Accurate and efficient lung and colon cancer classification is vital for early detection and treatment planning. Traditional methods require manual effort and expert analysis, leading researchers to explore deep learning models. However, deep learning-based lung and colon cancer classification models face challenges such as generalization, overfitting, gradient vanishing, and hyperparameter tuning. To overcome these challenges, we propose an efficient Deep Attention module and a Residual block-based lung and colon cancer classification Network (DARNet). It comprises three key components such as residual blocks, attention modules, and fully connected layers. Residual blocks (RBs) are utilized to refine the DARNet's ability to learn and capture residual information which allows DARNet to perceive complex patterns and improve accuracy. Attention module (AM) enhances feature extraction and captures useful information in the input data. Finally, to achieve better generalization performance, we employ Bayesian Optimization (BO) to fine-tune the hyperparameters of DARNet. Extensive experimental results indicate that the proposed BO-based DARNet achieved superior performance over competitive models on benchmark lung and colon cancer datasets, with a median accuracy of 98.86% and lower variance.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.

Abstract Spotted hyena optimizer (SHO) is a new metaheuristic algorithm that replicates spotted hyenas' hunting and social behaviour. This article proposes novel SHO algorithm that utilizes chaotic maps for fine‐tuning of control parameters. The chaotic maps help SHO to enhance the searching behaviour and preclude the solution to get trapped in local optima. The authors suggest 10 novel chaotic versions of SHO. The algorithms' performance is evaluated using 29 standardized test functions. The finding reveal that some of the presented algorithms outperform the standard SHO in terms of search capability and solution quality. In addition, five competitive approaches are compared with the suggested algorithms. It is observed from the results that chaos‐based spotted hyena optimizer (CSHO) achieved approximately 3% improvement over SHO in terms of fitness value. CSHO is also tested using five engineering design problems. CSHO achieved a 3%–5% improvement over the existing metaheuristic algorithms in terms of optimal design cost. Experimental results reveal that CSHO outperforms the existing metaheuristic algorithms.

Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%.

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate. Therefore, in this paper, to provide explanatory information to the doctors and patients, an efficient deep ensemble medical image captioning network (DCNet) is proposed. DCNet ensembles three well-known pre-trained models such as VGG16, ResNet152V2, and DenseNet201. Ensembling of these models achieves better results by preventing an over-fitting problem. However, DCNet is sensitive to its control parameters. Thus, to tune the control parameters, an evolving DCNet (EDC-Net) was proposed. Evolution process is achieved using the self-adaptive parameter control-based differential evolution (SAPCDE). Experimental results show that EDC-Net can efficiently extract the potential features of biomedical images. Comparative analysis shows that on the Open-i dataset, EDC-Net outperforms the existing models in terms of BLUE-1, BLUE-2, BLUE-3, BLUE-4, and kappa statistics (KS) by 1.258%, 1.185%, 1.289%, 1.098%, and 1.548%, respectively.

Remote sensing images taken during poor environmental conditions are degraded by the scattering of atmospheric particles, which affects the performance of many imaging systems. Hence, an efficient visibility restoration model is required to remove haze from distorted images. However, the design of visibility restoration models is an ill-posed problem as the physical information, such as depth information and attenuation model, is usually unknown. The physical parameters computed using existing models, such as dark channel prior and gradient channel prior, are not accurate, especially for images with large haze gradients. Therefore, in this article, an evolving visibility restoration model is proposed for remote sensing images. Initially, the fusion-based transmission map is computed from the foreground and sky regions. The transmission map is further improved by designing a hybrid constraint-based variational model. Finally, a dynamic differential evolution is utilized to optimize the control parameters of the proposed model. The proposed model is validated on 50 synthetic benchmarks and 50 real-life remote sensing images. For comparative analysis, ten well-known restoration models are also considered. The comparative analysis demonstrates that the proposed model outperforms the existing restoration models.

There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

In recent years, many image encryption approaches have been proposed on the basis of chaotic maps. The various types of chaotic maps such as one‐dimensional and multi‐dimensional have been used to generate the secret keys. Chaotic maps require some parameters and value assignment to these parameters is very crucial. Because, poor value assignments may make the chaotic map un‐chaotic. Therefore, hyper‐parameter tuning of chaotic maps is required. Recently, meta‐heuristic based image encryption approaches have been designed by researchers to resolve this issue. However, the majority of the techniques suffer from poor computational speed and stuck in local optima problems. Therefore, in this study, a strength Pareto evolutionary algorithm‐II based meta‐heuristic approach is proposed to tune the hyper‐parameters of the four‐dimensional chaotic map. The proposed approach is also implemented in a parallel fashion to enhance the computational speed. The effectiveness of the proposed approach is evaluated through extensive experiments. Comparative analyses show that the proposed approach outperforms the competitive approaches in terms of entropy, NPCR, UACI, and PSNR by , , , and , respectively.

Remote sensing images taken in hazy situations are degraded by scattering of atmospheric particles, which greatly influences the efficiency of visual systems. Therefore, the visibility restoration of hazy images becomes a significant area of research. In this study, a fourth‐order partial differential equations based trilateral filter (FPDETF) dehazing approach is proposed to enhance the coarse estimated atmospheric veil. FPDETF is able to reduce halo and gradient reversal artefacts. It also preserves the radiometric information of haze‐free images. The visibility restoration phase is also refined to reduce the colour distortion of dehazed images. The proposed technique has been evaluated on ten well‐known remote sensing images and also compared with seven well‐known existing dehazing approaches. The experimental results reveal that the proposed technique outperforms others in terms of contrast gain and percentage of saturated pixels.

In block discrete cosine transform‐based image compression, the blocking artefacts are the main cause of degradation, especially at higher compression ratio. It is of interest to be able to numerically assess the degree of blocking artefacts as it plays an important role in the design, optimisation and assessment of image and video coding systems. In this work, comparison of novel algorithms based on different modelling functions for blocking artefact detection in compressed images is proposed. The authors’ experiment results show that for all types of images, the proposed methods detect blocking artefacts more accurately as compared with other post‐processing methods/techniques and is very efficient and stable since the signal need not be compressed/decompressed.