Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has sig...
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Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an archimedes optimization algorithm with Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models.
Solar photovoltaic (PV) technology stands as a promising alternative to conventional fossil fuel-based power generation, offering pollution-free and low-maintenance energy production. To harness its potential effectiv...
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Solar photovoltaic (PV) technology stands as a promising alternative to conventional fossil fuel-based power generation, offering pollution-free and low-maintenance energy production. To harness its potential effectively, understanding the power generation process and accurately modeling solar PV systems are essential. Unfortunately, manufacturers often do not provide the necessary parameters for modeling solar cells, making it challenging for researchers. This research employs the archimedes optimization algorithm (AOA), an optimization technique, to determine unknown parameters for the PVM752 GaAs thin film solar cell and the RTC France solar cell. The modeling of these solar cells utilizes both a Single Diode Model (SDM) and a Double Diode Model (DDM). Performance evaluations are conducted using the sum of individual absolute errors (SIAE) and a novel root mean square error (RMSE) method. Comparing the effectiveness of the AOA with other optimization methods, The RMSEs for the AOA applied to the SDM and DDM of RTC France solar cell were 3.7415 x 10-3 and 1.0033 x 10-3. Similarly, for PVM752 GaAs thin film solar cell were 1.6564 x 10-3, and 0.00106365, respectively. The SIAE values for both solar diode models of RTC France cells were 0.071845 and 0.021268, respectively. For the PVM752 GaAs thin film, the corresponding SIAE values were 0.031488 and 0.040224. The results highlight the efficiency of the AOA-based approach, showcasing consistent convergence and a high level of accuracy in obtained solutions. The suggested approach produces superior results with a lower RMSE compared to other algorithms, demonstrating its efficacy in determining solar PV parameters for modeling purposes.
Energy consumption is getting rising gradually around the planet. Therefore, the importance of energy management has increased for all nations worldwide, and long-term energy demand estimation is becoming a vital prob...
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Energy consumption is getting rising gradually around the planet. Therefore, the importance of energy management has increased for all nations worldwide, and long-term energy demand estimation is becoming a vital problem for all countries. In this study, linear, quadratic and exponential models based six different archimedes optimization algorithms (AOA) such as AOA-Linear, AOA-Quadratic, AOA-Exponential, IAOA-Linear, IAOA-Quadratic and IAOA-Exponential have been proposed to make some future projections of Turkey for the years (2021-2050). The previous studies in the literature were used the data set of Turkey, such as observed energy demand (OED), population, gross domestic product (GDP), export and import data for the years (1979-2005) or (1979-2011) obtained from the Turkish Statistical Institute (TUIK) and the Ministry of Energy and Natural Resources (MENR). However, in this study, a new data set is organized with the OED, population, GDP, export and import data of Turkey for the years (1997-2020) to make some long-term energy demand estimations of Turkey, and this dataset is used for the first time in this study. AOA-Linear, AOA-Quadratic and AOA-Exponential algorithms are based on linear, quadratic and exponential mathematical models and the basic AOA method. IAOA-Linear, IAOA-Quadratic and IAOA-Exponential algorithms are also based on linear, quadratic and exponential mathematical models and the improved AOA (For short, IAOA) proposed in this study. Once a sensitivity analysis is made for determining the effect of algorithmic parameters of AOA and IAOA, the proposed algorithms are realized for Turkey's long-term energy demand estimation for the years (2021-2050) with three different future scenarios. According to the experimental results, the quadratic model-based proposed IAOA produces better or comparable performance on the problem dealt with in this study in terms of solution quality and robustness.
Cloud computing has changed the technology landscape for over a decade and led to an astounding growth in the number of applications it may be used for. Consequently, there has been a significant spike in the demand f...
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Cloud computing has changed the technology landscape for over a decade and led to an astounding growth in the number of applications it may be used for. Consequently, there has been a significant spike in the demand for improved algorithms to schedule workflows efficiently. These were mostly concerned with heuristic, metaheuristic, and hybrid approaches to workflow scheduling that mostly suffer from the problem of local optima entrapment. Due to such heavy traffic on the cloud resources, there is still a need for less computationally complex approaches. In light of this, this article proposes a novel approach: a multi-objective Modified Local Escaping archimedesoptimization (MLEAO) algorithm for workflow scheduling. This strategy involves initialization of the population of archimedes optimization algorithm through the HEFT algorithm to provide an inclination towards the solutions with improved makespan while achieving a cost-efficient workflow scheduling decision and avoiding the problem of local optima entrapment using a local escaping operation. To validate the efficacy of our approach, we conducted extensive experiments using scientific workflows as benchmarks. Through our investigations, we significantly improved makespan, cost, resource utilization, and energy consumption. Moreover, the effectiveness of our proposed approach is also verified by performance metrics such as hypervolume, s-metric, and dominance relationships between the proposed and state-of-the-art approaches.
