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Selective harmonic Elimination using dwarf mongoose optimization in Single-Phase Seven-Level switched capacitor inverter with reduced components

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2025年 195卷

作者： Singh, Ankit Jately, Vibhu Kala, Peeyush Singhal, Rahul Yang, Yongheng Univ Petr & Energy Studies Dept Elect & Elect Engn Dehra Dun 248007 India Elect Res & Dev Assoc ERDA R&D GIDC Rd Vadodara 390010 Gujarat India Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

The advancement of high-performance multilevel inverters is essential for contemporary power electronics applications. Nonetheless, attaining a compact topology that offers high gain, minimized component count, and enhanced harmonic mitigation continues to pose a difficulty. This study presents a single-phase seven-level switched capacitor multilevel inverter (SCMLI) that employs ten switches, two capacitors, and a single DC source, utilizing the self-voltage balancing properties of switched capacitors to eliminate the necessity for auxiliary circuits or complex control mechanisms. The dwarf mongoose optimization Algorithm (DMOA) improves the optimization of switching angles, hence enhancing selective harmonic removal and providing substantial control over individual harmonics. Comparative analysis and experimental results validate the inverter's resilience and exceptional performance, exhibiting a threefold voltage gain. Moreover, DMOA surpasses conventional and state-of-the-art optimization algorithms in terms of both speed and convergence accuracy, and achieves the best accuracy of around 1 x 10-52 which helps in significant reductions in the overall harmonic distortion, especially in the 5th and 7th harmonics. The inverter's effectiveness in reducing switching losses, attributed to its low switching frequency and high operating efficiency, makes it an effective option for industrial applications.

关键词： Selective Harmonic Elimination Switched Capacitor Multilevel Inverter Self-voltage balancing dwarf mongoose optimization

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dwarf mongoose Chimp optimization Enabled RMDL for Sentiment Categorization Using Cell Phone Data

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2025年第1期24卷 197-222页

作者： Abraham, Minu P. Reddy, K. R. Udaya Kumar Nitte Deemed Univ NMAM Inst Technol NMAMIT Dept Comp Sci & Engn Karkala 574110 Karnataka India Dayananda Sagar Univ Bengaluru Karnataka India

Sentiment analysis is the process of looking through digital text to determine if the emotional tone of a text is positive, negative, or neutral. It helps companies improve their product, but a serious problem arises in classifying the polarity of certain texts with information, sentences or features to forecast their opinion. Therefore, sentiment classification should be done using new technology that classifies reviews as positive or negative so that users can make effective decisions. This research paper develops an effective model to classify sentiment using cell phone data. Initially, the Amazon phone document is passed to the BERT tokenization stage to split the acquired reviews. Then, the Aspect Term Extraction (ATE) is applied and the Term Frequency-Inverse Document Frequency (TF-IDF) is extracted as the first output. Afterward, Wordnet ontology features are extricated as the second output. Moreover, features like statistical, sarcasm linguistic, and N-gram features are extracted from BERT tokenization and considered as the third output. Finally, the sentiment is classified by subjecting the obtained three outputs to Random Multimodal Deep Learning (RMDL), which is tuned by dwarf mongoose Chimp optimization (DMCO). DMCO is created by the combination of the dwarf mongoose optimization (DMO) and the Chimp optimization Algorithm (ChOA). The developed DMCO-RMDL approach attained high accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, recall, and F1-score values of 93%, 92.8%, 92.2%, 91.5%, 94.1%, and 94.8%, respectively.

关键词： Aspect term extraction cell phone review data sentiment classification random multimodal deep learning dwarf mongoose chimp optimization dwarf mongoose optimization chimp optimization algorithm

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Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters

AIMS ENERGY

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AIMS ENERGY 2023年第1期12卷 84-118页

作者： Chen, Shijian Zhou, Yongquan Luo, Qifan Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial weights to achieve a balance between exploration and exploitation. The DMOWOA performances are tested through the solving of the single diode model, double diode model, and photovoltaic (PV) modules. Finally, the DMOWOA is compared with six well-known algorithms and other optimization methods. The experimental results demonstrate that the proposed DMOWOA exhibits remarkable competitiveness in convergence speed, robustness, and accuracy.

