Showing 1-25 of 50,195 resultsfor "Index Terms":Structure Learning
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Design and Verification of Online Dialogue Promoting Deep Learning Supported by Intelligent Technology
2023 IEEE International Conference on Unmanned Systems (ICUS)
Year: 2023 | Conference Paper |
Intelligent learning environments supported by intelligent technology extend the traditional face-to-face dialogues and communication methods, expand the space of intelligent learning, and serve as a bridge to stimulate learners' higher-order thinking and realize the transition from shallow learning to deep learning. The study takes “online dialog” as the perspective to design an online dialog mec...Show More
Identifying Personalized Context-Aware Knowledge Structure for Individual User in Ubiquitous Learning Environment
Fifth IEEE International Conference on Wireless, Mobile, and Ubiquitous Technology in Education (wmute 2008)
Year: 2008 | Conference Paper |
Cited by: Papers (16)
Ubiquitous learning extends e-learning from indoor to outdoor but also overcomes the weakness of mobile learning which only provides the specific domain knowledge to learners in particular learning environment. A ubiquitous learning environment covers the knowledge of different domains, hence, how to offer individual learner the learning sequence according to the learner's preference is an importa...Show More
Identifying Personalized Context-Aware Knowledge Structure for Individual User in Ubiquitous Learning Environment
Year: 2008 | Conference Paper |
A Study and Application on Machine Learning of Artificial Intellligence
2009 International Joint Conference on Artificial Intelligence
Year: 2009 | Conference Paper |
Cited by: Papers (33)
This thesis elaborated the concept, significance and main strategy of machine learning as well as the basic structure of machine learning system. By combining several basic ideas of main strategies, great effort are laid on introducing several machine learning methods, such as Rote learning, Explanation-based learning, Learning from instruction, Learning by deduction, Learning by analogy and Induc...Show More
Unsupervised Structure-Adaptive Graph Contrastive Learning
Graph contrastive learning, which to date has always been guided by node features and fixed-intrinsic structures, has become a prominent technique for unsupervised graph representation learning through contrasting positive–negative counterparts. However, the fixed-intrinsic structure cannot represent the potential relationships beneficial for models, leading to suboptimal results. To this end, we ...Show More
A dynamic learning content pattern for adaptive learning environment
2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
Structuring content has a positive effect on students' metacognitive skills. Our work in designing content is to approach the use of gaze tracking techniques in adaptive learning strategy that implicates structuring a learning content that will insure the adaptability in real time, taking into account the profile of each learner in the learning process. We present in this paper a learning content ...Show More
Predicting Local Protein 3D Structures Using Clustering Deep Recurrent Neural Network
Since protein 3D structure prediction is very important for biochemical study and drug design, researchers have developed many machine learning algorithms to predict protein 3D structures using the sequence information only. Understanding the sequence-to-structure relationship is key for the successful structure prediction. Previous approaches including the single shallow learning model, the singl...Show More
Anomaly detection of industrial control systems based on transfer learning
Tsinghua Science and Technology
Cited by: Papers (87)
Industrial Control Systems (ICSs) are the lifeline of a country Therefore, the anomaly detection of ICS traffic is an important endeavor. This paper proposes a model based on a deep residual Convolution Neural Network (CNN) to prevent gradient explosion or gradient disappearance and guarantee accuracy. The developed methodology addresses two limitations: most traditional machine learning methods c...Show More
A self directed adaptive e-portfolio architecture for learning
2012 IEEE International Conference on Technology Enhanced Education (ICTEE)
Year: 2012 | Conference Paper |
The learners of the new millennium prefer to be self-learners. They prefer self evaluation by taking self-test and creating their own knowledge after a learning session. Autonomy in the learning process is the key focus of the modern learning environment. This paper describes the architecture of a self-learning environment which incorporates adaptive and intelligent features in the learning proces...Show More
A Comparative Study on Filtering Protein Secondary Structure Prediction
Petros Kountouris;Michalis Agathocleous;Vasilis J. Promponas;Georgia Christodoulou;Simos Hadjicostas;Vassilis Vassiliades;Chris Christodoulou
Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.Show More
Collaborative Global-Local Structure Network With Knowledge Distillation for Imbalanced Data Classification
Multi-expert networks have shown great superiority for imbalanced data classification tasks due to their complementary and diverse. We have summarized two aspects for further explorations: (1) uncontrollable results, arising from the performance differences of individual experts and variations in sample difficulty; (2) insufficient exploration of the internal data structure. These factors result i...Show More
An Analysis of AI-based SQL Injection (SQLi) Attack Detection
2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
The SQL injection attack is a highly perilous vulnerability in the digital realm, especially for web pages, as recognized by the Open Web Application Security Project (OWASP) ranking. It is a type of code injection attacks. This kind of attacks basically breaches the virtual portion of the databases. Many web applications accept to store the user’s private information (e.g., login credentials, cre...Show More
Learning Hypergraphs From Signals With Dual Smoothness Prior
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes crucial when a hypergraph topology is not readily available in the datasets. There are two challenges that lie at the heart of this problem: 1) how to handle the huge search space of potential hyperedges, and 2) how to ...Show More
A design of IL-CyTIS for automated cyber threat detection
2018 International Conference on Information Networking (ICOIN)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
As cyber squabbling has been intensified, the necessity of sharing cyber threat information has increased Therefore, attempts to develop a technology to upgrade and deepen the related system will continue. In particular, it is anticipated that automated response and analysis using machine learning will be actively conducted In this paper, we design and propose IL-CyTIS (a unified and lightened inf...Show More