Cloud computing has revolutionized various domains over the past decade, providing accessible computational and storage resources at reduced costs. The exponential growth in data volumes and processing complexity, par...
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Cloud computing has revolutionized various domains over the past decade, providing accessible computational and storage resources at reduced costs. The exponential growth in data volumes and processing complexity, particularly due to the proliferation of IoT devices and applications across fields such as business, education, and agriculture, requires scalable computing resources and efficient processing. Workflow scheduling in cloud computing, an NP-hard optimization problem, involves allocating resources to tasks within a workflow and determining their execution sequence. Despite numerous heuristic, metaheuristic, and hybrid approaches, there remains a need for scheduling algorithms with lesser computational complexity to optimize makespan and cost efficiency, as well as ensure SLA compliance. This paper introduces a novel multi-objective metaheuristic solution, the Deadline and Budget constrained archimedes optimization algorithm (ADB), which addresses workflow scheduling by optimizing makespan and cost while adhering to deadline and budget constraints. Extensive experiments on a well-known cloud simulation tool, Workflowsim, using scientific workflows demonstrate significant improvements in makespan (20%), cost (5%), resource utilization (15%), and energy consumption (9%). Performance observations on Pareto optimality metrics show that our approach has a higher hypervolume for 80% cases, it dominates state of the art by at least 83%, and the s-metric value of our approach is lower for 95% cases, alongside statistical validation using t-tests and ANOVA, confirming the efficacy of our method compared to state-of-the-art approaches.
Optimal reactive power dispatch (ORPD) is essential for addressing power system challenges related to distributed generation (DG), particularly from renewable energy (RE) sources such as wind and solar. The intermitte...
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Optimal reactive power dispatch (ORPD) is essential for addressing power system challenges related to distributed generation (DG), particularly from renewable energy (RE) sources such as wind and solar. The intermittent nature and uncertainty of these energy sources, influenced by varying wind speeds and solar irradiation, complicate their integration into power systems. This paper proposes a solution to the ORPD problem in systems with RE-DG integration using the archimedes optimization algorithm (AOA). The uncertainties of wind and solar power generation were modelled using Weibull and lognormal probability density functions (PDFs), respectively, and the optimization model was tested using a scenario-based method. The AOA was applied to the IEEE 57 bus system to minimize power loss, voltage deviation, and voltage stability index (VSI). The results demonstrated that AOA contributed to a 15.7% reduction in power loss, and an 83.9% enhancement in VSI compared to the base case. In the multi-objective optimization scenario, AOA achieved a 7.1% reduction in power loss, with an additional 11.6% improvement upon the integration of DGs. The performance of AOA was also compared with other metaheuristic algorithms, demonstrating superior results in terms of tracking accuracy and convergence speed. AOA outperformed the multi-objective ant lion optimization (MOALO) and the Levy-based Interior Search algorithm (LISA) in terms of power loss reduction and voltage stability. AOA achieved a 1.83% lower power loss and a 29.67% lower VSI compared to MOALO. When compared to LISA, AOA achieved a 1.68% lower power loss, demonstrating its superior optimization capabilities. These findings confirm that AOA is a highly effective method for solving the ORPD problem, accounting for renewable energy uncertainties and improving overall system performance.
The Internet of Things (IoT) and its devices have become an integral part of the people 's daily lives recently. The growing demand for intelligent applications indicates that the IoT improves regular automation a...
