Search results for: teaching and learning model

Authors: Tareq Oshan

Abstract:

Facility location and size decisions are important to any company because they affect profitability and success. However, warehouses are exposed to various risks of failure that affect their activity. This paper presents a mixed-integer non-linear mathematical model that can be used to determine optimal warehouse locations and sizes, which warehouses to fortify, and which branches should be assigned to specific warehouses when there is a risk of warehouse failure. Every branch is assigned to a fortified primary warehouse or a nonfortified primary warehouse and a fortified backup warehouse. The standard method and an introduced method, based on the average probabilities, for linearizing this mathematical model were used. A Canadian case study was used to demonstrate the developed mathematical model, followed by some sensitivity analysis.

Keywords: supply chain network design, fortified warehouse, mixed-integer mathematical model, warehouse failure risk

Procedia PDF Downloads 236

Authors: Victor Radich, Tania Basso, Regina Moraes

Abstract:

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

Procedia PDF Downloads 65

Authors: Rashid Ahmed , John N. Avaritsiotis

Abstract:

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

Procedia PDF Downloads 436

Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

Abstract:

Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

Procedia PDF Downloads 164

Authors: Jaisoon Baek, Gyuhwan Oh

Abstract:

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

Procedia PDF Downloads 130

Authors: Scott Snyder

Abstract:

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

Procedia PDF Downloads 88

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

Abstract:

Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

Procedia PDF Downloads 207

Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 407

Authors: Muhammad Mooneeb Ali

Abstract:

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.

Procedia PDF Downloads 61

Authors: Erva Akin

Abstract:

– 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

Procedia PDF Downloads 73

Authors: C. W. Kan

Abstract:

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

Procedia PDF Downloads 287

Authors: Victor William Harris

Abstract:

This research is a validation study of the Social Emotional Ability Inventory (SEAI), a multi-dimensional self-report instrument informed by positive psychology, emotional intelligence, social intelligence, and sociocultural learning theory. Designed for use in tandem with the Social Emotional Development (SEAD) theoretical model, the SEAI provides diagnostic-level guidance for professionals and individuals interested in investigating, identifying, and understanding social, emotional strengths, as well as remediating specific social competency deficiencies. The SEAI was shown to be psychometrically sound, exhibited strong internal reliability, and supported the a priori hypotheses of the SEAD. Additionally, confirmatory factor analysis provided evidence of goodness of fit, convergent and divergent validity, and supported a theoretical model that reflected SEAD expectations. The SEAI and SEAD hold potentially far-reaching and important practical implications for theoretical guidance and diagnostic-level measurement of social, emotional competency across a wide range of domains. Strategies researchers, practitioners, educators, and individuals might use to deploy SEAI in order to improve quality of life outcomes are discussed.

Keywords: emotion, emotional ability, positive psychology-social emotional ability, social emotional ability, social emotional ability instrument

Procedia PDF Downloads 226

Authors: Motahareh Fadaei

Abstract:

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

Procedia PDF Downloads 304

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

Abstract:

Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: K. M. Anusha, R. Krishnaveni

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Crossing a decade of the 21st century, the paradigm of human resources can be seen evolving with the strategic gene induced into it. There seems to be a radical shift descending as the corporate sector calls on its HR team to become strategic rather than administrative. This transferal eventually requires the metrics employed by these HR teams not to be just operationally reactive but to be aligned to an evidence-based strategic thinking. Realizing the growing need for a prescriptive metric model for effective HR analytics, this study has designed a conceptual framework for a basic metric model that can assist IT-HRM professionals to transition to a practice of evidence-based decision-making to enhance organizational performance.

