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AOA-guided hyperparameter refinement for precise medical image segmentation

ALEXANDRIA ENGINEERING JOURNAL 2025年 120卷 547-560页

作者： Balaha, Hossam Magdy Bahgat, Waleed M. Aljohani, Mansourah Bamaqa, Amna Atlam, El-Sayed Badawy, Mahmoud Elhosseini, Mostafa A. Univ Louisville JB Speed Sch Engn Bioengn Dept Louisville KY 40208 USA Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura 35516 Egypt Mansoura Univ Fac Comp & Informat Informat Technol Dept Mansoura 35516 Egypt Taibah Univ Appl Coll Comp Sci & Informat Dept Medina 41461 Saudi Arabia Taibah Univ Coll Comp Sci & Engn Comp Sci Dept Yanbu 46421 Saudi Arabia Tanta Univ Dept Comp Sci Tanta 31527 Egypt Taibah Univ Coll Comp Sci & Engn Informat Syst Dept Yanbu 46421 Saudi Arabia

Medical image segmentation faces significant challenges, including the need for extensive annotated data, the impact of hyperparameters, and the limitations of traditional CNN models. Breast cancer (BC) and COVID-19 imaging, in particular, require precise segmentation for accurate diagnosis and treatment planning. This study introduces a novel framework that utilizes the archimedes optimization algorithm (AOA) to optimize hyperparameters for medical image segmentation, aiming to enhance accuracy and efficiency. We propose a four-stage framework for hyperparameter optimization using AOA. The framework consists of: (1) Population Initialization, (2) Fitness Function Evaluation, (3) Population Updating, and (4) Results Logging. The framework optimizes key hyperparameters, including activation functions, loss functions, optimizers, and batch sizes, using a hybrid loss function combining Focal Tversky and IoU. The proposed framework was rigorously evaluated on two medical datasets: the BUSI dataset for BC and the COVID-19 CT scan lesion segmentation dataset. The R2 U-Net 2D model achieved an accuracy of 95.7% on the BUSI dataset, while the V-Net model achieved 99.2% accuracy on the COVID-19 dataset. The AOA-guided framework demonstrated superior performance compared to existing methods, with Dice coefficients of 0.675 and 0.723 for the BUSI and COVID-19 datasets, respectively. Convergence curves and performance metrics validated the stability and efficiency of AOA in optimizing hyperparameters. The AOA-guided framework significantly improves medical image segmentation by automating hyperparameter optimization. The results highlight the potential of AOA as a powerful tool for enhancing segmentation accuracy and robustness.

关键词： archimedes optimization algorithm Breast cancer COVID-19 Deep learning (DL) Hyperparameter optimization Medical image segmentation

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An efficient hybrid approach for high-gain DC-DC converter for hydropower operated plants on DC load with super-lift converters

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ELECTRICAL ENGINEERING 2025年第1期107卷 1315-1329页

作者： Chamundeeswari, V. Niraimathi, R. St Josephs Coll Engn Chennai 600119 Tamil Nadu India Mohamed Sathak Engn Coll Kilakarai Tamil Nadu India

Power converters play a crucial part in optimizing energy conversion in hydropower plants, but traditional designs often face challenges such as limited efficiency, high component stress, and complex control. This manuscript presents a hybrid approach for high-gain DC-DC converters in hydropower applications. The proposed hybrid technique is the joined execution of archimedes optimization algorithm (AOA) and dynamic differential annealed optimization (DDAO). Hence, it is named as AOA-DDAO approach. The study explores super-lift converters, including the positive Luo converter and modified negative Luo converter (MLC), enhancing their efficiency and adaptability. The AOA is used to optimize the design parameters of the high-gain DC-DC converters, and DDAO is used to control the dynamic performance of the converters. The objective of the AOA-DDAO method is to improve system performance by reducing overshoot, settling time, rise time, and steady-state error, while enhancing convergence speed, robustness, and overall efficiency. The proposed method, implemented in MATLAB, achieves a notable efficiency of 98% and a power loss of 3%, demonstrating superior performance compared to existing techniques. This advancement promises improved voltage conversion ratios and better compatibility with various hydro turbine input voltages, marking a significant contribution to sustainable energy solutions.

