Machine learning Helps in Quickly Diagnosis Cases of "New Corona"

Mesopotamian Journal of Artificial Intelligence in Healthcare, Journal Year: 2024, Volume and Issue: 2024, P. 16 - 19

Published: Jan. 16, 2024

Machine learning is considered one of the most significant techniques that play a vital role in diagnosing Coronavirus. It set advanced algorithms capable analysing medical data and identifying patterns behaviours diseases. used to interpret images, giving details each image with high accuracy efficiency, such as chest X-ray images. These are trained on large images recognise indicate presence infection Coronavirus (COVID-19). This article will provide brief overview importance machine COVID-19 by processing helping physicians healthcare workers distinguished influential care for patients infected this virus.

Language: Английский

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Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

Deleted Journal, Journal Year: 2024, Volume and Issue: 4(2), P. 20 - 62

Published: May 23, 2024

Cutting-edge technologies have been widely employed in healthcare delivery, resulting transformative advances and promising enhanced patient care, operational efficiency, resource usage. However, the proliferation of networked devices data-driven systems has created new cybersecurity threats that jeopardize integrity, confidentiality, availability critical data. This review paper offers a comprehensive evaluation current state context smart healthcare, presenting structured taxonomy its existing cyber threats, mechanisms essential roles. study explored (SHSs). It identified discussed most pressing attacks SHSs face, including fake base stations, medjacking, Sybil attacks. examined security measures deployed to combat SHSs. These include cryptographic-based techniques, digital watermarking, steganography, many others. Patient data protection, prevention breaches, maintenance SHS integrity are some roles ensuring sustainable healthcare. The long-term viability depends on constant assessment risks harm providers, patients, professionals. aims inform policymakers, practitioners, technology stakeholders about imperatives best practices for fostering secure resilient ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, management, sustainability research. Understanding recent is controlling escalating networks encouraging intelligent delivery.

Language: Английский

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Systematic literature review on intrusion detection systems: Research trends, algorithms, methods, datasets, and limitations

Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 33(1)

Published: Jan. 1, 2024

Abstract Machine learning (ML) and deep (DL) techniques have demonstrated significant potential in the development of effective intrusion detection systems. This study presents a systematic review utilization ML, DL, optimization algorithms, datasets research from 2018 to 2023. We devised comprehensive search strategy identify relevant studies scientific databases. After screening 393 papers meeting inclusion criteria, we extracted analyzed key information using bibliometric analysis techniques. The findings reveal increasing publication trends this domain frequently used with convolutional neural networks, support vector machines, decision trees, genetic algorithms emerging as top methods. also discusses challenges limitations current techniques, providing structured synthesis state-of-the-art guide future research.

Language: Английский

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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease

Biomedicines, Journal Year: 2025, Volume and Issue: 13(2), P. 427 - 427

Published: Feb. 10, 2025

The application of artificial intelligence (AI) and machine learning (ML) in medicine healthcare has been extensively explored across various areas. AI ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, prediction, workflow optimization, resource utilization. This review summarizes current advancements concerning disease, including their clinical investigation use primary cardiac imaging techniques, common categories, research, patient care, outcome prediction. We analyze discuss commonly used models, algorithms, methodologies, highlighting roles improving outcomes while addressing limitations future applications. Furthermore, this emphasizes the transformative potential practice decision making, reducing human error, monitoring support, creating more efficient workflows for complex conditions.

Language: Английский

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Big Data and AI for Smart Maintenance: Literature review on the impact on plants Resilience

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 253, P. 1959 - 1971

Published: Jan. 1, 2025

Language: Английский

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Advanced deep learning and large language models: Comprehensive insights for cancer detection

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495

Published: March 1, 2025

Language: Английский

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Exploring Federated Learning Tendencies Using a Semantic Keyword Clustering Approach

Information, Journal Year: 2024, Volume and Issue: 15(7), P. 379 - 379

Published: June 28, 2024

This paper presents a novel approach to analyzing trends in federated learning (FL) using automatic semantic keyword clustering. The authors collected dataset of FL research papers from the Scopus database and extracted keywords form collection representing landscape. They employed natural language processing (NLP) techniques, specifically pre-trained transformer model, convert into vector embeddings. Agglomerative clustering was then used identify major thematic sub-areas within FL. study provides granular view landscape captures broader dynamics activity key focus areas are divided theoretical practical applications make their results publicly available. data-driven moves beyond manual literature reviews offers comprehensive overview current evolution

Language: Английский

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Skin Disease Recognition Based on Deep Learning Algorithms: A Review

Indonesian Journal of Computer Science, Journal Year: 2024, Volume and Issue: 13(3)

Published: June 15, 2024

The sharp increase in cases of melanoma and other skin cancers worldwide highlights the urgent need for improved diagnostic methods. Because lesions vary widely access to dermatological knowledge is limited resource-poor areas, traditional methods - which rely on visual inspection clinical experience have difficulty identifying diseases accurately. This situation requires innovative approaches improve accessibility accuracy. To address these issues, this work uses deep learning (DL) convolutional neural networks (CNNs). paper trying transform cancer diagnosis through use large databases dermoscopic images advanced artificial intelligence algorithms. In order evaluate effectiveness CNNs DL diseases, we conducted a comprehensive analysis literature, focusing accuracy type classification. Our approach focused model architectures, data preparation methods, performance indicators while examining existing research using AI algorithms diagnose cancer. With ultimate goal improving patient outcomes early detection accurate classification conditions, not only underscores great potential CNN transcending limitations, but also continued development AI-based tools pathology. Dermatology. Diagnosis.

Language: Английский

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Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 159, P. 103024 - 103024

Published: Nov. 21, 2024

Language: Английский

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A Secure federated learning framework based on autoencoder and Long Short-Term Memory with generalized robust loss function for detection and prevention of data poisoning attacks

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 102, P. 107320 - 107320

Published: Dec. 13, 2024

Language: Английский

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