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bat optimization algorithm for wrapper-based feature selection and performance improvement of android malware detection

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IET NETWORKS 2021年第3期10卷 131-140页

作者： Varma, Ravi Kiran P. Mallidi, S. Kumar Reddy Jhansi, Santosh K. Latha, Pushpa D. MVGR Coll Engn Dept CSE Vizianagaram AP India Sri Vasavi Coll Engn Dept CSE Tadepalligudem AP India

Android malware is a serious threat to the mobile users and their data. The losses incurred are unimaginable, which stretch to the extent of identity theft, financial loss, sensitive information loss, espionage, sabotage, cyber fraud, to mention a few. Android application's permission attributes can be analysed for malware detection using machine learning. However, the high-dimensional permission attributes are the bottleneck in designing optimized malware detection system. Identification of useful permission attributes is an NP-hard problem. bat optimization algorithm for Wrapper-based Feature Selection (BOAWFS) is proposed in this article and evaluated on the CICInvesAndMal2019 benchmark dataset. The performance of BOAWFS is also compared with that of Cuckoo Search optimization for Wrapper-based Feature Selection (CSOWFS) and Grey Wolf optimization for Wrapper-based Feature Selection (GWOWFS). Five classifiers, Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbour (KNN), Decision Tree (DT), and Nearest Centroid (NC) are compared for wrapper feature selection. BOAWFS outperformed consistently with all the five classifiers. With 200 agents and 100 iterations, the BOAWFS-DT outperformed with 93.73% accuracy after reducing the features to 518 from 4115. The considerable contribution of BOAWFS is that a 1.67% improvement in accuracy with 87.41% redundancy removal in features is achieved for the very high-dimensional permission-based android malware dataset.

关键词： optimized malware detection system Data security learning (artificial intelligence) Optimisation techniques bat optimization algorithm mobile computing search problems android malware detection invasive software pattern classification wrapper-based feature selection optimisation smart phones Ubiquitous and pervasive computing feature selection Combinatorial mathematics Android (operating system) wrapper feature selection android application BOAWFS high-dimensional permission attributes decision trees support vector machines high-dimensional permission-based android malware Other topics in statistics

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Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

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JOURNAL OF SUPERCOMPUTING 2022年第9期78卷 11975-12023页

作者： Mohar, Satinder Singh Goyal, Sonia Kaur, Ranjit Punjabi Univ Patiala Dept Elect & Commun Engn Patiala Punjab India

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

关键词： Node localization bat optimization algorithm Computation time Target nodes Localization error

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An inverse damage location problem applied to AS-350 rotor blades using bat optimization algorithm and multiaxial vibration data

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 145卷

作者： Gomes, Guilherme Ferreira Souza Chaves, Joao Artur de Almeida, Fabricio Alves Fed Univ Itajuba UNIFEI Mech Engn Inst Itajuba MG Brazil Fed Univ Itajuba UNIFEI Inst Ind Engn & Management Itajuba MG Brazil

In this study, a damage identification method is proposed using both the finite element method and the bat optimization algorithm applied to the AS-350 helicopter main rotor blade. First, the structure is numerically modeled and evaluated with and without the presence of induced damages. In a second approach, an inverse problem of optimization is constructed in order to identify certain damages in terms of its position and severity level. Three different objective functions are evaluated according to the modal parameters of the rotor blade (vibrations in x, y and z directions). Numerical results, through analysis of variance, showed that local damage significantly modifies the modal response into a non-linear aspect. The modal response used was able to identify, with great efficiency, the actual (noise simulated) damages induced in terms of location and severity. Accordingly, a damage identification method is developed in order to better handle any measurement data (to find/regarding) structural changes (or damages) in complex aerospace structures. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy, even under high measurement noise. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Damage identification Inverse problem bat optimization algorithm Helicopter rotor blade RSM Structural health monitoring

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Location-Based Explosion Detection in Wireless Optical Pressure Sensor Networks Using bat optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期127卷 845-868页

作者： Luo, Renze Li, Guodong Fan, Shunli Safara, Fatemeh Southwest Petr Univ Sch Elect Informat Chengdu 610500 Peoples R China Southwest Petr Univ Sch Mech & Elect Engn Chengdu 610500 Peoples R China Islamic Azad Univ Dept Comp Engn Islamshahr Branch Islamshahr Iran

Explosion detection is one the important issues to protect people's lives from terrorist attacks. Technological advances have significantly reduced the possibility of terrorist attacks. One of the technologies that improved the explosion detection accuracy is Wireless Sensor Networks (WSN), which has gained researches' attention. WSN is widely used in medical systems, industries and military systems to collect data from sensor nodes placed in particular locations. For precise and high speed communications, optical sensor nodes are widely used recently. In this paper, the main objective is to detect explosion in an optical pressure sensor network. To meet this goal, an evolutionary algorithm, bat optimization algorithm, is employed. The proposed method results in reducing energy consumption and improving the service quality. Simulation results indicates the superiority of the proposed algorithm for explosion detection in compare to previous methods proposed for the same problem.

