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Usability feature selection via MBbat: A novel approach

JOURNAL OF COMPUTATIONAL SCIENCE 2017年 23卷 195-203页

作者： Gupta, Deepak Ahlawat, Anil K. MAIT Delhi India KIET Comp Sci & Engn Dept Ghaziabad India

In this paper, a metaheuristic algorithm has been introduced for software usability feature selection and evaluation. Usability is becoming one of the most significant aspects of quality of software. The term 'usability' has already been defined by the authors in their previous work in a reference to the hierarchical software usability model. This model combines various usability factors and features in a hierarchical manner. Here, we introduced MBbat (Modified binary bat algorithm) for usability feature selection to get an optimal solution for the search of useful usability features out of a given set of usability features. MBbat is an extension of binary bat algorithm(BBA) which is based on the bat's behavior and to the best of our knowledge, this algorithm is introduced for the first time in software engineering practices. The selected number of features and accuracy of proposed MBbat algorithm is compared with the original BBA and the proposed metaheuristic algorithm outperforms the original BBA as it generates a fewer number of selected features and having low accuracy. (C) 2017 Elsevier B.V. All rights reserved.

关键词： bat algorithm binary bat algorithm Modified binary bat algorithm Software usability model Feature selection

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Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis

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JOURNAL OF SUPERCOMPUTING 2023年第6期79卷 7014-7036页

作者： Souaidia, Chouaib Thelaidjia, Tawfik Chenikher, Salah Larbi Tebessi Univ Elect Engn Dept LABGET Lab Tebessa Algeria

This paper develops a new architecture for bearing fault diagnosis based on independent vector analysis, feature selection, and extreme learning machines classifiers. The suggested method applied to vibration signals includes the following steps: First, the independent vector analysis is introduced to separate vibration signal components from each other. Second, statistical parameters are extracted from all the obtained sources. Then three binary optimisation algorithms such as binary bat algorithm, binary particle swarm optimisation and binary grey wolf optimisation are employed for feature selection one by one. Finally, three classifiers based on extreme learning, artificial neural networks and random forest are used to perform the classification step. The obtained results show that independent vector analysis followed by feature selection based on binary grey wolf optimisation and classification using an extreme learning machine provides an optimal input vector which contains only five features for each sample and a very small misdiagnosis rate equal to 0.76%. The obtained results also prove that the suggested methodology gives the best classification results and high visibility compared to the other studied approaches.

关键词： Bearing fault diagnosis Independent vector analysis binary bat algorithm binary particle swarm optimisation binary grey wolf Extreme learning machine

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Designing an efficient security framework for detecting intrusions in virtual network of cloud computing

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COMPUTERS & SECURITY 2019年第0期85卷 402-422页

作者： Patil, Rajendra Dudeja, Harsha Modi, Chirag Natl Inst Technol Goa Ponda Ponda 403401 Goa India

Cloud computing has grown for various IT capabilities such as IoTs, Mobile Computing, Smart IT, etc. However, due to the dynamic and distributed nature of cloud and vulnerabilities existing in the current implementations of virtualization, several security threats and attacks have been reported. To address these issues, there is a need of extending traditional security solutions like firewall, intrusion detection/prevention systems which can cope up with high-speed network traffic and dynamic network configuration in the cloud. In addition, identifying feasible network traffic features is a major challenge for an accurate detection of the attacks. In this paper, we propose a hypervisor level distributed network security (HLDNS) framework which is deployed on each processing server of cloud computing. At each server, it monitors the underlying virtual machines (VMs) related network traffic to/from the virtual network, internal network and external network for intrusion detection. We have extended a binary bat algorithm (BBA) with two new fitness functions for deriving the feasible features from cloud network traffic. The derived features are applied to the Random Forest classifier for detecting the intrusions in cloud network traffic and intrusion alerts are generated. The intrusion alerts from different servers are correlated to identify the distributed attack and to generate new attack signature. For the performance and feasibility analysis, the proposed security framework is tested on the cloud network testbed at NIT Goa and using recent UNSW-NB15 and CICIDS-2017 intrusion datasets. We have performed a comparative analysis of the proposed security framework in terms of fulfilling the cloud network security needs. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cloud computing Virtual network security Intrusion detection binary bat algorithm Random Forest

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Relational database security using digital watermarking and evolutionary techniques

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COMPUTATIONAL INTELLIGENCE 2019年第4期35卷 693-716页

作者： Tufail, Hina Zafar, Kashif Baig, Abdul Rauf Natl Univ Comp & Emerging Sci Lahore Pakistan Al Imam Muhammad Ibn Saud Islamic Univ IMSIU Riyadh Saudi Arabia

