The detection of a Denial of Service (DoS) attacks is a key challenge in network security, directly impacting the availability and reliability of networks. Such attacks have to be mitigated by implementing an accurate and timely detection mechanism to ensure the integrity of the network infrastructure. Driven by the shortcomings of conventional attack detection methods and the growing complexity of the network attacks, this work proposes a new customized Long Short-Term Memory (LSTM) model for DoS attacks detection. The proposed deep learning approach utilizes LSTM's strength in learning long-range dependencies in sequential data to model network traffic patterns over time. The effectiveness of the model is evaluated through comparative experiments. The primary outcome is that the proposed LSTM model has improved detection performance across all metrics of precision, recall, F1-score, and accuracy. The results demonstrate that the proposed LSTM architecture is a promising and trustworthy solution for enhancing intrusion detection systems (IDSs) and protecting network systems against DoS attacks.

With the exponential growth of available data and computational power, deep convolutional neural networks (CNNs) have become as powerful tools for a wide range of applications, ranging from image classification to natural language processing. However, during last decade, remote sensing imagery has emerged as one of the most prominent areas in image processing. Variations in image resolution, size, aircraft types and complex backgrounds in remote sensing images challenge the aircraft classification task. This study proposes an effective aircraft classification model based on CNN architecture. The CNN network architecture is improved to achieve more accuracy rate and to avoid overfitting and underfitting problems. To validate the proposed model, a new public aircraft dataset called multi-type aircraft remote sensing images 2 (MTARSI2) has been used. Through an analysis of existing methodologies and experimental validation, the model shows the superior performance of the proposed CNN model in comparison to state-of-the-art deep learning approaches.

This study performs an independent examination of the performance exhibited by NoSQL and SQL databases regarding key-value stores in parallel processing using a simulation model containing 15 nodes. Specifically, the focus is on comparing the efficiency of read, write, delete, and instantiate operations within the context of key-value stores implemented with both NoSQL (MongoDB, RavenDB, and Cassandra) and SQL (MS-SQL) databases within the average time of three runs. The results show that SQL achieved better read operation times, while NoSQL databases, especially RavenDB, had better results in the other operations.

Over the past few years, object detection has experienced remarkable advancements, primarily attributable to significant progress in deep learning architectures. Nonetheless, the task of identifying aircraft targets within remote sensing images remains a challenging and actively explored area. Presently, there are two main approaches employed for this task: one utilizing convolutional neural network (CNN) techniques and the other relying on conventional methods. In this work, a CNN based architecture is proposed to recognize aircraft types using remote sensing images. The experiments performed on multi-type aircraft remote sensing images (MTARSI) dataset show that the proposed architecture achieves 97.07%, 94.81%, and 94.44% accuracy rates for training, validation and testing sets. The results approve that, the architecture outperforms state of the art models.

During last decade, medical imaging has attracted great deal of research interests. Deep learning applications has revolutionized medical image analysis and diseases diagnosis. Convolutional neural networks (CNNs)-a class of deep learning-have been widely used for classification and feature extraction, and they revealed good performance for various imaging applications. However, despite the advances in medicine, malaria remains among the world’s deadliest diseases. Only in 2020, malaria recorded 241 million clinical episodes, and 627,000 deaths. The disease is examined visually through a microscope, which depends on the pathologists experience and skills and results may vary in different laboratories. This paper proposes an efficient CNN architecture that could be used in diagnosing of malaria disease. By processing on 27,558 red blood smear cell images with balanced samples of parasitized and unparasitized cells on a publicly available malaria dataset from the National Institute of Health, the proposed model achieves high accuracy rate with 99.8%, 98.2, and 97.7% for training, validation and testing sets. Furthermore, the statistical results approve that the proposed model is outperforming the state-of-the-art models.