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Novel nonlinear fractional order Parkinson's disease model for brain electrical activity rhythms: Intelligent adaptive Bayesian networks

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Chaos, Solitons and Fractals 2024年 180卷

作者： Mukhtar, Roshana Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Chaudhary, Naveed Ishtiaq Shu, Chi-Min Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

In this study, a novel investigation in developing intelligent adaptive Bayesian networks (IABN) is carried out to solve the fractional order Parkinson's disease model (FOPDM) represented with a system of three fractional differential equations reflecting the brain's electrical activity rhythms at different cerebral cortex positions. The IABN are developed by using the competency of the multi-layer architecture of neural networks with backpropagation through Bayesian regularization optimization scheme. The reference dataset for IABN is created through Grunwald-Letnikov fractional derivative-based numerical solver for FOPDM in case of varying sensor locations on the cerebral cortex, as well as, considering different fractional orders in FOPDM. The proposed IABN is applied to the created datasets arbitrarily partitioned into training and testing sets by optimizing the fitness criterion based on mean square error (MSE) metric. The detailed analyses of the proposed IABN through extensive simulations and comparison with the reference numerical solutions of FOPDM in terms of MSE training/testing plots, absolute error, training/testing regression plots, and error histogram results, endorse the worth of the proposed intelligent scheme for different fractional orders. © 2024 Elsevier Ltd

关键词： Bayesian networks

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Propagation of Two-Soliton in a Compressible Hyperelastic Rod 2nd

Propagation of Two-Soliton in a Compressible Hyperelastic...

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2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024

作者： Chettri, Yogesh Micheal, David Raj Abdul Rahim, M.F. Department of Mathematics Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Vellore Institute of Technology Tamil Nadu Vellore India University Malaysia of Computer Science and Engineering Aras 1 and 2 Block 2 VSQ@PJ City Centre Jalan Utara Section 14 Selangor Petaling Jaya46200 Malaysia

ISBN: (纸本)9783031668739

The research delves into investigating the propagation of two-soliton in a compressible hyperelastic rods in the framework of the KdV equation. Using suitable transformation, the KdV equation is transformed to the standard KdV equation. By utilizing the Hirota bilinear method, the two-soliton solution of the KdV equation is obtained. Time evaluation of the propagation of two-soliton are plotted. Effect of various parameters on the two soliton solution are also investigated. Phase shift and their variation for different parameter values are also presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Solitons

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Energetic and Smart Design of Battery Management Scheme to Improve the Lifetime of Electric Vehicles

Energetic and Smart Design of Battery Management Scheme to I...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Varatharaju, V.M. Chidambaranathan, C.M. Karthik, Mamidala Vijay Sathi, G. Pandi, V. Samuthira Shobana, D. University of Technology and Applied Science Electrical Section Engineering Department Ibra Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Cmr Engineering College Department of Electrical & Electronics Engineering Telangana Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

In order to maximize efficiency, electric vehicles use Battery Management Systems (BMS) to manage their rechargeable batteries. The battery's safety and dependability are ensured by the battery management system, and the battery's age is extended without being damaged. Various monitoring methods are used to keep track of the battery pack's charging capacity, voltage-levels, current-ratings and ecological pressure ratio. A variety of analogue and digital sensors are employed with microcontrollers for this purpose. Several powerful batteries have been developed as a result of the tremendous advances in the field of batteries over the past ten years. High-power batteries with suitable battery management systems are essential for the safe and reliable operation of Electric Vehicles (EVs). This paper will address the power sources utilized by electric automobiles and the primary problems of battery control systems, as well as compare both the lithium ion battery as well as the metal based nickel-hydride batteries in regards to growing older along with the impact caused by climate through the use of the State-of-Charge (SOC) and closed device electricity. This study's goal is to compare and contrast different intelligence techniques along with the control methods used in electric vehicle battery management platforms. The study assesses the characteristics, structure, configuration, accuracy, benefits, and downsides of intelligent algorithms for estimating the condition of a battery. Focusing on parts, characteristics, aims, outcomes, advantages, and restrictions, this research analyses the various controllers used for warming, cooling, balancing, and protecting batteries. © 2023 IEEE.

