This research will analyze the efficiency of using sentiment analysis on news articles and social media to predict stock market trends. By analyzing public sentiment from sthis study'sces like Twitter, Reddit, and...
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ISBN:
(数字)9798331507244
ISBN:
(纸本)9798331507251
This research will analyze the efficiency of using sentiment analysis on news articles and social media to predict stock market trends. By analyzing public sentiment from sthis study'sces like Twitter, Reddit, and major financial news platforms, this research will try to capture the psychological influence that the market has on stock prices. Work use Natural Language Processing (NLP) techniques to label textual data as positive, negative, or neutral and aggregate sentiment scores over specific time intervals. These scores are added to the machine learning model: logistic regression, random forest, and Long Short-Term Memory (LSTM). Thus, the proposed approach along with the developed model is tested for determining its predictability pothis researchr when historical data are involved using various evaluation measures for quantifying its predictive pothis researchr after being trained and validated, showing very good and reliable results with better precision obtained by the integration of features based on sentiment analysis. This method brings out the strength of sentiment analysis as an ancillary tool for financial prediction. Investors can gain good knowledge about market trends by tracking the dynamics of public sentiments.
Mobile edge computing (MEC) improves resource-limited mobile devices by transferring demanding computational activities to edge servers and cloud infrastructure, thereby mitigating issues related to user mobility and ...
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Mobile edge computing (MEC) improves resource-limited mobile devices by transferring demanding computational activities to edge servers and cloud infrastructure, thereby mitigating issues related to user mobility and network fluctuations. This paper introduces Flexible Mobility-Based Task Offloading (FlexiMRO), a mobility-oriented, fault-tolerant framework for adaptive task offloading in MEC and cloud settings. The suggested methodology incorporates a long short-term memory model to forecast user trajectories and network states with high transmission precision, alongside a deep reinforcement learning strategy utilizing Q-Deep networks for optimal job distribution at the edge and cloud layers. A random forest classifier guarantees fault tolerance by accurately predicting server failures and facilitating the reallocation of ongoing jobs. Comprehensive simulations illustrate the superiority of FlexiMRO compared to current methodologies, achieving a latency reduction of up to 70%, an enhancement in energy efficiency of 60%, and a failure rate of 80% in contrast to 70%. FlexiMRO offers a scalable and adaptable solution for 5G and IoT applications by minimizing latency, energy consumption, and quality of experience, efficiently utilizing edge and cloud computing to guarantee uninterrupted service delivery in dynamic MEC environments.
Noise in histopathology images from hardware limitations, preparation artifacts, and environmental factors complicates disease analysis and increases risks. With growing workloads and the complexity of histopathology ...
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a radar system built on the Internet of Things (IoT) that uses ultrasonic sensors to find things within 150 degrees. The system's rotation is controlled by a servo motor, and a Graphical User Interface (GUI) built...
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Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to f...
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ISBN:
(数字)9798331533311
ISBN:
(纸本)9798331533328
Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model’s capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos.
With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networ...
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ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network.
This work presents a broadband and low-profile printed Inverted-F antenna (PIFA). The design deploys a 1.5 mm thick FR-4 substrate with dimensions ( $\mathbf{4 0} \times \mathbf{2 5} \mathbf{ m m}^{2}$ ). A broadband ...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
This work presents a broadband and low-profile printed Inverted-F antenna (PIFA). The design deploys a 1.5 mm thick FR-4 substrate with dimensions ( $\mathbf{4 0} \times \mathbf{2 5} \mathbf{ m m}^{2}$ ). A broadband operation is achieved through modifying a coplanar waveguide feed (CPW). Two $\lambda / 8$ stubs are created to enhance the entire operational frequency bandwidth. The simulation result of the reflection parameter achieves about 87 % fractional bandwidth ( $2.05-5.2 \text{GHz}$ ) based on the $\mathbf{- 1 0} \mathbf{d B}$ impedance bandwidth criterion. The design has total radiation efficiency above $\mathbf{7 2 \%}$ . Far-field radiation patterns results show a dualpattern (pattern diversity) among excited resonance modes frequencies. Such results make the proposed antenna an excellent candidate for current secure- and selective sub-6 GHz 5G communications systems.
The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an importan...
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Image Captioning involves generating a textual description of an image, in the most accurate way possible. It requires a combination of computer Vision and Natural Language Processing techniques, which can both be enh...
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As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, susta...
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As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.
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