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

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bioSYSTEMS ENGINEERING 2022年第0期216卷 132-146页

作者： Rahimzadeh, Hassan Sadeghi, Morteza Mireei, Seyed Ahmad Ghasemi-Varnamkhasti, Mahdi Isfahan Univ Technol Coll Agr Dept Biosyst Engn Esfahan *** Iran Shahrekord Univ Fac Agr Dept Mech Engn Biosyst Shahrekord Iran

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 nonaromatic 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. (C) 2022 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Automatic clustering bio-inspired algorithms Gas sensors Machine learning Postharvest technology Unsupervised modelling

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Identity – Attribute Inference in Online Social Network(s) Using bio-inspired algorithms and Machine Learning Approaches

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Journal of Mobile Multimedia 2023年第3期19卷 645-678页

作者： Shetty, Nisha P. Muniyal, Balachandra Teja, Daita Ravi Maben, Leander Melroy Vinil, Tummala Srinag Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 567104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 567104 India

Twitter is one of the most popular social networking sites today, and it has become a critical tool for gathering data from numerous individuals throughout the world. The platform hosts a variety of debates spanning from current events and news to entertainment, advertising, and technology. In contrast to earlier approaches, the proposed work employs the concept of both direct (via tweets) and indirect stance detection (via homophily elements) to infer sensitive attributes. Along with attribute-based inference, the proposed work also matches user profiles across cross platforms via user-generated posts. Unlike prior efforts, usernames are not included in the feature set here since they are a bit of a giveaway. bio-inspired algorithms are used along with ensemble methods to extract the best set of features. © 2023 River Publishers.

关键词： attribute inference bio-inspired algorithms ensemble homophily Identity inference

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A survey on dynamic populations in bio-inspired algorithms

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2024年第2期25卷 19-19页

作者： Farinati, Davide Vanneschi, Leonardo Univ Nova Lisboa NOVA Informat Management Sch NOVA IMS Campus Campolide P-1070312 Lisbon Portugal

Population-Based bio-inspired algorithms (PBBIAs) are computational methods that simulate natural biological processes, such as evolution or social behaviors, to solve optimization problems. Traditionally, PBBIAs use a population of static size, set beforehand through a specific parameter. Nevertheless, for several decades now, the idea of employing populations of dynamic size, capable of adjusting during the course of a single run, has gained ground. Various methods have been introduced, ranging from simpler ones that use a predefined function to determine the population size variation, to more sophisticated methods where the population size in different phases of the evolutionary process depends on the dynamics of the evolution itself and events occurring within the population during the run. The common underlying idea in many of these approaches, is similar: to save a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.

关键词： Population-based algorithms bio-inspired algorithms Population size Dynamic population Adaptive population

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Route Scheduling System for Multiple Self-driving Cars Using K-means and bio-inspired algorithms 23rd

Route Scheduling System for Multiple Self-driving Cars Using...

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23rd International Conference on Engineering Applications of Neural Networks (EAAAI/EANN)

作者： Silva, Clenio E. Cesar, Tiago S. Gomes, Iago P. Silva, Junior A. R. Wolf, Denis F. Alves, Raulcezar Souza, Jefferson R. Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Paulo Brazil

ISBN: (纸本)9783031082238;9783031082221

This paper presents the development of a hybrid approach as a solution to the multiple Traveling Salesman Problem (mTSP) applied to the route scheduling for self-drive cars. First, we use k-means to generate routes that equality distribute delivery locations among the cars. Then, these routes are set as the initial population for bio-inspired algorithms, such as Genetic Algorithm (GA) and Ant Colony System (ACS), that perform an evolutionary process in order to find a route which minimizes the overall distance while keeping the balance of individual tours of each car. The experiments were conducted with our route scheduling system in real and virtual environments. We compared our hybrid approaches using k-means in conjunction with GA and ACS against GA, ACS and Particle Swarm Optimization (PSO) initialized with random population. The results showed that, as the number of cars and target locations increase, the hybrid approaches outperform GA, ACS and PSO without any pre-processing.

