Assessing the performance of machine translation systems is of critical value, especially to languages with lower resource availability. Due to the large evaluation effort required by the translation task, studies oft...
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Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection amo...
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Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev
The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and f...
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The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks an...
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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).
Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has ha...
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In the rapidly advancing field of genomics, the identification of Single Nucleotide Polymorphisms (SNPs) plays a crucial role in understanding complex phenotypic *** study introduces "PentaPen", an innovativ...
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作者:
Lokesh, GudivadaBaseer, K.K.
Dept. of Computer Science and Engineering Tirupati India
Department of Data Science Tirupati India
Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model ...
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Diabetes prediction is crucial for early intervention and personalized treatment. This study uses a multimodal strategy, including prediction algorithms, downsampling, feature engineering, exploratory data analysis (E...
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The Partial Credit Model (PCM) of Andrich (1978) and Masters (1982) is a fundamental model within the psychometric literature with wide-ranging modern applications. It models the integer-valued response that a subject...
The Partial Credit Model (PCM) of Andrich (1978) and Masters (1982) is a fundamental model within the psychometric literature with wide-ranging modern applications. It models the integer-valued response that a subject gives to an item where there is a natural notion of monotonic progress between consecutive response values, such as partial scores on a test and customer ratings of a product. In this paper, we introduce a novel, time-efficient and accurate statistical spectral algorithm for inference under the PCM model. We complement our algorithmic contribution with in-depth non-asymptotic statistical analysis, the first of its kind in the literature. We show that the spectral algorithm enjoys the optimal error guarantee under three different metrics, all under reasonable sampling assumptions. We leverage the efficiency of the spectral algorithm to propose a novel EM-based algorithm for learning mixtures of PCMs. We perform comprehensive experiments on synthetic and real-life datasets covering education testing, recommendation systems, and financial investment applications. We show that the proposed spectral algorithm is competitive with previously introduced algorithms in terms of accuracy while being orders of magnitude faster. Copyright 2024 by the author(s)
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