Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Cancer Medicine, Journal Year: 2024, Volume and Issue: 13(7)

Published: April 1, 2024

Abstract Background The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution AI technologies based on deep learning, particularly advent convolutional neural networks (CNNs), presents an expanded horizon applications lung cancer screening, including segmentation, nodule detection, false‐positive reduction, classification, prognosis. Methodology This review initially analyzes current status technologies. It then explores assesses potential enhancing sensitivity detection reducing rates. Finally, it addresses challenges future directions screening. Results holds substantial prospects demonstrates significant improving sensitivity, rates, classifying nodules, while also showing value predicting growth pathological/genetic typing. Conclusions offers a promising supportive approach presenting considerable nodules. However, universality interpretability results need further enhancement. Future research should focus large‐scale validation new learning‐based algorithms multi‐center studies improve efficacy

Language: Английский

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IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Blockchain-Based Approach Toward a Trustworthy Cloud Computing

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 105479 - 105498

Published: Jan. 1, 2023

Cloud computing has revolutionized organizational operations by providing convenient, on-demand access to resources. The emergence of the Internet Things (IoT) introduced a new paradigm for collaborative computing, leveraging sensors and devices that generate process vast amounts data, thereby resulting in challenges related scalability security, making significance conventional security methods even more pronounced. Consequently, this paper, we propose novel Scalable Secure Architecture (SSCA) integrates IoT cryptographic techniques, aiming develop scalable trustworthy cloud systems, thus enabling multi-user systems facilitating simultaneous resources multiple users. design adopts decentralized approach, utilizing nodes handle user requests efficiently incorporates Multicast Broadcast Rekeying Algorithm (MBRA) ensure privacy confidentiality information, hybrid cryptosystem combines MBRA, Post Quantum Cryptography (PQC) blockchain technology. Leveraging devices, architecture gathers data from distributed sensing ensures collected information through robust MBRA-PQC encryption algorithms, while confidential is stored immutable records. proposed approach applied several datasets effectiveness validated various performance metrics, including response time, throughput, scalability, reliability. results highlight SSCA, showcasing notable reduction time 1.67 seconds 0.97 250 1000 respectively, comparison MHE-IS-CPMT. Likewise, SSCA demonstrated significant improvements AUC values, exhibiting enhancements 6.30%, 6.90%, 7.60%, 7.30% at 25-user level, impressive gains 5.20%, 9.30%, 11.50%, 15.40% 50-user level when compared MHE-IS-CPMT, EAM, SCSS, SHCEF models, respectively.

Language: Английский

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A systematic review of applying grey wolf optimizer, its variants, and its developments in different Internet of Things applications

Internet of Things, Journal Year: 2024, Volume and Issue: 26, P. 101135 - 101135

Published: Feb. 22, 2024

Language: Английский

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Edge Computing in Healthcare: Innovations, Opportunities, and Challenges

Future Internet, Journal Year: 2024, Volume and Issue: 16(9), P. 329 - 329

Published: Sept. 10, 2024

Edge computing promising a vision of processing data close to its generation point, reducing latency and bandwidth usage compared with traditional cloud architectures, has attracted significant attention lately. The integration edge in modern systems takes advantage Internet Things (IoT) devices can potentially improve the systems’ performance, scalability, privacy, security applications different domains. In healthcare domain, IoT nowadays be used gather vital parameters information that fed Artificial Intelligence (AI) techniques able offer precious insights support professionals. However, issues regarding privacy security, AI optimization, computational offloading at pose challenges adoption AI. This paper aims explore current state art by using Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) methodology analyzing more than 70 Web Science articles. We have defined relevant research questions, clear inclusion exclusion criteria, classified works three main directions: AI-based optimization methods, techniques. findings highlight many advantages integrating wide range use cases requiring near real-time decision-making, efficient communication links, potential transform future services eHealth applications. further is needed enforce new security-preserving methods better orchestrating coordinating load distributed decentralized scenarios.

Language: Английский

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Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 170, P. 108007 - 108007

Published: Jan. 16, 2024

Language: Английский

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Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Electronics, Journal Year: 2024, Volume and Issue: 13(3), P. 669 - 669

Published: Feb. 5, 2024

Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In era where IoT increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process monitoring identifying diseases for patients healthcare professionals. IoT-based systems, safeguarding data utmost importance, to prevent unauthorized access intermediary assaults. The motivation this research lies addressing growing security concerns within IoT. proposed paper, we combine Multi-Step Deep Q Learning Network (MSDQN) with (DLN) enhance privacy data. DLN employed authentication identify authenticated devices intermediate attacks between them. MSDQN, on other hand, harnessed detect counteract malware Distributed Denial Service (DDoS) during transmission locations. Our method’s performance assessed based such parameters as energy consumption, throughput, lifetime, accuracy, Mean Square Error (MSE). Further, have compared effectiveness our approach existing method, specifically, Learning-based (LDQN).

Language: Английский

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FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 18, 2024

Abstract The Internet of Things (IoT) is an extensive system interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. IoT's promise capturing interest as its value in healthcare continues grow, it can overlay on top challenges dealing the rising burden chronic disease management aging population. To address difficulties associated IoT-enabled healthcare, we propose a secure routing protocol that combines fuzzy logic Whale Optimization Algorithm (WOA) hierarchically. suggested method consists two primary approaches: trust strategy WOA-inspired clustering methodology. first methodology plays critical role determining trustworthiness connected IoT equipment. Furthermore, WOA-based framework implemented. A fitness function assesses likelihood acting cluster heads. This formula considers factors such centrality, range communication, hop count, remaining energy, trustworthiness. Compared other algorithms, proposed outperformed them terms network lifespan, energy usage, packet delivery ratio by 47%, 58%, 17.7%, respectively.

Language: Английский

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Enhanced Fox Optimizer for Internet of Things Powered Personalized Healthcare Systems

International Journal of Communication Systems, Journal Year: 2025, Volume and Issue: 38(7)

Published: March 31, 2025

ABSTRACT The Internet of Things (IoT) paradigm has recently opened up new research opportunities in many academic and industrial fields, particularly medicine. IoT‐enabled technology transformed healthcare from a centralized model to personalized system driven by ubiquitous wearable devices smartphones. implementation IoT faces critical challenges, including energy efficiency, network reliability, task response time, availability services. An Adaptive Fox Optimizer (AFO) is proposed as novel IoT‐supported method for providing zero‐orientation nature AFO mitigated quasi‐oppositional learning. A reinitialization plan also presented improve exploration skills. Furthermore, an additional stage implemented with two movement techniques optimize search capabilities. In addition, multi‐best methodology used deviate the local optimum manage population more efficiently. Ultimately, greedy selection accelerates convergence exploitability. was rigorously evaluated, demonstrating significant improvements across key performance metrics. Compared conventional approaches, enhances 83.33%, reliability 11.32%, reduces consumption 19.12%, decreases times 25.14%. These results highlight AFO's ability resource allocation, enhance fault tolerance, prolong lifespan environments. By addressing this contributes developing efficient, reliable, responsive systems, paving way advancements health monitoring, telemedicine, smart hospital management.

Language: Английский

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Machine Learning Models

Published: Jan. 1, 2025

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Real-Time AI Responses

Published: Jan. 1, 2025

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