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

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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

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An Approach for Fake News Detection in Social Media Using Hybrid Classifier

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CYBERNETICS AND SYSTEMS 2024年第4期55卷 894-917页

作者： Kanagavalli, N. Baghavathi Priya, S. Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai India Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai 600124 India

The users of social networks get ruined by fake news, which brings a vast effect on the offline community. A very significant aim is to improve the trust of data in online social networks by discovering fake news in an appropriately timed manner. A novel technique for identifying fake news on social media has been developed as a result. The feature extraction module receives the preprocessed output after the initial preprocessing stage on the news story. The extracted features are then analyzed using the generative adversarial network (GAN) and neural network (NN) to discover fake scores. Here, the rider shuffled shepherd optimization (RSSO), which was developed by integrating the rider optimization algorithm and the shuffled shepherd optimization algorithm, is used to train the NN classifier. Data labeling is considered an expensive task. GANs are unsupervised, so labeling of data is not required to train them. The score obtained from GAN and NN are utilized for computing the final score, which is evaluated using the weighted average model for detecting fake news. With the highest accuracy of 91.2%, the highest sensitivity of 92.1%, and the highest specificity of 88.8%, the proposed RSSO-based NN + GAN provided improved performance.

关键词： Fake detection generative adversarial network neural network rider optimization algorithm shuffled shepherd optimization algorithm

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Predicting the settlement of pile based on a hybrid form of the model by considering Least Square Support Vector Regression

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 529-542页

作者： Chen, Qiang Chongqing Vocat Coll Transportat Sch Marxism Chongqing 402247 Peoples R China

Understanding the vertical settlement is crucial when designing the pile and foundation type used in real estate, specifically regarding the pile settlement (Sp). This issue is of utmost importance due to the numerous variables involved in designing piles that penetrate rock. Despite multiple efforts, clear and precise theoretical explanations regarding the interactions between soil and piles are currently unclear. As a result, many studies have opted to employ artificial intelligence techniques for determining the subsidence rate of piles over time under different loading conditions. This study presents a machine learning (ML) that effectively predicts the values of Sp, namely Least Square Support Vector Regression (LSSVR). In addition, the proposed model coupled with three meta-heuristic algorithms, including the Flow Direction algorithm (FDA), Chimp optimization algorithm (ChOA), and rider optimization algorithm (ROA), to improve the performance and obtain the optimal results as a framework of hybrid. As a result, LSFD determined the most suitable effects with R2 and RMSE values equal to 0.2503 and 0.9952, respectively. Overall, using LSSVR with FDA, ChOA, and ROA can improve the accuracy and robustness of the model in predicting pile settlement, making it a valuable tool for geotechnical engineers designing foundation systems.

关键词： Pile settlement Least Square support vector regression Flow direction algorithm Chimp optimization algorithm rider optimization algorithm

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

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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

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rider and Sunflower optimization-driven neural network for image classification

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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

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Big Data Classification in IOT Healthcare Application Using Optimal Deep Learning

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2023年第1期17卷 33-58页

作者： Akhtar, Md Mobin Ahamad, Danish Shatat, Abdallah Saleh Ali Shatat, Ahmad Saleh Ali Riyadh Elm Univ Dept Basic Sci Riyadh Saudi Arabia Atheeb Intergraph Saudi Co AISC Riyadh Saudi Arabia Appl Sci Univ Coll Adm Sci Dept Management Informat Syst Eker Bahrain Appl Sci Univ Coll Adm Sci MIS Dept Eker Bahrain

The Internet of Things (IoT) system is composed of several numbers of sensor nodes and systems, which are wirelessly interlinked to the internet. Generally, big data is the storage of a huge amount of information, which causes the classification process to be very challenging. Numerous big data classification approaches are implemented, but the computational time and secure handling of information are the major problems. The aim of the study is the development of big data approach in Internet of Things (IoT) healthcare application. Hence, this paper presents the proposed Dragonfly rider Competitive Swarm optimization-based Deep Residual Network (DRCSO-based DRN) for big data classification in IoT. First, the IoT nodes are simulated, and the heart disease patient data are collected through sensors. The routing is done using the Multi-objective Fractional Gravitational Search algorithm (Multi-objective FGSA). In the Base Station (BS), the big data classification is done. Here, the classification is done using MapReduce (MR) framework, which includes two phases, like mapper and the reducer phase. The input data is initially fed to the mapper phase in the map-reduce (MR) framework. In the mapper phase, feature selection is carried out based on Dragonfly rider optimization algorithm (DROA) in order to select the appropriate features for further processing. The DROA is modeled through merging Dragonfly algorithm (DA) and rider optimization algorithm (ROA). In the reducer phase, the classification is performed using DRN, which is trained by the developed DRCSO algorithm. The DRCSO is modeled by incorporating DA, ROA and Competitive Swarm optimization (CSO). In addition, the performance of the developed method is outperformed than the existing approaches such as Linguistic Fuzzy Rules with Canopy Mapreduce (LFR-CM) + Fuzzy classifier, Machine learning-dependent k-nearest neighbors (FML-KNN), MapReduce-Fuzzy Integral-dependent Ensemble Learning Model+Single hidden layer feed

关键词： Mapreduce framework Dragonfly algorithm competitive swarm optimization deep residual network rider optimization algorithm

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Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment

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DATA TECHNOLOGIES AND APPLICATIONS 2022年第1期56卷 103-131页

作者： Vinolin, V. Sucharitha, M. Noorul Islam Ctr Higher Educ Thuckalay India Malla Reddy Coll Engn & Technol Maisammaguda India

Purpose With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images. Design/methodology/approach This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola-Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images. Findings Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy. Originality/value The Taylor-ROA is developed by integra

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

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

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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

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Image tampering detection in image forensics using earthworm-rider optimization

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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

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

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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

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