Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

Diagnostics, Journal Year: 2023, Volume and Issue: 13(4), P. 814 - 814

Published: Feb. 20, 2023

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying diagnosing pneumonia is X-ray imaging. However, early discrimination difficult radiologists doctors because similarities between tuberculosis. As a result, patients do not receive proper care, which in turn does prevent from spreading. The goal this study to extract hybrid features using variety techniques order achieve promising results differentiating In study, several approaches identification distinguishing were suggested. first proposed system uses techniques, VGG16 + support vector machine (SVM) ResNet18 SVM. second an artificial neural network (ANN) based on integrating ResNet18, before after reducing high dimensions principal component analysis (PCA) method. third ANN separately with handcrafted extracted by local binary pattern (LBP), discrete wavelet transform (DWT) gray level co-occurrence matrix (GLCM) algorithms. All systems have achieved superior differentiation LBP, DWT GLCM (LDG) reached accuracy 99.6%, sensitivity 99.17%, specificity 99.42%, precision 99.63%, AUC 99.58%.

Language: Английский

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Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 448, P. 141559 - 141559

Published: March 7, 2024

Language: Английский

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Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions

BMC Medicine, Journal Year: 2024, Volume and Issue: 22(1)

Published: Sept. 2, 2024

Abstract The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding the complex interplay between biological, social, environmental factors that shape health trajectories across lifespan. This perspective summarizes current applications, discusses future potential challenges, provides recommendations for harnessing ML AI technologies develop innovative public solutions. have been increasingly applied epidemiological studies, demonstrating their ability handle large, datasets, identify intricate patterns associations, integrate multiple multimodal data types, improve predictive accuracy, enhance causal inference methods. In epidemiology, these can help sensitive periods critical windows intervention, model interactions risk factors, predict individual population-level disease trajectories, strengthen observational studies. By leveraging five principles research proposed by Elder Shanahan—lifespan development, agency, time place, timing, linked lives—we discuss a framework applying uncover novel insights inform targeted interventions. However, successful faces challenges related quality, interpretability, bias, privacy, equity. To fully realize fostering interdisciplinary collaborations, developing standardized guidelines, advocating decision-making, prioritizing fairness, investing training capacity building are essential. responsibly power AI, we take significant steps towards creating healthier more equitable futures life course.

Language: Английский

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Artificial Intelligence in Ecology: A Commentary on a Chatbot's Perspective

Bulletin of the Ecological Society of America, Journal Year: 2023, Volume and Issue: 104(4)

Published: June 29, 2023

Abstract The potential of artificial intelligence (AI) to shape research and education is a highly topical issue. recent release ChatGPT (Chat Generative Pre‐trained Transformer) by OpenAI on November 30, 2022 has opened up new possibilities for the use chatbot services in ecological education. In this perspective article, we address associated contemporary topics including ecology academic writing, application AI ecology, environmental impact, ethical considerations using such services. Several logistical, factors were identified that should be considered context research. We argue can help reduce workload researchers, generate insights ideas, serve as personal instructor assistant students. While show how chatbots have useful assets ecologists, several challenges arose. includes limited ability algorithms capture complexity nuance, dependence models data quality, concerns about construction operation also impacts but may provide benefits comparison with other conventional approaches, all which evaluated. Despite these limitations challenges, consider valuable tool could enhance speed efficiency automating certain tasks (e.g. collection management) analyzing large amounts data. However, emphasize importance taking responsible, sustainable transparent approach education, while remaining mindful impact environment, society, concerns.

Language: Английский

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Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China

Sustainability, Journal Year: 2023, Volume and Issue: 15(16), P. 12437 - 12437

Published: Aug. 16, 2023

While artificial intelligence (AI) has had a great impact on the global economy, it also brought new hope and opportunities for environmental protection. In this context, authors of paper collected balanced panel data 30 Chinese provinces during 2006–2019 studied AI development local carbon emissions by using two-way fixed-effect model. The results show that significantly lowered emissions. Using series robustness tests instrumental variable (IV) analysis, was found are still reliable. Furthermore, mechanism analysis revealed mainly reduces improving energy structure technological innovation. lower dependence fossil energy, higher innovation becomes, better reduction effect AI. addition, regional heterogeneity test detected emission is best in East, followed West, not significant Central region. Therefore, to fully exploit positive effects emissions, suggests accelerating intelligent transformation, formulating differentiated strategies, promoting green transformation usage, strengthening human capital accumulation.

