Search results for: deep learning

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 73

Authors: Xinyu Wang

Abstract:

In the context of the rapid development of Internet technology and the increasing prevalence of online social media, this study investigates the impact of digital communication on social learning. Through three behavioral experiments, we explore both affective and cognitive social learning in online environments. Experiment 1 manipulates the content of experimental materials and two forms of feedback, emotional valence, sociability, and repetition, to verify whether individuals can achieve online emotional social learning through reinforcement using two social learning strategies. Results reveal that both social learning strategies can assist individuals in affective, social learning through reinforcement, with feedback-based learning strategies outperforming frequency-dependent strategies. Experiment 2 similarly manipulates the content of experimental materials and two forms of feedback to verify whether individuals can achieve online knowledge social learning through reinforcement using two social learning strategies. Results show that similar to online affective social learning, individuals adopt both social learning strategies to achieve cognitive social learning through reinforcement, with feedback-based learning strategies outperforming frequency-dependent strategies. Experiment 3 simultaneously observes online affective and cognitive social learning by manipulating the content of experimental materials and feedback at different levels of social pressure. Results indicate that online affective social learning exhibits different learning effects under different levels of social pressure, whereas online cognitive social learning remains unaffected by social pressure, demonstrating more stable learning effects. Additionally, to explore the sustained effects of online social learning and differences in duration among different types of online social learning, all three experiments incorporate two test time points. Results reveal significant differences in pre-post-test scores for online social learning in Experiments 2 and 3, whereas differences are less apparent in Experiment 1. To accurately measure the sustained effects of online social learning, the researchers conducted a mini-meta-analysis of all effect sizes of online social learning duration. Results indicate that although the overall effect size is small, the effect of online social learning weakens over time.

Keywords: online social learning, affective social learning, cognitive social learning, social learning strategies, social reinforcement, social pressure, duration

Procedia PDF Downloads 22

Authors: Nesengani Elelwani Clinton

Abstract:

This paper presents reflective learning as an opportunity commonly available and used for food waste learning in a food processing company in the transition to sustainable and just food systems. In addressing how employees learn about food waste during food processing, the opportunities available for food waste learning were investigated. Reflective learning appeared to be the most used approach to learning about food waste. In the case of food waste learning, reflective learning was a response after employees wasted a substantial amount of food, where process controllers and team leaders would highlight the issue to employees who wasted food and explain how food waste could be reduced. This showed that learning about food waste is not proactive, and there continues to be a lack of structured learning around food waste. Several challenges were highlighted around reflective learning about food waste. Some of the challenges included understanding the language, lack of interest from employees, set times to reach production targets, and working pressures. These challenges were reported to be hindering factors in understanding food waste learning, which is not structured. A need was identified for proactive learning through structured methods. This is because it was discovered that in the plant, where food processing activities happen, the signage and posters that are there are directly related to other sustainability issues such as food safety and health. This indicated that there are low levels of awareness about food waste. Therefore, this paper argues that food waste learning should be proactive. The proactive learning approach should include structured learning materials around food waste during food processing. In the structuring of the learning materials, individual trainers should be multilingual. This will make it possible for those who do not understand English to understand in their own language. And lastly, there should be signage and posters in the food processing plant around food waste. This will bring more awareness around food waste, and employees' behaviour can be influenced by the posters and signage in the food processing plant. Thus, will enable a transition to a just and sustainable food system.

Keywords: sustainable and just food systems, food waste, food waste learning, reflective learning approach

Procedia PDF Downloads 92

Authors: Vania Helena Tonussi Vidal, Ester Eliane Jeunon

Abstract:

