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Advanced Technologies in Intelligent Detection of Biological Information
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Advanced Technologies in Intelligent Detection of Biological Information

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 4673

Special Issue Editors

Dr. Jie Liu

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: machine vision; agricultural robot; near infrared spectroscopy; nondestructive measurement; signal processing
Dr. Shanmei Liu

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: multimodal data fusion; multimodal deep learning; brain-like computing; application of FPGA technology
Dr. Fang Yang

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: networked control system; visual navigation; multi machine collaborative control
Prof. Dr. Khalid Sayood

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, University of Nebraska-Lincoln, 209N Scott Engineering Center, P.O. Box 880511, Lincoln, NE 68588-0511, USA
Interests: data compression; joint source-channel coding; bioinformatics; metagenomics; neuroscience of cognition and memory; biological signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As one of the basic technologies for the design, development, and application of automated and intelligent equipment, biological information detection is of great significance in the fields of medicine, food, and agriculture. The combination of high-throughput, non-destructive biological information detection technology and intelligent information processing technology enables developing more intelligent and convenient application devices.

This Special Issue of Information will provide a current overview of the most significant research carried out in the field of advanced technologies in biological information intelligent detection. Scientists and researchers from all over the world are invited to submit original research and review articles related to, but not limited to, the following topics:

  • Biological information detection or measurement;
  • Intelligent detection;
  • Advanced information assessment;
  • Intelligent signal processing;
  • Other related intelligent detection theory of technology in the biological information acquisition.

Dr. Jie Liu
Dr. Shanmei Liu
Dr. Fang Yang
Prof. Dr. Khalid Sayood
Guest Editors

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. Information 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 1600 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

  • biological information
  • intelligent
  • detection
  • sensor
  • measurement
  • information processing

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Published Papers (4 papers)

