This paper presents a resilience-driven framework leveraging advanced control technologies, particularly a Markov chain approach, to enhance the robustness of peer-to-peer (P2P) energy trading networks under Low Proba...
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Recent advances in neural architecture search (NAS) techniques have provided effective solutions for managing high-dimensional data. These methods algorithmically automate the design process of NAS, aiming to identify...
A popular and lucrative area of research has al-ways been stock prediction. Stock prediction using traditional deep learning has been proven to provide better accuracy and returns. However, as artificial intelligence ...
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The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer ...
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The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor
Communication is one of the fields that adopts quantum characteristics to improve its potential. In particular, quantum teleportation (QT) is considered a fundamental tool for enabling quantum networks. This paper foc...
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Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantu...
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Online learning, particularly Multi-Armed Bandit (MAB) algorithms, has been extensively adopted in various real-world networking applications. In certain applications, such as fair heterogeneous networks coexistence, ...
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Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and ...
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Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.
The distributed data infrastructure in Internet of Things (IoT) ecosystems requires efficient data-series compression methods, as well as the capability to meet different accuracy demands. However, the compression per...
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Tourism route planning is widely applied in the smart tourism *** Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions proble...
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Tourism route planning is widely applied in the smart tourism *** Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism *** by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on *** method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage *** crowding degree mechanism between extreme and intermediate populations is used in the two-stage *** neighborhood is determined according to the weight of the subproblem for crossover ***,Pareto layering is used to improve the updating efficiency and population diversity of the *** two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same *** with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)*** the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better *** proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.
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