Search results for: teaching report writing for innovative learning

Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Nino Gogatishvili, Davit Kvernadze, Giorgi Japharidze

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Background and aims: Landau Kleffner syndrome (LKS) is a rare disorder with epileptic seizures and acquired aphasia. It usually starts in initially healthy children. The first symptoms are language regression and behavioral disturbances, and the sleep EEG reveals abnormal epileptiform activity. The aim was to discuss the efficacy of Clobazam for Landau Kleffner syndrome. Case report: We report a case of an 11-year-old boy with an uneventful pregnancy and delivery. He began to walk at 11 months and speak with simple phrases at the age of 2,5 years. At the age of 18 months, he had febrile convulsions; at the age of 5 years, the parents noticed language regression, stuttering, and serious behavioral dysfunction, including hyperactivity, temper outbursts. The epileptic seizure was not noticed. MRI was without any abnormality. Neuropsychological testing revealed verbal auditory agnosia. Sleep EEG showed abundant left fronto-temporal spikes, reaching over 85% during non-rapid eye movement sleep (non-REM sleep). Treatment was started with Clobazam. After ten weeks, EEG was improved. Stuttering and behavior also improved. Results: Since the start of Clobazam treatment, stuttering and behavior improved. Now, he is 11 years old, without antiseizure medication. Sleep EEG shows fronto-temporal spikes on the left side, over 10-49 % of non-REM sleep, bioccipital spikes, and slow-wave discharges and spike-waves. Conclusions: This case provides further support for the efficacy of Clobazam in patients with LKS.

Keywords: Landau-Kleffner syndrome, antiseizure medication, stuttering, aphasia

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Authors: Sunantha Teyarachakul

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We consider the worker-learning Newsvendor Model, under the case of lost-sales for unmet demand, with the research objective of proposing the cost-minimization order policy and lot size, scheduled to arrive at the beginning of the selling-period. In general, the New Vendor Model is used to find the optimal order quantity for the perishable items such as fashionable products or those with seasonal demand or short-life cycles. Technically, it is used when the product demand is stochastic and available for the single selling-season, and when there is only a one time opportunity for the vendor to purchase, with possibly of long ordering lead-times. Our work differs from the classical Newsvendor Model in that we incorporate the human factor (specifically worker learning) and its influence over the costs of processing units into the model. We describe this by using the well-known Wright’s Learning Curve. Most of the assumptions of the classical New Vendor Model are still maintained in our work, such as the constant per-unit cost of leftover and shortage, the zero initial inventory, as well as the continuous time. Our problem is challenging in the way that the best order quantity in the classical model, which is balancing the over-stocking and under-stocking costs, is no longer optimal. Specifically, when adding the cost-saving from worker learning to such expected total cost, the convexity of the cost function will likely not be maintained. This has called for a new way in determining the optimal order policy. In response to such challenges, we found a number of characteristics related to the expected cost function and its derivatives, which we then used in formulating the optimal ordering policy. Examples of such characteristics are; the optimal order quantity exists and is unique if the demand follows a Uniform Distribution; if the demand follows the Beta Distribution with some specific properties of its parameters, the second derivative of the expected cost function has at most two roots; and there exists the specific level of lot size that satisfies the first order condition. Our research results could be helpful for analysis of supply chain coordination and of the periodic review system for similar problems.

Keywords: inventory management, Newsvendor model, order policy, worker learning

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Authors: Suttipong Boonphadung, Thassanant Unnanantn

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The research study aimed to (1) compare the critical thinking of the teacher students of Suan Sunandha Rajabhat University before and after applying Miller’s Model learning activities and (2) investigate the students’ opinions towards Miller’s Model learning activities for improving the critical thinking. The participants of this study were purposively selected. They were 3 groups of teacher students: (1) fourth year 33 student teachers majoring in Early Childhood Education and enrolling in semester 1 of academic year 2013 (2) third year 28 student teachers majoring in English and enrolling in semester 2 of academic year 2013 and (3) third year 22 student teachers majoring in Thai and enrolling in semester 2 of academic year 2013. The research instruments were (1) lesson plans where the learning activities were settled based on Miller’s Model (2) critical thinking assessment criteria and (3) a questionnaire on opinions towards Miller’s Model based learning activities. The statistical treatment was mean, deviation, different scores and T-test. The result unfolded that (1) the critical thinking of the students after the assigned activities was better than before and (2) the students’ opinions towards the critical thinking improvement activities based on Miller’s Model ranged from the level of high to highest.

