Search results for: statistical machine learning

Authors: Flavio Campos, Patricia Masmo, Fernanda Yamamoto

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The report presents a research about teaching professional competencies through projects, considering the student as an active learner and curriculum development. Considering project based-learning, the report articulate the result of research about curriculum development for professional competencies and teaching-learning strategies to help the development of professional competencies in learning environments in the courses of National Learning Service in São Paulo, Brazil. There so, intend to demonstrate fundamentals to elaborate curriculum to learning environment, specific about teaching methodologies to enrich student-learning process, using projects. The practice that has been taking place since 2013 indicates the needs of rethinking knowledge and practice in courses that prepared students to labor.

Keywords: curriculum design, active learning, professional competencies, project based-learning

Procedia PDF Downloads 427

Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Michele Gattullo, Vito M. Manghisi, Alessandro Evangelista, Enricoandrea Laviola

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In this work, we explored the use of Augmented Reality (AR) to support students in laboratory lectures in Distance Learning (DL), designing an application that proved to be ready for use next semester. AR could help students in the understanding of complex concepts as well as increase their motivation in the learning process. However, despite many prototypes in the literature, it is still less used in schools and universities. This is mainly due to the perceived limited advantages to the investment costs, especially regarding changes needed in the teaching modalities. However, with the spread of epidemiological emergency due to SARS-CoV-2, schools and universities were forced to a very rapid redefinition of consolidated processes towards forms of Distance Learning. Despite its many advantages, it suffers from the impossibility to carry out practical activities that are of crucial importance in STEM ("Science, Technology, Engineering e Math") didactics. In this context, AR perceived advantages increased a lot since teachers are more prepared for new teaching modalities, exploiting AR that allows students to carry on practical activities on their own instead of being physically present in laboratories. In this work, we designed an AR application for the support of engineering students in the understanding of assembly drawings of complex machines. Traditionally, this skill is acquired in the first years of the bachelor's degree in industrial engineering, through laboratory activities where the teacher shows the corresponding components (e.g., bearings, screws, shafts) in a real machine and their representation in the assembly drawing. This research aims to explore the effectiveness of AR to allow students to acquire this skill on their own without physically being in the laboratory. In a preliminary phase, we interviewed students to understand the main issues in the learning of this subject. This survey revealed that students had difficulty identifying machine components in an assembly drawing, matching between the 2D representation of a component and its real shape, and understanding the functionality of a component within the machine. We developed a mobile application using Unity3D, aiming to solve the mentioned issues. We designed the application in collaboration with the course professors. Natural feature tracking was used to associate the 2D printed assembly drawing with the corresponding 3D virtual model. The application can be displayed on students’ tablets or smartphones. Users could interact with selecting a component from a part list on the device. Then, 3D representations of components appear on the printed drawing, coupled with 3D virtual labels for their location and identification. Users could also interact with watching a 3D animation to learn how components are assembled. Students evaluated the application through a questionnaire based on the System Usability Scale (SUS). The survey was provided to 15 students selected among those we participated in the preliminary interview. The mean SUS score was 83 (SD 12.9) over a maximum of 100, allowing teachers to use the AR application in their courses. Another important finding is that almost all the students revealed that this application would provide significant power for comprehension on their own.

Keywords: augmented reality, distance learning, STEM didactics, technology in education

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine

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Authors: Wiem Ben Khalifa, Dalila Souilem, Mahmoud Neji

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The evolutions of Social Web and Semantic Web lead us to ask ourselves about the way of supporting the personalization of learning by means of intelligent filtering of educational resources published in the digital networks. We recommend personalized courses of learning articulated around a first educational course defined upstream. Resuming the context and the stakes in the personalization, we also suggest anchoring the personalization of learning in a community of interest within a group of learners enrolled in the same training. This reflection is supported by the display of an active and semantic system of learning dedicated to the constitution of personalized to measure courses and in the due time.

