Search results for: Gagne’s learning model

Authors: Jinan Fiaidhi, Sabah Mohammed

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Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning.

Keywords: Siamese neural network, few-shot learning, meta-learning, metric-based learning, thick data transformation and analytics

Procedia PDF Downloads 59

Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Arzu Firlarer, Duygu Gocmen, Gokhan Uysal

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There is currently a growing trend among universities towards blended learning. Blended learning is becoming increasingly important in higher education, with the aims of better accomplishing course learning objectives, meeting students’ changing needs and promoting effective learning both in a theoretical and practical dimension like interior architecture discipline. However, the practical dimension of the discipline cannot be supported in the university environment. During the undergraduate program, the practical training which is tried to be supported by two different internship programs cannot fully meet the requirements of the blended learning. The lack of education program frequently expressed by our graduates and employers is revealed in the practical knowledge and skills dimension of the profession. After a series of meetings for curriculum studies, interviews with the chambers of profession, meetings with interior architects, a gap between the theoretical and practical training modules is seen as a problem in all interior architecture departments. It is thought that this gap can be solved by a new education model which is formed by the cooperation of University-Industry in the concept of blended learning. In this context, it is considered that theoretical and applied knowledge accumulation can be provided by the creation of industry-supported educational environments at the university. In the application process of the Interior Architecture discipline, the use of materials and technical competence will only be possible with the cooperation of industry and participation of students in the production/manufacture processes as observers and practitioners. Wood manufacturing is an important part of interior architecture applications. Wood productions is a sustainable structural process where production details, material knowledge, and process details can be observed in the most effective way. From this point of view, after theoretical training about wooden materials, wood applications and production processes are given to the students, practical training for production/manufacture planning is supported by active participation and observation in the processes. With this blended model, we aimed to develop a training model in which theoretical and practical knowledge related to the production of wood works will be conveyed in a meaningful, lasting way by means of university-industry cooperation. The project is carried out in Ankara with Arcora Architecture and Furniture Company and Başkent University Department of Interior Design where university-industry cooperation is realized. Within the scope of the project, every week the video of that week’s lecture is recorded and prepared to be disseminated by digital medias such as Udemy. In this sense, the program is not only developed by the project participants, but also other institutions and people who are trained and practiced in the field of design. Both academicians from University and at least 15-year experienced craftsmen in the wood metal and dye sectors are preparing new training reference documents for interior architecture undergraduate programs. These reference documents will be a model for other Interior Architecture departments of the universities and will be used for creating an online education module.

Keywords: blended learning, interior design, sustainable training, effective learning.

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Authors: Rukminingsih Rukmi

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This paper addresses the issue of L2 teaching speaking to ESL students by fostering their active learning through project-based learning. Project-based learning was employed in classrooms where teachers support students by giving sufficient guidance and feedback. The students drive the inquiry, engage in research and discovery, and collaborate effectively with teammates to deliver the final work product. The teacher provides the initial direction and acts as a facilitator along the way. This learning approach is considered helpful for fostering students’ active learning. that the steps in implementing of project-based learning that fosters students’ critical thinking in TEFL class are in the following: (1) Discussing the materials about Speaking Class, (2) Working with the group to construct scenario of ways on speaking practice, (3) Practicing the scenario, (4) Recording the speaking practice into video, and (5) Evaluating the video product. This research is aimed to develop a strategy of teaching speaking by implementing project-based learning to improve speaking skill in the second Semester of English Department of STKIP PGRI Jombang. To achieve the purpose, the researcher conducted action research. The data of the study were gathered through the following instruments: test, observation checklists, and questionnaires. The result was indicated by the increase of students’ average speaking scores from 65 in the preliminary study, 73 in the first cycle, and 82 in the second cycle. Besides, the results of the study showed that project-based learning considered to be appropriate strategy to give students the same amount of chance in practicing their speaking skill and to pay attention in creating a learning situation.

