Rider Optimization Algorithm Research Articles

Blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered‐based energy efficient routing protocol in wireless sensor network

AbstractIn this manuscript, a blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered based energy efficient routing protocol in wireless sensor network (WSN) is proposed for secure data transmission by finding the optimum cluster head (CH) in the network. Here, MayflyWater Wave Optimization (MF‐WWO) algorithm is used for CH selection accurately. After the selection of CH, the malicious node occurs in the cluster. Hence, the multi objective whale optimization algorithm is proposed to find the trust path from the several paths. At last, the optimized selected trust paths are given to the blockchain for communications in the network that is safe and more trustworthy. The simulation is done in MATLAB. The proposed MF‐WWO‐WO achieves high throughput, high efficiency, high network life time, high packet delivery ratio, low delay, low energy consumption, and low packet loss ratio. The outcomes shows the better performance of the proposed approach when compared to the existing approaches, like blockchain and clustered based secured data aware energy efficient using fitness averaged rider optimization algorithm FA‐ROA blockchain and clustered based secured data aware energy efficient using trusted moth flame optimization and genetic optimization algorithm TMFO‐GOA. Finally, the proposed approach attains better energy efficiency and security.

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce.

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.

Deep Recurrent Encoder Network and Spark Model for Angiographic Disease Risk Classification

Analysis and extraction of effective results from the medical big data is very complex due to the existence of large volume of data and it is also very difficult to classify the risk of angiographic diseases. The challenges faced by the conventional methods are complexity in classifying the data and performance degradation for a large-sized dataset. Hence, a hybrid approach named Deep Recurrent Encoder Network and Spark Model for Angiographic Disease Risk Classification (DRE-NET) is developed in this research to achieve accurate results for classifying the risk of angiographic diseases using Spark architecture. The developed Adaptive RCOA integrates Rider Optimization Algorithm (ROA) and Chicken Swarm Optimization (CSO) with the adaptive concept. The disease risk classification process is obtained by the Recurrent Neural Network (RNN) and Deep Stacked Autoencoder (DSAE) in such a way that the weights are optimally trained by the developed Adaptive RCOA. The optimal solution is obtained by evaluating the fitness function such that the fitness with minimal error value is considered the best solution. Moreover, the developed Adaptive RCOA-based RNN+DSAE attained better result with the metrics such as specificity, accuracy, and sensitivity with the values of 100%, 97.69%, and 99.19%, respectively, using the KDD Cup dataset.

Glaucoma detection using hybrid architecture based on optimal deep neuro fuzzy network

Glaucoma represents dangerous ailment, which affected the nervous model and causes loss of vision. Several researchers developed automated discovery of glaucoma, but redundancy elimination is still challenging. Hence, this research study introduces an effective method for detecting glaucoma with deep neurofuzzy network (DNFN). Initially, the retinal image is input for preprocessing to remove the noises. Then, the optic disc (OD) detection and blood vessel segmentation are employed using the blackhole entropy fuzzy clustering algorithm and the DeepJoint model, respectively. Finally, the obtained OD and blood vessels are fed to the DNFN, wherein DNFN training is performed with newly devised MultiVerse Rider Wave Optimization (MVRWO). The newly developed MVRWO integrates the Water Wave Optimization, Rider Optimization Algorithm, and MultiVerse Optimizer. Finally, the output is classified based on the loss function of the DNFN. The developed MVRWO–DNFN obtained an elevated accuracy of 92.214%, a sensitivity of 93.422%, and a specificity of 92.34%.

Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Currently, medical data clustering is a very active and effective part of the research area to take proper decisions at the medical field from medical data sets. But medical data clustering is a very challenging issue due to limitless receiving data, vast size, and high frequencies. To achieve this and improve the performance with fast and effective clustering, this paper proposes a hybrid optimization technique, namely, the K-means-based rider sunflower optimization (RSFO) algorithm for medical data. In this research, initially, the data preprocessing phase has been carried out to clean the current input medical data, and then in the second phase, important features are chosen with the help of the Tversky index with holoentropy. Finally, medical data clustering has been carried out by using hybrid K-means-based rider sunflower optimization (RSFO) algorithm. RSFO is designed to produce optimum clustering centroid, which is the combination of two optimization techniques, such as rider optimization algorithm (ROA) and sunflower optimization (SFO). This hybrid algorithm can get the advantages of both K-means and RSFO technique and avoid premature convergence of K-means algorithm and high computation cost of optimization technique. K-Means clustering algorithm is used to cluster the medical data by using an optimum centroid. The efficiency of the proposed K-means-based rider sunflower optimization algorithm is examined with a heart disease data set and analyzed based on three different performance metrics.

Enhancement of energy efficient distribution generation integrated with solid state transformer using improved rider optimization algorithm

ABSTRACT Nowadays, the usage of the latest technologies and pure energies like photovoltaic energy, wind energy, and electrical vehicles are increasing in demand to minimise environmental pollution. Hence, to hold this kind of technologies, the distribution systems in the future have to be converted to AC–DC hybrid for considering all kinds of DGs and (DC and AC) of loads. This paper attempts to develop an integration of DG with AC–DC hybrid distribution systems connected with solid state transformer (SST). The main intention of this paper is to make an energy-efficient system, by enhancing the voltage profile, reducing greenhouse gas emissions and reducing real and reactive power losses, when DG with SST is connected to a hybrid distribution system. With these constraints, a multi-objective function is formulated, which is solved by a new improved meta-heuristic algorithm in optimal location and sizing of DG. The new algorithm adopted in this paper is the improved Rider Optimisation Algorithm (ROA) termed as Fitness Rate-based ROA (FR-ROA). The experimental analysis on single and multi-objective functions proves the efficacy of SST in providing an energy-efficient system model.

Trust-based secure routing in IoT network based on rider foraging optimization algorithm

Due to the mobility and frequent topology changes, routing the data poses a challenging issue in the Internet of Things (IoT). Security poses a fundamental requirement factor in most IoT applications particularly, secure routing among the IoT nodes. The aim of this paper is to develop secure routing in IoT with minimal delay, minimal energy consumption and high throughput. Thus, a new algorithm, named Rider Foraging Optimization algorithm (RFO) is designed by the incorporation of standard Rider Optimization algorithm (ROA) and Bacterial Foraging optimization algorithm (BFO) that render an optimal solution, which is the optimal path for transmitting the information. Initially, the trust of the nodes is evaluated and the cluster head (CH) is chosen based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol to securely transmit the data. Then, the proposed RFO algorithm selects the optimal path using the fitness factors, such as trust, energy, and delay constraints. The results revealed that the energy consumption of the nodes is reduced and the lifetime of the network is enhanced through the implementation of the proposed RFO, which attained a maximal throughput of 1, and minimal delay and energy consumption of 0.1169 and 0.0002, respectively.

Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

In Mobile Adhoc Networks, a Trustworithness of Data Transmission Using HTCMR

Mobile Ad hoc Network is a self-configured network, the main responsibility faced in the network is security. Malicious nodes are mostly to blame for the insecurity of MANET. The malicious attacker nodes will disrupt the overall functioning of the network, and many security solution approaches are utilized for safe and reliable communication in MANET to stop such harmful activity. This routing, known as Hybrid Trust Cluster based Multiple Routing (H-TCMR), helps to minimize the amount of messages carried inside the network, lowering the network's total energy usage. It utilizes Hybrid Time Division Multiple Access (HTDMA) for time slots and the Unified Slot Assignment Protocol (USAP) for cluster allocation. Using Hybrid Rider Optimization Algorithm (HROA) approaches, a group of riders will drive together to a specified location in order to obtain the cluster head slot. The suggested approach encrypts the data before sending it to its final destination. It also helps to extend the life of mobile networks. Some of the factors considered in simulation studies include connection, energy, average delay, throughput, as well as overall cluster quality. The simulation result demonstrates a performance study of HTCMR's dependable security, as well as increased system efficiency and improved data transfer security.

