chamith S. Rajapakse

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Machine learning requires data that is categorized into features that models train on. This topic is important to the field of sports medicine due to the many tools it provides to physicians such as diagnosis support and risk assessment. Physician note that healthcare professionals take are usually unclean and not suitable for model training. The objective of this study was to develop and evaluate an advanced approach for extracting key features from sports medicine data without the need for extensive model training or data labeling. An LLM (Large Language Model) was given a narrative (Physician’s Notes) and prompted to extract four features (details about the patient). The narrative was found in a datasheet that contained six columns: Case Number, Validation Age, Validation Gender, Validation Diagnosis, Validation Body Part, and Narrative. The validation columns represent the accurate responses that the LLM attempts to output. With the given narrative, the LLM would output its response and extract the age, gender, diagnosis, and injured body part with each category taking up one line. The output would then be cleaned, matched, and added to new columns containing the extracted responses. Five ways of checking the accuracy were used: unclear count, substring comparison, LLM comparison, LLM re-check, and hand-evaluation. The unclear count essentially represented the extractions the LLM missed. This can be also understood as the recall score ([total - false negatives] over total). The rest of these correspond to the precision score ([total - false positives] over total). Substring comparison evaluated the validation (X) and extracted (Y) columns’ likeness by checking if X’s results were a substring of Y's findings and vice versa. LLM comparison directly asked an LLM if the X and Y’s results were similar. LLM Re-check prompted the LLM to see if the extracted results can be found in the narrative. Lastly, A selection of 1,000 random narratives was also selected and hand-evaluated to give an estimate of how well the LLM-based feature extraction model performed. With a selection of 10,000 narratives, the LLM-based approach had a recall score of roughly 98%. However, the precision scores of the substring comparison and LLM comparison models were around 72% and 76% respectively. The reason for these low figures is due to the minute differences between answers. For example, the ‘chest’ is a part of the ‘upper trunk’ however, these models cannot detect that. On the other hand, the LLM re-check and subset of hand-tested narratives showed a precision score of 96% and 95%. If this subset is used to extrapolate the possible outcome of the whole 10,000 narratives, the LLM-based approach would be strong in both precision and recall. These results indicated that an LLM-based feature extraction model could be a useful way for medical data in sports to be collected and analyzed by machine learning models. Wide use of this method could potentially increase the availability of data thus improving machine learning algorithms and supporting doctors with more enhanced tools.

Keywords: AI, LLM, ML, sports

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Authors: M. Kalyuga

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Prepositions have long been considered to be one of the major challenges for second language learners, since they have multiple uses that differ greatly from one language to another. The traditional approach to second language teaching supplies students with a list of uses of a preposition that they have to memorise and no explanation is provided. Contrary to the traditional grammar approach, the cognitive linguistic approach offers an explanation for the use of prepositions and provides strategies to comprehend and learn prepositions that would be otherwise seem obscure. The present paper demonstrates the use of the cognitive approach for the explanation of prepositions through the example of the Russian preposition 'за'. The paper demonstrates how various spatial and non-spatial uses of this preposition are linked together through metaphorical and metonymical mapping. The diversity of expressions with за is explained by the range of spatial scenes this preposition is associated with.

Keywords: language teaching, Russian, preposition 'за', cognitive approach

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Authors: Ilaisa Kacivakanadina, Samson Viulu, Brad Carte, Katy Soapi

