HPC as a Service: A naive model

Applications like Big Data, Machine Learning, Deep Learning and even other Engineering and Scientific research requires a lot of computing power; making High-Performance Computing (HPC) an important field. But access to Supercomputers is out of range from the majority. Nowadays Supercomputers are actually clusters of computers usually made-up of commodity hardware. Such clusters are called Beowulf Clusters. The history of which goes back to 1994 when NASA built a Supercomputer by creating a cluster of commodity hardware. In recent times a lot of effort has been done in making HPC Clusters of even single board computers (SBCs). Although the creation of clusters of commodity hardware is possible but is a cumbersome task. Moreover, the maintenance of such systems is also difficult and requires special expertise and time. The concept of cloud is to provide on-demand resources that can be services, platform or even infrastructure and this is done by sharing a big resource pool. Cloud computing has resolved problems like maintenance of hardware and requirement of having expertise in networking etc. An effort is made of bringing concepts from cloud computing to HPC in order to get benefits of cloud. The main target is to create a system which can develop a capability of providing computing power as a service which to further be referred to as Supercomputer as a service. A prototype was made using Raspberry Pi (RPi) 3B and 3B+ Single Board Computers. The reason for using RPi boards was increasing popularity of ARM processors in the field of HPCComment: 2019 8th International Conference on Information and Communication Technologies (ICICT), Karachi, Pakistan, 201

Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques