Search results for: supervised machine learning algorithm
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11234

Search results for: supervised machine learning algorithm

6314 A Comparative Evaluation of Cognitive Load Management: Case Study of Postgraduate Business Students

Authors: Kavita Goel, Donald Winchester

Abstract:

In a world of information overload and work complexities, academics often struggle to create an online instructional environment enabling efficient and effective student learning. Research has established that students’ learning styles are different, some learn faster when taught using audio and visual methods. Attributes like prior knowledge and mental effort affect their learning. ‘Cognitive load theory’, opines learners have limited processing capacity. Cognitive load depends on the learner’s prior knowledge, the complexity of content and tasks, and instructional environment. Hence, the proper allocation of cognitive resources is critical for students’ learning. Consequently, a lecturer needs to understand the limits and strengths of the human learning processes, various learning styles of students, and accommodate these requirements while designing online assessments. As acknowledged in the cognitive load theory literature, visual and auditory explanations of worked examples potentially lead to a reduction of cognitive load (effort) and increased facilitation of learning when compared to conventional sequential text problem solving. This will help learner to utilize both subcomponents of their working memory. Instructional design changes were introduced at the case site for the delivery of the postgraduate business subjects. To make effective use of auditory and visual modalities, video recorded lectures, and key concept webinars were delivered to students. Videos were prepared to free up student limited working memory from irrelevant mental effort as all elements in a visual screening can be viewed simultaneously, processed quickly, and facilitates greater psychological processing efficiency. Most case study students in the postgraduate programs are adults, working full-time at higher management levels, and studying part-time. Their learning style and needs are different from other tertiary students. The purpose of the audio and visual interventions was to lower the students cognitive load and provide an online environment supportive to their efficient learning. These changes were expected to impact the student’s learning experience, their academic performance and retention favourably. This paper posits that these changes to instruction design facilitates students to integrate new knowledge into their long-term memory. A mixed methods case study methodology was used in this investigation. Primary data were collected from interviews and survey(s) of students and academics. Secondary data were collected from the organisation’s databases and reports. Some evidence was found that the academic performance of students does improve when new instructional design changes are introduced although not statistically significant. However, the overall grade distribution of student’s academic performance has changed and skewed higher which shows deeper understanding of the content. It was identified from feedback received from students that recorded webinars served as better learning aids than material with text alone, especially with more complex content. The recorded webinars on the subject content and assessments provides flexibility to students to access this material any time from repositories, many times, and this enhances students learning style. Visual and audio information enters student’s working memory more effectively. Also as each assessment included the application of the concepts, conceptual knowledge interacted with the pre-existing schema in the long-term memory and lowered student’s cognitive load.

Keywords: cognitive load theory, learning style, instructional environment, working memory

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6313 The Implications in the Use of English as the Medium of Instruction in Business Management Courses at Vavuniya Campus

Authors: Jeyaseelan Gnanaseelan, Subajana Jeyaseelan

Abstract:

The paper avails, in a systemic form, some of the results of the investigation into nature, functions, problems, and implications in the use of English as the medium of Instruction (EMI) in the Business Management courses at Vavuniya Campus of the University of Jaffna, located in the conflict-affected northern part of Sri Lanka. It is a case study of the responses of the students and the teachers from Tamil and Sinhala language communities of the Faculty of Business Studies. This paper analyzes the perceptions on the use of the medium, the EMI background, resources available and accessible, language abilities of the teachers and learners, learning style and pedagogy, the EMI methodology, the socio-economic and socio-political contexts typical of a non-native English learning context. The analysis is quantitative and qualitative. It finds out the functional perspective of the EMI in Sri Lanka and suggests practical strategies of contextualization and acculturation in the EMI organization and positions. The paper assesses the learner and teacher capacity in the use of English. The ethnic conflict and linguistic politics in Sri Lanka have contributed multiple factors to the current use of English as the medium. It has conflicted with its domestic realities and the globalization trends of the world at large which determines efficiency and effectiveness.

