Showing 1-25 of 6,339 resultsfor "Index Terms":Deep Learning-based Methods
"Index Terms":Deep Learning-based Methods
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Deepfake Generation, Detection and Datasets: a Rapid-review
2022 15th International Conference on Information Security and Cryptography (ISCTURKEY)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Deepfake is the general expression of fake images or videos. Images and videos created with Deep Neural Networks (DNN) can cause both personal and national problems. Deepfake technology has been making progress day by day. Accordingly, both the detection methods and the diversity of the data sets to be trained are increasing. In this article, datasets created in deepfake technology and application...Show More
Facial Expression Recognition: A Survey and its Applications
2021 23rd International Conference on Advanced Communication Technology (ICACT)
Year: 2021 | Conference Paper |
Cited by: Papers (11)
Automatic facial expression recognition is an important component for efficient human-computer interaction system, and over the past decades, it has become a highly active research area. Numerous algorithms have been proposed in the literature to cope with the problem of face expression recognition (FER). General speaking, current existing FER methods can be categorized into two main groups, i.e.,...Show More
Facial Expression Recognition: A Survey and its Applications
2022 24th International Conference on Advanced Communication Technology (ICACT)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Automatic facial expression recognition is an important component for efficient human-computer interaction system, and over the past decades, it has become a highly active research area. Numerous algorithms have been proposed in the literature to cope with the problem of face expression recognition (FER). General speaking, current existing FER methods can be categorized into two main groups, i.e.,...Show More
Deep Learning-Based Inversion Method for Imaging Problems in Electrical Capacitance Tomography
Electrical capacitance tomography exhibits great potentials in the visualization measurement of industrial processes, and high-precision images are of great significance for the reliability and usefulness of measurement results. In this paper, we propose a deep learning-based inversion method to ameliorate the reconstruction accuracy. With the aid of the deep learning methodology, the prior from t...Show More
Deep Learning-based Anomaly Detection for Compressors Using Audio Data
2021 Annual Reliability and Maintainability Symposium (RAMS)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
SUMMARY & CONCLUSIONSBy pressurizing natural gas in pipelines, the compression system interlocks upstream gas production and downstream consumer use. Considering the installation cost of $1 to $2 million US dollars for a compressor, the failure of the component can be costly. Therefore, the anomaly detection for the compression system is essential. In this paper, a deep learning-based anomaly dete...Show More
Deep learning-based spatial modeling method for 3D formation drillability field
2021 40th Chinese Control Conference (CCC)
Year: 2021 | Conference Paper |
3D formation drillability field is crucial for drilling optimization and control due to its vital role in describing the spatial formation environment. Conventional geostatistical and machine learning methods are introduced to establish the field. However, the modeling accuracy should be further improved to meet the high-level requirement of drilling engineering. In this paper, a novel deep learni...Show More
Self-supervised Deep Learning for Flower Image Segmentation
Sudipan Saha;Nasrullah Sheikh;Biplab Banerjee;Sumedh Pendurkar
2020 14th International Conference on Innovations in Information Technology (IIT)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Segmentation plays an important role in imagebased plant phenotyping applications. Deep learning has led to a dramatic improvement in segmentation performance. Most deep learning-based methods are supervised and require abundant application-specific training data. Considering the wide range of plant phenotyping applications, such data may not be always available. To mitigate this problem, we intro...Show More
A Comparison of Artificial Intelligence-Based Methods in Traffic Prediction
2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Traffic plays an important role in our society as its state can affect individuals and industries in various ways. Traffic congestion can bring negative impacts to the society and can lead to bigger problems if let be without a solution to mitigate it. Thus, traffic prediction serves as a solution to said problem. In this systematic literature review, AI-based traffic prediction methods are compar...Show More
Deep Learning Based Techniques to Enhance the Performance of Microgrids: A Review
Sheraz Aslam;Herodotos Herodotou;Nasir Ayub;Syed Muhammad Mohsin
2019 International Conference on Frontiers of Information Technology (FIT)
Year: 2019 | Conference Paper |
Cited by: Papers (14)
In the last few years, carbon emissions and energy demand have increased dramatically around the globe due to a surge in population and energy-consuming devices. The integration of renewable energy resources (RERs) in a power supply system provides an efficient solution in terms of low energy cost with lower carbon emissions. However, renewable sources like solar panels have irregular nature of po...Show More
A Multiple Decoder Cnn For Inverse Consistent 3d Image Registration
Abdullah Nazib;Clinton Fookes;Olivier Salvado;Dimitri Perrin
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
The application of deep learning approaches in medical image registration has decreased the registration time and increased registration accuracy. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration...Show More
Graph Signal Denoising Using Nested-Structured Deep Algorithm Unrolling
Masatoshi Nagahama;Koki Yamada;Yuichi Tanaka;Stanley H. Chan;Yonina C. Eldar
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM (PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimization algorithm which contains trainable parameters at each layer. We also propose a nested-structur...Show More
A Survey on Deep Learning-Based Source Code Defect Analysis
2020 5th International Conference on Computer and Communication Systems (ICCCS)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
