Faculty Publications
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Item Prediction of crime hot spots using spatiotemporal ordinary kriging(Springer Verlag service@springer.de, 2019) Deshmukh, S.S.; Annappa, B.Prediction can play a very important role in many types of domains, including the criminal justice system. Even a little information can be gained from proper police assignments, which can increase the efficiency of the crime patrolling system. Citizens can also be aware and alert for possible future criminal incidents. This was identified previously, but the proposed solutions use many complex features, which are difficult to collect, especially for developing and underdeveloped countries, and the maximum accuracy obtained to date using simple features is around 66%. Few of these countries have even started collecting such criminal records in digital format. Thus, there is a need to use simple and minimal required features for prediction and to improve prediction accuracy. In the proposed work, a spatiotemporal ordinary kriging model is used. This method uses not only minimal features such as location, time and crime type, but also their correlation to predict future crime locations, which helps to increase accuracy. Past crime hot spot locations are used to predict future possible crime locations. To address this, the Philadelphia dataset is used to extract features such as latitude, longitude, crime type and time of incident, and prediction can be given for every 0.36 square km per day. The city area is divided into grids of 600 × 600 m. According to the evaluation results, the average sensitivity and specificity obtained for these experiments is 90.52 and 88.63%, respectively. © Springer Nature Singapore Pte Ltd. 2019.Item ARIMA-PID: container auto scaling based on predictive analysis and control theory(Springer, 2024) Joshi, N.S.; Raghuwanshi, R.; Agarwal, Y.M.; Annappa, B.; Sachin, D.N.Containerization has become a widely popular virtualization mechanism alongside Virtual Machines (VMs) to deploy applications and services in the cloud. Containers form the backbone of the modern architectures around microservices and provide a lightweight virtualization mechanism for IoT and Edge systems. Elasticity is one of the key requirements of modern applications with various constraints ranging from Service Level Agreements (SLA) to optimization of resource utilization, cost management, etc. Auto Scaling is a technique used to attain elasticity by scaling the number of containers or resources. This work introduces a novel mechanism for auto-scaling containers in cloud environments, addressing the key elasticity requirement in modern applications. The proposed mechanism combines predictive analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model and control theory utilizing the Proportional-Integral-Derivative (PID) controller. The major contributions of this work include the development of the ARIMA-PID algorithm for forecasting resource utilization and maintaining desired levels, comparing ARIMA-PID with existing threshold mechanisms, and demonstrating its superior performance in terms of CPU utilization and average response times. Experimental results showcase improvements of approximately 10% in CPU utilization and 30%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.