Bio-inspired Methods Research Articles

Physical and biomimetic treatment methods to reduce microplastic waste accumulation.

BackgroundSince the Covid-19 pandemic in 2019, the use of plastics has increased exponentially, so it is imperative to manage and dispose of these plastic wastes safely.ObjectivesThis review focuses on the management strategies governed by the policies of each country to reduce plastic waste through physical collection methods and methods that use eco-imitation technologies.ResultsThus far, physical treatment methods have been applied to sewage and drinking water treatment. The abilities of bio-inspired treatment methods are being assessed in terms of capturing microplastics (MPs) and nanoplastics (NPs), extracting substances from marine organisms, reducing toxicity, and developing alternatives to petroleum-based plastics.ConclusionsVarious post-treatment methods have been proposed to collect and remove MPs and NPs that have reached into aquatic ecosystems and subsequently reduce their toxicity. However, there are limitations that the effectiveness of these methods is hindered by the lack of policies governing the entire process of plastic use before the post-treatment.Purpose of ReviewWe purpose to reduce plastic waste through methods that use eco-imitation technologies.Recent FindingsThese eco-imitation methods are attracting attention as viable future plastic waste treatment options in line with the goals of sustainable development.

Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review

Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer.

Improving the Quality of Service (QoS) and Resource Allocation in Vehicular Platoon Using Meta-Heuristic Optimization Algorithm

The concept of vehicular platooning is emerging due to its self-driving capability, whereas a set of cars are arranged closely and that can drive safely even in high speed. Moreover the communication can also takes place between two platoons and hence the platoon leader can control the speed and direction. Traffic difficulties, like as collisions and delay at road junctions, need the use of platoon mechanisms. The most efficient vehicle traffic management in platoons is acknowledged in Intelligent Transportation System (ITS) for improving energy efficiency, road capacity, and road safety. Essentially, it is critical to analyse the connection characteristic of any two platoons in order to improve connectivity. As a result, under bi-directional platoon architecture, the possibility of connectivity between platoon members and Road Side Units (RSUs) is generated. Providing Quality of Service (QoS) to automotive network is difficult issue because elements such as asynchronous transmission or high mobility, crowded and fragmented channels, hardware defects, and various vehicular gadgets. As a result, it is extremely desired to utilize and distribute resources efficiently. Despite the existence of other bio-inspired meta-heuristic optimization methods, the Bacterial Foraging Algorithm has the benefit of simplicity and efficiency, and so may be used to a wide range of engineering applications. However, in the event of sophisticated analysis, the constraint is a lower convergence rate. To improve resource allocation, this study employs the Improved Bacterial Foraging Optimization based Channel Allocation (IBFOCA) technique. Set of channels allocated to platoon members are signified as bacteria’s position. By using latency, Signal-to-Interference Ratio (SINR) and Quality of Service (QoS), available channel is initially determined. This approach is used to find a channel that is acceptable for platoon members. Simulation results are used to examine the presentation of multi-channel distributions in platoons utilizing suggested optimization strategy. Signal-to-interference ratio is lowered by 29 decibel, throughput is raised by 69 kilobits per second, collision probability is reduced by 21.4 percent, and average batch size is 57 percent with a delay of 49 milliseconds.

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants, where an ant selects the next city to visit according to the pheromone on the trail and the visibility heuristic (inverse of distance). ACO assigns higher heuristic desirability to the nearest city without considering the issue of returning to the initial city or starting point once all the cities are visited. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which intelligently adopts a generalized formula of the visibility heuristic associated with the final destination city. ACOAV uses a new distance metric that includes proximity and eventual destination to select the next city. Including the destination in the metric reduces the tour cost because such adaptation helps to avoid using longer links while returning to the starting city. In addition, partial updates of individual solutions and 3-Opt local search operations are incorporated in the proposed ACOAV. ACOAV is evaluated on a suite of 35 benchmark TSP instances and rigorously compared with ACO. ACOAV generates better solutions for TSPs than ACO, while taking less computational time; such twofold achievements indicate the proficiency of the individual adoption techniques in ACOAV, especially in AV and partial solution update. The performance of ACOAV is also compared with the other ten state-of-the-art bio-inspired methods, including several ACO-based methods. From these evaluations, ACOAV is found as the best one for 29 TSP instances out of 35 instances; among those, optimal solutions have been achieved in 22 instances. Moreover, statistical tests comparing the performance revealed the significance of the proposed ACOAV over the considered bio-inspired methods.