With the rapid development of information technology, various software applications are flooding our daily lives. The development of these application software inevitably generates a lot of source code. How to detect and analyze various defects in the source code, such as API/Function call errors, array misuse, and expression syntax error, etc., which is known as source code defect analysis (SCDA)...Show More
Performance Evaluation of Partial Offloading under Various Scenarios in Mobile Edge Computing
Seokbeom Oh;Soojeong Lee;Yong-Geun Hong;Gyuyeol Kong;Hyun-Kook Kahng
2022 24th International Conference on Advanced Communication Technology (ICACT)
Year: 2022 | Conference Paper |
The problem of offloading policy is addressed for mobile edge computing (MEC) in this paper. We proposed a deep learning-based partial offloading method to reduce user equipment’s energy consumption and service delay. The proposed method consists of two deep neural networks (DNNs) to find the best partitioning of a single task and their offloading policy, respectively. Multiclass classification is...Show More
Improving GAN-Based Calligraphy Character Generation using Graph Matching
2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Year: 2019 | Conference Paper |
Cited by: Papers (6)
The Chinese character generation with specific style is the key of personal calligraphy font generation. In recent years, with the development of artificial intelligence, researchers have used deep learning to solve the calligraphy generation problem, which can automatically generate personal calligraphy fonts. However, the end-to-end approaches possibly generate wrong results in terms of glyph st...Show More
A Deep Reinforcement Learning Control Method for a Four-Link Brachiation Robot
2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Brachiation is a common way for primates to move between treetops. This movement has the characteristics of adapting to complex, discontinuous environment and low energy consumption. However, traditional control methods are often difficult to complete such tasks where the support is point-contact and discrete. Reinforcement learning (RL) gives a solution to such tasks due to its strong ability to ...Show More
Electromyography (EMG) shows excellent potential for human-machine interaction (HMI) tasks. It reflects the physiological intention of human beings, which contributes to a more intuitive human-machine interface. The sequence of EMG signals acquiring during a period is most commonly used for feature extraction and gesture recognition. The instant EMG graph, which is always thought to be useless due...Show More
Comparative Analysis of Hate Speech Detection: Traditional vs. Deep Learning Approaches
2024 IEEE Conference on Artificial Intelligence (CAI)
Year: 2024 | Conference Paper |
Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learnin...Show More
Deep Learning-based Fault Detection, Classification, and Locating in Shipboard Power Systems
2021 IEEE Electric Ship Technologies Symposium (ESTS)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
This paper proposes a new deep learning-based framework for fault detection, classification, and location identification simultaneously in shipboard power systems (SPS). Specifically, three different neural networks based fault detection methods, including deep neural network, gated recurrent unit, and long short-term memory, are developed and compared to detect different faults in SPS. The develo...Show More
Research on the Key Technology of Chinese Text Sentiment Analysis
2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
In the era of big data, text sentiment analysis is of great significance to the analysis of public opinion. In general, there are two broad approaches on sentiment analysis, lexicon-based and machine learning-based method. In fact, sentiment analysis belongs to the classification technique as well. Therefore, this paper also studied the method based on deep learning. This paper implemented three a...Show More
Medical Imaging-based Techniques for Alzheimer's Disease Detection: A Survey
2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Alzheimer's disease (AD) detection corresponds one of the most powerful and challenging tasks in medical imaging processing. This paper describes the survey of recent AD detection techniques in the last ten years. The process of AD detection can include different stages such as preprocessing, feature extraction, feature selection, dimensionality reduction, segmentation and classification. In this ...Show More
In a multi-robot system, situation assessment evaluates the current situation quantitatively to help decision-makers make the best decision. Conventional situation assessment methods ignore the initiative of each robot, so it often encounters bottlenecks. Collaborative intelligence shows better performance than a single global decision. To address this problem, this work introduces a deep learning...Show More
Physics-driven Deep Neural Network for Fourier Phase Retrieval
TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)
Year: 2022 | Conference Paper |
Fourier Phase retrieval (PR), aiming at recovering a complex-valued signal from its Fourier intensity measurements, has attracted widespread attention due to its importance in many optical imaging applications. Recently, deep learning-based approaches were developed that achieved some success. These approaches require only a single Fourier intensity measurement without the need to impose any addit...Show More
Deep Unsupervised Hashing by Distilled Smooth Guidance
Xiao Luo;Zeyu Ma;Daqing Wu;Huasong Zhong;Chong Chen;Jinwen Ma;Minghua Deng
2021 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Hashing has been widely used in approximate nearest neighbor search recently. Deep supervised hashing methods are not widely-used because of the lack of labeled data, especially when the domain is transferred. Meanwhile, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable similarity signals. Here, we propose a novel deep unsupervised hashing met...Show More
Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration
2020 Digital Image Computing: Techniques and Applications (DICTA)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Image registration is a widely-used technique in analysing large scale datasets that are captured through various imaging modalities and techniques in biomedical imaging such as MRI, X-Rays, etc. These datasets are typically collected from various sites and under different imaging protocols using a variety of scanners. Such heterogeneity in the data collection process causes inhomogeneity or varia...Show More
Deepfake Detection: A Systematic Literature Review
Md Shohel Rana;Mohammad Nur Nobi;Beddhu Murali;Andrew H. Sung
Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fak...Show More
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