Question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with t...
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Question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given *** studies for the approach performed on the entire dataset or datasets within a category that the question writer manually *** contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the *** to the text classification model,we can effectively reduce the search space for finding the answers to a given ***,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference ***,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding *** real-world QA data sets,we evaluate the performance of the proposed QA matching *** a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%***,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.
In today's dynamic world of software development, the demand for efficient and rapid creation of high-quality code has never been more pronounced. Automated software source code generation (ASSCG) emerges as a com...
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This paper describes the creation of an ML-based tool for image-based identification of fish species commonly found in Indian oceans, addressing the issues that fishermen experience in accurately reporting their catch...
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In this work, a virtual toll booth system utilizing cutting-edge technologies like EasyOCR with optical character recognition, or OCR, and YOLOv8 for object detection is introduced. By connecting the accounts stored i...
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Automated detection of cardiovascular diseases based on heartbeats is a difficult and demanding task in signal processing because the routine analysis of the patient’s cardiac arrhythmia is crucial to reducing the mo...
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The main elements of academic calendars, such as semester or quarter structures, course schedules, registration periods, exam periods, and holidays, are covered in the abstract. It examines how academic calendars can ...
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Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes us...
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Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.
Anticipating and diagnosing coronary illness is the greatest test in the clinical business and depends on variables like the actual assessment, side effects and indications of the patient. Factors that impact coronary...
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Magnetic resonance imaging (MRI) has become a valuable diagnostic assessment means for the detection, segmentation, and characterization of brain tumors. However, low brightness and low contrast in MRI images pose a s...
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ISBN:
(纸本)9789819994410
Magnetic resonance imaging (MRI) has become a valuable diagnostic assessment means for the detection, segmentation, and characterization of brain tumors. However, low brightness and low contrast in MRI images pose a significant challenge for accurate tumor detection, especially in the early stages. Several approaches have been proposed to address this challenge, including image enhancement and filtering techniques. However, these methods often result in loss of image details, making it difficult to discern the tumor regions from the non-tumor ones. To overcome these limitations, deep learning-based approaches have gathered attention in recent years for their capability to automatically learn features from the input images and achieve high accuracy in various medical imaging tasks. The aim of our research is to present a deep learning-based methodology for detecting brain tumors in low-brightness and low-contrast MRI images. We employ a neural network with convolutions’ (CNN) architecture, which has been proven to be effective in acquiring complex image features. Previous studies have used deep learning techniques for brain tumor segmentation and detection (Ramin Ranjbarzadeh et al. in Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images [1]). However, these studies did not specifically address the problem of low brightness and low contrast in MRI images. In contrast, our proposed method is designed to capture the subtle differences between tumor regions and non-tumor regions in such MRI images. Our CNN model has been trained and validated on a larger dataset of MRI images, including both normal and tumor-containing images. Our results demonstrate that our proposed method achieves high accuracy and specificity in detecting brain tumors, even in low-brightness and low-contrast MRI images. Additionally, our method has the potential to aid healthcare professionals in precisely and promptly pronouncing tumors
Network Intrusion Detection System (NIDS) is most momentous safety techniques in mobile networks. Intrusion detection (ID) is significant approach aimed at identifying attacks then applying it on security procedures t...
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