Digital twins (DTs) have developed as a transformative technology in smart farming, facilitating real-time simulation, monitoring, and optimization of agricultural processes. This survey explores DTs' definition a...
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The convergence of machine learning and medical data presents an exciting frontier in the realm of healthcare, with the potential to revolutionize the early detection of diseases. In this study, we introduce innovativ...
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ISBN:
(纸本)9789819739363
The convergence of machine learning and medical data presents an exciting frontier in the realm of healthcare, with the potential to revolutionize the early detection of diseases. In this study, we introduce innovative machine learning models designed for the early prediction of three critical ailments: diabetes, heart disease, and liver disorders. To enhance the performance of these models, we rigorously fine-tuned their hyperparameters, a critical aspect of the model development process. Our approach involved the utilization of various classification algorithms, such as logistic regression (LR), extra tree (ET), support vector machine (SVM), Naïve Bayes (NB), decision tree (DT), and random forest (RF). Furthermore, we employed ensemble learning techniques like bagging and boosting, using the aforementioned traditional algorithms as base estimators. All these algorithms underwent extensive hyperparameter tuning to optimize their predictive capabilities. To assess the performance of these models, we conducted a thorough tenfold cross-validation, enabling us to make a comprehensive comparative analysis and identify the most effective models for each dataset. Notably, our efforts bore fruit with exceptional results. For instance, we achieved an impressive accuracy rate of 99.22% in predicting diabetes using the traditional SVM classifier. In the case of the Statlog heart dataset, we reached an accuracy of 85.67% by utilizing the random forest classifier within a bagging ensemble. In predicting liver disorders, we achieved a 73.75% accuracy by employing both boosting random forest and boosting extra tree classifiers. Additionally, we elucidated the reasons behind the variation in results, providing valuable insights. These experimental findings underscore the superiority of our proposed models over existing methods in terms of predictive accuracy. Consequently, our research represents a significant step forward in the early diagnosis and prevention of diseases within t
Optimization plays a vital role in science and engineering. Nonlinear problems demand optimized solutions, traditional Algorithms fail to solvethese kinds of problems. A Recent trend is to use Nature Inspired optimiza...
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Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic res...
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Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.
With the emerging technological revolutions, the higher education institutions are integrating Artificial Intelligence (AI) to enhance their website support and user experience. A GPT-2-based chatbot has been develope...
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Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by inte...
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Machine Translation (MT) is a specialized domain within the broader field of Natural Language Processing (NLP) that utilizes machine learning algorithms to translate speech or text from one language to another languag...
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Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes i...
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It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations a...
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The rise of digital transformation is driven by the convenience of internet of things (IoT) wireless connectivity, facilitating the development of smarter, more interconnected, and efficient solutions across diverse s...
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