Keywords: critical thinking, Miller’s model, opinions, pre-service teachers

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Authors: Sholeh Motaghian, Pekka Toivanen, Keiji Haataja

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The swift expansion of the transportation industry and its associated emissions have captured the focus of policymakers who are dedicated to upholding ecological sustainability. As a result, understanding the key contributors to transportation emissions is of utmost significance. Amidst the escalating transportation emissions, the significance of electric vehicles cannot be overstated. Electric vehicles play a critical role in steering us towards a low-carbon economy and a sustainable ecological setting. The effective integration of electric vehicles hinges on the development of energy consumption models capable of accurately and efficiently predicting energy usage. Enhancing the energy efficiency of electric vehicles will play a pivotal role in reducing driver concerns and establishing a vital framework for the efficient operation, planning, and management of charging infrastructure. In this article, the works done in this field are reviewed, and the advantages and disadvantages of each are stated.

Keywords: deep learning, electrical vehicle, energy consumption, machine learning, smart grid

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Authors: Danilsa Lorduy, Liliana Valle

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Assessment and evaluation are inextricable parts of the teaching learning process. Evaluation practices concerns are gaining popularity among curriculum developers an educational researchers, particularly in Colombian regions where English language is taught as a foreign language EFL. This study addressed one of those issues, which are the unbalanced in –services’ evaluation practices perceived in school classes. They present predominance on the written test among the procedures they use to evaluate; therefore, the purpose of this case study was to explore in-service teachers’ evaluation practices, their beliefs about evaluation and to establish an eventual connection between practices and beliefs. To this end, classroom observations, questionnaires, and a semi structured interview were applied to three in-service English teachers from different schools in a city in Colombia. The findings suggested that teachers’ beliefs indicate a formative inclination and they actually are using a variety of procedures different from test but they seem to have some issues regarding their appropriateness for application Moreover, it was found that teachers’ practices are being influenced by external factors such as school requirements and national policies. It could be concluded that the predominance in using tests is not only elicited by teachers’ beliefs but also by national test results 'Pruebas Saber' and law 115 demanding. It was also suggested that further quantitative research is needed to demonstrate connections between overuse of testing procedures and 'Pruebas Saber' national test.

Keywords: beliefs, evaluation, external factors, national test

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: A. van Staden, M. M. Coetzee

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Despite numerous efforts to increase the literacy level of South African learners, for example, through the implementation of educational policies such as the Revised National Curriculum statement, advocating mother-tongue instruction (during a child's formative years), in reality, the majority of South African children are still being educated in a second language (in most cases English). Moreover, despite the fact that a significant percentage of our country's budget is spent on the education sector and that both policy makers and educationalists have emphasized the importance of learning English in this globalized world, the poor overall academic performance and English literacy level of a large number of school leavers are still a major concern. As we move forward in an attempt to comprehend the nuances of English language and literacy development in our country, it is imperative to explore both extrinsic and intrinsic factors that contribute or impede the effective development of English as a second language. In the present study, the researchers set out to investigate how intrinsic factors such as motivation and self-concept contribute to or affect English language learning amongst high school learners in South Africa. Emanating from the above the main research question that guided this research is the following: Is there a significant relationship between high school learners' self-concept, motivation, and English second language performances? In order to investigate this hypothesis, this study utilized quantitative research methodology to investigate the interplay of self-concept and motivation in English language learning. For this purpose, we sampled 201 high school learners from various schools in South Africa. Methods of data gathering inter alia included the following: A biographical questionnaire; the Academic Motivational Scale and the Piers-Harris Self-Concept Scale. Pearson Product Moment Correlation Analyses yielded significant correlations between L2 learners' motivation and their English language proficiency, including demonstrating positive correlations between L2 learners' self-concept and their achievements in English. Accordingly, researchers have argued that the learning context, in which students learn English as a second language, has a crucial influence on students' motivational levels. This emphasizes the important role the teacher has to play in creating learning environments that will enhance L2 learners' motivation and improve their self-concepts.

Keywords: motivation, self-concept, language learning, English second language learners (L2)

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Authors: Victor Radich, Tania Basso, Regina Moraes

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Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis or opinion mining can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results obtained so far have shown that it is possible to extract data from social networks and combine the techniques for a more complete classification.

