One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.

Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.

The Internet of Things (IoT) is a concept that involves integrating diverse objects to enable seamless interaction between real-world and virtual entities. IoT is now connecting the physical world to networks. IoT devices can sense, process, transmit, and store data collected from the physical world. However, these devices are resource-constrained, creating significant security vulnerabilities in many IoT applications. Implementing effective security measures on such devices is challenging without compromising their performance or potentially causing damage. Consequently, there is a substantial gap between the security capabilities of current IoT devices and their security requirements. Computer security principles, namely Confidentiality, Integrity, and Availability (CIA), can be compromised by malicious intrusions or attacks on computers and information databases. This study proposed and compared 1D CNN and XGBoost for detecting malicious attacks in the IoT environment. The proposed techniques were evaluated on the five variants of NetFlow datasets. The experiments shows that the proposed techniques outperform the Ensemble Tree classifier, achieving better performance in binary and multi-class classification. The results for 1D CNN and XGBoost were compared on the basis of F1 measure, AUC, recall, correctness value, and ppv.. The comparison shows that XGBoost is the better-performing model across the NetFlow datasets. XGBoost's ability to capture complex patterns and optimize the classification task makes it robust and effective.