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Design and development of big data-based model for detecting fraud in healthcare insurance industry

SOFT COMPUTING 2023年第12期27卷 8357-8369页

作者： Mary, A. Jenita Claret, S. P. Angelin SRM Inst Sci & Technol Coll Sci & Humanities Dept Comp Sci Chennai 603203 Tamilnadu India

The advancement in healthcare services has been increasing widely to extend several services with intense quality. One of the important issues affecting the effective use of public funds is the detection of health insurance fraud. Previous techniques of detecting fraud pay close attention to characteristics of a single visit rather than many patient visits. Due to a higher false positive rate and poor profile construction, the common traits have reduced detection performance. This paper introduces a novel and intelligent Provider Fraud_Anomaly Detection System (PF_ADS) by combining big data and deep learning approaches for the healthcare insurance industry. The proposed framework contributes to improvising the preprocessing and classification phases to detect provider fraud at an untimely phase. Initially, the collected datasets are preprocessed using a Relative Risk-based MapReduce framework that builds an organized set of relationships between diseases, patients, and claiming variables. The classification phase is improvised using a proposed Recurrent Neural Network (RNN). It consists of sophisticated steps to consider the significant attributes using hyperparameter optimization. Recalling ability is one of the best parts of RNNs that defines the past and present states of the networks. Therefore, the ability of network state predictions and the tuning of parameters is studied by improved Decisional Score-based Bayesian optimization (DS_BO). Finally, the best attributes with the selective hyperparameters are fed into the input layer of the Recurrent Neural Networks (RNNs) to classify the anomalies from the provider's end. The proposed PF_ADS framework is experimented with and validated on the public repositories. The experimental results state that the proposed framework outperforms better than the other methods in terms of accuracy (88.09%), precision (14.15%), recall (32.80%), and 92.30 s computational time.

关键词： Insurance provider fraud hyperparameters optimization Recurrent neural networks

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Optimized Support Vector Machine using Sparrow Search optimization for Osteoporosis Disease Diagnosis 2

Optimized Support Vector Machine using Sparrow Search Optimi...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Kunjali, Sharifa Malarvizhi, S. Govt Arts & Sci Coll Women Dept Comp Sci Barugur Tamil Nadu India Thiruvalluvar Govt Arts Coll Dept Comp Sci Rasipuram Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Osteoporosis might potentially reduce fracture risk and enhance quality of life if detected early. A prominent intelligence technique for diagnosing osteoporosis is support vector machines (SVMs), and choosing the right SVM parameter is essential for precise model classification. This research study optimizes the kernel function parameter and penalty factor of SVM using the new and extremely efficient sparrow search algorithm (SSA) based on partial opposition-based learning (POBL) called POSSA called POSSA-SVM osteoporosis disease detection model. In comparison to the variants and conventional models, experiments demonstrate that the POSSA-SVM diagnostic model efficiently increases the accuracy of osteoporosis disease diagnosis. It also claims a strong optimization ability and a rapid convergence time.

关键词： support vector machine sparrow search algorithm partial opposition-based learning hyperparameters optimization salp swarm algorithm Osteoporosis detection

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SBS feature selection and AdaBoost classifier for specialization/major recommendation for undergraduate students

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第14期29卷 17867-17887页

作者： Mansouri, Nesrine Abed, Mourad Soui, Makram Polytech Univ Hauts de France INSA LAMIH Valenciennes France Polytech Univ Hauts de France LAMIH Valenciennes France Univ Michigan Flint Coll Innovat & Technol Flint MI USA

Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains challenging are unfamiliar with the job market, the demand for the required skills, and being in the proper placement in a major is not straightforward. Thus, an automatic recommendation system can be helpful for students to assist and guide them in the right decision. In this context, we developed a machine learning model to predict and recommend suitable specializations for undergraduate students according to the job market and student's academic history. Two hundred twenty-five records of students are considered to establish this work. The proposed approach encompasses four major steps, including data preprocessing to clean, scale, and prepare the data for training to avoid obtaining suboptimal results, accompanied by an oversampling process to equal the samples' distribution to prevent the model from being biased or poorly generalized. Furthermore, we conducted a feature selection step using Sequential Backward Selection (SBS) to extract the relevant features to improve the outcomes and reduce the risk of noise. The selected subset is used to train the model using the AdaBoost classifier. We deployed the Genetic algorithm to optimize the classifier's hyperparameters to maximize results. As a result, the findings of this study exhibit noticeable results compared to existing models, with an accuracy of 98.1%. The proposed model can be reliable in guiding undergraduate students through proper decisions regarding selecting their major.

