Efficient frameworks for statistical seizure detection and prediction

Khalil, Ali; Sayeid, Mostafa I. El; Ibrahim, Fatma E.; Khalaf, Ashraf A. M.; Gemeay, Entessar; Kasem, Hossam M.; Khamis, S.; El‐Banby, Ghada M.; El‐Shafai, Walid; El‐Rabaie, El‐Sayed M.; El‐Fishawy, Adel S.; Dessouky, Moawad I.; El-Dokany, Ibrahim M.; Alotaiby, Turkey; Alshebeili, Saleh A.; El‐Samie, Fathi E. Abd

doi:10.1007/s11227-022-05030-0

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, there is untapped potential in exploring the application of Independent Component Analysis (ICA), PCA, and NN for FDM experimentation. The study conducted in [25] has shown that the use of SDF (Statistical Detection Framework) has resulted in significant improvements in the identification of abnormalities seen in actual flight data.…”

Section: Feature Selectionmentioning

confidence: 99%

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

Helgo

2023

JRS

View full text Add to dashboard Cite

This paper examines the use of machine learning and deep learning algorithms in the aviation industry, with a specific emphasis on aircraft diagnosis/prognosis, predictive maintenance, feature selection, and flight data monitoring (FDM). This study highlights the potential use of these algorithms in enhancing the efficacy and effectiveness of various aircraft operations. In the field of aviation prognosis and diagnosis, many designs have been acknowledged as efficient for defect identification, calculation of remaining usable life, and prediction of excessive vibration in aero-engines. The architectural models discussed in this paper include deep autoencoders, deep belief networks, long short-term memory networks, and convolutional neural networks. The use of feature selection and scalar feature selection methodologies has been seen to augment the efficacy of FDM (Feature Detection and Matching) algorithms by means of identifying noteworthy features and detecting highly linked features. The application of machine learning algorithms in the domain of predictive maintenance enables real-time assessment of equipment health, hence reducing possible hazards and improving overall equipment performance. The research results emphasize the importance of flight data monitoring in improving safety and operational efficiency in the field of civil aviation. The application of machine learning approaches, namely classification algorithms, facilitates the analysis of flight data for the aim of identifying unsafe behaviors or violations from established operational standards.

show abstract

Section: Feature Selectionmentioning

confidence: 99%

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

Helgo

2023

JRS

View full text Add to dashboard Cite

show abstract

Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG Signals

Sharma,

Meena

2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

Advancements in Robotic Systems and Human Robot Interaction for Industry 4.0

Amalia

2023

JRS

View full text Add to dashboard Cite

Robotic systems are software and algorithms used to mechanize iterative human processes. Robotic Process Automation (RPA) operates based on simple principles and business logic, enabling it to engage with various information systems by using pre-existing graphical user interfaces. The process is the use of non-invasive software robots, often referred to as “bots,” to automate actions that are repetitive in nature and governed by predefined rules. The integration of data analytics, artificial intelligence (AI), process mining, and cognitive computing is now being used to expand the capabilities of RPA, enabling it to do more intricate jobs. This study investigates the progress made in robotic systems and the interaction between humans and robots in Industry 4.0 context. The paper examines the use of RPA, the incorporation of AI into robotic systems, and the advancement of autonomous driving and mobile robots. The study also emphasizes the significance of efficient human-robot interaction strategies and the possible influence of artificial intelligence (AI) on the prospective progress of intelligent and independent service robots. Furthermore, this study delves into the obstacles and forecasts pertaining to the development of sophisticated machine intelligence.

show abstract

Efficient frameworks for statistical seizure detection and prediction

Cited by 3 publications

References 11 publications

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG Signals

Advancements in Robotic Systems and Human Robot Interaction for Industry 4.0

Contact Info

Product

Resources

About