检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis between binary Grey Wolf and binary bat Optimization for Optimal PMU Placement with Complete Observability 6

Comparative Analysis between Binary Grey Wolf and Binary Bat...

引用

6th IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)

作者： Priyadarshini, Subhashree Panigrahi, Chinmoy Kumar KIIT Deemed Univ Sch Elect Engn Bhubaneswar India

ISBN: (纸本)9781665419178

This paper presents the comparative analysis between the binary grey wolf optimization (BGWO) and the binary bat algorithm (BBA) to find the solution of the optimal PMU placement (OPP) problem by minimizing the number of phasor measurement units (PMUs) with maximum measurement redundancy to achieve complete observability of the power system. The proposed techniques have been implemented on various IEEE standard buses such as 14 bus, 30 bus, 57 bus and 118 bus system both under the normal condition as well as contingency condition like single PMU loss. The comparative results show that BGWO is more efficient than the BBA approach.

关键词： binary bat algorithm binary grey wolf optimization maximum redundancy observability optimal PMUplacement phasor measurement unit

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Lesion analysis towards melanoma detection using soft computing techniques

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Relational database security using digital watermarking and evolutionary techniques

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Meta-Heuristic Feature Selection Method for Identification of Indian Spoken Languages From Audio Signals

引用

IEEE ACCESS 2020年 8卷 181432-181449页

作者： Das, Aankit Guha, Samarpan Singh, Pawan Kumar Ahmadian, Ali Senu, Norazak Sarkar, Ram Univ Calcutta Inst Radio Phys & Elect Kolkata 700009 India Jadavpur Univ Dept Informat Technol Kolkata 700106 India Natl Univ Malaysia UKM Inst IR 4 0 Bangi 43600 Malaysia Univ Putra Malaysia Inst Math Res Serdang 43400 Malaysia Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

With the recent advancements in the fields of machine learning and artificial intelligence, spoken language identification-based applications have been increasing in terms of the impact they have on the day-to-day lives of common people. Western countries have been enjoying the privilege of spoken language recognition-based applications for a while now, however, they have not gained much popularity in multi-lingual countries like India owing to various complexities. In this paper, we have addressed this issue by attempting to identify different Indian languages based on various well-known features like Mel-Frequency Cepstral Coefficient (MFCC), Linear Prediction Coefficient (LPC), Discrete Wavelet Transform (DWT), Gammatone Frequency Cepstral Coefficient (GFCC) as well as a few deep learning architecture based features like i-vector and x-vector extracted from the audio signals. After comparing the initial results, it is observed that the combination of MFCC and LPC produces the best results. Then we have developed a new nature-inspired feature selection (FS) algorithm by hybridizing binary bat algorithm (BBA) with Late Acceptance Hill-Climbing (LAHC) to select the optimal subset from the said feature vectors in order to reduce the model complexity and help it train faster. Using Random Forest (RF) classifier, we have achieved an accuracy of 92.35% on Indic TTS database developed by IIT-Madras, and an accuracy of 100% on the Indic Speech database developed by the Speech and Vision Laboratory (SVL) IIIT-Hyderabad. The proposed algorithm is also found to outperform many standard meta-heuristic FS algorithms. The source code of this work is available at: https://***/CodeChef97dotcom/Feature-Selection

关键词： Feature extraction Mel frequency cepstral coefficient Machine learning Classification algorithms Databases Complexity theory Discrete wavelet transforms Spoken language identification feature selection binary bat algorithm late acceptance hill climbing algorithm MFCC and LPC features

来源：评论

学校读者我要写书评

暂无评论

Performance investigation and element optimization of 2D array transducer using bat algorithm

引用

ARCHIVES OF ELECTRICAL ENGINEERING 2020年第3期69卷 561-579页

作者： Tantawy, Dina Mohamed Eladawy, Mohamed Hassan, Mohamed Alimaher Mubarak, Roaa Egyptian Chinese Univ Fac Engn Mechatron Engn & Automat Dept Ein Shams Cairo Governora Egypt Helwan Univ Fac Engn Elect & Commun Engn Dept Helwan Egypt Helwan Univ Fac Engn Dept Biomed Engn Helwan Egypt

One of the least expensive and safest diagnostic modalities routinely used is ultrasound imaging. An attractive development in this field is a two-dimensional (21)) matrix probe with three-dimensional (3D) imaging. The main problems to implement this probe come from a large number of elements they need to use. When the number of elements is reduced the side lobes arising from the transducer change along with the grating lobes that are linked to the periodic disposition of the elements. The grating lobes are reduced by placing the elements without any consideration of the grid. In this study, the binary bat algorithm (BBA) is used to optimize the number of active elements in order to lower the side lobe level. The results are compared to other optimization methods to validate the proposed algorithm.

关键词： 2D ultrasound arrays binary bat algorithm Genetic algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

Relevant Subnetwork Identification for Topology Error Detection in Power Systems

Relevant Subnetwork Identification for Topology Error Detect...

引用

IEEE International Conference on Environment and Electrical Engineering (EEEIC) / IEEE Industrial and Commercial Power Systems Europe (ICPS Europe)

作者： Hossam-Eldin, Ahmed A. Abdallah, Emtethal N. Kassem, Ahmed H. Youssef, Karim H. Alexandria Univ Dept Elect Engn Fac Engn Alexandria 21544 Egypt

ISBN: (纸本)9781538651865

The development of methods based on demonstrating portions of the power system network at the detailed level has brought about new perceptions for topology error identification in modeling power system in real-time. This paper reports the issue of defining the noteworthy portions of the network to be represented in details so as to guarantee satisfactory topological conditions for determination of topology inaccuracies. Beginning with the reduced network model, the suggested methodology executes error investigation and states l inkage indices used for suspicious branches determination. binary bat algorithm is then used to enlarge the suspicious branches into a significant subnetwork to be represented at the detailed level. This subnetwork contains all uncertain substations and shows the essential properties for determination topology inaccuracies. Simulation results for the proposed methodology using IEEE 14-bus and 30-bus test systems proved its effectiveness.

关键词： topology error identification reduced network model linkage indices binary bat algorithm significant subnetwork detailed level

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：