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.
The purpose of this study was to show the benefits of fuzzy logic in software scaling. We made use of fuzzy logic systems and logic, including sets rules and inference, to cope with uncertainty in making decisions;by ...
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Detecting Ethereum phishing scams is extremely urgent. In this paper, we propose a novel Hybrid Attention Model for Ethereum phishing scams detection called HATTM to solve the problem of irregular transaction series i...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Detecting Ethereum phishing scams is extremely urgent. In this paper, we propose a novel Hybrid Attention Model for Ethereum phishing scams detection called HATTM to solve the problem of irregular transaction series in Ethereum, fully extract account features and then improve detection performance. Specifically, we take a novel perspective by regarding each transaction of an account as a separate amount-time point to handle irregular data. In the hybrid attention model, we capture intra-account and inter-account trading features through intra-account attention of EPS-FORMER and inter-account attention of EPSGAT, respectively. We further extract Intra-account and Inter-account statistical features to enrich the account representation. The complete representation of accounts is composed of the above four types of features to detect phishing accounts. Experimental results on the real-world Ethereum dataset show that HATTM outperforms existing models and is far ahead in the recall, which indicates that our model can effectively detect Ethereum phishing scams.
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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In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. ...
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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.
Capuchin search algorithm (CSA) is a newly matured meta-heuristic algorithm based on the natural roaming habits of capuchin monkeys while foraging. The key flaws of this meta-heuristic include convergence to local opt...
Capuchin search algorithm (CSA) is a newly matured meta-heuristic algorithm based on the natural roaming habits of capuchin monkeys while foraging. The key flaws of this meta-heuristic include convergence to local optimums as well as early convergence. To get around these issues, this study presents a new variant of CSA, referred to as heterogeneous comprehensive opposite learning-based CSA (HCOLCSA), which combines heterogeneous comprehensive learning (HCL) and opposite-based learning (OBL) strategies. The HCL strategy is presented to modify the velocity term of all capuchins for the purpose of improving the exploration and exploitation behaviors in HCOLCSA, while the OBL strategy is embedded into HCOLCSA to boost its exploration and exploitation capabilities of the search space and prevent the trapping of local optimums. To verify the performance of the developed HCOLCSA algorithm, it was evaluated on a set of 29 benchmark functions of the IEEE CEC-2017 test group for dimensions 10, 30, 50, and 100, and then applied to the benchmark functions of the IEEE CEC-2011 evolutionary algorithm competition to demonstrate its reliability and suitability to solve real-world problems. Friedman’s tests, Holm’s tests, and convergence analysis were performed to assess the strength of HCOLCSA compared to others. The numerical findings demonstrate that in more than 92 percent of cases, HCOLCSA produced better results and earned the highest ranking. The results show that HCOLCSA outperforms competing algorithms, demonstrating that it can solve real-world problems represented by CEC-2011 standard functions.
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.
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