Keywords: Semantic Web, semantic system, ontology, evaluation, e-learning

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Authors: Sitthichai Laisema, Panita Wannapiroon

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This study is a research and development which is intended to: 1) design ubiquitous collaborative learning activities with virtual teams using CPS processes to develop creative thinking and collaboration skills, and 2) assess the suitability of the ubiquitous collaborative learning activities. Its methods are divided into 2 phases. Phase 1 is the design of ubiquitous collaborative learning activities with virtual teams using CPS processes, phase 2 is the assessment of the suitability of the learning activities. The samples used in this study are 5 professionals in the field of learning activity design, ubiquitous learning, information technology, creative thinking, and collaboration skills. The results showed that ubiquitous collaborative learning activities with virtual teams using CPS processes to develop creative thinking and collaboration skills consist of 3 main steps which are: 1) preparation before learning, 2) learning activities processing and 3) performance appraisal. The result of the learning activities suitability assessment from the professionals is in the highest level.

Keywords: ubiquitous learning, collaborative learning, virtual team, creative problem solving

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: autism spectrum disorder, clustering, optimization, unsupervised machine learning

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Authors: H. Zou, B. Wang

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Thermal distortion of the machine tool plays a critical role in its machining accuracy. This study investigates the thermal performance of a high-precision machine frame with future-oriented mineral casting components. A thermo-mechanical finite element model (FEM) was established to evaluate the thermal behavior of the frame under environmental thermal fluctuations. The validity of the presented FEM model was confirmed experimentally by a series of laser interferometer tests. Good agreement between numerical and experimental results demonstrates that the proposed model can accurately predict the thermal deformation of the frame with thermo-mechanical coupling effect. The results also show that keeping the workshop in thermally stable conditions is crucial for improving the machine accuracy of the system with large scale components. The goal of this paper is to investigate the feasibility of innovative mineral casting material applied in high-precision drilling machine and to provide a strategy for machine tool industry seeking a perfect substitute for classic frame materials such as cast iron and granite.

Keywords: thermo-mechanical model, finite element method, laser interferometer, mineral casting frame

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Authors: Eric Fang, Robert Chen, Ghim Song Chia, Bien Soo Tan

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Objective and Rationale: Training of radiology residents hinges on practical, on-the-job training in all facets and modalities of diagnostic radiology. Although residency is structured to be comprehensive, clinical exposure depends on the case mix available locally and during the posting period. To supplement clinical training, there are several e-learning resources available to allow for greater exposure to radiological cases. The objective of this study was to survey residents and faculty on the usefulness of these e-learning resources. Methods: E-learning resources were shortlisted with input from radiology residents, Google search and online discussion groups, and screened by their purported focus. Twelve e-learning resources were found to meet the criteria. Both radiology residents and experienced radiology faculty were then surveyed electronically. The e-survey asked for ratings on breadth, depth, testing capability and user-friendliness for each resource, as well as for rankings for the top 3 resources. Statistical analysis was performed using SAS 9.4. Results: Seventeen residents and fifteen faculties completed an e-survey. Mean response rate was 54% ± 8% (Range: 14- 96%). Ratings and rankings were statistically identical between residents and faculty. On a 5-point rating scale, breadth was 3.68 ± 0.18, depth was 3.95 ± 0.14, testing capability was 2.64 ± 0.16 and user-friendliness was 3.39 ± 0.13. Top-ranked resources were STATdx (first), Radiopaedia (second) and Radiology Assistant (third). 9% of responders singled out R-ITI as potentially good but ‘prohibitively costly’. Statistically significant predictive factors for higher rankings are familiarity with the resource (p = 0.001) and user-friendliness (p = 0.006). Conclusion: A good e-learning system will complement on-the-job training with a broad case base, deep discussion and quality trainee evaluation. Based on our study on twelve e-learning resources, no single program fulfilled all requirements. The perception and use of radiology e-learning resources depended more on familiarity and user-friendliness than on content differences and testing capability.

Keywords: e-learning, medicine, radiology, survey

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Authors: Dimitra Alexiou

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During the last 10 years, in spite of the economic crisis, the number of tourists arriving in Greece has increased, particularly during the tourist season from April to October. In this paper, the number of annual tourist arrivals is studied to explore their preferences with regard to the month of travel, the selected destinations, as well the amount of money spent. The collected data are processed with statistical methods, yielding numerical and graphical results. From the computation of statistical parameters and the forecasting with exponential smoothing, useful conclusions are arrived at that can be used by the Greek tourism authorities, as well as by tourist organizations, for planning purposes for the coming years. The results of this paper and the computed forecast can also be used for decision making by private tourist enterprises that are investing in Greece. With regard to the statistical methods, the method of Simple Exponential Smoothing of time series of data is employed. The search for a best forecast for 2017 and 2018 provides the value of the smoothing coefficient. For all statistical computations and graphics Microsoft Excel is used.