This article aims to analyze the change processes of management related bureaucracy and learning organization model. The theoretical framework was based on Beer and Nohria (2001) model, identified as E and O Theory. Based on this theory the empirical research was conducted in connection with six key dimensions: goal, leadership, focus, process, reward systems and consulting. We used a case study of an educational Institution located in Barbacena, Minas Gerais. This traditional center of technical knowledge for long time adopted the bureaucratic way of management. After many changes in a business model, as the creation of graduate and undergraduate courses they decided to make a deep change in management model that is our research focus. The data were collected through semi-structured interviews with director, managers and courses supervisors. The analysis were processed by the procedures of Collective Subject Discourse (CSD) method, develop by Lefèvre & Lefèvre (2000), Results showed the incremental growing of management model toward a learning organization. Many impacts could be seeing. As negative factors we have: people resistance; poor information about the planning and implementation process; old politics inside the new model and so on. Positive impacts are: new procedures in human resources, mainly related to manager skills and empowerment; structure downsizing, open discussions channel; integrated information system. The process is still under construction and now great stimulus is done to managers and employee commitment in the process.

Keywords: bureaucracy, organizational learning, organizational change, E and O theory

Procedia PDF Downloads 416

Authors: Pauline Logue

Abstract:

In recent decades, the Irish Department of Education and Skills has pro-actively promoted student-center learning methodologies. Similarly, the National Forum for the Enhancement of Teaching and Learning has advocated such strategies, aligning them with student success. These developments have informed the author’s professional practice as a teacher educator. This qualitative student-perspective study focuses on a review of one pilot initiative in the academic year 2020-2021, namely, the implementation of universal design for learning strategies within teacher education, employing student-centered learning strategies. Findings included: that student-centered strategies enhanced student performance and success overall, with some minor evidence of student resistance. It was concluded that a dialogical review with student teachers on prior learning experiences (from intellectual and affective perspectives) and learning environments (physical, virtual, and emotional) could facilitate greater student ownership of learning. It is recommended to more formally structure such a dialogical review in a future delivery.

Keywords: professional practice, student-centered learning, teacher education, universal design for learning

Procedia PDF Downloads 176

Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

Abstract:

Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

Procedia PDF Downloads 23

Authors: Walid El Kamash

Abstract:

Sand columns (or granular piles) can be employed as soil strengthening for flexible constructions such as road embankments, oil storage tanks in addition to multistory structures. The challenge of embedding the sand columns in soft soil is that the surrounding soft soil cannot avail the enough confinement stress in order to keep the form of the sand column. Therefore, the sand columns which were installed in such soil will lose their ability to perform needed load-bearing capacity. The encasement, besides increasing the strength and stiffness of the sand column, prevents the lateral squeezing of sands when the column is installed even in extremely soft soils, thus enabling quicker and more economical installation. This paper investigates the improvement in load capacity of the sand column by encasement through a comprehensive parametric study using the 3-D finite difference analysis for the soft clay of soil in Egypt. Moreover, the study was extended to include a comparison study between encased sand column and Deep Mixed columns (DM). The study showed that confining the sand by geosynthetic resulted in an increment of shear strength. That result paid the attention to use encased sand stone rather than deep mixed columns due to relative high permeability of the first material.

Keywords: encased sand column, Deep mixed column, numerical analysis, improving soft soil

Procedia PDF Downloads 365

Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

Abstract:

In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

Procedia PDF Downloads 45

Authors: Baoliang Xu

Abstract:

The future of education should focus on creating meaningful learning for learners. Play is a basic form and an important means of carrying out kindergarten educational activities, which promotes the creation and development of meaningful learning and is of great importance in the harmonious physical and mental development of young children. Through literature research and case studies, this paper finds that: meaningful learning has the characteristics of contextuality, interaction and constructiveness; teachers should pay great attention to the guidance of children's games, fully respect children's autonomy and create a prepared game environment; children's meaningful learning exists in games and hidden in things that interest them, and "the generation of questions The "generation of questions" fuels the depth of children's meaningful learning, and teachers' professional support helps children's meaningful learning to develop continuously. In short, teachers' guidance of young children's play should be emphasized to effectively provide scaffolding instruction to promote meaningful learning in a holistic manner.

