Analytical Study of the World's First EU Artificial Intelligence (AI) Act, 2024

International Journal of Research Publication and Reviews, Journal Year: 2024, Volume and Issue: 5(3), P. 7343 - 7364

Published: March 1, 2024

The world's first law governing "artificial inelegance" has arrived!The emergence of Artificial Intelligence (AI) technologies prompted a global discourse on the necessity regulatory frameworks to govern their development and deployment responsibly.With escalating integration into various facets human life, imperative for become paramount.On March 13, 2024, European Parliament formally adopted EU Act, 2024 1 ("AI 2024") with large majority 523-46 votes in favor legislation, horizontal standalone legislation dedicated exclusively AI governance.The represents watershed moment governance, aiming establish comprehensive guidelines safeguards development, deployment, use systems across diverse sectors.Through rigorous analysis Act's key components, including definitions, principles, obligations, enforcement mechanisms, this research seeks elucidate its potential impact stakeholders, innovation ecosystems, societal dynamics worldwide.This study employs multidisciplinary approach scrutinize intricate provisions implications encompassing legal, ethical, socio-economic, technological dimensions.A crucial aspect will be deep dive specific regulations outlined explore how Act tackles identification mitigation "inelegant biases" within systems.Additionally, analyze 2024's requirements explain-ability "inelegant" decisions, ensuring transparency accountability.The mechanisms established oversight also under scrutiny understand effectiveness upholding regulations.Furthermore, endeavors identify strengths, weaknesses, opportunities, threats inherent considering adaptability evolving landscapes, alignment fundamental rights capacity foster responsible while mitigating risks disparities.This contribute valuable insights ongoing discussions about navigating complexities artificial intelligence ethical manner.

Language: Английский

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Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 58349 - 58377

Published: Jan. 1, 2024

In the era of Industry 4.0, digital transformation has spurred swift advancement technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction remaining useful life plays a crucial role in these technologies as it extends power equipment's safe operational duration decreases costs associated with unforeseen shutdowns. Also, increased accessibility data for monitoring system conditions paved way more immense adoption machine learning models management transformers. At moment, ever-increasing demand electricity, there is corresponding increase degradation processes Transformers insulation importantly, paper happens to be principal part where prominent. Therefore, an insulating condition through its degree polymerization required guarantee reliability this regard, predictions, reliability, equipment can actualized by modeling transformer several frameworks. view, review been drafted not only serve guide researchers interested fields fault prognosis but also offer insights into potential research directions existing literature evaluating presented.

Language: Английский

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Combining Compressed Sensing and Neural Architecture Search for Sensor-Near Vibration Diagnostics

IEEE Transactions on Industrial Informatics, Journal Year: 2024, Volume and Issue: 20(8), P. 10488 - 10498

Published: May 13, 2024

Compressed sensing (CS) for sensor-near vibration diagnostics represents a suitable approach the design of network-efficient structural health monitoring systems. This article presents solution analysis based on deep neural networks (DNNs) trained compressed data. The envisioned maintenance system consists network nodes orchestrated by very constrained centralizing unit. latter is equipped with microcontroller unit (MCU) that predicts state using aggregated information. As major contribution, DNN architectures are generated automatically from data through procedure inspired hardware-aware (HW) architecture search (NAS), called as HW-NAS-CS, which uniquely refined additional constraints consider both peculiarities CS parameters and limitation embedded devices. proposed has been validated two real-world SHM datasets damage identification eventually deployed low-end computing platform (the STM32L5 MCU). Results demonstrate DNNs combined adapted schemes can attain classification scores always above 90% even in case huge compression levels (higher than 64x): these performances significantly improve ones attained state-of-the-art approaches field, utmost advantage being portable

Language: Английский

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Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification

Al-Nahrain Journal for Engineering Sciences, Journal Year: 2025, Volume and Issue: 28(1), P. 97 - 120

Published: April 7, 2025

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It more likely be successfully discovered at an early stage before it worsens. Distinguishing size, shape, and location of lymphatic nodes identify spread around these nodes. Thus, identifying lung remarkably helpful for doctors. diagnosed by expert doctors; however, their limited experience may misdiagnosis cause medical issues in patients. In line computer-assisted systems, many methods strategies used predict malignancy level that plays a significant role provide precise abnormality detection. this paper, use modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 2024) were extensively explored highlight different machine deep (DL) techniques models detection, classification, prediction cancerous tumors. The efficient model Tiny DL must built assist physicians who are working rural centers swift rapid diagnosis cancer. combination lightweight Convolutional Neural Networks resources could produce portable with low computational cost has ability substitute skill doctors needed urgent cases.

