Sentiment analysis of tweets employing convolutional neural network optimized by enhanced gorilla troops optimization algorithm

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 4, 2025

Sentiment analysis has become a difficult and important task in the current world. Because of several features data, including abbreviations, length tweet, spelling error, there should be some other non-conventional methods to achieve accurate results overcome issue. In words, because those issues, conventional approaches cannot perform well accomplish with high efficiency. Emotional feelings, such as fear, anxiety, or traumas, often stem from many psychological issues experienced during childhood that can persist throughout life. addition, people discuss share their ideas on social media, unconsciously representing hidden emotions comments. This study is about sentiment tweets shared by people. fact, determine whether comments are positive negative. The paper introduces use Convolutional Neural Network (CNN), kind neural network, optimized Enhanced Gorilla Troops Optimization Algorithm (CNN-EGTO). Two datasets provided SemEval-2016 used evaluate system, while polarity were manually determined. It was determined findings present suggested model could approximately values 98%, 95%, 96.47% for accuracy, precision, recall, F1-score, respectively, polarity. gain 97, 96, 98, 97.49 negative Consequently, it found outperform models considering performance These metrics represent sentence, negative, great

Language: Английский

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A novel deep learning model for stock market prediction using a sentiment analysis system from authoritative financial website’s data

Connection Science, Journal Year: 2025, Volume and Issue: 37(1)

Published: Jan. 24, 2025

Language: Английский

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Large Model Era: Deep Learning in Osteoporosis Drug Discovery

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in decline patients' life quality. In response to the increased incidence osteoporosis, related drug discovery has attracted more attention, but it faced with challenges due long development cycle high cost. Deep learning powerful data processing capabilities shown significant advantages field discovery. With technology, applied all stages particular, large models, which have been developed rapidly recently, provide new methods for understanding disease mechanisms promoting because their parameters ability deal complex tasks. This review introduces traditional models deep domain, systematically summarizes applications each stage discovery, analyzes application prospect osteoporosis Finally, limitations are discussed depth, order help future

Language: Английский

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Deep Convolutional Neural Networks in Medical Image Analysis: A Review

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 195 - 195

Published: March 3, 2025

Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex imaging datasets. This review provides a focused CNN evolution and architectures as applied to analysis, highlighting their application performance in different fields, including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, orthopedics. The paper also explores challenges specific outlines trends future research directions. aims serve valuable resource for researchers practitioners healthcare artificial intelligence.

Language: Английский

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A proximal policy optimisation algorithm-based algorithm for cardiovascular disorders detection

Journal of Medical Engineering & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: March 11, 2025

Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises mutual learning-based bee colony (ML-ABC) algorithm set initial weights proximal policy optimisation (PPO) address imbalanced classification. ML-ABC uses learning enhance process by updating positions of food sources with respect best fitness outcomes two randomly selected individuals. PPO makes updates ANN stable efficient improve model's reliability. Our approach formulates classification problem as series decision-making processes, rewarding every act higher rewards for correctly identifying instances minority class, hence handling class imbalance. We evaluated performance on diversified medical dataset including 26,002 who were examined within Polyclinic Occupational Health Sports Zagreb, further validated NCAA NHANES datasets verify generalisability. findings indicate that our outperforms existing models accuracies 0.88, 0.86 0.82 respective datasets. These results clinical application advance cardiovascular disorder detection methodologies.

Language: Английский

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Hybrid CNN-LSTM-GRU With Attention Mechanism for Efficient Cardio Vascular Disease Prediction in IoMT

Advances in medical technologies and clinical practice book series, Journal Year: 2025, Volume and Issue: unknown, P. 503 - 530

Published: Feb. 14, 2025

In this study, we develop a hybrid deep learning model for IoMT which is capable of delivering efficient predictive capability. The effectiveness was enhanced through feature selection pipeline using Pearson correlation, chi-square tests, and ExtraTreesClassifier ranking importance. By eliminating redundant attributes transforming categorical data with LabelEncoder, computational efficiency performance are enhanced. integrates CNN, LSTM, GRU layers, augmented by an attention mechanism. CNN component extracts spatial patterns from the input data, while LSTM layers capture temporal sequential dependencies. mechanism further enhances focusing on most relevant features, improving interpretability overall prediction accuracy. proposed demonstrates high level performance, achieving accuracy 98.9% curated dataset.

Language: Английский

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Enhancing brain tumor classification in MRI images: A deep learning-based approach for accurate classification and diagnosis

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105555 - 105555

Published: April 1, 2025

Language: Английский

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Criminal emotion detection framework using convolutional neural network for public safety

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 1, 2025

In the era of rapid societal modernization, issue crime stands as an intrinsic facet, demanding our attention and consideration. As communities evolve adopt technological advancements, dynamic landscape criminal activities becomes essential aspect that requires careful examination proactive approaches for public safety application. this paper, we proposed a collaborative approach to detect patterns emotions with aim enhancing judiciary decision-making. For same, utilized two standard datasets - dataset comprised different features crime. Further, emotion has 135 classes help AI model efficiently find emotions. We adopted convolutional neural network (CNN) get first trained on bifurcate non-crime images. Once is detected, faces are extracted using region interest stored in directory. Different CNN architectures, such LeNet-5, VGGNet, RestNet-50, basic CNN, used face. The models enhance framework evaluated evaluation metrics, training accuracy, loss, optimizer performance, precision-recall curve, complexity, time, inference time. detection, achieves remarkable accuracy 92.45% LeNet-5 outperforms other architectures by offering 98.6%.

Language: Английский

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Multitask learning multimodal network for chronic disease prediction

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 3, 2025

Chronic diseases are a critical focus in the management of elderly health. Early disease prediction plays vital role achieving prevention and reducing associated burden on individuals healthcare systems. Traditionally, separate models were required to predict different diseases, process that demanded significant time computational resources. In this research, we utilized nationwide dataset proposed multi-task learning approach combined with multimodal model. By leveraging patients' medical records personal information as input, model predicts risks diabetes mellitus, heart disease, stroke, hypertension simultaneously. This addresses limitations traditional methods by capturing correlations between these while maintaining strong predictive performance, even reduced number features. Furthermore, our analysis attention scores identified risk factors align previous enhancing model's interpretability demonstrating its potential for real-world applications.

Language: Английский

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Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study

Cardiovascular Diabetology, Journal Year: 2024, Volume and Issue: 23(1)

Published: Nov. 15, 2024

Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models predict mortality in such patients. We aimed develop and test an individualized machine learning model assess risk factors these From January 2012 December 2021, this study collected data on heart from the Chongqing Medical University Data Platform. Least absolute shrinkage selection operator was used recognizing key clinical variables. The optimal predictive chosen among eight algorithms basis of area under curve. SHapley Additive exPlanations Local Interpretable Model-agnostic Explanations employed interpret outcome model. This ultimately comprised 4647 individuals failure. Random Forest highest curve 0.850 (95% CI 0.789–0.897), high accuracy 0.738, recall 0.837, specificity 0.734 brier score 0.178. According results, most related were urea, length stay, neutrophils, albumin high-density lipoprotein cholesterol. developed as well Compared other algorithms, performed significantly better. Our successfully predicted identified associated

Language: Английский

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