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Hybridizing remora and aquila optimizer with dynamic oppositional learning for structural engineering design problems

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 462卷

作者： Varshney, Megha Kumar, Pravesh Abualigah, Laith Rajkiya Engn Coll Bijnor 246725 India Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

To solve global optimization problems, the Aquila Optimizer (AO) algorithm was created recently and is based on the hunting habits of Aquila birds. The remora optimization algorithm (ROA) is combined with a novel Aquila optimizer in this study to create a hybrid version that generates new local solutions based on the best available ones, thereby improving searchability. Additionally, the implementation of dynamic oppositional-based learning (DOL) techniques facilitates both the exploration and exploitation of a search field while preserving an appropriate balance between them. Designated RODAO, is the proposed algorithm. The fundamental characteristic of the proposed approach is the use of remora's ability to prevent premature convergence and local search problems, as well as the DOL strategy to preserve high-quality solutions and variety among the RODAO's solutions. In order to assess these competencies in RODAO, the IEEE CEC 2017 benchmark functions as well as a traditional set of well-known benchmark functions have been used. The robustness and efficiency of the method are guaranteed by a number of performance measurements used on RODAO, including statistical tests and convergence graphs. Three popular engineering optimization issues are also solved in the paper using the suggested RODAO technique. The analysis and numerical experiments show that real-world optimization issues can be successfully solved by the proposed algorithm or RODAO.

关键词： optimization Aquila optimizer remora optimization algorithm Dynamic oppositional learning Engineering optimization Problems

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Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments

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COMPUTER COMMUNICATIONS 2024年 226卷

作者： Sajnani, Dileep Kumar Li, Xiaoping Mahesar, Abdul Rasheed Southeast Univ Sch Comp Sci & Engn Nanjing 210007 Jiangsu Peoples R China

The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial remora optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the remora optimization algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.

关键词： Mobile edge computing Workflow scheduling Security remora optimization algorithm Feedback artificial tree

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DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging

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ABDOMINAL RADIOLOGY 2025年 1-31页

作者： Thanya, T. Jeslin, T. PET Engn Coll Vallioor India Universal Coll Engn & Technol Vallioor India

Computed Tomography (CT) imaging captures detailed cross-sectional images of the pancreas and surrounding structures and provides valuable information for medical professionals. The classification of pancreatic CT images presents significant challenges due to the complexities of pancreatic diseases, especially pancreatic cancer. These challenges include subtle variations in tumor characteristics, irregular tumor shapes, and intricate imaging features that hinder accurate and early diagnosis. Image noise and variations in image quality also complicate the analysis. To address these classification problems, advanced medical imaging techniques, optimization algorithms, and deep learning methodologies are often employed. This paper proposes a robust classification model called DeepOptimalNet, which integrates optimization algorithms and deep learning techniques to handle the variability in imaging characteristics and subtle variations associated with pancreatic tumors. The model uses a comprehensive approach to enhance the analysis of medical CT images, beginning with the application of the Gaussian smoothing filter (GSF) for noise reduction and feature enhancement. It introduces the Modified remora optimization algorithm (MROA) to improve the accuracy and efficiency of pancreatic cancer tissue segmentation. The adaptability of modified optimization algorithms to specific challenges such as irregular tumor shapes is emphasized. The paper also utilizes Deep Transfer CNN with ResNet-50 (DTCNN) for feature extraction, leveraging transfer learning to enhance prediction accuracy in CT images. ResNet-50's strong feature extraction capabilities are particularly relevant to fault diagnosis in CT images. The focus then shifts to a Deep Cascade Convolutional Neural Network with Multimodal Learning (DCCNN-ML) for classifying pancreatic cancer in CT images. The DeepOptimalNet approach underscores the advantages of deep learning techniques, multimodal learning, and cascade architect

关键词： Medical CT images Pancreatic tumor classification Deep learning (DL) Multimodal imaging remora optimization algorithm Cascade convolutional neural network

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Performance study and comparative analysis of optimization algorithms in Hindi document summarization

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2025年

作者： Bandari, Sumalatha Bulusu, Vishnu Vardhan GH Raisoni Skill Tech Univ Dept Comp Sci & Engn Shradha Pk Nagpur India Jawaharlal Nehru Technol Univ Dept Comp Sci & Engn Hyderabad India

