The Ocean boundaries can be protected by pursuing an underwater uncooperative target and permits for the exploitation of ocean resources. This research design a novel technique, named Remora Jaya Optimization (RJO)-enabled Deep Quantum Neural Network (DQN) by considering the impacts of an unknown underwater environment for effective underwater target tracking in radar images. Here, the image is modified effectively using the RJO algorithm because the input radar signal is applied to the image reconstruction process. The modified image is fed up to the gridding phase, where the image is partitioned into a number of grids. The feature extraction process is carried out to extract the significant features after the gridding step is over. This data augmentation method is carried out to increase the data dimensionality. Accordingly, the augmented result is forwarded to the DQN for target tracking, where the network is tuned efficiently by the algorithm of RJO, which is devised by the integration of Jaya Optimization and Remora optimization algorithm (ROA). Moreover, the RJO-based DQN has achieved a minimum Means Square Error (MSE) of 0.168 and maximum detection rate of 0.914, and minimum MSE of 0.168 based on Vertical-Vertical (VV) polarity. The proposed method showed higher effectiveness in detecting the underwater target system in the marine environment.

Secure communication between network resources is a key issue, and researchers are persistently developing new intrusion detection schemes to detect intruders to enhance security. However, the traditional intrusion detection techniques have security challenges due to their poor detection performance. Hence, an effective intrusion detection model, namely spider monkey social optimization algorithm (SMSOA)-based Deep Q network is introduced for the detection of intrusion in the network. Here, the intrusion is detected based on the following three steps, such as pre-processing, feature fusion step, and detection. Estimated value replaced the missed value using missing value imputation in the pre-processing phase. Jaccard index by means of deep belief network (DBN) is performed in feature fusion. Deep Q network is used for intrusion detection process, where SMSOA method is used for training. The developed SMSOA scheme is performed by the integration of the social optimization algorithm (SOA) and spider monkey optimization (SMO). In addition, the attack mitigation process prevents the intruder enter into the network. Moreover, the developed model attained a better performance with respect to f-measure, recall values, and precision of 0.9455, 0.9585, and 0.9590, respectively.

Summary People can use bank loans to make investment decisions thanks to technological advancements in the banking sector. The bank, however, only has a limited amount of resources, therefore it must grant to borrowers who can follow to the repayment schedule. Determining the candidate for a loan and finding a secure alternative for a bank are therefore crucial tasks. A deep recurrent neural network based on the whale social optimization algorithm (WSOA) is developed to predict loan eligibility. The WSOA is modified in this case to train Deep RNN for loan eligibility prediction in order to yield the most precise result. The proposed WSOA is developed by combining the social ski‐driver algorithm and the whale optimization algorithm. By selecting the trusted person to save bank assets, the risk factor is also decreased. The loan eligibility prediction is performed by processing massive data with Box‐Cox transformation. Moreover, wrapper feature selection is adapted for choosing imperative features. The proposed WSOA‐based Deep RNN provided increased efficiency with the highest accuracy of 94%, the highest sensitivity of 95.4%, and the highest specificity of 91.3%.

Summary In this article, water cycle spider monkey optimization‐based deep convolutional neural network (WCSMO‐based deep CNN) is designed to classify root diseases in alfalfa plants. In the proposed method, the alfalfa plant root images are accessed remotely using a camera and transferred to the sink node to classify the disease. The proposed water cycle spider monkey optimization (WCSMO) algorithm performs the routing. Once the root images are received at base station or sink node, the images undergo preprocessing, segmentation, and classification. In the preprocessing module, a median filter is utilized to reduce the image noise. The preprocessed output is subjected to the segmentation phase, which is carried out using enhanced fuzzy C‐means clustering. The segmented results are passed to the classification step, where the deep convolutional neural network (deep CNN) is employed to categorize the root illness after being trained by the proposed WCSMO algorithm, which combines the water cycle algorithm and spider monkey optimization. The developed method achieved effective performance in terms of the metrics like sensitivity, accuracy, and specificity with the value of 0.9169, 0.9198, and 0.92. Moreover, the proposed WCSMO algorithm obtained the average energy and average throughput of 0.26641 J, and 91.483%, respectively.

The video summarisation is an advanced mechanism for enabling users to handle and browse large videos in an effective manner. Various video summarisation methods are developed in recent days, in which handling of synchronisation and timing issues remain as the important challenge. The proposed video summarisation technique produces a short summary from the huge video stream. Initially, from an input database, the cricket videos containing number of frames are fed to keyframe extraction unit. Here, the keyframe extraction is done by the Euclidean distance and discrete cosine transform, and the best keyframes are selected based on the Euclidean distance. The residual frame is obtained by passing the input frames through deep convolutional neural network. Then, the similarity is calculated by Bhattacharyya distance. For video summarisation process, the optimal frameset is evaluated by matching residual keyframe with obtained keyframes. Here, input queries consisting of face object are subjected to object matching process, which is performed using the proposed mutual probability-based k-nearest neighbour (MP-KNN) to obtain relevant frames based on texture features. The performance of the proposed MP-KNN is superior based on precision, recall, and F-measure with values 0.963, 0.960, and 0.909, respectively.

