WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop int...
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Imbalanced data classification is one of the major problems in machine *** imbalanced dataset typically has significant differences in the number of data samples between its *** most cases,the performance of the machi...
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Imbalanced data classification is one of the major problems in machine *** imbalanced dataset typically has significant differences in the number of data samples between its *** most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced *** classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class *** this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was ***,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was *** this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated *** is the training of a balanced dataset using *** this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this *** this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization *** works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of *** at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.
The surge in video streaming has created a pressing need for efficient delivery mechanisms and robust real-time analysis capabilities. This paper presents a comprehensive video stream analysis system designed to opera...
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Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast ...
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Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the be
With the growing population and the consequent surge in vehicular traffic, there is an urgent need for heightened safety measures. Addressing this challenge requires the integration of advanced systems equipped with h...
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Heart failure is a major problem which affects many people across the *** paper proposes an integrated architecture combining advanced predictive modeling for the risk assessment of heart failure and medication recomm...
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In recent years, deep learning technologies have significantly advanced the field of multi-spectral image analysis. This paper presents a novel advancement through the development of a specialized Convolutional Neural...
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During disasters, such as natural catastrophes the immediate survival of victims is at stake. There have been instances where victims who were genuinely in dire need of assistance were not provided with it when they s...
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The task of choosing a suitable web service has become increasingly difficult in today’s landscape due to the growing number of services offering similar functionalities. This paper addresses the critical need f...
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In the industrial production of high-energy-density lithium batteries, accurately detecting electrode endpoint positions is crucial for maintaining appropriate electrode spacing, as improper spacing can lead to short ...
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