Keywords: lead qualification, sentiment analysis, opinion mining, machine learning, CRM, lead scoring

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Authors: Rashid Ahmed , John N. Avaritsiotis

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Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural networks are presented. First, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Second, minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a Minor Component Analysis (MCA(R)) learning algorithm to enhance the convergence, where a convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Preliminary results will be furnished to illustrate the convergences results achieved.

Keywords: Direction of Arrival, neural networks, Principle Component Analysis, Minor Component Analysis

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Buket Canbaz Öztürk, N. Füsun Çam, Günseli Yaprak, Osman Candan

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The external terrestrial radiation exposure is related to the types of rock from which the soils originate. Higher radiation levels are associated with igneous rocks, such as granite, and lower levels with sedimentary rocks. Therefore, this study aims to assess the gamma radiation exposure of soils associated with granitic rocks in Kapıdağ Peninsula, Turkey. In the ongoing study, a comprehensive survey carried out systematically as a part of the environmental monitoring program on radiologic impact of the granitoid areas in Western Anatolia. The activity measurements of the gamma emitters (238U, 232Th and 40K) in the surface soil samples and the granitic rocks carried out by means of NaI(Tl) gamma-ray spectrometry system. To evaluate the radiological hazard of the natural radioactivity, the absorbed dose rate (D), the annual effective dose rate (AED), the radium equivalent activity (Raeq) and the external (Hex) hazard index were calculated according to the UNSCEAR 2000 report. The corresponding absorbed dose rates in air from all natural radionuclides were always much lower than 200 nGy h-1 and did not exceed the typical range of worldwide average values noticed in the UNSCEAR (2000) report. Furthermore, the correlation between soil and granitic rock samples were utilized, and external gamma radiation exposure distribution was mapped in Kapıdağ Peninsula.

Keywords: external absorbed dose, granitic rocks, Kapıdağ Peninsula, soil

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Authors: Jaisoon Baek, Gyuhwan Oh

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In this study, we developed a puzzle game based on coding and a web-based management system to observe the user's learning status in real time and maximize the understanding of the coding of elementary students. We have improved upon and existing coding game which cannot be connected to textual language coding or comprehends learning state. We analyzed the syntax of various coding languages for the curriculum and provided a menu to convert icon into textual coding languages. In addition, the management system includes multiple types of tutoring, real-time analysis of user play data and feedback. Following its application in regular elementary school software classes, students reported positive effects on understanding and interest in coding were shown by students. It is expected that this will contribute to quality improvement in software education by providing contents with proven educational value by breaking away from simple learning-oriented coding games.

Keywords: coding education, serious game, coding, education management system

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Authors: Reza Karimpour

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Ports play a decisive role in the EU's external and internal trade, as about 74% of imports and exports and 37% of exchanges go through ports. Although ports, especially Deep Sea Shipping (DSS) ports, are integral nodes within multimodal logistic flows, Short Sea Shipping (SSS) and inland waterways are not so well integrated. The automated vessels and supply chain optimisations for sustainable shortsea shipping (MOSES) project aims to enhance the short sea shipping component of the European supply chain by addressing the vulnerabilities and strains related to the operation of large containerships. The MOSES concept can be shortly described as a large containership (mother-vessel) approaching a DSS port (or a large container terminal). Upon her arrival, a combined intelligent mega-system consisting of the MOSES Autonomous tugboat swarm for manoeuvring and the MOSES adapted AutoMoor system. Then, container handling processes are ready to start moving containers to their destination via hinterland connections (trucks and/or rail) or to be shipped to destinations near small ports (on the mainland or island). For the first case, containers are stored in a dedicated port area (Storage area), waiting to be moved via trucks and/or rail. For the second case, containers are stacked by existing port equipment near-dedicated berths of the DSS port. They then are loaded on the MOSES Innovative Feeder Vessel, equipped with the MOSES Robotic Container-Handling System that provides (semi-) autonomous (un) feeding of the feeder. The Robotic Container-Handling System is remotely monitored through a Shore Control Centre. When the MOSES innovative Feeder vessel approaches the small port, where her docking is achieved without tugboats, she automatically unloads the containers using the Robotic Container-Handling System on the quay or directly on trucks. As a result, ports with minimal or no available infrastructure may be effectively integrated with the container supply chain. Then, the MOSES innovative feeder vessel continues her voyage to the next small port, or she returns to the DSS port. MOSES exploitation activity mainly aims to exploit research outcomes beyond the project, facilitate utilisation of the pilot results by others, and continue the pilot service after the project ends. By the mid-lifetime of the project, the exploitation plan introduces the reader to the MOSES project and its key exploitable results. It provides a plan for delivering the MOSES innovations to the market as part of the overall exploitation plan.