Keywords: medium of instruction, English, business management, teaching and learning

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6312 Using Wearable Device with Neuron Network to Classify Severity of Sleep Disorder

Authors: Ru-Yin Yang, Chi Wu, Cheng-Yu Tsai, Yin-Tzu Lin, Wen-Te Liu

Abstract:

Background: Sleep breathing disorder (SDB) is a condition demonstrated by recurrent episodes of the airway obstruction leading to intermittent hypoxia and quality fragmentation during sleep time. However, the procedures for SDB severity examination remain complicated and costly. Objective: The objective of this study is to establish a simplified examination method for SDB by the respiratory impendence pattern sensor combining the signal processing and machine learning model. Methodologies: We records heart rate variability by the electrocardiogram and respiratory pattern by impendence. After the polysomnography (PSG) been done with the diagnosis of SDB by the apnea and hypopnea index (AHI), we calculate the episodes with the absence of flow and arousal index (AI) from device record. Subjects were divided into training and testing groups. Neuron network was used to establish a prediction model to classify the severity of the SDB by the AI, episodes, and body profiles. The performance was evaluated by classification in the testing group compared with PSG. Results: In this study, we enrolled 66 subjects (Male/Female: 37/29; Age:49.9±13.2) with the diagnosis of SDB in a sleep center in Taipei city, Taiwan, from 2015 to 2016. The accuracy from the confusion matrix on the test group by NN is 71.94 %. Conclusion: Based on the models, we established a prediction model for SDB by means of the wearable sensor. With more cases incoming and training, this system may be used to rapidly and automatically screen the risk of SDB in the future.

Keywords: sleep breathing disorder, apnea and hypopnea index, body parameters, neuron network

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6311 Model of Learning Center on OTOP Production Process Based on Sufficiency Economic Philosophy

Authors: Chutikarn Sriviboon, Witthaya Mekhum

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The purposes of this research were to analyze and evaluate successful factors in OTOP production process for the developing of learning center on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning center on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning center on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.

Keywords: production process, OTOP, sufficiency economic philosophy, learning center

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6310 ECE Teachers’ Evolving Pedagogical Documentation in MAFApp: ICT Integration for Collective Online Thinking in Early Childhood Education

Authors: Cynthia Adlerstein-Grimberg, Andrea Bralic-Echeverría

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An extensive and controversial research debate discusses pedagogical documentation (PD) within early childhood education (ECE) as integral to ECE teachers' professional development. The literature converges in acknowledging that ICT integration in PD can be fundamental for children's and teachers' collaborative learning by making their processes visible and open to reflection. Controversial issues about PD emerge around ICT integration and the use of multimedia applications and platforms, displacing the physical experience involved in this pedagogical practice. Authors argue that online platforms make PD become a passive device to demonstrate accountability and performance. Furthermore, ICT integration would make educators inform children and families of pedagogical processes, positioning them more as consumers instead of involving them in collective thinking and pedagogical decision-making. This article analyses how pedagogical documentation mediated by a multimedia application (MAFApp) allows for the positive strengthening of an ECE pedagogical online community that thinks collectively about learning environments. In doing so, the paper shows how ICT integration supports ECE teachers' collective online thinking, enabling them to move from the controversial version of online PD, where they only act as informers of children's learning and assume a voyeuristic perspective, towards a collective online thinking that builds professional development and supports pedagogical decision-making about learning environments. This article answers How ECE teachers' pedagogical documentation evolves with ICT integration using the MAFApp multimedia application in a national ECE online community. From a posthumanist stance, this paper draws on an 18-month collaborative ethnographic immersion in Chile's unique public ECE online PD community. It develops a unique case study of an online ECE pedagogical community mediated by a multimedia application called MAFApp. This ECE online community includes 32 Chilean public kindergartens, 45 ECE teachers, and 72 assistants, who produced 534 pedagogical documentation. Fieldwork included 35 in-depth interviews, 13 discussion groups, and the constant comparison method for the PD coding. Findings show ICT integration in PD builds collective online thinking that evolves through four moments of growing complexity: 1) teachernalism of built environments, 2) onlookerism of children's anecdotes in learning environments; 3) storytelling of children's place-making, and 4) empowering pedagogies for co-creating learning environments. ICT integration through the MAFApp multimedia application enabled ECE teachers to build collective online thinking, making pedagogies of place visible and engaging children in co-constructing learning environments. This online PD is a continuous professional learning space for ECE teachers, empowering pedagogies of place. In conclusion, ICT integration into PD progressively empowers pedagogies of place in Chilean public ECE. Strengthening collective online thinking using the MAFApp multimedia application sharply contrasts with some recent PD research findings. ICT integration to PD enabled strong collective online thinking. Doing so makes PD operate as a place of professional development, pedagogical reflective encounters, and experimentation while inhabiting their own learning environments with children.

