Edge AI: A survey

Internet of Things and Cyber-Physical Systems, Journal Year: 2023, Volume and Issue: 3, P. 71 - 92

Published: Jan. 1, 2023

Artificial Intelligence (AI) at the edge is utilization of AI in real-world devices. Edge refers to practice doing computations near users network's edge, instead centralised location like a cloud service provider's data centre. With latest innovations efficiency, proliferation Internet Things (IoT) devices, and rise computing, potential has now been unlocked. This study provides thorough analysis approaches capabilities as they pertain or AI. Further, detailed survey computing its paradigms including transition presented explore background each variant proposed for implementing Computing. Furthermore, we discussed approach deploying algorithms models on which are typically resource-constrained devices located network. We also technology used various modern IoT applications, autonomous vehicles, smart homes, industrial automation, healthcare, surveillance. Moreover, discussion leveraging machine learning optimized environments presented. Finally, important open challenges research directions field have identified investigated. hope that this article will serve common goal future blueprint unite stakeholders facilitates accelerate development

Language: Английский

---

Advanced Manufacturing in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends

IEEE Transactions on Industrial Informatics, Journal Year: 2023, Volume and Issue: 20(2), P. 1055 - 1068

Published: May 8, 2023

A revolution in advanced manufacturing has been driven by digital technology the fourth industrial revolution, also known as Industry 4.0, and resulted a substantial increase profits for industry. In new paradigm of 5.0, will step further be capable offering customized products better user experience. number key enabling technologies are expected to play crucial roles assisting 5.0 meeting higher demands data acquisition processing, communications, collaborative robots process. The aim this survey is provide novel insights into summarizing latest progress technologies, such artificial intelligence things (AIoT), beyond 5G robotics. Finally, directions future research enable vision become reality, metaverse, outlined.

Language: Английский

---

Advances in wound dressing based on electrospinning nanofibers

Journal of Applied Polymer Science, Journal Year: 2023, Volume and Issue: 141(1)

Published: Oct. 3, 2023

Abstract In recent years, there has been a significant focus on bioactive dressings suitable for treating chronic and acute wounds. Electrospinning nanofibers are considered advanced dressing options due to their high porosity permeability air water, effective barrier properties against external pathogens, excellent resemblance the extracellular matrix wound healing skin regeneration. This article reviews advancements in application of electrospinning healing. The review begins with an overview process methods. It then explores advantages disadvantages different synthetic natural polymers used preparation dressings. discussed this include collagen, gelatin, silk fibroin, chitosan, hyaluronic acid, sodium alginate. Additionally, delves into commonly like polyvinyl alcohol, chloride, polyethylene lactone, polylactide, polyurethane applications. Furthermore, examines blending create high‐performance also incorporation functional additives, such as antimicrobial agents, growth factors, extracts, expedite tissue repair. conclusion, is emerging technology that provides unique opportunities designing more care products.

Language: Английский

---

Should I share it? Factors influencing fake news-sharing behaviour: A behavioural reasoning theory perspective

Technological Forecasting and Social Change, Journal Year: 2023, Volume and Issue: 193, P. 122647 - 122647

Published: May 30, 2023

Language: Английский

---

Attention-LSTM based prediction model for aircraft 4-D trajectory

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Sept. 15, 2022

Abstract Aviation activities are constantly increasing as a result of the growth global economic system. How to increase airspace capacity within limited resources while ensuring smooth and safe aircraft operations is challenge for civil aviation today. Air traffic safety supported by accurate trajectory prediction. The way-points relatively sparse, there many uncertain factors in flight, which greatly increases difficulty So, it vital enhance prediction accuracy. An attention-LSTM model proposed this paper, split into two parts. time-series features flight extracted initial stage using long-short-term memory neural network (LSTM). In second part, attention mechanism employed process sequence features. impact secondary elements reduced influence primary ones increased according mechanism. We used advanced models comparison models, such LSTM, support vector machine (SVM), back propagation (BP) network, Hidden Markov Model (HMM), convolutional long-term (CNN-LSTM). we superior above based on quantitative analysis comparison.

Language: Английский

---

Edge AI for Internet of Energy: Challenges and perspectives

Internet of Things, Journal Year: 2023, Volume and Issue: 25, P. 101035 - 101035

Published: Dec. 15, 2023

Language: Английский

---

Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

International Journal on Semantic Web and Information Systems, Journal Year: 2023, Volume and Issue: 19(1), P. 1 - 20

Published: June 9, 2023

Machine learning and deep are one of the most sought-after areas in computer science which finding tremendous applications ranging from elementary education to genetic space engineering. The machine techniques for development smart cities have already been started; however, still their infancy stage. A major challenge Smart City developments is effective waste management by following proper planning implementation linking different regions such as residential buildings, hotels, industrial commercial establishments, transport sector, healthcare institutes, tourism spots, public places, several others. experts perform an important role evaluation formulation efficient scheme can be easily integrated with overall plan complete city. In this work, we offered automated classification model urban into multiple categories using Convolutional Neural Networks. We represented being implemented Fine Tuning Pretrained Network Model new datasets litter classification. With help model, software, hardware both developed low-cost resources deployed at a large scale it issue associated healthy living provisions across cities. main significant aspects models use pre-trained utilize transfer fine-tuning specific task.

Language: Английский

---

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm

Heliyon, Journal Year: 2024, Volume and Issue: 10(11), P. e31766 - e31766

Published: May 22, 2024

This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance convergence accuracy speed optimization algorithm. The study assesses SCMSSA algorithm's performance against other algorithms using six test functions show efficacy enhancement strategies. Furthermore, its in improving Support Vector Regression (SVR) models for CO

Language: Английский

---

A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102773 - 102773

Published: Aug. 24, 2024

Language: Английский

---

Short-term wind power forecasting and uncertainty analysis based on FCM–WOA–ELM–GMM

Energy Reports, Journal Year: 2022, Volume and Issue: 9, P. 807 - 819

Published: Dec. 15, 2022

With large-scale wind power connected to the grid, accurate short-term forecasting has become a key technology for safe, economic grid operation. Therefore, and uncertainty analysis method based on FCM-WOA-ELM-GMM was proposed. Fuzzy C-means (FCM) used cluster numerical weather prediction (NWP) farm data, data points with similar meteorological information are classified into one class. Using rapid convergence high accuracy of whale optimization algorithm (WOA), input weight hidden layer threshold extreme learning machine (ELM) model were optimized improve ELM calculation speed accuracy. The WOA-ELM trained using clustered NWP projected model. To accurately calculate error probability density distribution, Gaussian mixture (GMM) applied forecast confidence intervals under different climatic conditions time scales calculated. accuracies WOA-ELM, ELM, PSO-LSSVM, LSSVM, LSTM, PSO-BP, WNN models compared analyzed, RMSE values 4-h results in April as follows: 5.95%; 26.73%; 3.78%; 5.19%; 23.71%; 15.63%; WNN, 23.23%. 24-h were: 6.62%; 19.86%; 9.91%; 13.73%; 23.69%; 14.08%; 20.11%. 72-h 5.24%; 13.64%; 12.03%; 13.67%; 16.61%; 15.46%; 20.22%. According results, scales, is higher than those other models.

Language: Английский

---