The healthcare industry has been suffering from fraud in many facets for decades, resulting in millions of dollars lost to fictitious claims at the expense of other patients who cannot afford appropriate care. As such...
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The healthcare industry has been suffering from fraud in many facets for decades, resulting in millions of dollars lost to fictitious claims at the expense of other patients who cannot afford appropriate care. As such, accurately identifying fraudulent claims is one of the most important factors in a well-functioning healthcare system. However, over time, fraud has become harder to detect because of increasingly complex and sophisticated fraud scheme development, data unpreparedness, as well as data privacy concerns. Moreover, traditional methods are proving increasingly inadequate in addressing this issue. To solve this issue a novel evolutionary dynamic weighted search space approach (DW-WOA-SVM) is presented in the current study. The approach has different levels that work simultaneously, where the optimization algorithm is responsible for tuning the Support Vector Machine (SVM) parameters, applying the weighting procedure for the features, and using a dynamic search space to adjust the range values. Tuning the parameters benefits the performance of SVM, and the weighting technique makes it updated with importance and lets the algorithm focus on data structure in addition to optimization objectives. The dynamic search space enhances the search range during the process. Furthermore, large language models have been applied to generate the dataset to improve the quality of the data and address the lack of good dimensionality, helping to enhance the richness of the data. The experiments highlighted the superior performance of this proposed approach than other algorithms.
Advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm is one of the satellite navigation integrity enhancement technologies to assist civil aviation approach. The basic ARAIM algorithm distributes the in...
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The data collection relies as the major problem while deploying the sensor nodes in Wireless Sensor Network (WSNs). The collection of data using Mobile Sink (MS) is an effective approach to rectify issues in data coll...
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The maximum power point tracking (MPPT) algorithms are essential for ensuring optimal energy conversion and efficient power transfer between the photovoltaic (PV) system and the load. This paper provides a comprehensi...
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The maximum power point tracking (MPPT) algorithms are essential for ensuring optimal energy conversion and efficient power transfer between the photovoltaic (PV) system and the load. This paper provides a comprehensive review of emerging MPPT algorithms for PV systems under different weather conditions, with a focus on their challenges and future trends. The review covers various types of converters, inverters, MPPT techniques including traditional, optimization, and artificial intelligence (AI)-based control strategies used in PV systems. The converters play a crucial role in converting the DC power generated by the PV panels into usable power that can be consumed by different loads. The paper highlights the working principle, limitations, challenges, and comparison of these techniques to choose most suitable algorithm for a specific application. Furthermore, the review discusses the future trends and enhancement in MPPT algorithms, such as the use of AI and optimization techniques to improve the performance and efficiency of PV systems. Overall, this paper provides valuable perspectives into the current state of MPPT algorithms for PV systems and the potential avenues for future research in this field.
The measurement of pile bearing capacity is crucial for the design of pile foundations, where in -situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use...
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The measurement of pile bearing capacity is crucial for the design of pile foundations, where in -situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy -based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems ( ANF/S ) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer ( FHD ) and equilibrium optimizer ( ED ) with the ANF/S , referred to as ANF/S FH0 and ANF/S E0 , respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANF/S FH0 and ANF/S E0 both have amazing potential for precisely calculating pile bearing capacity. The R 2 values obtained for ANF/S FH0 were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANF/S FH0 system had less uncertainty than the ANF/S E0 model. The research found that the ANF/S FH0 model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.
Real world optimization problems contain multiple complexities that are often not tractable if one completely relies on mathematical programming or metaheuristic approaches, as each approach has unique strengths and l...
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This paper introduces a seismic-inspired simplified earthquake optimization algorithm inspired by seismic wave propagation principles for solving optimization problems. Proposed algorithm combines exploration with loc...
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The integration of distributed power sources and electric vehicles introduces significant stochasticity to the distribution grid. In order to better adapt to real-world applications, a model for uncertain scenarios co...
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The traditional neural network algorithm applied to the forecasting of stock price which is easy to fall into local optima. To enhance the accuracy of stock price forecasting and reduce the forecasting time, this pape...
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This paper presents the Infection Susceptible Artificial Intelligence optimization Model (SIMO, susceptible-infected- removed model optimizer), an innovative learned heuristic inspired by biological systems and Deep L...
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This paper presents the Infection Susceptible Artificial Intelligence optimization Model (SIMO, susceptible-infected- removed model optimizer), an innovative learned heuristic inspired by biological systems and Deep Learning (DL) techniques. The SIMO optimization algorithm estimates the susceptibility of the population to infection, active infections and the recovering population at any point in time, inspired by the epidemiological partition model with Infection-Sensitive Artificial Intelligence. SIMO integrates the IA method into the initialisation method and parameter tuning components to improve the search process, so that it can exhibit intelligent and autonomous behaviour. The integration of the IO facilitates the generation of initial solutions based on neural models, which allows the algorithm to be guided towards efficient, effective and robust search results. This approach improves the performance of the algorithm by obtaining high-level solutions, allowing it to converge faster, increasing its robustness and reducing its computational requirements. Two datasets from the 2017 IEEE Congress on Evolutionary Computing (CEC 2017) benchmarking functions are used to validate the effectiveness of the SIMO algorithm and the experimental results are compared with innovative algorithms. Detailed comparisons show that SIMO outperforms many similar models, offering high performance solutions using fewer control parameters. Furthermore, the performance of SIMO is adapted to real-life problems. The results clearly show that integrating the learning process into SIMO provides superior accuracy and computational efficiency compared to other optimization approaches in the existing literature.
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