Advancement in the field of communication facilitates the world for electronic sharing of huge amount of data. Relational databases most widely used and shared through Internet by the communities and organizations. They work in collaborative environment for sharing of knowledge and for applying data mining techniques for finding different market trends and interests for research groups. Proof of ownership and database security are major issues related to online sharing of database. Watermarking facilitates solution for ownership protection and reversible watermarking ensures data recovery in case of any kind of malicious cyberattacks. The major challenge of watermarking are (1) imperceptibility of watermark, (2) robustness of watermark against any kind of data alteration attacks, and (3) data recovery. In this research, a reversible watermarking technique for numerical relational databases by using evolutionary techniques has been proposed that ensures the integrity of underlying data and robustness of watermark. Moreover, maximum Relevance and Minimum Redundancy (mRMR) technique has been used for feature subset selection. binary bat algorithm (BBA) as constraints optimization technique used for watermark creation. Result shows effectiveness of the proposed scheme against data tempering attacks. The proposed BBA provides better results as compare to Genetic algorithm.

关键词： binary bat algorithm database watermarking evolutionary techniques feature subset selection

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Feature selection and computational optimization in high-dimensional microarray cancer datasets via InfoGain-modified bat algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第25期81卷 36505-36549页

作者： Hambali, Moshood A. Oladele, Tinuke O. Adewole, Kayode S. Sangaiah, Arun Kumar Gao, Wei Fed Univ Wukari Dept Comp Sci Wukari Taraba State Nigeria Univ Ilorin Dept Comp Sci Ilorin Kwara State Nigeria Natl Yunlin Univ Sci & Technol Touliu 633102 Yunlin Taiwan Yunnan Normal Univ Sch Informat Sci & Technol Kunming Yunnan Peoples R China

Achieving a satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task, most importantly in heterogeneous multimedia data. One of the major drawbacks in cancer study is recognizing informative genes from thousands of available genes in microarray data. Traditional feature selection algorithms have failed to scale on large space data like microarray data. Therefore, an effective feature selection algorithm is required to explore the most significant subset of genes by removing non-predictive genes from the dataset without compromising the accuracy of the classification algorithm. The study proposed an information Gain - Modified bat algorithm (InfoGain-MBA) features selection model for selecting relevant and informative features from high dimensional Microarray cancer datasets and evaluate the approach with four classifiers - C4.5, Decision Tree, Random Forest and classification and regression tree (CART). The results obtained show that the proposed approach is promising for the classification of microarray cancer data. The random forest has 100% accuracy with few genes in all seven datasets used. Further investigations were also conducted to determine the optimal threshold for each of the datasets.

关键词： Feature selection binary bat algorithm Information gain Cancer classification Microarray data Random forest Computational optimization

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A wrapper approach-based key temperature point selection and thermal error modeling method

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第3-4期106卷 907-920页

作者： Tan, Feng Deng, Congying Xiao, Hong Luo, Jiufei Zhao, Shuang Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

A wrapper approach-based key temperature point selection and thermal error modeling method is proposed to concurrently screen the optimal key temperature points and construct the thermal error model. This wrapper approach can strengthen the intrinsic relation between the key temperature points and the thermal error model to ensure the strong prediction performance. On the whole, the least squares support vector machine (SVM) is used as the basic thermal error modeling method and the binary bat algorithm (BBA) is used as the optimization algorithm. The selection status of temperature points and the values of hyperparameters gamma and sigma(2) of SVM are coded in separate binary parts of the artificial bat's position vector of BBA. The cost function is designed by balancing the prediction error and the number of key temperature points. For verification, the thermal error experiment was conducted on a horizontal machining center. Feeding the collected experimental temperature data and thermal error data to the proposed method, three optimal key temperature points were screened out and the corresponding optimal hyperparameters were simultaneously searched. To verify the superiority of the proposed method, the prediction performance comparison analysis was conducted with the conventional filter-based method. Specifically, in the conventional method, the key temperature points were screened by combining fuzzy c means (FCM) clustering and correlation analysis, and the multiple linear regression (MLR), the backpropagation neural network (BPNN), and the SVM were used to build the thermal error model, respectively. Comparison results showed that the prediction accuracy of the proposed method increased by up to 44.0% compared to the conventional method, which suggests the superior prediction performance of the proposed method.

