检索结果-内蒙古大学图书馆

RSMO: rider Spider Monkey optimization-Based Artificial Noise Precoding Technique for Physical Layer Security in 5G Networks

引用

WIRELESS PERSONAL COMMUNICATIONS 2024年第4期135卷 2355-2377页

作者： Sekhar, P. Chandra Murthy, T. S. N. JNTU Kakinada Kakinada 533003 Andhra Pradesh India Chaitanya Bharathi Inst Technol A Dept ECE Hyderabad Telangana India JNTU GV Coll Engn Dept ECE Vizianagaram 535003 Andhra Pradesh India

The concern about security in wireless communication is increased, as Fifth Generation (5G) wireless communication is entered into very field of life. In recent days, privacy, confidentiality maintenance, integrity, and information security and data security are essential concerns. Thus, in this paper, rider Spider Monkey optimization (rider SMO) approach is developed for physical layer security (PLS) in 5G network. Traditionally, noise and interference are considered as catastrophic essentials in a wireless communication. Additionally, it has been exposed that identified interference is useful for signal prediction capability at receiving end. Moreover, the security rates of conventional sensible secrecy coding techniques are moderately low to assure security necessity of 5G communications. Therefore, concepts of Non-Orthogonal Multiple Access (NOMA) and massive Multiple-Input-Multiple-Output (mMIMO) and are incorporated for PLS of 5G wireless communication network through Artificial Noise (AN)-based precoding algorithm. The precoding method is modified based on the developed RSMO approach. Here, the developed RSMO method is devised by integrating rider optimization algorithm (ROA) and Spider Monkey optimization (SMO) approach. The developed RSMO technique obtained better performance with respect to power, secrecy rate, Bit Error Rate (BER) of 0.3561, 0.7629 and 0.0000502 Watts respectively.

关键词： AN-based precoding Spider monkey optimization rider optimization algorithm Physical layer security Bit error rate

来源：评论

学校读者我要写书评

暂无评论

rider and Sunflower optimization-driven neural network for image classification

引用

WEB INTELLIGENCE 2021年第1-2期19卷 41-61页

作者： Nadendla, Hanumantha Rao Srikrishna, A. Rao, K. Gangadhara JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India RVR & JC Coll Engn Dept Comp Sci & Engn Chowdavaram Andhra Pradesh India RVR & JC Coll Engn Dept Informat Technol Guntur Andhra Pradesh India Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India

Image classification is the classical issue in computer vision, machine learning, and image processing. The image classification is measured by differentiating the image into the prescribed category based on the content of the vision. In this paper, a novel classifier named RideSFO-NN is developed for image classification. The proposed method performs the image classification by undergoing two steps, namely feature extraction and classification. Initially, the images from various sources are provided to the proposed Weighted Shape-Size Pattern Spectra for pattern analysis. From the pattern analysis, the significant features are obtained for the classification. Here, the proposed Weighted Shape-Size Pattern Spectra is designed by modifying the gray-scale decomposition withWeight-Shape decomposition. Then, the classification is done based on Neural Network (NN) classifier, which is trained using an optimization approach. The optimization will be done by the proposed Ride Sunflower optimization (RideSFO) algorithm, which is the integration of rider optimization algorithm (ROA), and Sunflower optimization algorithm (SFO). Finally, the image classification performance is evaluated using RideSFO-NN based on sensitivity, specificity, and accuracy. The developed RideSFO-NN method achieves the maximal accuracy of 94%, maximal sensitivity of 93.87%, and maximal specificity of 90.52% based on K-Fold.

关键词： Image classification Weighted Shape-Size Pattern Spectra rider optimization algorithm Sunflower optimization algorithm neural network

来源：评论

学校读者我要写书评

暂无评论

Improved rider optimization for optimal container resource allocation in cloud with security assurance

引用

INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2020年第3期16卷 235-258页

作者： Vhatkar, Kapil Netaji Bhole, Girish P. Veermata Jijabai Technol Inst Dept CE&IT Mumbai Maharashtra India

Purpose The containerization application is one among the technologies that enable microservices architectures, which is observed to be the model for operating system (OS) virtualization. Containers are the virtual instances of the OS that are structured as the isolation for the OS atmosphere and its file system, which are executed on the single kernel and a single host. Hence, every microservice application is evolved in a container without launching the total virtual machine. The system overhead is minimized in this way as the environment is maintained in a secured manner. The exploitation of a microservice is as easy to start the execution of a new container. As a result, microservices could scale up by simply generating new containers until the required scalability level is attained. This paper aims to optimize the container allocation. Design/methodology/approach This paper introduces a new customized rider optimization algorithm (C-ROA) for optimizing the container allocation. The proposed model also considers the impact of system performance along with its security. Moreover, a new rescaled objective function is defined in this work that considers threshold distance, balanced cluster use, system failure, total network distance and security as well. At last, the performance of proposed work is compared over other state-of-the-art models with respect to convergence and cost analysis. Findings For experiment 1, the implemented model at 50th iteration has achieved minimal value, which is 29.24%, 24.48% and 21.11% better from velocity updated grey wolf optimisation (VU-GWO), whale random update assisted LA (WR-LA) and rider optimization algorithm (ROA), respectively. Similarly, on considering Experiment 2, the proposed model at 100th iteration attained superior performance than conventional models such as VU-GWO, WR-LA and ROA by 3.21%, 7.18% and 10.19%, respectively. The developed model for Experiment 3 at 100th iteration is 2.23%, 5.76% and 6.56% superior to VU-

