There is arbitrariness in optimum solutions of graph-theoretic problems that can give rise to unfairness. Incorporating fairness in such problems, however, can be done in multiple ways. For instance, fairness can be d...
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Beluga Whale optimization (BWO) algorithm faces challenges such as slower convergence, lower accuracy, and instability for dealing with unimodal functions. To tackle these problems, we propose an enhanced version of B...
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In the last few decades, poor prognosis of pancreatic tumor has been an issue of concern in spite of the recent advancements in the different imaging modalities. Small size, similar attenuation to normal sized pancrea...
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In the last few decades, poor prognosis of pancreatic tumor has been an issue of concern in spite of the recent advancements in the different imaging modalities. Small size, similar attenuation to normal sized pancreas or concealed position of pancreas during CT scans are the factors that leads to failure in early diagnosis of pancreatic tumor. In this research, an organized framework is proposed for monitoring, classifying and diagnosing of pancreatic tumor. The suggested model integrates the technique of Deep Neural Network (DNN) and optimistic aspects of nature-inspired algorithms;this model aims to achieve a harmonious combination of both the techniques. The proposed model examines the medical images obtained from CT scans for the presence of pancreatic tumor using SSA-ML image segmentation on CT dataset. Evaluation of suggested model in comparison to other contemporary models IDLDMS, ODL, weighted KLM, Kernel-ELM, and ELM models is also performed in reference to sensitivity, specificity, accuracy and F1 score. The classification accuracy of our model is 99.44 % which reflects its supremacy over other recent models.
In response to the RGV speed tracking problem, a multi-strategy enhanced particle swarm optimization (PSO) algorithm was proposed to optimize the Fuzzy-PID controller. Firstly, the operation of the RGV was modeled, an...
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In response to the RGV speed tracking problem, a multi-strategy enhanced particle swarm optimization (PSO) algorithm was proposed to optimize the Fuzzy-PID controller. Firstly, the operation of the RGV was modeled, and the transfer function was obtained using the system identification toolbox. Then, by integrating ICMIC mapping, adaptive parameters, Levy flights, and dynamic feedback learning strategies, the PSO algorithm was improved and the Fuzzy-PID controller was optimized. Simulation results show that compared to PID, PSO-PID, and Fuzzy-PID, the ILR-PSO-Fuzzy-PID effectively reduces the sum of squared errors (SSE) and average peak error, exhibiting better dynamic performance.
A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. It...
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A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO's behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO.
X-ray computed tomography (CT) has been widely celebrated for its ability to visualize the anatomical information of patients, but has been criticized for high radiation exposure. Statistical image reconstruction algo...
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X-ray computed tomography (CT) has been widely celebrated for its ability to visualize the anatomical information of patients, but has been criticized for high radiation exposure. Statistical image reconstruction algorithms in X-ray CT can provide improved image quality for reduced dose levels in contrast to the conventional reconstruction methods like filtered back-projection (FBP). However, the statistical approach requires substantial computation time, more than half an hour for commercial 3D X-ray CT products. Therefore, this dissertation focuses on developing iterative algorithms for statistical reconstruction that converge within fewer iterations and that are amenable to massive parallelization in modern multi-processor implementations. Ordered subsets (OS) methods have been used widely in tomography problems, because they reduce the computational cost by using only a subset of the measurement data per iteration. This dissertation first improves OS methods so that they better handle 3D helical cone-beam CT geometries. OS methods have been used in commercial positron emission tomography (PET) and single-photon emission CT (SPECT) since 1997. However, they require too long a reconstruction time in X-ray CT to be used routinely for every clinical CT scan. In this dissertation, two main approaches are proposed for accelerating OS algorithms, one that uses new optimization transfer approaches and one that combines OS with momentum algorithms. First, the separable quadratic surrogates (SQS) methods, a widely used optimization transfer method with OS methods yielding simple, efficient and massively parallelizable OS-SQS methods, have been accelerated in three different ways; a nonuniform SQS (NU-SQS), a SQS with bounded interval (SQS-BI), and a quasi-separable quadratic surrogates (QSQS) method. Among them, a new NU-SQS method that encourages larger step sizes for the voxels that are expected to change more between the current and the final image has highly accelerat
We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters. This improves on prior work which requires knowing at l...
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The Maximum Power Point Tracking (MPPT) algorithm subjected the PV parameters to changes under faulty and weather operations conditions during runtime, which influenced the results obtained. The aim of this research i...
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In order to ensure population diversity and overcome premature convergence, differential evolution strategy was introduced and a whale particle swarm hybrid algorithm was proposed. The proposed method has been achieve...
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At present, with the continuous increase of Internet users in rural China, the scale of online shopping is also expanding, and the development prospect of rural e-commerce is obvious. The scattered rural population an...
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