Abstract Object detection and localization attract the researchers to address the challenges associated with the computer vision. The literature presents numerous unsupervised methods to detect and localize the objects, but with inaccuracies and inconsistencies. The problem is tackled through proposing a novel model based on the optimization algorithm. The object in the image is detected using the Sparse Fuzzy C-Means (Sparse FCM) that is the enhanced Fuzzy C-Means algorithm used to manage the high-dimensional data. The detected objects are subjected to the object localization, which is performed using the proposed Cat Crow Optimization (CCO)-based Deep Convolutional Neural Network. The proposed CCO is the integration of Cat Swarm Optimization Algorithm and Crow Search Algorithm and inherits the advantages of both the optimization algorithms. The experimentation of the proposed method is performed using images obtained from the Visual Object Classes Challenge 2012 dataset. The analysis revealed that the proposed method acquired an average accuracy, precision, and recall of 0.8278, 0.8549, and 0.7911, respectively.

Abstract This paper proposes an effective classification method named Rider Chicken Optimization Algorithm-based Recurrent Neural Network (RCOA-based RNN) to perform big data classification in spark architecture. Initially, the input data are collected from the network by the master node and then forwarded to the slave node. These nodes are responsible for storing the data and performing computations. The features are effectively selected in the slave node using the proposed RCOA. The selected features are forwarded to the master node. The big data classification is achieved in the master node by using the RNN classifier, and the training of the classifier is done using the proposed RCOA algorithm, which is the integration of the Rider optimization algorithm (ROA) with the standard Chicken Swarm Optimization (CSO). The experimentation is done by using the Switzerland dataset, Cleveland dataset, Hungarian dataset and Skin disease dataset, in which the proposed RCOA-based RNN attained better performance based on the quantitative properties, such as sensitivity, accuracy and specificity with the values of 9.3E+01%, 9.4E+01% and 9.3E+01% using Hungarian dataset. The existing learning methods failed to address the complex classification problems at a reasonable time, which is overcome by the proposed method.

Summary 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.

Summary The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.

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.

Abstract The advancements in the area of object localization are in great progress for analyzing the spatial relations of different objects from the set of images. Several object localization techniques rely on classification, which decides, if the object exist or not, but does not provide the object information using pixel-wise segmentation. This work introduces an object detection and localization technique using semantic segmentation network (SSN) and deep convolutional neural network (Deep CNN). Here, the proposed technique consists of the following steps: Initially, the image is denoised using the filtering to eliminate the noise present in the image. Then, pre-processed image undergoes sparking process for making the image suitable for the segmentation using SSN for object segmentation. The obtained segments are subjected as the input to the proposed Stochastic-Cat Crow optimization (Stochastic-CCO)-based Deep CNN for the object classification. Here, the proposed Stochastic-CCO, obtained by integrating stochastic gradient descent and the CCO, is used for training the Deep CNN. The CCO is designed by the integration of cat swarm optimization (CSO) and crow search algorithm and takes advantages of both optimization algorithms. The experimentation proves that the proposed Stochastic-CCO-based Deep CNN-based technique acquired maximal accuracy of 98.7.

Abstract Rainfall prediction is the active area of research as it enables the farmers to move with the effective decision-making regarding agriculture in both cultivation and irrigation. The existing prediction models are scary as the prediction of rainfall depended on three major factors including the humidity, rainfall and rainfall recorded in the previous years, which resulted in huge time consumption and leveraged huge computational efforts associated with the analysis. Thus, this paper introduces the rainfall prediction model based on the deep learning network, convolutional long short-term memory (convLSTM) system, which promises a prediction based on the spatial-temporal patterns. The weights of the convLSTM are tuned optimally using the proposed Salp-stochastic gradient descent algorithm (S-SGD), which is the integration of Salp swarm algorithm (SSA) in the stochastic gradient descent (SGD) algorithm in order to facilitate the global optimal tuning of the weights and to assure a better prediction accuracy. On the other hand, the proposed deep learning framework is built in the MapReduce framework that enables the effective handling of the big data. The analysis using the rainfall prediction database reveals that the proposed model acquired the minimal mean square error (MSE) and percentage root mean square difference (PRD) of 0.001 and 0.0021.

Forgery detection from the images is gaining remarkable interest as there are a lot of editing tools that enable to cause edition with manipulation or removal of the objects from the images. This study proposes a new forgery detection scheme that is based on the supervised learning approach. The supervised learning is brought about by using the support vector neural network and the optimisation is enabled using the fruit fly optimisation algorithm. Initially, the images are fed to the texture descriptor and the face is detected using the Viola–Jones algorithm. The face detected images are subjected to the feature extraction using the Gabor filter + wavelet + texture operator and the features are concatenated to present the input to the classifier. Then, the classifier which is trained using the fruit fly optimisation classifies the features to detect the presence of the manipulation. The performance of the proposed scheme is evaluated with the existing methods for the evaluation metrics accuracy, sensitivity, and specificity using two datasets, namely DSO‐1 and DSI‐1. The analysis shows that the proposed scheme attained an accuracy of 0.9523, the sensitivity of 0.94, and the specificity of 0.9583, which are greater when compared to the existing methods.