RETRACTED ARTICLE: Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies

Jayanthi, E.; Ramesh, T.; Kharat, Reena; Veeramanickam, M. R. M.; Bharathiraja, N.; Venkatesan, R.; Marappan, Raja

doi:10.1007/s00500-023-07954-y

Cited by 15 publications

(4 citation statements)

References 20 publications

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“…An identifiable database of credit card activities was used to facilitate the integrated probabilistic and neuro-adaptive technique [7]. This particular combo exemplified a high level of fraudulent activity.…”

Section: Literature Survey and Analysismentioning

confidence: 99%

Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems

Maithili,

Sathish Kumar,

Subha

et al. 2024

MATEC Web Conf.

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Recent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit card fraud identification and must be selected carefully. This study suggests a An Efficient Machine Learning Algorithm for Reliable Credit Card Fraud Identification (EMLA-RCCFI) was constructed using ML, which utilizes the Genetic Algorithm (GA) to select features. Once the optimum characteristics are determined, the suggested detecting module utilizes the subsequent ML-based classifications. The proposed EMLA-RCCFI system is assessed using a dataset produced by European cardholders to confirm its efficacy. Based on the results, the suggested EMLA-RCCFI method surpassed existing systems regarding accuracy, precision, and F score.

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Section: Literature Survey and Analysismentioning

confidence: 99%

Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems

Maithili,

Sathish Kumar,

Subha

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…A frequency analysis-based FoG detection model is presented in [12], where on body acceleration sensors are used to measure the movements of patients to detect FoG. An expert system is designed to classify gait patterns in [13], which computes pearson correlation measure to classify the patterns. The skin conductance and EEG signals are more important in classifying FoG pattern and an anomaly based algorithm is presented in [14] to classify the patterns.…”

Section: Related Workmentioning

confidence: 99%

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Ezhilarasi Jegadeesan,

Senthil Kiumar Thillaigovindhan

2024

ARASET

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The problem of predicting Freeze of Gait (FoG) on Parkinson diseased patients has been well studied. There exists number of approaches in predicting FoG, which uses sensory features, EEG data and so on. However, the methods suffer to achieve higher performance. To handle this issue, an efficient Time Orient Acceleration Gait pattern based FoG prediction model (TOAGP-FoG) is presented in this paper. The model designed to attach accelerometer sensors at different ankle and joints of the body. The sensor signals are recorded at different gait movement in long term. The sensory signals are passed to the central data server which tracks the movement signals. With the time variant signals stored by the model, the method generates Acceleration Gait Pattern with number of features. At each movement, the method analyses the patterns to compute FOG Risk Support (FoGRS) towards various gait movement. The FoG Risk Support is measured according to the movement forces produced by the patient for various gait movement in different time stamp and computes minimum gait force to be produced. Based on the FoGRS value, the method performs FoG prediction. The proposed method improves the performance of FoG prediction with higher accuracy. Other notable aspects of the suggested model include comparable performance, resiliency, real-time prediction capabilities, FOG-specific integration of data, and advanced deep learning methodologies for accurate prediction. The Special Features of TOAGP-FoG include the Multi-Sensor Configuration, Temporal Analysis, Adaptive Thresholding, Dynamic FoG Risk Support (FoGRS), and Enriched Feature Extraction. The TOAGP-FoG model offers an important breakthrough in the predictive modelling of Parkinson's disease since it integrates several features such as temporal flexibility, dynamic FoGRS computation, adaptive thresholding, enriched feature extraction, and multi-sensor configurations.

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“…It is obvious that the multilayer perceptron performs worse than CNN in terms of accuracy metrics; however, when it is combined with CNN, the accuracy metrics increase. Using deep learning, Noushin Davari et al [25] have described a technique for analyzing UV-Visible video. The kind of incipient defect and its severity level is identified for every scene based mostly on the system's logs of unexpected power outages as well as scheduled inspections throughout the year.…”

Section: Related Workmentioning

confidence: 99%

Hybrid ICHO-HSDC Model For Accurate Covid-19 Detection and Classification From CT Scan And X-Ray Images

Alekhya¹,

Sasikumar²,

Kumar³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The worldwide demand for medical care has increased due to the increasing expansion of Covid-19 cases. Therefore, in this case, prompt and precise identification of this illness is crucial. Health professionals are using additional screening techniques including CT imaging as well as chest Xrays for this. Pre-processing the CT scan pictures to eliminate the areas of areas, normalize image contrast, and minimize image noise, however, receives little attention. The seriousness of the Covid- 19 infection must be assessed in addition to the Covid-19 detection and categorization. An ICHOHYBRID model for Covid-19 identification and classification from X-ray, as well as CT scan images, is offered as a solution to these issues. Histogram and morphological image processing methods are used for CT-scan images. The Improved Chicken Swarm Optimization (ICHO) technique is used to find the input image’s histogram threshold. The extracted areas are categorized using the Convolutional Neural Network method based on a feature vector. When infections are found, the CNN algorithm is used to categorize them as severe, moderate, or extremely severe using Support Vector Machine. To eliminate the noise from the test pictures for X-ray imaging, the Adapted Anisotropic Diffusion Filtering (A2DF) approach is used. Once the preprocessing is completed, features are extracted using an Image profile (IP) and Histogram-oriented gradient (HOG) to create a fused HOG and IP feature. Using the HYBRID method, the FHI characteristics are divided into 3 classes. When compared to SVM and CNN, the study provides the best accuracy, with scores of 94.6 for CT scan pictures and 95.6 for X-ray images.

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RETRACTED ARTICLE: Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies

Cited by 15 publications

References 20 publications

Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems

Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Hybrid ICHO-HSDC Model For Accurate Covid-19 Detection and Classification From CT Scan And X-Ray Images

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