Brain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam baldeagle optimization-based Shepard Convolutional Neural Network (ABE...
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Brain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam baldeagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the bald Border Collie Firefly Optimization algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization algorithm (FA) and baldeaglesearch (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.
The rational selection of subway station locations is an interdisciplinary problem encompassing architecture, transportation, and other fields. Few evaluation index systems and quantitative evaluation methods exist fo...
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The rational selection of subway station locations is an interdisciplinary problem encompassing architecture, transportation, and other fields. Few evaluation index systems and quantitative evaluation methods exist for choosing subway station locations;thus, this paper establishes a novel evaluation framework. Overall, 21 indicators covering the construction and operation phases are selected by a literature review, providing a basis for planning decision makers. The Projection Pursuit Method (PPM) and the baldeaglesearch (BES) algorithm are employed to assign objective weights. The Continuous Ordered Weighted Averaging (COWA) operator is utilized to obtain subjective weights. A combination weighting method is used based on game theory to improve the accuracy of weight calculation. Game theory and extension cloud theory are applied to develop an improved extension cloud model and evaluate the suitability based on optimal cloud entropy. We conduct a case study of 15 stations on the Chengdu Metro Line 11, China. The results reveal that the coordination of the development plans, the alignment with the land use plan, and regional population density are the most crucial tertiary indicators that should be considered in selecting subway station locations. These findings agree with the actual conditions, demonstrating the scientific validity of the proposed evaluation method, which outperforms classical evaluation methods. The proposed method is efficient and feasible for selecting subway station locations.
Green ammonia synthesis is an important industrial chemical process, which is widely applied in fields such as fertilizers, petrochemicals and fuel cells. In order to improve green ammonia production and reduce energy...
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Green ammonia synthesis is an important industrial chemical process, which is widely applied in fields such as fertilizers, petrochemicals and fuel cells. In order to improve green ammonia production and reduce energy consumption, this article focuses on a deeper understanding of the kinetic behavior of ammonia synthesis process system. To this end, a physics-informed sparse identification modeling and optimization framework for ammonia synthesis plant is proposed in this paper, which highlights in-depth exploration of reaction mechanisms, kinetic equations, and optimization methods. The proposed method can deal with the time series information generated by the complicated ammonia synthesis process system with noise. More importantly, the proposed method is found to have distinctive interpretability that from the parameters of differential equation governing the observable data can be deduced. A bald eagle search algorithm is used to solve the maximum yield problem of green ammonia, which can obtain the optimal reactor length and the maximum ammonia profit under physical limitation conditions. The simulation results illustrated that the proposed optimization method was highly competitive with other state-of-art global optimization methods. Finally, the effectiveness and robustness of the proposed method have been demonstrated on ammonia synthesis plant by achieving good and competitive model interpretation and accuracy.
With an increase in electrification trends, the energy consumption of households is expected to increase significantly in the near future. To reduce electricity bills in the electrified era, management of the demand s...
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With an increase in electrification trends, the energy consumption of households is expected to increase significantly in the near future. To reduce electricity bills in the electrified era, management of the demand side is inevitable. Different demand response programs, such as real-time pricing can be introduced from the grid side. The consumption side needs to adjust its consumption patterns in accordance with the price signals. Therefore, an innovative home appliance scheduling method is proposed considering the rooftop solar panels as part of energy suppliers. First, an optimization problem is formulated considering different objectives such as energy price, consumer satisfaction, and peak-to-average load ratio. Then, home appliances are divided into three major categories (shiftable, flexible, and normally operated) to adjust schedules of different load types. Then, the developed model is solved using the baldeaglesearch optimization algorithm (BESOA). Finally, the performance of the proposed BESOA-based method is compared with other well-known heuristic/meta-heuristic methods. In addition, an analysis of different equipment's maximal operation time is conducted for all the methods. In order to provide feedback on user comfort levels, digital twin technology is used allowing the user to adjust the scheduling of the appliances to ensure that they are comfortable. By leveraging digital twin technology, users would be able to optimize the scheduling of their rooftop solar home appliances to maximize efficiency and minimize costs. Simulation results have shown the superiority of the BESOA method over other methods in terms of daily average energy cost, peak-to-average load ratio, and consumer satisfaction.
The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can emp...
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The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has a huge impact on the lives of physically disabled people. Therefore, there is need to develop a robot that can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid's position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. Fast gradient filters using a fuzzy inference system (FIS) are employed to separate the iris from its surroundings. Then, the baldeaglesearch (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using the true success rate (TSR) and compared to those of other existing models. These results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.
ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam baldeagle optimization-based Shepard Convolutional Neural Netw...
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ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam baldeagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the bald Border Collie Firefly Optimization algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization algorithm (FA) and baldeaglesearch (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.
ABSTRACTIn this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete W...
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ABSTRACTIn this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete Wavelet Transform (DWT), Tetrolet transform and Saliency maps. Here, the outputs gained from images by the Tetrolet transform are fused and allowed for inverse Tetrolet transform. Moreover, the DWT process done with two images is fused and the output gained is allowed for inverse DWT. Similarly, the same fusion process is carried out with image outputs from Saliency maps. Finally, three image outputs that are considered as input to TL with newly devised optimization. Here, Convolutional Neural Network (CNN) is used with hyperparameters from trained models, such as SqueezeNet and AlexNet, where weights are updated using Adam Based baldeaglealgorithm (ABBEA). This ABBEA is obtained by combining the baldeaglesearch (BES) algorithm and Adam algorithm. Further, the ABBEA has Peak Signal-to-Noise Ratio (PSNR) with maximal of 38.95, Mean Squared Error (MSE) with lesser value of 20.14, Structural Similarity Index Measure (SSIM) with maximal value of 0.92, Mutual Information (MI) with maximal value of 0.86, Signal-to-Noise Ratio (SNR) with lesser value of 0.38.
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