The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for ...
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Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge server...
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This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation...
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Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create effici...
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A Brain Tumors are highly dangerous illnesses that significantly reduce the life expectancy of patients. The classification of brain tumors plays a crucial role in clinical diagnosis and effective treatment. The misdi...
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A Brain Tumors are highly dangerous illnesses that significantly reduce the life expectancy of patients. The classification of brain tumors plays a crucial role in clinical diagnosis and effective treatment. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients Precisely diagnosing brain tumors is of utmost importance for devising suitable treatment plans that can effectively cure and improve the quality of life for patients afflicted with this condition. To tackle this challenge, present a framework that harnesses deep convolutional layers to automatically extract crucial and resilient features from the input data. Systems that use computers and with the help of convolutional neural networks have provided huge success stories in early detection of tumors. In our framework, utilize VGG19 model combined with fuzzy logic type-2 where used fuzzy logic type-2 that applied to enhancement the images brain where Type-2 fuzzy logic better handles uncertainty in medical images, improving the interpretability of image enhancement by managing noise and subtle differences with greater precision than Type-1 fuzzy logic for MRI images often contain ambiguous or low-contrast areas where noise, lighting conditions different and greatly improve accuracy. while used the VGG19 architecture to feature extraction and classify Tumor and non- Tumor. This approach enhances the accuracy of tumors classification, aiding in the development of targeted treatment strategies for patients. The method is trained on the Br35H dataset, resulting in a training accuracy of 0.9983 % and Train loss of 0.2118 while the validation accuracy of 0.9953 % validation loss of 0.2264. This demonstrates effective pattern learning and generalization capabilities. The model achieves outstanding accuracy, with a best accuracy for the model of 0.9983 %, While the test accuracy of the model reached of 99 %, and both of sensitivity and specificity at 0.9967
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Fish classification and object detection are crucial tasks in the fishery industry. The use of computer vision and deep learning techniques can help automate these tasks and improve the efficiency of the fishery indus...
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Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-...
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Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband ***,they consume important and scarce net...
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Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband ***,they consume important and scarce network resources such as bandwidth and processing *** have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial *** paper draws its motivation from such real network disaster incidents attributed to signaling *** this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and *** provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding *** important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a *** paper presents an update and an extension of our earlier conference *** our knowledge,no similar survey study exists on the subject.
Since author’s writing styles are often ambiguous, writer recognition is an appealing research problem for handwritten manuscript investigation. Pattern identification allows for recognizing the author of a handwritt...
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