Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral imaging (HSI) has been widely used in a wide range of real-world application areas due to the in-depth spectral information stored within each pixel. Noticeably, the detailed features, i.e., a nonlinear correlation between the obtained spectral data and the correlating HSI data object, generate efficient classification results that are complex for traditional techniques. Deep learning (DL) has recently been validated as an influential feature extractor that efficiently identifies the nonlinear issues that have arisen in various computer vision challenges. This motivates using DL for HSIC, which shows promising results. This survey provides a brief description of DL for HSIC and compares cutting-edge methodologies in the field. We will first summarize the key challenges for HSIC, and then, we will discuss the superiority of DL and DL ensemble in addressing these issues. In this article, we divide state-of-the-art DL methodologies and DL with ensemble into spectral features, spatial features, and combined spatial–spectral features in order to comprehensively and critically evaluate the progress (future research directions as well) of such methodologies for HSIC. Furthermore, we will take into account that DL involves a substantial percentage of labeled training images, whereas obtaining such a number for HSI is time and cost consuming. As a result, this survey describes some methodologies for improving the classification performance of DL techniques, which can serve as future recommendations.

Cyber-attacks are becoming more common against Internet users due to the increasing dependency on online communication in their daily lives. X.509 Public-Key Infrastructure (PKIX) is the most widely adopted and used system to secure online communications and digital identities. However, different attack vectors exist against the PKIX system, which attackers exploit to breach the security of the reliant protocols. Recently, various projects (e.g., Let’s Encrypt and Google Certificate Transparency) have been started to encrypt online communications, fix PKIX vulnerabilities, and guard Internet users against cyber-attacks. This survey focuses on classical PKIX proposals, certificate revocation proposals, and their implementation on blockchain as well as ledger technologies. First, we discuss the PKIX architecture, the history of the World Wide Web, the certificate issuance process, and possible attacks on the certificate issuance process. Second, a taxonomy of PKIX proposals, revocation proposals, and their modern implementation is provided. Then, a set of evaluation metrics is defined for comparison. Finally, the leading proposals are compared using 15 evaluation metrics and 13 cyber-attacks before presenting the lessons learned and suggesting future PKIX and revocation research.

Online shopping platforms are growing at an unprecedented rate all over the world. These platforms mostly rely on search engines, which are still primarily based on the knowledge-base and use keywords matching for finding similar products. However, customers want an interactive setup that is convenient and reliable for querying related products. In this paper, we propose a novel idea of searching for products in an online shopping system using an image-based approach. A user can provide, select, or click an image, and similar image-based products will be presented to the user. The proposed recommendation system is based on content-based image retrieval and is composed of two major phases; Phase 1 and Phase 2. In Phase 1, the proposed approach learns the class/type of the product. In Phase 2, the proposed recommendation system retrieves closely matched similar products. For Phase 1, the proposed approach creates a model of products using Machine Learning (ML). The model is then used to find the category of the test products. From the ML perspectives, we employ the Random Forests (RF) classifier, and for feature extraction, we use the JPEG coefficients. The dataset used for proof of concepts includes 20 categories of products. For image-based recommendation, the proposed RF model is evaluated for Phase 1 and Phase 2. In Phase 1, the evaluation of the proposed model generates a 75% accurate model. For performance enhancements, the RF model has been integrated into the Deep Learning (DL) setup achieving 84% accurate predictions. Based on the custom evaluation approach for Phase 2, the proposed recommendation approach achieves 98% correct recommendations, thus demonstrating its efficacy for the product recommendation in practical applications.

In the modern world, people face an explosion of information and difficulty to find the right choice of their interest. Nowadays, people show interest in online shopping to meet their demands increasingly. For researchers and students, finding and buying the desired books from online shops is very tedious work. Recently Recommender System is an excellent tool to deal with such problems, but the Recommender System is suffering from multiple problems such as data sparsity, cold-start, and inaccuracy. To address these problems, we propose Deep Edu a novel Deep Neural Collaborative Filtering for educational services recommendation. A Deep Edu architecture consists of three parts of a Deep Neural Network model (such as input layer, a multilayered perceptron, and an output layer). The Deep Edu provides the following contributions: first, the users’ identifier and books identifier features are mapped into N-dimensional dense embedding vectors, second, the Multi-Layer-Perceptron (MLP) takes the N-dimensional and non-linear features. To increase the performance of Deep Edu in all metrics, we proposed the advance Loss function. Equipped with the following, Deep Edu not only capable of learning the N-dimensional and non-linear interactions between users’ identifier and books identifier, but moreover, it also considerably mitigates the cold-start, data sparsity, and inaccuracy problem. Over significant experiments performed on real-world good books dataset, the results show that Deep Edu’s recommendation performance obviously outperforms existing Educational services recommendation methods.

The deployment of convolutional neural networks (CNNs) to classify hyperspectral images is extensively discussed in the research study. A number of different algorithms and approaches are applied, including 2-D CNN, 3-D CNN, support vector machine (SVM), regression models, and other state-of-the-art deep learning models, although these methods do not show good performance for hyperspectral image classification. Furthermore, 3-D CNNs require a lot of computational power and are not mainly employed, whereas 2-D CNNs do not constitute multiresolution image processing and exclusively focus on spatial features. However, 3D–2D CNNs aim to incorporate spectral and spatial features, and their efficiency while being evaluated on various datasets tends to be limited. Moreover, a number of deep learning models have been proposed recently, but their performance is still limited. In order to solve these problems, in this article, we propose a novel deep hyperspectral shot, a deep smooth wavelet CNN shots ensemble for hyperspectral image classification. A deep smooth wavelet CNN utilizes layers of wavelet transform to extract spectral features. The computation of a wavelet transform is less intensive as compared to the computation of a 3-D CNN. After that, the extracted spectral features are integrated into 2-D CNN, which generates spatial features, as a result, generates a spatial–spectral feature vector for classification. Furthermore, we introduce the snapshots generation method and employ a cyclic annealing schedule to converge to several local minima along its optimization path and save the models. We build several snapshots of the deep hyperspectral shots model to enhance the performance of our proposed method. We propose the snapshots optimization and ensemble selection approach in order to solve the optimization problem within ensemble creation and further enhance the performance. In addition, we also introduce a novel activation function called Relish to increase spatial–spectral feature propagation and advance for smoother gradients. Overall, we ensemble the snapshots of our proposed method and achieved that can classify multiresolution HSI data with high accuracy. Experiments performed on benchmark datasets, our proposed method, deep hyperspectral shots, achieved overall accuracies of 99.96%, 97.91%, and 99.49% on the Salinas, Indian Pines, and Pavia University datasets against the state-of-the-art methods.