On Applicability of Imagery-Based CNN to Computational Offloading Location Selection

Ometov, Aleksandr; Mezina, Anzhelika; Nurmi, Jari

doi:10.1109/access.2022.3232469

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Following its development, the model was subjected to rigorous testing on test datasets to ascertain its efficacy and generalizability. We evaluated the model's performance using the following metrics: mean absolute error (MAE) root mean squared error (RMSE), and coefficient of determination (R 2 ), as defined in Equations ( 8)-( 10) [45,46]. These metrics were helpful in comparing our model with others so as to verify its performance and gain insights into its strength and weakness in the context of flight fare analysis.…”

Section: Gru Modelmentioning

confidence: 99%

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Degife,

Lin

2024

Applied Sciences

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This paper presents an advanced method for forecasting flight fares that combines aspect-based sentiment analysis (ABSA) with deep learning techniques, particularly the gated recurrent unit (GRU) model. This approach leverages historical airline ticket transaction data and customer reviews to better understand airline fare dynamics and the impact of customer sentiments on pricing. The aspect analysis extracts key service aspects from customer feedback and provides insightful correlations with airfare. These were further categorized into nine groups for sensitivity analysis, which offered a deeper understanding of how each group influences customers’ attitudes. This ABSA-driven forecasting method marks a departure from traditional models by utilizing sentiment data alongside airline transaction data to improve the predictive accuracy. Its effectiveness is demonstrated through metrics including a root mean square error (RMSE) of 0.0071, a mean absolute error (MAE) of 0.0137, and a coefficient of determination (R2) of 0.9899. Additionally, this model shows strong prediction performance in both short- and long-term fare predictions. It not only advances airfare forecasting methods but provides valuable insights for decision makers of airline industry to refine the pricing strategies or make improvements when it is indicated some services require further attention.

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Section: Gru Modelmentioning

confidence: 99%

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Degife,

Lin

2024

Applied Sciences

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“…These evaluation metrics play a vital role in comparing and contrasting the performance of different variations of the stacked GRU-LSTM model, allowing for the identification of the model that proves superior performance. These performance evaluation metrics, MSE, MAE, and RMSE, are computed in Equations ( 2)-( 4) [32], respectively, as follows:…”

Section: The Proposed Stacked Gru-lstm Model Trainingmentioning

confidence: 99%

Integrated Sensor-Optics Communication System Using Bidirectional Fiber and FSO Channels and Hybrid Deep Learning Techniques

Dehnaw,

Manie,

et al. 2023

Sensors

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This paper introduces a new bidirectional integration approach that combines fiber sensor/free space optics (FSO) communication using an intensity and wavelength division multiplexer (IWDM) techniques-based long-distance fiber Bragg grating (FBG) sensor strain-sensing system. By implementing coarse wavelength division multiplexing (CWDM), the system achieves the simultaneous transmission of optical communication and fiber optical sensor (FOS) sensing signals, resulting in a highly capable, flexible, and cost-effective solution. The proposed FSO transmission technique addresses complex fiber cable installation concerns with topographical limitations. This bidirectional structure ensures the reliability and stability of the long-distance FBG sensor system, supported by extensive research and experimentation. A hybrid stacked gated recurrent units and long short-term memory (SGRU-LSTM) model is proposed to enhance strain measurement accuracy by predicting and measuring the central wavelength of overlapped strain-sensing FBG sensor signals. The results demonstrate the superiority of the proposed model in peak wavelength detection accuracy. The primary benefit of integrating communication and sensing is the significant reduction in construction costs by eliminating the requirement for two individual fiber optic systems, as the integration allows for a single system to fulfill both functions, resulting in more efficient and cost-effective implementation. Overall, this paper contributes to advancing long-distance FBG sensor systems by integrating fiber sensor/FSO communication and deep learning techniques, improving transmission distance, multiplexing capacity, measurement accuracy, system survivability, and cost-effectiveness.

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“…Examples of improvement heuristics algorithms include the GA [34], ACO [35], and PSO [36], [37]. Some scholars have also applied various optimization methods in machine learning to solve TSP and have made progress [38], [39], [40]. Based on the literature mentioned above, the proposed approach in this research aims to address the problem of location priority in TSP by employing fuzzy inference systems combined with metaheuristic algorithms such as the Greedy Algorithm, Dynamic Programming, and Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

Liu

et al. 2023

IEEE Access

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Tidying up objects poses a complex challenge for service robots, particularly when it involves scheduling operations that require real-time action for achieving optimal results. This paper presents a decision system aimed at selecting priority locations to address the problem of robots searching for miscellaneous items in indoor environments. Initially, three datasets with varying complexities are generated using real environmental data, and the environmental information is integrated into feature matrices. Subsequently, the rule base of the fuzzy inference system is trained and optimized using an equilibrium optimizer, and its performance is compared with other commonly used algorithms. The feature matrices and the optimized rule base are then incorporated into the fuzzy inference system, leveraging the traveling salesman problem to determine the optimal sequence for visiting locations. The accuracy and efficiency of the proposed method are validated through physical implementation tests using analog data. Finally, the system is applied and evaluated in real-world scenarios to validate its effectiveness. The video of experiment is available at https://youtu.be/rjMDgopM-9M.INDEX TERMS Autonomous mobile robot, equilibrium optimizer, fuzzy inference system, tidying up object problem, travelling salesman problem.

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On Applicability of Imagery-Based CNN to Computational Offloading Location Selection

Cited by 4 publications

References 41 publications

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Integrated Sensor-Optics Communication System Using Bidirectional Fiber and FSO Channels and Hybrid Deep Learning Techniques

Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

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