关键词： Key temperature points Thermal error Support vector machine binary bat algorithm Prediction performance

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Improved DV-Hop Localization algorithm Based on Variable Communication Radius for Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期138卷 1703-1725页

作者： Cao, Yuxiao Wu, Xiaobin Xu, Jinbao Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Jiangsu Peoples R China

In the field of wireless sensor networks, node localization is a key research issue. As a classical range-free localization scheme, the localization accuracy of distance vector-hop (DV-Hop) algorithm needs to be further improved. Therefore, an improved DV-Hop location algorithm based on variable communication radius (VCR IDV-Hop) is proposed in this paper. Firstly, by changing the communication radius of the anchor nodes, hop counts between nodes are refined from integer to decimal, which improves the accuracy of estimated distance between nodes. Secondly, the calculation of the average hop size of anchor nodes is abstracted into a combinatorial optimization problem, and the binary bat algorithm (BBA) is applied to solve it. Finally, when the unknown node is located, only part of anchor nodes with smaller hop counts from the unknown node are selected to participate in the localization. Simulation results show that VCR IDV-Hop greatly improves the localization accuracy of nodes compared with other excellent localization algorithms.

关键词： Wireless sensor networks Localization Variable communication radius Combinatorial optimization binary bat algorithm

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Bayes-Q-Learning algorithm in Edge Computing for Waste Tracking

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Intelligent Automation & Soft Computing 2023年第5期36卷 2425-2440页

作者： D.Palanikkumar R.Ramesh Kumar Mehedi Masud Mrim M.Alnfiai Mohamed Abouhawwash Department of Computer Science and Engineering Dr.NGP Institute of TechnologyCoimbatore641048India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the city,municipalities in the aspects of the environment,and *** efﬁcient supply chain with edge computing technology is used in managing medical *** supply chain operations include processing of waste collec-tion,transportation,and disposal of *** research works have been applied to improve the management of *** main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and *** over-come these issues,in this paper we implement an efﬁcient Naive Bayes classiﬁer algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical *** minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is *** accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

关键词： binary bat algorithm naïve bayes supply chain edge medical wastage

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Enhanced Intrusion Detection System Based on bat algorithm-support Vector Machine 11

Enhanced Intrusion Detection System Based on Bat Algorithm-s...

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11th International Conference on Security and Cryptography (SECRYPT)

作者： Enache, Adriana-Cristina Sgarciu, Valentin Univ Politehn Bucuresti Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9789898565952

As new security intrusions arise so does the demand for viable intrusion detection systems. These solutions must deal with huge data volumes, high speed network traffics and countervail new and various types of security threats. In this paper we combine existing technologies to construct an Anomaly based Intrusion Detection System. Our approach improves the Support Vector Machine classifier by exploiting the advantages of a new swarm intelligence algorithm inspired by the environment of microbats (bat algorithm). The main contribution of our paper is the novel feature selection model based on binary bat algorithm with Levy flights. To test our model we use the NSL-KDD data set and empirically prove that Levy flights can upgrade the exploration of standard binary bat algorithm. Furthermore, our approach succeeds to enhance the default SVM classifier and we obtain good performance measures in terms of accuracy (90.06%), attack detection rate (95.05%) and false alarm rate (4.4%) for unknown attacks.

关键词： Intrusion Detection SVM bat algorithm binary bat algorithm Levy Flights

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Lesion analysis towards melanoma detection using soft computing techniques

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CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH 2020年第2期8卷 501-508页

作者： Gaonkar, Rohan Singh, Kanwaljeet Prashanth, G. R. Kuppili, Venkatanareshbabu Natl Inst Technol Dept Elect & Commun Engn Veling Goa India

Introduction: Melanoma has been increasing worldwide. An efficient method based on bio-inspired algorithms and neural networks has been suggested in this study. Objective: The main goal of this study is to reduce the complexity of classifier using feature selection method thereby reducing time for classification and to balance specificity and sensitivity. Materials and methods: The approach to the problem has been divided into three basic steps;lesion segmentation feature extraction and the last step is classification. Segmentation and feature extraction was performed using image processing techniques. A novel fitness function has been proposed that will be optimized using binary bat algorithm (BBA) to obtain the most relevant feature set. Result: Support Vector Machine (SVM) and Radial Basis Function Network (RBFN) were used for classification process. SVM and RBFN produced accuracy of 87% and 91% respectively for K10 protocol. Specificity and Sensitivity for SVM in K10 protocol was obtained to be 82% and 92% respectively. As for RBFN specificity and sensitivity was obtained to be 90% and 93% respectively. We were able to obtain balance between specificity and sensitivity through our approach. Conclusion: With simple network structure like RBFN and SVM we were able to obtain results better than other complex networks.

关键词： Skin lesion binary bat algorithm Epiluminicence Gray level Co-Variance matrix (GLCM) Morphological operations

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