Keywords: automated vessels, exploitation, shortsea shipping, supply chain

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Authors: Muhammad Mooneeb Ali

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Institutions, where religious knowledge is given are known as madrassas. They also give formal education along with religious education. This study will be a pioneer to explore if MALL can be beneficial for madrassa students or not in formal educational situations. For investigation, an experimental study was planned in Punjab where the sample size was 100 students, 10 each from 10 different madrassas of Punjab, who are studying at the intermediate level (i.e., 11th grade). The madrassas were chosen through a convenient sampling method, whereas the learners were chosen by a simple random sampling method. A pretest was conducted, and on the basis of the results, the learners were divided into two equal groups (experimental and controlled). After two months of treatment, a posttest was conducted, and the results of both groups were compared. The results indicated that the performance of the experimental group was significantly better than the control one. This indicates that MALL elevates the performance of Madrassa students.

Keywords: english language learners, madrassa students, formal education, mobile assisted language learning (MALL), Pakistan.

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Authors: Luo Jiaoyang, Yu Hongyang

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In this paper, we explore a method that can be used in the working scene of intelligent industrial robots to confirm the identity information of operators to ensure that the robot executes instructions in a sufficiently safe environment. This approach uses three information modalities, namely visible light, depth, and sound. We explored a variety of fusion modes for the three modalities and finally used the joint feature learning method to improve the performance of the model in the case of noise compared with the single-modal case, making the maximum noise in the experiment. It can also maintain an accuracy rate of more than 90%.

Keywords: multimodal, kinect, machine learning, distance image

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Authors: Erva Akin

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– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: Scott Snyder

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Grading approaches in higher education have not changed meaningfully in over 100 years. While there is variation in the types of grades assigned across countries, most use approaches based on simple ordinal scales (e.g, letter grades). While grades are generally viewed as an indication of a student's performance, challenges arise regarding the clarity, validity, and reliability of letter grades. Research about grading in higher education has primarily focused on grade inflation, student attitudes toward grading, impacts of grades, and benefits of plus-minus letter grade systems. Little research is available about alternative approaches to grading, varying approaches used by faculty within and across colleges, and faculty attitudes toward grades and alternative approaches to grading. To begin to address these gaps, a survey was conducted of faculty in a sample of departments at three diverse colleges in a southeastern state in the US. The survey focused on faculty experiences with and attitudes toward grading, the degree to which faculty innovate in teaching and grading practices, and faculty interest in alternatives to the point system approach to grading. Responses were received from 104 instructors (21% response rate). The majority reported that teaching accounted for 50% or more of their academic duties. Almost all (92%) of respondents reported using point and percentage systems for their grading. While all respondents agreed that grades should reflect the degree to which objectives were mastered, half indicated that grades should also reflect effort or improvement. Over 60% felt that grades should be predictive of success in subsequent courses or real life applications. Most respondents disagreed that grades should compare students to other students. About 42% worried about their own grade inflation and grade inflation in their college. Only 17% disagreed that grades mean different things based on the instructor while 75% thought it would be good if there was agreement. Less than 50% of respondents felt that grades were directly useful for identifying students who should/should not continue, identify strengths/weaknesses, predict which students will be most successful, or contribute to program monitoring of student progress. Instructors were less willing to modify assessment than they were to modify instruction and curriculum. Most respondents (76%) were interested in learning about alternative approaches to grading (e.g., specifications grading). The factors that were most associated with willingness to adopt a new grading approach were clarity to students and simplicity of adoption of the approach. Follow-up studies are underway to investigate implementations of alternative grading approaches, expand the study to universities and departments not involved in the initial study, examine student attitudes about alternative approaches, and refine the measure of attitude toward adoption of alternative grading practices within the survey. Workshops about challenges of using percentage and point systems for determining grades and workshops regarding alternative approaches to grading are being offered.