Keywords: active learning, project-based learning, speaking ability, L2 teaching speaking

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Authors: Wasila Lawan Gadanya

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This paper examines the problems of learning English vowel pronunciation. The objective is to identify some of the factors that affect the learning of English vowel sounds and their proper realization in words. The theoretical framework adopted is based on both error analysis and contrastive analysis. The data collection instruments used in the study are questionnaire and word list for the respondents (students) and observation of some of their lecturers. All the data collected were analyzed using simple percentage. The findings show that it is not a single factor that affects the learning of English vowel pronunciation rather many factors concurrently do so. Among the factors examined, it has been found that lack of correlation between English orthography and its pronunciation, not mother-tongue (which most people consider as a factor affecting learning of the pronunciation of a second language), has the greatest influence on students’ learning and realization of English vowel sounds since the respondents in this study are from different ethnic groups of Nigeria and thus speak different languages but having the same or almost the same problem when pronouncing the English vowel sounds.

Keywords: English vowels, learning, Nigeria, pronunciation

Procedia PDF Downloads 453

Authors: Tesfaye Mengistu

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This thesis aims to investigate the effectiveness of Reinforcement Learning (RL) for Autonomous Energy Management solutions. The study explores the potential of Model Free RL approaches, such as Monte Carlo RL and Q-learning, to improve energy management by autonomously adjusting energy management strategies to maximize efficiency. The research investigates the implementation of RL algorithms for optimizing energy consumption in a single-agent environment. The focus is on developing a framework for the implementation of RL algorithms, highlighting the importance of RL for enabling autonomous systems to adapt quickly to changing conditions and make decisions based on previous experiences. Moreover, the paper proposes RL as a novel energy management solution to address nations' CO2 emission goals. Reinforcement learning algorithms are well-suited to solving problems with sequential decision-making patterns and can provide accurate and immediate outputs to ease the planning and decision-making process. This research provides insights into the challenges and opportunities of using RL for energy management solutions and recommends further studies to explore its full potential. In conclusion, this study provides valuable insights into how RL can be used to improve the efficiency of energy management systems and supports the use of RL as a promising approach for developing autonomous energy management solutions in residential buildings.

Keywords: artificial intelligence, reinforcement learning, monte carlo, energy management, CO2 emission

Procedia PDF Downloads 84

Authors: H. S. Saini, K. Vijayalakshmi, Rishi Sayal

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Network-based education has been growing rapidly in size and quality. Knowledge clustering becomes more important in personalized information retrieval for web-learning. A personalized-Learning service after the learners’ knowledge has been classified with clustering. Through automatic analysis of learners’ behaviors, their partition with similar data level and interests may be discovered so as to produce learners with contents that best match educational needs for collaborative learning. We present a specific mining tool and a recommender engine that we have integrated in the online learning in order to help the teacher to carry out the whole e-learning process. We propose to use sequential pattern mining algorithms to discover the most used path by the students and from this information can recommend links to the new students automatically meanwhile they browse in the course. We have Developed a specific author tool in order to help the teacher to apply all the data mining process. We tend to report on many experiments with real knowledge so as to indicate the quality of using both clustering and sequential pattern mining algorithms together for discovering personalized e-learning systems.

Keywords: e-learning, cluster, personalization, sequence, pattern

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Authors: Edicio M. Faller

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The use of technology in the delivery of teaching and learning is vital nowadays especially in education. Computer Assisted Learning Module (CALM) software is the use of computer in the delivery of instruction with a tailored fit program intended for a specific lesson or a set of topics. The CALM software developed in this study is intended to supplement the traditional teaching methods in technical-vocational (TECH-VOC) instruction specifically the Consumer Electronics Servicing course. There are three specific objectives of this study. First is to create a learning enhancement and review materials on the selected lessons. Second, is to computerize the end-of-chapter quizzes. Third, is to generate a computerized mock exam and summative assessment. In order to obtain the objectives of the study the researcher adopted the Agile Model where the development of the study undergoes iterative and incremental process of the Software Development Life Cycle. The study conducted an acceptance testing using a survey questionnaire to evaluate the CALM software. The results showed that CALM software was generally interpreted as very satisfactory. To further improve the CALM software it is recommended that the program be updated, enhanced and lastly, be converted from stand-alone to a client/server architecture.

Keywords: computer assisted learning module, software development life cycle, computerized mock exam, consumer electronics servicing

Procedia PDF Downloads 395

Authors: Andrea Treviño Gavito, Diego Klabjan, Sanjiv J. Shah

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Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we introduce a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, and 25.9% in accuracy and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses.