关键词： parameters extraction photovoltaic models dwarf mongoose optimization whale optimization algorithm hybrid metaheuristic algorithm

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An efficient early detection of diabetic retinopathy using dwarf mongoose optimization based deep belief network

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第28期34卷 e7364-e7364页

作者： Abirami, A. Kavitha, R. SRM Univ SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India SRM Univ SRM Inst Sci & Technol Dept Informat Technol Chennai Tamil Nadu India

In general, diabetic retinopathy (DR) is a common ocular disease that causes damage to the retina due to blood leakage from the vessels. Earlier detection of DR becomes a complicated task and it is necessary to prevent complete blindness. Various physical examinations are employed in DR detection but manual diagnosis results in misclassification results. Therefore, this article proposes a novel technique to predict and classify the DR disease effectively. The significant objective of the proposed approach involves the effective classification of fundus retinal images into two namely, normal (absence of DR) and abnormal (presence of DR). The proposed DR detection utilizes three vital phases namely, the data preprocessing, image augmentation, feature extraction, and classification. Initially, the image preprocessing is done to remove unwanted noises and to enhance images. Then, the preprocessed image is augmented to enhance the size and quality of the training images. This article proposes a novel modified Gaussian convolutional deep belief network based dwarf mongoose optimization algorithm for effective extraction and classification of retinal images. In this article, an ODIR-2019 dataset is employed in detecting and classifying DR disease. Finally, the experimentation is carried out and the proposed approach achieved 97% of accuracy. This implies that our proposed approach effectively classifies the fundus retinal images.

关键词： augmentation detection diabetic retinopathy dwarf mongoose optimization modified Gaussian convolutional deep belief network

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Artificial Ecosystem-Based optimization with dwarf mongoose optimization for Feature Selection and Global optimization Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 102-102页

作者： Al-Shourbaji, Ibrahim Kachare, Pramod Fadlelseed, Sajid Jabbari, Abdoh Hussien, Abdelazim G. Al-Saqqar, Faisal Abualigah, Laith Alameen, Abdalla Jazan Univ Dept Comp & Network Engn Jazan 45142 Saudi Arabia Univ Hertfordshire Dept Comp Sci Hatfield AL10 9AB England Ramrao Adik Inst Technol Dept Elect & Telecomm EngineeringNerul Navi Mumbai 400706 Maharashtra India Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Prince Sattam Bin Abdulaziz Univ Coll Arts & Sci Dept Comp Sci Al Kharj Saudi Arabia

Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. dwarf mongoose optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.

关键词： Feature selection Machine learning Metaheuristic algorithms Artificial ecosystem-based optimization dwarf mongoose optimization

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Improved forecasting of the compressive strength of ultra-high-performance concrete (UHPC) via the CatBoost model optimized with different algorithms

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STRUCTURAL CONCRETE 2025年第1期26卷 212-235页

作者： Katlav, Metin Ergen, Faruk Bitlis Eren Univ Vocat Sch Tech Sci Bitlis Turkiye Inonu Univ Fac Engn Dept Civil Engn Malatya Turkiye

This paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra-high-performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO-CatBoost, DMO-CatBoost, and ASO-CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the "black box" problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO-CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R-2, MAPE, and MAE values of the DMO-CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC.

关键词： CatBoost compressive strength dwarf mongoose optimization optimization techniques ultra-high-performance concrete

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Selective harmonic elimination in Novel 27-level trinary DC source inverter using optimization algorithm for hybrid renewable energy sources

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ELECTRICAL ENGINEERING 2025年第1期107卷 775-793页

作者： Revathi, Shanmugaiya Pandiyan Mohan Deborah, Lazarus Jegatha Balamurugan, Chinnapettai Ramalingam Univ Coll Engn Dept Elect & Commun Engn Arni Tamil Nadu India Univ Coll Engn Tindivanam Dept Comp Sci & Engn Melpakkam 604001 India Er Perumal Manimekalai Coll Engn Elect & Elect Engn Hosur 635117 Tamilnadu India

A multi-level inverter (MLI) is essential to improve the performance and efficiency of the inverter, which is widely accepted for high-power and high-voltage applications. Its performance is significantly superior due to higher DC link voltages, lower electromagnetic interference, and lower harmonic distortion. However, it has some drawbacks, like voltage balancing problems, complex pulse width modulation methods, and an increased number of components. This paper investigated the selective elimination of harmonics in optimization-based cascaded H-bridge (CHB) 27-level inverters for hybrid renewable energy sources. This method uses hybrid renewable energy sources such as solar photovoltaics, wind, and tidal turbines with single maximum power point tracking (MPPT). A single MPPT with a dwarf Mungo optimization (DMO) algorithm is implemented to obtain high performance from hybrid sources simultaneously. The proposed inverter comprises ten semiconductor switches connected to three direct current (DC) sources. The Osprey optimization algorithm (OOA) is used in the 27-level inverter to calculate the switching angles for selective harmonic elimination of pulse width modulation (SHE-PWM). The proposed approach is used on a Simulink platform to assess performance, and its efficacy is demonstrated by contrasting it with an existing approach. The simulation obtained 1.98% THD by using OOA with SHE-PWM. The total cost obtained by the proposed method is 40.03$, and the computational time for DMO and OOA is 1 s and 1.2 s at the 100th iteration. The experimental validation is established using a dSPACE RTI1104 controller to validate the proposed method. The experimental results obtained 1.99% of harmonics with the same output voltage as the simulation.

