Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectati...
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This look at explores the efficiency of diverse photo recovery strategies, inclusive of noise reduction, histogram change, facet enhancement, polishing, and smoothing, in digital picture processing. An extensive evalu...
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The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text ***,humans naturally use speech for visualization ***,this paper proposes an architecture...
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The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text ***,humans naturally use speech for visualization ***,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN *** proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate *** paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the *** GAN model successfully generates artwork from descriptions using speech and text *** outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input *** model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse *** proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output *** capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.
Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output ...
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Hemorrhagic transformation (HT) is a severe complication of acute ischemic stroke (AIS) that can lead to disability or death. Accurate and timely risk assessment of HT is essential for clinicians to design effective t...
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While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heuristics can lead to failure on challe...
Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning *** analytical and statistical models are avai...
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Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning *** analytical and statistical models are available to infer a variety of user sentiments in posts on social *** amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing *** paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning *** articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of *** proposed method classies tweets related to the outbreak of dengue into positives and *** were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue *** proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.
Breast cancer causes the highest death among all types of cancers in women. Early detection and diagnosis leading to early treatment can save the life. The computer-assisted methodologies for breast dynamic contrast-e...
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Owing to its therapeutic importance, several Deep-Learning (DL) methodologies are frequently utilized in hospitals for disease detection through medical imaging. Renal CT (RCT) is an imaging modality utilized to asses...
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ISBN:
(数字)9798331502768
ISBN:
(纸本)9798331502775
Owing to its therapeutic importance, several Deep-Learning (DL) methodologies are frequently utilized in hospitals for disease detection through medical imaging. Renal CT (RCT) is an imaging modality utilized to assess abnormalities in the kidney. Kidney stones (KS) are a prevalent issue, and prompt detection and treatment are crucial. This research utilized a DL-method to classify the selected RCT slices into normal/KS category. The DL-tool comprises several stages: (i) image collection and resizing, (ii) deep-features extraction and classification using SoftMax, (iii) best three models selection and Ensemble Deep-Features (EDF) vector generation, and (iv) classification and 3-fold cross-validation. This study analyzed 1250 RCT images per class, revealing that conventional-features based identification achieves >88.5% accuracy, whereas EDF based detection attains an accuracy >98%. The results validate that this DL-tool functions effectively on the selected RCT database, and in the future, the performance of DL-tool can be assessed with clinically acquired RCT images.
Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people over the age of sixty. Currently, there is no such cure for PD. However, Treatments are available to help relieve the symp...
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ISBN:
(数字)9798350361155
ISBN:
(纸本)9798350361162
Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people over the age of sixty. Currently, there is no such cure for PD. However, Treatments are available to help relieve the symptoms related to PD. A rating tool called MDS-UPDRS III is used to gauge the severity and progression and stages of disease severity are measured by the Hoehn and Yahr (NHY) staging system. This study performs a comparative analysis of the effectiveness of various machine learning models such as AdaBoost, XGBoost, Gradient Boosting Classifier (GBC), Decision Trees (DT), K-Nearest Neighbor (KNN), Logistic regression (LR), and Gaussian Naïve Bayes (GNB) on a dataset containing a variety of PD-related features obtained from 2861 persons. Local Interpretable Model-agnostic Explanations (LIME) is employed to perform the analysis of the strength of each feature toward the outcome prediction for various classification tasks. It is observed that the AdaBoost classifier delivers superior performance of 93.2% accuracy and 84.2% F1-Score for the dataset.
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