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A three-tier BERT based transformer framework for detecting and classifying skin cancer with HSCGS algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期83卷 51441-51467页

作者： George, Joseph Rao, Anne Koteswara Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Srivilliputhur 626126 Tamil Nadu India

Skin cancer is the process of identifying and diagnosing, a disease in which abnormal skin cells grow and spread uncontrollably. An innovative deep learning-based skin cancer detection model is introduced in this research work. The proposed model is divided into five main phases: (a) Pre-Processing (b) Segmentation (c) Feature Extraction (d) 3-Tier Classification (e) post-processing. Initially, the collected raw image is pre-processed via contrast enhancement, decimal scaling, and augmentation methods. From the pre-processed image, the important feature is extracted by using the statistical features like mean, variance, and information gain. Then, from the extracted image, the region of interest ROI is identified via fuzzy assisted Kapur's multi-level thresholding. The optimal features are selected using the hybrid Self-Improved chimp optimization algorithm with Glow Swarm optimization algorithm (HSCGS). The three-tier classification using the BERT based Transformer with HSCGS based Gated Recurred Unit (GRU), BiLSTM, and Graph Neural Network is projected for classification. The proposed model is implemented using the PYTHON platform. The findings are evaluated in terms of accuracy, sensitivity, precision, FPR, FNR, etc. using the present models. The proposed model has recorded the highest detection accuracy as 97% and highest MCC and NPV values. Proposed model has shown the best performance and has outperformed other models.

关键词： Skin cancer detection chimp optimization algorithm Glow swarm optimization Gated recurrent unit BiLSTM

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A Deep Belief Network Model for Automatic Atrial Fibrillation Detection

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IETE JOURNAL OF RESEARCH 2024年第2期70卷 1303-1315页

作者： Suresh, P. Reka, S. Sofana Vellore Inst Technol Sch Elect Engn Chennai India Vellore Inst Technol Ctr Smart Grid Technol Sch Elect Engn Chennai India

Atrial fibrillation is an abnormal heart rhythm categorized by the rapid and irregular pulse of the atrial chambers in the heart. The Electrocardiogram is normally used for the analysis of Atrial Fibrillation which is used to check a patient's rhythm and electrical activity. The diagnosing method involved in this process is used to classify the electrocardiogram signal as normal or atrial fibrillation. In this analysis, Adaptive Dual Threshold Filter and Discrete Wavelet Transform methods are utilized to remove noises present in ECG signals representing Electromyogram, Power line interference, Baseline wandering, White Gaussian Noise, electrode motion artifact, and muscle contraction noises. In this process, a combination of heart rate variability and atrial activity is used to do the feature extraction. This work proposes a chimp optimization algorithm-based parameter-optimized Deep Belief classifier for effective Atrial Fibrillation detection. To validate the results comparison analysis is done with the three methods, Random Forest Classifier, Logistic Regression, and Decision Tree Classifier, to the check the outcomes of the proposed model. The simulation outcomes demonstrate that the accuracy of the proposed method achieves 10.81%, 10.81%, and 1.86% better than the existing methods.

关键词： Atrial activity Atrial fibrillation chimp optimization algorithm Deep belief classifier Electrocardiogram Heart rate variability

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Hybrid optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection

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IEEE ACCESS 2022年 10卷 65611-65622页

作者： Deore, Bhushan Bhosale, Surendra Veermata Jijabai Technol Inst Dept Elect Engn Mumbai 400019 Maharashtra India

Nowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring attacks. An intrusion detection system (IDS) is a security device similar to other measures, including firewalls, antivirus software, and access control models devised to strengthen communication and information security. Network intrusion detection system (NIDS) plays a vital function in defending computer networks and systems. However, several issues concerning the sustainability and feasibility of existing techniques are faced with recent networks. These concerns are directly related to the rising levels of necessary human interactions and reducing the level of detection accuracy. Several approaches are designed to detect and manage various security threats in a network. This study uses chimp Chicken Swarm optimization-based Deep Long Short-Term Memory (ChCSO-driven Deep LSTM) for the intrusion detection process. A CNN feature extraction process is necessary for effective intrusion detection. Here, the Deep LSTM is applied for detecting network intrusion, and the Deep LSTM is trained using a designed optimization technique to enhance the detection performance.

关键词： Feature extraction Network intrusion detection Deep learning Computational modeling Training Security optimization Intrusion detection deep long short-term memory chimp optimization algorithm chicken swarm optimization algorithm convolutional neural network features

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Crop Yield Prediction Using Improved Extreme Learning Machine

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COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 2023年第1期54卷 1-21页

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh Chandra Amity Univ Comp Sci & Engn Noida Uttar Pradesh India Natl Inst Technol Agartala Comp Sci & Engn Agartala India

Crop yield prediction (CYP) is a critical challenge for decision-makers of any kind of levels, including regional and national decision-making. Farmers might use effective CYP model to help them determine what to cultivate or when to grow it. The primary aim of precision agriculture (PA) is to increase crop growth and yield thereby decreasing production costs and emissions. Various developmental factors influence potential yield, including soil properties, irrigation, weather, fertilizer maintenance, and topography. CYP is critical to worldwide agricultural production. To improve the global food security, policymakers depend on reliable forecasts to make timely exports and imports decision. The main aim of this paper is to predict the crop yield using machine-learning technique. The collected data are pre-processed using Kalman filter algorithm and then certain features are extracted using the Linear Discriminant Analysis (LDA). The CYP is done using the improved extreme learning machine (IELM). The output weight of the extreme learning machine is improved using the chimp optimization algorithm (COA). The implementation tool used in this method is PYTHON and the dataset used for the CYP is farm yield prediction. The experimental results showed an accuracy of 99.99% which is better when compared to existing methods.

