In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from or...
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In this paper, the image restoration mechanism of objects through ground glass is studied by digital holography. The holographic scattering imaging system is designed and built, the mathematical model of ground glass ...
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Virtual Palette is a cutting-edge tool designed to enhance audience participation by providing an alternative to conventional jamboards for educators. Leveraging object tracking, a fundamental component of computer vi...
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ISBN:
(数字)9798350379990
ISBN:
(纸本)9798350391558
Virtual Palette is a cutting-edge tool designed to enhance audience participation by providing an alternative to conventional jamboards for educators. Leveraging object tracking, a fundamental component of computer vision, it facilitates seamless and intuitive interactions with the audience. The video analysis process comprises three primary steps: object detection, object tracking across frames, and object behavior analysis. This sophisticated approach necessitates meticulous consideration of elements such as accurate object representation, feature selection for tracking, object recognition, and object tracking. One of the primary advantages of Virtual Palette over traditional mouse or touchpaddevices is its ability to support clear and legible drawing and effective interaction in virtual environments. This feature is particularly beneficial for individuals with hearing impairments, aiding in the identification of patterns anddrawings. The development of Virtual Palette involves overcoming challenges such as precise three-dimensional finger movement tracking and optimizing the technology for virtual backgrounds. Overall, Virtual Palette offers a dynamic and interactive teaching experience, akin to the advantages provided by screen recording for creating tutorials and virtual documentation.
Extreme weather events have posed tremendous challenges to the operation of distribution networks. In this paper, we propose a decision-dependent chance-constrained model for the optimal planning of diesel generators,...
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Cryptocurrency markets, exemplified by the notable volatility in Bitcoin prices, have become pivotal arenas for financial exploration and investment. In response to this, ourresearch undertakes a comprehensive compar...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Cryptocurrency markets, exemplified by the notable volatility in Bitcoin prices, have become pivotal arenas for financial exploration and investment. In response to this, ourresearch undertakes a comprehensive comparison of diverse machine-learning models to predict Bitcoin prices. The models scrutinized include Linearregression, ridge regression, decision Tree, random Forest, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Neural Networks. This study evaluates and contrasts these models based on performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), andr -squared. This work encompasses the collection and preprocessing of historical Bitcoin price data, the engineering of pertinent features, and the division of the dataset into training and test sets. Each machine learning model is meticulously trained on the training set, allowing for the tuning of hyperparameters to optimize predictive capabilities. Subsequently, the models’ performance is systematically assessed on the test set, providing valuable insights into their accuracy andreliability. The scope of this research is delimited by a specific timeframe, focusing on historical Bitcoin price data up to 15/11/2023. By addressing these objectives, this research aspires to guide investors, researchers, and analysts in navigating the intricate landscape of cryptocurrency investment decisions. The findings contribute to an enhanced understanding of the nuanced strengths and limitations inherent in different machine learning models when applied to the volatile context of Bitcoin price prediction. Ultimately, this research aims to facilitate more informed and strategic decision-making processes in the cryptocurrency market.
A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project impl...
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Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks i...
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The computational design of functional materials relies heavily on large-scale atomistic *** simulations are often problematic for conventional classical force fields,which require tedious and time-consuming parameter...
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The computational design of functional materials relies heavily on large-scale atomistic *** simulations are often problematic for conventional classical force fields,which require tedious and time-consuming parameterization of interaction *** problem can be solved using a quantum mechanically derived force field(QMdFF)—a system-specific force fieldderiveddirectly from the first-principles *** present a computational approach for atomistic simulations of complex molecular systems,which include the treatment of chemical reactions with the empirical valence bond *** accuracy of the QMdFF is verified by comparison with the experimental properties of liquid *** illustrate the capabilities of our methodology to simulate functional materials in several case studies:chemical degradation of material in organic light-emitting diode(OLEd),polymer chain packing,material morphology of organometallic *** presented methodology is fast,accurate,and highly automated,which allows its application in diverse areas of materials science.
People can share theirdigital data or information with others through networks with the aid of file sharing techniques. Peer-to-peer, cloud storage, andremovable storage are the three ways that file sharing can be a...
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The challenge of WAd (web attack detection) is growing as hackers continuously refine their methods to evade traditional detection. deep learning models excel in handling complex unknown attacks due to their strong ge...
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