OPTIMAL SWITCHING LOSS REDUCTION IN THREE-PHASE VSI USING THRESHOLDED RIDER OPTIMIZATION ALGORITHM

This paper intends to develop a variable switching frequency system for switching loss minimization in a three-phase voltage source inverter. Switching losses are associated with the present value in switching instants, device voltage, and the number of commutations. Here, the number of commutations is minimized by varying the switching frequency over the fundamental period. The reduced switching loss is attained by optimizing the modulation index (MI) and reference angle. Thus, optimal switching patterns are generated with the optimized constraints, where the average value of switching losses over an entire period is determined, which has to be reduced. Accordingly, here, the optimization of MI and reference angle is carried out by thresholded rider optimization algorithm, which is the modification of the rider optimization algorithm. Finally, the experimental analysis is carried out for MI and a reference angle with respect to the switching loss parameter and total harmonic distortion.

PROPOSING NEW ROUTING PROTOCOL BASED ON CHAOS ALGORITHM

In MANET, the multicast routing is considered as a non-deterministic polynomial (NP) complexity, it contains assorted objectives and restrictions. In the multicast issue of MANET, the Quality of services (QoS) based upon cost, delay, jitter, bandwidth, these are constantly deemed as multi-objective for intending multiple cast routing protocols. Conversely, mobile node has finite battery energy and the lifetime of network depending on its mobile node battery energy. If the mobile node has high battery energy consumption, it automatically reduces the network’s life time because of their route breaks. Alternatively, node’s battery energy has to be consumed to ensure higher level quality of services in multicast routing for transmitting the accurate data anywhere and anytime. Therefore, the network lifespan is deemed as multi-objective in the multicast routing (MR) problem. To overcome these issues, QoS and Network Lifetime Aware Reliable Multicast Routing Protocol are proposed by applying Chaos integrated Cuckoo Search Rider Optimization Algorithm for effectual data transmission in MANET (QOS-MRP-CSROA-MANET). The proposed method is the joint execution of both the Cuckoo Search Algorithm with chaos theory (Chaotic-CSA) and Rider Optimization (ROA) algorithm and hence it is called as Chaotic-CSA-ROA, which is utilized to solve the MR problem of MANET. Here, the MR problem of MANET have five objectives, viz cost, delay, jitter, bandwidth, network lifetime are optimized with the help of Chaotic-CSA-ROA. Then the proposed method is simulated in NS2 simulator for validating the performance of the proposed QOS-MRP-CSROA-MANET system. Here, evaluation metrics, via delay, delivery ratio, drop, Network lifespan, overhead and throughput are analyzed with node, rate and speed. The proposed QOS-MRP-CSROA-MANET provide higher throughput in node as 32.9496% and 65.5839%, higher throughput in rate as 16.6049% and 30.4654%, higher through put in speed as 10.1298% and 7.0825%, low packet drop in node as 63.7313% and 52.2255%, low packet drop in rate as 51.5528% and 25.6220%, low packet drop in speed as 18.0857% and 24.5953% compared with existing methods, like QOS aware of multicast routing protocol using particle swarm optimization algorithm in MANET (QOS-MRP-PSOA-MANET) and QOS aware of multicast routing protocol using genetic algorithm in MANET (QOS-MRP-GA-MANET) respectively.

Brain Tumor Classification using the Probabilistic-Deformable Fuzzy system and RideNN

In the medical field, brain tumor classification is a very significant treatment process. The manual segmentation and classification of brain tumors is a tedious and time-consuming process. Hence, automatic classification and segmentation methods are very important. This paper presents brain tumor classification using a Probabilistic-deformable fuzzy system (PDFS) with RideNN. The overall procedure of the proposed approach involves four steps, such as pre- processing, segmentation, feature extraction, and classification. Initially, the input image is subjected to pre-processing. Then, the brain tumor segmentation is done using a new model called the Probabilistic-Deformable Fuzzy system. It is developed by combining a deformable model and a fuzzy system. Then the result is combined using probability-based fusion. After the segmentation, feature extraction is carried out by extracting texture-based features such as Local Gradient Pattern (LGP) and tetrolet transform. Finally, the classification is performed based on the features extracted using RideNN which employs Rider optimization Algorithm (ROA) for training the Neural network (NN). The performance of the brain tumor classification is based on PDFS-RideNN and evaluated based on sensitivity, specificity, and accuracy. The proposed PDFS-RideNN method achieves a maximal sensitivity of 93.18%, a maximal specificity of 98.67%, and a maximal accuracy of 98.25%.