Keywords: meaningful learning, young childhood, game, case study

Procedia PDF Downloads 52

Authors: Alison Power

Abstract:

Games-based learning aims to enhance students’ engagement with and enjoyment of learning opportunities using games-related principles to create a fun yet productive learning environment. Motivating students to learn in an online setting can be particularly challenging, so a cross-Faculty synchronous online session provided students with the opportunity to engage with ‘GAMING’: an interactive, flexible and scalable e-resource for students to work synchronously in groups to complete a series of e-tivities designed to enhance their skills of leadership, collaboration and negotiation. Findings from a post-session online survey found the majority of students had a positive learning experience, finding 'GAMING' to be an innovative and engaging e-resource which motivated their group to learn.

Keywords: collaboration, games-based learning, groupwork, synchronous online learning, teamwork

Procedia PDF Downloads 109

Authors: Mimi Mohaffyza Mohamad, Yee Mei Heong, Nurfirdawati Muhammad Hanafi, Tee Tze Kiong

Abstract:

This study is conducted to investigate the disparity of between learning styles and cognitive abilities specifically in Vocational Education. Felder and Silverman Learning Styles Model (FSLSM) was applied to measure the students’ learning styles while the content in Building Construction Subject consists; knowledge, skills and problem solving were taken into account in constructing the elements of cognitive abilities. There are four dimension of learning styles proposed by Felder and Silverman intended to capture student learning preferences with regards to processing either active or reflective, perception based on sensing or intuitive, input of information used visual or verbal and understanding information represent with sequential or global learner. The study discovered that students are tending to be visual learners and each type of learner having significant difference whereas cognitive abilities. The finding may help teachers to facilitate students more effectively and to boost the student’s cognitive abilities.

Keywords: learning styles, cognitive abilities, dimension of learning styles, learning preferences

Procedia PDF Downloads 386

Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

Abstract:

Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

Procedia PDF Downloads 86

Authors: Noawanit Songkram

Abstract:

This paper is a report on the findings of a study conducted on e–learning system in virtual learning environment to develop problem solving ability and team learning for learners in higher education. The methodology of this study was R&D research. The subjects were 18 undergraduate students in Faculty of Education, Chulalongkorn University in the academic year of 2013. The research instruments were a problem solving ability assessment, a team learning evaluation form, and an attitude questionnaire. The data was statistically analyzed using mean, standard deviation, one way repeated measure ANOVA and t–test. The research findings discovered the e –learning system in virtual learning environment to develop problem solving ability and team learning for learners in higher education consisted of five components:(1) online collaborative tools, (2) active learning activities, (3) creative thinking, (4) knowledge sharing process, (5) evaluation and nine processes which were (1) preparing in group working, (2) identifying interested topic, (3) analysing interested topic, (4) collecting data, (5) concluding idea (6) proposing idea, (7) creating workings, (8) workings evaluation, (9) sharing knowledge from empirical experience.

Keywords: e-learning system, problem solving ability, team leaning, virtual learning environment

Procedia PDF Downloads 425

Authors: Nabila Daly, Hamdi Ellouzi, Hela Ltifi

Abstract:

In last year’s, several recent researches’ that focus on e-learning approaches having as goal to improve pedagogy and student’s academy level assessment. E-learning-related works have become an important research file nowadays due to several problems that make it impossible for students join classrooms, especially in last year’s. Among those problems, we note the current epidemic problems in the word case of Covid-19. For those reasons, several e-learning-related works based on Artificial Intelligence techniques are proposed to improve distant education targets. In the current paper, we will present a short survey of the most relevant e-learning based on Artificial Intelligence techniques giving birth to newly developed e-learning tools that rely on new technologies.

Keywords: artificial intelligence techniques, decision, e-learning, support system, survey

Procedia PDF Downloads 208

Authors: Amira Khelil, Habib Affes

Abstract:

The XXIst century is characterized by promoting teamwork as one of the main drivers of firms` performance. Collective competence is becoming crucial in developing and maintaining a firm’s competitive advantage, as well as its contributions to organizational innovation. In other words, the improvement of collective competence for a firm is no longer a choice, but rather an obligation. Learning capabilities of a firm in the context of knowledge management are assumed to be the main drivers of collective competence. Although there are some efforts to consider these concepts together; they are mostly discussed separately in the management theory. Thus, this paper aims to offer a holistic approach for development collective competence on the basis of Knowledge Management and Organizational Learning Capabilities. A theoretical model that defines a relationship between knowledge management, organizational learning and collective competence is presented at the end of this paper.

