Rider Optimization Research Articles

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Hybrid Long Short-Term Memory Fused Convolution Neural Network for Weather Forecasting

Weather forecasting is an effort by meteorologists to predict whether states at a few prospective times and the conditions of weather that might be estimated. With new modern technology, present weather forecasting methods are highly precise. To achieve high accuracy, the methods developed for weather forecasting were much more complicated owing to many factors. Here, the usage of time series data for weather forecasting is done by the devised Long-Short Term Memory fused Convolutional neural network (LSTMFCNN). At first, the acquisition of input time series data from the specific dataset is done. In The feature extraction technical features are done by considering the input time series data. Then, the feature extraction is done utilizing the Rider Optimization Algorithm-Based Neural Network (RideNN) with the Soergel metric. RideNN is the integration of the Rider Optimization Algorithm (ROA) with the Neural Network (NN) classifier. Thus, the feature fusion step reduces the complexity and improves the accuracy. Thereafter, the oversampling technique is utilized for the data augmentation (DA) process. Finally, weather forecasting is done utilizing the newly designed LSTMFCNN and is obtained by the integration of Convolutional Neural Network (CNN) and Deep Long-Short Term Memory (DLSTM).

MOTOR IMAGERY SIGNAL CLASSIFICATION FOR BRAIN–COMPUTER INTERFACE USING RideNN WITH HOLO-ENTROPY FEATURES

The brain–computer interface (BCI) database’s motor assessment depends heavily on the motor imagery (MI) signal classification. By examining the multiple patterns of different creative tasks in the electroencephalogram (EEG) signals, the intention of humans is translated into computer-based commands in the MI-based signals of BCI data. Nevertheless, low accuracy and efficiency are issues with MI–EEG signals’ classification because of the low signal-to-noise ratio, huge individual differences, overall volatility, and complexity in the signal. To overcome these problems, this research proposes a rider optimization algorithm-based neural network (ROA-based NN) to classify the MI signals effectively. Pre-processing is done after collecting the dataset of raw EEG signals. The suitable electrodes, such as C3, C4, and Cz, are subsequently chosen from the MI signals. Using the holo-entropy-based WPD feature extractor, tunable Q-factor wavelet transform (T-QWT), and common spatial patterns in the model, the pertinent features are extracted from the chosen electrodes. The developed holo-entropy-based WPD feature examines the electrode structure’s association. As a result, the most diverse signals are removed from the chosen electrodes before being input into the proposed RideNN classifier, where the proposed ride optimization algorithm optimizes classification performance and correctly predicts and classes the output from the MI signals that have been analyzed. The developed RideNN classifier recognizes the patterns more accurately processes more data and tackles the noise and incomplete data effectively. Utilizing the parameters of accuracy, sensitivity, and specificity, the results are evaluated. The PROA-based RideNN and the combined features obtain the maximum accuracy of 92.24%, sensitivity of 92.26%, and specificity of 92.14% for the BCI competition-IV 2a database. The qPROA-based RideNN and the combined features obtain the maximum accuracy of 92.11%, sensitivity of 91.98%, and specificity of 92.35% for the BCI competition-IV 2b database.

A Hybrid Rider Optimization with Deep Learning Driven Intrusion Detection Farmwork in Wireless Sensor Network