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Two bioactive compounds namely Vulgamycin (also known as enterocin A) and 5-deoxyenterocin were purified from a marine bacterial strain 1903. Strain 1903 was isolated from marine sediments collected from the Solomon Islands. Morphological features of strain 1903 showed that it belongs to the genus Streptomyces. The two secondary metabolites were extracted using EtOAc and purified by chromatographic methods using EtOAc and hexane solvents. Mass spectrum and NMR data of pure compounds were used to elucidate the chemical structures. In this study, results showed that both compounds were strongly active against Wild Type Staphylococcus aureus (WTSA) (MIC < 1 µg/mL) and in Brine shrimp assays (BSA) (MIC < 1 µg/mL). 5-deoxyenterocin was also active against Rifamycin resistant Staphylococcus aureus (RRSA) (MIC, 250 µg/mL) while vulgamycin showed bioactivity against Methicillin resistant Staphylococcus aureus (MRSA) (MIC 250 µg/mL). To the best of our knowledge, this is the first study that showed the bio-activity of 5-deoxyenterocin. This is also the first time that Vulgamycin has been reported to be active in a BSA. There has not been any mechanism of action studies for these two compounds against pathogens. This warrants further studies on their mechanism of action against microbial pathogens.

Keywords: 5-deoxyenterocin, bioactivity, brine shrimp assay (BSA), vulgamycin

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Salam Kodsi

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In this research, we sought to focus on constructive play and examine its components in the context of two different educational approaches: Waldorf and normative schools. When they are free to choose, construction is one of the forms of play most favored by children. Its short-term and long-term cognitive contributions are apparent in various areas of development. The lack of empirical studies about play in Waldorf schools, which addresses the possibility of this incidental learning inspired the need to enrich the body of existing knowledge. 90 children (4-6 yrs.old) four preschools ( two normative, two Waldorf) participated in a small homogeneous city. Naturalistic observations documented the time frame, physical space, and construction materials related to the freeform building; processes of construction among focal representative children and its products. The study’s main finding with respect to the construction output points to a connection between educational approach and level of construction sophistication. Higher levels of sophistication were found at the Waldorf preschools than at the mainstream preschools. This finding emerged due to the differences in the level of sophistication among the older children in the two types of preschools, while practically no differences emerged among the younger children. Discussion of the research findings considered the differences between the play environments in terms of time, physical space, and construction materials. The construction processes were characterized according to the design model stages. The construction output was characterized according to the sophistication scale dimensions and the connections between approach, age and gender, and sophistication level.

Keywords: constructive play, preschool, design process model, complexity

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Authors: Peng Liu

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Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework.

Keywords: deep reinforcement learning, deep Q networks, portfolio optimization, multi-period optimization

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Authors: Roberta Gentry

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A mixed methods study that examined candidate’s perceptions of the use of computerized simulation as an effective tool to learn classroom management will be presented. The development, implementation, and assessment of the simulation and candidate data on the feasibility of the approach in comparison to other methods will be presented.

Keywords: behavior management, simulations, teacher preparation, teacher education

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Authors: G. S. Androulakis, G. Deli, M. Kaisari, N. Mihos

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Teaching practices at the university level have changed and developed during the last decade. Implementation of inverted classroom method in secondary education consists of a well-formed basis for academic teachers. On the other hand, distance learning is a well-known field in education research and widespread as a method of teaching. Nonetheless, the new pandemic found many Universities all over the world unprepared, which made adaptations to new methods of teaching a necessity. In this paper, we analyze a model of an inverted university classroom in a distance learning context. Thus, the main purpose of our research is to investigate students’ difficulties as they transit to a new style of teaching and explore their learning development during a semester totally different from others. Our teaching experiment took place at the Business Administration department of the University of Patras, in the context of two courses: Calculus, a course aimed at first-year students, and Statistics, a course aimed at second-year students. Second-year students had the opportunity to attend courses in the university classroom. First-year students started their semester with distance learning. Using a comparative study of these two groups, we explored significant differences in students’ learning procedures. Focused group interviews, written tests, analyses of students’ dialogues were used in a mixed quantity and quality research. Our analysis reveals students’ skills, capabilities but also a difficulty in following, non-traditional style of teaching. The inverted classroom model, according to our findings, offers benefits in the educational procedure, even in a distance learning environment.