关键词： Kalman filter algorithm linear discriminant analysis improved extreme learning machine CYP chimp optimization algorithm

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Predicting the settlement of pile based on a hybrid form of the model by considering Least Square Support Vector Regression

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 529-542页

作者： Chen, Qiang Chongqing Vocat Coll Transportat Sch Marxism Chongqing 402247 Peoples R China

Understanding the vertical settlement is crucial when designing the pile and foundation type used in real estate, specifically regarding the pile settlement (Sp). This issue is of utmost importance due to the numerous variables involved in designing piles that penetrate rock. Despite multiple efforts, clear and precise theoretical explanations regarding the interactions between soil and piles are currently unclear. As a result, many studies have opted to employ artificial intelligence techniques for determining the subsidence rate of piles over time under different loading conditions. This study presents a machine learning (ML) that effectively predicts the values of Sp, namely Least Square Support Vector Regression (LSSVR). In addition, the proposed model coupled with three meta-heuristic algorithms, including the Flow Direction algorithm (FDA), chimp optimization algorithm (ChOA), and Rider optimization algorithm (ROA), to improve the performance and obtain the optimal results as a framework of hybrid. As a result, LSFD determined the most suitable effects with R2 and RMSE values equal to 0.2503 and 0.9952, respectively. Overall, using LSSVR with FDA, ChOA, and ROA can improve the accuracy and robustness of the model in predicting pile settlement, making it a valuable tool for geotechnical engineers designing foundation systems.

关键词： Pile settlement Least Square support vector regression Flow direction algorithm chimp optimization algorithm Rider optimization algorithm

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Improved market prediction using meta-heuristic algorithms and time series model and testing market efficiency

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Iran Journal of Computer Science 2023年第1期6卷 29-61页

作者： Shahvaroughi Farahani, Milad Farrokhi-Asl, Hamed Department of Finance Faculty of Management Khatam University Tehran Iran Sheldon B. Lubar College of Business University of Wisconsin-Milwaukee Milwaukee WI United States

This study aims to evaluate the efficiency of using technical indicators such as closing price, lowest price, highest price, and exponential moving average in the prediction of stock prices. We use a genetic algorithm (GA) and a hybrid of grey wolf optimization and particle swarm optimization binary algorithm (GWO-PSO) as feature selection methods. In addition, we train our neural network by using some metaheuristic algorithms such as harmony search algorithm (HS), particle swarm optimization algorithm (PSO), modified particle swarm optimization algorithm (MPSO), modified particle swarm optimization algorithm with time-varying acceleration coefficients (MPSO-TVAC), moth flame optimization (MFO), wolf optimization algorithm (WOA) and chimp optimization algorithm (ChOA). The experimental results show that using metaheuristic algorithms to fortify neural networks may increase their ability in finding optimal solutions. We also compare the results of our proposed algorithms with the results of the autoregressive integrated moving average (ARIMA) model. To compare the performance of the proposed algorithms and select the best one, we introduce eight estimation criteria for error assessment. Moreover, market efficiency is another important factor that is checked in this paper to avoid abnormal returns. Briefly speaking, it is the first time that ChOA and MFO algorithms have been used for the prediction of stock prices and to improve ANN. In addition, we use two algorithms (i.e., GA and GWO-PSO) for improving the feature selection process. Finally, experimental results show that WOA has the best performance among applied algorithms. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishi

关键词： Artificial neural network chimp optimization algorithm Feature selection Meta-heuristic algorithms Moth flame optimization algorithm Technical indicators

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Research on Short-Term Power Load Forecasting Method Based on CEEMDAN-ChOA-GRU

Research on Short-Term Power Load Forecasting Method Based o...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Yang, Xiaoyan Guo, Xiaoxuan Chen, Chongmin Xie, Mancheng Zhang, Jing Shu, Jie Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd. Guangxi Nanning China Guangxi Power Grid Co. Ltd. Electric Power Resarch Institute Guangxi Nanning China Chinese Academy of Sciences Guangzhou Institute of Energy Conversion Guangdong Guangzhou China

ISBN: (纸本)9781665471640

Short-term load forecasting (STLF) have a significant meaning for the planning, construction, and stable operation of new power systems. However, traditional load forecast methods generally have the problems of insufficient accuracy and low model applicability. In view of this, an STLF method based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and chimp optimization algorithm (ChOA) is proposed. The CEEMDAN-ChOA-GRU model proposed in this paper integrates the advantages of CEEMDAN and ChOA, which can effectively realize the accurate forecasting of power loads. First, CEEMDAN-GRU is shown to have the best performance in the comparison of different mode decomposition methods to improve GRU in STLF. Furthermore, ChOA is used to optimize GRU hyper-parameters, gaining higher forecast accuracy than existing models. In the best case studied, the mean error of this method is less than 2%, and R-Squared (R2) is greater than 0.98, which shows good performance and excellent application potential. © 2023 IEEE.

