Arya Institute of Engineering & Technology

This research investigates the crucial role of cloud computing services in web development for creating scalable applications. Our study explores the benefits, challenges, and best practices associated with leveraging these services. Real-world case studies will be examined to showcase their significance. Additionally, we examine the integration of blockchain technologies in cloud-based web development and their potential impact on scalability. This paper presents a comprehensive analysis of the indispensability of cloud computing services in building scalable applications for the web. This research project delves into the transformative impact of cloud computing services on the field of web development. By providing scalable and flexible infrastructure, these services have revolutionized the way applications are built. Through an in-depth analysis of benefits, challenges, and best practices, along with real- world case studies, the project highlights the indispensable nature of cloud computing in creating scalable web applications. Furthermore, the exploration of blockchain integration within cloud-based web development offers insights into potential advancements in scalability and opens new avenues for innovation in the field. Additionally, the integration of blockchain technologies in cloud-based web development is investigated, providing insights into its potential impact on scalability and fostering innovation in the field. The research presents a comprehensive analysis of the indispensability of cloud computing services in creating scalable web applications and highlights their transformative impact on the development process.

Concrete the spirit of the infrastructures and it’s a combination of cement, water and coarse aggregate, fine aggregate and add mixtures. Sand and cement is consider as critical material in significant mix design due reality that collecting of cement and uncovering sand is impacting out. In case consider concrete the manufacturing of it release out CO 2 and other green house gases and in other hand sand expulsion furthermore is lead us to stream bed declination, so most ideal choice for the both of materials ought to be taken vital notes. Through this paper an imaginative focus on utilization of Pareva Dust as a replacement to concrete and Quartz Sand as replacement to sand at different level (5%,10%15%) is utilized and assist with getting's mom earth. Through this examination paper study is given for the mechanical and quality properties of which guide as choice in concrete.

For many people, social media has become an essential part of daily life. While many people began by exchanging data in the form of text and images in the media sphere, others moved on to sharing test papers, coursework, and masterclasses in the academic domain and e-learning materials, marketing, and a performance of the business clientele in the amusement sphere as well as jokes, music, and recordings in the entertainment sphere. Even the tiniest of Internet users would prefer long-range social media to the current Internet culture because of its widespread use. Sharing personal information on social media may be fun, but it also demands a great deal of security and safety. Data about customers should be kept private if it is to be kept private.

In the present-day tech-stack, cloud computing is evolving as a successful and one of the popular fields of technology where the new businesses are achieving success by deploying their functionalities, products, data, and services on cloud instead of on-premises system and that also without depending on any physical component. Infrastructure as code (IaC) is a set of methodologies which uses code to set up the install packages, virtual machines and networks, and configure environments. A successful IaC implementation and adoption by developers requires a broad set of skills and knowledge. It is DevelopmentOperations’ tactic of provisioning an application’s infrastructure and managing it through binary readable configuration files, instead of any hardware configuration.

Brain tumors have been linked to an increase in death rates. To improve patients’ life expectancy, an early and accurate discovery of tumors is the first step, and categorization is used for a more accurate diagnosis of the tumor. Brain tumor identification is the most demanding and intriguing thing to conduct in medical image processing. Tumor location, size, shape, type, and contrast of tumor tissues are used in computer-aided diagnosis (CAD). In the instance of brain imaging analysis, a machine learning algorithm is a feasible option. Convolutional neural network (CNN) is now the most common and effective approach for image categorization utilizing brain magnetic resonance imaging. It is, however, sluggish and lacking accuracy. The application of this well-known technology is DNN which is a model that is modified in the feature-extraction and segmentation phases to increase accuracy. This work presents a unique approach of detecting and classifying brain tumors from MRI scans of patients utilizing deep learning methods, as well as CNN for classification. The proposed system is made up of many steps, including pre-processing, feature extraction, segmentation, tumor detection, and tumor classification. MRI pictures were chosen because they outperform other imaging modalities in terms of brain tumor analysis. The MRI image datasets for testing and training would be derived from WHO medical image databases. In this also discusses other image processing approaches. Following a study of the comparative research, this is an extension of earlier studies that identifies and classifies tumors with more accuracy, sensitivity, and precision, shorter processing time with bigger datasets, and better performance than other systems that employed SVM and ANN classifiers.

The aim of this work is to design, simulate, and analyze a bi-axial piezoresistive MEMS (Micro-Electro-Mechanical System) force sensor, which has the capability of flexibility, high sensitivity and sensing forces in nano-Newton ranges. To achieve this, a novel combination of polydimethylsiloxane (PDMS) as substrate material for microcantilever and graphene as piezoresistors are taken in this study. Force to be sensed is applied on the cantilever beam which generates its output in the form of displacement and by using smart piezoresistive sensing mechanism displacement is converted into corresponding voltage. Finite element analysis approach is used for designing and simulation of the proposed force sensor. The force sensitivity and stiffness are achieved as 0.566524 mV/nN and, 0.263 nN/µm in ‘y’ direction whereas 0.63039 mV/nN and, 0.039 nN/µm in ‘z’ direction, respectively. It is found in this study the stiffness of cantilever beam plays significant role in affecting the sensitivity of the sensor. The designed force sensor has ability to sense bi-axial forces and therefore suitable for microbotics and health care applications, while operating range of the sensors is ideal for a wide range of applications including microbotics, living cell handling, microassembly, nano-scale material characterization, minimal invasive surgeries and heath care applications.