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A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 15977

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Dr. Huiling Chen

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College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
Interests: computational intelligence; machine learning; data mining; medical diagnosis; evolutionary algorithms
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Dr. Shuihua Wang

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Department of Mathematics, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: deep learning; unsupervised learning
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Prof. Dr. Yudong Zhang

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Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: deep learning; artificial intelligence; machine learning
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Special Issue Information

Dear Colleagues,

The Special Issue "Bionic Artificial Neural Networks and Artificial Intelligence" focuses on the application of bionic-inspired neural networks and artificial intelligence (AI) in various fields. The Special Issue calls for papers that discuss the use of bionic neural networks and AI in areas such as image processing, speech recognition, robotics, and control systems. We hope the papers in this Special Issue highlight the benefits of using bionic-inspired neural networks, which are designed to mimic the structure and function of the brain, in solving complex problems.

Dr. Huiling Chen
Dr. Shuihua Wang
Prof. Dr. Yudong Zhang
Guest Editors

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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

bionic artificial neural networks
bionic artificial intelligence
deep learning
optimization
machine learning
evolutionary machine learning
global optimization
metaheuristic
image processing
bionic engineering

Published Papers (10 papers)

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Editorial

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3 pages, 374 KiB

Open AccessEditorial

Bionic Artificial Neural Networks in Medical Image Analysis

by Shuihua Wang, Huiling Chen and Yudong Zhang

Biomimetics 2023, 8(2), 211; https://doi.org/10.3390/biomimetics8020211 - 22 May 2023

Cited by 1 | Viewed by 1519

Abstract

Bionic artificial neural networks (BANNs) are a type of artificial neural network (ANN) [...] Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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Research

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18 pages, 7871 KiB

Open AccessArticle

Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images

by Fadwa Alrowais, Faiz Abdullah Alotaibi, Abdulkhaleq Q. A. Hassan, Radwa Marzouk, Mrim M. Alnfiai and Ahmed Sayed

Biomimetics 2023, 8(7), 538; https://doi.org/10.3390/biomimetics8070538 - 10 Nov 2023

Cited by 1 | Viewed by 1105

Abstract

Breast cancer (BC) is a prevalent disease worldwide, and accurate diagnoses are vital for successful treatment. Histopathological (HI) inspection, particularly the detection of mitotic nuclei, has played a pivotal function in the prognosis and diagnosis of BC. It includes the detection and classification of mitotic nuclei within breast tissue samples. Conventionally, the detection of mitotic nuclei has been a subjective task and is time-consuming for pathologists to perform manually. Automatic classification using computer algorithms, especially deep learning (DL) algorithms, has been developed as a beneficial alternative. DL and CNNs particularly have shown outstanding performance in different image classification tasks, including mitotic nuclei classification. CNNs can learn intricate hierarchical features from HI images, making them suitable for detecting subtle patterns related to the mitotic nuclei. In this article, we present an Enhanced Pelican Optimization Algorithm with a Deep Learning-Driven Mitotic Nuclei Classification (EPOADL-MNC) technique on Breast HI. This developed EPOADL-MNC system examines the histopathology images for the classification of mitotic and non-mitotic cells. In this presented EPOADL-MNC technique, the ShuffleNet model can be employed for the feature extraction method. In the hyperparameter tuning procedure, the EPOADL-MNC algorithm makes use of the EPOA system to alter the hyperparameters of the ShuffleNet model. Finally, we used an adaptive neuro-fuzzy inference system (ANFIS) for the classification and detection of mitotic cell nuclei on histopathology images. A series of simulations took place to validate the improved detection performance of the EPOADL-MNC technique. The comprehensive outcomes highlighted the better outcomes of the EPOADL-MNC algorithm compared to existing DL techniques with a maximum accuracy of 97.83%. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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18 pages, 8533 KiB

Open AccessArticle

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

by Hany Mahgoub, Ghadah Aldehim, Nabil Sharaf Almalki, Imène Issaoui, Ahmed Mahmud and Amani A. Alneil

