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A robust framework for enhancing cardiovascular disease risk prediction using an optimized category boosting model

Mathematical Biosciences and Engineering 2024年第2期21卷 2943-2969页

作者： Qiu, Zhaobin Qiao, Ying Shi, Wanyuan Liu, Xiaoqian School of Mathematics and Information Sciences North Minzu University Yinchuan China Ningxia Collaborative Innovation Center for Scientific Computing and Intelligent Information Processing North Minzu University Yinchuan China

Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and it is of utmost importance to accurately assess the risk of cardiovascular disease for prevention and intervention purposes. In recent years, machine learning has shown significant advancements in the field of cardiovascular disease risk prediction. In this context, we propose a novel framework known as CVD-OCSCatBoost, designed for the precise prediction of cardiovascular disease risk and the assessment of various risk factors. The framework utilizes Lasso regression for feature selection and incorporates an optimized category-boosting tree (CatBoost) model. Furthermore, we propose the opposition-based learning cuckoo search (OCS) algorithm. By integrating OCS with the CatBoost model, our objective is to develop OCSCatBoost, an enhanced classifier offering improved accuracy and efficiency in predicting CVD. Extensive comparisons with popular algorithms like the particle swarm optimization (PSO) algorithm, the seagull optimization algorithm (SOA), the cuckoo search algorithm (CS), K-nearest-neighbor classification, decision tree, logistic regression, grid-search support vector machine (SVM), grid-search XGBoost, default CatBoost, and grid-search CatBoost validate the efficacy of the OCSCatBoost algorithm. The experimental results demonstrate that the OCSCatBoost model achieves superior performance compared to other models, with overall accuracy, recall, and AUC values of 73.67%, 72.17%, and 0.8024, respectively. These outcomes highlight the potential of CVD-OCSCatBoost for improving cardiovascular disease risk prediction. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Decision trees

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Hedge-Embedded Linguistic Fuzzy Neural Networks for Systems Identification and Control

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4928-4937页

作者： Rafiei, Hamed Akbarzadeh-T, Mohammad-R Ferdowsi University of Mashhad Department of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Mashhad*** Iran Chalmers University of Technology Department of Electrical Engineering Center for Bionics and Pain Research Mölndal Gothenburg412 96 Sweden

In the realm of natural language processing, hedge-embedded structures have contributed considerably by appreciating linguistic variables and distinguishing overlapped classes. This aspect of natural languages considerably affects the building of linguistically interpretable architectures for fuzzy neural networks (FNNs). Here, we propose extending the idea of hedge-embedded linguistic fuzzy neural networks (LiFNNs) to the systems identification and control paradigm. This perspective leads us to the universal approximation property for this mathematical construct using the Stone–Weierstrass theorem and the proof of stability for the resulting nonlinear system identification process using the Lyapunov function. Furthermore, the power activation functions in the membership degrees of the proposed network enable linguistic hedge interpretation and more precise learning. Finally, the proposed LiFNN, optimized using a backpropagation learning algorithm, is evaluated on several problems in function approximation (periodic functions and quadratic Hermite function), system identification (a nonlinear system), and direct adaptive control fields. Results show that memberships are more distinguishable in the proposed LiFNN, leading to ∼50% less error on the average and higher granulation and interpretability. © 2020 IEEE.

关键词： Linguistics

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Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search for Feature Selection 31

Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search...

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31st International Conference on Electrical Engineering, ICEE 2023

作者： Farjadi, Seyed Amirhossein Akbarzadeh-T, M.-R. Ferdowsi University of Mashhad Center of Excellence on Soft Computing and Intelligent Information Processing Department of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350312560

Feature selection (FS) is an essential preprocessing step in machine learning and data mining. FS algorithms eliminate the irrelevant and redundant features from a feature vector, thereby increasing the accuracy and robustness of the data mining process while offering higher transparency and lower computation. Here, we suggest using the coral reefs optimization algorithms combined with a betahill climbing algorithm as part of a mimetic search strategy. The Coral Reefs is a population-based global search technique that combines well with local search techniques, as suggested here. However, such a combination is also computationally expensive. They could also converge prematurely with few iterations or insufficient population diversity. Accordingly, we propose here a more efficient search in the problem space by transforming the brooding operator into a function of the ranks of coral larvae. In this way, the chance of disturbing a larva with a higher rank is reduced, and at the same time, the exploratory role of larvae with a lower rank is used best. The performance of the proposed method has been evaluated using three KNN, Decision Tree, and SVM classifiers on eight UCI standard datasets using Precision, Recall and f1 score criteria. Results show that the rank-based adaptive brooding approach is superior to the fixed brooding approach in the high-dimensional problem space. © 2023 IEEE.

关键词： Optimization

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RGB Image-Based Hand Pose Estimation: A Survey on Deep Learning Perspective 20

RGB Image-Based Hand Pose Estimation: A Survey on Deep Learn...

