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Biomimetics
Special Issues
Bioinspired Artificial Intelligence Applications
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Bioinspired Artificial Intelligence Applications

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 18566

Special Issue Editors

Dr. Haoran Wei

E-Mail Website
Guest Editor
Department of ECE, University of Texas at Dallas, Richardson, TX 75081, USA
Interests: artificial intelligence; audio and music processing; image and video processing; multimodal
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Tao

E-Mail Website
Guest Editor
Amazon, 410 Terry Ave N, Seattle, WA 98109, USA
Interests: multimodal; speech processing; machine learning; bio-signal processing
Dr. Zhenghua Huang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: computer vision; image analysis and processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yanhua Long

E-Mail Website
Guest Editor
The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201412, China
Interests: intelligence speech signal processing; machine learning; data mining

Special Issue Information

Dear Colleagues,

With rapid development of Artificial Intelligence (AI), researchers have found many bioinspired AI applications, such as bioinspired images and speech processing, which can increase accuracy; bioinspired AI models deployed on edge devices can reduce cost and energy usage; bioinspired AI noise reduction technologies could enhance the bio-signal quality; bioinspired AI applications for audio analysis can be used for detecting COVID-19; smart watch-generated bio-signals can be utilized by AI to monitor users' health condition, and so on.

On the other hand, improper AI utilization also creates challenges for human beings, such as synthesised images by AI can be used to generate fake news, AI synthesised speech can simulate a target speaker and cause security issues for personal devices, AI generated speech and text can be used for phone scams, and so on.

To explore the potential of AI applications and also deal with these challenges for improper AI utilization, state-of-the-art AI-based technologies in image processing, video processing, speech and audio processing, natural language processing, multi-modality processing, internet-of-things, edge computing, autonomous deriving, and smart healthcare, can adding intelligence to human-centered AI applications and solved the challenges associated with improper AI utilization.

The aim of this Special Issue is to present a multidisciplinary state-of-the-art reference regarding theoretical and real-world challenges, and innovative solutions by inviting high-quality research papers for bioinspired AI applications.

The topics of interest for this Special Issue include, but are not limited to, the following:

  • Bioinspired image classification, noise reduction, segmentation, and object detection.
  • Bioinspired video surveillance, object detection, object tracking, and noise reduction applications.
  • Bioinspired speech recognition (ASR), speech synthesis (TTS), speech noise reduction, and speaker ID applications.
  • Personalized audio, image, and video processing for human-related applications.
  • Bioinspired natural language processing (NLP) applications.
  • Bioinspired multi-modality applications.
  • Bioinspired wearable biosensors and Internet of Things (IoT) applications.
  • Edge computing models and lite deep learning models bioinspired applications.
  • Other bioinspired Artificial Intelligence applications.

Dr. Haoran Wei
Dr. Fei Tao
Dr. Zhenghua Huang
Dr. Yanhua Long
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. Biomimetics 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 2200 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

  • bioinspired image processing
  • bioinspired signal processing
  • bioinspired audio signal processing
  • bioinspired video processing
  • artificial intelligence
  • deep learning
  • machine learning
  • healthcare monitoring system
  • Internet-of-Things
  • wearable sensors

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Published Papers (7 papers)

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Editorial

Jump to: Research

3 pages, 174 KiB  
Editorial
Bioinspired Artificial Intelligence Applications 2023
by Haoran Wei, Fei Tao, Zhenghua Huang and Yanhua Long
Biomimetics 2024, 9(2), 80; https://doi.org/10.3390/biomimetics9020080 - 28 Jan 2024
Viewed by 2208
Abstract
With rapid development of Artificial Intelligence (AI), researchers have found many bioinspired AI applications, such as bioinspired images and speech processing, which can increase accuracy [...] Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)

