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Biomimetics
Special Issues
Artificial Intelligence (AI) 2.0
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Artificial Intelligence (AI) 2.0

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 (1 May 2023) | Viewed by 14350

Special Issue Editors

Dr. Isa Ebtehaj

E-Mail Website1 Website2
Guest Editor
Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V0A6, Canada
Interests: climate change; deep learning; hydroinfomatics; machine learning; sediment transport; time series; water resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Sayed M. Bateni

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: satellite data processing; land surface product algorithm; remote sensing classification with machine learning;agrometeorology; agrometeorological disater monitoring with remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Practical issues are becoming progressively more important in the bionics and biomimicry. However, so far only limited attention has been paid to how these issues can be used in the most basic aspects of living organisms and the transfer their properties to human applications.

Artificial intelligence (AI) techniques and machine learning approaches will revolutionize the field of biomimetics in the coming years; this Special Issue will establish an excellence platform for scholars in this field.

The biomimetic mechanism and design are still not systematically benefited by automated data processing, data analysis, and predictive modelling assistance for real-time monitoring, and adjusting appropriate forecasting models using data-driven techniques with the full capacity of Artificial Intelligence (AI) techniques. The accurate analysis and modeling of biomimetics is a challenging task due to the randomness inherent of models as representations of any real system over time.

Our goal in proposing this Special Issue entitled “Artificial Intelligence " is to combine many of the ongoing research activities on application of AI techniques in biomimicry and bionics into a single open-source document. The contributions to this Special Issue will encompass wide topics in Biomimetics in many regions around the world, including, but not limited to, the application and development of more efficient of AI techniques in experimental, theoretical, and review contributions from a multidisciplinary community of physicists, material scientists, biologists, and engineers working on functional materials.

Dr. Isa Ebtehaj
Dr. Sayed M. Bateni
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

  • artificial intelligence
  • renewable raw materials
  • biomaterials
  • bioinspired intelligence
  • bio-Inspiration
  • biomimetic and evolutionary techniques
  • plant biomechanics
  • biomimetic mechanism and design