Keywords: tourism, statistical methods, exponential smoothing, land spatial planning, economy

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Pimploi Tirastittam, Sawanath Treesathon, Amornrath Ongkawat

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Learning Management System (LMS) is the system which uses to manage the learning in order to grouping the content and learning activity between the lecturer and learner including online examination and evaluation. Nowadays, it is the borderless learning era so the learning activities can be accessed from everywhere in the world and also anytime via the information technology and media. The learner can easily access to the knowledge so the different in time and distance is not a constraint for learning anymore. The learning pattern which was used in this research is the integration of the in-class learning and online learning via internet and will be able to monitor the progress by the Learning management system which will create the fast response and accessible learning process via the social media. In order to increase the capability and freedom of the learner, the system can show the current and history of the learning document, video conference and also has the chat room for the learner and lecturer to interact to each other. So the objectives of the “The Design and Applied of Learning Management System via Social Media on Internet: Case Study of Operating System for Business Subject” are to expand the opportunity of learning and to increase the efficiency of learning as well as increase the communication channel between lecturer and student. The data of this research was collect from 30 users of the system which are students who enroll in the subject. And the result of the research is in the “Very Good” which is conformed to the hypothesis.

Keywords: Learning Management System, social media, Operating System, information technology

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Authors: A. Ishag, E. Witruk, C. Altmayer

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Motivation and attitudes are considered as hypothetical psychological constructs in explaining the process of second language learning. Gardner (1985) – who first systematically investigated the motivational factors in second language acquisition – found that L2 achievement is related not only to the individual learner’s linguistic aptitude or general intelligence but also to the learner’s motivation and interest in learning the target language. Traditionally language learning motivation can be divided into two types: integrative motivation – the desire to integrate oneself with the target culture; and instrumental motivation – the desire to learn a language in order to meet a specific language requirement such as for employment. One of the Gardner’s main ideas is that the integrative motivation plays an important role in second language acquisition. It is directly and positively related to second language achievement more than instrumental motivation. However, the significance of integrative motivation reflects a rather controversial set of findings. On the other hand, Students’ attitudes towards the target language, its speakers and the learning context may all play some part in explaining their success in learning a language. Accordingly, the present study aims at exploring the significance of motivational and attitudinal factors in learning foreign languages, namely English and German among Sudanese undergraduate students from a psycholinguistic and interdisciplinary perspective. The sample composed of 221 students from the English and German language departments respectively at the University of Khartoum in Sudan. The results indicate that English language’s learners are instrumentally motivated and that German language’s learners have positive attitudes towards the German language community and culture. Furthermore, there are statistical significant differences in the attitudes toward the two languages due to gender; where female students have more positive attitudes than their male counterparts. However, there are no differences along the variables of academic grade and study level. Finally, the reasons of studying the English or German language have also been indicated.

Keywords: motivation and attitudes, foreign language learning, english language, german language

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Authors: Bassem Nassar, Wessam Hussein, Medhat Mokhtar

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The crucial concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems in order to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important in order to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the aforementioned problem coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions and the results shows that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: space telemetry monitoring, multivariate analysis, PCA algorithm, space operations

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Authors: Chris Landorf, Stephen Ward

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This paper addresses a virtual environment approach to work integrated learning for students in construction-related disciplines. The virtual approach provides a safe and pedagogically rigorous environment where students can apply theoretical knowledge in a simulated real-world context. The paper describes the development of a 4-dimensional digital construction environment and associated learning activities funded by the Australian Office for Learning and Teaching. The environment was trialled with over 1,300 students and evaluated through questionnaires, observational studies and coursework analysis. Results demonstrate a positive impact on students’ technical learning and collaboration skills, but there is need for further research in relation to critical thinking skills and work-readiness.

