Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder

Fraudulent activities in financial fields are continuously rising. The fraud patterns tend to vary with time, and no consistency can be observed in this regard. The incorporation of new technology by fraudsters is the reason for the execution of online fraud transactions. Given the volatility of the fraud patterns, a good fraud detection model must be able to evolve and update itself to the changing patterns. Thus we aim in this paper to analyze the fraud cases that are unable to be detected based on supervised learning or previous history, create an Auto-encoder model based on deep learning and Compare and assess the performance of the model based on data from different parts of the world and check for the demographic diversity of fraud patterns thereby inferring that the data from which part of the world the model fits the best. The proposed algorithm, deep learning based on the auto-encoder (AE) network is an unsupervised learning algorithm that utilizes backpropagation by setting the inputs and outputs identical. In this research, the Tensorflow package from Google has been employed to implement AE by using deep learning. The accuracy, precision, recall, F1 score and area under the curve(AUC) are all executed to assess the performance of the model

Towards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(1), (2021): E99-E122, https://doi.org/10.1175/BAMS-D-19-0005.1.The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.The development of the Red Sea modeling system is being supported by the Virtual Red Sea Initiative and the Competitive Research Grants (CRG) program from the Office of Sponsored Research at KAUST, Saudi Aramco Company through the Saudi ARAMCO Marine Environmental Center at KAUST, and by funds from KAEC, NEOM, and RSP through Beacon Development Company at KAUST

Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder

Fraudulent activities in financial fields are continuously rising. The fraud patterns tend to vary with time, and no consistency can be observed in this regard. The incorporation of new technology by fraudsters is the reason for the execution of online fraud transactions. Given the volatility of the fraud patterns, a good fraud detection model must be able to evolve and update itself to the changing patterns. Thus we aim in this paper to analyze the fraud cases that are unable to be detected based on supervised learning or previous history, create an Auto-encoder model based on deep learning and Compare and assess the performance of the model based on data from different parts of the world and check for the demographic diversity of fraud patterns thereby inferring that the data from which part of the world the model fits the best. The proposed algorithm, deep learning based on the auto-encoder (AE) network is an unsupervised learning algorithm that utilizes backpropagation by setting the inputs and outputs identical. In this research, the Tensorflow package from Google has been employed to implement AE by using deep learning. The accuracy, precision, recall, F1 score and area under the curve(AUC) are all executed to assess the performance of the model