The Pathway to a High Impact Journal and Scopus Indexation–New Achievement of the International Journal of Cardiovascular Sciences

Mesquita, Cláudio Tinoco

doi:10.36660/ijcs.20220030

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Firstly, although it is a common scientific guideline to use several databases to search for relevant papers, we have chosen to use only one database, Scopus. It should be noted that Scopus covers more than 25,100 titles (including over 23,452 peer-reviewed journals) from approximately 5000 publishers [ 75 ] and thus offers a wider range of academic disciplines than Web of Science [ 76 ]. Moreover, Scopus is claimed to be the most authentic, comprehensive [ 77 ], and complete database of global scientific research [ 78 ], with 1.4 billion citations and 16 million author records [ 79 ].…”

Section: Discussionmentioning

confidence: 99%

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Bałdyga,

Barański,

Belter

et al. 2024

Sensors

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To date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging infrastructure, and adverse environmental conditions on the one hand, and the scale, complexity, and critical safety implications of railway systems on the other. Our study is underpinned by the three objectives. Specifically, we aim to identify time series anomaly detection methods applied to railway sensor device data, recognize the advantages and disadvantages of these methods, and evaluate their effectiveness. To address the research objectives, the first part of the study involved a systematic literature review and a series of controlled experiments. In the case of the former, we adopted well-established guidelines to structure and visualize the review. In the second part, we investigated the effectiveness of selected machine learning methods. To evaluate the predictive performance of each method, a five-fold cross-validation approach was applied to ensure the highest accuracy and generality. Based on the calculated accuracy, the results show that the top three methods are CatBoost (96%), Random Forest (91%), and XGBoost (90%), whereas the lowest accuracy is observed for One-Class Support Vector Machines (48%), Local Outlier Factor (53%), and Isolation Forest (55%). As the industry moves toward a zero-defect paradigm on a global scale, ongoing research efforts are focused on improving existing methods and developing new ones that contribute to the safety and quality of rail transportation. In this sense, there are at least four avenues for future research worth considering: testing richer data sets, hyperparameter optimization, and implementing other methods not included in the current study.

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Section: Discussionmentioning

confidence: 99%