关键词： Hydropower plant DC-DC converter archimedes optimization algorithm Dynamic differential annealed optimization Luo converters

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Anomaly Detection in IoT Network Traffic Using Bidirectional 3D Quasi-Recurrent Neural Network Optimize With Coati optimization algorithm

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第1期36卷

作者： Deivakani, M. PSNA Coll Engn & Technol Dept Elect & Commun Engn Dindigul Tamil Nadu India

Anomaly detection in Internet of Things (IOT) network traffic involves identifying abnormal patterns or behaviors, enabling early detection of potential security threats or system malfunctions in the IOT ecosystem. IoT encompasses a variety of networks, addressing not only security challenges within sensor networks, the internet, and mobile communication networks but also specifically focusing on issues related to privacy protection, information management, network authentication, and access control. In this manuscript, Anomaly Detection in IoT Network Traffic using a Bidirectional 3D Quasi-Recurrent Neural Network with Coati optimization algorithm (ADIOT-B3DQRNN-COA) is proposed. Initially, the input data are collected from the DS2OS Dataset. Then, the collected data is fed into pre-processing utilizing an Implicit Unscented Particle Filter (IUPF). The IUPF is used to remove the invalid data. Subsequently, the preprocessed data are sent into the archimedes optimization algorithm (AOA) to select features. Seven characteristics from the DS2OS dataset are chosen using AOA. The selected features are then fed into a Bidirectional 3D Quasi-Recurrent Neural Network (Bi-3DQRNN) to classify anomaly detection in an Internet of Things network into the following categories: data probing, malicious control, malicious operation, scan, spying, incorrect configuration, DOS attack, and normal. To guarantee accurate classification of anomaly detection in IoT networks, Bi-3DQRNN generally does not express any adaptation of optimization algorithms for figuring out the best parameters. Hence, the Coati optimization algorithm (COA) to optimize Bi-3DQRNN accurately classifies anomaly detection in the IOT network. The proposed ADIOT-B3DQRNN-COA approach is implemented in MATLAB. The performance of the proposed method was examined utilizing performance metrics like Accuracy, Computational Time, F-measure, Precision, Recall, and ROC. The proposed ADIOT-B3DQRNN-COA approach contains 32.15%,

关键词： anomaly detection archimedes optimization algorithm bidirectional 3D quasi-recurrent neural network coati optimization algorithm and implicit unscented particle filter

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Power system economic emission dispatch considering uncertainties of wind, solar, and small runoff hydropower via a hybrid multi-objective optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Xiong, Guojiang Liu, Qinghua Wang, Yang Fu, Xiaofan Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China State Grid Chongqing Elect Power Co Wanzhou Power Supply Branch Chongqing 404100 Peoples R China

Economic emission dispatch (EED) plays a key role for the power system operation. With the integration of renewable energy sources (RESs), their uncertainties pose great challenges to the EED. This work establishes an EED model of thermal generators, wind, solar, and small runoff hydropower and considers varied practical factors and constraints. Overestimation and underestimation models are used to describe the uncertainties. Meanwhile, to solve this model effectively, a hybrid multi-objective method MOAGT by combining archimedes optimization algorithm, artificial gorilla troops optimizer, and teaching-learning-based optimization is presented via both parallelization and serialization. Some techniques including chaotic opposition initialization, Morlet wavelet mutation, memory retention, and modified compromise solution selection are utilized to raise the performance. The validity of the proposed model with MOAGT is demonstrated on two systems. Results compared with peer algorithms and the CPLEX solver show that MOAGT achieves better solutions in less costs and lower emissions, which can make the systems run in a more economical and low-carbon state under the premise of meeting the system reliability. Besides, the established EED model has good portability that supports the transition to a cleaner energy system. Furthermore, MOAGT can be seen as an effective alternate to the EED problem with RESs.

关键词： Artificial gorilla troops optimizer archimedes optimization algorithm Economic emission dispatch Renewable energy Teaching-learning-based optimization Uncertainty

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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment

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IEEE ACCESS 2025年 13卷 11280-11294页

作者： Al Mazroa, Alanoud Albogamy, Fahad R. Khairi Ishak, Mohamad Mostafa, Samih M. Princess Nourah bint Abdulrahman Univ PNU Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Taif Univ Turabah Univ Coll Comp Sci Program Taif 21944 Saudi Arabia Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates South Valley Univ Fac Comp & Informat Comp Sci Dept Qena 83523 Egypt New Assiut Technol Univ NATU Fac Ind & Energy Technol New Assiut City 71684 Egypt

The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the archimedes optimization algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.

