The project “AI-Based Aircraft Recognition System” aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) i...
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ISBN:
(数字)9798350391244
ISBN:
(纸本)9798350391251
The project “AI-Based Aircraft Recognition System” aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) in various sectors, including aviation, has driven the need for more efficient and accurate recognition systems. Aircraft recognition has attracted attention due to its potential uses in areas like military operations, air traffic control, and aviation surveillance. This project proposes the use of advanced AI algorithms to address gaps in current aircraft recognition systems by enhancing accuracy, performance, and real-time application.
In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the ...
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Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood agg...
Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall
We present a new method to estimate the rate-distortion-perception function in the perfect realism regime (PR-RDPF), for multivariate continuous sources subject to a single-letter average distortion constraint. The pr...
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ISBN:
(数字)9798350382846
ISBN:
(纸本)9798350382853
We present a new method to estimate the rate-distortion-perception function in the perfect realism regime (PR-RDPF), for multivariate continuous sources subject to a single-letter average distortion constraint. The proposed approach is not only able to solve the specific problem but also two related problems: the entropic optimal transport (EOT) and the output-constrained rate-distortion function (OC-RDF), of which the PR-RDPF represents a special case. Using copula distributions, we show that the OC-RDF can be cast as an
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-projection problem on a convex set, based on which we develop a parametric solution of the optimal projection proving that its parameters can be estimated, up to an arbitrary precision, via the solution of a convex program. Subsequently, we propose an iterative scheme via gradient methods to estimate the convex program. Lastly, we characterize a Shannon lower bound (SLB) for the PR-RDPF under a mean squared error (MSE) distortion constraint. We support our theoretical findings with numerical examples by assessing the estimation performance of our iterative scheme using the PR-RDPF with the obtained SLB for various sources.
Breathing disturbances that persist more than ten seconds during sleep are the hallmark of the widespread disorder known as sleep apnea. These interruptions may manifest as a series of Apnea or Hypopnea events, each l...
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ISBN:
(数字)9798350343878
ISBN:
(纸本)9798350343885
Breathing disturbances that persist more than ten seconds during sleep are the hallmark of the widespread disorder known as sleep apnea. These interruptions may manifest as a series of Apnea or Hypopnea events, each lasting for a brief duration. The AHI can be used to assess the severity of this disease. Traditionally, the recognition of sleep apnea (SA) has relied on costly and expert-intensive Polysomnography (PSG) technique, requiring continuous monitoring by medical professionals, given its high prevalence, there is a pressing need to identify and address Sleep Apnea at its early stages. To tackle this challenge, an annotated dataset of ECG data has been employed to detect the early onset of Sleep Apnea (SA). This data can be harnessed to classify individuals and assess their risk of developing Sleep Apnea, offering a more accessible and cost-effective approach to detection and intervention. This innovative approach not only provides a more cost-effective and scalable method for detecting Sleep Apnea but also reduces the burden on expert medical resources. Early identification and treatment can lower the health risks related to sleep apnea and greatly enhance quality of life.
The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry's digital transformation resul...
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Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar an...
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The incorporation of Software Defined Networking (SDN) with IoT emerges as an auspicious strategy for enhancing security and access control mechanisms. Nevertheless, substantial threats from DDoS attacks persist in Io...
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ISBN:
(数字)9798350365269
ISBN:
(纸本)9798350365276
The incorporation of Software Defined Networking (SDN) with IoT emerges as an auspicious strategy for enhancing security and access control mechanisms. Nevertheless, substantial threats from DDoS attacks persist in IoT networks, with the potential for execution over botnet or zombie attack, incorporating a machine learning-based detection scheme enables establishing it a framework have been introduced to analyze the performance of IoT devices. Profiles that inform decision-making processes are amassed by these frameworks, ultimately protecting the security of the IoT devices. This work presents a machine learning-based approach for detecting DDoS attacks in an SDN-WISE IoT controller. The incorporation of a machine learning-based detection scheme enables the establishment of a testbed environment for simulating the traffic of DDoS attacks. A logging mechanism within the SDN-WISE controller detects the traffic, generating network logs that are pre-processed and transformed into a dataset. The machine learning DDoS detection module entrenched in the SDN-WISE controller uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to categorize SDN-IoT data. The proposed framework's performance is assessed across various traffic simulation scenarios, and the results from the machine learning DDoS detection module are analyzed. The proposed framework exhibits an accuracy rate of 98.2%, 97.1%, and 97.18% for NB, SVM, and DT, respectively. The attack detection module consumes up to 36% of memory and CPU usage, saving approximately 64% of memory while maintaining up to 71% CPU availability to analyze SD-IoT network traffic. The proposed framework achieves an accuracy of 98.2% which proves the efficiency of the work over state of the work models.
Write-ahead log and data encryption technologies are employed to ensure both crash consistency and data security for persistent memory (PM). The encryption/decryption of both data and log requests increase the memory ...
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The extraction of EXIF (Exchangeable Image File Format) metadata, which provides information about the aspects of data in digital camera images, is essential in various applications such as geolocation, privacy protec...
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ISBN:
(数字)9798331532956
ISBN:
(纸本)9798331532963
The extraction of EXIF (Exchangeable Image File Format) metadata, which provides information about the aspects of data in digital camera images, is essential in various applications such as geolocation, privacy protection protocols, and cyber analysis. This project presents a novel approach using Python with the ‘piexif’ and ‘pyheif’ libraries, which support the extraction of metadata from diverse image formats such as JPG, JPEG, and HEIC. Using these libraries, to effortlessly retrieve date, time, and GPS coordinates, and finally link these geo-coordinates to Maps in a speedy and flexible manner working across any platforms and environments. Our project demonstrates a lightweight, efficient solution without the overhead of too many modules.
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