... and machine learning techniques to repair anomalies. The authors evaluated the model using four data sets, demonstrating improved data quality in real-time data streams by correctly assigning repair techniques to the detected and classified anomalies. Findings By eliminating manual intervention...

... 2025 30 12 2025 © 2026 Emerald Publishing Limited 2026 Emerald Publishing Limited Licensed re-use rights only Video Medical Cyber-physical systems Deep learning Machine learning Anomaly detection Anomaly detection refers to recognizing abnormal observations or items...

... sections. Tulsi Pawan Fowdur can be contacted at: p.fowdur@uom.ac.mu 21 06 2022 25 08 2022 30 08 2022 © Emerald Publishing Limited 2022 Emerald Publishing Limited Licensed re-use rights only Network analytics Machine learning Classification Regression Cloud...

... protocol predictor in quantum key distribution is a critical step toward commercialization of QKD. Design/methodology/approach The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random...

... about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than...

... these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM...

...Venkatesh Naramula; Kalaivania A. Purpose This paper aims to focus on extracting aspect terms on mobile phone (iPhone and Samsung) tweets using NLTK techniques on multiple aspect extraction is one of the challenges. Then, also machine learning techniques are used that can be trained on supervised...

... and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization...

... metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection. Findings In reference to this, this paper determined a regression...

... these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which...

... safety Driver monitoring Heart rate variability (HRV) Machine learning Figure 1. Overview of the proposed drowsiness detection system Table I. Karolinska Sleepiness Scale (KSS) 1 Extremely alert 2 Very alert 3 Alert 4 Rather alert 5 Neither alert nor...

... Limited 2017 Emerald Publishing Limited Licensed re-use rights only Activity recognition Change detection Concept drift Machine learning Model adaptation The basic procedure for activity recognition involves: collection of labelled data from the subjects that perform sample...

... attention. © Emerald Group Publishing Limited 2015 User behavior Context Machine learning Semantic place User modeling where the dependent variable C is the semantic place information, e.g. as in our case: C = {Home, Office/School...