Keywords: distance learning, higher education, inverted classroom, mathematics teaching

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Authors: Olukemi E. Adetuyi-Olu-Francis

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Learning is natural for living. One stops learning when life ends. Hence, there is no negotiating life-long learning. An individual has the innate ability to learn as many languages as he/she desires as long as life exists. French language education to every Nigerian teacher-trainee is a necessity. Nigeria’s geographical location requires that the French language should be upheld for economic and cultural co-operations between Nigeria and the francophone countries sharing borders with her. The French language will enhance the leadership roles of the teacher-trainees and their ability to function across borders. The 21st century learning tools are basically digital, and many apps are complementing the actual classroom interactions. This study examined the communicative competence in the French language to equip Nigerian teacher-trainees in the new-normal society using mobile apps as a lifelong learning tool. Three research questions and hypotheses guided the study, and the researcher adopted a pre-test, a post-test experimental design, using a sample size of 87 teacher-trainees in South-south geopolitical zone of Nigeria. Results showed that the use of mobile apps is effective for learning the French language. One of the recommendations is that the use of mobile apps should be encouraged for all Nigerian youths to learn the French language for enhancing leadership roles in the world of work and for international interactions for socio-economic co-operations with Nigerian neighboring countries.

Keywords: communicative competence, french language, life long learning, mobile apps, new normal society, teacher trainees

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Authors: Nada Souissi, Mourad Mroua

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The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index.

Keywords: Financial asset return predictability, Economic factors, Investor's psychology index, Bayesian approach, Probabilistic networks, Parametric learning

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Authors: Kayondoamunmose Kamafrika

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Experimental based Engineering education approach plays a vital role in the development of student’s deep understanding of both social and physical sciences. Experimental based education approach through laboratory class activities prepare students to meet national demand for high-tech skilled individuals in the government and private sector. However, students across the country are faced with difficulties in classes that involve laboratory activities: poor experimental based exposure in their early development of student’s education-life-cycle, lack of student engagement in scientific method practical thinking approach, lack of communication between students and the instructor during class, a large number of students in one classroom, lack of instruments and improper equipment calibration. The purpose of this paper is to help students develop their own scientific knowledge and understanding, develop their methodologies in the design of experiments, collect and analyze data, write laboratory reports, present and explain their findings. Experimental based laboratory activities allow students to learn with high-level understanding as well as engage in the design processes of constructing knowledge through practical means of doing science. Experimental based education systems approach will act as a catalyst in the development of practical-based-educational methodologies in social and physical science and engineering domain of learning; thereby, converting laboratory classes into pilot industries and students into professional experts in finding a solution for complex problems, research, and development of super high- tech systems.

Keywords: experimental, engineering, innovation, practicability

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Authors: Agah Tugrul Korucu

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Significant changes have taken place for the better in the bulk of information and in the use of technology available in the field of education induced by technological changes in the 21st century. It is mainly the job of the teachers and pre-service teachers to integrate information and communication technologies into education by means of conveying the use of technology to individuals. While the pre-service teachers are conducting lessons by using technology, the methods they have developed are important factors for the requirements of the lesson and for the satisfaction levels of the students. The study of this study is to examine the satisfaction levels of pre-service teachers as regards e-learning in a technological environment in which there are lesson activities conducted through an online learning environment in terms of various variables. The study group of the research is composed of 156 pre-service teachers that were students in the departments of Computer and Teaching Technologies, Art Teaching and Pre-school Teaching in the academic year of 2014 - 2015. The qualitative research method was adopted for this study; the scanning model was employed in collecting the data. “The Satisfaction Scale regarding the E-learning Process”, developed by Gülbahar, and the personal information form, which was developed by the researcher, were used as means of collecting the data. Cronbach α reliability coefficient, which is the internal consistency coefficient of the scale, is 0.91. SPSS computerized statistical package program and the techniques of medium, standard deviation, percentage, correlation, t-test and variance analysis were used in the analysis of the data.

