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—The recommendation system will help users to figure out a decision on selection of right combinations of food. The system makes recommendations on prior likely experience. The proposed system recommends the food item that mostly be eaten with the searched food item. This system is very helpful as most of the people still have confusions on what food is to be eaten in combinations with a main course. It may lead to customers having bad experience as they don’t choose the apt combinations. So food recommendation system helps to identify healthy and likely food item that mostly appeals to the selected main course. It is done with the help of methods like K Nearest Neighbour, content based filtering etc.

— The vast selection of books available in libraries can make it challenging for users to find books that suit their preferences. To overcome this issue, personalized book recommendation systems have been developed that use machine learning techniques. This article proposes a book recommendation system that combines content-based and collaborative filtering methods. It employs the K-nearest neighbor (KNN) algorithm and the cosine similarity matrix to find books that are similar to a user's preferences, based on two datasets, one containing book information and the other containing user ratings. The system recommends top-rated books. To implement this system, it uses several Python libraries, including pandas, numpy, scipy, and scikit-learn. The proposed system is highly effective in recommending books based on user preferences, improving the user experience. The experimental results demonstrate that the system is effective, resulting in a significant increase in accuracy and user satisfaction.

— The use of deep learning models to identify lesions on cotton leaves based on images of the crop in the field is proposed in this work. It has become a target for a wide range of agricultural pests and diseases as a result of its tropical cultivation, demanding treatment. Furthermore, early indicators of significant pests and infections might be difficult to spot, making precise lesion diagnosis challenging for growers. The current research proposes a deep learning-based solution for cotton leaf screening to aid in the problem's resolution, allowing for better monitoring of the cotton crop's health and management decisions. The automated classifier CNN will be utilized in this technique to classify those two categories based on learning with certain training samples. Finally, simulation findings demonstrate that using a network classifier reduces training errors while improving classification accuracy.

— By acquiring a deeper understanding of the user's preferences, recommendation systems are utilized to boost user appeal. These systems have grown in popularity as a result of their capacity to provide users with customized material that is relevant to the interests of them.The implementation of these systems can be done by collaborative filtering ,hybrid models,contenet-based, and also by neural networks. The goal of this research is to see if the machine learning approaches like Content-based filtering and Cosine similarity can be used to detect content similarities and suggest related movies. Cosine similarity and Euclidean distance are two ways to find similarity in genres.We have uses the many metrics that are used to compute similarity between products/movies in this study.

Cloud is collection of huge resources like computing power in terms of virtual machines, server storage, RAM memory which will be available to the users on-demand. Cloud provides many services in comparatively very low price. The users can setup their own datacenters on-premises that can cost huge amount for purchasing as well as for maintenance. Cloud computing refers to services and applications which run in a distributed network using virtualized resources. A revolutionary new approach was adopted in cloud computing where the infrastructure, storage, computers and network capacity are leased or rented. Resources are limitless, scalable with agility and universally available. Computing has become reliable and faster and as a result dependency on the services offered by cloud has increased drastically. There are many cloud service providers which offers cloud services to customers. Choosing the vendors which offers maximum features at reasonable and affordable price is needed. In this paper we are comparing the features offered by Moodle Cloud and GCP Moodle Cloud on various parameters. &nbsp

This study proposes the Oracle load balancing solution for cloud computing. Load balancing is a strategy for distributing work load and resources in cloud computing. The users can get access to the resources they required and services are available on demand as a Pay-For-What You use model. The Oracle Cloud Infrastructure load balancer provides excellent availability, scalability, and resource usage. The paper discuss the load balancing concept in cloud computing and presents the step by step process of implementing load balancer in OCI. &nbsp

—The aim of this paper is to identify a web application that can be used to test the effectiveness of Burp Suite web application vulnerability scanners. Burp suite is a prominent penetration testing and vulnerability detection tool created by the Portswigger firm. Burp Suite is designed to be an all-in-one toolkit, and its capabilities can be expanded by installing add-ons calledBApps. Its simplicity makes it a better choice than free options like OWASP ZAP.

Faster release of features to the production environment is the need of the hour of every IT organizations. This faster and continuous software delivery is made possible through DevOps. DevOps is a set of practices that bridges the gap between development team and operations team. The different aspects of DevOps came from agile methodology. Continuous integration is the paramount of DevOps life cycle. In a continuous integration development practice, the development code and the applications code are continuously integrated several times a day. Developers follow the practice of committing changes frequently into the version control system such as git. A change in the version control system triggers the continuous integration system such as Jenkins. This exploratory study presents the ways that support the developers during the build break in continuous integration environment and thereby improve the efficiency of continuous integration process. Our findings contribute that the selected list of plugins in the Jenkins support the developers during the build break, thus developers can come up with faster recovery of build failures which in turn increase the efficiency of continuous integration environment.