DETECTING SPAM EMAIL WITH MACHINE LEARNING OPTIMIZED WITH BIO-INSPIRED METAHEURISTIC ALGORITHMS

Groups and individuals used email regularly, for sending and receiving messages. When spammers get to know any valid email address, then they can exploit it easily by sending unsolicited emails, which will directly reach our mail inbox. Detecting spam using the machine learning technique is a known way, but we want to improve the accuracy, so the ML algorithms were optimized with bio-inspired methods to achieve better accuracy. The research was done to implement ML models using many algorithms, such as Naive Bayes, Random Forest, Support Vector Machine, and Decision Tree on a particular email dataset, along with feature extraction and pre-processing. The techniques occurring in nature can be used to achieve better accuracy, viz. Genetic Algorithm and Particle Swarm Optimization.

A Transendothelial Leukocyte Transmigration Model Based on Computational Fluid Dynamics and BP Neural Network.

The mechanism of immune infiltration involving immune cells is closely related to various diseases. A key issue in immune infiltration is the transendothelial transmigration of leukocytes. Previous studies have primarily interpreted the leukocyte infiltration of from biomedical perspective. The physical mechanism of leukocyte infiltration remains to be explored. By integrating the immune cell transmigration computational fluid dynamics (CFD) data, the paper builds a time-dependent leukocyte transmigration prediction model based on the bio-inspired methods, namely back propagation neural networks (BPNN) model. The model can efficiently predict the immune cell transmigration in a special microvascular environment, and obtain good prediction accuracy. The model accurately predicted the cell movement and flow field changes during the transmigration. In the test data set, it has high prediction accuracy for cell deformation, motion velocity and flow lift forces during downstream motion, and maintains a good prediction accuracy for drag force. The two prediction models achieved the prediction of leukocyte transmigration in a specific microvascular environment and maintained a high prediction accuracy, indicating the feasibility and robustness of the BPNN model applied to the prediction of immune cell infiltration. Compared with traditional CFD simulations, BPNN models avoid complex and time-dependent physical modeling and computational processes.

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

Microbial Content Generation for Natural Terrains in Computer Games

Procedural content generation (PCG) has been applied since several decades to fulfill various game-related design needs. Besides bio-inspired methods, living (non-human) organisms were used in computer games for various purposes, such as behavior generation, data gathering, and player education. No living organisms were used for the generation of virtual terrains in games. Such an approach to terrain generation could benefit from morphological similarity between natural terrains and colonies of microbial organisms, real-time development of terrains over time, and educational opportunities. We successfully executed an experiment in which we used growing bacterial and fungal cultures for generating naturally appearing virtual terrains in real-time. Concludingly, we confirm the feasibility of using living organisms in real-time non-behavioral PCG and reflect on its potential impact.

Soil moisture estimation using novel bio-inspired soft computing approaches

ABSTRACT Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.

Neuro-Immune Model Based on Bio-Inspired Methods for Medical Diagnosis

In this article, a hybrid bio-inspired algorithm called neuro-immune is proposed based on Multi-Layer Perceptron Neural Network (MLPNN) and the Clonal Selection Classification (CSC) principle of the Artificial Immune System (AIS) for the classifying and diagnosing of medical disease. The proposed approach consists in the first phase to code the weights and biases of MLPNN concatenation vector of the input samples into an antigen vector and to decompose it into new weights to generate population memory cells which will be applied by the processes of the CSC algorithm clone and mutate in the second phase, to optimize the accuracy class of data and updating the MLPNN weights to minimize the mean squared error. Experimental results show that the proposed hybrid neuro-immune model allows obtaining a high diagnosis performance on a set of medical data problems from the UCI repository with an improved classification accuracy compared to existing works in the literature.

An overview of edge and object contour detection

In computer vision, edge and object contour detection is essential for higher-level vision tasks, such as shape matching, visual salience, image segmentation, and object recognition. It has attracted much attention during the past several decades, and many excellent methods have been proposed. In this paper, we make a comprehensive introduction to representative edge and object contour detection methods in the past two decades. Based on the development of these methods, we mainly classify them into two categories: traditional methods and learning-based methods. We further divide traditional methods into local pattern methods, edge grouping methods, active contour models, and bio-inspired methods. Further, we divide learning-based methods into classical learning-based methods and deep learning-based methods. At the same time, we introduce the most popular benchmarks and evaluation measures and quantitatively compare the performances of these promising methods. Moreover, we discuss current challenges in edge and object contour detection and suggest some future trends to bridge gaps with human vision. We believe that this overview will benefit newcomers and promote the development of edge and object contour detection.