Keywords: online learning environment, integration of information technologies, e-learning, e-learning satisfaction, pre-service teachers

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Authors: Manthekeleng Linake, Fesi Liziwe

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The ongoing argument over the use of English as a learning and teaching language in schools was examined in this study. The nature of the language proficiency gap is particularly relevant in light of the present emphasis on learning and educational quality in contemporary debates, as well as the education sustainable development goal. As a result, an interpretivist paradigm, a qualitative technique, and a case study-based research design were used in the work. Two school principals, two teachers, two members of the School Governing Body (SGB), and four learners were chosen using purposive sampling from two schools in the Amathole West Education District. The researchers were able to acquire in-depth information on the disputes surrounding the use of English as a language of learning and teaching by using semi-structured interview questions and focus groups. Despite knowing that they do not have the potential to do well in English, teachers found that despite appreciating the value of mother tongue and cultural identity, they prefer to use English as the language of teaching in schools. The findings, on the other hand, revealed that proponents of mother-language-based education argue that learning one's mother tongue is a human right.

Keywords: English first additional language learners, social justice, human capabilities, language proficiency

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Authors: Rodrique Watong Tchounkeu

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This paper investigated the implementation and impact of inclusive education practices in early childhood settings (ages 3-6) with the overarching aim of fostering a diverse learning environment. The primary objectives were to assess the then-current state of inclusive practices, identify effective methodologies for accommodating diverse learning needs, and evaluate the outcomes of implementing inclusive education in early childhood settings. To achieve these objectives, a mixed-methods approach was employed, combining qualitative interviews with early childhood educators and parents, along with quantitative surveys distributed to a diverse sample of participants. The qualitative phase involved semi-structured interviews with 30 educators and 50 parents, selected through purposive sampling. The interviews aimed to gather insights into the challenges faced in implementing inclusive education, the strategies employed, and the perceived benefits and drawbacks. The quantitative phase included surveys administered to 300 early childhood educators across various settings, measuring their familiarity with inclusive practices, their perceived efficacy, and their willingness to adapt teaching methods. The results revealed a significant gap between the theoretical understanding and practical implementation of inclusive education in early childhood settings. While educators demonstrated a high level of theoretical knowledge, they faced challenges in effectively translating these concepts into practice. Parental perspectives highlighted the importance of collaboration between educators and parents in supporting inclusive education. The surveys indicated a positive correlation between educators' familiarity with inclusive practices and their willingness to adapt teaching methods, emphasizing the need for targeted professional development. The implications of this study suggested the necessity for comprehensive training programs for early childhood educators focused on the practical implementation of inclusive education strategies. Additionally, fostering stronger partnerships between educators and parents was crucial for creating a supportive learning environment for all children. By addressing these findings, this research contributed to the advancement of inclusive education practices in early childhood settings, ultimately leading to more inclusive and effective learning environments for diverse groups of young learners.

Keywords: inclusive education, early childhood settings, diverse learning, young learners, practical implementation, parental collaboration

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Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath

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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.

Keywords: pronunciation variations, dynamic programming, machine learning, natural language processing

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Authors: Kyle Saltmarsh

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Acoustic testing possesses many benefits due to its non-destructive nature and practicality. There hence exists many scenarios in which using acoustic testing for condition testing shows powerful feasibility. A wealth of information is contained within the acoustic and vibration characteristics of structures, allowing the development meaningful features for the classification of their respective condition. In this paper, methods, results, and discussions are presented on the use of non-destructive acoustic testing coupled with acoustic feature extraction and machine learning techniques for the condition testing of manufactured circular steel plates subjected to varied levels of damage.

Keywords: plates, deformation, acoustic features, machine learning

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Authors: Mohd Afif Md Nasir, Mohd Faizal Amin Nur, Jamaluddin Hasim, Abd Samad Hasan Basari, Mohd Halim Sahelan

