Summary Cloud computing is considered as the most fabulous paradigm to accommodate various kinds of user information. However, the privacy conflicts integrated with computational complexities need more hybridized resource efficiency for hassle‐free accession. For this purpose, this article presents a cloud assisted, secured and privacy preserved protocol based on the elliptical curve cryptography (ECC) and blockchain consortium. Blockchain provides an innovative approach for storing information, establishing trust, and various other transactions in an open platform. There exists no single technology as a panacea to obtain optimum privacy and security in the complex cloud environment that needs several desired characteristics. Hence, an elite integration of multiple cryptographic methods by carefully analyzing the potential harms and pitfalls has to be framed to balance the trade‐off between privacy and security. The proposed technique highlights the user privacy preservation through guaranteeing that user information is safeguarded from unauthorized use or access. In accordance with that the present study enrolled the advantages of the ECC algorithm, vectorization, and blockchain methods to rule out the limitations of state of art methods. This article attempts to provide the most required privacy with optimum key generation, encryption, and decryption time. The present study has obtained optimal outcomes and the maximum percentage reduction w.r.t the existing method in key generation time, encryption time, and decryption time is given as 14.89%, 16.67%, and 12.5%, respectively.

In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

Abstract Aim Fruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth. Methods This study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five‐layer stacked sparse autoencoder was used as the classifier. Results Ten runs on the test set showed our method achieved a micro‐averaged F1 score of 95.08% for an 18‐category fruit dataset. Conclusion Our method gives better micro‐averaged F1 score than 10 state‐of‐the‐art approaches.

Abstract Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre‐trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well‐known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising.

Abstract Malaria is considered one of the deadliest diseases in today's world, which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium, which infects the red blood cells in human beings. Diagnosis of malaria requires identification and manual counting of parasitized cells in microscopic blood smears by medical practitioners. Its diagnostic accuracy is primarily affected by extensive scale screening due to the unavailability of resources. State of the art Computer‐Aided Diagnostic techniques based on deep learning algorithms such as CNNs, which perform an end to end feature extraction and classification, have widely contributed to various image recognition tasks. In this paper, we evaluate the performance of Mosquito‐Net, a custom made convnet to classify the infected and uninfected blood smears for malaria diagnosis. The CADx system can be deployed on IoT and mobile devices due to its fewer parameters and computation power, making it wildly preferable for diagnosis in remote and rural areas that lack medical facilities. Statistical analysis demonstrates that the proposed model achieves greater accuracy than the previous SOTA architectures for malaria diagnosis despite being 10 times lighter in parameters and inference time. Mosquito‐Net achieves an AUC of 99.009% and an F‐1 score of 96.7% on the validation set.

The sixth-generation (6G) communication networks being highly data-intensive and enabled by the Internet of Everything (IoE) are envisaged to find applications in various domains including smart healthcare, smart industry, and gaming. In this article, a novel hybrid 6G-based cybertwin cooperative architecture is studied and an analytical framework for the secrecy outage analysis is presented. The base station (BS) considered utilizes nonorthogonal-multiple-access for content request transmission to the cybertwin host and the server with a direct communication link present between the BS and the server. A wireless link experiencing Nakagami-$m$fading is considered which is further assisted in the communication network by a wired link which is a power line communication link and experiences Rayleigh fading, for the communication between the cybertwin host and the server. Secrecy error probability expressions are derived for the cybertwin host and the server for the scenario when both the wireless and the wired links are available for communication and only the wireless link is available for communication. Furthermore, the presented analytical results are corroborated with simulation results which demonstrate the optimality of operating the wireless links at lower signal-to-noise ratio (SNR) values. It is observed that for the lower values of Nakagami fading parameter, better secrecy performance is observed with the usage of wired link along with the wireless link, and also the secrecy performance of the cybertwin host and the server is dependent on the system parameters like the fading parameter and the average SNR.

Summary Resource provisioning in the cloud is the most popular business model for any service provider due to profit that is based on how resources are distributed among users in Industry 4.0. Moreover, much research carried out to provide a better resource provisioning system to service providers; however, efficient resource provisioning may save the environments too. This study presents an extensive analysis of different resource provisioning systems based on concert parameters. More than 250 relative research articles have been considered for this study, out of these 125 articles has been processed for comparative analysis on the basis of different performance metrics. This study highlights the classifications of resource management techniques, objective functions, and open research challenges and issues while analyzing resource management techniques. It also provides the depth knowledge of uses of performance metrics utilization based on its classifications.

In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis on image data. However, one critical problem is that Deep Learning comes with complicated, black-box models so it is not possible to analyze their trust level directly. So, Explainable Artificial Intelligence (XAI) methods are used to build additional interfaces for explaining how the model has reached the outputs by moving from the input data. Of course, that's again another competitive problem to analyze if such methods are successful according to the human view. So, this paper comes with two important research efforts: (1) to build an explainable deep learning model targeting medical image analysis, and (2) to evaluate the trust level of this model via several evaluation works including human contribution. The target problem was selected as the brain tumor classification, which is a remarkable, competitive medical image-based problem for Deep Learning. In the study, MR-based pre-processed brain images were received by the Subtractive Spatial Lightweight Convolutional Neural Network (SSLW-CNN) model, which includes additional operators to reduce the complexity of classification. In order to ensure the explainable background, the model also included Class Activation Mapping (CAM). It is important to evaluate the trust level of a successful model. So, numerical success rates of the SSLW-CNN were evaluated based on the peak signal-to-noise ratio (PSNR), computational time, computational overhead, and brain tumor classification accuracy. The objective of the proposed SSLW-CNN model is to obtain faster and good tumor classification with lesser time. The results illustrate that the SSLW-CNN model provides better performance of PSNR which is enhanced by 8%, classification accuracy is improved by 33%, computation time is reduced by 19%, computation overhead is decreased by 23%, and classification time is minimized by 13%, as compared to state-of-the-art works. Because the model provided good numerical results, it was then evaluated in terms of XAI perspective by including doctor-model based evaluations such as feedback CAM visualizations, usability, expert surveys, comparisons of CAM with other XAI methods, and manual diagnosis comparison. The results show that the SSLW-CNN provides good performance on brain tumor diagnosis and ensures a trustworthy solution for the doctors.

The three-dimensional (3D) mesh is moderately novel media type that realizes a rising success in various applications through data transfer via the Internet, which requires security approaches. Technological copyright protection of digital contents has become a challenging task in the current digital epoch. In this work, a robust watermarking algorithm of polygonal meshes for copyright protection purposes is proposed. The watermark insertion was achieved by quantization of the vertex norms variance in order to insert the watermark bits. In addition, this method is based on a blind detection scheme, so the watermark can be extracted without referring to the original mesh. The experimental results established the quality of the watermarked object as well as the inserted watermark robustness against various types of attacks, which were evaluated to prove the validity of the proposed algorithm. The results proved the proposed method efficiency in terms of robustness and imperceptibility against several signal processing distortions. A comparison with other reported method with similar purposes is also provided. The comparison depicted the outstanding robustness of the proposed method compared to the other reported method.