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

19 pages, 811 KiB  
Article
A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features
by Rahul Nijhawan, Mukul Kumar, Sahitya Arya, Neha Mendirtta, Sunil Kumar, S. K. Towfek, Doaa Sami Khafaga, Hend K. Alkahtani and Abdelaziz A. Abdelhamid
Biomimetics 2023, 8(4), 351; https://doi.org/10.3390/biomimetics8040351 - 7 Aug 2023
Cited by 4 | Viewed by 3148
Abstract
Parkinson’s disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world’s population, this number is expected to rise. The early detection of PD and avoiding its severe [...] Read more.
Parkinson’s disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world’s population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject’s voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network’s potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution’s space and time complexity. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) 2.0)
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24 pages, 2312 KiB  
Article
Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
by Amel Ali Alhussan, Abdelaziz A. Abdelhamid, S. K. Towfek, Abdelhameed Ibrahim, Laith Abualigah, Nima Khodadadi, Doaa Sami Khafaga, Shaha Al-Otaibi and Ayman Em Ahmed
Biomimetics 2023, 8(3), 270; https://doi.org/10.3390/biomimetics8030270 - 26 Jun 2023
Cited by 12 | Viewed by 3524
Abstract
Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with [...] Read more.
Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) 2.0)
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20 pages, 3586 KiB  
Article
Deep-Learning-Based Reduced-Order Model for Power Generation Capacity of Flapping Foils
by Ahmad Saeed, Hamayun Farooq, Imran Akhtar, Muhammad Awais Tariq and Muhammad Saif Ullah Khalid
Biomimetics 2023, 8(2), 237; https://doi.org/10.3390/biomimetics8020237 - 5 Jun 2023
Cited by 1 | Viewed by 2001
Abstract
Inspired by nature, oscillating foils offer viable options as alternate energy resources to harness energy from wind and water. Here, we propose a proper orthogonal decomposition (POD)-based reduced-order model (ROM) of power generation by flapping airfoils in conjunction with deep neural networks. Numerical [...] Read more.
Inspired by nature, oscillating foils offer viable options as alternate energy resources to harness energy from wind and water. Here, we propose a proper orthogonal decomposition (POD)-based reduced-order model (ROM) of power generation by flapping airfoils in conjunction with deep neural networks. Numerical simulations are performed for incompressible flow past a flapping NACA-0012 airfoil at a Reynolds number of 1100 using the Arbitrary Lagrangian–Eulerian approach. The snapshots of the pressure field around the flapping foil are then utilized to construct the pressure POD modes of each case, which serve as the reduced basis to span the solution space. The novelty of the current research relates to the identification, development, and employment of long-short-term neural network (LSTM) models to predict temporal coefficients of the pressure modes. These coefficients, in turn, are used to reconstruct hydrodynamic forces and moment, leading to computations of power. The proposed model takes the known temporal coefficients as inputs and predicts the future temporal coefficients followed by previously estimated temporal coefficients, very similar to traditional ROM. Through the new trained model, we can predict the temporal coefficients for a long time duration that can be far beyond the training time intervals more accurately. It may not be attained by traditional ROMs that lead to erroneous results. Consequently, the flow physics including the forces and moment exerted by fluids can be reconstructed accurately using POD modes as the basis set. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) 2.0)
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17 pages, 4121 KiB  
Article
LQR Control and Optimization for Trajectory Tracking of Biomimetic Robotic Fish Based on Unreal Engine
by Ming Wang, Kunlun Wang, Qianchuan Zhao, Xuehan Zheng, He Gao and Junzhi Yu
Biomimetics 2023, 8(2), 236; https://doi.org/10.3390/biomimetics8020236 - 4 Jun 2023
Cited by 7 | Viewed by 2941
Abstract
A realistic and visible dynamic simulation platform can significantly facilitate research on underwater robots. This paper uses the Unreal Engine to generate a scene that resembles real ocean environments, before building a visual dynamic simulation platform in conjunction with the Air-Sim system. On [...] Read more.
A realistic and visible dynamic simulation platform can significantly facilitate research on underwater robots. This paper uses the Unreal Engine to generate a scene that resembles real ocean environments, before building a visual dynamic simulation platform in conjunction with the Air-Sim system. On this basis, the trajectory tracking of a biomimetic robotic fish is simulated and assessed. More specifically, we propose a particle swarm optimization algorithm-based control strategy to optimize the discrete linear quadratic regulator controller for the trajectory tracking problem, as well as tracking and controlling discrete trajectories with misaligned time series through introducing a dynamic time warping algorithm. Simulation analyses of the biomimetic robotic fish following a straight line, a circular curve without mutation, and a four-leaf clover curve with mutation are carried out. The obtained results verify the feasibility and effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) 2.0)
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13 pages, 2746 KiB  
Article
Multi-Scopic Cognitive Memory System for Continuous Gesture Learning
by Wenbang Dou, Weihong Chin and Naoyuki Kubota
Biomimetics 2023, 8(1), 88; https://doi.org/10.3390/biomimetics8010088 - 21 Feb 2023
Cited by 4 | Viewed by 1752
Abstract
With the advancement of artificial intelligence technologies in recent years, research on intelligent robots has progressed. Robots are required to understand human intentions and communicate more smoothly with humans. Since gestures can have a variety of meanings, gesture recognition is one of the [...] Read more.
With the advancement of artificial intelligence technologies in recent years, research on intelligent robots has progressed. Robots are required to understand human intentions and communicate more smoothly with humans. Since gestures can have a variety of meanings, gesture recognition is one of the essential issues in communication between robots and humans. In addition, robots need to learn new gestures as humans grow. Moreover, individual gestures vary. Because catastrophic forgetting occurs in training new data in traditional gesture recognition approaches, it is necessary to preserve the prepared data and combine it with further data to train the model from scratch. We propose a Multi-scopic Cognitive Memory System (MCMS) that mimics the lifelong learning process of humans and can continuously learn new gestures without forgetting previously learned gestures. The proposed system comprises a two-layer structure consisting of an episode memory layer and a semantic memory layer, with a topological map as its backbone. The system is designed with reference to conventional continuous learning systems in three ways: (i) using a dynamic architecture without setting the network size, (ii) adding regularization terms to constrain learning, and (iii) generating data from the network itself and performing relearning. The episode memory layer clusters the data and learns their spatiotemporal representation. The semantic memory layer generates a topological map based on task-related inputs and stores them as longer-term episode representations in the robot’s memory. In addition, to alleviate catastrophic forgetting, the memory replay function can reinforce memories autonomously. The proposed system could mitigate catastrophic forgetting and perform continuous learning by using both machine learning benchmark datasets and real-world data compared to conventional methods. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) 2.0)
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