Keywords: metric model, evidence based HR, HR analytics, strategic HR practices, IT sector

Procedia PDF Downloads 392

Authors: Nikolaos Reppas, Yilin Gui

Abstract:

A dual-porosity finite element-code will be presented for the stability analysis of the wellbore during CO₂ injection. An elastoplastic damage response will be considered to the model. The Finite Element Method (FEM) will be validated using experimental results from literature or from experiments that are planned to be undertaken at Newcastle University. The main target of the research paper is to present a constitutive model that can help industries to safely store CO₂ in geological rock formations and forecast any changes on the surrounding rock of the wellbore. The fully coupled elastoplastic damage Thermo-Hydraulic-Mechanical (THM) model will determine the pressure and temperature of the injected CO₂ as well as the size of the radius of the wellbore that can make the Carbon Capture and Storage (CCS) procedure more efficient.

Keywords: carbon capture and storage, Wellbore stability, elastoplastic damage response for rock, constitutive THM model, fully coupled thermo-hydraulic-mechanical model

Procedia PDF Downloads 161

Authors: Bernice Badal

Abstract:

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: N. Guo, C. Xu, Z. C. Yang

Abstract:

In order to ensure the high reliability of an aircraft, the accurate structural dynamics analysis has become an indispensable part in the design of an aircraft structure. Therefore, the structural finite element model which can be used to accurately calculate the structural dynamics and their transfer relations is the prerequisite in structural dynamic design. A dynamic finite element model updating method is presented to correct the uncertain parameters of the finite element model of a structure using measured modal parameters. The coordinate modal assurance criterion is used to evaluate the correlation level at each coordinate over the experimental and the analytical mode shapes. Then, the weighted summation of the natural frequency residual and the coordinate modal assurance criterion residual is used as the objective function. Moreover, the hybrid pattern search (HPS) optimization technique, which synthesizes the advantages of pattern search (PS) optimization technique and genetic algorithm (GA), is introduced to solve the dynamic FE model updating problem. A numerical simulation and a model updating experiment for GARTEUR aircraft model are performed to validate the feasibility and effectiveness of the present dynamic model updating method, respectively. The updated results show that the proposed method can be successfully used to modify the incorrect parameters with good robustness.

Keywords: model updating, modal parameter, coordinate modal assurance criterion, hybrid genetic/pattern search

Procedia PDF Downloads 145

Authors: Azyz Sharafy

Abstract:

3D Text Toys is an innovative and creative approach that utilizes 3D text objects to enhance creativity, literacy, and basic learning in an enjoyable and gamified manner. By using 3D Text Toys, children can develop their creativity, visually learn words and texts, and apply their artistic talents within their creative abilities. This process incorporates haptic engagement with 2D and 3D texts, word building, and mechanical construction of everyday objects, thereby facilitating better word and text retention. The concept involves constructing visual objects made entirely out of 3D text/words, where each component of the object represents a word or text element. For instance, a bird can be recreated using words or text shaped like its wings, beak, legs, head, and body, resulting in a 3D representation of the bird purely composed of text. This can serve as an art piece or a learning tool in the form of a 3D text toy. These 3D text objects or toys can be crafted using natural materials such as leaves, twigs, strings, or ropes, or they can be made from various physical materials using traditional crafting tools. Digital versions of these objects can be created using 2D or 3D software on devices like phones, laptops, iPads, or computers. To transform digital designs into physical objects, computerized machines such as CNC routers, laser cutters, and 3D printers can be utilized. Once the parts are printed or cut out, students can assemble the 3D texts by gluing them together, resulting in natural or everyday 3D text objects. These objects can be painted to create artistic pieces or text toys, and the addition of wheels can transform them into moving toys. One of the significant advantages of this visual and creative object-based learning process is that students not only learn words but also derive enjoyment from the process of creating, painting, and playing with these objects. The ownership and creation process further enhances comprehension and word retention. Moreover, for individuals with learning disabilities such as dyslexia, ADD (Attention Deficit Disorder), or other learning difficulties, the visual and haptic approach of 3D Text Toys can serve as an additional creative and personalized learning aid. The application of 3D Text Toys extends to both the English language and any other global written language. The adaptation and creative application may vary depending on the country, space, and native written language. Furthermore, the implementation of this visual and haptic learning tool can be tailored to teach foreign languages based on age level and comprehension requirements. In summary, this creative, haptic, and visual approach has the potential to serve as a global literacy tool.

