A Comprehensive Analysis of Hypertension Disease Risk-Factors, Diagnostics, and Detections Using Deep Learning-Based Approaches

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(4), P. 1939 - 1958

Published: Dec. 14, 2023

Language: Английский

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Multiple Infectious Disease Diagnosis and Detection Using Advanced CNN Models

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 45 - 58

Published: Jan. 1, 2024

Language: Английский

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Exploring the relationship between artificial intelligence literacy and English language learning motivation

Published: Dec. 30, 2024

Artificial intelligence has been transforming every field of life. It's critical to comprehend how artificial affects foreign language learning. can improve real-time feedback and individualized learning experiences, which may boost student motivation. The study assesses students' literacy English motivation levels. Data were gathered through in-person surveys from 397 participants using the Intelligence Literacy Language Learning Motivation Scales. findings showed that (65.02) in (61.95) above average. There statistically significant positive correlations between total scores (p < 0.01). These results imply a greater learn is related better level literacy. Also, incorporating into instruction engagement. More research examine other variables impacting this relationship also needed. offer insightful information educators legislators who seek enhance quickly changing educational environment.

Language: Английский

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A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors

International Journal of experimental research and review, Journal Year: 2024, Volume and Issue: 46, P. 1 - 18

Published: Dec. 30, 2024

Cardiovascular Diseases (CVDs), particularly heart diseases, are becoming a significant global public health concern. This study enhances CVD detection through novel approach that integrates obesity prediction using machine learning (ML) models. Specifically, model trained on an dataset was used to add 'Obesity level' feature the disease dataset, leveraging relation of high with increased risk. We have also calculated BMI and added as in dataset. evaluated this transfer learning-based alongside eight ML Performance these models assessed precision, recall, accuracy F1-score metrics. Our research aims provide healthcare practitioners reliable tools for early diagnosis. Results indicate ensemble methods, which combine strengths multiple models, significantly improve compared other classifiers. able achieve 74% score along 0.72 F1 score, 0.77 precision 0.80 AUC XGBoost classifier, followed closely by DNN 73.7% 0.75 0.798 our proposed model. seek enhance efficiency promote integrating AI-based solutions into medical practice. The findings demonstrate potential techniques effectiveness incorporating obesity-related features optimized cardiovascular detection.

Language: Английский

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COPD Assessment Through Multimodal Analysis: Exploiting the Synergy of CNNs and LSTM Networks

Published: Oct. 18, 2023

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition that requires accurate assessment for effective management. The paper proposes novel approach leverages the combined power of CNNs and LSTM networks COPD through multimodal analysis. objective study to enhance accuracy reliability diagnosis by exploiting synergy between using comprehensive dataset comprising lung function measurements, clinical history, imaging data. Existing systems often rely on single-modal analysis, limiting effectiveness diagnosis. In contrast, our proposed integrates multiple modalities, including data, capture more representation disease. Experimental evaluation showcases superior performance model, achieving an above 95 % outperforming existing in terms precision, recall, Fl-score. fusion enables model extract relevant features temporal dependencies, enhancing overall performance. These findings highlight potential analysis reliable early detection COPD. research contributes improving management treatment outcomes debilitating condition.

Language: Английский

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Automated Detection of Polycystic Ovaries using Pretrained Deep Learning Models

Published: Nov. 16, 2023

One way to diagnose PCOS, a hormonal disorder that impacts female pregnancy, is ultrasound imaging.. To overcome the manual difficulties in identifying disorders by physicians an automated deep learning approach suggested this paper. The bulk of imaging traits are used determine illness's diagnosis. Due overlapping follicles, intrinsic equipment noise, and shortage operator knowledge, it primarily based on expertise execution, typical appearance PCOS image becomes more challenging, lengthening diagnosis process. This study suggests for prediction makes use transfer tools including Alexnet, VGG16, Inception V3, hybrid models. classification was developed using proposed approach. Here, effort made propose process would train model improve accuracy Applying performance metrics such as accuracy, precision, Recal, F1score each network's evaluated. detection method produces 87%.

Language: Английский

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Automated System for Prediction and Prognosis of Infection Diseases Using Deep Learning-Based Approaches

Indian Journal of Science and Technology, Journal Year: 2023, Volume and Issue: 16(34), P. 2730 - 2739

Published: Sept. 15, 2023

Objectives: This study explores the potential of deep learning-based techniques to improve disease management and intervention by focusing on their use in infectious prediction prognosis. Methods: The research used learning models EfficientNetB0, NASNetLarge, DenseNet169, ResNet152V2, InceptionResNetV2. For this study, a dataset comprising 29,252 images different diseases such as COVID-19, MERS, Pneumonia, SARS, tuberculosis. To visualize pixel intensity, exploratory data analysis was performed pictures. Preprocessing eliminated disruptive signals via image augmentation contrast enhancement. After that, Otsu thresholding contour feature morphological values retrieved relevant features. Findings: best successful model found be EfficientNetB0. During training, it obtained 90.22% accuracy rate, loss 0.279, having an RMSE value 0.578. However, InceptionResNetV2 showed accuracy, loss, throughout testing. precise results were 88%, 0.399, 0.631, respectively. Novelty: novelty resides exploring methods based for predicting prognosticating diseases, with handling strategies intervention, public health decisions. Keywords: Tuberculosis; Pneumonia; Infectious diseases; Deep learning;

Language: Английский

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Advancements in Image Classification for Malaria Diagnosis

Published: Nov. 23, 2023

Malaria, a dangerous disease transmitted through mosquito bites and caused by Plasmodium parasites, presents substantial threat to human health. The primary aim is streamline the process, rendering it quicker, more straightforward, highly efficient. foremost objective create robust computer model capable of swiftly distinguishing cells in thin blood samples obtained from standard microscope slides. These will be categorized as either infected or uninfected, employing advanced image processing techniques facilitate prompt effective testing. Additionally, authors intend harness capabilities machine learning for classifying cell images. purpose firmly rooted desire enhance accuracy speed malaria diagnosis, ultimately contributing early identification management this life-threatening ailment.

Language: Английский

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