Keywords: artificial intelligence, echocardiographic view detection, echocardiography, machine learning, self-supervised representation learning, unsupervised learning

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Authors: Seongjun Kim, Jinwooung Kim, Sung-Ah Kim

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The development of information and communication technology also affects human cognition and thinking, especially in the field of design, new techniques are being tried. In architecture, new design methodologies such as machine learning or data-driven design are being applied. In particular, these methodologies are used in analyzing the factors related to the value of real estate or analyzing the feasibility in the early planning stage of the apartment housing. However, since the value of apartment buildings is often determined by external factors such as location and traffic conditions, rather than the interior elements of buildings, data is rarely used in the design process. Therefore, although the technical conditions are provided, the internal elements of the apartment are difficult to apply the data-driven design in the design process of the apartment. As a result, the designers of apartment housing were forced to rely on designer experience or modular design alternatives rather than data-driven design at the design stage, resulting in a uniform arrangement of space in the apartment house. The purpose of this study is to propose a methodology to support the designers to design the apartment unit plan with high consumer preference by deriving the correlation and importance of the floor plan elements of the apartment preferred by the consumers through the machine learning and reflecting this information from the early design process. The data on the pre-sale competition rate and the elements of the floor plan are collected as data, and the correlation between pre-sale competition rate and independent variables is analyzed through machine learning. This analytical model can be used to review the apartment unit plan produced by the designer and to assist the designer. Therefore, it is possible to make a floor plan of apartment housing with high preference because it is possible to feedback apartment unit plan by using trained model when it is used in floor plan design of apartment housing.

Keywords: apartment unit plan, data-driven design, design methodology, machine learning

Procedia PDF Downloads 269

Authors: Linhan Sun

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With the gradual strengthening of China's economic development, more and more people around the world are learning Chinese due to economic and trade needs, which has also promoted the research related to Chinese language education for foreigners. Because the Chinese language system is different from the Western language system, learning Chinese is not easy for many learners. In addition, language learning cannot be separated from the learning and understanding of culture. How to integrate cultural learning into the curriculum of Chinese language education for foreigners is the focus of this study. Through a semi-structured in-depth interview method, 15 foreigners who have studied or are studying Chinese participated in this study. This study found that cultural learning and Chinese as a foreign language are relatively disconnected. In other words, learners were able to acquire a certain degree of knowledge of the Chinese language through textbooks or courses but did not gain a deeper understanding of Chinese culture.

Keywords: Chinese language education, Chinese culture, qualitative methods, intercultural communication

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Authors: Ellen A. Kalinga

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Moodle is an open source learning management system that enables creation of a powerful and flexible learning environment. Many organizations, especially learning institutions have customized Moodle open source LMS for their own use. In general open source LMSs are of great interest due to many advantages they offer in terms of cost, usage and freedom to customize to fit a particular context. Tanzania Secondary School e-Learning (TanSSe-L) system is the learning management system for Tanzania secondary schools. TanSSe-L system was developed using a number of methods, one of them being customization of Moodle Open Source LMS. This paper presents few areas on the way Moodle OS LMS was customized to produce a functional TanSSe-L system fitted to the requirements and specifications of Tanzania secondary schools’ context.

Keywords: LMS, Moodle, e-learning, Tanzania, secondary school

Procedia PDF Downloads 394

Authors: Sara Moridpour

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To meet emerging industry needs, engineering students must learn different software packages and enhance their computational skills. Traditionally, face-to-face is selected as the preferred approach to teaching software packages. Face-to-face tutorials and workshops provide an interactive environment for learning software packages where the students can communicate with the teacher and interact with other students, evaluate their skills, and receive feedback. However, COVID-19 significantly limited face-to-face learning and teaching activities at universities. Worldwide lockdowns and the shift to online and remote learning and teaching provided the opportunity to introduce different strategies to enhance the interaction among students and teachers in online and virtual environments and improve the learning and teaching of software packages in online and blended teaching methods. This paper introduces a blended strategy to teach engineering software packages to undergraduate students. This article evaluates the effectiveness of the proposed blended learning and teaching strategy in students’ learning by comparing the impact of face-to-face, online and the proposed blended environments on students’ software skills. The paper evaluates the students’ software skills and their software learning through an authentic assignment. According to the results, the proposed blended teaching strategy successfully improves the software learning experience among undergraduate engineering students.