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This study is aimed to investigate the suitability of Computer-Based Training (CBT) as one of the approaches in skills competency development at the Centre of Instructor and Advanced Skills Training (CIAST) Shah Alam Selangor and National Youth Skills Institute (NYSI) Pagoh Muar Johor. This study has also examined the perception among trainees toward Virtual Learning Environment (VLE) as to realize the development of skills in Welding Technology. The significance of the study is to create a computer-based skills development approach in welding technology among new trainees in CIAST and IKBN as well as to cultivate the element of general skills among them. This study is also important in elevating the number of individual knowledge workers (K-Workers) working in manufacturing industry in order to achieve the national vision which is to be an industrial nation in the year 2020. The design is a survey of research which using questionnaires as the instruments and is conducted towards 136 trainees from CIAST and IKBN. Data from the questionnaires is proceeding in a Statistical Package for Social Science (SPSS) in order to find the frequency, mean and chi-square testing. The findings of the study show the welding technology skills have developed in the trainees as a result of the application of the Virtual Reality simulator at a high level (mean=3.90) and the respondents agreed the skills could be embedded through the application of the Virtual Reality simulator (78.01%). The Study also found that there is a significant difference between trainee skill characteristics through the application of the Virtual Reality simulator (p<0.05). Thereby, the Virtual Reality simulator is suitable to be used in the development of welding skills among trainees through the skills training institute.

Keywords: computer-based training, virtual learning environment, welding technology, virtual reality simulator, virtual learning environment

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Authors: Namubiru Zula, Aganyira Kelle, Van der Linden Josje, Openjuru George Laadah

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Learning to know is a key principle to lifelong learning, with self-direction as the cornerstone. This study sought to initiate self-direction for lifelong learning through social constructivism among fishers; with the major goal of creating a community of fishers who continuously learn from each other for sustainable fishing. Government of Uganda has instituted several mechanisms like co-management with Beach Management Unit (BMU) System against illegal fishing. However, illegal fishing persists, there is reduced fish stocks with several outcry on how fishers are handled. Some studies have indicated that it’s the poor orientation of BMU leaders and fishers which are top down. This initial engagement of fishers was conducted through a meeting and use of stake holder’s analysis tool to discuss the relevance of the study; harnessing fishers’ knowledge for sustainable fisheries on Lake Victoria, its objectives, the key stake holders to enable them fish sustainably. It revealed initial attempt to learn from each other and learning to know among fishers, with some elements of self-direction. However, fishers attempt to learning and self-direction are affected by prior brutal enforcement experiences. This meeting led to fishers gain some sense of hope towards enforcement brutality. The key stakeholders highlighted include MAAIF, FAO, UNBS, NaFIRRI, LVFO, BMU, UFPEA, Fishers m employers, Fisheries Protection Unit, GIZ, and any Non-Government organization but declined the Association of Fisheries and Lake Users in Uganda.

Keywords: self direction, lifelong learning, social constructivism, sustainable fishing

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Authors: Wenqi Liu, Reginald Bailey

Abstract:

This study proposes a comprehensive and effective approach to business-to-business (B2B) sales forecasting by integrating advanced machine learning models with a rule-based decision-making framework. The methodology addresses the critical challenge of optimizing sales pipeline performance and improving conversion rates through predictive analytics and actionable insights. The first component involves developing a classification model to predict the likelihood of conversion, aiming to outperform traditional methods such as logistic regression in terms of accuracy, precision, recall, and F1 score. Feature importance analysis highlights key predictive factors, such as client revenue size and sales velocity, providing valuable insights into conversion dynamics. The second component focuses on forecasting sales value using a regression model, designed to achieve superior performance compared to linear regression by minimizing mean absolute error (MAE), mean squared error (MSE), and maximizing R-squared metrics. The regression analysis identifies primary drivers of sales value, further informing data-driven strategies. To bridge the gap between predictive modeling and actionable outcomes, a rule-based decision framework is introduced. This model categorizes leads into high, medium, and low priorities based on thresholds for conversion probability and predicted sales value. By combining classification and regression outputs, this framework enables sales teams to allocate resources effectively, focus on high-value opportunities, and streamline lead management processes. The integrated approach significantly enhances lead prioritization, increases conversion rates, and drives revenue generation, offering a robust solution to the declining pipeline conversion rates faced by many B2B organizations. Our findings demonstrate the practical benefits of blending machine learning with decision-making frameworks, providing a scalable, data-driven solution for strategic sales optimization. This study underscores the potential of predictive analytics to transform B2B sales operations, enabling more informed decision-making and improved organizational outcomes in competitive markets.

Keywords: machine learning, XGBoost, regression, decision making framework, system engineering

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Authors: C. W. Kan

Abstract:

Heat setting is a commonly used technique in textile industry for treating synthetic fibers. In this study, we examined the effect of heat-setting process on the dyeing properties of polyester fabric. The heat setting conditions were varied, and these conditions would affect the dyeing results. The aim of this study is to illustrate the proper application method of heat setting process to polyester fabric, and the results could provide guidance note to the students in learning this topic. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: learning materials, heat setting, polyester, dyeing

Procedia PDF Downloads 247

Authors: Erzen Muharemi, Emmanuel De Jaeger, Jos Knockaert

Abstract:

The work presented here investigates the modeling of power electronics converters in terms of their harmonic production. Specifically, it addresses high-frequency emissions in the range of 2-150 kHz, referred to as supraharmonics. This paper models a conventional converter, namely the boost converter used as an active power factor corrector (APFC). Furthermore, the modeling is extended to the case of the interleaved boost converter, which offers advantages such as halving the emissions. Finally, a comparison between the theoretical, numerical, and experimental results will be provided.

Keywords: APFC, boost converter, converter modeling, double fourier series, supraharmonics

Procedia PDF Downloads 42

Authors: Vedant Janapaty

Abstract:

Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

Procedia PDF Downloads 104

Authors: Joy Chadwick

Abstract:

This Study of Teaching and Learning (SoTL) research focused on the experiences of teacher candidates involved in an inclusive education methods course within a four-year direct entry Bachelor of Education program. The placement of this course within the final fourteen-week practicum semester is designed to facilitate deeper theory-practice connections between effective inclusive pedagogical knowledge and the real life of classroom teaching. The course focuses on supporting teacher candidates to understand that effective instruction within an inclusive classroom context must be intentional, responsive, and relational. Diversity is situated not as exceptional but rather as expected. This interpretive qualitative study involved the analysis of twenty-nine teacher candidate reflective journals and six individual teacher candidate semi-structured interviews. The journal entries were completed at the start of the semester and at the end of the semester with the intent of having teacher candidates reflect on their beliefs of what it means to be an effective inclusive educator and how the course and practicum experiences impacted their understanding and approaches to teaching in inclusive classrooms. The semi-structured interviews provided further depth and context to the journal data. The journals and interview transcripts were coded and themed using NVivo software. The findings suggest that instructional frameworks such as universal design for learning (UDL), differentiated instruction (DI), response to intervention (RTI), social emotional learning (SEL), and self-regulation supported teacher candidate’s abilities to meet the needs of their students more effectively. Course content that focused on specific exceptionalities also supported teacher candidates to be proactive rather than reactive when responding to student learning challenges. Teacher candidates also articulated the importance of reframing their perspective about students in challenging moments and that seeing the individual worth of each child was integral to their approach to teaching. A persisting question for teacher educators exists as to what pedagogical knowledge and understanding is most relevant in supporting future teachers to be effective at planning for and embracing the diversity of student needs within classrooms today. This research directs us to consider the critical importance of addressing personal attributes and mindsets of teacher candidates regarding children as well as considering instructional frameworks when designing coursework. Further, the alignment of an inclusive education course during a teaching practicum allows for an iterative approach to learning. The practical application of course concepts while teaching in a practicum allows for a deeper understanding of instructional frameworks, thus enhancing the confidence of teacher candidates. Research findings have implications for teacher education programs as connected to inclusive education methods courses, practicum experiences, and overall teacher education program design.

Keywords: inclusion, inclusive education, pre-service teacher education, practicum experiences, teacher education

Procedia PDF Downloads 68

Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail

Abstract:

Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.

Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation

Procedia PDF Downloads 181