关键词： Battery Management Systems BMS Electric Vehicles EVs SOC State-of-Charge

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Detection and Identification of Respiratory Disease Using DWT and SVM 23

Detection and Identification of Respiratory Disease Using DW...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Garcia Rios, Elizabeth Escamilla Hernandez, Enrique Espino Lopez, Angela Gabriela Perez Meana, Héctor Manuel Mendoza Guzman, Lorena Academic Area of Computer Systems Tecnológico Nacional de México Instituto Tecnológico Superior del Occidente del Estado de Hidalgo Hgo Mixquiahuala42700 Mexico Section of Graduate Studies and Engineering Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Culhuacan CDMX04440 Mexico

ISBN: (纸本)9781643685380

This article proposes a computer-aided diagnosis (CAD) system for the detection and identification of respiratory diseases, such as COVID-19, pneumonia, etc., in addition to differentiating a healthy case. Starting then, the proposal is comprised of the Discrete Wavelet Transform (DWT) with the Symlet10 function for the extraction of main features, together with the Limited Contrast Adaptive Histogram (CLAHE) method for contrast enhancement. For the classification of the cases, the Support Vector Machine (SVM) was used. The results showed considerable performance with the Medium Gaussian SVM model delivering 82.4% of correctly estimated values. Improve the capacity of detection and identification based on a supervised learning algorithm without the need to use high computational performance, considering that, in most of the health systems in Mexico, there is not the necessary hardware for the installation and operation of systems with high computational demand requirements. © 2024 IOS Press. All rights reserved.

关键词： Lung cancer

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Performance Analysis of Machine Learning Algorithm for the Credit Risk Analysis in the Banking Sector 7

Performance Analysis of Machine Learning Algorithm for the C...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Hegde, Sandeep Kumar Hegde, Rajalaxmi Krishna Priya, R. Sree Southry, S. Marthanda, A.V.G.A. Logu, K. Karkala Taluk Udupi India Department of Engineering Electrical Section University of Technology and Applied Sciences Shinas campus PO Box 66Al-Aqur Shinas324 Oman Department of ECE Sona College of Technology Salem India Department of EEE Laki Reddy Bali Reddy College of Engineering Mylavaram Permanently Affiliated to JNTU Andhrapradesh Kakinada India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9781665464086

The banking sector has advanced in recent years. Thus, there is an increase in the demand for bank loans. The bank must distribute and sell each of the limited number of available slots to a select group of people. As a result, a usual stage is to identify who will be unable to return the loan and who will prove to be a more trustworthy option to the bank. In order to save the bank time and costs, in the proposed paper machine learning based approach is introduced to reduce the risk involved with finding the safe individual. In order to decide whether or not to grant someone a loan, this paper presents a method of loan approval based on predetermined criteria. The machine learning model for credit approval was implemented using logistic regression, XG Boost, random forest and naïve bayes model. The experimental results indicates that logistic regression model is more accurate for the credit risk analysis. © 2023 IEEE.

关键词： Python

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Integration of FDOSM and FWZIC under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

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International Journal of Information Technology and Decision Making 2024年第4期23卷 1559-1599页

作者： Alsattar, H.A. Qahtan, Sara Mohammed, R.T. Zaidan, A.A. Albahri, O.S. Kou, Gang Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Al-Samarraay, Mohammed S. Malik, R.Q. Jasim, Ali Najm Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Faculty of Engineering and IT The British University in Dubia United Arab Emirates Computer Techniques Engineering Department Mazaya University College Nassiriya Thi-Qar Iraq School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Foundation of Alshuhda Baghdad Iraq

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-Attribute decision-Analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. © 2024 World Scientific Publishing Company.

关键词： COVID-19

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Development of Low Cost Plasmonic Tracker Via a Microstepping with PC Based System

Development of Low Cost Plasmonic Tracker Via a Microsteppin...

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2023 International Conference on engineering Technology and Technopreneurship, ICE2T 2023

作者： Abdullah, Muhammad Rosli Harun, Noor Hasmiza Wahab, Azimah Abdul Ibrahim, S Noorjannah Jamilan, Mohd Azerulazree Rahim, Nur Aina Syuhada Universiti Kuala Lumpur British Malaysian Institute Medical Engineering Technology Section Selangor Malaysia Universiti Kuala Lumpur Institute of Medical Science Technology Selangor Malaysia Engineering International Islamic University Department Of Electrical & Computer Gombak Selangor 53100 Malaysia Ministry of Health Malaysia NMCRC Institute for Medical Research Selangor Shah Alam40170 Malaysia

