Showing 1-25 of 230,264 resultsfor "Index Terms":Deep neural network
"Index Terms":Deep neural network
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Review of Deep Learning Methods
2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)
Year: 2024 | Conference Paper |
Deep Learning falls within the realm of artificial intelligence as a subset of Machine Learning. It plays a crucial role in our everyday lives. The field of Deep learning has expanded significantly in recent years and finds application in wide array of contexts. Deep learning (DL) has made its impact in many areas predictive forecasting, voice generation and recognition, audio, video, image genera...Show More
Estimation of Joint Angle From sEMG and Inertial Measurements Based on Deep Learning Approach
Alfredo Lobaina Delgado;Adson F. Da Rocha;Alexander Suárez León;Andrés Ruiz-Olaya;Klaus Ribeiro Montero;Alberto López Delis
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
Continuous kinematics estimation from surface electromyography (sEMG) allows more natural and intuitive human-machine collaboration. Recent research has suggested the use of multimodal inputs (sEMG signals and inertial measurements) to improve estimation performance. This work focused on assessing the use of angular velocity in combination with myoelectric signals to simultaneously and continuousl...Show More
High-rate emphasized DeepLabV3Plus for Semantic Segmentation of Breast Cancer-related Hematoxylin and Eosin-stained Images
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
Deep learning algorithms have been successfully adopted to extract meaningful information from digital images, yet many of them have been untapped in the semantic image segmentation of histopathology images. In this paper, we propose a deep convolutional neural network model that strengthens Atrous separable convolutions with a high rate within spatial pyramid pooling for histopathology image segm...Show More
Applications of Deep Learning for Disease Management
2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The current scenario indicates that every person suffers from a disease. Thus, in the current framework, disease management is a must. It includes disease detection, diagnosis, prediction, and analysis. Deep learning (DL) plays a dynamic role in the healthcare area. The Healthcare sector uses various DL technologies such as Convolutional Neural Network (CNN), Stacked Encoders (SAE), Recurrent Neur...Show More
Automatic COVID-19 Detection from Chest X-ray using Deep MobileNet Convolutional Neural Network
Noor Kamal Al-Qazzaz;Alaa A. Aldoori;Sawal Hamid Bin Mohd Ali;Siti Anom Ahmad
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
As the COVID-19 pandemic has put a strain on healthcare systems around the world, accurate and rapid virus detection has become increasingly important. Lung issues caused by COVID-19 can be detected using a chest X-ray (CXR). In order to automatically determine whether or not a patient’s CXR data are consistent with COVID-19, this work provides a deep learning transfer learning MobileNetV2 model f...Show More
Deep Learning Enabled Automatic Abnormal EEG Identification
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2018 | Conference Paper |
Cited by: Papers (35)
In hospitals, physicians diagnose brain-related disorders such as epilepsy by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians or neurophysiologists and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and rate at which new data is acquired makes interpretation a time-cons...Show More
Comprehensive Analysis of Text based Sentiment Analysis using Deep Learning
2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)
Year: 2024 | Conference Paper |
Vast growth in the customers of the social media platform leads to automatic sentiment analysis based on the review, post, profile, and comment text to characterize the emotional status of customers. Text-based sentiment analysis is challenging due to variants in the context, content, semantics, grammar, and understanding of the particular language. This paper provides a comparative analysis of De...Show More
Survey of the Image Recognition Based on Deep Learning Network for Autonomous Driving Car
2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
Recently, autonomous driving car is a hot area of research and the image recognition technology is one of the key technologies for autonomous driving cars to drive safely on the road. With the development of times and technology, deep learning has been widely applied in image recognition technology, and it plays an important role in this field. In this paper, the development of image recognition t...Show More
Recurrent Residual Networks Contain Stronger Lottery Tickets
Ángel López García-Arias;Yasuyuki Okoshi;Masanori Hashimoto;Masato Motomura;Jaehoon Yu
Accurate neural networks can be found just by pruning a randomly initialized overparameterized model, leaving out the need for any weight optimization. The resulting subnetworks are small, sparse, and ternary, making excellent candidates for efficient hardware implementation. However, finding optimal connectivity patterns is an open challenge. Based on the evidence that residual networks may be ap...Show More
Deep Learning Methods for Facial Expression Recognition
2019 7th International Conference on Mechatronics Engineering (ICOM)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
Deep learning is very popular methods for facial expression recognition (FER) and classification. Different types of deep learning algorithms have been used for FER such as deep belief network (DBN) and convolutional neural network (CNN). In this paper, we analyze various deep learning methods and their results. We have chosen Deep convolutional neural network as the best algorithms for facial exp...Show More
Justification of STL-10 dataset using a competent CNN model trained on CIFAR-10
2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
Deep Learning is a subfield of machine learning concerned through algorithms stimulated by the edifice and purpose of the brain called ANN (artificial neural networks). A convolutional neural network (CNN) is a class of deep neural networks, utmost generally applied to examining painterly images. It uses a distinction of multilayer perceptrons intended to necessitate nominal preprocessing. The CIF...Show More
Automatic Modulation Classification in Deep Learning
2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)
Year: 2022 | Conference Paper |
Cited by: Papers (6)
Due to the evolution and availability of vast amounts of data for transferring, receiving, and detection, the field of signal recognition and modulation classification has become vital in various fields and applications. Additionally, the classical approaches to machine learning (ML) no more can satisfy the current needs. Hence, this urged researchers to apply deep learning (DL) algorithms that ha...Show More