An efficient automatic brain tumor classification using optimized hybrid deep neural network

A significant topic of investigation in the area of medical imaging is brain tumor classification. Since precision is significant for classification, computer vision researchers have developed several approaches, but they still struggle with poor accuracy. In this paper, an automatic optimized hybrid deep neural network (OHDNN) is suggested for brain tumors. The proposed approach consists of two phases such as pre-processing and brain tumor classification. At first, the images are composed of the data, and then the collected imageries are pre-processed by using the following steps such as image enhancement and noise removal. Then the pre-processed images are fed to the classification stage. For the classification process, in this paper, OHDNN is used. The HDNN is a combination of a convolution neural network and long short-term memory (CNN-LSTM). Here, the CNN classifier is used for feature map generation and the classification process LSTM classifier is used. Besides, to improve the performance of the CNN-LSTM classifier, the parameter extant in the classifiers is randomly selected utilizing the adaptive rider optimization (ARO) algorithm. For the experimental process, an MRI image dataset is utilized. The experimental results show proposed approach attained the maximum accuracy of 97.5.

Optimal Deep Transfer Learning Model for Histopathological Breast Cancer燙lassification

Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate. One of the commonly utilized imaging modalities for breast cancer is histopathological images. Since manual inspection of histopathological images is a challenging task, automated tools using deep learning (DL) and artificial intelligence (AI) approaches need to be designed. The latest advances of DL models help in accomplishing maximum image classification performance in several application areas. In this view, this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer (DTLRO-HCBC) technique. The proposed DTLRO-HCBC technique aims to categorize the existence of breast cancer using histopathological images. To accomplish this, the DTLRO-HCBC technique undergoes pre-processing and data augmentation to increase quantitative analysis. Then, optimal SqueezeNet model is employed for feature extractor and the hyperparameter tuning process is carried out using the Adadelta optimizer. Finally, rider optimization with deep feed forward neural network (RO-DFFNN) technique was utilized employed for breast cancer classification. The RO algorithm is applied for optimally adjusting the weight and bias values of the DFFNN technique. For demonstrating the greater performance of the DTLRO-HCBC approach, a sequence of simulations were carried out and the outcomes reported its promising performance over the current state of art approaches.

Navigation Control using Fractional Rider Optimization Algorithm for Autonomous Sailing Robots

Navigation Control using Fractional Rider Optimization Algorithm for Autonomous Sailing Robots

OA-PU Algorithm-to Enhance WSN Life Time with Cluster Head Selection

Clustering is the most popular strategy for increasing the lifetime of a wireless sensor network, which immediately leads to a stronger routing process. This process requires the processing of sensor nodes into clusters and assigning relevant cluster heads to each cluster. This paper aims to implement a new hybrid algorithm called Over taker Assisted Political Update (OA-PU) for selecting an efficient cluster head. This cluster head is selected based upon four factors, namely energy, distance, cluster radius, and time. The hybrid algorithm is a combination of the Fusion Rider Optimization Algorithm (F-ROA) as well as a nature-inspired Political Optimization Algorithm (PO). Fusion-ROA depends on groups of riders that are attempting to reach the goal and PO is a state of the art meta-heuristic optimization methodology for global and structural analysis performance prediction in the WSN environment. In terms of alive node analysis, cost function analysis, and energy analysis, the proposed OA-PU outperforms conventional approaches. Consequently, the lifetime of WSN is increased and an optimal system is developed.

Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

Internet of Things (IoT) and blockchain receive significant interest owing to their applicability in different application areas such as healthcare, finance, transportation, etc. Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks. This paper presents a new wind driven optimization algorithm based medical image encryption (WDOA-MIE) technique for blockchain enabled IoT environments. The WDOA-MIE model involves three major processes namely data collection, image encryption, optimal key generation, and data transmission. Initially, the medical images were captured from the patient using IoT devices. Then, the captured images are encrypted using signcryption technique. In addition, for improving the performance of the signcryption technique, the optimal key generation procedure was applied by WDOA algorithm. The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio (PSNR). Upon successful encryption of images, the IoT devices transmit to the closest server for storing it in the blockchain securely. The performance of the presented method was analyzed utilizing the benchmark medical image dataset. The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

T2FRF Filter: An Effective Algorithm for the Restoration of Fingerprint Images

Images captured in dim light are hardly satisfactory and increasing the International Organization for Standardization (ISO) for a short duration of exposure makes them noisy. The image restoration methods have a wide range of applications in the field of medical imaging, computer vision, remote sensing, and graphic design. Although the use of flash improves the lighting, it changed the image tone besides developing unnecessary highlight and shadow. Thus, these drawbacks are overcome using the image restoration methods that recovered the image with high quality from the degraded observation. The main challenge in the image restoration approach is recovering the degraded image contaminated with the noise. In this research, an effective algorithm, named T2FRF filter, is developed for the restoration of the image. The noisy pixel is identified from the input fingerprint image using Deep Convolutional Neural Network (Deep CNN), which is trained using the neighboring pixels. The Rider Optimization Algorithm (ROA) is used for the removal of the noisy pixel in the image. The enhancement of the pixel is performed using the type II fuzzy system. The developed T2FRF filter is measured using the metrics, such as correlation coefficient and Peak Signal to Noise Ratio (PSNR) for evaluating the performance. When compared with the existing image restoration method, the developed method obtained a maximum correlation coefficient of 0.7504 and a maximum PSNR of 28.2467dB, respectively.

OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network

Face recognition is considered as important research in computer vision applications, and it is regarded as the basic biometric security system. Research related to face recognition has been done in the past several years. Still, many more challenges associated with this field need to be addressed. Some literary works have designed the face recognition model on the relatively controlled environments; yet, their performance in general settings has been substandard This paper develops an Optimal Face Recognition Network (OPFaceNet) to recognize the face images affected by high noise and occlusion. The feature patterns subjected to noise like LBP, FLBP, and NRLBP are extracted. The average of all three patterns is given to the proposed Convolutional Neural Network (CNN) classifier. As the main contribution, the CNN model is enhanced by optimizing the Fitness Sorted Rider Optimization Algorithm (FS-ROA). This algorithm optimizes the hyperparameters of CNN like Convolutional layer, Pooling Layer, Fully connected layer, number of Hidden layers, and Type of Pooling. Finally, the simulation results show that the system achieves a good recognition rate of 97.2% and is robust against variations in terms of occlusion and noise when benchmarked over diverse datasets.

Particle Rider Mutual Information and Dendritic-Squirrel Search Algorithm With Artificial Immune Classifier for Brain Tumor Classification

AbstractMagnetic Resonance Images (MRI) is an imperative imaging modality employed in the medical diagnosis tool for detecting brain tumors. However, the major obstacle in MR images classification is the semantic gap between low-level visual information obtained by MRI machines and high-level information alleged by the clinician. Hence, this research article introduces a novel technique, namely Dendritic-Squirrel Search Algorithm-based Artificial immune classifier (Dendritic-SSA-AIC) using MRI for brain tumor classification. Initially the pre-processing is performed followed by segmentation is devised using sparse fuzzy-c-means (Sparse FCM) is employed for segmentation to extract statistical and texture features. Furthermore, the Particle Rider mutual information (PRMI) is employed for feature selection, which is devised by integrating Particle swarm optimization, Rider optimization algorithm and mutual information. AIC is employed to classify the brain tumor, in which the Dendritic-SSA algorithm designed by combining dendritic cell algorithm and Squirrel search algorithm (SSA). The proposed PRMI-Dendritic-SSA-AIC provides superior performance with maximal accuracy of 97.789%, sensitivity of 97.577% and specificity of 98%.