Keywords: alternative approaches to grading, grades, higher education, letter grades

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Authors: C. W. Kan

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Cotton fibre is a commonly-used natural fibre because of its good fibre strength, high moisture absorption behaviour and minimal static problems. However, one of the main drawbacks of cotton fibre is wrinkling after washing, which is recently overcome by wrinkle-resistant treatment. 1,2,3,4-butanetetracarboxylic acid (BTCA) could improve the wrinkle-resistant properties of cotton fibre. Although the BTCA process is an effective method for wrinkle resistant application of cotton fabrics, reduced fabric strength was observed after treatment. Therefore, this paper would explore the use of atmospheric pressure plasma treatment under different discharge powers as a pretreatment process to enhance the application of BTCA process on cotton fabric without generating adverse effect. The aim of this study is to provide learning information to the users to know how the atmospheric pressure plasma treatment can be incorporated in textile finishing process with positive impact.

Keywords: learning materials, atmospheric pressure plasma treatment, cotton, wrinkle-resistant, BTCA

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Bo-Bin Xiaoa, Chia-Yi Liua

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In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Bernice Badal

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The South African English curriculum emphasizes the adoption of the Communicative Approach (CA) using Communicative Language Teaching (CLT) methodologies to develop English as a second language (ESL) learners’ communicative competence in contexts such as township schools in South Africa. However, studies indicate that the adoption of the approach largely remains a rhetoric. Poor English language proficiency among learners and poor student performance, which continues from the secondary to the tertiary phase, is widely attributed to a lack of English language proficiency in South Africa. Consequently, this qualitative study, using a mix of classroom observations and interviews, sought to investigate teacher knowledge of Communicative Competence and the methods and strategies ESL teachers used to develop their learners’ communicative competence. The success of learners’ ability to develop communicative competence in contexts such as township schools in South Africa is inseparable from materials, tasks, teacher knowledge and how they implement the approach in the classrooms. Accordingly, teacher knowledge of the theory and practical implications of the CLT approach is imperative for the negotiation of meaning and appropriate use of language in context in resource-impoverished areas like the township. Using a mix of interviews and observations as data sources, this qualitative study examined teachers’ definitions and knowledge of Communicative competence with a focus on how it influenced their classroom practices. The findings revealed that teachers were not familiar with the notion of communicative competence, the communication process, and the underpinnings of CLT. Teachers’ narratives indicated an awareness that there should be interactions and communication in the classroom, but a lack of theoretical understanding of the types of communication necessary scuttled their initiatives. Thus, conceptual deficiency influences teachers’ practices as they engage in classroom activities in a superficial manner or focus on stipulated learner activities prescribed by the CAPS document. This study, therefore, concluded that partial or limited conceptual and coherent understandings with ‘teacher-proof’ stipulations for classroom practice do not inspire teacher efficacy and mastery of prescribed approaches; thus, more efforts should be made by the Department of Basic Education to strengthen the existing Professional Development workshops to support teachers in improving their understandings and application of CLT for the development of Communicative competence in their learners. The findings of the study contribute to the field of teacher knowledge acquisition, teacher beliefs and practices and professional development in the context of second language teaching and learning with a recommendation that frameworks for the development of communicative competence with wider applicability in resource-poor environments be developed to support teacher understanding and application in classrooms.

Keywords: communicative competence, CLT, conceptual understanding of reforms, professional development

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

Abstract:

This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: AnjalI A. Prasad

Abstract:

Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: Lassaad Smirani

Abstract:

In this paper we propose a Neurosymbolic Learning System (NLS) as a channel estimator for Long Term Evolution Advanced (LTE-A) uplink. The proposed system main idea based on Neural Network has modules capable of performing bidirectional information transfer between symbolic module and connectionist module. We demonstrate various strengths of the NLS especially the ability to integrate theoretical knowledge (rules) and experiential knowledge (examples), and to make an initial knowledge base (rules) converted into a connectionist network. Also to use empirical knowledge witch by learning will have the ability to revise the theoretical knowledge and acquire new one and explain it, and finally the ability to improve the performance of symbolic or connectionist systems. Compared with conventional SC-FDMA channel estimation systems, The performance of NLS in terms of complexity and quality is confirmed by theoretical analysis and simulation and shows that this system can make the channel estimation accuracy improved and bit error rate decreased.