Artificial Neural Network Design for Enabling Relay Selection by Supervised Machine Learning
Hongfei Dang;Yuehan Liang;Linchang Wei;Chengzhong Li;Shuping Dang
2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
Our recent work has proved the effectiveness of utilizing supervised machine learning to perform relay selection in cooperative networks. However, the structure of artificial neural network provided is rather heuristic and simple, where a symmetric architecture is adopted with only a single hidden layer and the number of neurons in the hidden layer is the same as the number of inputs/outputs. Howe...Show More
Road Accident Analysis using Structure from Motion and Machine Learning
2022 IEEE 7th International conference for Convergence in Technology (I2CT)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Frequency of accidents have increased along with an increase in the number of vehicles on the roads. Many people suffer injuries, with many incurring a disability as a result of their injury. This work focuses on analyzing road accidents by using machine learning and videogrammetry techniques with the help of videos and images obtained from unmanned aerial vehicles (UAV). Machine learning model in...Show More
Knowledge structuring for learning by level
2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA)
Year: 2015 | Conference Paper |
Learning plays a very important role in the evolution of human knowledge; in teaching area, teachers notice more and more that the learning level is getting down, on the other hand learners are unable to use their knowledge in new situation in an effective way, they claim themselves did not have a good learning or they have misunderstood the course. However, to solve this matter, we believe that t...Show More
Semi-Supervised Learning Algorithm Based on Lie Group
2009 WRI Global Congress on Intelligent Systems
Year: 2009 | Conference Paper |
Semi-supervised learning is an important research area in machine learning, which is mainly combined with a little labeled training data from reality, studies the data structure and distribution information from the large number of unlabeled data and makes full use of this information to improve the performance of classification algorithms, and researches the symmetry between the labeled and unlab...Show More
Design and Development of a Machine Learning Tool for an Innovation-Based Learning MOOC
Lauren Singelmann;Ellen Swartz;Mary Pearson;Ryan Striker;Enrique Alvarez Vazquez
Massive Open Online Courses (MOOCs) are allowing education to become accessible to all learners. However, computers are currently not able to provide the same individual expertise and support as a human instructor, making it difficult to tailor the course for each student. This work proposes using machine learning analytics to provide useful individualized feedback to learners in a Cardiovascular ...Show More
Simplified Algorithm for Seismic Action of High-rise Building Structures Based on Deep Learning
2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC)
Year: 2024 | Conference Paper |
At present, the seismic fortification level of “small earthquake is not bad, medium earthquake can be repaired, and big earthquake can not fall” adopted by various countries is to ensure life safety as the single fortification goal. The structure of high-rise building is complex, and the degree of freedom is huge. When the active seismic control method is adopted, the accurate model of the structu...Show More
Machine Learning Techniques for Protein Structure Prediction in Bioinformatics
Aliyev Nurillo Abdiqayumovich;Jaisudha J;Mohammed Al-Farouni;Neha Khare;Gurmandeep Kaur;Siva Sai Koduru
2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)
Year: 2024 | Conference Paper |
This work analyses and discusses the use of machine learning methods in the field of bioinformatics concerning protein structure prediction. The steps of the proposed methodology include: acquiring and cleaning the relevant proteins sequences datasets; and creation and training of deep learning models, including Convolutional Neural Networks (CNNs), and Recurrent Neural Networks, (RNNs). Overall, ...Show More
Machine Learning Methods and Their Application Research
2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing
Year: 2011 | Conference Paper |
Cited by: Papers (11)
This paper introduces definition of machine learning and the basic structure, describes a variety of machine learning methods, compares and analyzes their advantages and their limitations, introduces the main research areas of machine learning, especially introduces technological application of machine learning methods.Show More
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that h...Show More
An epistemology for software tool design that promotes knowledge production: Experiences with students learning computer programming languages
2013 Second International Conference on E-Learning and E-Technologies in Education (ICEEE)
Year: 2013 | Conference Paper |
This article presents the results of applying an epistemological model for students learning computer programming languages. The proposal is based on an analysis of learning theories, teaching methods and experiences with groups in promoting learning and research in different institutions. The main focus of the proposal is on applying learning principles guided with an epistemological model using ...Show More
Combination of Deep Learning and Syntactical Approaches for the Interpretation of Interactions Between Text-Lines and Tabular Structures in Handwritten Documents
2019 International Conference on Document Analysis and Recognition (ICDAR)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
In this article, we present our work on baseline detection in images of historical documents. This work focuses on handwritten documents containing tabular structures. One of the difficulties of this kind of documents is the strong interaction between text and tabular structures. This interaction leads to ambiguous cases for which recognition systems often over-or sub-segment baselines. The intere...Show More
Structure Guided Deep Neural Network for Unsupervised Active Learning
Unsupervised active learning has become an active research topic in the machine learning and computer vision communities, whose goal is to choose a subset of representative samples to be labeled in an unsupervised setting. Most of existing approaches rely on shallow linear models by assuming that each sample can be well approximated by the span (i.e., the set of all linear combinations) of the sel...Show More
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