The tone captures how the leaders of listed company confidence to the performance and tone changes correlate with revisions of future outlook. Current predicting stock market behaviour is using numerous quantitative f...
The tone captures how the leaders of listed company confidence to the performance and tone changes correlate with revisions of future outlook. Current predicting stock market behaviour is using numerous quantitative financial factors. Recent publications have demonstrated that some implied sentiment information such as tone changes in annual reports can be successfully used to predict the stock price in the U.S market. However, the investors' reflection to the tone changes in annual reports in Asia market, especially in Asian financial center Hong Kong, is still unknown. In this paper, the chairman's statement tone changes in annual reports from the Hong Kong market have been studied in the first time. This study evaluates three different tone changes methods and combing with financial indicators to predict the stock price. The experimental results prove that the tone changes of annual reports can predict the stock price in the long trend, which implies the low market efficiency in Hong Kong. Moreover, some experiments have been investigated whether the financial crisis can be predicted from the chairman's tone changes.
We investigate the structural vulnerability of complex networks under different edge-based attacks. The attacks are induced by removing a certain ratio of edges according to four kinds of weighting methods strategies ...
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Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine lear...
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Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat
The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm ...
The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013. These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set. The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it's very efficient and practicable.
This In this paper, we study the problem of detecting packet loss distortion and estimating the perceived visibility of such distortion in decoded video. Our analysis is based on the features of the decoded video sign...
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This In this paper, we study the problem of detecting packet loss distortion and estimating the perceived visibility of such distortion in decoded video. Our analysis is based on the features of the decoded video signal, and we assume that no information about actual packet losses is available from the underlying network or video decoder. First, we present a full-reference method for assessing packet loss visibility at the macroblock, frame and sequence levels. Second, we propose a no-reference method for detecting defected frames, based on spatiotemporal features and machine learning. Experimental results show that the proposed no-reference method achieves a high correlation with the full-reference method at both sequence and frame level. At sequence level, the no-reference method can also predict the subjective quality ratings at high accuracy.
Consumer photos taken in low light conditions often suffer from substantial undesired capture artifacts, such as shakiness and sensor noise. In this paper, we use rank ordering method to assess the subjective preferen...
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Consumer photos taken in low light conditions often suffer from substantial undesired capture artifacts, such as shakiness and sensor noise. In this paper, we use rank ordering method to assess the subjective preferences among different postprocessing methods used to alleviate capture artifacts. The results show that most users prefer sharpened photos, even in the presence of substantial sensor noise. However, there are also systematic differences in individual preferences between users. Therefore, user preferences need to be considered in addition to the image characteristics, when selecting the post-processing algorithms and parameters for photo quality enhancement.
A graph G is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function w is defined on its vertices. Then G is w-well-covered if all maximal independent sets are of th...
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The Internet of Things (IoT) global market is growing significantly fast in recent years, from a wearable small watch to a giant airplane. The Smart-home is one of the most popular applications which can improve the r...
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The Internet of Things (IoT) global market is growing significantly fast in recent years, from a wearable small watch to a giant airplane. The Smart-home is one of the most popular applications which can improve the resident's life quality. However, the security and privacy issues of homeowner bring emerging challenges while more and more data are collecting and sharing by the instances of different IoT devices within the Smart-home. Confront the challenge, an emerging charming technology blockchain provides a private, secure, and decentralized mechanism for the data usage. But, there are still many challenges for the application of blockchain such as the efficiency, storage, and energy cost, etc. This paper proposes IoT architecture in Smart-home environment based on blockchain and smart contract with the comprehensive consideration of the primary challenges. Our approach exemplifies the three core components in Smart-home: smart contract, private blockchain, and public blockchain. Each Smart-home contains their unique private chain. We present the application scenarios in our architecture and discuss the principles of blockchain smart contract application for Smart-home.
A graph G is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function w is defined on its vertices. Then G is w-well-covered if all maximal independent sets are of th...
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