Keywords: early childhood education, ICT integration, multimedia application, online collective thinking, pedagogical documentation, professional development

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6309 Affects Associations Analysis in Emergency Situations

Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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6308 Integrating Historical Narratives with Merge Games as Tools for Pedagogy In Education

Authors: Aathira H.

Abstract:

Digital games can act as catalysts for educational transformation in the current scenario. Children and adolescence acquire this digital knowledge quickly and hence digital games can act as one of the most effective media for technology-mediated learning. Mobile gaming industries have seen the rise of a new trending genre of games, i.e., “Merge games” which is currently thriving in the market. This paper analysis on how gamifying historic and cultural narratives with merge mechanics can be an effective way to educate school children. Through the study of how merge mechanics in games have currently emerged as a trend., this paper argues how it can be integrated with a strong narrative which can convey history in an engaging way for education.

Keywords: game-based learning, merge mechanics, historical narratives, gaming innovations

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6307 A Fuzzy Multiobjective Model for Bed Allocation Optimized by Artificial Bee Colony Algorithm

Authors: Jalal Abdulkareem Sultan, Abdulhakeem Luqman Hasan

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With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use.

Keywords: bed allocation problem, fuzzy logic, artificial bee colony, multi-objective optimization

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6306 Creating a Professional Knowledge Base for Multi-Grade Teaching: Case Studies

Authors: Matshidiso Joyce Taole, Linley Cornish

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Teacher’s professional knowledge has become the focus of interest over decades and the interest has intensified in the 21st century. Teachers are expected to develop their professional academic expertise continually, on an ongoing basis. Such professional development may relate to acquiring enhanced expertise in terms of leadership, curriculum development, teaching and learning, assessment of/for learning and feedback for enhanced learning. The paper focuses on professional knowledge base required for teachers in multi-grade contexts. This paper argues that although teacher knowledge is strongly related to individual experiences and contexts, there are elements of teacher knowledge that are particular to multi-grade context. The study employed qualitative design using interviews and observations. The participants were multi-grade teachers and teaching principals. The study revealed that teachers need to develop skills such as learner grouping, differentiating the curriculum, planning, time management and be life-long learners so that they stay relevant and up to date with developments not only in the education sector but globally. This will help teachers to learn increasingly sophisticated methods for engaging the diverse needs of students in their classrooms.

Keywords: curriculum differentiation, multi-grade, planning, teacher knowledge

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6305 Design and Field Programmable Gate Array Implementation of Radio Frequency Identification for Boosting up Tag Data Processing

Authors: G. Rajeshwari, V. D. M. Jabez Daniel

Abstract:

Radio Frequency Identification systems are used for automated identification in various applications such as automobiles, health care and security. It is also called as the automated data collection technology. RFID readers are placed in any area to scan large number of tags to cover a wide distance. The placement of the RFID elements may result in several types of collisions. A major challenge in RFID system is collision avoidance. In the previous works the collision was avoided by using algorithms such as ALOHA and tree algorithm. This work proposes collision reduction and increased throughput through reading enhancement method with tree algorithm. The reading enhancement is done by improving interrogation procedure and increasing the data handling capacity of RFID reader with parallel processing. The work is simulated using Xilinx ISE 14.5 verilog language. By implementing this in the RFID system, we can able to achieve high throughput and avoid collision in the reader at a same instant of time. The overall system efficiency will be increased by implementing this.

Keywords: antenna, anti-collision protocols, data management system, reader, reading enhancement, tag

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6304 Prospective English Language Teachers’ Views on Translation Use in Foreign Language Teaching

Authors: Ozlem Bozok, Yusuf Bozok

Abstract:

The importance of using mother tongue and translation in foreign language classrooms cannot be ignored and translation can be utilized as a method in English Language Teaching courses. There exist researches advocating or objecting to the use of translation in foreign language learning but they all have a point in common: Translation should be used as an aid to teaching, not an end in itself. In this research, prospective English language teachers’ opinions about translation use and use of mother tongue in foreign language teaching are investigated and according to the findings, some explanations and recommendations are made.