Introduction: an array of hazards currently exists in cyberspace, prompting extensive research to tackle these concerns. Intrusion Detection Systems (IDS) are a mechanism used to provide security in Wireless Sensor Networks (WSN). The IDS continue to encounter significant challenges in accurately identifying unknown attacks. Conventional Intrusion Detection Systems (IDS) commonly rely on Deep Learning (DL) algorithms, which utilise binary classifiers to classify attacks. The data dimension attribute is affected inside large-scale high-dimensional data sets. Methods: this research introduces a hybrid GFSO (HGFSO) model combined with Deep Learning Driven Intrusion Detection (HGFSO-DLIDS) to tackle this problem. The HGFSO approach is developed by merging the parameter selection methods of the Felis Margarita Swarm Optimisation (FMSO), the Grampus optimisation algorithm (GOA), and the Deep Convolutional Neural Network (DCNN) with BiLSTM (Bidirectional Long Short-Term Memory) algorithm. Results: the model training utilised real-time traffic statistics, including the KDDCup 99 and WSN-DS datasets. After being trained and validated using the datasets, the model’s performance is assessed by multi-class classification, achieving accuracy rates of 99,89 % and 99,64 % respectively. Conclusion: as a result, this occurrence leads to a decrease in the overall effectiveness of detecting assaults. Deep learning may enhance the creation of an intrusion detection system by eliminating complex features in the raw data, resulting in a more precise classification method.

Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm.

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the "STrutred Analysis of the REtina (STARE) Database" accessed on Oct. 2020 (https://cecas.clemson.edu/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

Secure medical data on cloud storage via DNA homomorphic encryption technique

BackgroundCloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. MethodsHowever, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. ResultsThe efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10 %, 20 %, and 35 % than OHE, HE and ECC algorithms.

Hybrid trust-based optimized virtual machine migration for dynamic load balancing and replica management in heterogeneous cloud

Cloud computing is an upcoming technology that has garnered interest from academic as well as commercial domains. Cloud offers the advantage of providing huge computing capability as well as resources that are positioned at multiple locations irrespective of time or location of the user. Cloud utilizes the concept of virtualization to dispatch the multiple tasks encountered simultaneously to the server. However, allocation of tasks to the heterogeneous servers requires that the load is balanced among the servers. To address this issue, a trust based dynamic load balancing algorithm in distributed file system is proposed. Load balancing is performed by predicting the loads in the physical machine with the help of the Rider optimization algorithm-based Neural Network (RideNN). Further, load balancing is carried out using the proposed Fractional Social Deer Optimization (FSDO) algorithm, where the virtual machine migration is performed based on the load condition in the physical machine. Later, replica management is accomplished for managing the replica in distributed file system with the help of the devised FSDO algorithm. Moreover, the proposed FSDO based dynamic load balancing algorithm is evaluated for its performance based on parameters, like predicted load, prediction error, trust, cost and energy consumption with values 0.051, 0.723, 0.390 and 0.431J correspondingly.

Performance Analysis of Routing Protocol Using Trust-Based Hybrid FCRO-AEPO Optimization Techniques

Mobile Ad hoc Networks (MANETs) offer numerous benefits and have been used in different applications. MANETs are dynamic peer-to-peer networks that use multi-hop data transfer without the need for-existing infrastructure. Due to their nature, for secure communication of mobile nodes, they need unique security requirements in MANET. In this work, a Hybrid Firefly Cyclic Rider Optimization (FCRO) algorithm is proposed for Cluster Head (CH) selection, it efficiently selects the CH and improves the network efficiency. The Ridge Regression Classification algorithm is presented in this work to sense the malicious nodes in the network and the data is transmitted using trusted Mobile nodes for the QoS enactment metric improvement. A trust-based routing protocol (TBRP) is introduced utilizing the Atom Emperor Penguin Optimization (AEPO) algorithm, it identifies the best-forwarded path to moderate the routing overhead problem in MANET. The planned method is implemented using Matlab software and the presentation metrics are packet delivers ratio, packet loss ratio (PLR), routing overhead, throughput, end-to-end delay (E2ED), transmission delay, energy consumption and network lifetime. The suggested AEPO algorithm is compared with the prevailing PSO-GA, TID-CMGR, and MFFA. The AEPO algorithm’s performance is approximately 1.5%, 3.2%, 2%, 3%, and 4% higher than the existing methods for PLR, packet delivers ratio, throughput, and E2ED and network lifetime. The sender nodes can increase their information transmission rates and reduce delays in appreciation of this evaluation. Additionally, the suggested technique has a perfect benefit in terms of demonstrating the genuine contribution of distinct nodes to trust evaluation (TE).

