Digital signature schemes are practical mechanisms for achieving message integrity, authenticity, and non-repudiation. Several asymmetric encryption techniques have been proposed in the literature, each with its proper limitations. RSA and El Gamal prove their robustness, but are unsuitable in several domains due to their computational complexity. Other asymmetric encryption schemes have been proposed to provide a cloud homomorphic encryption service, where the researchers focused only on how to ensure the homomorphic property. This paper proposes a new digital signature scheme dedicated to a family of encryption techniques. The proposal consists of two parts: the first focused on the secret key, and the second focused on the public key. Signature validity checking was performed by multiplying these two parts to reform again the sender’s public key, then comparing the result with the decrypted message. The validation of the decrypted message guarantees data integrity, where the signer public key is used to ensure authenticity. The proposed scheme takes a shorter execution time for the entire signature operation, including signing and verification, compared to other modern techniques. The analysis showed its robustness against private key recovery and forgery attacks. The implementation results of the proposed scheme showed promising performance in terms of complexity and robustness. The results confirmed that the proposed scheme is efficient and effective for signature generation and verification.

In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. Hardware and software limitations of IoT devices can be mitigated using the massive on-demand cloud resources. However, IoT cloud-based solutions pose some security and privacy concerns, specifically when an untrusted cloud is used. This calls for strong encryption schemes that allow operations on data in an encrypted format without compromising the encryption. This paper presents an asymmetric multi-key and partially homomorphic encryption scheme. The scheme provides the addition operation by encrypting each decimal digit of the given integer number separately using a special key. In addition, data integrity processes are performed when an untrusted third party performs homomorphic operations on encrypted data. The proposed work considers the most widely known issues like the encrypted data size, slow operations at the hardware level, and high computing costs at the provider level. The size of generated ciphertext is almost equal to the size of the plaintext, and order-preserving is ensured using an asymmetrical encryption version.

The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life and the world economy. By ensuring early prediction accuracy, remote sensing enters the scene to safeguard the globe against weather-related threats and natural disasters. Convolutional neural networks (CNNs), which are a reflection of deep learning, have been used more recently to reliably identify land use in remote sensing images. This work proposes a novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition from remote sensing images. First, we proposed using the fast neural approach to generate cloud-effect satellite images. In neural style, we proposed a 5-layered residual block CNN to estimate the loss of neural-style images. After that, we proposed two novel architectures, named 3-layered bottleneck CNN architecture and 3-layered bottleneck self-attention CNN architecture, for the classification of land use images. Training has been conducted on both proposed and original neural-style generated datasets for both architectures. Subsequently, features are extracted from the deep layers and merged employing an innovative serial approach based on weighted entropy. By removing redundant and superfluous data, a novel chimp optimization technique is applied to the fused features in order to further refine them. In conclusion, selected features are classified using the help of neural network classifiers. The experimental procedure yielded respective accuracy rates of 99.0% and 99.4% when applied to both datasets. When evaluated in comparison to state-of-the-art methods, the outcomes generated by the proposed framework demonstrated enhanced precision and accuracy.

The diversity, noise, interimage interference, image distortion, and increase in the number of classes in aerial remotely sensed dataset cause exertion in the classification. The efficacy and stability of convolutional neural networks increase in image classification with the specified use of feature selection algorithm that causes remarkably improved decision making. To address the associated difficulties, a fuzzy deep learning architecture has been designed with a super-resolution technique that consists of 40 convolutional, four polling, four inverted bottleneck blocks, and one fully connected layer. The fuzzy optimistic formula is implemented in 4 blocks as an activation function where information is fused from the previous layers and present block while the rest are using the ReLU transfer function to handle the issue of noise and interimage interference. Feature selection is performed based on the physics of chaotic particle swarm optimization hybrid with the active set algorithm. The accuracy of the proposed architecture is examined on three diverse datasets: Bijie earth landslide/nonlandslide, EuroSAT, and NWPU-RESISC45, comprised of varying classes. The results are compared with state-of-the-art models, such as the hybrid version of VGGNet-16, Yolov4, ResNet-50, DenseNet-121, and other reported techniques. Moreover, the stability and computational complexity of the presented architecture are computed on 50 independent runs. It has been observed that the proposed architecture is stable, accurate, and viable and exploits a smaller number of learnable parameters than the models considered in comparison.