Keywords: exposure to foreign language translation, foreign language learning, prospective teachers’ opinions, use of L1

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6303 Exclusive Value Adding by iCenter Analytics on Transient Condition

Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

Abstract:

During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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6302 Optimisation of Intermodal Transport Chain of Supermarkets on Isle of Wight, UK

Authors: Jingya Liu, Yue Wu, Jiabin Luo

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This work investigates an intermodal transportation system for delivering goods from a Regional Distribution Centre to supermarkets on the Isle of Wight (IOW) via the port of Southampton or Portsmouth in the UK. We consider this integrated logistics chain as a 3-echelon transportation system. In such a system, there are two types of transport methods used to deliver goods across the Solent Channel: one is accompanied transport, which is used by most supermarkets on the IOW, such as Spar, Lidl and Co-operative food; the other is unaccompanied transport, which is used by Aldi. Five transport scenarios are studied based on different transport modes and ferry routes. The aim is to determine an optimal delivery plan for supermarkets of different business scales on IOW, in order to minimise the total running cost, fuel consumptions and carbon emissions. The problem is modelled as a vehicle routing problem with time windows and solved by genetic algorithm. The computing results suggested that accompanied transport is more cost efficient for small and medium business-scale supermarket chains on IOW, while unaccompanied transport has the potential to improve the efficiency and effectiveness of large business scale supermarket chains.

Keywords: genetic algorithm, intermodal transport system, Isle of Wight, optimization, supermarket

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6301 Modeling Aeration of Sharp Crested Weirs by Using Support Vector Machines

Authors: Arun Goel

Abstract:

The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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6300 Enhancing Email Security: A Multi-Layered Defense Strategy Approach and an AI-Powered Model for Identifying and Mitigating Phishing Attacks

Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

Abstract:

Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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6299 The Potential Benefits of Multimedia Information Representation in Enhancing Students’ Critical Thinking and History Reasoning

Authors: Ang Ling Weay, Mona Masood

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This paper discusses the potential benefits of an interactive multimedia information representation in enhancing students’ critical thinking aligned with history reasoning in learning history between Secondary School students in Malaysia. Two modes of multimedia information representation implemented which are chronological and thematic information representation. A qualitative study of an unstructured interview was conducted among two history teachers, one history education lecturer, two i-think expert and program trainers and five form 4 secondary school students. The interview was to elicit their opinions on the implementation of thinking maps and interactive multimedia information representation in history learning. The key elements of interactive multimedia (e.g. multiple media, user control, interactivity, and use of timelines and concept maps) were then considered to improve the learning process. Findings of the preliminary investigation reveal that the interactive multimedia information representations have the potential benefits to be implemented as instructional resource in enhancing students’ higher order thinking skills (HOTs). This paper concludes by giving suggestions for future work.

Keywords: multimedia information representation, critical thinking, history reasoning, chronological and thematic information representation

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6298 2D Hexagonal Cellular Automata: The Complexity of Forms

Authors: Vural Erdogan

Abstract:

We created two-dimensional hexagonal cellular automata to obtain complexity by using simple rules same as Conway’s game of life. Considering the game of life rules, Wolfram's works about life-like structures and John von Neumann's self-replication, self-maintenance, self-reproduction problems, we developed 2-states and 3-states hexagonal growing algorithms that reach large populations through random initial states. Unlike the game of life, we used six neighbourhoods cellular automata instead of eight or four neighbourhoods. First simulations explained that whether we are able to obtain sort of oscillators, blinkers, and gliders. Inspired by Wolfram's 1D cellular automata complexity and life-like structures, we simulated 2D synchronous, discrete, deterministic cellular automata to reach life-like forms with 2-states cells. The life-like formations and the oscillators have been explained how they contribute to initiating self-maintenance together with self-reproduction and self-replication. After comparing simulation results, we decided to develop the algorithm for another step. Appending a new state to the same algorithm, which we used for reaching life-like structures, led us to experiment new branching and fractal forms. All these studies tried to demonstrate that complex life forms might come from uncomplicated rules.

