For the robustness analysis of the security mechanism, the time complexity and asymptotic security of the algorithm are analysed and used. Therefore, this work examines the resilience of the zero-knowledge proofs (ZKP) technique utilising the RSA (Rivest, Shamir, and Adleman) problem. The result of this research is a better option for Internet of Things (IoT) devices to address key security challenges (attacks on confidentiality, authentication, integrity, and availability). The zero-knowledge proofs technique has been used with the RSA problem. It is analysed and compared with Elliptic Curve Cryptography (ECC) to see how they are correlated with each other and whether ZKP using the RSA problem can address the security issues of IoT devices in a better way or not. The final correlation result shows that both ZKP using RSA for Pollard's rho algorithm and ECC are highly correlated with one another. Asymptotic time complexity and the graph of ZKP using RSA for Pollard's rho algorithm and ECC clearly show that RSA for Pollard's rho algorithm has better time complexity than ECC. So, it can address the security issues required by IoT devices.
Brain tumor is a group of unfamiliar cells present in the cerebrum that may lead to cancer. Brain tumor can be diagnosed by several easy ways but among them MRI imaging is the best way to discover a tumor in the brain. To form images of a brain it uses radio waves and magnetic field so that the information regarding the abnormal tissue growth in the brain can be identified. The detection of brain tumor is done by using deep learning algorithm which somewhat a branch or a subset of machine learning and in this proposed system we will use the CNN(Convolution neural network) model to determine whether the brain has a tumor or not and all of this can only be done by using MRI(Magnetic resonance imaging) scans. CNN is a kind of network architecture for deep learning algorithms and is used specifically for image recognition and processing the pixel data. Brain tumor detection is done quickly with higher accuracy when these algorithms were used on the MRI scans Images which also helps in providing treatment to the diagnosed person. These predictions also help the radiologists in making the decisions quickly. The steps for creating this project is as follows: A collection of data containing images from MRI scans from various sources. In addition to that the data will be pre- processed and augmented. Next, a neural network model for tumor detection will be developed and in the end model training and testing will be performed. In generally, CNN offers a fairly high training accuracy and the error rate is also very low as compared to other techniques
In order to measure the aspects of employee-tourist interactions that have an effect on tourist experiences, text analytics is used in this article. Rather than the conventional, constrained research method, a big data analytics technique is used to create a methodological evaluation of aspects of employee-tourist interactions. We assess the concept and the significance of the components for optimizing opinions of fulfilment, service, and value. The findings show how crucial the various experience value components are in fostering favourable visitor views during employee-tourist interactions. This contributes a knowledge base to destination management organizations to encourage the implementation of official tourism statistics systems using big data.
One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective.
The procedure of detecting dyslexia is difficult and needs a multidisciplinary approach. Early dyslexia detection is essential for providing people with this learning disability with adequate assistance and intervention. This study categorizes most relevant and recently published approaches proposed for dyslexia detection on the basis of physiological traits used as dataset and the algorithms used for the same. The analysis showed application of various traits such as handwriting, neurological scans and eye-based movements. A detailed analysis has been presented in terms of accuracy as well. The study has successfully condensed a multitude of the state of the arts methods for dyslexia detection leading towards possibly more physiological traits based study.
