Aim of this paper is to deduce computational cost using a token generation method compared to homomorphic algorithms in cloud storage. Token generation and homomorphic algorithms are compared and the study contains two groups with sample size N=10. Accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Homomorphic algorithm provides 1.2 times better performance compared to token generation algorithm for various datasets. Results were obtained with statistical significance of two algorithms: sig (2-tailed) with p-value observed is 0.002 (p<0.05) for both accuracy and loss. Homomorphic algorithms give significantly better performance in comparison with Token Generation algorithm.
The main objective is to reduce the noise in Magnetic Resonance Imaging of brain images using novel Contour coding technique and biologically inspired Support Vector Machine algorithm. Two calculations were applied, one is Novel Contour coding technique and another is biologically inspired Support Vector Machine. Sample size N=5 is taken for both algorithms. The computation processes were executed and verified for exactness. Novel Contour coding technique was applied and it has attained the improved mean accuracy of 93.2%, and it performs better than support vector machine having 87.4%. The performance comparison indicates that there is a statistically significant difference between Support Vector Machine clustering and Contour coding technique. The results were obtained with a level of significance value of 0.003 (p<0.05), with a pretest G-power value of 80% using SPSS tools.
The concept of Convolution Neural Networks (CNN) has been becoming highly significant in computer-vision-based applications. Their applications in disaster management, like fire detection, will definitely improve the social and ecological environment. Most of the existing fire detection systems fail to detect fire in certain environments like smoke, fog and so on. In this paper, we propose a Squeeze-Net framework-based CNN for detection of fire, localization and understanding the scene of fire. The method uses smaller convolution layers with no dense layers, thus minimizing the computational power. Experimental results suggest improved performance in terms of accuracy and loss parameters for both known and unpredictable image settings. Despite the low computational power, the method provides more accuracy than state-of-art techniques.
The software size estimation is the basic and essential process in the Software Development Life Cycle (SDLC) for budgeting, developing and delivering in the scheduled manner. The modern computer software like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Data Analytics are highly dynamic, distributed and parallel processing. To identify the size of this versatile environment is highly difficult and risk based. A small fault may cause negative consequences and also suspension of the project. This article gives a study on the functional, social, economic, political, organizational and nonfunctional risks associated to the parallel computing applications.
EARLYBUDDY is a smart alarm clock app for Android devices. It primarily uses traffic data to help the user wake up on time when there is an estimated delay. Users can choose a timeframe within which they must awaken rather than a single wake-up time. The app will then select a time inside that window based on the projected delay based on frequent traffic data updates. If it detects that the user will be late to their destination, it will change the existing alarm to wake them up. Users can also specify their morning habits, which are factored into the total delay estimate. These routines are optional, but they are encouraged because they help the user to organize their morning and provide more data for the application to use when calculating delays. All humans are not made equal when it comes to sleep. EARLYBUDDY features a provision that prevents users from turning off the phone to silence the alarm if they find it difficult to get up from bed in the morning. Users must tap the stop alarm or snooze button, which only appears in the app itself at the center of the screen, to turn off the alarm. If you press anyplace else on the screen or push the snooze button incorrectly, the alarm will ring again. The basic goal here is to actively wake up the body and mind before hitting the snooze button. Users can also place their phones on the mattress nextto them, and EARLYBUDDY will analyze their sleeping habits using the device’s sensors. This may be used to assess the quality of the user’s night’s sleep and set the alarm at the most natural time.
