Commercial airports require routine recertification to ensure the safety of planes and passengers. To support this process, software has been developed to integrate Digital Elevation Models (DEMs) from the United Stat...
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ISBN:
(纸本)9798400705328
Commercial airports require routine recertification to ensure the safety of planes and passengers. To support this process, software has been developed to integrate Digital Elevation Models (DEMs) from the United States Geological Survey (USGS) into two flight navigational aid multipath modeling programs: the Ohio University Glideslope Model (OUGS), which predicts glide slope system performance in non-uniform terrain, and the Ohio University NAVAID Performance Prediction Model (OUNPPM), which simulates localizer, glide slope, and VHF omnidirectional ranging (VOR) system behavior. This integration aims to improve model accuracy for enhanced flight safety. The new software collects DEM data from the USGS's National map in the form of Geo-Tagged Image File Format (GeoTIFF) files, which store elevation data as raster grids, where each pixel represents reflects the elevation at a specific geographic point. A webscraper downloads and saves these TIFF files, which are accessed via an Application Programming Interface (API). The API constructs a 2D array of elevation data for the selected area with data from saved DEMs. This array is shrunk using bi-linear interpolation to reduce its size while preserving the data's integrity. By reducing the resolution from 300px to 150px, the size of the dataset drops from 1.28 MB to 327 KB while keeping accuracy relatively unchanged. Processing time is reduced from 5.83s to 1.45s. which allows for fast and efficient data input with little loss of detail in terrain. Interpolation can be applied to create smaller datasets, reducing size while maintaining relative accuracy.
Counting small subgraphs, referred to as motifs, in large graphs is a fundamental task in graph analysis, extensively studied across various contexts and computational models. In the sublinear-time regime, the relaxed...
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Counting small subgraphs, referred to as motifs, in large graphs is a fundamental task in graph analysis, extensively studied across various contexts and computational models. In the sublinear-time regime, the relaxed problem of approximate counting has been explored within two prominent query frameworks: the standard model, which permits degree, neighbor, and pair queries, and the strictly more powerful augmented model, which additionally allows for uniform edge sampling. Currently, in the standard model, (optimal) results have been established only for approximately counting edges, stars, and cliques, all of which have a radius of one. This contrasts sharply with the state of affairs in the augmented model, where algorithmic results (some of which are optimal) are known for any input motif, leading to a disparity which we term the "scope gap" between the two models. In this work, we make significant progress in bridging this gap. Our approach draws inspiration from recent advancements in the augmented model and utilizes a framework centered on counting by uniform sampling, thus allowing us to establish new results in the standard model and simplify on previous results. In particular, our first, and main, contribution is a new algorithm in the standard model for approximately counting any Hamiltonian motif in sublinear time, where the complexity of the algorithm is the sum of two terms. One term equals the complexity of the known algorithms by Assadi, Kapralov, and Khanna (ITCS 2019) and Fichtenberger and Peng (ICALP 2020) in the (strictly stronger) augmented model and the other is an additional, necessary, additive overhead. Our second contribution is a variant of our algorithm that enables nearly uniform sampling of these motifs, a capability previously limited in the standard model to edges and cliques. Our third contribution is to introduce even simpler algorithms for stars and cliques by exploiting their radius-one property. As a result, we simplify all previo
This paper presents a comparative analysis of four generative AI models namely ChatGPT, Gemini, Copilot, and Stable Diffusion - evaluated on metrics such as visual quality, prompt adherence, creativity, usability, and...
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ISBN:
(数字)9798331520762
ISBN:
(纸本)9798331520779
This paper presents a comparative analysis of four generative AI models namely ChatGPT, Gemini, Copilot, and Stable Diffusion - evaluated on metrics such as visual quality, prompt adherence, creativity, usability, and processing time. Visual and quantitative results highlight Gemini and Copilot's superiority in artistic and imaginative tasks, while Stable Diffusion excels in customization for advanced users. ChatGPT demonstrates ease of use but is limited in complexity. We also provides the best practices in selecting and using such tools to improve creative work. This study emphasizes the importance of evaluating generative AI tools based on diverse requirements and practical use cases.