Keywords: channel estimation, SC-FDMA, neural network, hybrid system, BER, LTE-A

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Authors: Blanca Fatela Cantillo, Silvia Iglesias Cerrato, Guadalupe Garrido Ceca

Abstract:

Purpose: Bleb dysfunction is a late complication following glaucoma filtration surgery. We describe our surgical technique for excision and reconstruction of a hypertrophic bleb complication using bovine pericardium patch graft (Tutopatch®) and amniotic membrane. Material and methods: The case report presents a hypertrophic bleb over the cornea with good intraocular pressure control. The hanging bleb without leak caused dysesthesia and high irregular astigmatism. Bleb reconstruction involved the excision of corneal fibrous material and avascular conjunctiva, preserving the original scleral and tennon. Bovine pericardium patch graft (Tutopatch®) was sited over these with fixed sutures, reinforcing the underlying scleral, and the conjunctiva advanced. The superior epithelium corneal defect was covered using an amniotic membrane. Conclusion: Repair of bleb dysfunction with varied techniques has been reported, including conjunctival advancement, use of scleral patch graft, dural patch graft, or pericardium. Additional use of amniotic membrane promotes epithelialization and exhibits anti-fibrotic and anti-inflammatory features. Reconstruction with bovine pericardium patch graft and amniotic membrane resulted in pain relief, visual rehabilitation, and good aesthetic results, with preservation of bleb function.

Keywords: reconstruction, hypertrophic bleb, bovine pericardium, amniotic membrane, dysesthesia of the bleb

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Authors: Hoi Nga Ng, Kam Weng Boey, Chi Wai Kwan

Abstract:

Supporters of Inclusive education proclaim that all students, regardless of disabilities or special educational needs (SEN), have the right to study in the normal school setting. It is asserted that students with SEN would benefit in academic performance and psychosocial adjustment via participation in common learning activities within the ordinary school system. When more and more students of SEN completed their early schooling, institute of higher education become the setting where students of SEN continue their learning. This study aimed to investigate the school well-being, social relationship, and academic self-concept of students of SEN in higher education. The Perception of Inclusion Questionnaire (PIQ) was used as the measuring instruments. PIQ was validated and incorporated in a questionnaire designed for online survey. Participation was voluntary and anonymous. A total of 90 students with SEN and 457 students without SEN responded to the online survey. Results showed no significant differences in school well-being and social relationship between students with and without SEN, but students with SEN, particularly those with learning and development impairment and those with mental illness and emotional problems, were significantly poorer in academic self-concept. Implications of the findings were discussed.

Keywords: ccademic self-concept, school well-being, social relationship, special educational needs

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Authors: Arjumand Warsy

Abstract:

Among the Muslims, the Holy Quran is taught from early childhood and generally by the age of 7-8 years the reading of the entire Quran is completed by most of the children in Muslim families. During this period excellent reciter’s are selected to teach and emphasis is laid on correct reading, pronunciation and memorization. Following these years, the parents lay emphasis on the recitation of the Quran on daily basis. During the month of Ramadan the entire Quran is read one or more times and there are considerable number of Muslims who complete the entire Quran once or more each calendar month. Many Muslims do not know Arabic and for them message in the Quran is what others tell them and often they have no idea about this Guidance sent to them. This deficiency is reflected in many ways, both among people living in Muslim or non-Muslim countries. Due to the deficiency in knowledge about Islamic teachings, the foundations of Islam are being eroded by a variety of forces. In an attempt to guard against the non-Islamic influences, every Muslim must have a clear understanding of the Islamic teachings and requirements. The best guidance can be provided by the understanding of the Holy Quran. However, we are faced with the problem that often the Quran is taught in a way that fails to develop an interest and understanding of the message from Allah. Looking at the teaching of other subjects both scientific and non-scientific, at school, college or University levels, it is obvious that the advances in teaching methodologies using electronic technology have had a major impact, where both the understanding and the interest of the students are significantly elevated. We attempted to teach the meaning of the Holy Quran to children and adults using a scientific and modern approach using slide presentation and animations. The results showed almost 100% increase in the understanding of the Quran message; all attendees claimed they developed an increased interest in the study of the Holy Quran and did not lose track or develop boredom throughout the lectures. They learnt the information and remembered it more effectively. The love for Allah and Prophet Mohammad (PBUH) increased significantly. The fear of Allah and love of Heaven developed significantly. Historical facts and the stories of the past nations became clearer and the Greatness of the Creator was strongly felt. Several of attendees wanted to become better Muslims and to spread the knowledge of Islam. In this presentation, the adopted teaching method will be first presented and demonstrated to the audience using a short Surah from the Quran, followed by discussion on the results achieved during our study. We will endeavor to convey to the audience that there is a need to adopt a more scientific approach to teach the Quran so that a greater benefit is achieved by all.

Keywords: The Holy Quran, Muslims, presentations, technology

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