Language: Английский

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Enhancing water and air pollution monitoring and control through ChatGPT and similar generative artificial intelligence implementation

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

This research delves into the utilization of advanced artificial intelligence (AI), specifically ChatGPT or Bard, to improve strategies for monitoring and controlling water air pollution. Given escalating concerns surrounding environmental degradation its repercussions on public health, there is a pressing demand innovative pollution management techniques. investigation centers harnessing capabilities ChatGPT, an language model, address real-time data analysis, decision-making, engagement challenges within realm quality. Incorporating cutting-edge methods in monitoring, such as sensor networks, satellite imagery, IoT devices, this aims obtain comprehensive understanding dynamics. Nevertheless, substantial volume presents processing extracting meaningful insights. employed intelligent tool proficient comprehending natural queries delivering insightful analyses. integration streamlines interpretation intricate sets, enabling swift decision-making control authorities. Moreover, assumes pivotal role by serving user-friendly interface disseminating information levels, regulatory measures, preventive actions. Through interactive conversations, it enhances communication between agencies general public, cultivating awareness encouraging participation initiatives. paper underscores significance collaborative human-AI approach tackling multifaceted The also ethical considerations associated with AI-driven emphasizing importance responsible AI implementation. As technologies progress, proposed framework contribute ongoing discourse sustainable involvement. By synergizing state-of-the-art techniques, seeks offer efficacious solution advancing contemporary landscape.

Language: Английский

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Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 13, 2024

Language: Английский

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Mitigating Climate Change

Practice, progress, and proficiency in sustainability, Journal Year: 2024, Volume and Issue: unknown, P. 161 - 200

Published: Aug. 27, 2024

The existential threat presented by climate change demands an unprecedented response. Existing environmental regulations are insufficient for the pollution concerns that arise from our complicated and integrated global economy. AI has potential to completely revolutionize existing regulatory frameworks dramatically improve mitigation with superior data collection, modeling & new enforcement capabilities. Using a doctrinal approach, it studied both national international laws found best practices as well legal obstacles, such need privacy algorithmic bias concerns. It discovered health law regulation compliance of in public health. concluded artificial intelligence had vastly partially but theoretically, strict can curb worst impulses unscrupulous AI. recommended policymakers collaborate experts researchers ensure quality action.

Language: Английский

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Nanoplasmonic biosensors for environmental sustainability and human health

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(21), P. 10491 - 10522

Published: Jan. 1, 2024

Monitoring the health conditions of environment and humans is essential for ensuring human well-being, promoting global health, achieving sustainability. Innovative biosensors are crucial in accurately monitoring conditions, uncovering hidden connections between understanding how environmental factors trigger autoimmune diseases, neurodegenerative infectious diseases. This review evaluates use nanoplasmonic that can monitor diseases according to target analytes different sizes scales, providing valuable insights preventive medicine. We begin by explaining fundamental principles mechanisms biosensors. investigate potential techniques detecting various biological molecules, extracellular vesicles (EVs), pathogens, cells. also explore possibility wearable physiological network healthy connectivity humans, animals, plants, organisms. will guide design next-generation advance sustainable healthcare environment, planet.

Language: Английский

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Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air

Earth and Space Science, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Abstract An automated air quality forecasting system (AI‐Air) was developed to optimize and improve for different typical cities, combined with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Model (CUACE), used in a inland city of Zhengzhou coastal Haikou China. The performance evaluation results show that PM 2.5 forecasts, correlation coefficient (R) is increased by 0.07–0.13, mean error (ME) root square (RMSE) decreased 3.2–3.5 3.8–4.7 μg/m³. Similarly, O 3 R value improved 0.09–0.44, ME RMSE values are reduced 7.1–22.8 9.0–25.9 μg/m³, respectively. Case analyses operational also indicate AI‐Air can significantly pollutant concentrations effectively correct underestimation, or overestimation phenomena compared CUACE model. Additionally, explanatory were performed assess key meteorological factors affecting cities topographic climatic conditions. highlights potential AI techniques forecast accuracy efficiency, promising applications field forecasting.

Language: Английский

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