Keywords: hexagonal cellular automata, self-replication, self-reproduction, self- maintenance

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6297 Performance Study of Classification Algorithms for Consumer Online Shopping Attitudes and Behavior Using Data Mining

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

Abstract:

With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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6296 Instructional Game in Teaching Algebra for High School Students: Basis for Instructional Intervention

Authors: Jhemson C. Elis, Alvin S. Magadia

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Our world is full of numbers, shapes, and figures that illustrate the wholeness of a thing. Indeed, this statement signifies that mathematics is everywhere. Mathematics in its broadest sense helps people in their everyday life that is why in education it is a must to be taken by the students as a subject. The study aims to determine the profile of the respondents in terms of gender and age, performance of the control and experimental groups in the pretest and posttest, impact of the instructional game used as instructional intervention in teaching algebra for high school students, significant difference between the level of performance of the two groups of respondents in their pre–test and post–test results, and the instructional intervention can be proposed. The descriptive method was also utilized in this study. The use of the certain approach was to that it corresponds to the main objective of this research that is to determine the effectiveness of the instructional game used as an instructional intervention in teaching algebra for high school students. There were 30 students served as respondents, having an equal size of the sample of 15 each while a greater number of female teacher respondents which totaled 7 or 70 percent and male were 3 or 30 percent. The study recommended that mathematics teacher should conceptualize instructional games for the students to learn mathematics with fun and enjoyment while learning. Mathematics education program supervisor should give training for teachers on how to conceptualize mathematics intervention for the students learning. Meaningful activities must be provided to sustain the student’s interest in learning. Students must be given time to have fun at the classroom through playing while learning since mathematics for them was considered as difficult. Future researcher must continue conceptualizing some mathematics intervention to suffice the needs of the students, and teachers should inculcate more educational games so that the discussion will be successful and joyful.

Keywords: instructional game in algebra, mathematical intervention, joyful, successful

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6295 Impact of E-Commerce Integrated for Export Marketing on Performance of Thai Export Businesses

Authors: Peerawat Chailom, Pimgarn Suwan-Natada

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The objective of this study is to examine the effects of e-commerce integrated for export marketing strategy on export advantage and firm performance. This study indicates that e-commerce infrastructure, organizational learning for e-commerce, and internet dissemination were antecedent of e-commerce integrated for export marketing strategy. In additional, export expertise is moderating variable of the research. In this study, 151 export businesses in Thailand are the sample of study. The results of study indicate that e-commerce integrated for export marketing strategy has significant positive influences on export advantage and export performance. Moreover, e-commerce infrastructure, organizational learning for e-commerce, and internet dissemination are have positive effects on e-commerce integrated for export marketing strategy. For moderating effect, export expertise significant influences on the relationships between e-commerce integrated for export marketing strategy and export advantage, and significant influences on the relationships between e-commerce integrated for export marketing strategy and export performance. Theoretical and practical implications are presented. Conclusion and suggestions for future research are also discussed.

Keywords: e-commerce integrated for export marketing, e-commerce infrastructure, organizational learning for e-commerce, export performance

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6294 Enquiry Based Approaches to Teaching Grammar and Differentiation in the Senior Japanese Classroom

Authors: Julie Devine

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This presentation will look at the approaches to teaching grammar taken over two years with students studying Japanese in the last two years of high school. The main focus is an enquiry based approach to grammar introduction and a three tier system using videos and online support material to allow for differentiation and personalised learning in the classroom. The aim is to create space for motivated students to do some higher order activities using the target pattern to solve problems and create scenarios. Less motivated students have time to complete basic exercises and struggling students have some time with the teacher in smaller groups.

Keywords: differentiation, digital technologies, personalised learning plans, student engagement

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6293 Relevance of Lecture Method in Modern Era: A Study from Nepal

Authors: Hari Prasad Nepal

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Research on lecture method issues confirm that this teaching method has been practiced from the very beginnings of schooling. Many teachers, lecturers and professors are convinced that lecture still represents main tool of contemporary instructional process. The central purpose of this study is to uncover the extent of using lecture method in the higher education. The study was carried out in Nepalese context with employing mixed method research design. To obtain the primary data this study employed a questionnaire involving items with close and open answers. 120 teachers, lecturers and professors participated in this study. The findings indicated that 75 percent of the respondents use the lecture method in their classroom teaching. The study reveals that there are advantages of using lecture method such as easy to practice, less time to prepare, high pass rate, high students’ satisfaction, little comments on instructors, appropriate to large classes and high level students. In addition, the study divulged the instructors’ reflections and measures to improve the lecture method. This research concludes that the practice of lecture method is still significantly applicable in colleges and universities in Nepalese contexts. So, there are no significant changes in the application of lecture method in the higher education classroom despite the emergence of new learning approaches and strategies.

