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Electrocardiogram (ECG) is a graphical visualization of the electrical activity of the human heart that is recorded by placing a surface electrode at standardized position on a person’s chest. ECG signals suffer from artifacts/noises due to baseline wander (BW), electrode artifacts, muscle artifacts, power-line interference and channel noises during acquisition and transmission of the ECG signals. Reduction of these artifacts is crucial for efficient diagnosis and interpretation of the human heart condition. In this paper, an effective adaptive noise canceller (ANC) based on empirical mode decomposition (EMD)-Jaya algorithm is proposed for denoising electrocardiogram. In this approach, intrinsic mode functions (IMFs) produced by EMD are used as reference and Jaya algorithm is used to calculate optimum weights of finite impulse response (FIR) filter. This scheme is compared with EMD, wavelet transform (WT) thresholding, and hybrid EMD-least mean square (LMS) approaches through extensive simulation on noise corrupted ECG besides verifying the robustness with real ECG signals. The performance of the proposed technique is assessed using standard metric signal-to-noise ratio (SNR) with different contamination levels. The results obtained demonstrate the superiority of the hybrid when compared to other competing approaches.

Structural damage identification based on the long short-term memory (LSTM) neural network (NN) is proposed in this study. To address the hyperparameters selection problem for the LSTM, the Jaya algorithm is applied to minimize the difference between the observed and predicted data in the validation datasets and determine the LSTM network’s optimal hyperparameters, including the number of nodes, learning rate, and maximum iteration number. Frequency-domain data, such as natural frequencies and mode shapes, are used as input of the network, and then damage locations and extents are utilized as output. Measurement uncertainties are introduced during NN training to improve the robustness of the model. Numerical and experimental studies showed that the proposed method can identify structural damage accurately when measurement noise is considered, even for damage scenarios beyond the training datasets.

Stroke is a disease that is caused due to the blockage and burst in the blood vessels of the brain, thus resulting in abrupt brain dysfunction, like sensory or motor disorders, unconsciousness, limb paralysis, and pronunciation disorders. The existing stroke prediction algorithms have some limitations because of the lengthy testing procedures and hefty testing expenses. The main goal of this study is to develop and implement the proposed fusion-based, optimized deep learning model for stroke disease prediction using multimodalities. For that, this research considers the Computed Tomography (CT) and electroencephalogram (EEG) signals as input, and all of these inputs are processed separately to predict the stroke disease. While predicting the stroke disease with a CT image, the bilateral filter performs the pre-processing and the disease prediction is done with the DenseNet model, which is tuned by the proposed Jaya Fractional Reptile Search Algorithm (Jaya FRSA). Similar to how the proposed FRSA does CNN-LSTM training, the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) predicts the stroke disease using the EEG data as an input after the Gaussian filter removes signal noise. Additionally, the CT image and the EEG signal are processed independently from the image and signal properties. Additionally, the CNN-LSTM model and DenseNet model results are combined using the overlap coefficient to get the final disease prediction. According to the experimental study, the suggested method achieved the maximum image accuracy, sensitivity, and specificity of 0.924, 0.930, and 0.935.

The advancements in Mobile Ad-hoc Network (MANET) are suitable to wide applications, which involve military applications, civilian domains, and disaster recovery systems. It is assumed that the nodes present in the routing protocols of MANETs are cooperative and trustworthy. This assumption makes MANET more prone to manipulation and interception, which generates several possibilities of different suspicious attacks, such as Denial of Service (DoS) attacks. Hence, security is considered as an important parameter as the network is fond of suspicious attacks. This paper addresses the security issues by proposing an optimization algorithm, named Jaya Cuckoo Search (JCS) algorithm, which is the combination of Jaya algorithm and Cuckoo Search (CS) algorithm for initiating secure route among the MANET nodes so that the path attained is feasible and secure. The proposed JCS algorithm uses a fitness function, which considers a multiobjective function using parameters, such as distance, link lifetime, delay, energy, trust, along with the reputation factor to select a secured path. The JCS algorithm shows maximum performance with energy, throughput, and PDR values as 27.826J, 0.554, and 0.628, respectively.

Cloud computing has become a powerful mechanism for initiating secure communication among users. The advancements in the technology lead to provide various services, like accessing network, resources, and platform. However, handling large datasets and security are major issues in the cloud systems. Hence, this paper proposes a technique, namely, Jaya–Whale Optimization (JWO), which is the integration of Jaya algorithm and Whale optimization algorithm (WOA) and adapts homomorphic encryption for initiating secure data transmission in the cloud. The original data are preserved by generating Data Protection (DP) coefficient using the proposed JWO algorithm. In the proposed algorithm, the fitness is calculated based on privacy and utility parameters for selecting the optimal solution. Also, the sanitized data are generated by EXORing the Key Information Product (KIP) matrix and key vector. Finally, the data owner provides the key to the users for retrieving the original data from the sanitized data. The experimentation is carried out using Cleveland, Hungarian, and Switzerland datasets in terms of BD, accuracy, and fitness and the analysis shows that the proposed JWO provides superior performance in terms of BD, accuracy, and fitness parameters with values 0.720, 0.822, and 0.722.

Catastrophic forgetting is a well-known characteristic of diverse parameterized supervised learning models. Artificial Neural Networks (ANN) face severe catastrophic forgetting or catastrophic interference in the sequential learning of information that is intolerable to both engineering applications and the human memory model. This problem can be solved through the machine learning community. The main intent of this paper is to implement a novel continuous learning model that could overcome the problem of catastrophic forgetting. The stream data from diverse benchmark sources are used for experimenting. The proposed model adopts the Deep Neural Network (DNN) with testing weight updates. When new data are given, the hybrid meta-heuristic algorithm helps to update the weight that manages to prevent the learning from catastrophic forgetting. The weight update strategy for solving the catastrophic forgetting is done by the hybridization of Shark Smell Optimization (SSO) and Jaya Algorithm (JA) that is named as Hybrid Shark Smell with Jaya Optimization (HSS-JO). A multi-objective function concerning the parameters associated with catastrophic forgetting like accuracy and remembering is used for framing the proposed continuous learning. The proof of the proposed model over conventional models on three publicly available datasets is given for final validation. From the analysis, in Table 3, the accuracy of the proposed HSS-JO-DNN is 38%, 0.5%, 0.55%, 0.14%, 0.98%, and 0.5% superior to DT, NB, KNN, SVM, NN, and RNN, respectively. For dataset 2, the FNR of the proposed HSS-JO-DNN is 90% superior to DT, 89% improved than NB, KNN, and NN, 94% improved than SVM, and 24% improved than RNN. Similarly, the proposed HSS-JO performance is higher in all the results. Thus, the proposed HSS-JO performs better than all the other traditional methods.

Automatic generation control (AGC) is a tool which helps the system to maintain the frequency within its targeted value by balancing the active power in the system. In this work, a novel membrane computing inspired Jaya algorithm (MCJA) is proposed and applied to tune the controller parameters for AGC of multi-area interconnected system. The proposed algorithm is designed by incorporating the concept of membrane computing in basic Jaya algorithm (JA). A two area non-reheat thermal plant is considered for AGC in this study. The objective function is designed by considering the integral errors of the frequency deviations of two areas and tie-line power deviation. Several cases with different sets of disturbances are considered to test the efficacy of the proposed controller. To validate the superior performance of the proposed controller, it is compared to particle swarm optimization (PSO), Jaya algorithm (JA) and membrane computing (MC) based controllers. Time-domain simulations are presented to depict better performance of the proposed controller. Additionally, a comparative statistical analysis is carried out to examine the robust and stable nature of the proposed algorithm.