Nowadays, the explosion of textual information occurs due to the emerging development of the internet, which is easily available to anyone at any time. Generally, the textual information provided in text documents can be analyzed using different Natural Language Processing (NLP) techniques. In recent years, various text summarization techniques have been implemented. Among these, the inter-party election-based text document summarization is not analyzed properly. In this research analysis, for Hindi text word document summarization, the Coot remora optimization (CRO) algorithm-based Deep Recurrent Neural Network (DRNN) is introduced. Here, the sentence scores are generated using DRNN, which is trained using the CRO algorithm. The superiority of the developed method is compared with recent popularity-based optimization algorithmic approaches, such as Coot optimization (COOT), remora optimization algorithm (ROA), Political Optimizer (PO), Elephant Herding optimization (EHO), Particle Swarm optimization (PSO), Genetic algorithm (GA), and Political Elephant Herding optimization (PEHO) algorithm. Additionally, the performance of the CRO-based DRNN technique is analyzed using four evaluation metrics, precision, recall, f-measure, and rouge. The proposed CRO-based DRNN achieved high-performance values of 95.1%, 95.1%, 94.31%, and 79% for precision, recall, f-measure, and rouge, respectively.

关键词： Hindi word document summarization Coot optimization remora optimization algorithm Political Elephant Herding optimization Coot remora optimization

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remora-CNN: A Novel and Effective Method for Rice Leaf Disease Detection and Classification

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JOURNAL OF PHYTOPATHOLOGY 2024年第5期172卷

作者： Chaudhari, Devchand J. Karunakaran, Malathi Govt Coll Engn Dept Comp Sci & Engn Nagpur MS India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamilnadu India

For millions of people worldwide, rice is one of the main food crops. Nevertheless, while being grown, rice is susceptible to many diseases. Most rice plant diseases are influenced by biotic and abiotic factors, including nematodes, viroids, fungus, viruses, bacteria, and other microorganisms, as well as temperature and other environmental factors. Thus, an automatic early classification of leaf disease is necessary to improve the rice yield. In this paper, for identifying and categorizing the rice leaf disease, a convolutional neural network (CNN) model is used, and the CNN is trained using the remora optimization algorithm (ROA). A better classification outcome is attained by performing the segmentation process using K-means with the Fractional Tangential-Spherical Kernel (FTSK) algorithm. Furthermore, the developed remora optimization- Convolutional Neural Network (remora-CNN) method achieved the optimal performance based on the testing accuracy, sensitivity and specificity of 0.925, 0.931, and 0.941 using the Rice Leaf Disease Image Samples Dataset.

关键词： convolutional neural network fractional tangential-spherical kernel fractional theory remora optimization algorithm spherical kernels

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remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 210卷

作者： Pingale, Subhash V. Sutar, Sanjay R. Dr Babasaheb Ambedkar Technol Univ Riagad India

Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named remora Whale optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the remora optimization algorithm (ROA) and Whale optimization algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively.

关键词： Security Intrusion detection Deep learning remora optimization algorithm Whale optimization algorithm

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Modeling of remora optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar

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CHEMOSPHERE 2022年第Part2期303卷 135065页

作者： Almalawi, Abdulmohsen Khan, Asif Irshad Alqurashi, Fahad Abushark, Yoosef B. Alam, Md Mottahir Qaiyum, Sana King Abdulaziz Univ Comp Sci Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Univ Teknol PETRONAS Ctr Res Data Sci Seri Iskandar 32610 Perak Malaysia

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of remora optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL

关键词： Biochar Sorption efficiency Predictive model Deep learning remora optimization algorithm Heavy metals

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Three Dimensional DenseUNet with CKHA Segmentation Technique for Monkeypox Disease Prediction

Three Dimensional DenseUNet with CKHA Segmentation Technique...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Madhu, H.S. Rallapalli, Sreekanth Thatikonda, Ramya Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India University of the Cumberlands Department of Information Technology CharlotteNC United States