Research

Jump to: Editorial

15 pages, 20626 KiB  
Article
CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency
by Yongfeng Dong, Jiawei Li, Zhen Wang and Wenyu Jia
Biomimetics 2024, 9(2), 92; https://doi.org/10.3390/biomimetics9020092 - 3 Feb 2024
Cited by 2 | Viewed by 1977
Abstract
Inspired by the biological nervous system, deep neural networks (DNNs) are able to achieve remarkable performance in various tasks. However, they struggle to handle label noise, which can poison the memorization effects of DNNs. Co-teaching-based methods are popular in learning with noisy labels. [...] Read more.
Inspired by the biological nervous system, deep neural networks (DNNs) are able to achieve remarkable performance in various tasks. However, they struggle to handle label noise, which can poison the memorization effects of DNNs. Co-teaching-based methods are popular in learning with noisy labels. These methods cross-train two DNNs based on the small-loss criterion and employ a strategy using either “disagreement” or “consistency” to obtain the divergence of the two networks. However, these methods are sample-inefficient for generalization in noisy scenarios. In this paper, we propose CoDC, a novel Co-teaching-basedmethod for accurate learning with label noise via both Disagreement and Consistency strategies. Specifically, CoDC maintains disagreement at the feature level and consistency at the prediction level using a balanced loss function. Additionally, a weighted cross-entropy loss is proposed based on information derived from the historical training process. Moreover, the valuable knowledge involved in “large-loss” samples is further developed and utilized by assigning pseudo-labels. Comprehensive experiments were conducted on both synthetic and real-world noise and under various noise types. CoDC achieved 72.81% accuracy on the Clothing1M dataset and 76.96% (Top1) accuracy on the WebVision1.0 dataset. These superior results demonstrate the effectiveness and robustness of learning with noisy labels. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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19 pages, 5568 KiB  
Article
Optimizing Robotic Task Sequencing and Trajectory Planning on the Basis of Deep Reinforcement Learning
by Xiaoting Dong, Guangxi Wan, Peng Zeng, Chunhe Song and Shijie Cui
Biomimetics 2024, 9(1), 10; https://doi.org/10.3390/biomimetics9010010 - 27 Dec 2023
Cited by 3 | Viewed by 2358
Abstract
The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a [...] Read more.
The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a local optimum solution. To address this problem, this paper formulates a co-optimization model that integrates the task sequencing problem and trajectory planning problem into a holistic problem, abbreviated as the robot TSTP problem. To solve the TSTP problem, we model the optimization process as a Markov decision process and propose a deep reinforcement learning (DRL)-based method to facilitate problem solving. To validate the proposed approach, multiple test cases are used to verify the feasibility of the TSTP model and the solving capability of the DRL method. The real-world experimental results demonstrate that the DRL method can achieve a 30.54% energy savings compared to the traditional evolution algorithm, and the computational time required by the proposed DRL method is much shorter than those of the evolutionary algorithms. In addition, when adopting the TSTP model, a 18.22% energy reduction can be achieved compared to using the sequential optimization model. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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19 pages, 24141 KiB  
Article
YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities
by Fangzheng Song and Peng Li
Biomimetics 2023, 8(6), 480; https://doi.org/10.3390/biomimetics8060480 - 9 Oct 2023
Cited by 9 | Viewed by 3443
Abstract
Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges [...] Read more.
Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges such as slow detection speeds and increased costs. To address these challenges, we introduce the YOLOv5-MS model, an YOLOv5-based solution for target detection. Initially, we optimize the multi-threaded acquisition of video streams within YOLOv5 to ensure image stability and real-time performance. Subsequently, leveraging reparameterization, we replace the original BackBone convolution with RepvggBlock, streamlining the model by reducing convolutional layer channels, thereby enhancing the inference speed. Additionally, the incorporation of a bioinspired “squeeze and excitation” module in the convolutional neural network significantly enhances the detection accuracy. This module improves target focusing and diminishes the influence of irrelevant elements. Furthermore, the integration of the K-means algorithm and bioinspired Retinex image augmentation during training effectively enhances the model’s detection efficacy. Finally, loss computation adopts the Focal-EIOU approach. The empirical findings from our internally developed smart city dataset unveil YOLOv5-MS’s impressive 96.5% mAP value, indicating a significant 2.0% advancement over YOLOv5s. Moreover, the average inference speed demonstrates a notable 21.3% increase. These data decisively substantiate the model’s superiority, showcasing its capacity to effectively perform pedestrian detection within an Intranet of over 50 video surveillance cameras, in harmony with our stringent requisites. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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17 pages, 6094 KiB  
