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Human activity recognition in IoHT applications using arithmetic optimization algorithm and deep learning

MEASUREMENT 2022年 199卷

作者： Dahou, Abdelghani Al-qaness, Mohammed A. A. Elaziz, Mohamed Abd Helmi, Ahmed Univ Ahmed DRAIA Math & Comp Sci Dept Adrar 01000 Algeria Wuhan Univ State Key Lab Informat Engn SurveyingMapping & Re Wuhan 430079 Peoples R China Galala Univ Fac Sci & Engn Suez 435611 Egypt Zagazig Univ Fac Engn Dept Comp & Syst Engn Zagazig 44519 Egypt Univ Ahmed Draia Fac Sci & Technol LDDI Lab Adrar 01000 Algeria Sanaa Univ Fac Engn Sanaa 12544 Yemen Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Buraydah Private Coll Coll Engn & Informat Technol Buraydah 51418 Saudi Arabia Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates

Nowadays, people use smart devices everywhere and for different applications such as healthcare. The Internet of Healthcare Things (IoHT) generates enormous amounts of data daily, which need exploitation and analysis to help healthcare professionals make decisions and provide a fast diagnosis. Human Activity Recognition (HAR) has received more attention due to its importance in elderly care, lifestyle improvement, and IoT systems. This paper presents a novel HAR system based on optimizing two algorithms: convolutional neural network (CNN) and the recently proposed optimization algorithm, arithmetic optimization algorithm (AOA), to boost the HAR performance with fewer resources. The proposed CNN is applied to learn and extract features from input data where a modified AOA algorithm, called Binary AOA (BAOA), is used to select the most optimal features. Finally, the support vector machine (SVM) is adopted to classify the selected feature based on different activities. We evaluate the proposed HAR model with three different public datasets, UCI-HAR, WISDM-HAR, and KU-HAR datasets. Moreover, we compare the feature selection method, BAOA, to various optimization algorithms using several evaluation measures, and we found that BAOA recorded the best performance. Furthermore, we compare the proposed model to several existing HAR methods. The outcomes confirmed the competitive performance of the proposed model, which achieved 95.23%, 99.5%, and 96.8% for UCI-HAR, WISDM-HAR, and KU-HAR datasets, respectively.

关键词： Human Activity Recognition (HAR) Human-computer interaction (HCI) Internet of Healthcare Things (ioHT) Feature selection arithmetic optimization algorithm

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Nondestructive detection for adulteration of panax notoginseng powder based on hyperspectral imaging combined with arithmetic optimization algorithm-support vector regression

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JOURNAL OF FOOD PROCESS ENGINEERING 2022年第9期45卷 e14096-e14096页

作者： Zhang, Fujie Shi, Lei Li, Lixia Zhou, Yufeng Tian, Liquan Cui, Xiuming Gao, Yongping Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Yunnan Peoples R China Key Lab Crop Harvesting Equipment Technol Zhejian Jinhua Zhejiang Peoples R China Yunnan Prov Key Lab Panaxnotoginseng Kunming Yunnan Peoples R China Yixintang Pharmaceut Grp Ltd Kunming Yunnan Peoples R China

Illegal merchants adulterate the powder of panax notoginseng rhizome and main root with the powder of panax notoginseng fibrous root, which reduces the efficacy and hygiene of panax notoginseng powder. To detect the adulteration of panax notoginseng powder nondestructively and rapidly, in this study, the hyperspectral images of 80 samples of adulterated rhizome powder and adulterated main root powder (AR & AM) were collected by using a hyperspectral image acquisition system (400-1000 nm). Then, Savitzky-Golay and standard normalized variable (SG-SNV) were used to preprocess the spectrum and improve the signal-to-noise ratio of the data. Next, competitive adaptive reweighed sampling (CARS) and iteratively retains informative variables (IRIV) methods were respectively adopted to select feature wavelengths from the spectra data after pretreatment. Subsequently, support vector regression (SVR) was used to establish the prediction models based on the selected variables and the full spectra data. Also, the arithmetic optimization algorithm (AOA) was used to optimize the parameters of the SVR model. The results showed that CARS-AOA-SVR was the optimal model for AR & AM, achieving RP2$$ {\mathrm{R}}_{\mathrm{P}}<^>2 $$ of .9693 and .9667, RMSEP of .0266 and .0264, respectively. Therefore, the hyperspectral imaging combined with the CARS-AOA-SVR is a feasible method to detect the adulteration concentration of panax notoginseng powder. Practical Applications The traditional detection methods for adulteration of panax notoginseng powder depended on manual detection and chemical analysis. In order to detect the adulterated concentration of panax notoginseng powder rapidly and accurately, the adulterated panax notoginseng powder was studied based on hyperspectral imaging. The results showed that hyperspectral imaging combined with CARS-AOA-SVR model could be used to quantitatively detect the adulteration concentration of panax notoginseng powder, which could be a fast, accurate,

