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Modeling and Theoretical Analysis of Artificial Neural Networks for Life...
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Modeling and Theoretical Analysis of Artificial Neural Networks for Life Sciences

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (16 February 2023) | Viewed by 4187

Special Issue Editor

Dr. Xin Jin

E-Mail Website
Guest Editor
School of Software, Yunnan University, Kunming, China
Interests: machine learning; artificial neural networks; image processing; bioinformatics; artificial-intelligence-based information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial neural networks are usually inspired by biological nervous systems, such as multilayer perceptron, feedback neural networks, convolutional neural networks, and spiking neural networks, which partly simulate the natural nervous system. Recently, artificial neural networks have been widely used in life sciences, and deep neural networks in particular have shown great vitality in many related fields, such as medical biology, bioinformatics, and computational biology. However, there are still some problems to be solved in artificial neural networks. First, more effective and powerful biomimetic neural networks are still required to address the application problems of life sciences, because there is still plenty of room to solve problems in the real world for the existing artificial neural networks that are far from being regarded as strong artificial intelligence. Second, theoretical analysis of existing artificial neural networks is urgently needed because most of them lack interpretability, meaning that we still cannot explain why neural networks make the decisions they make, which also limits their use in some situations. Third, a systematic study of modeling methods in the applications of artificial neural networks is needed because designing neural networks for specific tasks is still expensive, and we need universal modules and modeling methods to help engineers to make better use of them in life sciences. Fourth, research in adversarial attacks for deep neural networks has shown that there is great destructive power in their applications, and well-designed adversarial examples can fool deep neural-network-based systems. Thus, research on attack and defense techniques for deep neural networks is urgent for them to be applied in life sciences. This Special Issue focuses on new ideas, methods, and techniques in modeling and theoretical analysis of artificial neural networks for life sciences, and related application research on neural networks is also welcome.

Dr. Xin Jin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial neural networks
  • neurodynamic analysis
  • computational neuroscience
  • brain-inspired modeling
  • neural network modeling for life sciences
  • machine learning for life sciences
  • bioinformatics
  • computational biology
  • deep learning for medicine
  • adversarial attack and defense on neural networks

Published Papers (2 papers)

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Research

14 pages, 1414 KiB  
Article
Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis
by Mehdhar S. A. M. Al-Gaashani, Nagwan Abdel Samee, Rana Alnashwan, Mashael Khayyat and Mohammed Saleh Ali Muthanna
Life 2023, 13(6), 1277; https://doi.org/10.3390/life13061277 - 29 May 2023
Cited by 9 | Viewed by 2013
Abstract
The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of [...] Read more.
The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20–40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the convolutional neural network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing that of current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector. Full article
(This article belongs to the Special Issue Modeling and Theoretical Analysis of Artificial Neural Networks for Life Sciences)
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14 pages, 3293 KiB  
Article
BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images
by Haichou Chen, Yishu Deng, Bin Li, Zeqin Li, Haohua Chen, Bingzhong Jing and Chaofeng Li
Life 2023, 13(3), 743; https://doi.org/10.3390/life13030743 - 09 Mar 2023
Cited by 1 | Viewed by 1420
Abstract
Background: Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions [...] Read more.
Background: Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions are solid and smooth. Methods: To overcome these problems and to provide an efficient, accurate, robust and concise solution that simplifies the whole segmentation pipeline in AI-assisted applications, we propose the BézierSeg model which outputs Bézier curves encompassing the region of interest. Results: Directly modeling the contour with analytic equations ensures that the segmentation is connected and continuous, and that the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of precision, the Bézier contour can be resampled and overlaid with images of any resolution. Moreover, clinicians can conveniently adjust the curve’s control points to refine the result. Conclusions: Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models. Full article
(This article belongs to the Special Issue Modeling and Theoretical Analysis of Artificial Neural Networks for Life Sciences)
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