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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Mehta, Gaurav Jain, Shaily Das, Prasenjit Tripathi, Vikas Chitkara University Chitkara University School of Engineering & Technology Himachal Pradesh India Chitkara University Department of Computer Science and Engineering Himachal Pradesh India Chandigarh University Apex Institute of Technology Punjab Mohali India Graphic Era Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350313413

The prevalence and sophistication of malware today pose a challenge to conventional signature-based malware detection techniques. This paper provides an approach to the identification and classification of malware that makes use of bio-inspired algorithms in conjunction with machine learning (ML) approaches. The main purpose of this paper is to design a system that can accurately recognize and classify malware samples as they are encountered in real time. This paper uses different bio-inspired algorithms, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), to achieve this. These algorithms take cues from biological processes. These techniques are used to fine-tune the feature selection process, which plays a major role in determining the quality of the detection and classification models. These methods improve the system's overall performance by simulating natural evolutionary processes to find the most discriminative and relevant features among a large set of candidate features. To predict the classification accuracy different ML algorithms are used like Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbour, Decision Tree, Multilayer Perceptron, Support Vector Machine, JRip, Random Tree, REPTree along with ACO and PSO. The results shows that bio-inspired algorithms improved the accuracy of the malware detection as compared to the basic ML algorithms. © 2023 IEEE.

关键词： bio inspired algorithms Classification algorithms Machine Learning Malware Detection

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A survey on identification of influential users in social media networks using bio inspired algorithms

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Procedia Computer Science 2023年 218卷 2110-2122页

作者： Sebin Jose P. Victer Paul Indian Institute of Information Technology Kottayam 686576 India

There are 4.5 billion active social media users who are continuously generating very large amounts of social big data. This data can be a vital source for prescriptive data analytics as it contains various insights about the day-to-day involvements and activities of the user. Among the large number of user profiles in a social media network, it is critical to recognize the best nodes with maximum influentiality in order to propagate messages through them so as to reach maximum number of nodes. After identifying, this seed set of nodes can be used for effective marketing through the platform. Marketing messages which are propagated via social media networks is evaluated to be the most promising form of advertisement for any product. As in the case of any other advertising strategy, the marketing team will be looking for maximum reachability of the advertisement with minimum cost. They will select the influencers in such a way that the advertisement is propagated to the maximum nodes possible, but with only limited number of initial influencers. In this study we are evaluating the suitability of bio inspired algorithms to the influential node identification problem. We are considering a sub class of bio inspired algorithms called as ”Swarm Intelligence” algorithms as they exhibits the collective behaviour of an organized group of bio species analogous to a group of profiles in social networks. This study has attempted to compare 4 algorithms which were already used for predicting the seed-set of influential users. The aim is to identify the most suitable one for the target problem and improve the same in future works.

关键词： social media data analytics social big data tweet analysis influential user identification viral marketing bio inspired algorithms swarm intelligence algorithms Literature review Nature-inspired computing soft computing

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Analysis of bio-inspired approaches and Social Media Analytics in Twitter datasets for Misinformation Detection 1

Analysis of Bio-Inspired approaches and Social Media Analyti...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Nicholas, Neomi Nelin Nirmalrani, V. Sathyabama Institute of Science and Technology Faculty of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350342796

bio-inspired algorithms, such as those with regard to collective intelligence and evolutionary algorithms have been increasingly used in social media analytics in recent years. These algorithms have been shown to be effective in many areas, such as sentiment analysis, community identification, and misinformation detection. This paper explains the underlying principles and features of the most recent bio-inspired techniques. The effectiveness of bio-inspired algorithms for the sentiment analysis categorization on Twitter datasets is studied. In this paper, several social media spam detection techniques that are currently in use are tabulated, and detailed study on the use of social media analytics and bio-inspired algorithms to detect misinformation in social media datasets is performed. The findings of the proposed analysis throws light into the efficiency of these algorithms and gaps for more research, which is inevitable to further improve their performance and robustness for misinformation detection. © 2023 IEEE.

关键词： bio inspired algorithms misinformation detection optimization social media analytics twitter datasets

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Parameter estimation of induction motor with bioinspired algorithms 7

Parameter estimation of induction motor with bioinspired alg...

