Advanced Artificial Intelligence Models and Its Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 4072

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School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: computer vision; machine learning; medical image analysis; AI in healthcare
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Special Issue Information

Dear Colleagues,

The field of Artificial Intelligence (AI) has experienced tremendous growth since the mid-20th century, as evidenced by its application in a wide range of engineering and science problems. Over the last decade, AI has seen a breakthrough, owing to the introduction of deep learning, which has enabled the utilization of various AI models in a diverse range of domains.

This Special Issue intends to provide a forum for researchers developing and reviewing new AI models in various fields, including science, engineering, industry, education, health, and transportation. We are inviting authors to submit relevant original results, literature reviews, theoretical studies, or papers addressing AI’s real-world applications.

Prof. Dr. Tao Zhou
Guest Editor

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Keywords

  • machine learning
  • deep learning
  • pattern recognition
  • computer vision
  • multimedia retrieval and analysis
  • multimodal representation learning
  • statistical learning
  • medical image analysis
  • security applications
  • big data and analysis
  • benchmark dataset

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Published Papers (4 papers)

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Research

14 pages, 3740 KiB  
Article
Towards Human-Interactive Controllable Video Captioning with Efficient Modeling
by Yoonseok Heo, Taehoon Kim, Seunghwan Kim, Jungyun Seo and Juae Kim
Mathematics 2024, 12(13), 2037; https://doi.org/10.3390/math12132037 - 30 Jun 2024
Viewed by 437
Abstract
Video captioning is a task of describing the visual scene of a given video in natural language. There have been several lines of research focused on developing large-scale models in a transfer learning paradigm, with major challenge being the tradeoff between scalability and [...] Read more.
Video captioning is a task of describing the visual scene of a given video in natural language. There have been several lines of research focused on developing large-scale models in a transfer learning paradigm, with major challenge being the tradeoff between scalability and performance in limited environments. To address this problem, we propose a simple yet effective encoder–decoder-based video captioning model integrating transformers and CLIP, both of which are widely adopted in the vision and language domains, together with appropriate temporal feature embedding modules. Taking this proposal a step further, we also address the challenge of human-interactive video captioning, where the captions are tailored to specific information desired by humans. To design a human-interactive environment, we assume that a human offers an object or action in the video as a short prompt; in turn, the system then provides a detailed explanation regarding the prompt. We embed human prompts within an LSTM-based prompt encoder and leverage soft prompting to tune the model effectively. We extensively evaluated our model on benchmark datasets, demonstrating comparable results, particularly on the MSR-VTT dataset, where we achieve state-of-the-art performance with 4% improvement. In addition, we also show potential for human-interactive video captioning through quantitative and qualitative analysis. Full article
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33 pages, 9006 KiB  
Article
CALSczNet: Convolution Neural Network with Attention and LSTM for the Detection of Schizophrenia Using EEG Signals
by Norah Almaghrabi, Muhammad Hussain and Ashwaq Alotaibi
Mathematics 2024, 12(13), 1989; https://doi.org/10.3390/math12131989 - 27 Jun 2024
Viewed by 368
Abstract
Schizophrenia (SZ) is a serious psychological disorder that affects nearly 1% of the global population. The progression of SZ disorder causes severe brain damage; its early diagnosis is essential to limit adverse effects. Electroencephalography (EEG) is commonly used for SZ detection, but its [...] Read more.
Schizophrenia (SZ) is a serious psychological disorder that affects nearly 1% of the global population. The progression of SZ disorder causes severe brain damage; its early diagnosis is essential to limit adverse effects. Electroencephalography (EEG) is commonly used for SZ detection, but its manual screening is laborious, time-consuming, and subjective. Automatic methods based on machine learning have been introduced to overcome these issues, but their performance is not satisfactory due to the non-stationary nature of EEG signals. To enhance the detection performance, a novel deep learning-based method is introduced, namely, CALSczNet. It uses temporal and spatial convolutions to learn temporal and spatial patterns from EEG trials, uses Temporal Attention (TA) and Local Attention (LA) to adaptively and dynamically attend to salient features to tackle the non-stationarity of EEG signals, and finally, it employs Long Short-Term Memory (LSTM) to work out the long-range dependencies of temporal features to learn the discriminative features. The method was evaluated on the benchmark public-domain Kaggle dataset of the basic sensory tasks using 10-fold cross-validation. It outperforms the state-of-the-art methods on all conditions with 98.6% accuracy, 98.65% sensitivity, 98.72% specificity, 98.72% precision, and an F1-score of 98.65%. Furthermore, this study suggested that the EEG signal of the subject performing either simultaneous motor and auditory tasks or only auditory tasks provides higher discriminative features to detect SZ in patients. Finally, it is a robust, effective, and reliable method that will assist psychiatrists in detecting SZ at an early stage and provide suitable and timely treatment. Full article
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27 pages, 5136 KiB  
Article
maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism
by Turki Turki and Y-h. Taguchi
Mathematics 2024, 12(10), 1536; https://doi.org/10.3390/math12101536 - 15 May 2024
Viewed by 803
Abstract
Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational [...] Read more.
Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server. Full article
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31 pages, 7299 KiB  
Article
Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder
by Hasan Alkahtani, Theyazn H. H. Aldhyani, Zeyad A. T. Ahmed and Ahmed Abdullah Alqarni
Mathematics 2023, 11(22), 4698; https://doi.org/10.3390/math11224698 - 20 Nov 2023
Viewed by 2067
Abstract
This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for [...] Read more.
This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD. Full article
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