Keywords: teaching software packages, undergraduate students, blended learning and teaching, authentic assessment

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

Procedia PDF Downloads 86

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Suriani Oxley

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All teachers aim to conduct a successful and an effective teaching. Teacher will use a variety of teaching techniques and methods to ensure that students achieve the learning objectives but often the teaching and learning processes are interrupted by a number of things such as noisy students, students not paying attention, the students play and so on. Such disturbances must be addressed to ensure that students can concentrate on their learning activities. This qualitative study observed and captured a video of numerous tricks that teachers in Labuan have implemented in helping the students to pay attentions in the classroom. The tricks are such as Name Calling, Non-Verbal Clues, Body Language, Ask Question, Offer Assistance, Echo Clapping, Call and Response & Cues and Clues. All of these tricks are simple but yet interesting language learning strategies that helped students to focus on their learning activities.

Keywords: paying attention, observation, tricks, learning strategies, classroom

Procedia PDF Downloads 567

Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: Nguyen Duc An

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To the movement towards learning community, creating friendly, positive and appropriate learning environments which best suit the local features is the most salient and decisive factor of the development and success of that learning society. With the aim at building such an English language learning community for the inhabitants in Moc Chau - the national tourist zone, Tay Bac University has successfully designed and deployed the program ‘English in Tourism’ in the project ‘English for Community’. With the strong attachment to the local reality and close knit to the certain communicative situations, this program which was carefully designed and compiled with interesting and practical activities, has greatly helped the locals confidently introduce and popularize the natural beauty, unique culture and specific characteristics of Moc Chau to the foreign tourists; in addition, reinforce awareness of the native culture of the local people as well as improve the professional development in tourism and service.

Keywords: English for community, learning society, learning community, English in tourism

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Ertekin Kotbas

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Teaching English as a Foreign Language (EFL) is on the front burner of many countries in the world, in particular for English Language Teaching departments that train EFL teachers. Under the head of motivational theories in foreign language education, there are numerous researches in literature. However; researches comprising English Language Self-Efficacy and Teachers’ Learning Goal Orientation which has a positive impact on learning teachings skills are scarce. Examination of these English Language self-efficacy beliefs and Learning Goal Orientations of Pre-Service EFL Teachers may broaden the horizons, in consideration the importance of self-efficacy and goal orientation on learning and teaching activities. At this juncture, the present study aims to investigate the relationship between English Language Self-Efficacy and Teachers’ Learning Goal Orientation from Turkish context.

Keywords: English language, learning goal orientation, self-efficacy, pre-service teachers

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Authors: Aroona Hashmi

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The prime motive for this endeavor was to explore the tenth grade English class students’ preferred learning styles studying in government secondary school so that English subject teachers could tailor their pedagogical strategies in relation to their students learning needs. The further aim of this study was to identify any significance difference among the students on a gender basis, area basis and different categories of school basis. The population of this study consisting of all the secondary level schools working in the government sector and positioned in the province of Punjab. The multi-stage cluster sampling method was employed while selecting the study sample from the population. The scale used for the identification of students’ learning styles in this study was developed by Grasha-Riechmann. The data collected through learning style scale was analyzed by employing descriptive statistics technique. The results from data analysis depict that learning styles of the majority of students found to be Collaborative and Competitive. Overall, no considerable difference was surfaced between male-female, urban-rural, general-other categories of 10th grade English class students learning styles.

Keywords: learning style, learning style scale, grade, government sector

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Authors: Sayed-Mahdi Hashemi-Dehkordi, Ian Baker

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This paper investigates the benefits of implementing distance learning in training courses for the oil and gas industries to reduce lean. Due to the remote locations of many oil and gas operations, scheduling and organizing in-person training classes for employees in these sectors is challenging. Furthermore, considering that employees often work in periodic shifts such as day, night, and resting periods, arranging in-class training courses requires significant time and transportation. To explore the effectiveness of distance learning compared to in-class learning, a set of questionnaires was administered to employees of a far on-shore refinery unit in Iran, where both in-class and distance classes were conducted. The survey results revealed that over 72% of the participants agreed that distance learning saved them a significant amount of time by rating it 4 to 5 points out of 5 on a Likert scale. Additionally, nearly 67% of the participants acknowledged that distance learning considerably reduced transportation requirements, while approximately 64% agreed that it helped in resolving scheduling issues. Introducing and encouraging the use of distance learning in the training environments of oil and gas industries can lead to notable time and transportation savings for employees, ultimately reducing lean in a positive manner.