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2024 Congress of Argentina

作者： Antonelli, Sofia L. Donolo, Pablo D. Pezzani, Carlos M. De Angelo, Cristian H. UNRC CONICET IITEMA UTN FRSNGEA Fac Ingn Rio Cuarto Argentina UNRC GEA Fac Ingn IITEMA CONICET Rio Cuarto Argentina

ISBN: (纸本)9798350365948;9798350365931

The identification of induction motor (IM) parameters is crucial for developing accurate models that simulate IM behavior under various conditions. This, in turn, facilitates the optimal design of control systems, early fault diagnosis, and predictive maintenance. Additionally, accurate parameter identification enables the implementation of energy efficiency strategies, reducing consumption and optimizing IM performance. This review article will analyze and elaborate on bioinspired optimization-based methods that have been widely employed in the parameter estimation of IMs.

关键词： Parameter estimation induction motor bio inspired algorithms

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Enhanced Antlion optimization based resource allocation in vehicular cloud environment

Enhanced Antlion optimization based resource allocation in v...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Jayasudha, V. Nazrin Salma, S. Saravanan, V. Nivedha, M. Vinuja, G. Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India Thamirabharani Engineering College Department of Eee Tirunelveli India Saveetha School of Engineering Department of Nano Electronics Materials and Sensors Chennai India Arasu Engineering College Department of Eee Kumbakonam India

ISBN: (纸本)9798350315455

There exists several complications with contemporary modes of transport in terms of security, accessibility, sustainability, and limitations in space. Vehicular networks are frequently seen as a viable strategy for enhancing ease and happiness in commuting. However, guaranteeing the optimal use of resources and addressing the rising needs continue to be difficult given the soaring popularity of vehicle users and the expanding diversity of requests for different services. By improving resource allocation from the perspectives of both suppliers and clients, the gaps in the prior research is closed and concentrate on resource allocation in vehicular cloud computing (VCC). The goal of our multi-objective optimisation approach for this issue is to increase levels of acceptance while conserving service supplier expenses. By adjusting the initial sample size in accordance with the pairing variable, unique overlap likelihood to favour exceptional members and increase demographic variation, we develop the Enhanced Ant lion optimization to handle such issues. The simulation results demonstrate that our suggested strategy outperforms the earlier methods in terms of performance. © 2023 IEEE.

关键词： Ant lion optimization bio inspired algorithms Resource allocation Vehicular cloud computing

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A bio-inspired localization-free stochastic coverage algorithm with verified reachability

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bioINSPIRATION & bioMIMETICS 2021年第5期16卷 056009-056009页

作者： Khan, Ayesha Al-Abri, Said Mishra, Vivek Zhang, Fumin Georgia Inst Technol North Ave NW Atlanta GA 30332 USA

Dependence on constant availability to an external localization service is often unreliable and infeasible in mobile robots. In this paper, we take inspiration from a continuous fish motion model, the persistent turning Walker (PTW), to devise a strategy which is able to achieve 2D and 3D coverage in an unknown environment in the absence of a localization service, such as a global positioning system (GPS). This is achieved by converting the continuous-time dynamical system into a discrete-time Markov chain which is then shown to exhibit strongly connected properties that are verifiable through numerical methods. The aforementioned proposed framework can also be used to study the continuous-time dynamics of other biological systems and evaluate their properties. The performance of the PTW model is also compared with two existing random search strategies, simple random walks (SRW) and correlated random walks (CRW) by using analytical bounds, simulation results, and statistical tests. The simulation results show that the proposed PTW algorithm covers a given search-space at a faster rate compared to the CRW and SRW models. Hence, the PTW may be effectively used as a coverage strategy by mobile robots in underwater or underground environments where the availability of a GPS cannot be guaranteed at all times.

