With the increasing popularity of smart portable electronic gadgets, voice-based online person verification systems have become prevalent. However, these systems are susceptible to attacks where illegitimate individua...
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With the increasing popularity of smart portable electronic gadgets, voice-based online person verification systems have become prevalent. However, these systems are susceptible to attacks where illegitimate individuals exploit the recorded voices of legitimate users, leading to false confirmations—spoofing attacks. To overcome this limitation, this article presents an innovative solution by combining speech and online handwritten signatures to mitigate the risks associated with spoofing attacks in voice-based authentication systems because a person has to be present in front of the system to produce an online handwritten signature. To accomplish this objective, this work proposes a novel bidirectional Legendre memory unit (BLMU), a type of recurrent neural network (RNN), for person authentication (verification) and recognition. The Legendre memory unit (LMU) is an innovative memory cell for RNNs that efficiently retains temporal/non-temporal sequential information over a long period with minimal resources. It achieves information orthogonalization by solving coupled ordinary differential equations (ODEs) and leveraging Legendre polynomials, ensuring effective data representation. The proposed framework for person authentication and recognition comprises seven convolution layers, four BLMU layers, two dense layers, and one output layer. The performance of the proposed BLMU-based deep learning framework has been evaluated on a self-generated/private dataset of combined feature matrix of voice signals and online handwritten signatures in the Devanagari script. To assess performance, experiments have also been conducted using various RNN architectures, such as LSTM, BLSTM, and ordinary differential equation recurrent neural network (ODE-RNN), to have a performance comparison with the proposed BLMU-based deep learning (DL) framework. The results demonstrate the superiority of the proposed BLMU-based DL framework in enhancing the accuracy of person verification systems,
The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by le...
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The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by learning few samples in new tasks,providing a solution for many scenarios that lack *** paper summarizes few-shot learning algorithms in recent years and proposes a ***,we introduce the few-shot learning task and its ***,according to different implementation strategies,few-shot learning methods in recent years are divided into five categories,including data augmentation-based methods,metric learning-based methods,parameter optimization-based methods,external memory-based methods,and other ***,We investigate the application of few-shot learning methods and summarize them from three directions,including computer vision,human-machine language interaction,and robot ***,we analyze the existing few-shot learning methods by comparing evaluation results on mini Image Net,and summarize the whole paper.
Nowadays, Android-based devices such as smart phones, tablets, smart watches, and virtual reality headsets have found increasing use in our daily lives. Along with the development of various applications for these dev...
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Background: Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence,...
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Background: Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence, obstetricians check the fetal health state by monitoring the fetal heart rate (FHR). Cardiotocography (CTG) is a technique used by obstetricians to access the physical well-being of fetal during pregnancy. It provides information on the fetal heart rate and uterine respiration, which can assist in determining whether the fetus is normal or suspect or pathology. CTG data has typically been evaluated using machine learning (ML) algorithms in predicting the wellness of the fetal and speeding up the detection process. Methods: In this work, we developed LightGBM with a Grid search-based hyperparameter tuning model to predict fetal health classification. The classification results are analysed quantitatively using the performance measures, namely, precision, Recall, F1-Score, and Accuracy Comparisons were made between different classification models like Logistic Regression, Decision Tree, Random Forest, k-nearest neighbors, Bagging, ADA boosting, XG boosting, and LightGBM, which were trained with the CTG Dataset obtained by the patented fetal monitoring system of 2,216 data points from pregnantwomen in their third trimester available in the Kaggle dataset. The dataset contains three classes: normal, suspect, and pathology. Our proposed model will give better results in predicting fetal health classification. Results: In this paper, the performance of the proposed algorithm LightGBM is compared and experimented with various Machine learning Techniques namely LR, DT, RF, KNN, Boosting, Ada boosting, and XG Boost and the classification accuracy of the respective algorithms are 84%, 94%, 93%, 88%, 94%, 89%, 96%. The LightGBM achieved a performance of 97% and outperforms the former models. Conclusion: The LightGBM-based fetal health classification has been pres
Multimodal sentiment analysis on images with textual content is a research area aiming to understand the sentiment conveyed by visual and textual elements in the images. While multimodal sentiment analysis on images a...
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Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis ...
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This article introduces a novel algorithm, named 'CrowdDC,' that aims to solve the issue of ranking large datasets based on subjective factors using crowdsourced paired comparisons. In traditional paired compa...
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Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focu...
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Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).
Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure fo...
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Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure formed by interconnecting blocks1),which are stored by full replication method,where each node stores a replica of all blocks and the data consistency is maintained by the consensus *** decrease the storage overhead,previous approaches such as BFT-Store and Partition Chain store blocks via erasure ***,existing erasure coding based methods utilize static encoding schema to tolerant f malicious nodes,but in the typical cases,the number of malicious nodes is much smaller than f as described in previous *** redundant parities to tolerate excessive malicious nodes introduces unnecessary storage *** solve the above problem,we propose Dynamic-EC,which is a Dynamic Erasure Coding method in permissioned blockchain *** key idea of Dynamic-EC is to reduce the storage overhead by dynamically adjusting the total number of parities according to the risk level of the whole system,which is determined by the number of perceived malicious nodes,while ensuring the system *** demonstrate the effectiveness of Dynamic-EC,we conduct several experiments on an open source blockchain software *** results show that,compared to the state-of-the-art erasure coding methods,Dynamic-EC reduces the storage overhead by up to 42%,and decreases the average write latency of blocks by up to 25%,respectively.
Information spreads swiftly via social media in response to breaking news, modifying several aspects of the data. Nevertheless, it's crucial to understand that early modifications can have been brought about by he...
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