Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

IEEE Communications Surveys & Tutorials, Journal Year: 2023, Volume and Issue: 26(1), P. 496 - 533

Published: Sept. 11, 2023

Over the past decade, Unmanned Aerial Vehicles (UAVs) have provided pervasive, efficient, and cost-effective solutions for data collection communications. Their excellent mobility, flexibility, fast deployment enable UAVs to be extensively utilized in agriculture, medical, rescue missions, smart cities, intelligent transportation systems. Machine learning (ML) has been increasingly demonstrating its capability of improving automation operation precision many UAV-assisted applications, such as communications, sensing, collection. The ongoing amalgamation UAV ML techniques is creating a significant synergy empowering with unprecedented intelligence autonomy. This survey aims provide timely comprehensive overview used operations communications identify potential growth areas research gaps. We emphasize four key components which can significantly contribute, namely, perception feature extraction, interpretation regeneration, trajectory mission planning, aerodynamic control operation. classify latest popular tools based on their applications conduct gap analyses. also takes step forward by pointing out challenges upcoming realm ML-aided automated It revealed that different dominate modules While there an increasing trend cross-module designs, little effort devoted end-to-end framework, from extraction unveiled reliability trust require attention before full cooperation between humans come fruition.

Language: Английский

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An Incorrect Data Detection Method for Big Data Cleaning of Machinery Condition Monitoring

IEEE Transactions on Industrial Electronics, Journal Year: 2019, Volume and Issue: 67(3), P. 2326 - 2336

Published: March 13, 2019

The presence of incorrect data leads to the decrease condition-monitoring big quality. As a result, unreliable or misleading results are probably obtained by analyzing these poor-quality data. In this paper, improve quality, an detection method based on improved local outlier factor (LOF) is proposed for cleaning. First, sliding window technique used divide into different segments. These segments considered as objects and their attributes consist time-domain statistical features extracted from each segment, such mean, maximum peak-to-peak value. Second, kernel-based LOF (KLOF) calculated using evaluate degree segment being Third, according KLOF values threshold value, detected. Finally, simulation vibration generated defective rolling element bearing three real cases concerning fixed-axle gearbox, wind turbine, planetary gearbox verify effectiveness method, respectively. demonstrate that able detect both missing abnormal segments, which two typical data, effectively, thus helpful cleaning machinery condition monitoring.

Language: Английский

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Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

Security and Communication Networks, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 41

Published: Aug. 27, 2022

The integration of the Internet Things (IoT) connects a number intelligent devices with minimum human interference that can interact one another. IoT is rapidly emerging in areas computer science. However, new security problems are posed by cross-cutting design multidisciplinary elements and systems involved deploying such schemes. Ineffective implementation protocols, i.e., authentication, encryption, application security, access network for their essential weaknesses security. Current approaches also be improved to protect environment effectively. In recent years, deep learning (DL)/machine (ML) has progressed significantly various critical implementations. Therefore, DL/ML methods turn system protection from simply enabling safe contact between intelligence This review aims include an extensive analysis ML state-of-the-art developments DL improve enhanced device methods. On other hand, insights machine securities illustrate how it could help future research. risks relating or threats identified, as well attacks possible associated each surface. We then carefully analyze present approach’s benefits, possibilities, weaknesses. discusses potential challenges limitations. works, recommendations, suggestions included.

Language: Английский

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A comprehensive study on disease risk predictions in machine learning

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Journal Year: 2020, Volume and Issue: 10(4), P. 4217 - 4217

Published: June 13, 2020

Over recent years, multiple disease risk prediction models have been developed. These use various patient characteristics to estimate the probability of outcomes over a certain period time and hold potential improve decision making individualize care. Discovering hidden patterns interactions from medical databases with growing evaluation model has become crucial. It needs many trials in traditional clinical findings that could complicate prediction. Comprehensive survey on different strategies used predict is conferred this paper. Applying these techniques healthcare data, improvement find out patients who would get benefit management programs reduce hospital readmission cost, but results endeavours shifted.

Language: Английский

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A Survey On Missing Data in Machine Learning

Research Square (Research Square), Journal Year: 2021, Volume and Issue: unknown

Published: June 17, 2021

Abstract Machine learning has been the corner stone in analysing and extracting information from data often a problem of missing values is encountered. Missing occur as result various factors like completely at random, random or not random. All these may be system malfunction during collection human error pre-processing. Nevertheless, it important to deal with before since ignoring omitting biased misinformed analysis. In literature there have several proposals for handling values. this paper we aggregate some on particularly focusing machine techniques. We also give insight how approaches work by highlighting key features proposed techniques, they perform, their limitations kind are most suitable for. Finally, experiment K nearest neighbor forest imputation techniques novel power plant induced fan offer possible future research direction.

Language: Английский

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FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk

IMF Working Paper, Journal Year: 2019, Volume and Issue: 2019(109), P. 1 - 1

Published: May 1, 2019

Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution reduce the cost of credit increase financial inclusion.However, machine learning (ML) methods that lie at heart remained largely black box for nontechnical audience.This paper contributes literature by discussing potential strengths weaknesses ML-based assessment through (1) presenting core ideas most common techniques ML audience; and(2) fundamental challenges risk analysis.FinTech has enhance inclusion outperform traditional scoring leveraging nontraditional sources improve borrower's track record; (2) appraising collateral value;(3) forecasting income prospects; (4) predicting changes general conditions.However, because central role analysis, relevance should be ensured, especially situations when deep structural change occurs, borrowers could counterfeit certain indicators, agency problems arising from information asymmetry not resolved.To avoid exclusion redlining, variables trigger discrimination used assess rating.

Language: Английский

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Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

Computational Materials Science, Journal Year: 2021, Volume and Issue: 196, P. 110524 - 110524

Published: May 6, 2021

Language: Английский

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SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 35643 - 35662

Published: Jan. 1, 2024

This paper focuses on the vulnerabilities of ADS-B, one avionics systems, and countermeasures taken against these proposed in literature. Anomaly detection methods based machine learning deep algorithms among ADS-B are analyzed detail. The advantages disadvantages using an anomaly system data investigated. Thanks to advances last decade, it has become more appropriate use systems detect anomalies systems. To best our knowledge, this is first survey focused studies that for security. In context; addresses research topic from different perspectives, draws a road map future research, searches five questions related used

Language: Английский

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Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

mHealth, Journal Year: 2017, Volume and Issue: 3, P. 45 - 45

Published: Oct. 19, 2017

Cardiovascular diseases are one of the top causes deaths worldwide. In developing nations and rural areas, difficulties with diagnosis treatment made worse due to deficiency healthcare facilities. A viable solution this issue is telemedicine, which involves delivering health care sharing medical knowledge at a distance. Additionally, mHealth, utilization mobile devices for care, has also proven be feasible choice. The integration mHealth computer-aided systems fields machine deep learning enabled creation effective services that adaptable multitude scenarios. objective review provide an overview heart disease management, especially within context healthcare, as well discuss benefits, issues solutions implementing algorithms improve efficacy relevant applications.

Language: Английский

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Guided parallelized stochastic gradient descent for delay compensation

Applied Soft Computing, Journal Year: 2021, Volume and Issue: 102, P. 107084 - 107084

Published: Jan. 14, 2021

Language: Английский

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