Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the a...
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Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled ***,3D networks can be computationally expensive and require significant training *** research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or *** proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI *** and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive *** proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset *** results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art ***,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.
With the rise of the Internet of Things(IoT),the word“intelligent medical care”has increasingly become a major *** medicine adopts the most advanced IoT technology to realize the interaction between patients and peo...
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With the rise of the Internet of Things(IoT),the word“intelligent medical care”has increasingly become a major *** medicine adopts the most advanced IoT technology to realize the interaction between patients and people,medical institutions,andmedical ***,with the openness of network transmission,the security and privacy of information transmission have become a major ***,Masud et *** a lightweight anonymous user authentication protocol for IoT medical treatment,claiming that their method can resist various ***,through analysis of the protocol,we observed that their protocol cannot effectively resist privileged internal attacks,sensor node capture attacks,and stolen authentication attacks,and their protocol does not have perfect forward ***,we propose a new protocol to resolve the security vulnerabilities in Masud’s protocol and remove some redundant parameters,so as tomake the protocolmore compact and *** addition,we evaluate the security and performance of the new protocol and prove that the overall performance of the new protocol is better than that of other related protocols.
This paper attempts to conceptualize a potent methodology by combining the African vultures optimization algorithm (AVOA) with a multi-orthogonal-oppositional strategy (M2OS), named AVO-M2OS, to address the nonconvexi...
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This paper attempts to conceptualize a potent methodology by combining the African vultures optimization algorithm (AVOA) with a multi-orthogonal-oppositional strategy (M2OS), named AVO-M2OS, to address the nonconvexity and multidimensional nature of the combined heat and power economic dispatch (CHPED) problem under both crisp and uncertainty aspects. The AVO-M2OS uses the M2OS to simultaneously explore the search region, improving solutions’ diversity as well as solution quality. Therefore, AVO-M2OS can perform deeper exploration and exploitation features and thus mitigate the trapping at local optima, especially when tackling the more complicated nature of the CHPED problem. A three-stage analysis is conducted to assess the effectiveness of the proposed AVO-M2OS algorithm. During the first stage, the algorithm’s performance is evaluated on benchmark problems such as CEC 2005 and CEC 2019, employing statistical verifications and convergence characteristics. In the second stage, the significance of the results is evaluated using the nonparametric Friedman test to demonstrate that the results did not occur by chance. The results indicate that the AVO-M2OS algorithm outperforms the best existing algorithm (AVOA) by an average rank of the Friedman test exceeding 26% for the CEC 2005 suite while outperforming the gray wolf optimization (GWO) by 60% for the CEC 2019 suite. Moreover, the AVO-M2OS demonstrates exceptional performance compared to existing state-of-the-art algorithms, surpassing the best algorithm available by an average rank of the Friedman test that exceeds 41%. Finally, the AVO-M2OS’s applicability is achieved by minimizing the operational costs by finding the optimal power and heat generation scheduling for the CHPED problem. The recorded results realize that the AVO-M2OS algorithm offers accurate performance compared to competing optimizers, where it saves the operational cost of the 48-unit system by 24% on the original AVO variant. Furthermore, the u
This study proposes the design of a photovoltaic (PV) system to power agricultural activities in rural communities, with a focus on Sub-Saharan Africa. Considering the high costs of most PV systems for rural economies...
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Research supporting improved anomaly detection performance benefits a wide range of technical applications. Thus, the definition of anomalies and the subsequent means to detect them are wide-ranging. This treatment pr...
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This paper introduces machine learning (ML) as a solution for the detection and range localization of jamming attacks targeting the global positioning system (GPS) technology, with applications to unmanned aerial vehi...
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We investigate the learning of implicit neural representation (INR) using an overparameterized multilayer perceptron (MLP) via a novel nonparametric teaching *** latter offers an efficient example selection framework ...
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We investigate the learning of implicit neural representation (INR) using an overparameterized multilayer perceptron (MLP) via a novel nonparametric teaching *** latter offers an efficient example selection framework for teaching nonparametrically defined (***-closed-form) target functions, such as image functions defined by 2D grids of *** address the costly training of INRs, we propose a paradigm called Implicit Neural Teaching (INT) that treats INR learning as a nonparametric teaching problem, where the given signal being fitted serves as the target *** teacher then selects signal fragments for iterative training of the MLP to achieve fast *** establishing a connection between MLP evolution through parameter-based gradient descent and that of function evolution through functional gradient descent in nonparametric teaching, we show for the first time that teaching an overparameterized MLP is consistent with teaching a nonparametric *** new discovery readily permits a convenient drop-in of nonparametric teaching algorithms to broadly enhance INR training efficiency, demonstrating 30%+ training time savings across various input modalities. Copyright 2024 by the author(s)
Recently, deep reinforcement learning (DRL) has been employed in flexible job-shop scheduling problems (FJSP) to minimize makespan within flexible manufacturing systems (FMS). In practice, numerous modern enterprises ...
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作者：
Lee, HyunjongJung, Han HeeKwak, JeonghoYea, JunwooChoi, Jihwan P.Jang, Kyung-In
Department of Electrical Engineering and Computer Science Daegu Korea Republic of Dgist
Department of Robotics and Mechatronics Engineering Daegu Korea Republic of Dgist
Department of Electrical Engineering and Computer Science Daegu Korea Republic of
Department of Aerospace Engineering Dajeon Korea Republic of
Analyzing wine using taste data is a promising field due to the explosive expansion of online commerce. However, because of the wide variety of wine types with different flavors and aromas, it is difficult for consume...
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In this paper, a solution for automatic MIMO radar channel selection utilizing convolutional neural networks is presented for the task of advancing robust remote heart rate monitoring. A millimeter-wave radar with a h...
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