关键词： adulteration detection arithmetic optimization algorithm hyperspectral imaging Panax notoginseng powder support vector regression

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Optimal Placement of multiple Distributed Generators using a Novel Voltage Stability Indicator employing arithmetic optimization algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2023年第1期110卷

作者： Khan, Mohd Tauseef Singh, Pushpendra Chauhan, Anurag Arya, Rajesh Verma, Aanchal Titare, L. S. Choube, S. C. AKTU Lucknow Rajkiya Engn Coll Banda Dept Elect Engn Lucknow 210201 Uttar Pradesh India Med Caps Univ Dept Elect Engn Indore 453331 MP India Indian Inst Technol Dept Elect Engn Varanasi 221005 UP India Goverment Engn Coll Dept Elect Engn Ujjain 456010 MP India UIT RGPV Dept Elect & Elect Engn Bhopal 452003 Madhya Pradesh India

With the extensive application of renewable energy operated distributed generation plants in the power system, certain issues need to be addressed. One such problem is the optimal location of these DG units, as their random placement may result in congestion in certain paths of the transmission network. This paper proposed a novel voltage stability indicator (VSI) that is incorporated in the objective function along with real power loss reduction and minimum voltage deviation. The prominent feature of this VSI is the utilization of phasor data available through Phasor Measurement Units (PMUs) in the recent power systems. This VSI performs satisfactorily for both mesh and radial networks. The developed method under the paper is tested on IEEE-30 bus network. To optimize the objective function, arithmetic optimization algorithm (AOA) is used and the obtained results have been compared with Particle swarm optimization. Simulation results justified the applicability of this technique for reducing losses in the network.

关键词： Voltage stability indicator distributed generation phasor measurement unit optimal location arithmetic optimization algorithm

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A new MPPT design using arithmetic optimization algorithm for PV energy storage systems operating under partial shading conditions

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期289卷

作者： Chtita, Smail Derouich, Aziz Motahhir, Saad EL Ghzizal, Abdelaziz SMBA Univ Ind Technol & Serv Lab EST Fes Morocco SMBA Univ Syst & Applicat Lab Engn ENSA Fes Morocco SMBA Univ Innovat Technol Lab EST Fes Morocco

arithmetic optimization is a new metaheuristic algorithm that has shown great strength and good performance in handling and solving complex problems. In photovoltaic systems, and especially in the case of a partial shading condition, the power-voltage curve of the photovoltaic array exhibits multiple peaks of which only one is global, making the tracking of the global maximum power point more complex, and thus requiring a powerful metaheuristic maximum power point tracking algorithm. Therefore, to address this problem, this paper introduces a new design of an arithmetic optimization based maximum power point tracking controller for battery charging schemes through photovoltaic systems operating under partial shading conditions. The main objective of the arithmetic optimization based maximum power point tracking proposed here is to maximize the power extracted from the photovoltaic array under all types of climatic conditions in order to efficiently charge the battery through the maximization of its charging current. The feasibility and effectiveness of the proposed arithmetic optimization based maximum power point tracking method are verified through MATLAB simulations and processor-in-the-loop test using the low-cost STM32F429 embedded board. Moreover, a comparison of its performance with existing maximum power point tracking methods such as, hybrid of grey wolf optimization and particle swarm optimization, particle swarm optimization and perturb and observe is also carried out while considering real climatic conditions as well as uniform and partial shading scenarios. Simulation and processorin-the-loop results have demonstrated the good performance of the proposed method in terms of battery charging efficiency as well as the ability to track the global maximum power point with an efficiency of 99.99% and a tracking time of 0.85 s.