关键词： Wireless sensor networks Explosion detection Optical pressure sensors bat optimization algorithm Evolutionary algorithms

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Optimum deployment of sensor nodes in wireless sensor network using hybrid fruit fly optimization algorithm and bat optimization algorithm for 3D Environment

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第6期15卷 2694-2718页

作者： Mohar, Satinder Singh Goyal, Sonia Kaur, Ranjit Punjabi Univ Dept Elect & Commun Engn Patiala Punjab India

Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influences the performance of wireless sensor network. For better performance of wireless sensor network it is essential to increase the coverage of nodes by locating the nodes at optimum positions with help of efficient optimization algorithm. In this paper the sensor nodes are located by using Hybrid fruit fly optimization algorithm and bat optimization algorithm in three dimensional environment. The exploration feature of fruit fly optimization algorithm is combined with exploitation characteristics of bat optimization algorithm in proposed algorithm. The grid points covered once by sensor node are removed from entire grid points for remaining nodes. With removal of grid points the overload on sensor nodes is reduced in proposed algorithm. The simulation results of Hybrid fruit fly optimization algorithm and bat optimization algorithm are compared in terms of variance, standard deviation, coverage rate and coefficient of dispersion with fruit fly optimization algorithm and bat optimization algorithm. Moreover, to verify the efficiency of proposed algorithm the results are also compared with other optimization algorithms such as artificial bee colony algorithm with dynamic search strategy, grey wolf optimization algorithm, enhanced grey wolf optimization algorithm, whale optimization algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm, differential evolution algorithm, shuffled frog leaping algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm based on differential evolution respectively. The simulation result signifies that proposed Hybrid fruit fly optimization algorithm and bat optimization algorithm is efficient than above stated existing optimization algorithm in terms of average coverage rate.

关键词： Sensor nodes bat optimization algorithm Wireless sensor network Node deployment Fruit fly optimization algorithm

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Hybrid Deep Learning-Improved bat optimization algorithm for Soil Classification Using Hyperspectral Features

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Computer Systems Science & Engineering 2023年第4期45卷 579-594页

作者： S.Prasanna Bharathi S.Srinivasan G.Chamundeeswari B.Ramesh Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105TamilnaduIndia Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India

Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better *** technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its *** are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation *** are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction *** overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved bat optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral *** HDIB,we propose a spontaneous bat optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)***-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking ***,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of ***,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.

关键词： HDIB bat optimization algorithm recurrent deep learning neural network convolutional neural network single layer perceptron hyperspectral images deep metric learning

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Discovering the maximum k-clique on social networks using bat optimization algorithm

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Computational Social Networks 2021年第1期8卷 1-15页

作者： Khodadadi, Akram Saeidi, Shahram Department of Industrial Engineering Islamic Azad University Tabriz Branch Tabriz Iran

The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic algorithm (GA) and Ant Colony optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5. © 2021, The Author(s).

关键词： Ant colony optimization bat optimization algorithm Genetic algorithm K-clique problem

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Power System Stabilizer Design Using bat optimization algorithm in Multimachine Power System 11

Power System Stabilizer Design Using BAT Optimization Algori...

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IEEE Student Conference on Research and Developement

作者： Islam, Naz Niamul Hannan, M. A. Shareef, Hussain Mohamed, Azah Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi Selangor Malaysia

ISBN: (纸本)9781479926565

This paper proposes a new nature inspired optimization technique known as bat optimization algorithm ( batOA) for optimal design of Power System Stabilizer ( PSS) in order to damp power system oscillations usually tempt to grow in multi machine power system when a fault is occurred. The design problem of PSS is formulated as an optimization problem to minimize the value of objective function. bat algorithm is then employed to search for best parameters of PSS for which the value of objective function is global minimum under all boundary conditions. In the simulation, the linearized model of power system and conventional lead-lag controller as PSS structure are used. The results obtained from batOA are compared with the obtained results using Particle Swarm optimization ( PSO) algorithm. The comparison of both algorithm reveals the effectiveness of batOA over PSO in tuning of PSS parameters for multi machine power system.

关键词： Power System Stabilizer (PSS) Damping Controller bat optimization algorithm optimization Multi Machine Power System

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Automatic Diagnosis and Grading of Diabetic Retinopathy using bat optimization algorithm-Refined Deep Residual Network

Automatic Diagnosis and Grading of Diabetic Retinopathy usin...

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

作者： Lakshmi, B. Haritha Kaneti, Venkata Ramana Soundariya, R.S. Nayak, Sanjib Kumar Rajesh, N. Malla Reddy Engineering College for Women It Department Hyderabad India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Diabetic Retinopathy (DR) is one of the most severe sight-threatening disorders resulting from diabetes, and can eventually lead to blindness and visual impairment. Early detection and medical therapy can assist in controlling and preventing DR progression. However, manual grading is extremely difficult and time-consuming because of the retina's complicated structure. In this research, the bat optimization algorithm- Refined Deep Residual Network (BOA-RDRN) is proposed for automatic diagnosis and grading of DR. Initially, the DIARETDB0 dataset is employed for the proposed method, and then pre-processing is performed for image denoising which eliminates noise. The Attention-based Fusion Network (AFU-Net) is used to segment the lesion region. The Grey-Level Co-occurrence Matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract the features and BOA is employed to find an optimal subset of features in retinal images. Finally, the RDRN is utilized to classify the anomalies as normal or abnormal. The BOA-RDRN achieves an accuracy of 99.42% compared to the existing methods such as Tunicate Swarm Spider Monkey optimization-based Refined Deep Residual Network (TSSMO-RDRN), Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Function (WKFM-DBF), Improved Harris Hawk optimization-based Convolution Neural Network (IHHO-CNN), and Enhanced Fuzzy C-Means Clustering-Resnet 152 respectively. © 2023 IEEE.

关键词： bat optimization algorithm Diabetic Retinopathy Grey-Level Co-occurrence Matrix Local Ternary Pattern Refined Deep Residual Network

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Intrusion Detection System in Mobile Cloud Computing Using bat optimization algorithm-Support Vector Machine 2

Intrusion Detection System in Mobile Cloud Computing Using B...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Alabdeli, Haider Yamsani, Nagendar Anitha, D. Chaithra, K.N. Bindu, G. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317558

Mobile Cloud Computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations;hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The bat optimization algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively. © 2024 IEEE.

关键词： bat optimization algorithm Intrusion Detection System Machine Learning Mobile Cloud Computing Support Vector Machine

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