In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellu...
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Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a c...
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In [E. G. Birgin, R. Castillo and J. M. Martínez, Computational Optimization and Applications 31, pp. 31–55, 2005], a general class of safeguarded augmented Lagrangian methods is introduced which includes a larg...
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In [E. G. Birgin, R. Castillo and J. M. Martínez, Computational Optimization and Applications 31, pp. 31–55, 2005], a general class of safeguarded augmented Lagrangian methods is introduced which includes a large number of different methods from the literature. Besides a numerical comparison including 65 different methods, primal-dual global convergence to a KKT point is shown under a (strong) regularity condition. In the present work, we generalize this framework by considering also classical/non-safeguarded Lagrange multipliers updates. This is done in order to give a rigorous theoretical study to the so-called hyperbolic augmented Lagrangian method, which is not safeguarded, while also including the classical Powell-Hestenes-Rockafellar augmented Lagrangian method. Our results are based on a weak regularity condition which does not require boundedness of the set of Lagrange multipliers. Somewhat surprisingly, in non-safeguarded methods, we show that the penalty parameter may be kept constant at every iteration even in the lack of convexity assumptions. Numerical experiments with all the problems in the Netlib and CUTEst collections are reported to compare and discuss the different approaches.
In the age of swift digital advancement, social media has emerged as a vital and readily available information source for users. Individuals utilize social media to communicate, articulate thoughts, and disseminate th...
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ISBN:
(数字)9798331508616
ISBN:
(纸本)9798331508623
In the age of swift digital advancement, social media has emerged as a vital and readily available information source for users. Individuals utilize social media to communicate, articulate thoughts, and disseminate their viewpoints, both textually and visually. Twitter is a social media platform that people extensively utilize to disseminate both favorable and unfavorable information regarding GoFood services. This study intends to apply the Convolutional Neural Network (CNN) algorithm within the GoFood service on Twitter. This research also seeks to assess the accuracy of the CNN algorithm in relation to GoFood services. Employing deep learning techniques, particularly the convolutional neural network algorithm, to analyze Twitter user sentiment can enhance the efficiency, accuracy, and simplicity of classifying answers to GoFood services. The findings indicated that the Convolutional Neural Network technique was effectively employed to classify positive, negative, and neutral feelings. The optimal accuracy rate was attained through preprocessing with Stemming, yielding 51% accuracy for multiclass classification and 72% for binary class classification. The accuracy percentage for classification without stemming was 49% for multiclass classification and 70% for binary classification. This research holds possible consequences for Gojek firms and can serve as evaluative and assessment material for GoFood services, enabling them to implement necessary corrective measures.
The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. Th...
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ISBN:
(数字)9798350364101
ISBN:
(纸本)9798350364118
The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm.
In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very importa...
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Voice synthesizers still present several challenges in the speech of mathematical content, as spoken mathematics has quite peculiar rules. In the synthesized speech, pauses help blind and visually impaired students id...
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To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work w...
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Being one essential part of the solutions we are developing to provide accessibility for blind persons, synthesized speech of mathematical content, although having evolved in naturalness in recent years, still keeps a...
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Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem...
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