Keywords: distance learning, in-class learning, lean, oil and gas, scheduling, time, training programs, transportation

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Authors: Omer Agail

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The purpose of this study was to assess the social skills of students with and without learning disabilities in primary education in Saudi Arabia. A Social Skills Rating Scale for Teachers Form (SSRS-TF) was used to evaluate students' social skills as perceived by teachers. A randomly-selected sample was chosen from students with and without learning disabilities. Descriptive statistics were used to describe the demographic characteristics of participants. Analysis indicated that there were statistically significant differences in SSRS-TF by academic status, i.e. students with learning disabilities exhibit less social skills compared to students without learning disabilities. In addition, analysis indicated that there were no statistically significant differences in SSRS-TF by gender. A conclusion and recommendations are presented.

Keywords: primary education, students with learning disabilities, social skills, social competence

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Gavin J. Baxter, Mark H. Stansfield

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This paper reviews the internal use of blogs and their potential effectiveness as organisational learning tools. Prior to and since the emergence of the concept of ‘Enterprise 2.0’ there still remains a lack of empirical evidence associated with how organisations are applying social media tools and whether they are effective towards supporting organisational learning. Surprisingly, blogs, one of the more traditional social media tools, still remains under-researched in the context of ‘Enterprise 2.0’ and organisational learning. The aim of this paper is to identify the theoretical linkage between blogs and organisational learning in addition to reviewing prior research on organisational blogging with a view towards exploring why this area remains under-researched and identifying what needs to be done to try and move the area forward. Through a review of the literature, one of the principal findings of this paper is that organisational blogs, dependent on their use, do have a mutual compatibility with the interpretivist aspect of organisational learning. This paper further advocates that further empirical work in this subject area is required to substantiate this theoretical assumption.

Keywords: Enterprise 2.0, blogs, organisational learning, social media tools

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Authors: Rida Kanwal, Wang Yuhui, Song Weiguo

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Wildfires are one of the most prominent threats to ecosystems, human health, and economic activities, with wind acting as a critical driving factor. This study combines machine learning (ML) and remote sensing (RS) to assess the effects of wind on wildfires in Chongqing Province from August 16-23, 2022. Landsat 8 satellite images were used to estimate the difference normalized burn ratio (dNBR), representing prefire and postfire vegetation conditions. Wind data was analyzed through geographic information system (GIS) mapping. Correlation analysis between wind speed and fire radiative power (FRP) revealed a significant relationship. An autoregressive integrated moving average (ARIMA) model was developed for wind forecasting, and linear regression was applied to determine the effect of wind speed on FRP. The results identified high wind speed as a key factor contributing to the surge in FRP. Wind-rose plots showed winds blowing to the northwest (NW), aligning with the wildfire spread. This model was further validated with data from other provinces across China. This study integrated ML, RS, and GIS to analyze wildfire behavior, providing effective strategies for prediction and management.

Keywords: wildfires, machine learning, remote sensing, wind speed, GIS, wildfire behavior

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Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 127

Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

Abstract:

In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

Procedia PDF Downloads 231

Authors: Naoki Okamura

Abstract:

From a perspective of moral education, this study has examined the experiences of a group of college students who volunteered in disaster areas after the magnitude 9.0 Earthquake, which struck the Northeastern region of Japan in March, 2011. The research, utilizing the method of grounded theory, has uncovered that most of the students have gone through positive changes in their development of moral and social characters, such as attaining deeper sense of empathy and caring personalities. The study expresses, in identifying the nature of those transformations, that the importance of volunteer work should strongly be recognized by the colleges and universities in Japan, in fulfilling their public responsibility of creating and building learning communities that are responsible and caring.

Keywords: moral development, moral education, service learning, volunteer learning

Procedia PDF Downloads 322