关键词： Specialization/major Recommender systems Machine learning Feature selection Genetic algorithm hyperparameters optimization

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Use of a delayed input for simple and effective optimisation of physical reservoir computers

Use of a delayed input for simple and effective optimisation...

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Conference on Emerging Topics in Artificial Intelligence (ETAI)

作者： Picco, Enrico Jaurigue, Lina Luedge, Kathy Massara, Serge Univ Libre Bruxelles ULB Lab Informat Quant CP224 Av FD Roosevelt 50 B-1050 Brussels Belgium Tech Univ Ilmenau Inst Phys Weimarer Str 25 D-98693 Ilmenau Germany

ISBN: (纸本)9781510678972;9781510678965

Reservoir computing is a promising framework for signal processing applications, but the optimization of such physical reservoirs remains an important challenge in the field. In this work we address this challenge with a new technique based on the use of a delayed input and we test it using an experimental optoelectronic reservoir. We demonstrate that this technique can replace the standard hyperparameters optimisation of reservoirs with a much simpler approach based on the scan of only two parameters. We test this approach on tasks of different nature, confirming its superiority in terms of ease of use and implementation to the standard hyperparameters scan.

关键词： Reservoir Computing photonics time-multiplexing hyperparameters optimization time-series prediction

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An assessment of modeling dynamics in EEG signals using learned sparse nonlinear representations 30

An assessment of modeling dynamics in EEG signals using lear...

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30th IEEE International Conference on Engineering, Technology, and Innovation

作者： Popescu, Dragos Constantin Mateescu, Andrei Stefan, Ioana Livia Vlasceanu, Ioana Miruna Sacala, Joan Stefan Dumitrache, Joan Univ POLITEHN Fac Automat Control & Comp Bucharest Romania

ISBN: (纸本)9798350362442;9798350362435

This work focuses on the challenge of modeling the nonlinear dynamics components of EEG signals, with the long term goal to extract valuable information regarding brain state behavior or emotional and psychological condition. At the core of the approach is the SINDy (Sparse Identification of Nonlinear Dynamics) method, in which the task of choosing the adequate nonlinear signal transformations and hyperparameters of the sparse regression is accomplished by using a Genetic Algorithm and a Bayesian optimization. The performance of the two solutions is analyzed and compared against the MindBigData-EP dataset. The first results show that very good accuracy in modeling the dynamics of EEG signals, of over 80% on 60 seconds long recordings, is achieved, however, the precision is a trade-off against the complexity.

关键词： EEG signals sparse nonlinear dynamics SINDy hyperparameters optimization Genetic Algorithm Bayesian optimization

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Investigating the influence of hyperparameters on the optimal time-series prediction ability of generative large language models 3

Investigating the influence of hyperparameters on the optima...

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3rd Conference on Information Technology and Data Science

作者： Pandy, Arpad Lakatos, Robert Pollner, Peter Csernai, Eszter Hajdu, Andras Univ Debrecen Fac Informat Dept Data Sci & Visualizat Debrecen Hungary Semmelweis Univ Hlth Serv Management Training Ctr Budapest Hungary GE HealthCare Chicago IL USA

ISBN: (纸本)9798350387896;9798350387889

This study investigates the optimal hyperparameter settings for Large Language Models (LLMs) in predicting time series data. We conducted extensive experiments using both synthetic datasets and real agricultural data to evaluate the performance of several LLMs. Our findings reveal that the predictive accuracy of these models is highly dependent on the configuration of the temperature and Top-p hyperparameters. Specifically, models demonstrated optimal performance when the ratio between these two parameters was maintained below a threshold, with the most effective ratios being under 2.0 for Mistral-small and around 4.0 for Gemma-7B. Furthermore, Mistral-small showed lower variability in performance across different settings, indicating higher determinism. By aggregating data from multiple experimental conditions, we identified that maintaining a temperature to Top-p ratio below 1.36 covers 50% of top-performing pairs for Mistral-small and below 2.75 for Gemma-7B. If we consider only hyperparameters, that has such ratios, we can decrease the MSE value by 32% on average. These insights provide a foundation for enhancing the accuracy of LLMs in time-series forecasting, offering practical guidelines for hyperparameter optimization. Our results underscore the potential of LLMs to outperform traditional methods, particularly in datasets with periodic patterns, thereby contributing to the growing literature on the application of generative models in time-series analysis.