关键词： Microservices System Performance Container Resource Allocation rider optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

RBorderNet: rider Border Collie optimization-based Deep Convolutional Neural Network for road scene segmentation and road intersection classification

引用

DIGITAL SIGNAL PROCESSING 2022年第0期129卷

作者： Mahesh, K. Michael Veluchamy, S. Thirumalai, J. Sureshkanna, P. St Joseph Coll Engn Dept Elect & Commun Engn Chennai 602117 India Sri Venkateswara Coll Engn Dept ECE Chennai India SMK Fomra Inst Technol ECE Dept Chennai India Cyient Ltd Pointe Claire India

Intersection is an important component in urban traffic networks, as it connects pedestrian flows and vehicles between the network links. Intersection classification plays an active role in mitigating traffic congestion, increasing the level of road safety and traffic efficiency. Various intersection classification methods are adopted to classify the intersection point in the road segment, but detecting the accurate location results from a complex task in an automatic driving system. Hence, this paper proposes a rider Border Collie optimization-based Deep Convolutional Neural Network (RBorderNet) for road scene segmentation intersection classification. The RBCO model is modeled by combining the rider optimization algorithm (ROA) with Border Collie optimization (BCO). Here, Fresnel Transform (FrT) is employed to detect the keyframes from the video based on the angular distance. DCNN classifier is used for classifying the intersection of road segments by considering the optimal region extracted from the segmented frames. The training of the DCNN classifier is accomplished by the RBCO algorithm. The developed RBCO-based DCNN achieved higher performance with the metrics, like accuracy, training error, precision, and recall is 94.51%, 5.49%, 96.43%, and 95.29%, respectively, by considering vehicles and traffic scenarios.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Intersection classification Lane detection Deep Convolutional Neural Network Border Collie optimization rider optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Image tampering detection in image forensics using earthworm-rider optimization

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第26期34卷

作者： Cristin, Rajan Premnath, Sasi Padma Ananth, John Patrick GMR Inst Technol Rajam Dept Comp Sci & Engn Rajam Andhra Pradesh India Sri Krishna Coll Engn & Technol Coimbatore Coimbatore Tamil Nadu India

This article introduces a novel earthworm-rider optimization algorithm (EW-ROA), by integrating the earthworm algorithm in the rider optimization algorithm for the detection of the tampered regions optimally. Initially, the individual binary maps are generated using the image blocks of the input image are fed to five forensic tools, and then they are concatenated into a single binary map. The features are extracted from the binary map moreover they are feed to classifier for the detection of the tampered image via the deep belief neural network, which is trained using the proposed earthworm-rider optimization algorithm. The proposed system attains a highest accuracy of 0.9402, sensitivity of 0.98, and specificity of 0.98 that shows the superiority of the proposed system in effective tampering detection.

关键词： earthworm algorithm forensic tools image forgery rider optimization algorithm tampering detection

来源：评论

学校读者我要写书评

暂无评论

rider Manta Ray Foraging optimization-based Generative Adversarial Network and CNN feature for detecting glaucoma

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期73卷 103425-103425页

作者： Jain, Supiksha Indora, Sanjeev Atal, Dinesh Kumar DeenbandhuChhotu Ram Univ Sci & Technol Dept Comp Sci & Engn Sonipat Haryana India DeenbandhuChhotu Ram Univ Sci & Technol Dept Biomed Engn Sonipat Haryana India

Glaucoma is a leading and rapidly increasing disease in recent years, which affects the eye's optic nerve and causes everlasting vision loss. The early recognition of glaucoma is a significant one for decreasing the risk of permanent loss of sight. Therefore, it is necessary to detect glaucoma disease in the initial stage. Moreover, optic cup segmentation from retinal fundus images is an essential procedure for automatic glaucoma detection. In this paper, an efficient glaucoma detection approach is developed using rider Manta-Ray Foraging optimization based General Adversarial Network ((rider MRFO-based GAN). Here, the optic disc segmentation process is performed by the Fuzzy Local Information C-Means clustering (FLICM clustering). In addition, the sparking process is also employed in this glaucoma detection method for the blood vessel detection process. Several significant features in this detection model, namely mean, standard deviation, variance, kurtosis, skewness, entropy, and CNN features, are extracted to detect glaucomatous images further. The developed rider MRFO approach is newly developed by the MRFO technique and rider optimization algorithm (ROA). Additionally, the developed glaucoma detection technique performs better based on various parameters, like specificity, sensitivity, and accuracy. Hence, the developed rider MRFO-based GAN model showed improved results with the highest accuracy of 0.96, the sensitivity of 0.94, and the specificity of 0.89.

