This study provides insights into the current limitations of quantum machine learning compared to classical machine learning and identifies areas for future research. We present a novel approach that utilizes real IBM quantum computers to classify celestial objects within the extensive Sloan Digital Sky Survey Data Release 18 (SDSS-V DR18) dataset. Despite persistent challenges in both hardware and software, quantum computers are being explored as tools for enhancing machine learning performance in comparison to classical methods due to potential upside. This investigation delves into the untapped potential of quantum machine learning and quantum neural networks in tackling the complexities of processing vast telescope data. By leveraging quantum technologies, we aim to expedite the analysis of large complex data, unveiling hidden patterns, and propelling specialized fields such as astronomical research into the quantum era.
Network traffic has increased substantially due to the introduction of advanced network-enabled applications and devices. The introduction of software defined networks (SDNs) and machine learning (ML) has empowered optimizing network operations and network traffic monitoring, resulting in improved complex traffic operations and security with faster malicious intention detections. This paper focuses on network traffic data collection systems, and the data is evaluated using a survey of ML algorithms, depending on the data type (tabular or image). Adhering to system architecture best practices including a decoupled design to integrate with existing network monitoring infrastructures and cybersecurity standards; and online and offline data collection via packet capture (PCAP) standards. For packet based network traffic data analysis, we convert captured data into images and feed into a convolutional neural network to classify the data based on requirements. For statistical based network traffic data analysis, we apply feature engineering on tabular data and feed into various ML systems to classify based on requirements. Finally, We show that the same ML algorithm outperforms publicly available datasets using our collection method.
Sea navigation and operations within areas of interest has been a major focus of naval research. Documents such as Raster Navigational Charts (RNC) that help with sea navigation tasks are critically important. A RNC is a copy of a navigational paper chart in image form. Therefore, RNC contains important information such as navigational channels, water depths, rocky areas etc. However, a RNC is hard to interpret by computers and even humans as it contains very dense information due to the different layers of drawings from the information mentioned above. In this paper, we introduce a reverse engineering approach using computer vision to extract features from the RNC image. We use optical character recognition to extract text features and templates matching for symbolic features. With the new approach, we show that RNC will become machine readable, and the features extracted can be used to draw tactical regions of interest.
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