关键词： bio inspired algorithms area coverage mobile robotics Markov chains fish behavior persistent turning Walker

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Chaotic step length artificial bee colony algorithms for protein structure prediction

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2020年第2期23卷 617-629页

作者： Saxena, Akash Shekhawat, Shalini Sharma, Ajay Sharma, Harish Kumar, Rajesh Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur 302017 Rajasthan India Swami Keshvanand Inst Technol Management & Gramot Dept Math Jaipur 302017 Rajasthan India Govt Engn Coll Dept Elect Engn Jhalawar 326023 Rajasthan India Rajasthan Tech Univ Dept Comp Sci & Engn Kota 324010 Rajasthan India Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India

Protein Structure Prediction (PSP) is an important problem of bio informatics. It has a paramount importance due to the fact that accurately predicted structure can help scientist and doctors to fight with the chronic diseases. Recently some studies reported efficacy of the bio inspired algorithms to solve these problems reported in the literature. It has also been a known fact now that Fitness Landscape Analysis (FLA) of Artificial Bee Colony Algorithm (ABC) is suitable for these problems. inspired with these facts, authors of this paper present new variants of ABC as Chaotic Step Length driven ABC algorithms named as Enhanced Chaotic Artificial Bee Colony algorithms (ECABC). In these variants, 10 different Chaotic Step Lengths (CSLs) are embedded in the position update phase of onlooker bee phase. 10 different variants based on CSL are proposed and evaluated on artificial and real protein structure benchmarks. Different statistical analyses reveal that the performances of ECABCs are superior to the parent algorithm. These algorithms may become a suitable choice to solve protein structure problem.

关键词： bio inspired algorithms Protein structure prediction Boxplot analysis.

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ANT_FDCSM: A novel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期36卷 747-760页

作者： Anuradha Singh, Akansha Gupta, Gaurav NorthCap Univ Dept CSE Gurgaon India NorthCap Univ Dept IT Gurgaon India Galgotias Univ Sch Comp Sci & Engn Greater Noida India Kean Univ Wenzhou Peoples R China

The ever increasing advances in the field of biotechnology and health information sciences have led to large electronic health records (EHRs), which in turn contains important genetic and clinical information. Machine learning and data mining techniques are playing vital and indispensible efforts to intelligently convert available data into useful information for effective medical diagnosis. But, designing effective prediction and diagnosis techniques for diabetes mellitus (DM) are getting more attention than ever before. Thus, a novel fuzzy rule miner (ANT_FDCSM) derived from ant colony meta-heuristic for diagnosis of diabetic patients has been proposed in this paper. A few important improvements have been suggested to improve the performance of traditional ant colony optimization induced decision tree classifier. The first improvement is done to optimize search space of construction graph by employing a novel approach for optimal split point selection. Secondly, to compute heuristic information, a hybrid node split measure (SW_FDCSM) is presented. SW_FDCSM is a combination of attribute significance weight (SW) with a new fuzzy variant (Fuzzy_DCSM) of famous distinct class split measure (DCSM). The improvements have been proposed to generate comprehensive rule set while maintaining good accuracy, sensitivity and specificity count. A 10 fold cross validation (10-FNo) is applied on Pima Indian Diabetes (PID) data set to validate the performance of the proposed fuzzy rule miner (ANT_FDCSM).

关键词： Feature extraction fuzzy classifiers fuzzy clustering decision tree classifier medical diagnosis bio inspired algorithms

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Extending ACO for fast path search in huge graphs and social networks

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 86卷 292-306页

作者： Calle, Javier Rivero, Jesica Cuadra, Dolores Isasi, Pedro Univ Carlos III Comp Sci Dept Avda Univ 30 Leganes 28911 Spain

This paper presents the bio-inspired algorithm SoSACO-v2 that is explained as an extension of the Ant Colony Optimization in which the ants are empowered with the sense of smell, directing them straightly to privileged nodes when they are near enough of them. This algorithm is an evolution of a former version which main feature is efficiency through path search task in huge graphs of high connectivity. New requirements regarding this task in most applications include processing vast graphs, immediate comeback, and dealing with dynamicity. The here proposed algorithm gives response to new needs the former approaches cannot fulfill: fast finding of paths between two nodes through vast dynamic graphs. SoSACO-v2 does not provide the optimum path, but it is the quicker algorithm in providing a response. It stands for domains where optimality is not required, and often the path search takes more time than covering the path. The approach is evaluated, both in a generic huge graph and in a small-world type graph from a real social network, showing satisfactory results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Huge graphs ACO bio inspired algorithms Path search Social networks

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