Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classi...
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This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and...
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This study conducts a systematic literature review (SLR) that focuses on optimizing deep-learning techniques for post-earthquake building damage detection. By employing the PRISMA protocol, this review aimed to collec...
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ISBN:
(数字)9798350392036
ISBN:
(纸本)9798350392043
This study conducts a systematic literature review (SLR) that focuses on optimizing deep-learning techniques for post-earthquake building damage detection. By employing the PRISMA protocol, this review aimed to collect, analyze, and summarize the findings of various studies published between 2014 and 2024. This research identifies and evaluates optimization methods such as hyperparameter tuning, transfer learning, and data augmentation, highlighting their effectiveness and implementation. Challenges such as data quality, computational costs, and model interpretability were also discussed. The findings indicate that while deep learning techniques significantly enhance the accuracy and efficiency of damage detection, further research is required to address existing challenges and improve model robustness in diverse earthquake scenarios.
An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) ...
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This study aims to increase the number of access users by limiting the sample size to 30 users while ensuring that every Optical Network Unit (ONU) receives data from the Optical Line Terminal (OLT). The proposed solu...
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This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to expl...
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Currently, online project-based learning is one of the methodologies used in university student assessment. Furthermore, this study is supported by several factors and current conditions applied to learning, such as t...
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This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. The...
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information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on ...
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ISBN:
(纸本)9798350304084
information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. Data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application's interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar
The proliferation of large-scale applications has led to the generation of vast datasets across diverse scientific domains. The subsequent need to transfer such expansive data across geographical distances is essentia...
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ISBN:
(数字)9798350370997
ISBN:
(纸本)9798350371000
The proliferation of large-scale applications has led to the generation of vast datasets across diverse scientific domains. The subsequent need to transfer such expansive data across geographical distances is essential for collaborative data storage and analysis. While reserving bandwidth on dedicated links within high-performance networks (HPNs) has proved as an efficient means for such extensive data transfers, certain crucial challenges remain to be investigated. In this paper, we delve into the intricate tradeoff between cost and completion time of data transfers using bandwidth reservation on fixed paths with fixed bandwidth of the HPNs, the most common type of bandwidth reservation or data transfer paths. Our focus centers on the scheduling of two types of bandwidth reservation requests (BRRs) that encapsulate this tradeoff: (i) minimizing data transfer cost within prescribed deadlines, and (ii) achieving the earliest data transfer completion time while adhering to predefined cost constraints. We propose two algorithms to optimize the scheduling of individual BRRs of these two types. We then compare the proposed algorithms with existing ones from the perspective of different performance metrics, and efficacy of the proposed algorithms is verified through extensive simulations.
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