The use of Natural Language Processing (NLP) in machine translation has grown in significance as technologies as well as computers have become more prevalent in our daily lives. This essay's goal is to evaluate th...
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This work modeled and simulated a robotic arm with a conveyer belt that picks and places objects from one spot to another. Daily production is increasing and increasing production rates while increasing profit margins...
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In this paper, we have proposed testing the graphical user interface (GUI) applications using the idea of finite state machines and then comparing the results with the model generated using Matlab: which represents th...
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Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit pr...
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Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.
SVMs had been effectively applied to various signal processing tasks in Telecommunications, along with goal detection and category in cognitive radio networks, channel estimation, exploiting spatial variety for multip...
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In response to the challenges posed by missed electricity bill payments and unauthorized power consumption, this study introduces enhancements to existing smart energy meter systems. The proposed improvements include ...
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The scalable production of inexpensive, efficient, and robust catalysts for oxygen evolution reaction (OER) that can deliver high current densities at low potentials is critical for the industrial implementation of wa...
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The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest ***, reinforcement learning (RL)-based sequential planning met...
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The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest ***, reinforcement learning (RL)-based sequential planning methods have exhibited advantages in solving efficiency and ***, these methods fail to exploit the problem-specific properties in learning representations, resulting in less effective features for decoding optimal *** paper considers the sequential planning process of min-max VRPs as two coupled optimization tasks: customer partition for different routes and customer navigation in each route (i.e., partition and navigation).To effectively process min-max VRP instances, we present a novel attention-based Partition-and-Navigation encoder (P&N Encoder) that learns distinct embeddings for partition and ***, we utilize an inherent symmetry in decoding routes and develop an effective agent-permutation-symmetric (APS) loss *** results demonstrate that the proposed Decoupling-Partition-Navigation (DPN) method significantly surpasses existing learning-based methods in both single-depot and multi-depot minmax *** code is available at. Copyright 2024 by the author(s)
People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,incl...
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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) ca...
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