Keywords: instructors, learning approaches, learning strategies, lecture method

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6292 Cost Sensitive Feature Selection in Decision-Theoretic Rough Set Models for Customer Churn Prediction: The Case of Telecommunication Sector Customers

Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

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In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: churn prediction, data mining, decision-theoretic rough set, feature selection

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6291 Hardware in the Loop Platform for Virtual Commissioning: Case Study of a Hydraulic-Press Model Simulated in Real-Time

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Ana Maria Macarulla

Abstract:

Hydraulic-press commissioning consumes a great amount of man-hours, due to the fact that it takes place several miles away from where it has been designed. This factor became exacerbated due to control designers’ lack of knowledge about which will be the final controller gains before they start working with it. Virtual commissioning has been postulated as an optimal solution to deal with this lack of knowledge. Here, a case study is presented in which a controller is set up against a real-time model based on a hydraulic-press. The press model is designed following manufacturer specifications and it is embedded in a real-time simulator. This methodology ensures that the model achieves similar responses as the real machine that would be placed on the industry. A deterministic communication protocol is in charge of the bidirectional information transmission between the real-time model and the controller. This platform allows the engineer to test and verify the final control responses with exactly the same hardware that is going to be installed in the hydraulic-press, in other words, realize a virtual commissioning of the electro-hydraulic actuator. The Hardware in the Loop (HiL) platform validates in laboratory conditions and harmless for the machine the control algorithms designed, which allows embedding them afterwards in the industrial environment without further modifications.

Keywords: deterministic communication protocol, electro-hydraulic actuator, hardware in the loop, real-time, virtual commissioning

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6290 The Analysis of Emergency Shutdown Valves Torque Data in Terms of Its Use as a Health Indicator for System Prognostics

Authors: Ewa M. Laskowska, Jorn Vatn

Abstract:

Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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6289 Working Memory Capacity and Motivation in Japanese English as a Foreign Language Learners' Speaking Skills

Authors: Akiko Kondo

Abstract:

Although the effects of working memory capacity on second/foreign language speaking skills have been researched in depth, few studies have focused on Japanese English as a foreign language (EFL) learners as compared to other languages (Indo-European languages), and the sample sizes of the relevant Japanese studies have been relatively small. Furthermore, comparing the effects of working memory capacity and motivation which is another kind of frequently researched individual factor on L2 speaking skills would add to the scholarly literature in the field of second language acquisition research. Therefore, the purposes of this study were to investigate whether working memory capacity and motivation have significant relationships with Japanese EFL learners’ speaking skills and to investigate the degree to which working memory capacity and motivation contribute to their English speaking skills. One-hundred and ten Japanese EFL students aged 18 to 26 years participated in this study. All of them are native Japanese speakers and have learned English as s foreign language for 6 to 15. They completed the Versant English speaking test, which has been widely used to measure non-native speakers’ English speaking skills, two types of working memory tests (the L1-based backward digit span test and the L1-based listening span test), and the language learning motivation survey. The researcher designed the working memory tests and the motivation survey. To investigate the relationship between the variables (English speaking skills, working memory capacity, and language learning motivation), a correlation analysis was conducted, which showed that L2 speaking test scores were significantly related to both working memory capacity and language learning motivation, although the correlation coefficients were weak. Furthermore, a multiple regression analysis was performed, with L2 speaking skills as the dependent variable and working memory capacity and language learning motivation as the independent variables. The results showed that working memory capacity and motivation significantly explained the variance in L2 speaking skills and that the L2 motivation had slightly larger effects on the L2 speaking skills than the working memory capacity. Although this study includes several limitations, the results could contribute to the generalization of the effects of individual differences, such as working memory and motivation on L2 learning, in the literature.