Rice is one of the most significant primary crops and provides a substantial portion of the world’s food for billions of people worldwide. However, several leaf-related diseases pose significant risks to the rice cro...
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Within the domain of medical diagnostics, the integration of machine vision strategies holds guarantee for upgrading precision and effectiveness in disease discovery. Lung cancer remains a critical worldwide wellbeing...
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ISBN:
(数字)9798331508685
ISBN:
(纸本)9798331519476
Within the domain of medical diagnostics, the integration of machine vision strategies holds guarantee for upgrading precision and effectiveness in disease discovery. Lung cancer remains a critical worldwide wellbeing concern, requiring progressed imaging advances for early and precise conclusion. This paper presents an advanced symptomatic framework enabled by machine vision, especially centring on image segmentation techniques. Through a comprehensive survey of existing writing, we investigate the scene of image segmentation procedures connected to lung cancer location. Hence, we propose a novel system leveraging state-of-the-art machine vision calculations to segment lung images successfully. Our framework points to move forward upon current symptomatic hones by robotizing the division prepare, subsequently encouraging more precise and opportune distinguishing proof of cancerous districts. We examine the potential suggestions of this approach in clinical settings, including its capacity to help radiologists in elucidation and decision-making. Besides, we highlight avenues for future investigate and advancement to refine and optimize the proposed framework for broader clinical selection.
The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration ...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration also introduces significant cyber security vulnerabilities even though it brings about flexibility, resource efficiency, and operational agility. Current IDS using traditional machine learning fail to classify cyber attacks precisely because of complexity in data, limited availability, and mislabeling issues. To overcome these challenges, this study presents a scalable and effective ensemble detection framework that utilizes Pyramidal Recurrent Units (PRUs) and Decision Tree (DT) models. This framework aims to detect and counter cyber attacks across large IIoT networks, especially in SCADA-based environments. The propose of developing this project is to ensure scalable and efficient Deep Learning and Decision Tree based ensemble cyber attack detection framework to resolve trustworthiness issues in the SCADA based IIoT networks. Our proposed detection method can be applied to various IIoT domains. It is easy to implement and deploy, improving efficiency and accuracy while addressing the limitations of earlier *** framework enhances the security of SCADA-based IIoT systems, making industrial networks more reliable, trustworthy, and resilient. The results indicates that the system performs well across different network settings, showcasing its adaptability and robustness in detecting IIoT-based SCADA systems.
Fake news detection has emerged as a critical challenge in the digital age, where misinformation spreads rapidly across social media and news platforms. This survey explores the efficacy of using multivariate feature ...
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ISBN:
(数字)9798350355611
ISBN:
(纸本)9798350355628
Fake news detection has emerged as a critical challenge in the digital age, where misinformation spreads rapidly across social media and news platforms. This survey explores the efficacy of using multivariate feature selection combined with hybrid deep learning approaches to enhance the accuracy and robustness of fake news detection systems. The paper reviews various feature selection techniques, including chi-square, information gain, and correlation-based methods, that help in identifying the most relevant features for detecting fake news. These techniques are crucial in reducing dimensionality, improving processing time, and enhancing the performance of deep learning models. Furthermore, the study examines hybrid deep learning frameworks, which integrate multiple architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers to capture both linguistic and contextual information from news data. This survey provides a comprehensive analysis of recent advancements, highlighting key research contributions, datasets, and evaluation metrics used in fake news detection. The integration of multivariate feature selection with hybrid deep learning models offers a promising approach to addressing the complexities of fake news identification. Finally, the paper outlines the challenges and future research directions in improving model generalization, handling adversarial content, and adapting to evolving patterns of misinformation.
Intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient ...
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ISBN:
(数字)9798331508050
ISBN:
(纸本)9798331508067
Intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces.
The Dual Active Bridge (DAB) is a reliable and efficient converter capable of providing bi-directional power transfer and galvanic isolation. An ac-ac DAB can control both active and reactive power flow. The present w...
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