In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learnin...
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Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent ...
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Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the *** and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed *** recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis ***:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical ***:Radiological imaging is an extremely important tool in modern medicine to assist in medical *** work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.
GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for in...
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Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
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Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a c...
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Multi-path avoidance routing for wireless sensor networks (WSNs) is a secure routing paradigm against adversaries with unbounded computational power. The key idea of avoidance routing is to encode a message into sever...
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Multi-path avoidance routing for wireless sensor networks (WSNs) is a secure routing paradigm against adversaries with unbounded computational power. The key idea of avoidance routing is to encode a message into several pieces by the XOR coding, and each piece is routed via different paths. Then, an adversary cannot obtain the original message unless she eavesdrops on all message pieces from all the paths. In this paper, we extend such an approach into secure multicast routing, which is a one-to-many communication primitive. To this end, we propose the multi-tree-based avoidance multicast routing protocol (AMRP) for WSNs, in which a set of adversary disjoint trees is discovered, i.e., a set of multicast trees with no common adversaries. When a set of multicast trees is adversary disjoint, no adversary can eavesdrop on all message pieces to recover the original message. In addition, optimized AMRP (OAMRP) is proposed in order to reduce the control overhead of AMRP, where additional multicast trees are used for only a subset of destination nodes with no single safe tree. The simulation results demonstrate that the proposed protocols achieve higher secure delivery rates than a simple extension of the existing unicast avoidance routing protocol. IEEE
Spam emails are sent to recipients for advertisement and phishing purposes. In either case, it disturbs recipients and reduces communication quality. Addressing this issue requires classifying emails on servers as eit...
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We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning to improve the learning process with causal inference. Q-Cogni achieves improved policy quality and l...
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Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced *** study addresses these challenges by...
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Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced *** study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant *** framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment *** the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word ***,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment *** evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification *** incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis *** results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.
The use of Amazon Web Services is growing rapidly as more users are adopting the *** has various functionalities that can be used by large corporates and individuals as *** analysis is used to build an intelligent sys...
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The use of Amazon Web Services is growing rapidly as more users are adopting the *** has various functionalities that can be used by large corporates and individuals as *** analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related *** this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter *** data is managed to the EC2 by using elastic load *** collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative *** accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.
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