Article
Intelligent Breast Mass Classification Approach Using Archimedes Optimization Algorithm with Deep Learning on Digital Mammograms
by Mohammed Basheri
Biomimetics 2023, 8(6), 463; https://doi.org/10.3390/biomimetics8060463 - 1 Oct 2023
Cited by 5 | Viewed by 1937
Abstract
Breast cancer (BC) has affected many women around the world. To accomplish the classification and detection of BC, several computer-aided diagnosis (CAD) systems have been introduced for the analysis of mammogram images. This is because analysis by the human radiologist is a complex [...] Read more.
Breast cancer (BC) has affected many women around the world. To accomplish the classification and detection of BC, several computer-aided diagnosis (CAD) systems have been introduced for the analysis of mammogram images. This is because analysis by the human radiologist is a complex and time-consuming task. Although CAD systems are used to primarily analyze the disease and offer the best therapy, it is still essential to enhance present CAD systems by integrating novel approaches and technologies in order to provide explicit performances. Presently, deep learning (DL) systems are outperforming promising outcomes in the early detection of BC by creating CAD systems executing convolutional neural networks (CNNs). This article presents an Intelligent Breast Mass Classification Approach using the Archimedes Optimization Algorithm with Deep Learning (BMCA-AOADL) technique on Digital Mammograms. The major aim of the BMCA-AOADL technique is to exploit the DL model with a bio-inspired algorithm for breast mass classification. In the BMCA-AOADL approach, median filtering (MF)-based noise removal and U-Net segmentation take place as a pre-processing step. For feature extraction, the BMCA-AOADL technique utilizes the SqueezeNet model with AOA as a hyperparameter tuning approach. To detect and classify the breast mass, the BMCA-AOADL technique applies a deep belief network (DBN) approach. The simulation value of the BMCA-AOADL system has been studied on the MIAS dataset from the Kaggle repository. The experimental values showcase the significant outcomes of the BMCA-AOADL technique compared to other DL algorithms with a maximum accuracy of 96.48%. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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18 pages, 7989 KiB  
Article
YOLO-DRS: A Bioinspired Object Detection Algorithm for Remote Sensing Images Incorporating a Multi-Scale Efficient Lightweight Attention Mechanism
by Huan Liao and Wenqiu Zhu
Biomimetics 2023, 8(6), 458; https://doi.org/10.3390/biomimetics8060458 - 1 Oct 2023
Cited by 6 | Viewed by 2275
Abstract
Bioinspired object detection in remotely sensed images plays an important role in a variety of fields. Due to the small size of the target, complex background information, and multi-scale remote sensing images, the generalized YOLOv5 detection framework is unable to obtain good detection [...] Read more.
Bioinspired object detection in remotely sensed images plays an important role in a variety of fields. Due to the small size of the target, complex background information, and multi-scale remote sensing images, the generalized YOLOv5 detection framework is unable to obtain good detection results. In order to deal with this issue, we proposed YOLO-DRS, a bioinspired object detection algorithm for remote sensing images incorporating a multi-scale efficient lightweight attention mechanism. First, we proposed LEC, a lightweight multi-scale module for efficient attention mechanisms. The fusion of multi-scale feature information allows the LEC module to completely improve the model’s ability to extract multi-scale targets and recognize more targets. Then, we propose a transposed convolutional upsampling alternative to the original nearest-neighbor interpolation algorithm. Transposed convolutional upsampling has the potential to greatly reduce the loss of feature information by learning the feature information dynamically, thereby reducing problems such as missed detections and false detections of small targets by the model. Our proposed YOLO-DRS algorithm exhibits significant improvements over the original YOLOv5s. Specifically, it achieves a 2.3% increase in precision (P), a 3.2% increase in recall (R), and a 2.5% increase in mAP@0.5. Notably, the introduction of the LEC module and transposed convolutional results in a respective improvement of 2.2% and 2.1% in mAP@0.5. In addition, YOLO-DRS only increased the GFLOPs by 0.2. In comparison to the state-of-the-art algorithms, namely YOLOv8s and YOLOv7-tiny, YOLO-DRS demonstrates significant improvements in the mAP@0.5 metrics, with enhancements ranging from 1.8% to 7.3%. It is fully proved that our YOLO-DRS can reduce the missed and false detection problems of remote sensing target detection. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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18 pages, 6312 KiB  
Article
Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
by Amani Abdulrahman Albraikan, Mohammed Maray, Faiz Abdullah Alotaibi, Mrim M. Alnfiai, Arun Kumar and Ahmed Sayed
Biomimetics 2023, 8(6), 449; https://doi.org/10.3390/biomimetics8060449 - 23 Sep 2023
Cited by 8 | Viewed by 2187
Abstract
In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. [...] Read more.
In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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