The main idea mentioned in the study is the use of Dynamic Mode Decomposition (DMD) to analyze CO2 and greenhouse gas emissions and other related information. The DMD assessment is used as a test where scientists meas...
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Early brain stroke detection is essential for improved results and prompt treatments. Standared techniques work suggests a novel method for machine learning that makes use of CT or MRI scans images. In orderly to trai...
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In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, a...
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In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, and varying lighting conditions, all of which exacerbate the difficulty of recognition. In recent years, the DETR model based on the Transformer architecture has eliminated traditional post-processing steps such as NMS(Non-Maximum Suppression), thereby simplifying the object detection process and improving detection accuracy, which has garnered widespread attention in the academic community. However, DETR has limitations such as slow training convergence, difficulty in query optimization, and high computational costs, which hinder its application in practical fields. To address these issues, this paper proposes a new object detection model called OptiDETR. This model first employs a more efficient hybrid encoder to replace the traditional Transformer encoder. The new encoder significantly enhances feature processing capabilities through internal and cross-scale feature interaction and fusion logic. Secondly, an IoU (Intersection over Union) aware query selection mechanism is introduced. This mechanism adds IoU constraints during the training phase to provide higher-quality initial object queries for the decoder, significantly improving the decoding performance. Additionally, the OptiDETR model integrates SW-Block into the DETR decoder, leveraging the advantages of Swin Transformer in global context modeling and feature representation to further enhance the performance and efficiency of object detection. To tackle the problem of small object detection, this study innovatively employs the SAHI algorithm for data augmentation. Through a series of experiments, It achieved a significant performance improvement of more than two percentage points in the mAP (mean Average Precision) metric compared to current mainstream object detection models. Furthermore, ther
This electronic Digital information, computer systems, networks, and data are highly in need of protection from damage, unauthorized access, and internal and external threats. Cyber security involves the implementatio...
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In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system i...
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Despite the demonstrated potential of large language models (LLMs) in diverse NLP tasks, their causal reasoning capability appears inadequate when evaluated within the context of the Event Causality Identification (EC...
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Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitati...
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The rapid evolution of artificial intelligence technologies, with the corresponding impact on human well-being, calls for an increase in ethical awareness to influence the computerscience students who will be creatin...
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ISBN:
(纸本)9798400705311
The rapid evolution of artificial intelligence technologies, with the corresponding impact on human well-being, calls for an increase in ethical awareness to influence the computerscience students who will be creating the future. Interdisciplinary teams of scholars are attempting to build frameworks for explaining appropriate ethical considerations for responsible computing. Although professional computing codes of ethics are intended for managing ethical dilemmas in computing, there are no single fixed solutions to all possible challenges which computer scientists face in their professional work. The use of narratives is one way to engage computerscience (CS) students as they explore the ethical implications of their work and critically examine the implications of decisions that are made as part of professional practice. This experience report shares the story of a team of faculty (computerscience and Applied Philosophy) working to integrate computing ethics into their undergraduate CS programs in different instructional contexts;a liberal arts college in the USA and a technology and research university in Nigeria. This collaborative work in progress shares examples of modules and assessments refined over time based on evolving student interests and curriculum needs that are responsive to rapid advances in computing technologies. Contributions of this report include: 1) documentation of the origins of this interdisciplinary partnership, 2) a rationale for a narrative-based approach for fostering responsible computing principles and practices, and 3) preliminary data the impact of this approach within international computing environments.
The modern times have led to the adoption of distinctive meta-heuristic procedures for solving distinct class of optimization-problems. The meta-heuristics procedures have benefit above conventional algorithms because...
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Lung cancer is the most life-threatening cancer worldwide, emphasizing the critical importance of its timely detection and treatment for patient recovery. Colon cancer is also a very hazardous cancer if not treated ti...
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