Deep learning method requires a substantial amount of labeled data to achieve the state-of-the-art performance. However, annotating a large volume of data is often costly and impractical. Active Learning is a approach...
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We describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons a...
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Social network data, and web-graph data in general, sees great amounts of research and is widely used commercially. Due to privacy or other concerns it often cannot be shared verbatim. Synthetic counterparts to such d...
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This paper presents an experiment and results of the modified CNN algorithm, it was developed by combining a compact 1D convolution neural network with a tuned signal filter (low-pass filter in this experiment). The a...
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Accurate classification of brain tumors from MRI scans is crucial for effective diagnosis and treatment planning in neuro-oncology. This study presents a comprehensive framework leveraging advanced deep-learning techn...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Accurate classification of brain tumors from MRI scans is crucial for effective diagnosis and treatment planning in neuro-oncology. This study presents a comprehensive framework leveraging advanced deep-learning techniques and visualization methods to achieve precise tumor classification. Our methodology involves merging two diverse datasets, performing data augmentation, and standardizing image dimensions to facilitate robust model training. We propose an attention-based multiscale fusion model, which integrates spatial attention and multi-scale fusion layers, achieving an accuracy of 99.17%, surpassing other models such as DenseNet201 (87.21%), InceptionV3 (82.30%), and MobileNet V3 (92.01%). Advanced visualization techniques, including Grad-CAM, enhance interpretability and confidence in diagnostic assessments. Moving forward, future work could explore integrating multi-modal imaging data and addressing challenges related to data scarcity and class imbalances to enhance diagnostic accuracy further and personalize treatment recommendations. Collaboration between computer scientists and clinicians is essential for seamless integration of AI systems into clinical workflows, ensuring reliable and safe deployment in real-world settings.
Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past o...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. This study will propose a web application that uses advanced time series models to try and overcome such challenges. The system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models; Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis; Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting; Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets; and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. This solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. The goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model.
Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) relaxometry is a powerful non-destructive technique used to study molecular dynamics and structures in various systems, including food products. This study intro...
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Existing Automated Service Composition (ASC) approaches typically require inputs to be in a designated form. These, namely tuples, pose challenges due to the significant divergence from the most commonly used and stra...
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This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic s...
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The rapid adoption of electric vehicles (EVs) presents a significant challenge for EV charging stations in efficiently managing and allocating energy. This study develops an advanced ensemble learning framework to pre...
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