关键词： Glaucoma General Adversarial Network rider optimization algorithm CNN feature

来源：评论

学校读者我要写书评

暂无评论

Modified rider optimization-Based V Channel Magnification for Enhanced Video Super Resolution

引用

INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2021年第1期21卷 2150003-2150003页

作者： Jagdale, Rohita H. Shah, Sanjeevani K. Maharashtra Inst Technol Pune Maharashtra India Savitribai Phule Pune Univ Pune Maharashtra India Smt Kashibai Navale Coll Engn Pune Maharashtra India

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the rider optimization algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.

关键词： Video super resolution rider optimization algorithm RGB V-channel magnification HSV

来源：评论

学校读者我要写书评

暂无评论

Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment

引用

VISUAL COMPUTER 2021年第8期37卷 2369-2390页

作者： Vinolin, V. Sucharitha, M. Noorul Islam Ctr Higher Educ Dept Elect & Commun Engn Kanyakumari Tamil Nadu India Malla Reddy Coll Engn & Technol Dept Elect & Commun Engn Medchal Telangana India

Video forgery detection is one of the challenges in this digital era, where the focus is on discovering authenticity. Though there are so many methods available to detect forgeries in the video, there is no method that utilizes illumination-based forgery detection. Hence, this research focuses on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in the video. On the other hand, this paper proposes dual adaptive-Taylor-rider optimization algorithm-based deep convolutional neural network (DA-Taylor-ROA-based DCNN) for video forgery detection, where DCNN is trained using the dual adaptive-Taylor-rider optimization algorithm (DA-TROA) that inherits the adaptive concept and Taylor series within the standard rider optimization algorithm (ROA). For the detection process, the distance-based features from the light coefficients and face objects detected using the Viola-Jones algorithm from the video frames are used. The significance of the method is analyzed using the real images for varying noise conditions based on the performance metrics, such as accuracy, true positive rate, and true negative rate. The percentage improvement of accuracy for proposed DA-Taylor-ROA-based DCNN with respect to Taylor-ROA-Based deep CNN is 4.3626% in the absence of noise, and 1.5985% of accuracy improvement in the presence of speckle noise, respectively.

关键词： Video forgery detection Deep learning network rider optimization algorithm Taylor series Spliced images

来源：评论

学校读者我要写书评

暂无评论

A secure and efficient authentication and multimedia data sharing approach in IoT-healthcare

引用

IMAGING SCIENCE JOURNAL 2023年第3期71卷 277-298页

作者： Yempally, Sangeetha Singh, Sanjay Kumar Sarveshwaran, Velliangiri Amity Univ Rajasthan Dept Elect & Commun Engn Jaipur India SRM Inst Sci & Technol Dept Computat Intelligence Chennai India

Currently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data;therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the rider Horse Herd optimization algorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the rider optimization algorithm (ROA) and Horse herd optimization algorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.

关键词： Internet of things blockchain healthcare authentication multimedia data sharing rider optimization algorithm Horse herd optimization algorithm rider Horse herd optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Taylor-Based Least Square Estimation in MIMO-OFDM Systems for Multimedia Applications

引用

WIRELESS PERSONAL COMMUNICATIONS 2021年第1期120卷 609-631页

作者： Raut, Shital N. Jalnekar, Rajesh M. Vishwakarma Inst Technol Pune 411037 Maharashtra India

Recent mobile telecommunication systems are using multiple-input multiple-output system (MIMO) collective with the orthogonal frequency division multiplexing (OFDM), which is well-known as MIMO-OFDM, to offer robustness and higher spectrum efficiency. The most important challenge in this scenario is to achieve an accurate channel estimation to identify the information symbols once the receiver must have the channel state information to balance and process the received signal. Hence, an effective technique is introduced by proposing the Taylor-least square error algorithm (TLSE) to improve the performance of the MIMO-OFDM system in multimedia applications. In addition, the user admission control is done in multi user-MIMO (MU-MIMO) system using the priority-based scheduling based on Dolphin-rider optimization (DRO) algorithm that is integrated within the space-time block code (STBC) STBC-MIMO-OFDM system for efficient power allocation to ensure the energy efficiency. The DRO is the integration of rider optimization algorithm (ROA) and Dolphin Echolocation (DE). Here, channel estimation is done using the novel optimization algorithm, termed TLSE, which is designed by modifying LSE with the Taylor series. Moreover, the fitness parameters, such as power, priority, throughput, and Proportionally Fair, are computed. The experimentation is conducted in different fading environments with three modulation schemes, binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM) with the performance metrics, namely bit error rate (BER) and throughput. The developed TLSE + DRO (QAM) outperformed other methods with minimal BER of 0.0001 based on channel-2 and maximal throughput of 0.9965 with respect to channel-1.

关键词： Channel estimation MIMO-OFDM system Taylor series rider optimization algorithm Least square error algorithm

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：