关键词： multiple Traveling Salesman Problem (mTSP) Route scheduling system bio-inspired algorithms

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Recommendation system using bio-inspired algorithms for urban orchards

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INTERNET OF THINGS 2024年 26卷

作者： Nunez, Juan M., V Corchado, Juan M. Giraldo, Diana M. Rodriguez-Gonzalez, Sara De la Prieta, Fernando Univ Salamanca BISITE Res Grp Edificio Multiusos I D I Salamanca 37007 Spain Univ Autonoma Occidente Calle 25 115-85 Cali 760001 Valle Del Cauca Colombia

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

关键词： Internet of Things bio-inspired algorithms Urban orchards Lettuce crops Social design

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Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms

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APPLIED WATER SCIENCE 2024年第11期14卷 1-25页

作者： Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Abba, Sani I. Ali, Farman Choi, Soo-Mi Sejong Univ XR Res Ctr Dept Comp Sci & Engn & Convergence Engn Intelligen Seoul South Korea Prince Mohammad Bin Fahd Univ Dept Civil Engn Al Khobar Saudi Arabia Sungkyunkwan Univ Coll Comp & Informat Sch Convergence Dept Comp Sci & Engn Seoul 03063 South Korea

Groundwater resources are essential for ensuring a consistent water supply in many regions. Groundwater potential maps (GPMs) can be utilized in many ways to estimate the quantity, quality, and distribution of subsurface water, supporting the decision-making processes of numerous stakeholders. This study contributes to improving the accuracy of GPMs, focusing on implementing Geospatial Artificial Intelligence (GeoAI) models. For this purpose, the accuracy performance of the Extreme Gradient Boosting (XGBoost) algorithm is improved in this study. To do this, two such popular metaheuristic algorithms, i.e., invasive weed optimization (IWO) and biogeography-based optimization (BBO), are integrated into the XGBoost algorithm for modeling and spatial prediction of the areas prone to groundwater. Three models-XGBoost, XGBoost-IWO, and XGBoost-BBO-are implemented within the Python programming environments to execute spatial modeling and generate predictive maps. The evaluation of results unfolds in two stages: model validation and GPM validation. For the training data, the root mean square error (RMSE) and mean absolute error (MAE) indices were 0.165 and 0.121 for XGBoost, 0.13 and 0.087 for XGBoost-IWO, and 0.114 and 0.082 for XGBoost-BBO, respectively. The test data showed similar trends, with XGBoost yielding RMSE and MAE values of 0.424 and 0.295, XGBoost-IWO at 0.416 and 0.287, and XGBoost-BBO at 0.39 and 0.28. XGBoost-BBO, XGBoost-IWO, and XGBoost had a prediction accuracy higher than other models. The respective area under the curve (AUC) of GMPs using receiver operating characteristic (ROC) curves for XGBoost, XGBoost-IWO, and XGBoost-BBO were 81.8 %, 83.1 %, and 83.7 %. Using bio-inspired metaheuristic algorithms, the GPM accuracy rate has improved further. The study of groundwater resources demonstrated how geological feature extraction by GeoAI may help employ advanced techniques.

关键词： Groundwater resource Spatial prediction Geospatial artificial intelligence (GeoAI) bio-inspired algorithms Water scarcity

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Application of bio-inspired algorithms to the Cryptanalysis of Asymmetric Ciphers on the Basis of Composite Number

Application of Bio-inspired Algorithms to the Cryptanalysis ...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Shoshina, Anastasiia, V Borzunov, Georgii, I Ivanova, Ekaterina Y. Natl Res Nucl Univ MEPhI Moscow Russia

ISBN: (纸本)9781665404761

In some cases, the confidentiality of cryptographic algorithms used in digital communication is related to computational complexity mathematical problems, such as calculating the discrete logarithm, the knapsack problem, decomposing a composite number into prime divisors etc. This article describes the application of insolvability of factorization of a large composite number, and reviews previous work integer factorization using either the deterministic or the bio-inspired algorithms. This article focuses on the possibility of using bio-inspired methods to solve the problem of cryptanalysis of asymmetric encryption algorithms, which ones based on factorization of composite numbers. The purpose of this one is to reviewing previous work in integer factorization algorithms, developing a prototype of either the deterministic and the bio-inspired algorithm and the effectiveness of the developed algorithms and recommendations are made for future research paths.