Language: Английский

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A Comprehensive Survey on Deep Learning-based Predictive Maintenance

ACM Transactions on Embedded Computing Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

With the advent of Industrial 4.0 and push towards Industry 5.0, data generated by industries have become surprisingly large. This abundance significantly boosts machine deep learning models for Predictive Maintenance (PdM). The PdM plays a vital role in extending lifespan industrial equipment machines while also helping to reduce risk unscheduled downtime. Given its multidisciplinary nature, field has been approached from many different angles: this comprehensive survey aims provide an up-to-date overview focused on all learning-based strategies, discussing weaknesses strengths. is based Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) methodological flow, allowing systematic complete review literature. In particular, firstly, we explore main used PdM, mainly Convolutional Neural Networks (ConvNets), Autoencoders (AEs), Generative Adversarial (GANs), Transformers, giving newest such as diffusion foundation models. Then, discuss paradigms applied i.e. , supervised, unsupervised, ensemble, transfer, federated, reinforcement learning. Furthermore, work discusses pipeline data-driven benefits, practical applications, datasets, benchmarks. addition, evaluation metrics each stage state-of-the-art hardware devices are discussed. Finally, challenges future presented.

Language: Английский

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Reinforcement effects of bonding Fe-SMA in steel bridge diaphragms based on machine learning

Structures, Journal Year: 2025, Volume and Issue: 76, P. 108984 - 108984

Published: April 25, 2025

Language: Английский

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The design and testing of an ultrasensitive device with embedded phononic crystals for the detection and localisation of nonlinear guided waves

Journal of Sound and Vibration, Journal Year: 2025, Volume and Issue: unknown, P. 119155 - 119155

Published: April 1, 2025

Language: Английский

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A TinyDL Model for Gesture-Based Air Handwriting Arabic Numbers and Simple Arabic Letters Recognition

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 76589 - 76605

Published: Jan. 1, 2024

The application of tiny machine learning (TinyML) in human-computer interaction is revolutionizing gesture recognition technologies. However, there remains a significant gap the literature regarding effective complex scripts, such as Arabic, real-time applications. This research aims to bridge this by leveraging TinyML for accurate Arabic numbers and simple letters through gesture-based air handwriting. For first time, we introduce novel deep (TinyDL) model that utilizes lightweight convolutional neural network (CNN) architecture specifically designed handle intricacies script adaptable domain. Despite widespread use CNNs recognition, our stands out achieving an exceptional accuracy rate 97.5% decoding 2D inputs numerals letters. high level demonstrates effectiveness TinyDL addressing unique challenges posed thereby making it user-friendly accessible solution. Moreover, contributes advancement applications real-world apps, showcasing potential transforming between humans digital devices.

Language: Английский

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Compression-Accuracy Co-optimization Through Hardware-aware Neural Architecture Search for Vibration Damage Detection

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(19), P. 31745 - 31757

Published: June 26, 2024

Internet-of-Things (IoT) is a key enabler for the transition to Automatic Structural Health Monitoring (ASHM) of technical facilities, thanks seamless flow data from multitude always connected devices. Current IoT-ASHM installations, however, face double challenge ensure high accuracy while meeting requirement minimal energy consumption. The paper tackles these issues deep-learning perspective, and describes an IoT-enabled monitoring approach based on distributed end-to-end deep neural network (DNN). architecture supports both compression damage detection. A low-end microcontroller hosts specific local DNN; hardware-aware neural-architecture search strategy rules optimization, in order satisfy resource constraints set by computing features extracted feed aggregating unit, which includes stacked global classification layer full-scale After proper quantization, designed models are eventually deployed wireless accelerometer sensor. Finally, cost-benefit analysis evaluates system's impact sensor autonomy. Experiments well-known dataset proved that proposed solution could achieve state-of-the-art scores (all metrics above 98.4%) with transmission cost (less than 53 B average); as compared conventional approaches, described yielded reduction three orders magnitude

Language: Английский

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An Ultrasensitive Device with Embedded Phononic Crystals for the Detection and Localisation of Nonlinear Guided Waves

Published: Jan. 1, 2024

Language: Английский

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