关键词： Cyberattack optimization Feature extraction Data models Accuracy Security Neural networks Adaptation models Deep learning Computational modeling Cyber-physical systems deep learning cyberattack detection metaheuristics archimedes optimization algorithm

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Dual interactive Wasserstein generative adversarial networks optimized with hybrid archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第7期37卷 e5720-e5720页

作者： Mydhili, S. K. KGiSL Inst Technol Dept Elect & Commun Engn Coimbatore India KGiSL Inst Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Dual interactive Wasserstein generative adversarial networks optimized with hybrid archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM (DiWGAN-Hyb AOA-COA-MIMO-OFDM) is proposed in this manuscript. In OFDM, there is a non-stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA-COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN-Hyb AOA-COA-MIMO-OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%;lesser bit error rate 98.33%, 83.12%, and 88.96%;and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO-OFDM system using deep neural network and MN-based improved AMO model (DNN-IAMO-MIMO-OFDM), MIMO-OFDM systems using the deep learning and optimization (RBFNN-PSO-MIMO-OFDM), and MIMO-OFDM systems using hybrid neural network (HNN-CSI-MIMO-OFDM) respectively. Dual interactive Wasserstein generative adversarial networks optimized with hybrid archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM (DiWGAN-Hyb-AOA-COA-MIMO-OFDM) is proposed in this manuscript. During channel estimation in OFDM, there is a non-stationary channel physical appearance, problems arise to overcome this issue, and DiWGAN is used. Therefore in this work, hybrid archimedes optimization algorithm and chimp optimization algorithm (Hyb AOA-COA) are employed to enhance the DiWGAN weight parameters. The proposed is compared with existing methods, like DNN-IAMO-MIMO-OFDM, MIMO OFDM RBFNN-PSO-MIMO-OFDM, and HNN-CSI-MIMO-OFDM, respectively. image

关键词： archimedes optimization algorithm channel estimation algorithm chimp optimization algorithm dual interactive Wasserstein generative adversarial network multiple inputs multiple output and orthogonal frequency division multiplexing

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A Cloud-Based Platform for Soybean Plant Disease Classification Using archimedes optimization Based Hybrid Deep Learning Model

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WIRELESS PERSONAL COMMUNICATIONS 2022年第4期122卷 2995-3017页

作者： Annrose, J. Rufus, N. Herald Anantha Rex, C. R. Edwin Selva Immanuel, D. Godwin St Xaviers Catholic Coll Engn Dept Informat Technol Nagercoil Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Commun Engn Chennai Tamil Nadu India Vignana Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad India Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai Tamil Nadu India

Bean which is botanically called Phaseolus vulgaris L. belongs to the Fabaceae family. Unnecessary economic losses arise during bean disease identification due to a delay in treatment, inappropriate treatment, and a lack of understanding. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean Data Set. The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short-term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. To help the farmers in real-time, the proposed model is also deployed in a cloud-based collaboration framework. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

关键词： Bean disease classification Hybrid deep learning archimedes optimization algorithm LSTM WPD

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Hybrid archimedes Sine Cosine optimization enabled Deep Learning for multilevel brain tumor classification using MRI images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期87卷

作者： Geetha, M. Srinadh, V Janet, J. Sumathi, S. SA Engn Coll Dept Comp Sci & Engn Poonamallee Avadi Rd Chennai 600077 Tamil Nadu India GMR Inst Technol Dept Comp Sci & Engn Rajam Andhra Pradesh India Sri Krishna Coll Engn & Technol Dept CSE Coimbatore India Mahendra Engn Coll Dept EEE Namakkal India

The most terrible form of cancer caused by uncontrolled and aberrant cell division is the Brain Tumor (BT). The current methodologies are insufficient for precise categorization due to the variety of tumor sizes, forms, and placements in the brain. The main objective of the proposed method is to classify the presence of a brain tumor and also classify its type. It is necessary for effective treatment, and recovery and it improves the survival rate. The Sine cosine archimedes optimization algorithm (SCAOA) is used in this study to construct a highly successful model for BT classification. Primarily, input Magnetic Resonance Imaging (MRI) brain image is taken from databases, which is later promoted to pre-processing phase. Thereafter, in this pre-processing phase, a Gaussian filter is used to eliminate undesirable noises in the image. The BT segmentation is done by using SegNet, which is tuned by using SCAOA. The proposed SCAOA is established by the combination of archimedes optimization algorithm (AOA) and Sine Cosine algorithm (SCA). The segmented image samples are then exposed to the feature extraction phase. Features are sent to BT detection and then ShCNN is applied to detect BT with features obtained. If the detected output is found as a tumor, then the BT image is classified as Pituitary tumors, Gliomas, and Meningiomas using DenseNet, which is tuned by using the proposed SCAOA. Finally, SCAOA_DenseNet attained high accuracy of 93.0%, sensitivity of 92.3%, and specificity of 92.0%.

