Artificial Neural Network Model and Its Application in Signal Processing

Pan, Xianlu; Wang, Ying; Qian, Yu

doi:10.9734/ajarr/2023/v17i1459

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…In 2016, the advent of AlphaGo brought research enthusiasm for deep learning to a new height. Now ANN has been widely applied in various fields, such as image recognition [6][7][8], wireless signal processing [9][10][11][12], chemical process control and optimization [13][14][15][16], forecasting [17,18], security risk assessment [19], traditional Chinese medicine processing [20][21][22], aquatic products [23], intelligent driving [24,25], and so on.…”

Section: Research Progress Of Artificial Neural Networkmentioning

confidence: 99%

Advances in Neural Networks for Pharmaceutical Applications

Yang,

Chen

2024

AJARR

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Artificial neural networks (ANNs) are rapidly changing the landscape of the pharmaceutical industry. Their unique capabilities, including collective computing, adaptive learning, and fault tolerance, make them ideal for tackling complex challenges in drug discovery, analysis, and personalized medicine. This article summarizes the latest research progress in ANNs for pharmacy, highlighting breakthroughs in areas like QSAR modeling for drug design, pharmacokinetic prediction, and optimization of pharmaceutical preparations. With their immense potential to accelerate drug development, improve drug efficacy, and personalize healthcare, ANNs are poised to revolutionize the future of pharmaceuticals.

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Section: Research Progress Of Artificial Neural Networkmentioning

confidence: 99%

Advances in Neural Networks for Pharmaceutical Applications

Yang,

Chen

2024

AJARR

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“…They are a viable choice when high precision of speed or position control is required [1]. Additionally, with current advance in electrical drives and digital signal processing, many state-of-the-art solutions can be easily implemented [2]. However, sometimes control accuracy may be affected by the mechanical structure of the device.…”

Section: Introductionmentioning

confidence: 99%

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

2023

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Neural network approaches have commonly been used to solve complex mathematical equations in the literature. They have inspired the modifications of state controllers and are often implemented for electrical drives with an elastic connection. Given that the addition of a virtual signal can provide adaptive properties to classical controllers and that selected feedback signals can also be replaced with a virtual state variable from a neural network, several combinations can be considered and compared. In this paper, Radial Basis Function neural-network-based control algorithms are proposed in which online updating of the output weights is used. Analyses of simulation experiment results reveal that the proposed control algorithms significantly improve the operation of classic-state feedback controllers applied to two-mass systems in the presence of parameter uncertainty.

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Natural Language Processing and Social Determinants of Health in Mental Health Research: An Artificial Intelligence Assisted Scoping Review (Preprint)

Scherbakov,

Hubig,

Lenert

et al. 2024

Preprint

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BACKGROUND The usage of natural language processing (NLP) in mental health research is increasing with a wide range of applications and datasets being investigated. OBJECTIVE This review aims to summarize the usage NLP in mental health research, with a special focus on the types of text datasets and the usage of social determinants of health (SDOH) in NLP projects related to mental health. METHODS The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete. All citations were uploaded to Covidence online software. The screening and extraction process took place in Covidence with the help of a custom large language model (LLM) module developed by our team. This LLM module was calibrated and tuned to substitute human reviewers. RESULTS The screening process, assisted by the custom LLM, led to the inclusion of 1,768 studies in the final review. The majority of the reviewed studies (n=665, 42.8%) utilized clinical data as their primary text dataset, followed by social media datasets (n=523, 33.7%). The United States contributed the highest number of studies (n=568, 36.6%), with depression (n=438, 28.2%) and suicide (n=240, 15.5%) being the most frequently investigated mental health issues. Traditional demographic variables such as age (n=877, 56.5%) and gender (n=760, 49.0%) were commonly extracted, while SDOH factors were less frequently reported, with urban/rural status being the most used (n=19, 1.2%). Over half of the citations (n=826, 53.2%) did not provide clear information on dataset accessibility, although a sizable number of studies (n=304, 19.6%) made their datasets publicly available. CONCLUSIONS This scoping review underscores the significant role of clinical notes and social media in NLP-based mental health research. Despite the clear relevance of SDOH to mental health, their underutilization presents a gap in current research. This review can be a starting point for researchers looking for an overview of mental health projects using text data. Discovered datasets could be used to place more emphasis on SDOH in future studies.

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Artificial Neural Network Model and Its Application in Signal Processing

Cited by 3 publications

References 35 publications

Advances in Neural Networks for Pharmaceutical Applications

Advances in Neural Networks for Pharmaceutical Applications

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

Natural Language Processing and Social Determinants of Health in Mental Health Research: An Artificial Intelligence Assisted Scoping Review (Preprint)

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