Biomimetics 2023, 8(6), 493; https://doi.org/10.3390/biomimetics8060493 - 18 Oct 2023

Viewed by 1174

Abstract

Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to restaurant recommendation systems. By leveraging the developments in Deep-Learning (DL) techniques, especially the Convolutional Neural Network (CNN), food image classification has been developed as an effective process for interacting with and understanding the nuances of the culinary world. The deep CNN-based automated food image classification method is a technology that utilizes DL approaches, particularly CNNs, for the automatic categorization and classification of the images of distinct kinds of foods. The current research article develops a Bio-Inspired Spotted Hyena Optimizer with a Deep Convolutional Neural Network-based Automated Food Image Classification (SHODCNN-FIC) approach. The main objective of the SHODCNN-FIC method is to recognize and classify food images into distinct types. The presented SHODCNN-FIC technique exploits the DL model with a hyperparameter tuning approach for the classification of food images. To accomplish this objective, the SHODCNN-FIC method exploits the DCNN-based Xception model to derive the feature vectors. Furthermore, the SHODCNN-FIC technique uses the SHO algorithm for optimal hyperparameter selection of the Xception model. The SHODCNN-FIC technique uses the Extreme Learning Machine (ELM) model for the detection and classification of food images. A detailed set of experiments was conducted to demonstrate the better food image classification performance of the proposed SHODCNN-FIC technique. The wide range of simulation outcomes confirmed the superior performance of the SHODCNN-FIC method over other DL models. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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30 pages, 10408 KiB

Open AccessArticle

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

by Liguo Yao, Jun Yang, Panliang Yuan, Guanghui Li, Yao Lu and Taihua Zhang

Biomimetics 2023, 8(6), 492; https://doi.org/10.3390/biomimetics8060492 - 18 Oct 2023

Viewed by 1510

Abstract

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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26 pages, 1231 KiB

Open AccessArticle

Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor

by Ulbio Alejandro-Sanjines, Anthony Maisincho-Jivaja, Victor Asanza, Leandro L. Lorente-Leyva and Diego H. Peluffo-Ordóñez

Biomimetics 2023, 8(5), 434; https://doi.org/10.3390/biomimetics8050434 - 19 Sep 2023

Cited by 1 | Viewed by 2323

Abstract

Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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19 pages, 680 KiB

Open AccessArticle

Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features

by Imran Arshad Choudhry, Adnan N. Qureshi, Khursheed Aurangzeb, Saeed Iqbal and Musaed Alhussein

Biomimetics 2023, 8(5), 406; https://doi.org/10.3390/biomimetics8050406 - 01 Sep 2023

Viewed by 1167

Abstract

A recently discovered coronavirus (COVID-19) poses a major danger to human life and health across the planet. The most important step in managing and combating COVID-19 is to accurately screen and diagnose affected people. The imaging technology of lung X-ray is a useful imaging identification/detection approach among them. The help of such computer-aided machines and diagnoses to examine lung X-ray images of COVID-19 instances can give supplemental assessment ideas to specialists, easing their workload to some level. The novel concept of this study is a hybridized approach merging pertinent manual features with deep spatial features for the classification of COVID-19. Further, we employed traditional transfer learning techniques in this investigation, utilizing four different pre-trained CNN-based deep learning models, with the Inception model showing a reasonably accurate result and a diagnosis accuracy of 82.17%. We provide a successful diagnostic approach that blends deep characteristics with machine learning classification to further increase clinical performance. It employs a complete diagnostic model. Two datasets were used to test the suggested approach, and it did quite well on several of them. On 1102 lung X-ray scans, the model was originally evaluated. The results of the experiments indicate that the suggested SVM model has a diagnostic accuracy of 95.57%. When compared to the Xception model’s baseline, the diagnostic accuracy had risen by 17.58 percent. The sensitivity, specificity, and AUC of the proposed models were 95.37 percent, 95.39%, and 95.77%, respectively. To show the adaptability of our approach, we also verified our proposed model on other datasets. Finally, we arrived at results that were conclusive. When compared to research of a comparable kind, our suggested CNN model has a greater accuracy of classification and diagnostic effectiveness. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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22 pages, 5722 KiB

