Optimization of searching the best possible action depending on various states like state of environment, system goal etc. has been a major area of study in computer systems. In any search algorithm, searching best possible solution from the pool of every possibility known can lead to the construction of the whole state search space popularly called as minimax algorithm. This may lead to a impractical time complexities which may not be suitable for real time searching operations. One of the practical solution for the reduction in computational time is Alpha Beta pruning. Instead of searching for the whole state space, we prune the unnecessary branches, which helps reduce the time by significant amount. This paper focuses on the various possible implementations of the Alpha Beta pruning algorithms and gives an insight of what algorithm can be used for parallelism. Various studies have been conducted on how to make Alpha Beta pruning faster. Parallelizing Alpha Beta pruning for the GPUs specific architectures like mesh(CUDA) etc. or shared memory model(OpenMP) helps in the reduction of the computational time. This paper studies the comparison between sequential and different parallel forms of Alpha Beta pruning and their respective efficiency for the chess game as an application.
The threat landscape is exponentially increasing which become more worsen when a greater number of emerging devices are connected to internet such as Internet of Things (IoTs), Embedded Systems, Cyber Physical devices etc. To control the damage of cyber threats, there is a need to monitor the cyber criminals continuously to understand the tools and technique used by the attackers in order to develop cyber defense mechanism to protect cyber Eco-systems. In this research, a multi-Honeypot platform as a tool is presented for cyber threat intel generation to implement the multiple classes of Honeypots such as Windows, IoTs, Embedded etc. Honeypot is widely used by the security researchers, security companies to understand the tools and tactics about the attackers but these are quite complex to deploy and maintain especially due to diverse set of IT systems and intensive resource requirements to deploy High Interaction Honeypots. This complexity is reduced in this research by implementation of Para-Virtualization based approach to enable multiple classes of Honeypot sensors of different platforms on a light weight hardware. It is addressed that time window to collect the data and to conclude it as a cyber threat intel with support of evidences should be probabilistically determined. After applying the analysis such as behavior analysis and deep learning methods to determine about the unknown threat patterns, the attack data sets are correlated into different cyber threat events and converted into an actionable cyber threat intelligence to disseminate the information in an automated manner. In the end, threat intelligence is generated and experiments are documented. The deep learning-based analysis inspired by neural networks is integrated in the design to determine the unknown classified threat events.
Migration of customer to other companies is “Churn”. Customers who abandon services of any company or leave the organization are known as churners. Now a day churn prediction is one of the biggest challenge for organizations/Companies. Reputation/Ranking, finance and growth plans of company is directly affected by churn. This makes research on churn more valuable. In this paper python platform is used to implement two of the classification models of supervised machine learning. As the data used is a labeled data thus this is under the category of supervised domain of machine learning. Confusion matrix is observed to state the conclusion which says that KNN is a better approach to predict customer churn over Logistic Regression. KNN is 2.0% more accurate than Logistic Regression to predict customer churn. Here “accuracy” parameter says that Logistic algorithm is least effective to predict the customer churn as compare to K-Nearest Neighbors.
In Computer vision, object recognition is a very important component and also very challenging. Intention of This paper is to exploit a high confidence object detection framework that boosts up the classification performance with less computational burden and cost efficient. Features are extracted from images by using Histograms of Oriented Gradients (HOG) technique and then for generating principle components as well as reducing dimensions Principal Component Analysis (PCA) has been applied on the extracted features. For classification of objects Support Vector Machine (SVM), Random Forest, Input mapped classifier, M5P classifier and Gaussian process classifier have been employed. A comparative study on performance of those approaches have been conducted. Moreover, for better clarification of the dataset, statistical and automated analysis have been considered. Overall findings demonstrates that, Principle Component Analysis (PCA)based Support Vector Machine (PSO) outperforms other approaches by depicting accuracy of 94.02% and highest F-Score measurement.
Rooftop solar energy potential has traditionally been estimated by surveying the number of large buildings in a given area. In this work, we propose a fast and low-cost method to estimate the rooftop photovoltaic solar energy generated in a particular area by utilizing satellite imagery - even though it may be of low resolution. We employ a deep learning based approach to carry out image segmentation on low resolution satellite images of Bangalore, India. Three different model architectures (U-Net, SegNet, FCN) were trained on manually hand-labelled data and tested for the task of semantic segmentation of satellite images. U-Net was found to yield the best results. By using a custom modified U-Net to segment the images, we arrive at the building rooftop area available for solar panels. To calculate the annual solar energy generated in gigawatt-hour (GWh), the incident solar insolation values from the U.S. National Renewable Energy Laboratory (NREL) based on observed weather patterns in Bangalore, and standard values from datasheets of photovoltaic manufacturers are used.