Optimal Allocation of Hybrid Renewable Distributed Generation with Battery Energy Storage System Using MOEA/D-DRA Algorithm

The need for energy and environmental sustainability drives the adoption of renewable energy systems to reduce greenhouse gas emissions. Efficient planning of energy sources is crucial for a reliable distribution network. This article solves the optimal allocation of hybrid energy sources (i.e., Renewable Distribution Generators (RDGs) and Battery Energy Storage System (BESS)) problem, aiming to minimize the annual energy loss and investment costs simultaneously. The proposed formulation includes the uncertainties such as forced outages of RDG’s, intermittence in RDG power generation, Expected Energy Not Served (EENS) by DG units, and time-varying demand altogether in the existing formulation. To evaluate the effectiveness of the proposed formulation, a Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation (MOEA/D-DRA) is used. Simulation studies are conducted on IEEE 33-node and TNEB 84-node Radial Distribution Systems (RDSs), comparing results with the Rider Optimization Algorithm (ROA) and Hybrid Nelder Mead-Particle Swarm Optimization (HNMPSO) respectively. The proposed formulation for a Multi-objective Optimization of Hybrid Energy Sources allocation problem solved by the MOEA/D-DRA algorithm provides improved benefits like minimum annual energy loss, investment cost, CO2 emission, EENS by the DG units, and enhanced system voltage stability and voltage profile.

Feature optimization in CNN using MROA for disease classification

Electronic Medical Records (EMR) carry important information about a patient’s journey. The past decade shows substantial use of Natural Language Processing (NLP)-based Information Retrieval (IR) techniques to extract insights such as symptoms, diseases, and tests from these unstructured records. The state-of-the-art shows that convolutional neural networks (CNN) make a significant contribution to the disease classification task.A significant improvement in precise knowledge mining is possible with precise feature extraction. Feature selection addresses undesirable, unneeded, or irrelevant features. This article proposes a Modified Rider Optimization Algorithm (MROA) to choose important features by selecting optimal weights from a pool of randomly generated weights based on high accuracy and less training time in the CNN algorithm. A modified approach is trained on 114 N2C2 patients’ records to extract symptoms, disease, and tests are performed on them to perform disease classification tasks. The proposed approach is found to be accurate, with 97.77% accuracy in the disease classification and treatment prediction task from EMR.

Rider Water Wave-enabled deep learning for disease detection in rice plant

Rice diseases have degraded the production of the rice plant, which produces economic loss. To control and minimize the effects of attacks, the diseases are required to be recognized at a premature stage. Premature detection of infections can improve the yield from quantitative as well as qualitative losses, diminish the usage of pesticides, and improve the economic growth of the country. Hence, this paper devises a new method, namely Rider Water Wave-based neural network (RWW-NN) for finding the disease in the rice plant, where the training of NN is completed using the RWW, which is formed by assimilating Rider Optimization algorithm (ROA) and Water wave optimization (WWO). Initially, the pre-processing is done by using histogram equalization from the input image. Then, the segmentation is completed using Segmentation Network (SegNet), and then the CNN features are employed for feature mining in order to acquire the optimal features for disease recognition. These features are fed to NN for disease detection wherein the RWW is introduced for training the optimal weights. The RWW-based NN acquires greatest accuracy of 0.908, F-measure of 0.907, sensitivity of 0.862, and specificity of 0.947 based on K-value using Rice disease dataset.

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

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

ABSTRACT 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.

Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade

Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.

Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm

SummaryThe Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self‐adaptive meta‐heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self‐adaptive coyote optimization algorithm (SA‐COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA‐COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA‐COA. From the simulation findings, the best value of the designed SA‐COA‐LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self‐adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C‐ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.