Keywords: collective competence, exploitation learning, exploration learning, knowledge management, organizational learning capabilities

Procedia PDF Downloads 487

Authors: Mari A. Virtanen, Elina Haavisto, Eeva Liikanen, Maria Kääriäinen

Abstract:

Ubiquitous learning and the use of ubiquitous learning environments herald a new era in higher education. Ubiquitous environments fuse together authentic learning situations and digital learning spaces where students can seamlessly immerse themselves into the learning process. Definitions of ubiquitous learning are wide and vary in the previous literature and learning environments are not systemically described. The aim of this scoping review was to identify the criteria and the use of ubiquitous learning environments in higher education contexts. The objective was to provide a clear scope and a wide view for this research area. The original studies were collected from nine electronic databases. Seven publications in total were defined as eligible and included in the final review. An inductive content analysis was used for the data analysis. The reviewed publications described the use of ubiquitous learning environments (ULE) in higher education. Components, contents and outcomes varied between studies, but there were also many similarities. In these studies, the concept of ubiquitousness was defined as context-awareness, embeddedness, content-personalization, location-based, interactivity and flexibility and these were supported by using smart devices, wireless networks and sensing technologies. Contents varied between studies and were customized to specific uses. Measured outcomes in these studies were focused on multiple aspects as learning effectiveness, cost-effectiveness, satisfaction, and usefulness. This study provides a clear scope for ULE used in higher education. It also raises the need for transparent development and publication processes, and for practical implications of ubiquitous learning environments.

Keywords: higher education, learning environment, scoping review, ubiquitous learning, u-learning

Procedia PDF Downloads 246

Authors: A. Meydanlioglu, F. Arikan

Abstract:

In recent years, thanks to the development of information and communication technologies, the computer and internet have been used widely in higher education. Internet-based education is impacting traditional higher education as online components increasingly become integrated into face-to-face (FTF) courses. The goal of combined internet-based and traditional education is to take full advantage of the benefits of each platform in order to provide an educational opportunity that can promote student learning better than can either platform alone. Research results show that the use of hybrid learning is more effective than online or FTF models in higher education. Due to the potential benefits, an increasing number of institutions are interested in developing hybrid courses, programs, and degrees. Future research should evaluate the effectiveness of hybrid learning. This paper is designed to determine the impact of hybrid learning on higher education.

Keywords: e-learning, higher education, hybrid learning, online education

Procedia PDF Downloads 881

Authors: Aleksandra Ristic

Abstract:

The Coronavirus Disease – 2019 hit Europe in February 2020, and infections took place in four waves. It left consequences and demanded changes for the future. More than half of European countries responded quickly by declaring a state of emergency and introducing various containment measures that have had a major impact on individuals’ lives in recent years. Closing public lives was largely achieved by limited access and/or closing public institutions and services, including the closure of educational institutions. Teaching in classrooms converted to distance learning. In the research, we used a quantitative study to analyze various factors of distance learning that influenced pupils in different segments: teachers’ availability, family support, entire online conference learning, successful distance learning, time for themselves, reliable sources, teachers’ feedback, successful distance learning, online participation classes, motivation and teachers’ communication and theoretical review of the importance of digital skills, e-learning Index, World comparison of e-learning in the past, digital education plans for the field of Europe. We have gathered recommendations and distance learning solutions to improve the learning process by strengthening teachers and creating more tiered strategies for setting and achieving learning goals by the children.

Keywords: availability, digital skills, distance learning, resources

Procedia PDF Downloads 82

Authors: Mingxi Xiao

Abstract:

Children’s experiences in the early years build and shape the brain. The early years learning environment plays a significantly important role in children’s development. A well-constructed environment will facilitate children’s physical and mental well-being. This case study used an early learning centre in Australia called SDN Hurstville as an example, describing the learning environment in the centre, as well as analyzing the functions of the affordances. In addition, this report talks about the sustainability of learning in the centre, and how the environment supports cultural diversity and indigenous learning. The early years for children are significant. Different elements in the early childhood centre should work together to help children develop better. This case study found that the natural environment and the artificial environment are both critical to children; only when they work together can children have better development in physical and mental well-being and have a sense of belonging when playing and learning in the centre.

Keywords: early childhood center, early childhood education, learning environment, Australia

Procedia PDF Downloads 203

Authors: Yong Zhang

Abstract:

Light has proven to be useful in a wide range of biomedical applications such as fluorescence imaging, photoacoustic imaging, optogenetics, photodynamic therapy, photothermal therapy, and light controlled drug/gene delivery. Taking photodynamic therapy (PDT) as an example, PDT has been proven clinically effective in early lung cancer, bladder cancer, head, and neck cancer and is the primary treatment for skin cancer as well. However, clinical use of PDT is severely constrained by the low penetration depth of visible light through thick tissue, limiting its use to target regions only a few millimeters deep. One way to enhance the range is to use invisible near-infrared (NIR) light within the optical window (700–1100nm) for biological tissues, extending the depth up to 1cm with no observable damage to the intervening tissue. We have demonstrated use of NIR-to-visible upconversion fluorescent nanoparticles (UCNPs), emitting visible fluorescence when excited by a NIR light at 980nm, as a nanotransducer for PDT to convert deep tissue-penetrating NIR light to visible light suitable for activating photosensitizers. The unique optical properties of UCNPs enable the upconversion wavelength to be tuned and matched to the activation absorption wavelength of the photosensitizer. At depths beyond 1cm, however, tissue remains inaccessible to light even within the NIR window, and this critical depth limitation renders existing phototherapy ineffective against most deep-seated cancers. We have demonstrated some new treatment modalities for deep-seated cancers based on UCNP hydrogel implants and miniaturized, wirelessly powered optoelectronic devices for light delivery to deep tissues.

Keywords: upconversion, fluorescent, nanoparticle, bioimaging, photodynamic therapy

Procedia PDF Downloads 145

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

Abstract:

Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

Procedia PDF Downloads 60

Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

Procedia PDF Downloads 114

Authors: Jennifer Leach, Umashanger Thayasivam

Abstract:

The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

Procedia PDF Downloads 172

Authors: Victor Oluwaseyi Olowonisi

Abstract:

The paper aimed at presenting an overview on the concept of e-learning as it relates to higher education and how it provides opportunities for students, instructors and the government in developing the educational sector. It also touched on the benefits and challenges attached to e-learning as a new medium of reaching more students especially in the Nigerian context. The opportunities attributed to e-learning in the paper includes breaking boundaries barriers, reaching a larger number of students, provision of jobs for ICT experts, etc. In contrary, poor power supply, cost of implementation, poor computer literacy, technophobia (fear of technology), computer crime and system failure were some of the challenges of e-learning discussed in the paper. The paper proffered that the government can help the people gain more from e-learning through its financing. Also, it was stated that instructors/lecturers and students need to undergo training on computer application in order for e-learning to be more effective in developing higher education in Nigeria.

Keywords: e-learning, education, higher education, increasing literacy

Procedia PDF Downloads 253

Authors: Pinn Tsin Isabel Yee

Abstract:

Students are increasingly turning towards online learning materials to supplement their education. One such approach would be the gamified student engagement platforms (GSEPs) to instill a new learning culture. Data was collected from closed-ended questions via content analysis techniques. About 81.8% of college students from the Monash University Foundation Year agreed that GSEPs (Quizizz) was an effective tool for learning. Approximately 85.5% of students disagreed that games were a waste of time. GSEPs were highly effective among students to facilitate the learning process.

