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Biomimetics
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Artificial Intelligence (AI)
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Artificial Intelligence (AI)

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

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 18051

Special Issue Editors

Dr. Isa Ebtehaj

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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
Dr. Babak Mohammadi

E-Mail Website
Guest Editor
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden
Interests: surface water hydrology; snow hydrology; remote sensing; hydrological modeling
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 of their properties to human application.

Artificial intelligence (AI) techniques and machine learning approaches will revolutionize the field of biomimentics 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
Prof. Dr. Sayed M. Bateni
Dr. Babak Mohammadi
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

Published Papers (6 papers)

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Research

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14 pages, 1229 KiB  
Article
Cervical Cell Image Classification-Based Knowledge Distillation
by Wenjian Gao, Chuanyun Xu, Gang Li, Yang Zhang, Nanlan Bai and Mengwei Li
Biomimetics 2022, 7(4), 195; https://doi.org/10.3390/biomimetics7040195 - 10 Nov 2022
Cited by 4 | Viewed by 1737
Abstract
Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge [...] Read more.
Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI))
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17 pages, 1352 KiB  
Article
Explainable AI: A Neurally-Inspired Decision Stack Framework
by Muhammad Salar Khan, Mehdi Nayebpour, Meng-Hao Li, Hadi El-Amine, Naoru Koizumi and James L. Olds
Biomimetics 2022, 7(3), 127; https://doi.org/10.3390/biomimetics7030127 - 09 Sep 2022
Cited by 3 | Viewed by 2672
Abstract
European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally [...] Read more.
European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called “decision stacks” that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI))
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11 pages, 626 KiB  
Article
Biomimetic Incremental Domain Generalization with a Graph Network for Surgical Scene Understanding
by Lalithkumar Seenivasan, Mobarakol Islam, Chi-Fai Ng, Chwee Ming Lim and Hongliang Ren
Biomimetics 2022, 7(2), 68; https://doi.org/10.3390/biomimetics7020068 - 28 May 2022
Cited by 1 | Viewed by 2281
Abstract
Surgical scene understanding is a key barrier for situation-aware robotic surgeries and the associated surgical training. With the presence of domain shifts and the inclusion of new instruments and tissues, learning domain generalization (DG) plays a pivotal role in expanding instrument–tissue interaction detection [...] Read more.
Surgical scene understanding is a key barrier for situation-aware robotic surgeries and the associated surgical training. With the presence of domain shifts and the inclusion of new instruments and tissues, learning domain generalization (DG) plays a pivotal role in expanding instrument–tissue interaction detection to new domains in robotic surgery. Mimicking the ability of humans to incrementally learn new skills without forgetting their old skills in a similar domain, we employ incremental DG on scene graphs to predict instrument–tissue interaction during robot-assisted surgery. To achieve incremental DG, incorporate incremental learning (IL) to accommodate new instruments and knowledge-distillation-based student–teacher learning to tackle domain shifts in the new domain. Additionally, we designed an enhanced curriculum by smoothing (E-CBS) based on Laplacian of Gaussian (LoG) and Gaussian kernels, and integrated it with the feature extraction network (FEN) and graph network to improve the instrument–tissue interaction performance. Furthermore, the FEN’s and graph network’s logits are normalized by temperature normalization (T-Norm), and its effect in model calibration was studied. Quantitative and qualitative analysis proved that our incrementally-domain generalized interaction detection model was able to adapt to the target domain (transoral robotic surgery) while retaining its performance in the source domain (nephrectomy surgery). Additionally, the graph model enhanced by E-CBS and T-Norm outperformed other state-of-the-art models, and the incremental DG technique performed better than the naive domain adaption and DG technique. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI))
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13 pages, 1087 KiB  
Article
Bio-Inspired Control System for Fingers Actuated by Multiple SMA Actuators
by George-Iulian Uleru, Mircea Hulea and Adrian Burlacu
Biomimetics 2022, 7(2), 62; https://doi.org/10.3390/biomimetics7020062 - 13 May 2022
Cited by 6 | Viewed by 2457
Abstract
Spiking neural networks are able to control with high precision the rotation and force of single-joint robotic arms when shape memory alloy wires are used for actuation. Bio-inspired robotic arms such as anthropomorphic fingers include more junctions that are actuated simultaneously. Starting from [...] Read more.
Spiking neural networks are able to control with high precision the rotation and force of single-joint robotic arms when shape memory alloy wires are used for actuation. Bio-inspired robotic arms such as anthropomorphic fingers include more junctions that are actuated simultaneously. Starting from the hypothesis that the motor cortex groups the control of multiple muscles into neural synergies, this work presents for the first time an SNN structure that is able to control a series of finger motions by activation of groups of neurons that drive the corresponding actuators in sequence. The initial motion starts when a command signal is received, while the subsequent ones are initiated based on the sensors’ output. In order to increase the biological plausibility of the control system, the finger is flexed and extended by four SMA wires connected to the phalanges as the main tendons. The results show that the artificial finger that is controlled by the SNN is able to smoothly perform several motions of the human index finger while the command signal is active. To evaluate the advantages of using SNN, we compared the finger behaviours when the SMA actuators are driven by SNN, and by a microcontroller, respectively. In addition, we designed an electronic circuit that models the sensor’s output in concordance with the SNN output. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI))
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7 pages, 953 KiB  
Article
Innovation in Gastroenterology—Can We Do Better?
by Eyal Klang, Shelly Soffer, Abraham Tsur, Eyal Shachar and Adi Lahat
Biomimetics 2022, 7(1), 33; https://doi.org/10.3390/biomimetics7010033 - 19 Mar 2022
Cited by 5 | Viewed by 3084
Abstract
The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird’s eye view of gastroenterology’s innovative technologies via utilizing a text-mining technique. We analyzed five research [...] Read more.
The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird’s eye view of gastroenterology’s innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI))
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Review

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14 pages, 2378 KiB  
Review
Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions
by Shiva Rangwani, Devarshi R. Ardeshna, Brandon Rodgers, Jared Melnychuk, Ronald Turner, Stacey Culp, Wei-Lun Chao and Somashekar G. Krishna
Biomimetics 2022, 7(2), 79; https://doi.org/10.3390/biomimetics7020079 - 14 Jun 2022
Cited by 8 | Viewed by 3091
Abstract
The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic [...] Read more.
The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34–68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25–64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI))
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