Unsupervised modelling of rice aroma change during ageing based on electronic nose coupled with bio-inspired algorithms

Rice aroma profiles, consisting of different sorts of volatile organic compounds, go through complex alterations during storage period that affect its end-use quality. The aroma active compounds of rice are the key elements to determine its quality characteristics and freshness. This study evaluates the efficiency of electronic nose combined with modern biologically inspired unsupervised algorithms as a fast, real-time, and non-invasive technology for modelling rice aroma change during storage. The developed unsupervised algorithms could present successful results in clustering of the aroma change, and also could provide supplementary facilities for assessment of ageing in aromatic and non-aromatic rice samples. Self-organizing map neural network resulted in reliable grouping of the samples. The provided unified distance matrices were applied to interpret the pace of the aroma change. Fuzzy c-means was hybridized with differential evolution, and the fuzzifier parameter was applied for controlling of the samples’ memberships to the storage clusters. The developed fuzzy clustering proposed more reasonable solutions when there were overlappings among the clusters. The automatic clustering developed by artificial bee colony algorithm proposed original grouping of the aromatic samples. For the non-aromatic rice, the structures obtained by the automatic approach agreed satisfactorily with the aroma change during ageing. The automatic clustering eliminated the need for the number of clusters as well as the descriptive labelled samples, and presented smarter performance. The electronic nose system combined with the developed unsupervised algorithms could be utilized as a reliable and rapid tool for analysis of rice aroma change. • MOS based e-nose was applied for unsupervised modelling of rice aroma change. • Self-organizing map and the resulting U-matrix was applied for crisp clustering. • Differential evolution was hybridized with fuzzy c-means for fuzzy clustering. • Artificial bee colony was developed in automatic clustering framework. • E-nose combined with bio-inspired methods is fast and reliable in analyzing rice aroma.

Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

The Internet of Things (IoT) is now expanding and becoming more popular in most industries, which leads to vast growth in cloud computing. The architecture of IoT is integrated with cloud computing through web services. Recently, Dynamic Web Service Composition (DWSC) has been implemented to fulfill the IoT and business processes. In recent years, the number of cloud services has multiplied, resulting in cloud services providing similar services with similar functionality but varying in Quality of Services (QoS), for instance, on the response time of web services; however, existing methods are insufficient in solving large-scale repository issues. Bio-inspired algorithm methods have shown better performance in solving the large-scale service composition problems, unlike deterministic algorithms, which are restricted. Thus, an improved eagle strategy algorithm method is proposed to increase the performance that directly indicates an improvement in computation time in large-scale DWSC in a cloud-based platform and on both functional and non-functional attributes of services. By proposing the improved bio-inspired method, the computation time can be improved, especially in a large-scale repository of IoT.

Flipping food during grilling tasks, a dataset of utensils kinematics and dynamics, food pose and subject gaze

This paper presents a multivariate dataset of 2866 food flipping movements, performed by 4 chefs and 5 home cooks, with different grilled food and two utensils (spatula and tweezers). The 3D trajectories of strategic points in the utensils were tracked using optoelectronic motion capture. The pinching force of the tweezers, the bending force and torsion torque of the spatula were also recorded, as well as videos and the subject gaze. These data were collected using a custom experimental setup that allowed the execution of flipping movements with freshly cooked food, without having the sensors near the dangerous cooking area. Complementary, the 2D position of food was computed from the videos. The action of flipping food is, indeed, gaining the attention of both researchers and manufacturers of foodservice technology. The reported dataset contains valuable measurements (1) to characterize and model flipping movements as performed by humans, (2) to develop bio-inspired methods to control a cooking robot, or (3) to study new algorithms for human actions recognition.

Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications

These days, hybrid nature-inspired population-based intelligent optimization methods are a wide range of the algorithms, which are used very often to solve real-world (industrial) optimizationproblems. As it was shown in [item 1) in the Appendix] by Slowik and Cpalka, nature-inspired methods can be divided into several groups of algorithms. In these groups of nature-inspired algorithms, we can find physics-based algorithms (gravitational search algorithm, harmony search algorithm, big bang-big crunch algorithm, etc.) and bio-inspired methods. In bio-inspired methods, we can find evolutionary algorithms (genetic algorithms, evolution strategies, genetic programming, etc.), swarm intelligence algorithms (particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc.), immune algorithms (clonal selection algorithm, negative selection algorithm, etc.), and others (flower pollination algorithm, great Salomon run, Japanese tree frogs calling, etc.). These four selected groups of nature-inspired population-based optimization algorithm are commonly used in creating hybrid methods.

Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review

Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets, sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the area of fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves further study as it can show interesting results for intelligent fraud detection.