Keywords: 3D text toys, creative, artistic, visual learning for world literacy

Procedia PDF Downloads 51

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

Abstract:

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

Procedia PDF Downloads 93

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 107

Authors: Jin Sung Kim, Hoon Huh

Abstract:

The material properties of OFHC copper film was investigated with the High-Speed Material Micro Testing Machine (HSMMTM) at the high strain rates. The rate-dependent stress-strain curves from the experiment and the Johnson-Cook curve fitting showed large discrepancies as the plastic strain increases since the constitutive model implies no rate-dependent strain hardening effect. A new constitutive model was proposed in consideration of rate-dependent strain hardening effect. The strain rate hardening term in the new constitutive model consists of the strain rate sensitivity coefficients of the yield strength and strain hardening.

Keywords: rate dependent material properties, dynamic constitutive model, OFHC copper film, strain rate

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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: Riaan Kleyn

Abstract:

Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: M. Ndaw, G. Mendy, S. Ouya

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Risk management in banking sector is a key issue linked to financial system stability and its importance has been elevated by technological developments and emergence of new financial instruments. In this paper, we improve the model previously defined for quantifying internal control impact on banking risks by automatizing the residual criticality estimation step of FMECA. For this, we defined three equations and a maturity coefficient to obtain a mathematical model which is tested on all banking processes and type of risks. The new model allows an optimal assessment of residual criticality and improves the correlation rate that has become 98%.

Keywords: risk, control, banking, FMECA, criticality

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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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Authors: Fernando P. Silva, Valter J. S. Leite, Erivelton G. Nepomuceno

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In this paper, a nonlinear controller design for an omnidirectional mobile is presented. The robot controller consists of an inner-loop controller and an outer-loop controller, the first is designed using state feedback (robust allocation) and the second controller is designed based on Robust Multi Inversion (RMI) approach. The objective of RMI controller is rendering the robust inversion of the dynamic, when the model is affected by uncertainties. A model nonlinear MIMO of an omni-directional robot (small-league of Robocup) is used to simulate the RMI approach. The parameters of linear and nonlinear model are varied to cause modelling uncertainties among the model and the real model (real system) generating an error in inner-loop controller signal that must be compensated by RMI controller. The simulation test results show that the RMI is capable of compensating the uncertainties and keep the system stable and controlled under uncertainties.

Keywords: robust multi inversion, omni-directional robot, robocup, nonlinear control

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Authors: Farzad Khajavi, Farzaneh Jalilfar, Faranak Jafari, Leila Shokrani

Abstract:

Formulation of gliclazide in the form of extended-release tablet in 30 and 60 mg dosage forms was performed using hypromellose (HPMC K4M) as a retarding agent. Drug-release profiles were investigated in comparison with references Diamicron MR 30 and 60 mg tablets. The effect of size of powder particles, the amount of hypromellose in formulation, hardness of tablets, and also the effect of halving the tablets were investigated on drug release profile. A mathematical model which describes hypromellose behavior in initial times of drug release was proposed for the estimation of hypromellose content in modified-release gliclazide 60 mg tablet. This model is based on erosion of hypromellose in dissolution media. The model is applicable to describe release profiles of insoluble drugs. Therefore, by using dissolved amount of drug in initial times of dissolution and the model, the amount of hypromellose in formulation can be predictable. The model was used to predict the HPMC K4M content in modified-release gliclazide 30 mg and extended-release quetiapine 200 mg tablets.

Keywords: Gliclazide, hypromellose, drug release, modified-release tablet, mathematical model

Procedia PDF Downloads 209

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

Abstract:

The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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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

Procedia PDF Downloads 381