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Research

18 pages, 3838 KiB  
Article
DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image Sequences
by Shubao Yao, Jianhui Lin and Hao Bai
Information 2024, 15(7), 411; https://doi.org/10.3390/info15070411 - 16 Jul 2024
Viewed by 482
Abstract
Ginkgo leaf disease poses a grave threat to Ginkgo biloba. The current management of Ginkgo leaf disease lacks precision guidance and intelligent technologies. To provide precision guidance for disease management and to evaluate the effectiveness of the implemented measures, the present study [...] Read more.
Ginkgo leaf disease poses a grave threat to Ginkgo biloba. The current management of Ginkgo leaf disease lacks precision guidance and intelligent technologies. To provide precision guidance for disease management and to evaluate the effectiveness of the implemented measures, the present study proposes a novel disease progression prediction (DPP) method for Ginkgo leaf blight with a multi-level feature translation architecture and enhanced spatiotemporal attention module (eSTA). The proposed DPP method is capable of capturing key spatiotemporal dependencies of disease symptoms at various feature levels. Experiments demonstrated that the DPP method achieves state-of-the-art prediction performance in disease progression prediction. Compared to the top-performing spatiotemporal predictive learning method (SimVP + TAU), our method significantly reduced the mean absolute error (MAE) by 19.95% and the mean square error (MSE) by 25.35%. Moreover, it achieved a higher structure similarity index measure (SSIM) of 0.970 and superior peak signal-to-noise ratio (PSNR) of 37.746 dB. The proposed method can accurately forecast the progression of Ginkgo leaf blight to a large extent, which is expected to provide valuable insights for precision and intelligent disease management. Additionally, this study presents a novel perspective for the extensive research on plant disease prediction. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)
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14 pages, 12531 KiB  
Article
Application of Attention-Enhanced 1D-CNN Algorithm in Hyperspectral Image and Spectral Fusion Detection of Moisture Content in Orah Mandarin (Citrus reticulata Blanco)
by Weiqi Li, Yifan Wang, Yue Yu and Jie Liu
Information 2024, 15(7), 408; https://doi.org/10.3390/info15070408 - 14 Jul 2024
Viewed by 650
Abstract
A method fusing spectral and image information with a one-dimensional convolutional neural network(1D-CNN) for the detection of moisture content in Orah mandarin (Citrus reticulata Blanco) was proposed. The 1D-CNN model integrated with three different attention modules (SEAM, ECAM, CBAM) and machine learning [...] Read more.
A method fusing spectral and image information with a one-dimensional convolutional neural network(1D-CNN) for the detection of moisture content in Orah mandarin (Citrus reticulata Blanco) was proposed. The 1D-CNN model integrated with three different attention modules (SEAM, ECAM, CBAM) and machine learning models were applied to individual spectrum and fused information by passing the traditional feature extraction stage. Additionally, the dimensionality reduction of hyperspectral images and extraction of one-dimensional color and textural features from the reduced images were performed, thus avoiding the large parameter volumes and efficiency decline inherent in the direct modeling of two-dimensional images. The results indicated that the 1D-CNN model with integrated attention modules exhibited clear advantages over machine learning models in handling multi-source information. The optimal machine learning model was determined to be the random forest (RF) model under the fusion information, with a correlation coefficient (R) of 0.8770 and a root mean square error (RMSE) of 0.0188 on the prediction set. The CBAM-1D-CNN model under the fusion information exhibited the best performance, with an R of 0.9172 and an RMSE of 0.0149 on the prediction set. The 1D-CNN models utilizing fusion information exhibited superior performance compared to single spectrum, and 1D-CNN with the fused information based on SEAM, ECAM, and CBAM respectively improved Rp by 4.54%, 0.18%, and 10.19% compared to the spectrum, with the RMSEP decreased by 11.70%, 14.06%, and 31.02%, respectively. The proposed approach of 1D-CNN integrated attention can obtain excellent regression results by only using one-dimensional data and without feature pre-extracting, reducing the complexity of the models, simplifying the calculation process, and rendering it a promising practical application. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)
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17 pages, 26751 KiB  
Article
Multi-Level Attention Split Network: A Novel Malaria Cell Detection Algorithm
by Zhao Xiong and Jiang Wu
Information 2024, 15(3), 166; https://doi.org/10.3390/info15030166 - 15 Mar 2024
Viewed by 1359
Abstract
Malaria is one of the major global health threats. Microscopic examination has been designated as the “gold standard” for malaria detection by the World Health Organization. However, it heavily relies on the experience of doctors, resulting in long diagnosis time, low efficiency, and [...] Read more.
Malaria is one of the major global health threats. Microscopic examination has been designated as the “gold standard” for malaria detection by the World Health Organization. However, it heavily relies on the experience of doctors, resulting in long diagnosis time, low efficiency, and a high risk of missed or misdiagnosed cases. To alleviate the pressure on healthcare workers and achieve automated malaria detection, numerous target detection models have been applied to the blood smear examination for malaria cells. This paper introduces the multi-level attention split network (MAS-Net) that improves the overall detection performance by addressing the issues of information loss for small targets and mismatch between the detection receptive field and target size. Therefore, we propose the split contextual attention structure (SPCot), which fully utilizes contextual information and avoids excessive channel compression operations, reducing information loss and improving the overall detection performance of malaria cells. In the shallow detection layer, we introduce the multi-scale receptive field detection head (MRFH), which better matches targets of different scales and provides a better detection receptive field, thus enhancing the performance of malaria cell detection. On the NLM—Malaria Dataset provided by the National Institutes of Health, the improved model achieves an average accuracy of 75.9% in the public dataset of Plasmodium vivax (malaria)-infected human blood smear. Considering the practical application of the model, we introduce the Performance-aware Approximation of Global Channel Pruning (PAGCP) to compress the model size while sacrificing a small amount of accuracy. Compared to other state-of-the-art (SOTA) methods, the proposed MAS-Net achieves competitive results. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)
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16 pages, 1807 KiB  
Article
Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Information 2024, 15(1), 47; https://doi.org/10.3390/info15010047 - 15 Jan 2024
Viewed by 1423
Abstract
Smartphones have become ubiquitous, allowing people to perform various tasks anytime and anywhere. As technology continues to advance, smartphones can now sense and connect to networks, providing context-awareness for different applications. Many individuals store sensitive data on their devices like financial credentials and [...] Read more.
Smartphones have become ubiquitous, allowing people to perform various tasks anytime and anywhere. As technology continues to advance, smartphones can now sense and connect to networks, providing context-awareness for different applications. Many individuals store sensitive data on their devices like financial credentials and personal information due to the convenience and accessibility. However, losing control of this data poses risks if the phone gets lost or stolen. While passwords, PINs, and pattern locks are common security methods, they can still be compromised through exploits like smudging residue from touching the screen. This research explored leveraging smartphone sensors to authenticate users based on behavioral patterns when operating the device. The proposed technique uses a deep learning model called DeepResNeXt, a type of deep residual network, to accurately identify smartphone owners through sensor data efficiently. Publicly available smartphone datasets were used to train the suggested model and other state-of-the-art networks to conduct user recognition. Multiple experiments validated the effectiveness of this framework, surpassing previous benchmark models in this area with a top F1-score of 98.96%. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)
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