ISBN: (纸本)9798350304107

A plasmonic tracker had been developed for detecting a plasmonic energy at Total Internal Reflectance angle. It is crucial to determine thin film thicknesses, resonance angle and refractive index of a dielectric material. However, a low cost and flexible plasmonic tracker could offers a precise control for accurate measurement system. The main objective is to develop manual, automated and smart control of the plasmonic tracker at high resolution via a PC based system. The plasmonic tracker uses two 1.8° bipolar stepper motor as a prism and silicon photodiode stages. Both of the stepper motors were operated in microstepping that generate 3200 steps at 0.1125° pulse per step. A stable microstepping operation was observed at distance, period, phase angle, torque, RPM and power respectively at 1250 mm/ sec, 90 μS, 3.4°, 0.22 Nm, 200 RPM and 4.64 W. A plasmonic curve and resonance angle were plotted and displayed from a customized software interface which provides high sensitivity, throughput, performance and efficiency. © 2023 IEEE.

关键词： Plasmonics

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Improving the Strut Modelling of the European Space Agency Deep Space Antennas to Evaluate Efficiency and Sidelobe Impact

Improving the Strut Modelling of the European Space Agency D...

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European Conference on Antennas and Propagation, EuCAP

作者： Davide Arenare Fabio Pelorossi Filippo Concaro Marco Pasian Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Directorate of Operations GSA Section European Space Operations Centre European Space Agency Darmstadt Germany

Large reflector antennas necessitate the presence of struts to support, according to the configuration, feeds or sub-reflectors. This paper presents the results of an investigation, in terms of sidelobe level and efficiency, of the impact on the antenna performance of the struts, considering the upcoming European Deep Space Antenna 4 (DSA4). In particular, for the first time in the framework of the DSA4 project, the strut model includes a tapering of the diameter of the strut itself, and a service platform and ladder (needed to reach the sub-reflector for maintenance), thus modeling with greater accuracy the real situation. The simulations presented here, carried out using Physical Optics, represent a rare opportunity to investigate these aspects, most often discarded due to the large computational time, and show that the impact on the antenna performance, for such high-end antennas, may be not negligible.

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Model-Based DRL for Task Scheduling in Dynamic Environments for Cognitive Multifunction Radar

Model-Based DRL for Task Scheduling in Dynamic Environments ...

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IEEE National Conference on Radar

作者： Sunila Akbar Raviraj S. Adve Zhen Ding Peter W. Moo Department of Electrical and Computer Engineering University of Toronto Toronto Canada Radar Sensing and Exploitation Section Defence Research and Development Canada (DRDC) Ottawa Research Center Ottawa Canada

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

The uncertainty in the radar environment brings significant challenges to task scheduling in a cognitive multifunction radar (MFR). The recent radar task scheduling approaches assume the knowledge of the environment, which is unknown in real scenarios. To achieve online task scheduling for a cognitive radar which does not need to know the dynamics of the environment, this work investigates the model-based deep reinforcement learning (DRL), which learns the model of the environment to plan for scheduling tasks. The main idea of the model-based methods is to construct an abstract Markov-decision process (MDP) model such that planning in the abstract MDP is equivalent to planning in the real environment. Here, we tailor MuZero, proposed to learn to play games, to provide the needed model. The proposed algorithm is shown to be effective in MFR task scheduling while adapting to dynamic radar environments, without any a priori knowledge.

关键词： Training Adaptation models Schedules Uncertainty Heuristic algorithms Radar Predictive models

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Type II Solar Radio Burst Segmentation and Detection using Multi-Model Deep Learning Networks

Type II Solar Radio Burst Segmentation and Detection using M...

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Irish Signals and Systems Conference (ISSC)

作者： Jeremiah Scully Ronan Flynn Peter Gallagher Mark Daly Department of Computer and Software Engineering Technological University of the Shannon Athlone Ireland Astronomy & Astrophysics Section Dublin Institute of Advanced Studies Dublin Ireland

Type II Solar Radio Bursts (SRBs) are the result of particle acceleration by shock waves in the solar corona and interplanetary medium. The shocks are created by solar eruptions involving coronal mass ejections traveling at super-alfvenic speeds. The automatic detection, classification, and segmentation of such radio bursts is a challenge in solar radio physics due to their heterogeneous form. Large data rates produced by cutting-edge radio telescopes like the LOw-Frequency ARray (LOFAR) have made SRB detection and classification more feasible in recent years. In this study, we use a Generative Adversarial Network (GAN) to simulate Type II SRBs and then use this simulated data as a training set for a Mask R-CNN to detect and segment Type II SRBs automatically. Using this multi-model approach, we can accurately detect and segment Type II SRBs with a mean Average Precision (mAP) score of 78.90%.

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