Open AccessArticle

A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images

by Saeed Iqbal, Adnan N. Qureshi, Musaed Alhussein, Khursheed Aurangzeb and Seifedine Kadry

Biomimetics 2023, 8(4), 370; https://doi.org/10.3390/biomimetics8040370 - 16 Aug 2023

Cited by 1 | Viewed by 1271

Abstract

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model’s guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model’s superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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19 pages, 10182 KiB

Open AccessArticle

Fault Diagnosis of Planetary Gearbox Based on Dynamic Simulation and Partial Transfer Learning

by Mengmeng Song, Zicheng Xiong, Jianhua Zhong, Shungen Xiao and Jihua Ren

Biomimetics 2023, 8(4), 361; https://doi.org/10.3390/biomimetics8040361 - 12 Aug 2023

Cited by 1 | Viewed by 895

Abstract

To address the problem of insufficient real-world data on planetary gearboxes, which makes it difficult to diagnose faults using deep learning methods, it is possible to obtain sufficient simulation fault data through dynamic simulation models and then reduce the difference between simulation data and real data using transfer learning methods, thereby applying diagnostic knowledge from simulation data to real planetary gearboxes. However, the label space of real data may be a subset of the label space of simulation data. In this case, existing transfer learning methods are susceptible to interference from outlier label spaces in simulation data, resulting in mismatching. To address this issue, this paper introduces multiple domain classifiers and a weighted learning scheme on the basis of existing domain adversarial transfer learning methods to evaluate the transferability of simulation data and adaptively measure their contribution to label predictor and domain classifiers, filter the interference of unrelated categories of simulation data, and achieve accurate matching of real data. Finally, partial transfer experiments are conducted to verify the effectiveness of the proposed method, and the experimental results show that the diagnostic accuracy of this method is higher than existing transfer learning methods. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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26 pages, 5197 KiB

Open AccessArticle

Improved Colony Predation Algorithm Optimized Convolutional Neural Networks for Electrocardiogram Signal Classification

by Xinxin He, Weifeng Shan, Ruilei Zhang, Ali Asghar Heidari, Huiling Chen and Yudong Zhang

Biomimetics 2023, 8(3), 268; https://doi.org/10.3390/biomimetics8030268 - 21 Jun 2023

Cited by 5 | Viewed by 1446

Abstract

Recently, swarm intelligence algorithms have received much attention because of their flexibility for solving complex problems in the real world. Recently, a new algorithm called the colony predation algorithm (CPA) has been proposed, taking inspiration from the predatory habits of groups in nature. However, CPA suffers from poor exploratory ability and cannot always escape solutions known as local optima. Therefore, to improve the global search capability of CPA, an improved variant (OLCPA) incorporating an orthogonal learning strategy is proposed in this paper. Then, considering the fact that the swarm intelligence algorithm can go beyond the local optimum and find the global optimum solution, a novel OLCPA-CNN model is proposed, which uses the OLCPA algorithm to tune the parameters of the convolutional neural network. To verify the performance of OLCPA, comparison experiments are designed to compare with other traditional metaheuristics and advanced algorithms on IEEE CEC 2017 benchmark functions. The experimental results show that OLCPA ranks first in performance compared to the other algorithms. Additionally, the OLCPA-CNN model achieves high accuracy rates of 97.7% and 97.8% in classifying the MIT-BIH Arrhythmia and European ST-T datasets. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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33 pages, 14420 KiB

Open AccessArticle

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

by Karla Avilés-Mendoza, Neil George Gaibor-León, Víctor Asanza, Leandro L. Lorente-Leyva and Diego H. Peluffo-Ordóñez

Biomimetics 2023, 8(2), 255; https://doi.org/10.3390/biomimetics8020255 - 14 Jun 2023

Cited by 1 | Viewed by 2415

Abstract

About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15. Full article

(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)

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