Enhancing Natural Language Processing with Deep Learning for Sentiment Analysis
Om Prakash C;S. Vijaya Kumar;Pallavi;Jamal K. Abbas;Sarmad Jaafar Naser;Noor Hanoon Haroon
2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
Year: 2024 | Conference Paper |
Natural language processing (NLP) aids in the advancement of intelligent machines through its emphasis on etymologically grounded human-PC connections and a greater understanding of the human language. The demand and necessity to implement information-driven methodologies for automating semantic analysis have increased due to ongoing advancements in processing power and the accessibility of vast q...Show More
Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human–Robot Interaction
A coupled multimodal emotional feature analysis (CMEFA) method based on broad–deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracted using the broad and deep learning fusion network (BDFN). Considering that the bi-modal emotion is not completely independent of each other, canonica...Show More
Recommendation System For Social Platform Using Deep Learning Techniques
2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS)
Year: 2024 | Conference Paper |
This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and mutual friends, the system aims to provide accurate friend suggestions. This recommendation methodology can be applied across various domains, including search engine recommendations...Show More
Deep Learning Classifiers used in Speech Processing: A Literature Review
2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)
Year: 2023 | Conference Paper |
Speech is the most convenient way for people to express their emotions. It follows that this communication medium should be extended to computer programmes. This fresh research makes use of advancements in all domains of computers and technology, necessitating an update on the present approaches and techniques that enable Speech processing. Numerous strategies have been used in the literature of a...Show More
A Comparison of Hough Transform and Deep Neural Network Methods on Road Segmentation
2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Thanks to developments in the computer hardware systems, deep learning has been an attractive field for many researchers in different disciplines. Aim of deep learning is to extract the desired features of raw data as a learning method by operating many hidden layers. Accomplished results of learning methods on complex issues as face recognition, object detection, motion recognition etc. led resea...Show More
Deep and Wide Convolutional Neural Network Model for Highly Dense Crowd
2019 Innovations in Intelligent Systems and Applications Conference (ASYU)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
In this study, a novel and efficient deep learning model are proposed to estimate the number of people in highly dense crowd images. We present a convolutional neural network model consisting of two parallel modules which focus on various specific features of the images. Thus, while the general density map is derived by obtaining lower-level features from the first module, it is possible to identi...Show More
Deep learning has transformed data generation, particularly in creating synthetic sensor data. This capability is invaluable in fields like autonomous driving, robotics, and computer science. To achieve this, we train models using real data, enabling them to replicate sensor data closely. These models introduce variations and noise, enhancing diversity and realism. Prominent techniques, including ...Show More
Selection of Deep Neural Network Models for IoT Anomaly Detection Experiments
2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
Year: 2021 | Conference Paper |
Cited by: Papers (10)
This research is about selection of deep neural network models for anomaly detection in Internet of Things network traffic. We are experimentally evaluating deep neural network models using the same software, hardware and the same subsets of the UNSW-NB 15 dataset for training and testing. The assessment results are quality metrics of anomaly detection and the time spent on training models.Show More
Network Attack Detection in the PfSense Firewall via Deep Learning
2024 IEEE International Multi-Conference on Smart Systems & Green Process (IMC-SSGP)
Year: 2024 | Conference Paper |
The detection of network attacks is a crucial aspect in ensuring the sustainability and proper functioning of information systems. Complex threat patterns and malicious actors possess the ability to inflict significant damage to cyber systems. In this study, we propose novel approaches utilizing deep learning techniques to identify and respond to threats and alerts found within network logs acquir...Show More
Predicting Wheat Yield Using Sequential and Deep Convolutional Neural Networks
2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)
Year: 2024 | Conference Paper |
Estimating yield and monitoring agricultural practices are crucial for ensuring local and global food security. Almost every region or country is somehow facing loss of yield due to change in climate and other conditions, it is essential to recognize the yearly variability in agricultural production and its link to meteorological conditions. If it is possible to forecast the accurate yield on time...Show More
A fault diagnosis algorithm of the dedicated equipment based on CBAM-ResNET mechanism
2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA)
Year: 2023 | Conference Paper |
With the increase of the intellectualization of the dedicated equipment, the coupling degree between different electronic devices is also increasing, and the signals contained in the dedicated equipment are also growing. Therefore, it is difficult to use statistical methods for fault diagnosis, but now the fault diagnosis of equipments mainly adopts machine learning for small samples or deep learn...Show More
Automatic Searching and Pruning of Deep Neural Networks for Medical Imaging Diagnostic
The field of medical imaging diagnostic makes use of a modality of imaging tests, e.g., X-rays, ultrasounds, computed tomographies, and magnetic resonance imaging, to assist physicians with the diagnostic of patients’ illnesses. Due to their state-of-the-art results in many challenging image classification tasks, deep neural networks (DNNs) are suitable tools for use by physicians to provide diagn...Show More
Weed Detection Model Using the Generative Adversarial Network and Deep Convolutional Neural Network
Agriculture crop demand is increasing day by day because of population. Crop production can be increased by removing weeds in the agriculture field. However, weed detection is a complicated problem in the agriculture field. The main objective of this paper is to improve the accuracy of weed detection by combining generative adversarial networks and convolutional neural networks. We have implemente...Show More
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