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20th CSI International Symposium on Artificial Intelligence and Signal processing, AISP 2024

作者： Farjadi, Seyed Amirhossein Akbarzadeh-T, M.-R. Ghiasi-Shirazi, Kamaledin Ferdowsi University of Mashhad Center of Excellence on Soft Computing and Intelligent Information Processing Department of Computer Engineering Mashhad Iran Ferdowsi University of Mashhad Department of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350383942

The pervasive integration of modern artificial intelligence into daily life necessitates robust human-computer interaction, underscored by advancements in computer technology. As the primary human tool, hand position, and orientation are vital for applications like virtual reality, with hand pose estimation playing a pivotal role. Despite the challenges inherent in RGB image-based estimation, the coalescence of extensive datasets, neural networks, and heightened computational capabilities has catalyzed the utilization of deep learning in visual tasks, particularly advancing hand pose estimation. This review exclusively centers on methodologies of RGB image-based approaches within the realm of hand pose estimation. The paper explores recent advancements in this category, providing a comprehensive analysis of outcomes and charting future directions. Organized around discussions on challenges, advancements, and future directions, this review aims to contribute to the ongoing discourse in the dynamic RGB image-based hand pose estimation field. By addressing unique challenges specific to this method, the paper offers insights that contribute to developing precise and robust hand pose estimation systems, impacting the broader landscape of computer vision and human-machine interaction. © 2024 IEEE.

关键词： Computer vision

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Unsupervised Muscle Fatigue Clustering Based on sEMG Signals using DeepCNN Autoencoders 11

Unsupervised Muscle Fatigue Clustering Based on sEMG Signals...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Javanbakht, Mohammad Javad Fouji, Mohammad Reza Saffar, Amin Akhavan Akbarzadeh-T, Mohammad-R. Akbarzadeh, Alireza Mashhad Iran Mashhad Iran Ferdowsi University of Mashhad Center of Excellence on Soft Computing and Intelligent Information Processing Dept. of Electrical Engineering Mashhad Iran Mashhad Iran

ISBN: (纸本)9798350308105

Muscle fatigue assessment is key for optimizing physiotherapy, rehabilitation, and exercise. Muscle fatigue detection not only helps in reducing rehabilitation discomfort but also assists athletes in working out efficiently. The main motivation behind this research is to address the pressing need for a precise and reliable tool to assess muscle fatigue during physical activities. In addition to enhancing therapeutic interventions, this approach improves microtears-induced muscle strengthening by advancing the understanding of muscle fatigue levels. Previous studies have mainly employed supervised approaches to detect fatigue by processing surface electromyography (sEMG) signals through traditional methods in the Time, Frequency, and Time-Frequency domains. These approaches are vulnerable to challenges, such as signal sensitivity, noise interference, and inter-individual variability. Thus, the proposed method uses the spring energy model of the sEMG signal because of its excellent approximation with muscle characteristics. In order to handle noisy and ambiguous data, this paper employs a self-supervised Deep Convolutional Neural Network (DeepCNN) Autoencoder for feature extraction and K-means clustering due to their strong performance. Five healthy participants carried out two experiments using a knee rehabilitation robot (FUM-Physio). The results showcase a well-ordered clustering that closely mirrors the progression of muscle fatigue during these experiments. This indicates the robustness and generality of the proposed method. © 2023 IEEE.

关键词： Muscle

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A Distributed Learning Based on Robust Diffusion Sgd Over Adaptive Networks with Noisy Output Data

SSRN

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SSRN 2023年

作者： Barani, Fatemeh Savadi, Abdorreza Yazdi, Hadi Sadoghi Mashhad Iran Center of Excellence on Soft Computing and Intelligent Information Processing Mashhad Iran

Outliers and noises are unavoidable factors that cause the performance of the distributed learning algorithms to be severely reduced. Developing a robust algorithm is vital in applications such as system identification and forecasting stock market, in which noise on the desired signals may intensely divert the solutions. In this paper, we propose a Robust Diffusion SGD (RDSGD) algorithm based on the pseudo-Huber objective function which can significantly suppress the effect of Gaussian and non-Gaussian noises on estimation performances in the adaptive networks. Performance and convergence behavior of RDSGD are assessed in presence of the α-stable and Mixed-Gaussian noises in the stationary and non-stationary environments. Simulation results show that the proposed algorithm can achieve both higher convergence rate and lower steady-state misadjustment than the conventional diffusion algorithms and several robust algorithms in the presence of non-Gaussian noises. On the other hand, when the environment noise is Gaussian, the performance of RDSGD is similar to the diffusion SGD algorithm with the square loss function (DSGD), while outperforms the other mentioned algorithms. © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Autoencoders for Input Reduction in Interval Type-2 Hyperbolic Fuzzy System Identification and Control: Experimental Results