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The Internet of Things (IoT) and its devices have become an integral part of the people 's daily lives recently. The growing demand for intelligent applications indicates that the IoT improves regular automation and intelligent sensing, which improves quality of life. Data present in a variety of forms and formats is the fundamental element of the IoT ecosystem. Then, the gathered information is utilized to generate context awareness and arrive at significant conclusions. Numerous obstacles related to object security are used to maintain on-going services with many benefits using IoT. In this manuscript, Multi-Lead-Branch Fusion Network optimized using archimedes optimization algorithm for Securing Resource Constrained Environments (MLBF-ArOA-SRCE)is proposed. Initially, the data are acquired from the N-BaIoT dataset. The input data are pre-processed using Structural Interval Gradient Filtering (SIGF) which requires using the common organising techniques to put the data in an accessible format, like removing extra spaces and entries without values. Then,the pre-processed data are fed intoHexadecimal Local Adaptive Binary Pattern (HLABP) for extracting features. Then, the extracted features are provided to the Multi-Lead-Branch Fusion Network (MLBFN) which classifies the benign and malicious attack. TheMLBFN does not express any adoption of optimization strategies for scaling the ideal parameters for Securing Resource Constrained Environments. Hence, archimedes optimization algorithm (ArOA) is utilized to improve the MLBFN weight parameters. The performance of the proposed techniqueis examined using performance metrics like precision, recall, f-measure, specificity, and accuracy. The proposed MLBF-ArOA-SRCE method provides 38%, 14%, 29.93% higher recall;26.87%, 25.41%, 17.92 %higher accuracy;30.88%, 13.29%, 25.71% higher specificity compared with existing approaches like RER-EML, FOG-PDM, ALSN-SSP respectively.
Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In...
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Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved archimedes optimization algorithm with a Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize infill well placement for polymer flooding, an objective function that considers the economic influence of infill drilling was developed. The novel optimizationalgorithm (HS-AOA) for infill well placement was subsequently developed by combining the AOA with the Halton sequence. The codes were developed in MATLAB 2023a and connected to a commercial reservoir simulator, Computer Modeling Group (CMG) STARS, Calgary, AB, Canada to carry out infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into the AOA effectively increased the diversity of the initial objects in the AOA and prevented the HS-AOA from becoming trapped in the local optimal solutions. The HS-AOA outperformed the AOA. This approach was effective for optimizing the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically improve the polymer flooding performance in offshore oil reservoirs. The net present value (NPV) of the polymer flooding case with infill wells determined by HS-AOA reached USD 3.5 x 108, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for optimizing infill well placement for polymer flooding processes. It can also serve as a valuable reference for other optimization problems in the petroleum industry, such as joint optimization of well control and placement.
This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-based investigation (FBI) an...
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This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-based investigation (FBI) and archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods. This manuscript introduces FBIAOA, a novel method combining forensic-based investigation (FBI) and archimedes optimization algorithm (AOA) for precise electric vehicle (EV) modeling in smart grid (SG) reliability studies. The approach aims to maximize fast charging station (FCS) profits, reduce grid energy demand using storage and renewable sources, and assess SG reliability through aggregated stochastic EV parameters. Implemented in MATLAB, the proposed FBIAOA method demonstrates cost-effectiveness compared to existing techniques. image
The past dynasties literature has very few records as the literature of the earlier ones is a novelty and the hereditary secret historical and cultural assets are now at risk. In this study, the inheritance and innova...
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The past dynasties literature has very few records as the literature of the earlier ones is a novelty and the hereditary secret historical and cultural assets are now at risk. In this study, the inheritance and innovation of the Huizhou carving culture using a progressive conditional generative adversarial network enhanced using the archimedes optimization algorithm (HCC-PCGAN-AO) is proposed. Initially, the data are gathered using the onsite Huizhou culture website with three-dimensional data scanning technique. The progressive conditional generative adversarial network (PCGAN) is used to design the Huizhou carving. Then, the archimedes optimization algorithm (AOA) is proposed to optimize the Progressive PCGAN classifier, which precisely eliminates errors in the design. It demonstrates the 3D digital carving into architectural cultural property is required and achieves a essential role in preserving the advancing architectural cultural heritage. The proposed HCC-PCGAN-AO method attains 22.13%, 19.46% and 30.65% lower RMSE compared with the existing methods such as Inheritance and Protection of Temple Architectural Cultural Heritage utilizing in the Case of Three-Mountain Kings Ancestral Temple of Jiexi Lintian with Digital Media Technology (IPTA-CH-DMT), Research on Innovative design of tourism cultural and creative products from the perception of Huizhou intangible cultural heritage culture (RID-TCC-HICHC) and Research on the Inheritance along Development of Huizhou Culture in the Construction of New Countryside in Anhui Province (RID-HCC-NCAP) respectively.
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