关键词： Cascaded H-bridge dwarf mongoose optimization Hybrid energy system Multi-level inverter Osprey optimization algorithm Total harmonics distortion

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Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier

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IEEE ACCESS 2023年 11卷 117741-117751页

作者： Jain, Sweta Tripathy, Hrudaya Kumar Mallik, Saurav Qin, Hong Shaalan, Yara Shaalan, Khaled Deemed Univ Kalinga Inst Ind Technol Sch Comp Engn Bhubaneswar 751024 Orissa India Shri Ramdeobaba Coll Engn & Management Dept Comp Sci & Engn Nagpur 440013 India Harvard T H Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA Univ Tennessee Dept Comp Sci & Engn Chattanooga TN 37403 USA Misr Univ Sci & Technol Fac Med 6th October City 16878 Egypt British Univ Dubai Fac Engn & IT Dubai Int Acad City Dubai U Arab Emirates

The neurodevelopmental Autism Spectrum Disorder (ASD) causes problems in social communication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deep Convolutional Neural Network (CNN) with dwarf mongoose optimized Residual Network (DM-ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM-GMM segments the volume into predefined cortical and sub cortical regions. After segmentation, the features are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.

关键词： Feature extraction Magnetic resonance imaging Image segmentation Autism Signal processing algorithms Deep learning Classification algorithms Autism detection MRI images segmentation VGG feature extraction ResNet dwarf mongoose optimization

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Texture-driven super-resolution of ultrasound images using optimized deep learning model

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IMAGING SCIENCE JOURNAL 2024年第5期72卷 643-656页

作者： Markco, M. Kannan, S. EGS Pillay Engn Coll Dept Comp Sci & Engn Nagapattinam Tamil Nadu India Kings Coll Engn Dept Elect & Commun Engn Pudukottati Tamil Nadu India

While comparing to the additional medical imaging modalities, the low resolution (LR) with poor quality images are obtained because of natural intrinsic imaging characteristics. We proposed a novel deep learning- based super-resolution of ultrasound images that are texture-driven. In this study, Convolutional Neural Networks (CNNs) are used to speed up the process to increase the image quality. Additionally, the dwarf mongoose optimization (DMO) method is used to adjust the parameters of CNN thereby improving the quality of the image resolutions. The textures of the super-resolution images are examined with the Histogram of Oriented Gradients (HOG). The experimental works are handled using python software. Super-resolution per second and PSNR/dB values for the proposed model are 0.289 and 42.340, respectively. This model also offers FSO, GMSD, MAD, and MSSIM values of 0.9712, 0.013, 0.9802, and 0.9832 which is relatively higher than the existing techniques.

关键词： Super resolution ultra sound imaging Convolutional Neural Network Histogram of Oriented Gradients low resolution texture CAD diagnosis deep learning dwarf mongoose optimization

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Application of the optimal regression-based analysis to estimate the deformation of geogrid-reinforced soil structures

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3695-3708页

作者： Ren, Hongwei Chengdu Univ Technol Dept Resource Explorat & Civil Engn Engn & Tech Coll Leshan 614000 Sichuan Peoples R China

The analysis of deformation plays a crucial role in the design of Geosynthetic reinforced structure (GRS) constructions. However, the publications emphasize the potential of artificial prediction algorithms in addressing geotechnical engineering challenges. The main aim of the present study is to examine the possible employ of estimation methods in the prediction of the deformation (Dis) of GRS. The present paper shows and approve a new technique which integrates the dwarf mongoose Optimizer (DMO) framework with the Multi-layered perceptron (MLP) neural network and Support Vector Regression (SVR) (abbreviated as SVRDMO and MLPDMO). Afterwards, a whole of 166 finite element values performed in the publications were utilized so as to make the data collection. Based on the results obtained, it can be concluded that both SVRDMO and MLPDMO have considerable potential in properly forecasting the Dis. The R2 values for SVRDMO were 0.9835 throughout training and 0.9866 throughout testing. After careful examination of several types of performance tests and their comparison to valid publications, it has been determined which the SVRDMO offers a more suitable framework for calculating the Dis of GRS.

关键词： Geosynthetic reinforced soil Geogrid Deformation Prediction dwarf mongoose optimization Regression analysis

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