关键词： chimp optimization algorithm complete ensemble empirical mode decomposition with adaptive noise gated recurrent unit short-term load forecasting

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Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security

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CHAOS SOLITONS & FRACTALS 2023年第1期170卷

作者： Raj, Saurav Mahapatra, Sheila Babu, Rohit Verma, Sumit Inst Chem Technol Dept Elect Engn Marathwada Campus Jalna India Alliance Univ Dept Elect & Elect Engn Bangalore India IIT Kanpur Dept Ind & Management Engn Kanpur India

This work consolidates chaotic maps into a contemporary heuristic approach called chimp optimization algorithm (COA). Augmentation of ten chaotic maps to COA forms the hybrid Chaotic COA (CCOA) which generates solutions in search space with enhanced mobility. A bi-level strategy of validating the robustness of the proposed hybrid method is adopted by first benchmarking it on eighteen assorted benchmark test functions and later by performing statistical analysis. The result obtained candidly establishes that merging of chaotic maps (dominantly Chaotic 4 Iterative type-CCOA4) enhances the performance of COA. The inferences congregated from the benchmark functions and statistical analysis facilitates the application of the recommended algorithm to obtain techno-economic solution in reactive power dispatch (RPD) problem which is manifested as a challenging issue in power system operation. The RPD problem is analyzed implementing the proposed approach with series-shunt compensation in a competitive electricity market for secured operation and the proposed Iterative type-CCOA4 reduces the total operating cost by 3.3450 x 103$ with respect to initial operating cost.

关键词： Reactive power dispatch chimp optimization algorithm Chaotic Maps Chaotic chimp optimization algorithm TCSC SVC

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An adaptive hybrid fractal model for short-term load forecasting in power systems

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期207卷 107858-107858页

作者： Li, Xiaolan Zhou, Jun Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China

This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation function (CLFIF), iterative learning (IL) and chimp optimization algorithm (ChOA). More precisely, after selecting similar days in power load data, an amendatory CLFIF model is constructed for an-hour-ahead prediction in terms of hourly load curves. Then, iterative learning based on ChOA optimizes the parameters of the amendatory CLFIF model with higher accuracy. Moreover, to confirm effectiveness of the CLFIF-IL-ChOA model, numerical examples are tested on the historical power load data from PJM and ENTSOE. The numerical results show that the proposed method can obtain higher accuracy, compared to some common methods about time series analysis.

关键词： Adaptive Composite linear fractal interpolation function Iterative learning chimp optimization algorithm Short-term load forecasting

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optimization of hybrid energy management system based on high-energy solid-state lithium batteries and reversible fuel cells

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ENERGY 2023年第1期283卷

作者： Li, Xue Li, Minghai Habibi, Mostafa Najaafi, Neda Safarpour, Hamed North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450045 Henan Peoples R China Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710055 Shaanxi Peoples R China UTE Univ Fac Architecture & Urbanism Calle Rumipamba S-N & Bourgeois Quito Ecuador Saveetha Inst Med & Tech Sci Saveetha Dent Coll & Hosp Dept Biomat Chennai 600077 India Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Sharif Univ Technol Ctr Excellence Design Robot & Automat Dept Mech Engn Azadi AvePOB 11365-9567 Tehran Iran Iran Ind Design Co Tehran Iran Imam Khomeini Int Univ Fac Engn Dept Mech Qazvin Iran

Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT), small size, and competitive pricing for the overall source. A hybrid energy storage system (ESS) controlled by an intelligent energy management strategy (EMS) may be substantially included in multi-source EV design and development. Therefore, this paper proposes a hybrid chimp optimization algorithm (ChOA) and Levy walk technique to create an optimum EMS. The proposed technique reduces battery power (BP) stress and increases the LT, which is accomplished by using a hybrid ChOA-Levy walk (ChOA-LW) optimization algorithm with a rule-based approach in accordance with understanding the performance of LIB and SCAP. In order to optimize the rule-based EMS's control settings, the latter strategy is suggested. The control approach can be implemented online once the offline optimization procedure is finished. The presented technique is evaluated via simulation and on an experimental platform by means of a power emulator testbed of a LIB/SCAP hybrid ESS. In terms of BP stress and LT, the findings are compared with a conventional rule-based approach and a mono-source containing a regular high-power LIB. Results obtained demonstrate the effectiveness of the suggested technique, which enables the requested performance to be satisfied with better energy utilization. The assessment results also show notable LT improvements for the LIB, an improvement of up to 19% over the mono-source in reference to a conventional single cell LIB.

关键词： Battery lifetime Energy management strategy Hybrid energy storage system chimp optimization algorithm

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