AIMC Topic: Deep Learning

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A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format.

Computational intelligence and neuroscience
In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to s...

Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing.

Computational intelligence and neuroscience
Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (I...

Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018.

Revista da Sociedade Brasileira de Medicina Tropical
BACKGROUND: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are lo...

New deep learning method for efficient extraction of small water from remote sensing images.

PloS one
Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other inte...

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

International journal of radiation oncology, biology, physics
PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images.

Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis.

IEEE transactions on pattern analysis and machine intelligence
With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layou...

Robust Face Alignment via Deep Progressive Reinitialization and Adaptive Error-Driven Learning.

IEEE transactions on pattern analysis and machine intelligence
Regression-based face alignment involves learning a series of mapping functions to predict the true landmarks from an initial estimation of the alignment. Most existing approaches focus on learning efficacious mapping functions from some feature repr...

3D Human Pose, Shape and Texture From Low-Resolution Images and Videos.

IEEE transactions on pattern analysis and machine intelligence
3D human pose and shape estimation from monocular images has been an active research area in computer vision. Existing deep learning methods for this task rely on high-resolution input, which however, is not always available in many scenarios such as...

Deep Learning Adapted to Differential Neural Networks Used as Pattern Classification of Electrophysiological Signals.

IEEE transactions on pattern analysis and machine intelligence
This manuscript presents the design of a deep differential neural network (DDNN) for pattern classification. First, we proposed a DDNN topology with three layers, whose learning laws are derived from a Lyapunov analysis, justifying local asymptotic c...

Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL.

Sensors (Basel, Switzerland)
An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we pro...