ISBN: (纸本)9798350313413

Since several years ago, the monkeypox virus has been seen in Africa, where it has been connected to the emergence of skin lesions. Following the COVID-19 pandemic, lethal consequences of viral infections have caused public concern and anxiety. Given that COVID-19 has reached pandemic levels, quick detection techniques are essential. The hybrid pre-trained deep learning (DL) models are used in this study's research to identify the monkeypox virus. This method uses the Messy Krill Guide Procedure (CKHA) in conjunction with Kapur's entropy (KE) to segment images. The remora Optimisation algorithm (ROA) selects the Long-Short Term Memory (LSTM) weight in the classifier in the best possible way. For this, we first adjust them by adding familiar routine sheets for all of them, and then we examine the outcomes via tetrad familiar-known metrics: Accuracy, Remember, F1notch, and Correctness. With the aid of our suggested segmentation and classification strategy, the study runs its trials on a publically accessible dataset, yielding average values of 96.90% accuracy, 97.84% accuracy, 98.20% remember, and 98.67% F1-notch, respectively. These optimistic outcomes, which exceed cutting-edge techniques, imply that the suggested strategy is appropriate for mass screening by health practitioners. © 2023 IEEE.

关键词： Chaotic Krill Herd algorithm Deep learning Kapur's entropy Long-short term memory Monkeypox virus detection remora optimization algorithm

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Selfish node management in opportunistic mobile networks with improved social based watchdog and dynamic power AODV protocol

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3253-3264页

作者： Sherif, Bismin V. Salini, P. Department of Computer Science and Engineering Puducherry Technological University Puducherry India

The opportunistic mobile networks (OMNs) are gaining more attention due to the wide deployment of mobile wireless devices. For the proper functioning of OMNs, selfish behavior detection is necessary. In the existing selfish node detection techniques, the detection time is long and communication overhead is high because of the use of Ad hoc On-Demand Distance Vector (AODV) routing protocol for the optimal path determination. To overcome the issues in the existing selfish node detection system, a novel routing mechanism for OMNs is proposed in this paper. The nature inspired remora optimization algorithm (ROA) is integrated with Dynamic Power Ad hoc On-Demand Distance Vector (DP-AODV) routing protocol for the optimal routing. To manage the selfishness in the OMNs, a novel routing mechanism named remora-DP-AODV is integrated with the social based watchdog (So-Watch) that can differentiate the type of selfishness. The performance of the proposed remora-DP-AODV based So-Watch mechanism is compared with the existing approaches such as DP-AODV, conventional AODV, and So-Watch. The simulation results show that the proposed method is effective in managing selfishness in OMNs with increased throughput, detection rate, and reduced end-to-end delay, detection time, energy consumption, total overhead, and false positive rate. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Ad-hoc On-Demand Distance Vector Opportunistic mobile networks remora optimization algorithm Selfish node Social based watchdog

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DEEP JOINT RP-NET-BASED SEGMENTATION algorithm AND OPTIMIZED DEEP LEARNING FOR SEVERITY PREDICTION OF BRAIN TUMOR

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2024年第1期24卷 2350060-2350060页

作者： Kumar, R. Ramesh Nalinipriya, Ganapathi Vidyadhari, Ch Elwin, J. Granty Regina Sri Krishna Coll Technol Dept Informat Technol Coimbatore India Saveetha Engn Coll Dept Informat Technol Chennai India Gokaraju Rangaraju Inst Engn & Technol Dept Informat Technol Hyderabad India Sri Krishna Coll Engn & Technol Dept Informat Technol Coimbatore India

The uncontrolled growth of cells in a particular area is referred to as a tumor. The premature and precise identification of the tumor and its level have a straight impression on the patient's survival, treatment process, and tumor progression computation. However, in the medical field, picture segmentation and classification are more important and difficult processes. Typically, the Magnetic Resonance Imaging (MRI) modality can detect malignancy. Segmenting tumor images with respect to Cerebrospinal Fluid (CSF), Grey Matter (GM), and White Matter is the most important task in MRI identification or classification (WM). The introduction of medical image analysis based on radiology pictures is a result of the significant contributions of engineering, data sciences, and medicine. The precise and automatic segmentation of tumors affords excessive support to doctors in the medicinal area, speed detection in the treatment process, computer-aided operation, radiation treatment and so on. Thus, remora Aquila optimization (RAO)-enabled deep learning is devised for tumor classification and its severity classification. The deep learning approach is utilized for categorizing tumors as normal or abnormal as well as their severity grades. The RAO-aided deep learning system achieved improved performance with prediction error, specificity, sensitivity and testing accuracy of 0.072, 0.905, 0.925, and 0.917, respectively.

关键词： Deep neuro-fuzzy network local directional ternary pattern remora optimization algorithm recurrent prototypical network deep joint segmentation

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