Keywords: engagement, gamified, Quizizz, technology

Procedia PDF Downloads 89

Authors: Elson Szeto

Abstract:

Over the past 15 years, digital technology has ubiquitously penetrated societies around the world. New values of e-learning are emerging in the preparation of future talents, while e-learning is a key driver of widening participation and knowledge transfer in Chinese higher education. As a vibrant, Chinese society in Asia, Hong Kong’s new generation university students, perhaps the digital natives, have been learning with e-learning since their basic education. They can acquire new knowledge with the use of different forms of e-learning as a generic competence. These students who embrace this competence further their study journeys in higher education. This project reviews the Government’s policy of Information Technology in Education which has largely put forward since 1998. So far, primary to secondary education has embraced advantages of e-learning capability to advance the learning of different subject knowledge. Yet, e-learning capacity in higher education is yet to be fully examined in Hong Kong. The study reported in this paper is a pilot investigation into e-learning capacity in Chinese higher education in the region. By conducting a qualitative case study of Hong Kong, the investigation focuses on (1) the institutional ICT settings in general; (2) the pedagogic responses to e-learning in specific; and (3) the university students’ satisfaction of e-learning. It is imperative to revisit the e-learning capacity for promoting effective learning amongst university students, supporting new knowledge acquisition and embracing new opportunities in the 21st century. As a pilot case study, data will be collected from individual interviews with the e-learning management team members of a university, teachers who use e-learning for teaching and students who attend courses comprised of e-learning components. The findings show the e-learning capacity of the university and the key components of leveraging e-learning capability as a university-wide learning settings. The findings will inform institutions’ senior management, enabling them to effectively enhance institutional e-learning capacity for effective learning and teaching and new knowledge acquisition. Policymakers will be aware of new potentials of e-learning for the preparation of future talents in this society at large.

Keywords: capability, e-learning, higher education, student learning

Procedia PDF Downloads 260

Authors: Khlaid Albishi

Abstract:

This paper participates in giving new vision and explains the learning and acquisition processes of English language by analyzing a certain context. Five important factors in English language acquisition and learning are discussed and suitable solutions are provided. The factors are compared with the learners' linguistic background at Bisha College of Technology BCT attempting to link the issues faced by students and the research done on similar situations. These factors are phonology, age of acquisition, motivation, psychology and courses of English. These factors are very important; because they interfere and affect specific learning processes at BCT context and general English learning situations.

Keywords: language acquisition, language learning, factors, Bisha college

Procedia PDF Downloads 478

Authors: Reni Francis

Abstract:

Learning has transcended the classroom boundaries to create a learner centric, interactive, and integrative teaching learning environment. This study analysed the impact of iLearn on the teaching, learning, and evaluation among 100 teacher trainees. The objectives were to cater to the different learning styles of the teacher trainees, to incorporate innovative teaching learning activities, to assist in peer tutoring, to implement different evaluation processes. i: Identifying the learning styles among the teacher trainees through VARK Learning style checklist was followed by planning the teaching-learning process to meet the learning styles of the teacher trainees. L: Leveraging innovations in teaching- learning by planning and creating modules incorporating innovative teaching learning and hence the concept based year plan was prepared. E: Engage learning through constructivism using different teaching methodology to engage the teacher trainees in the learning process through Workshop, Round Robin, Gallery walk, Co-Operative learning, Think-Pair-Share, EDMODO, Course Networking, Concept Map, Brainstorming Sessions, Video Clippings. A: Assessing the learning through an Open Book assignment, Closed book assignment, and Multiple Choice Questions and Seminar presentation. R: Remediation through peer tutoring through Mentor-mentee approach in the tutorial groups, Group work, Library Hours. N: Norming new standards. This was done in the form of extended remediation and tutorials to understand the need of the teacher trainee and support them for further achievements in learning through Face to face interaction, Supervised Study Circle, Mobile (Device) learning. The findings of the study revealed the positive impact of iLearn towards student achievement and enhanced social skills.

Keywords: academic achievement, innovative strategy, learning styles, social skills

Procedia PDF Downloads 343

Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

Abstract:

Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

Procedia PDF Downloads 438

Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

Abstract:

To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

Procedia PDF Downloads 120