关键词： bio-inspired algorithms integer factorization fitness function genetic algorithm RSA quadratic sieve

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Improving software effort estimation using bio-inspired algorithms to select relevant features: An empirical study

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SCIENCE OF COMPUTER PROGRAMMING 2021年 205卷 102621-102621页

作者： Ali, Asad Gravino, Carmine Univ Salerno Dept Comp Sci Via Giovanni Paolo II132 I-84084 Fisciano SA Italy

Context: bio-inspired feature selection algorithms got the attention of the researchers in the domain of Software Development Effort Estimations (SDEE) because they can improve the prediction accuracy of existing estimation techniques, such as machine learning methods. Objective: This paper aims to analyze different feature selection algorithms and assess the role they can play to increase the accuracy of software development effort predictions. Method: We have performed an empirical study considering commonly used bio-inspired feature selection algorithms in the domain of SDEE, i.e., Genetic Algorithm (GA), Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Harmony Search (HS), and Firefly algorithm, and four traditional non-bio-inspired algorithms, i.e., Best-First Search (BFS), Greedy Stepwise, Subset Forward Selection, and Random Search, used in combination with five widely used estimation techniques and applied to eight widely used SDEE datasets. Results: The performed analysis suggests that almost all (bio-inspired) feature selection algorithms have outperformed the baseline estimation techniques (i.e., techniques employed without any feature selection algorithms) in the majority of the experiments and hence we can conclude that feature selection algorithms can help in the domain of SDEE to increase the prediction accuracy. Similarly, HS and GA are considered as best performed bio-inspired algorithms because they provided significantly better results than the non-bio-inspired algorithms in a greater number of experiments. Moreover, we also compared the results of various employed bio-inspired algorithms, and, again, GA and HS came out as the best performed bio-inspired feature selection algorithms. Conclusion: From our results, if we have to pick feature selection algorithms (from both bio- and non-bio-inspired) and recommend them for future investigations, we would suggest HS because it provided better effort predictions in more combinations o

关键词： bio-inspired algorithms Feature selection Effort estimation Estimation techniques

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Comparative Analysis of bio-inspired Optimization algorithms in Neural Network-Based Data Mining Classification

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-25页

作者： Murugan, Mathi T. Baburaj, Eppipanious Sathyabama Inst Sci & Technol Chennai Tamil Nadu India Marian Engn Coll Dept Comp Sci & Engn Trivandrum Kerala India

It always helps to determine optimal solutions for stochastic problems thereby maintaining good balance between its key elements. Nature-inspired algorithms are meta-heuristics that mimic the natural activities for solving optimization issues in the era of computation. In the past decades, several research works have been presented for optimization especially in the field of data mining. This paper addresses the implementation of bio-inspired optimization techniques for machine learning-based data mining classification by four different optimization algorithms. The stochastic problems are overcome by training the neural network model with techniques such as barnacles mating, black widow optimization, cuckoo algorithm, and elephant herd optimization. The experiments are performed on five different datasets, and the outcomes are compared with existing methods with respect to runtime, mean square error, and classification rate. From the experimental analysis, the proposed bio-inspired optimization algorithms are found to be effective for classification with neural network training.

关键词： bio-inspired algorithms Classification Data Mining Neural Networks Optimization

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Scheduling System for Multiple Self-driving Cars Using K-Means and bio-inspired Optimization algorithms

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SN Computer Science 2023年第5期4卷 647页

作者： Silva, Clênio E. César, Tiago S. Gomes, Iago P. Silva, Júnior A. R. Wolf, Denis F. Alves, Raulcézar Souza, Jefferson R. Faculty of Computing Federal University of Uberlândia Minas Gerais Uberlândia Brazil Institute of Mathematical and Computer Sciences University of São Paulo São Paulo São Carlos Brazil

This paper presents the development of a hybrid approach as a solution to the multiple Traveling Salesman Problem (mTSP) applied to the route scheduling for self-drive cars. First, we use k-means to generate routes that equality distribute delivery locations among the cars. Then, these routes are set as the initial population for bio-inspired algorithms, such as Genetic Algorithm (GA) and Ant Colony System (ACS), which perform an evolutionary process to find a route which minimizes the overall distance while keeping the balance of individual tours of each car. The experiments were conducted with our route scheduling system in real and virtual environments. We compared our hybrid approaches using k-means in conjunction with GA and ACS against GA, ACS, and Particle Swarm Optimization (PSO) initialized with random population. The results showed that, as the number of cars and target locations increase, the hybrid modeling approaches outperform GA, ACS, and PSO without any pre-processing. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bio-inspired algorithms Multiple Traveling Salesman Problem (mTSP) Route scheduling system Self-driving cars

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