With a vast increase in the present amount of digital information with regards to biomedical research, processing related data in an efficient manner assumes critical importance. This work proposes a hybrid summarizat...
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ISBN:
(数字)9798331544607
ISBN:
(纸本)9798331544614
With a vast increase in the present amount of digital information with regards to biomedical research, processing related data in an efficient manner assumes critical importance. This work proposes a hybrid summarization method combining extractive and abstractive techniques with the help of cutting-edge models in NLP. While BERT is used to select the most informative sentences capable of summarizing main ideas in a document, Pegasus has been used to generate smooth, concise summaries for the purpose. The results with the metrics like Cosine Similarity and BERTScore, incorporating precision, recall, and F1 score, show the system proposed performs very well by holding onto the meaning that preserves coherent summaries of high quality on generation. It is of great benefit to researchers as well as professionals regarding rapid extraction from vast amounts of complex data.
Knowledge Management encounters difficulties related to the speed and spread of information production during the modern era of digitization. This results in missing and unprocessed organizational information, which, ...
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Security vulnerabilities in telecommunications networks have become an important issue due to the significant growth of gadgets for smart homes. and its connectivity via the Internet of Things (IoT) This research supp...
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Traffic flow forecasting is crucial for efficient traffic management and congestion avoidance. Traditional methods are mainly based on statistical methods, which fail to capture the complex spatial-temporal correlatio...
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Dynamic Quality-of-Service (QoS) data capturing temporal variations in user-service interactions are essential source for service selection and user behavior understanding. Approaches based on Latent Feature Analysis ...
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Creating simulated synthetic aperture radar (SAR) images using electromagnetic algorithms is an effective way to augment insufficient measured SAR data in automatic target recognition applications. However, discrepanc...
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ISBN:
(数字)9798350350760
ISBN:
(纸本)9798350350777
Creating simulated synthetic aperture radar (SAR) images using electromagnetic algorithms is an effective way to augment insufficient measured SAR data in automatic target recognition applications. However, discrepancies in the modeling process and simplifications of the electromagnetic characteristics inevitably cause some differences between synthetic and mea-sured images. Various other data augmentation methods also exist, such as noise addition, rotation, and numerous generative adversarial network (GAN) techniques. This paper quantitatively discusses the effects of different augmentation methods and provides recommendations for SAR image augmentation.
While the Internet of Things (IoT) has greatly facilitated our daily lives, this trend has encountered numerous security challenges in data storing and sharing especially for sensitive personal data. Identity-Based Br...
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In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity ...
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Event-related potentials (ERPs) are widely used in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Because ERPs vary with age, individual differences, and task difficulty, identifying useful feature...
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ISBN:
(纸本)9781665499248
Event-related potentials (ERPs) are widely used in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Because ERPs vary with age, individual differences, and task difficulty, identifying useful features is challenging. In recent years, deep learning, in which features can be acquired via learning, has been used to identify such features. However, a large amount of BCI data is required for deep learning, and data collection can be difficult. Therefore, we attempted to verify the applicability of BCI to transfer learning based on a considerable amount of data obtained from clinical examinations. The components of an ERP vary depending on measurement conditions;therefore, they degrade the performance of transfer learning. Thus, we proposed to improve performance using latency correction to shift waveforms along the time axis. We trained EEGNet with 5600 responses obtained from a clinical auditory oddball paradigm. In the BCI experiment, 12 subjects input one of the four characters presented on a display. The use of deep learning resulted in an approximately 10% higher performance than the conventional method based on the P300 amplitude. Furthermore, an improvement of approximately 5% in accuracy was observed when latency correction was used.
Cyber security is still the main argument in any system development lifecycle that needs more concern. There are still numerous challenges associated with conducting a threat modeling approach for smart manufacturing ...
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