关键词： Gaussian filter archimedes optimization algorithm Sine Cosine algorithm DenseNet Shepard Convolutional Neural Network

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A Blockchain-Based Hybrid Hunger Game Search archimedes optimization Enabled Deep Learning for Multiclass Plant Disease Detection Using Leaf Images

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第0期

作者： Gajmal, Yogesh Manohar Jagtap, Arvind M. Kale, Kiran Dhanaji Gawade, Jawahar Sambhaji More, Pranav Finolex Acad Management & Technol Dept Informat Technol Ratnagiri 415639 Maharashtra India MIT Art Design & Technol Univ MIT Sch Comp Dept Comp Sci & Engn Pune Maharashtra India Presidency Univ Dept Elect & Commun Engn Bangalore India SVPMs Coll Engn Malegaon BK Dept Informat Technol Baramati Pune India Universal AI Univ Universal AI & Future Technol Sch Karjat Maharashtra India

Plants are susceptible to a wide range of diseases when they are growing. One of the crucial difficulties in agriculture is the earlier finding of plant diseases. If the diseases are not detected at the beginning, it may have an undesirable effect on the entire production. To avoid these issues, a blockchain-based hybrid optimized deep learning (DL) approach is devised in this work. The plant leaf images are stored in the blockchain network and the noise level of the images is minimized by the Kalman filter. In image segmentation, the Deep Joint segmentation technique is employed to segment the disease-affected portion of the image. The position and color augmentation are carried out to enhance the size and clarity of the image. Moreover, the statistical and speeded-up robust features (SURF) are extracted in the feature extraction stage. In the first level classification process, the developed hunger game search archimedes optimization (HGSAO) enabled SpinalNet is employed for classifying the plant type and the second level classification is carried out for multiclass disease identification using the proposed HGSAO optimized SpinalNet. Moreover, the proposed HGSAO with SpinalNet outperformed the accuracy of 0.972, True positive rate (TPR) of 0.963, true negative rate (TNR) of 0.951, false negative rate (FNR) of 0.936 and false positive rate (FPR) of 0. 942.

关键词： Plant leaf disease SpinalNet hunger game search optimization archimedes optimization algorithm Deep Joint segmentation

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Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers

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JOURNAL OF ENERGY STORAGE 2024年 86卷

作者： Peddakapu, K. Mohamed, M. R. Srinivasarao, P. Arya, Yogendra Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Pekan Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Tech Pekan Malaysia JC Bose Univ Sci Technol Dept Elect Engn YMCA Faridabad Haryana India

The stability of modern power systems faces significant challenges due to intermittent renewable generation and fluctuating load demands, resulting in excessive frequency variations. To address this issue and prevent potential blackouts, implementing a robust control strategy is crucial. This study introduces a cascaded control system, combining a novel fractional order integral derivative (FOID) with a fractional order proportional integral derivative with filter (FOPIDN) controllers, denoted as CFOID-FOPIDN. The gain parameters for CFOID-FOPIDN are determined using the archimedes optimization algorithm (AOA). The proposed two -area microgrid system incorporates various power -generating sources, including solar, wind turbine generators, fuel cells, micro -turbines, and battery energy storage technologies. To assess the AOA-tuned CFOID-FOPIDN controller's effectiveness, the system is tested under five different conditions: load fluctuations, changes in wind speed, variations in irradiance, combined wind and irradiance variations, and comprehensive changes across all conditions. Simulation results reveal that the AOA-based CFOID-FOPIDN outperforms other existing algorithms, such as particle swarm optimization (PSO), bat algorithm (BAT), moth flame optimization (MFO), and whale optimization algorithm (WOA). In dynamic variations across all sources, the AOA-tuned strategy demonstrates shorter settling times in area -1 (2.5 s), area -2 (2.2 s), and tie -line power (2.1 s) compared to PSO (3.5 s, 3.8 s, 3.4 s), WOA (3.2 s, 3.4 s, 3.1 s), BA (3.0 s, 3.1 s, 3.0 s), and MFO (2.9 s, 2.8 s, 2.9 s). Across all five scenarios, the AOA-tuned CFOIDFOPIDN strategy consistently demonstrates superior performance compared to the other strategies under consideration.

关键词： Renewable energy sources Microgrid Fuel cell Battery energy storage systems archimedes optimization algorithm

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