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  • 1
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    Online Resource
    Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-4-14), p. 1-9
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-4-14), p. 1-9
    Abstract: A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/3019194
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 2
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    Evolving Long Short-Term Memory Network-Based Text Classification
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-2-21), p. 1-11
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-2-21), p. 1-11
    Abstract: Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/4725639
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 3
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    Psychological Analysis for Depression Detection from Social Networking Sites
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-4-6), p. 1-14
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-4-6), p. 1-14
    Abstract: Rapid technological advancements are altering people’s communication styles. With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions. Psychological analysis analyzes text and extracts facts, features, and important information from the opinions of users. Researchers working on psychological analysis rely on social networks for the detection of depression-related behavior and activity. Social networks provide innumerable data on mindsets of a person’s onset of depression, such as low sociology and activities such as undergoing medical treatment, a primary emphasis on oneself, and a high rate of activity during the day and night. In this paper, we used five machine learning classifiers—decision trees, K-nearest neighbor, support vector machines, logistic regression, and LSTM—for depression detection in tweets. The dataset is collected in two forms—balanced and imbalanced—where the oversampling of techniques is studied technically. The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/4395358
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 4
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    Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-5-20), p. 1-19
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-5-20), p. 1-19
    Abstract: In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/7882924
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 5
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    Medical Image Captioning Using Optimized Deep Learning Model
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-3-9), p. 1-9
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-3-9), p. 1-9
    Abstract: Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/9638438
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 6
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    Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images
    Hindawi Limited ; 2021
    In:  Mathematical Problems in Engineering Vol. 2021 ( 2021-11-24), p. 1-8
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2021 ( 2021-11-24), p. 1-8
    Abstract: Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.
    Type of Medium: Online Resource
    ISSN: 1563-5147 , 1024-123X
    URL: Article
    DOI: 10.1155/2021/7627566
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 7
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    Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring
    Hindawi Limited ; 2022
    In:  Journal of Healthcare Engineering Vol. 2022 ( 2022-3-1), p. 1-15
    Details
    In: Journal of Healthcare Engineering, Hindawi Limited, Vol. 2022 ( 2022-3-1), p. 1-15
    Abstract: Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic’s beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient’s activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient’s breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient’s respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.
    Type of Medium: Online Resource
    ISSN: 2040-2309 , 2040-2295
    URL: Article
    DOI: 10.1155/2022/8732213
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2545054-2
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  • 8
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    An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction
    Hindawi Limited ; 2022
    In:  Scientific Programming Vol. 2022 ( 2022-2-16), p. 1-13
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2022 ( 2022-2-16), p. 1-13
    Abstract: The idea of multivariate and online stock price prediction via the kernel adaptive filtering (KAF) paradigm is proposed in this article. The prediction of stock prices is traditionally done with regression and classification, thereby requiring a large set of batch-oriented and independent training samples. This is problematic considering the nonstationary nature of a financial time series. In this research, we propose an online kernel adaptive filtering-based approach for stock price prediction to overcome this challenge. To examine a stock's performance and demonstrate the work's superiority, we use ten different KAF family of algorithms. In this paper, we take on this challenge and propose an approach for predicting stock prices. To analyze a stock's performance and demonstrate the work's superiority, we use ten distinct KAF algorithms. Besides, the results are analyzed on nine-time windows such as one day, sixty minutes, thirty minutes, twenty five minutes, twenty minutes, fifteen minutes, ten minutes, five minutes, and one minute. We are the first to experiment with several time windows for all fifty stocks on the Indian National Stock Exchange, to the best of our knowledge. It should be noted here that the experiments are performed on stocks making up the main index: Nifty-50. In terms of performance and compared to existing methods, we have a 66% probability of correctly predicting a stock's next upward or downward movement. This number clearly shows the edge that the proposed method has in actual deployment. Furthermore, the experimental findings show that KAF is not only a better option for predicting stock prices but that it may also be used as an alternative in high-frequency trading due to its low latency.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2022/3798734
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2070004-0
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  • 9
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    Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP
    Hindawi Limited ; 2021
    In:  Computational Intelligence and Neuroscience Vol. 2021 ( 2021-12-27), p. 1-19
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2021 ( 2021-12-27), p. 1-19
    Abstract: Security of the software system is a prime focus area for software development teams. This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. Various approaches in this context are explored using data of insurance domain-based software development. These approaches will facilitate an easy understanding of the practical challenges associated with actual-world implementation. This paper also discusses the capabilities of language modeling and its role in the knowledge system. The source code is modeled to build a deep software security analysis model. The proposed model can help software engineers build secure software by assessing the software security during software development time. Extensive experiments show that the proposed models can efficiently explore the software language modeling capabilities to classify software systems’ security vulnerabilities.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2021/8522839
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2388208-6
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