Intelligent Spectrum Sharing and Sensing in Cognitive Radio Network by Using AROA (Adaptive Rider Optimization Algorithm)

Wireless spectrum has been allocated to licensees for large geographic areas on a long-term basis in recent years. Cognitive Radio Networks (CRN) will offer mobile users with a huge amount of available bandwidth. Due to spectrum management issues such as spectrum sensing and sharing, CRN networks pose some challenges. Hence in this paper, Adaptive Rider Optimization (AROA) is developed to improve the energy efficiency for different spectrum sensing conditions. The proposed algorithm is utilized to compute the sensing time, sequence length, and detection threshold. In order to detect the spectrum with optimal values of transmission power and sensing bandwidth, the AROA uses the adaptive threshold detection method. The spectrum sensing and sharing of the CRN network are achieved with the help of the AROA algorithm. The proposed method is implemented in MATLAB and the performances such as Normalized Energy consumption, delay, SNR, Jitter, blocking probability, convergence analysis, and Throughput are evaluated. The proposed method is contrasted with the existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), respectively.

An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection

Vehicular Ad-hoc Network (VANET) offers expedient services in intellectual transportation models but is susceptible to several attacks. The Intrusion detection systems (IDSs) help to prevent security risks by discovering irregular network behaviours under local sub-networks inspite of complete VANET. An optimization-driven model is devised for detecting intrusion in SDN-VANET. The network comprises several entities, like the On-Board Unit (OBU), SDN, Road-Side Unit (RSU), and Authentication Server. The proposed approach involves five phases, namely registration, key generation, data encoding, authentication, and decoding. The authentication of OBU is done with the data packets. The detection of intrusion is performed using the optimized model. The selection of features is done from log files considering the Renyi entropy. The detection of intrusion is done using the Rider-based neural network (RideNN), trained with designed Rider-based Sea Lion Optimization (RBSLO), and is produced by blending the Rider optimization algorithm (ROA) and Sea Lion Optimization (SLnO). The proposed RBSLO-based RideNN offered to improve the achievement with the elevated precision of 92.5%, the recall of 95.4%, the F-measures of 94%, and the smallest computation time of 135.654 s.

Deep Learning-based Prioritized Packet Classification and Optimal Route Selection in Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks suffer network congestion due to increased traffic leading to data packet loss and increased energy consumption. Thus, it is essential to have a mechanism for priority-based packet classification to improve the performance and decrease the delay in real-time packets. This paper introduces priority-based packet classification using a word embedding mechanism to extract packet semantics and classification using the long short-term memory model that works based on the different characteristics of the packet header to decide the priority of each packet. Furthermore presents a hybrid meta-heuristic algorithm termed butterfly-based rider optimization algorithm, which considers the packet priority and other parameters in optimal route selection. This hybrid meta-heuristic algorithm is developed by merging rider optimization algorithm, and butterfly optimization algorithm. Finally, the experimental results validate the analytical result as well as the performance comparison between the proposed optimal route selection model and the conventional models.

Blood vessel detection using enhanced DeepJoint fuzzy clustering algorithm with deep Maxout network for glaucoma detection

SummaryGlaucoma became a leading reason for losing vision as it progresses in a gradual manner without any symptoms. The discovery of glaucoma at an earlier phase is imperative as it can help to accelerate the progress. The fundus images are an option for screening glaucoma and help to enable the observation of optic disc. The classical techniques are less accurate and time‐consuming. In this way, a highly accurate automatic glaucoma diagnosis is developed in this research. The noise in the images is eliminated during pre‐processing. Additionally, the DeepJoint model and improved fuzzy clustering technique are combined to develop the proposed Enhanced DeepJoint fuzzy clustering algorithm, which is used to segment blood vessels. The optic disc is additionally divided using black hole entropic fuzzy clustering (BHEFC). In order to identify glaucoma, the acquired segments are fed into a deep Maxout network that has been trained using the multi‐verse water rider optimization (MVRWO) algorithm. By combining the rider optimization algorithm, multi‐verse optimizer, and water wave optimization (WWO), the MVRWO is produced (ROA). The developed model has the greatest accuracy, sensitivity, and specificity scores, coming in at 93.91%, 95.61%, and 92.57% respectively.

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication