Special Issue "Deep Learning Trends in Intelligent Systems"

Submission Deadline: 15 December 2020 (closed)
Guest Editors
Dr. Gopal Chaudhary, Guru Gobind Singh Indraprastha University, India.
Dr. Manju Khari, Guru Gobind Singh Indraprastha University, India.
Dr. Bharat Rawal, Gannon University, USA.


Machine learning (ML) and artificial intelligence (AI) are turning out to be effective critical thinking procedures in numerous regions of research and industry, not least as a result of the ongoing accomplishments of deep learning (DL). They supplement one another, and the next advancement lies in pushing every one of them as well as in joining them. Various research disciplines, from computer science to medical science, pattern recognition, forensics science, and cyber-physical systems, as well as numerous organizations, accept that data-driven and “intelligent” solutions are essential to take care of a large number of their key issues. The vast use of these intelligent systems is due to its intelligent decision-making algorithms and techniques. These systems incorporate machine learning, deep learning, transfer learning, and neuro-fuzzy inference techniques, AI-based solutions that are material in the industrial Internet of Things, and machine-to-machine interfaces. The present pattern is to combine data from different sorts of sensors to have an increasingly gainful and progressively robust framework like assistive frameworks using adaptive learning and decision making.


Within this framework, this Special Issue tries to bring together all the latest developments in the area of “Deep Learning trends in Intelligent Systems.” It aims at promoting the recent advances in this research field while highlighting the main real-world challenges.

Potential topics include, but are not limited to, the following:
• Activity recognition: object recognition and pose estimation for assistive robotics, and emotion recognition
• Intelligent autonomous systems
• Deep learning-based intelligent control
• Intelligent modeling, identification and optimization
• Applications in vision, audio, speech, natural language processing, robotics, neuroscience, or any other field
• Deep network compression/acceleration in pattern recognition applications
• Deep neural network in safety-critical or low-cost pattern recognition
• Developing new models for multimodal deep learning
• Signal processing for intelligent systems
• Artificial intelligence for intelligent systems
• Big Data for intelligent sensors systems
• Low-cost solutions for intelligent systems
• Hardware design and solutions for intelligent systems
• Intelligent systems in the biomedical context
• New trends and applications for intelligent systems

Published Papers
  • Spatial-Resolution Independent Object Detection Framework for Aerial Imagery
  • Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) object detection framework to make… More
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  • An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification
  • Abstract Even though much advancements have been achieved with regards to the recognition of handwritten characters, researchers still face difficulties with the handwritten character recognition problem, especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset (EMNIST). The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability. Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset. The presence of intra-class variability is mainly due to different shapes written by different writers for the same character. In this… More
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  • Arabic Named Entity Recognition: A BERT-BGRU Approach
  • Abstract Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models… More
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  • Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment
  • Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing… More
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  • Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classification
  • Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by any of the above mentioned… More
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  • Detecting Driver Distraction Using Deep-Learning Approach
  • Abstract Currently, distracted driving is among the most important causes of traffic accidents. Consequently, intelligent vehicle driving systems have become increasingly important. Recently, interest in driver-assistance systems that detect driver actions and help them drive safely has increased. In these studies, although some distinct data types, such as the physical conditions of the driver, audio and visual features, and vehicle information, are used, the primary data source is images of the driver that include the face, arms, and hands taken with a camera inside the car. In this study, an architecture based on a convolution neural network (CNN) is proposed to… More
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  • Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images
  • Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.… More
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  • 1D-CNN: Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features
  • Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated convolutional neural network (1D-DCNN) for… More
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  • Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning
  • Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More
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  • A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis
  • Abstract Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy… More
  •   Views:634       Downloads:322        Download PDF

  • Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review
  • Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading… More
  •   Views:435       Downloads:303        Download PDF

  • A Novel Approach to Data Encryption Based on Matrix Computations
  • Abstract In this paper, we provide a new approach to data encryption using generalized inverses. Encryption is based on the implementation of weighted Moore–Penrose inverse AMN(nxm) over the nx8 constant matrix. The square Hermitian positive definite matrix N8x8 p is the key. The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge. We have provided NIST (National Institute of Standards and Technology) quality assurance tests for a random generated Hermitian matrix (a total of 10 different tests and additional analysis with approximate entropy and random digression). In the… More
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  • Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms
  • Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More
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  • 3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems
  • Abstract The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter… More
  •   Views:948       Downloads:504        Download PDF

  • Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification
  • Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then… More
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  • Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks
  • Abstract The diagnosis of multiple sclerosis (MS) is based on accurate detection of lesions on magnetic resonance imaging (MRI) which also provides ongoing essential information about the progression and status of the disease. Manual detection of lesions is very time consuming and lacks accuracy. Most of the lesions are difficult to detect manually, especially within the grey matter. This paper proposes a novel and fully automated convolution neural network (CNN) approach to segment lesions. The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly. The first CNN network is implemented to segment lesions accurately,… More
  •   Views:1124       Downloads:605        Download PDF