Autoencoders for Input Reduction in Interval Type-2 Hyperbol...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Behnaz Mohammadi Nazanin Ildarabadi Mohammad-R Akbarzadeh-T Dept. of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

One of the main challenges in real-time robot control is its computational complexity and poor performance due to environmental uncertainties and nonlinearities. Interval Type-2 Generalized Fuzzy Hyperbolic Systems (IT2-GFHS) offer a simpler inferencing process but at the cost of increased number of inputs. However, numerous inputs could lead to decreased accuracy and lower efficiency. This article presents an Interval Type-2 Generalized Fuzzy Hyperbolic Neural Network System (IT2-GFHNS) that utilizes an Autoencoder neural network to reduce the number of inputs and improve the system performance. The effectiveness of the proposed method is first examined through simulations on a quadcopter and then experimentally implemented on an EMG-driven Robotic Follower, where the system identification is achieved using IT2-GFHNS. Additionally, the sliding mode is employed as a quadcopter control mechanism, and PID control is utilized for an EMG-driven robot. This method outperforms IT2-GFHS in terms of reducing tracking error, even with the increasing number of inputs.

关键词： Uncertainty System performance Simulation Neural networks Robot control Feature extraction Real-time systems

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Taguchi-based Optimal Dry Electrode Design 11

Taguchi-based Optimal Dry Electrode Design

引用

11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Zakeri-K, Sorour Ghanaei, Mohsen Hakkak, Hediye Akbarzadeh-T, Mohammad-R. Akbarzadeh, Alireza Dept. of Electrical Engineering Mashhad Iran Dept. of Mechanical Engineering Mashhad Iran Center of Excellence on Soft Computing and Intelligent Information Processing Dept. of Electrical Engineering Mashhad Iran Dept. of Mechanical Engineering Mashhad Iran

ISBN: (纸本)9798350308105

Surface Electromyography (sEMG) electrodes, including both wet and dry classes, play a significant role in the EMG signals analysis and recording of muscle activation area. Users of prosthesis hands must occasionally manipulate and move their limbs for several hours during daily tasks. Hence, high-quality and durable muscle activation is crucial. Although the designs of electrodes with different probe shapes, sizes, metals, and roughness are presented in previous studies, finding the optimal electrode design is an open problem. Hence, this paper proposes a different application of the Taguchi method to specify a reduced set of examinations for determining the optimal electrode design. This study considers the length, width, and distance of probes as controllable parameters as well as subject dominant hand, skin humidity, and muscle fatigue as data recording noises. The design of the experiment process (DOE) selects reasonable orthogonal arrays for the inner and outer arrays of the DOE table. The probes of the electrodes are made of silver due to their high conductivity, less oxidation, and fewer skin allergies in the presented study. In this investigation, the primary focus lies in electrode optimization through the decrease in the error of grasp force prediction. The Support Vector Machine (SVM) method is used to evaluate the error of grasp force. For this purpose, the signal-To-noise ratio (SNR) assessment is performed, identifying each parameter's optimum level and significance. Results indicate that the electrodes with probes of 1 cm in length, 1 cm in width, and 2 cm apart lead to better sEMG signals. These results offer a design for significantly improved muscle activation signal based on the FUMCARE sEMG setup to use in hand prosthesis applications. © 2023 IEEE.

关键词： Support vector machines

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Impact-based Prioritization of Medical Equipment Maintenance for Small Medical centers

Impact-based Prioritization of Medical Equipment Maintenance...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Mina Ashrafiyan Mohammad-R. Akbarzadeh-T Department of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

The importance of prioritizing medical equipment maintenance in small medical centers (SMCs) cannot be overstated, especially since these facilities often do not have backups for much of their equipment and must run on considerably tighter budgets. When a device malfunctions in a SMC, the impact on the performance of the entire clinic is significant. Repair costs, along with additional costs associated with the failure can also rise. Yet, the current literature addresses maintenance scheduling only for larger medical providers. This study aims to propose an Analytic Hierarchy Process-based TOPSIS (AHP-TOPSIS) model to prioritize medical equipment in SMCs. For this purpose, we introduce a new “impact” factor to measure the device failure’s effect on other equipment. This model considers linguistic and numerical information types. Specifically, equipment age, criticality during urgent care, usage and failure frequencies, repair cost, and alternative back up are considered in the prioritization process. We then implement the model using actual databases from three 24-hour clinics in the city of Mashhad. Results indicate that equipment prioritization could vary considerably in different SMCs, leading to the conclusion that actual equipment usage data in SMCs is highly valuable for providing better care. The results also confirm the importance and effect of the impact factor on SMCs’ prioritization.

关键词：

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Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search for Feature Selection

Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Seyed Amirhossein Farjadi M.-R. Akbarzadeh-T. Department of Computer Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

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