Metaheuristic minimum dose path planning for nuclear power plant decommissioning

When a nuclear power plant reaches its end of operational life, decommissioning work needs to be carried out. One of the most important decommissioning strategies is to plan an optimal path for workers or robots to move around in a radioactive environment, keeping the radiation exposure as low as possible. This paper develops two bio-inspired metaheuristic methods for minimum dose path planning using a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), respectively. To evaluate the effectiveness of the two metaheuristic methods, two extreme hypothetical environments are simulated. The developed bio-inspired metaheuristic methods are compared with prior grid-based and sampling-based minimum dose path planning algorithms, in terms of cumulative dose, computational time, and distance. The results indicate that PSO outperforms prior grid-based and sampling-based algorithms in cumulative dose and distance. GA outperforms only the grid-based algorithms in cumulative dose and distance.

Global optimisation in Hilbert spaces using the survival of the fittest algorithm

Global optimisation problems in high-dimensional and infinite-dimensional spaces arise in various real-world applications such as engineering, economics and finance, geophysics, biology, machine learning, optimal control, etc. Among stochastic approaches to global optimisation, biology-inspired methods are currently very popular in the literature. Bio-inspired approaches imitate natural ecological and evolutionary processes and are reported to be efficient in a large number of practical study cases. However, many bio-inspired methods can possess some vital drawbacks. For example, due to their semi-empirical nature, convergence to the globally optimal solution cannot always be guaranteed. Another major obstacle is that the existing methods often struggle with higher dimensionality of the space of parameters, which results in a slow convergence. Moreover, it is often difficult to adjust the dimensionality of the space of parameters in the corresponding computer code for a practical realisation of the optimisation method. Here, we present a bio-inspired global stochastic optimisation method applicable in Hilbert function spaces. The proposed method is an evolutionary algorithm inspired by the Darwin’s famous idea of survival of the fittest and is, therefore, referred to as the ‘Survival of the Fittest Algorithm’ (SoFA). Mathematically, the convergence of SoFA is a consequence of the fundamental property of localisation of probabilistic measure in a Hilbert space and we rigorously prove the convergence of the introduced algorithm for a generic class of functionals. The approach is simple in terms of practical coding. As an insightful, real-world problem, we apply our method to find the globally optimal trajectory for the daily vertical migration of zooplankton in the ocean and lakes, this phenomenon is considered to be the largest synchronised movement of biomass on Earth. We maximise fitness in a function space derived from a von-Foerster stage-structured population model with biologically realistic parameters. We show that for problems of fitness maximisation in high-dimensional spaces, SoFA provides better performance as compared to some other stochastic global optimisation algorithms. We highlight the links between the new optimisation algorithm and natural selection processes in ecosystems occurring within a population via gradual exclusion of competitive con-specific strains.

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Vitamin B12 – Bioinspiration for Catalysis

Nature constructs and decomposes complex molecules with the help of enzymes and light as the ultimate energy source. However, despite enzyme’s fundamental benefits, their industrial application can be limited by their availability, instability towards certain reaction conditions, cost, scale, lack of suitable enzyme etc. A complementary approach to sustainable methodologies relies on the formation or cleavage of chemical bonds using bioinspired methods (methods imitating the efficient and elegant designs of biology) and recruiting light, the most abundant free energy source.Vitamin B12 - a cofactor of many enzymes seems inspirational.[1,2] This type of catalysis has been successfully translated into the laboratory and used in a small collection of reactions.[2, 3] The advantage of using vitamin B12 as a catalyst lays in the complete stability of the central cobalt ion and by the definition it is nontoxic. It has also been well documented that the reaction mechanism usually follows a radical pathway, bringing a new dimension to this already interesting field.Along this line, we have developed new vitamin B12-catalyzed reactions involving reduction of Co(III) to Co(I) or Co(II) and subsequent reactions with electrophiles or radicals. Under light irradiation vitamin B12 derivative unusually catalyzes a new olefinic sp2 C-H alkylation reaction, with diazo reagents as a carbene source, instead of the expected cyclopropanation, acylation of activated olefins. Light-induced vitamin B12-catalyzed methods are also suitable for C-O bond cleavage. These key findings emphasize the unique feature of vitamin B12 as a catalyst to achieve something unachievable with other methodologies or to find a greener approach.[1] Banerjee, R. Chemistry and Biochemistry of B12, John Wiley & Sons, Inc, 1999; [2] K. ó Proinsias, M. Giedyk, D. Gryko, Chem. Soc. Rev. 2013, 42, 6605-6619. [3] Giedyk, M.; Fedosov, S.; Gryko, D. Chem. Commun. 2014, 50, 4674-4676. Figure 1