Keywords: architectural education, construction industry, digital learning environments, immersive learning

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Authors: Babak Mohajeri, Sen Bao, Timo Nyberg

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Customer experience is increasingly the differentiator between successful companies and those who struggle. Currently, customer experiences become more dynamic; and they advance with each interaction between the company and a customer. Every customer conversation and any effort to evolve these conversations would be beneficial and should ultimately result in a positive customer experience. The aim of this paper is to analyze the evolving customer experience management landscape and the relevant challenges and opportunities. A case study on the “paper machine” companies is chosen. Hence, this paper analyzes the challenges and opportunities in customer experience management of paper machine companies for the case of “road to steel”. Road to steel shows the journey of steel from raw material to end product (i.e. paper machine in this paper). ALPHA (Steel company) and BETA (paper machine company), are chosen and their efforts to evolve the customer experiences are investigated. Semi-structured interviews are conducted with experts in those companies to identify the challenges and opportunities of the evolving customer experience management from their point of view. The findings of this paper contribute to the theory and business practices in the realm of the evolving customer experience management landscape.

Keywords: Customer Experience Management, Paper Machine , Value Chain Management, Risk Analysis

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Authors: Jenq-Shyong Chen, Ben-Fong Yu

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CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system.

Keywords: auto-tuning, CNC parameters, machining mode, high speed, high accuracy, high surface quality

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Authors: Fatemeh Keivanifard

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E-learning is becoming increasingly prominent in higher education, with universities increasing provision and more students signing up. This paper examines factors that predict students' attitudes to adapt e-learning at the Khuzestan province Iran. Understanding the nature of these factors may assist these universities in promoting the use of information and communication technology in teaching and learning. The main focus of the paper is on the university students, whose decision supports effective implementation of e-learning. Data was collected through a survey of 300 post graduate students at the University of dezful, shooshtar and chamran in Khuzestan. The technology adoption model put forward by Davis is utilized in this study. Two more independent variables are added to the original model, namely, the pressure to act and resources availability. The results show that there are five factors that can be used in modeling students' attitudes to adapt e-learning. These factors are intention toward e-learning, perceived usefulness of e-learning, perceived ease of e-learning use, pressure to use e-learning, and the availability of resources needed to use e-learning.

Keywords: e-learning, intention, ease of use, pressure to use, usefulness

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: They Shao Peng

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Machine vision system for inspection is an automated technology that is normally utilized to analyze items on the production line for quality control purposes, it also can be known as an automated visual inspection (AVI) system. By applying automated visual inspection, the existence of items, defects, contaminants, flaws, and other irregularities in manufactured products can be easily detected in a short time and accurately. However, AVI systems are still inflexible and expensive due to their uniqueness for a specific task and consuming a lot of set-up time and space. With the rapid development of mobile devices, smartphones can be an alternative device for the visual system to solve the existing problems of AVI. Since the smartphone-based AVI system is still at a nascent stage, this led to the motivation to investigate the smartphone-based AVI system. This study is aimed to provide a low-cost AVI system with high efficiency and flexibility. In this project, the object detection models, which are You Only Look Once (YOLO) model and Single Shot MultiBox Detector (SSD) model, are trained, evaluated, and integrated with the smartphone and webcam devices. The performance of the smartphone-based AVI is compared with the webcam-based AVI according to the precision and inference time in this study. Additionally, a mobile application is developed which allows users to implement real-time object detection and object detection from image storage.

Keywords: automated visual inspection, deep learning, machine vision, mobile application

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: M. Abbasi Layegh, S. Haghipour, K. Athari, R. Khosravi, M. Tafkikialamdari

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An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study.

Keywords: radif of Mirzâ Ábdollâh, Gushe, mel frequency cepstral coefficients, fuzzy c-mean clustering algorithm, k-nearest neighbors (KNN), gaussian mixture model (GMM), hidden markov model (HMM), support vector machine (SVM)

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Authors: Yara Bajnaid, Malak Zaidan, Eman Dakkak

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This research explores the use of Artificial Intelligence in Duolingo, a popular mobile application for language learning. Duolingo's success hinges on its gamified approach and adaptive learning system, both heavily reliant on AI functionalities. The research also analyzes user feedback regarding Duolingo's AI functionalities. While a significant majority (70%) consider Duolingo a reliable tool for language learning, there's room for improvement. Overall, AI plays a vital role in personalizing the learning journey and delivering interactive exercises. However, continuous improvement based on user feedback can further enhance the effectiveness of Duolingo's AI functionalities.