关键词： generative large language models hyperparameters optimization time-series prediction synthetic and agricultural data

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A Fully Connected Quantum Convolutional Neural Network for Classifying Ischemic Cardiopathy

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IEEE ACCESS 2022年 10卷 134592-134605页

作者： Ullah, Ubaid Jurado, Alain Garcia Olea Gonzalez, Ignacio Diez Garcia-Zapirain, Begonya Univ Deusto eVIDA Res Grp Bilbao 48007 Spain Basurto Univ Hosp Cardiol Serv Bilbao 48013 Spain

The prevalence of heart diseases is rising quickly throughout the world, which has an impact on both the world economy and public health. According to the recent statistical survey reports, the increasing mortality rate is due to high blood pressure, high cholesterol, the use of tobacco, obesity, and an inconsistent pulse rate. It is difficult and time-consuming to investigate the various variations of these factors and their impact on Coronary Artery Disease (CAD). Therefore, it is necessary to use modern approaches to diagnose the disease early and minimize the mortality rate. The fields of machine learning and data mining have a wide research dimension and various novel techniques that could help in the prediction of CAD in its early stages and identify their patterns and behaviors in a huge amount of data. The results of such predictions will aid the clinical staff in decision making and early diagnosis. In such a scenario, we proposed a quantum version of the Fully Convolutional Neural Network (FCQ-CNN) for Ischemic Heart Disease (IHD) classification. The proposed model evaluates the quantum circuit-based technique that was inspired by convolutional neural networks, a very successful machine learning model. This method provides O(log(n)) depth for n qubits, reducing the number of parameters and allowing for effective training and testing of real quantum devices. The model has been evaluated by considering the IHD dataset after the data has been cleaned and filtered through the Maximally Relevant Minimally Redundancy (MRMR) filter. For dimension reduction, a Support Vector Machine along with Recursive Feature Elimination (SVM-RFE) has been considered. Initially, the model is tested with 20% of the whole dataset and gets the promising results of a testing accuracy of 84.6% with a testing loss of 0.28. By taking into account the same optimal parameters, the proposed model outcomes are compared to those of the classical Optimized Convolution Neural Network (Optimiz

关键词： Ischemic cardiopathy classification feature selection quantum machine learning algorithm hyperparameters optimization

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Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

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IEEE ACCESS 2022年 10卷 45643-45662页

作者： Pirayeshshirazinezhad, Reza Biedron, Sandra G. Cruz, Jorge Alberto Diaz Guitron, Salvador Sosa Martinez-Ramon, Manel Univ New Mexico Dept Mech Engn Albuquerque NM 87131 USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Element Aero Chicago IL 60643 USA

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T, consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

关键词： Virtual telescope for X-ray observation (VTXO) space mission design Monte-Carlo simulation optimal control machine learning optimization satellites real-time control surrogate models high performance computing hyperparameters optimization

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An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第20期34卷 18015-18033页

作者： Houssein, Essam H. Emam, Marwa M. Ali, Abdelmgeid A. Minia Univ Fac Comp & Informat Al Minya Egypt

Breast cancer is the second leading cause of death in women;therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA

关键词： Breast cancer classification Deep learning Transfer learning Convolutional neural network Marine predators algorithm Opposition-based learning hyperparameters optimization

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Tool wear prediction based on convolutional bidirectional LSTM model with improved particle swarm optimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期123卷 4025-4039页

作者： Li, Xianwang Qin, Xuejing Wu, Jinxin Yang, Jinfei Huang, Zhongxiang Guangxi Univ Dept Mech Engn Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China

Accurate tool wear prediction is crucial for preventive maintenance on time. Most of the existing data-driven prediction methods still need complex feature engineering, which reduces the prediction accuracy and efficiency. To address this problem, a tool wear prediction model based on Improved Particle Swarm optimization (IPSO) Convolutional Neural Network (CNN) and Bidirectional long short-term memory (BiLSTM) network is proposed. Firstly, the cutting force, vibration, and acoustic emission signals are taken as the input features of the model. CNN is used for high-dimensional feature extraction in the raw signal. Then, the BiLSTM with long-term memory and time-series processing ability is used to model time-series data. Besides, IPSO is employed to enhance the prediction accuracy of the hybrid model. Finally, the fully connected layer obtains the tool wear prediction results. IEEE PHM 2010 challenge data was used to illustrate and validate this model. The experimental results show that the hybrid model has an average prediction error of 7.06% on this data set, with an average absolute error (MAE) and an average root mean square error (RMSE) of 1.49 and 1.81, respectively. The prediction performance of the proposed hybrid model outperforms other related deep learning models.

关键词： Tool wear prediction Feature extraction Bidirectional LSTM hyperparameters optimization

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