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Topic Review
Deep Learning Algorithms for Structural Health Monitoring
Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. 
  • 227
  • 22 Nov 2023
Topic Review
Deep Learning Algorithms in Agriculture
The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. 
  • 966
  • 18 Mar 2022
Topic Review
Deep Learning and Non-Orthogonal Multiple Access System
In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. Machine learning (ML) algorithms have the capability to adapt to variations in channel between user and base station (BS); therefore, ML is regarded as a strong contender for future radio networks.
  • 750
  • 13 Jun 2022
Topic Review
Deep Learning Applications for Optical Coherence Tomography
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a pivotal biomarker to assist in the clinical diagnosis of age-related macular diseases, diabetic macular edema, and retinal vein occlusion. In recent years, the advanced machine learning methods, such as deep learning paradigms, have attracted more and more attention from academia in the retinal fluid segmentation applications. The automatic retinal fluid segmentation based on deep learning can improve the semantic segmentation accuracy and efficiency of macular change analysis, which has potential clinical implications for ophthalmic pathology detection.
  • 959
  • 07 May 2022
Topic Review
Deep Learning Based Demand Forecasting in Smart Grids
Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role.
  • 404
  • 04 May 2023
Topic Review
Deep Learning Based Non-Orthogonal Multiple Access
Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system’s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios.
  • 1.1K
  • 14 Mar 2023
Topic Review
Deep Learning Defect Detection Methods for Industrial Products
Deep learning models based on convolutional neural networks (CNN) have had a lot of success in various computer vision fields, such as recognizing faces, identifying pedestrians, detecting text in images, and tracking targets. Additionally, these models are used in a wide range of industrial settings for defect detection.
  • 995
  • 21 Feb 2023
Topic Review
Deep Learning for Automated Visual Inspection
This article evaluates the state of the art of deep-learning-based automated visual inspection in manufacturing and maintenance applications and contrasts it to academic research in the field of computer vision. By doing so itidentifies to what extent computer vision innovations are already being used and which potential improvements could be realized by further transferring promising concepts. Existing work is either focused on specific industry sectors or methodologies but not on industrial VI as a whole or is outdated by almost two decades. We surveyed 196 open access publications from 2010 to March 2023 from the fields of manufacturing and maintenance with no restriction regarding industries. Our main findings were: The vast majority of publications utilize supervised learning approaches on relatively small datasets with convolutional neural networks. The timegap between publication of new approaches in deep learning-based computer vision and its first application in industrial visual inspection is approximately three years First vision transformer models emerge in 2022 and seem to outperform established models but their excellent self-supervised learning capabilities are not explored to date
  • 458
  • 26 Feb 2024
Topic Review
Deep Learning for Image Annotation in Agriculture
The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry.
  • 1.1K
  • 27 Jul 2022
Topic Review
Deep Learning in Brain Tumor Classification from MRI
The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. 
  • 252
  • 22 Sep 2023
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