关键词： PV systems Battery charging arithmetic optimization algorithm MPPT Partial shading conditions Processor-in-the-loop

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Parallel binary arithmetic optimization algorithm and its application for feature selection

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KNOWLEDGE-BASED SYSTEMS 2023年第1期275卷

作者： Zhuang, Zhongjie Pan, Jeng-Shyang Li, Junbao Chu, Shu-Chuan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China Flinders Univ S Australia Coll Sci & Engn 1284 South Rd Clovelly Pk SA 5042 Australia

arithmetic optimization algorithm (AOA) has simple structure, few parameters and is easy to implement. It utilizes the distribution behavior of the main arithmetic operators in mathematics. The Multiplication and Division operators are used for exploration, while the Subtraction and Addition are used for exploitation. In this manuscript, the AOA algorithm was converted into binary form with the Multiplication Mathematical Optimizer Operator (MOO) redesigned for better exploration. Then, four families of transfer functions are used in the binary AOA (BAOA). Moreover, in order to further improve the performance, the parallel mechanism is introduced to the BAOA and proposed the Parallel Binary AOA (PBAOA) algorithm. The proposed algorithms are applied for feature selection problem on 10 low-dimensional and 10 high-dimensional datasets from UCI and scikit-feature repository. The results show that the proposed BAOA and PBAOA algorithms are superior to the classical and state-of-the-art algorithms. The Z-shaped transfer functions are more suitable for the proposed algorithms on low dimensional datasets, while the V-shaped transfer functions are more suitable for high-dimensional datasets. Moreover, no matter what transfer function is used, the parallel mechanism is effective for BAOA.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Parallel Binary arithmetic optimization algorithm Feature selection

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A Novel Hybrid Harris Hawk-arithmetic optimization algorithm for Industrial Wireless Mesh Networks

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SENSORS 2023年第13期23卷 6224页

作者： Devan, P. Arun Mozhi Ibrahim, Rosdiazli Omar, Madiah Bingi, Kishore Abdulrab, Hakim Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar 32610 Malaysia Univ Teknol PETRONAS Dept Chem Engn Seri Iskandar 32610 Malaysia

A novel hybrid Harris Hawk-arithmetic optimization algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk optimization and the arithmetic optimization algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely arithmetic optimization algorithm, Moth Flame optimization, Sine Cosine algorithm, Grey Wolf optimization, and Harris Hawk optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.

关键词： arithmetic optimization algorithm Fractional-order Predictive PI Harris Hawks optimization industrial wireless mesh networks optimal node placement pressure process real-time control

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Distribution Network Reconfiguration Based on an Improved arithmetic optimization algorithm

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ENERGIES 2024年第8期17卷 1969-1969页

作者： Jia, Hui Zhu, Xueling Cao, Wensi North China Univ Water Resources & Elect Power Fac Elect Engn Zhengzhou 450045 Peoples R China

Aiming to address the defects of the arithmetic optimization algorithm (AOA), such as easy fall into local optimums and slow convergence speed during the search process, an improved arithmetic optimization algorithm (IAOA) is proposed and applied to the study of distribution network reconfiguration. Firstly, a reconfiguration model is established to reduce network loss, and a cosine control factor is introduced to reconfigure the math optimization accelerated (MOA) function to coordinate the algorithm's global exploration and local exploitation capabilities. Subsequently, a reverse differential evolution strategy is introduced to improve the overall diversity of the population and Weibull mutation is performed on the better-adapted individuals generated in each iteration to ensure the quality of the optimal individuals generated in each iteration and strengthen the algorithm's ability to approach the optimal solution. The performance of the improved algorithm is also tested using eight basis functions. Finally, simulation analysis is carried out by taking the IEEE33 and IEEE69 node systems and a real power distribution system as examples;the results show that the proposed algorithm can help to reconfigure the system quickly, and the system node voltages and network losses were significantly improved after the reconfiguration.