Keywords: AI, Duolingo, language learning, application

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: John Zanetich

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This paper uses a pragmatic research approach to investigate the relationships between Active Development of Tacit Knowledge (ADTK), social media (Facebook) and classroom learning communities. This paper investigates the use of learning communities and social media as the context and means for changing tacit knowledge to explicit and presents a dynamic model of the development of a classroom learning community. The goal of this study is to identify the point that explicit knowledge is converted to tacit knowledge and to test a way to quantify the exchange using social media and learning communities.

Keywords: tacit knowledge, knowledge management, college programs, experiential learning, learning communities

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

Procedia PDF Downloads 150

Authors: Muhammad Mohsin, Fawad Ahmad, Fatima Batool, Muhammad Kaab Zarrar

Abstract:

The integration of the Internet of Things (IoT) and edge computing devices offers a transformative approach to electronic waste management, particularly in the dismantling of printed circuit boards (PCBs). This paper explores how these technologies optimize operational efficiency and improve environmental sustainability by addressing challenges such as data security, interoperability, scalability, and real-time data processing. Proposed solutions include advanced machine learning algorithms for predictive maintenance, robust encryption protocols, and scalable architectures that incorporate edge computing. Case studies from leading e-waste management facilities illustrate benefits such as improved material recovery efficiency, reduced environmental impact, improved worker safety, and optimized resource utilization. The findings highlight the potential of IoT and edge computing to revolutionize e-waste dismantling and make the case for a collaborative approach between policymakers, waste management professionals, and technology developers. This research provides important insights into the use of IoT and edge computing to make significant progress in the sustainable management of electronic waste

Keywords: internet of Things, edge computing, waste PCB disassembly, electronic waste management, data security, interoperability, machine learning, predictive maintenance, sustainable development

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Authors: Dorottya Horváth, Tímea Harmath-Tánczos

Abstract:

Attention deficit hyperactivity disorder (ADHD) and learning disabilities are common neurocognitive disorders that can have a significant impact on a child's academic performance. ADHD is characterized by inattention, hyperactivity, and impulsivity, while learning disabilities are characterized by difficulty with specific academic skills, such as reading, writing, or math. The aim of this study was to investigate the working memory, executive, and attention functions of neurotypical children and children with ADHD and learning disabilities in order to fill the gaps in the Hungarian mean test scores of these cognitive functions in children with neurocognitive disorders. Another aim was to specify the neuropsychological differential diagnostic toolkit in terms of the relationships and peculiarities between these cognitive functions. The research question addressed in this study was: How do the working memory, executive, and attention functions of neurotypical children compare to those of children with ADHD and learning disabilities? A self-administered test battery was used as a research tool. Working memory was measured with the Non-Word Repetition Test, the Listening Span Test, the Digit Span Test, and the Reverse Digit Span Test; executive function with the Letter Fluency, Semantic Fluency, and Verb Fluency Tests; and attentional concentration with the d2-R Test. The data for this study was collected from 115 children aged 9-14 years. The children were divided into three groups: neurotypical children (n = 44), children with ADHD without learning disabilities (n = 23), and children with ADHD with learning disabilities (n = 48). The data was analyzed using a variety of statistical methods, including t-tests, ANOVAs, and correlational analyses. The results showed that the performance of children with neurocognitive involvement in working memory, executive functions, and attention was significantly lower than the performance of neurotypical children. However, the results of children with ADHD and ADHD with learning disabilities did not show a significant difference. The findings of this study are important because they provide new insights into the cognitive profiles of children with ADHD and learning disabilities and suggest that working memory, executive functions, and attention are all impaired in children with neurocognitive involvement, regardless of whether they have ADHD or learning disabilities. This information can be used to develop more effective diagnostic and treatment strategies for these disorders.

Keywords: ADHD, attention functions, executive functions, learning disabilities, working memory

Procedia PDF Downloads 94

Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

Procedia PDF Downloads 111