Keywords: individual differences, motivation, speaking skills, working memory

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6288 UAV Based Visual Object Tracking

Authors: Vaibhav Dalmia, Manoj Phirke, Renith G

Abstract:

With the wide adoption of UAVs (unmanned aerial vehicles) in various industries by the government as well as private corporations for solving computer vision tasks it’s necessary that their potential is analyzed completely. Recent advances in Deep Learning have also left us with a plethora of algorithms to solve different computer vision tasks. This study provides a comprehensive survey on solving the Visual Object Tracking problem and explains the tradeoffs involved in building a real-time yet reasonably accurate object tracking system for UAVs by looking at existing methods and evaluating them on the aerial datasets. Finally, the best trackers suitable for UAV-based applications are provided.

Keywords: deep learning, drones, single object tracking, visual object tracking, UAVs

Procedia PDF Downloads 140
6287 Investigating Safe Operation Condition for Iterative Learning Control under Load Disturbances Effect in Singular Values

Authors: Muhammad A. Alsubaie

Abstract:

An iterative learning control framework designed in state feedback structure suffers a lack in investigating load disturbance considerations. The presented work discusses the controller previously designed, highlights the disturbance problem, finds new conditions using singular value principle to assure safe operation conditions with error convergence and reference tracking under the influence of load disturbance. It is known that periodic disturbances can be represented by a delay model in a positive feedback loop acting on the system input. This model can be manipulated by isolating the delay model and finding a controller for the overall system around the delay model to remedy the periodic disturbances using the small signal theorem. The overall system is the base for control design and load disturbance investigation. The major finding of this work is the load disturbance condition found which clearly sets safe operation condition under the influence of load disturbances such that the error tends to nearly zero as the system keeps operating trial after trial.

Keywords: iterative learning control, singular values, state feedback, load disturbance

Procedia PDF Downloads 148
6286 Enhancing Students’ Performance in Basic Science and Technology in Nigeria Using Moodle LMS

Authors: Olugbade Damola, Adekomi Adebimbo, Sofowora Olaniyi Alaba

Abstract:

One of the major problems facing education in Nigeria is the provision of quality Science and Technology education. Inadequate teaching facilities, non-usage of innovative teaching strategies, ineffective classroom management, lack of students’ motivation and poor integration of ICT has resulted in the increase in percentage of students who failed Basic Science and Technology in Junior Secondary Certification Examination for National Examination Council in Nigeria. To address these challenges, the Federal Government came up with a road map on education. This was with a view of enhancing quality education through integration of modern technology into teaching and learning, enhancing quality assurance through proper monitoring and introduction of innovative methods of teaching. This led the researcher to investigate how MOODLE LMS could be used to enhance students’ learning outcomes in BST. A sample of 120 students was purposively selected from four secondary schools in Ogbomoso. The experimental group was taught using MOODLE LMS, while the control group was taught using the conventional method. Data obtained were analyzed using mean, standard deviation and t-test. The result showed that MOODLE LMS was an effective learning platform in teaching BST in junior secondary schools (t=4.953, P<0.05). Students’ attitudes towards BST was also enhanced through MOODLE LMS (t=15.632, P<0.05). The use of MOODLE LMS significantly enhanced students’ retention (t=6.640, P<0.05). In conclusion, the Federal Government efforts at enhancing quality assurance through integration of modern technology and e-learning in Secondary schools proved to have yielded good result has students found MOODLE LMS to be motivating and interactive. Attendance was improved.

Keywords: basic science and technology, MOODLE LMS, performance, quality assurance

Procedia PDF Downloads 286
6285 Exploring Public Opinions Toward the Use of Generative Artificial Intelligence Chatbot in Higher Education: An Insight from Topic Modelling and Sentiment Analysis

Authors: Samer Muthana Sarsam, Abdul Samad Shibghatullah, Chit Su Mon, Abd Aziz Alias, Hosam Al-Samarraie

Abstract:

Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education. However, their specific role within the educational context and the level of legal support for their implementation remain unclear. Therefore, this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. To achieve this objective, English tweets were collected from Twitter's free streaming Application Programming Interface (API). The Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the collected tweets. User sentiments, including disgust, surprise, sadness, anger, fear, joy, anticipation, and trust, as well as positive and negative sentiments, were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. This study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. The findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journey.

Keywords: generative artificial intelligence chatbots, bard, higher education, topic modelling, sentiment analysis

Procedia PDF Downloads 61