关键词： arithmetic optimization algorithm distribution network reconfiguration nodal voltage network losses

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Tuning Multi-Layer Perceptron by Hybridized arithmetic optimization algorithm for Healthcare 4.0

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Procedia Computer Science 2022年 215卷 51-60页

作者： Marko Stankovic Jelena Gavrilovic Dijana Jovanovic Miodrag Zivkovic Milos Antonijevic Nebojsa Bacanin Milos Stankovic Singidunum University Danijelova 32 11000 Belgrade Serbia College of Academic Studies ”Dositej” 11000 Belgrade Serbia

Healthcare 4.0 has been enabled by the recent advances in several different fields, including the artificial intelligence (AI) and Internet of things (IoT), among others. It aims to support the doctors in making decisions as fast as possible by providing early prediction of the illness, that could save lives of the patients. Typical workflow includes IoT sensors that collect the data of the patients, while the AI is employed to analyze this data by utilizing a variety of machine learning approaches to predict and classify the illness. This manuscript aims to suggest a healthcare framework that consists of a neural network that is optimized by a hybridized arithmetic optimization algorithm. In the proposed framework, metaheuristics is used to simultaneously perform the network training and tune the hyper-parameters of the multi-layer perceptron neural network. The performance of this framework has been evaluated on three healthcare benchmark datasets, with the task to classify the fatal conditions including heart diseases, breast cancer and diabetes. The obtained results were compared to five other cutting-edge metaheuristics that were employed in the same framework with the same task. The proposed hybridized arithmetic optimization algorithm achieved superior level of performance, indicating that this structure could assist the medical workers in early diagnostics.

关键词： Machine Learning Healthcare arithmetic optimization algorithm Metaheuristics Multi-layer Perceptron

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Enhanced arithmetic optimization algorithm Guided by a Local Search for the Feature Selection Problem

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Intelligent Automation & Soft Computing 2024年第3期39卷 511-525页

作者： Sana Jawarneh Computer Science Department Applied College DammamImam Abdulrahman Bin Faisal UniversityDammam32257Saudi Arabia

High-dimensional datasets present significant challenges for classification *** reduction,a crucial aspect of data preprocessing,has gained substantial attention due to its ability to improve classification ***,identifying the optimal features within high-dimensional datasets remains a computationally demanding task,necessitating the use of efficient *** paper introduces the arithmetic optimization algorithm(AOA),a novel approach for finding the optimal feature *** is specifically modified to address feature selection problems based on a transfer ***,two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision,slow convergence,and susceptibility to local *** first enhancement proposes a new method for selecting solutions to be improved during the search *** method effectively improves the original algorithm’s accuracy and convergence *** second enhancement introduces a local search with neighborhood strategies(AOA_NBH)during the AOA exploitation ***_NBH explores the vast search space,aiding the algorithm in escaping local *** results demonstrate that incorporating neighborhood methods enhances the output and achieves significant improvement over state-of-the-art methods.

关键词： arithmetic optimization algorithm classification feature selection problem optimization

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Improved arithmetic optimization algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

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Journal of Applied Mathematics and Physics 2024年第6期12卷 2212-2253页

作者： Yu Liu Minge Chen Ran Yin Jianwei Li Yafei Zhao Xiaohua Zhang School of Mathematics and Data Science Shaanxi University of Science & Technology Xian China School of Materials Science and Engineering Shaanxi University of Science & Technology Xian China

This article addresses the issues of falling into local optima and insufficient exploration capability in the arithmetic optimization algorithm (AOA), proposing an improved arithmetic optimization algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.

关键词： arithmetic optimization algorithm Adaptive Balance Factor Spiral Search Brownian Motion Whale Fall Mechanism

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