Containerization is a technique for lightweight virtualization of programs in cloud computing, which leads to the widespread use of cloud computing. It has a positive impact on both the development and deployment of software. Containers can be divided into two groups based on their setup. The Application Container and the System Container are two types of containers. A container is a user-space that is contained within another container, while a system container is a user-space that is contained within another container. This study compares and contrasts several container architectures and their organization in micro-hosting environments for containers.

Recommender systems utilize the times of yore experiences and preferences of the target customers as a basis to offer personalized recommendations for them as well as resolve the information overloading hitch. Personalized recommendation methods are primarily classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches have their own advantages, drawbacks and complementarities. Because conventional recommendation techniques don't consider the contextual information, the real factor why a customer likes a specific product is unable to be understood. Therefore, in reality, it often causes a decrease in the accuracy of the recommendation results and also persuades the recommendation quality. In this paper, we propose the integrated contextual information as the foundation concept of multidimensional recommendation model and use the Online Analytical Processing (OLAP) ability of data warehousing to solve the contradicting tribulations among hierarchy ratings. This work hopes that by establishing additional user profiles and multidimensional analysis to find the key factors affecting user perceptions.

Natural languages are written and spoken languages, and NLP (Natural Language Processing) is the ability of a computer program to recognize both written and spoken languages. Word Sense Disambiguation (WSD) is identified as a challenging area of research in Artificial Intelligence (AI), and Machine Translation (MT). WSD is the procedure for selecting the exact meaning of a word that has more than one meaning. This is an essential application for all-natural language processing applications. There are various knowledge-based, supervised, and unsupervised approaches to WSD process. The Naïve Bayes classifier as an example of approach supervised and unsupervised approaches is the most important method. In this paper, we emphasize on the use of the Naïve Bayes approach for text classification in WSD techniques. Bayes’ hypothesis is a probabilistic model and a reliable approach for text classification. Bayes’ hypothesis acknowledges that the occurrence of some other features is not dependent on the presence of a particular element in a class. This calculation can be used to solve multi-class prediction problems. This classifier performs better compared to the other methods of different approaches. This paper gives an itemized investigation of Naïve Bayes algorithms, which depicts its ideas, covering up Naïve Bayes’, text characterization, traditional innocent Bayes’, and machine learning. We have used the collocation method of feature extraction for the WSD of English sentences. Using this model we have disambiguate ambiguous English words by predicting part-of-speech inclusive of “noun,” “verb,” “adverb,” and “adjective.” This disambiguation module is an enhancement in machine translation. The system reported the performance measure of seventy-eight (78%) percent of the scale on F1-measure.

The advancement of information technology today depends on cloud computing. Every cloud consumer continues to search for the best services available, particularly in terms of security. The absence of uniform standards in the security architecture of the cloud, especially within the cloud computing community, has become an ongoing problem. Most cloud service providers automatically follow the best security practises and actively protect the operation of their systems. Enterprises are required to independently judge the security of their data, apps, and the cloud’s workload. The complexity of security concerns is increasing as the digital environment develops. Virtualization, multi-tenancy, security controls, rules, and risk profiling are all concepts in cloud security. DoS, service injection, user-to-root attacks, man-in-the-middle attacks, data loss leakage andloss, identity theft, and backdoor channel attacks are a few of the dangers associated with cloud computing. Cloud platforms, data outsourcing, data storage standardisation and security, data backup, and data recovery are all examples of cloud security services. Data storage, computation, untrusted computing, and virtualization security challenges are all a part of the cloud.

5G networks, the newest generation of wireless communication in an era of quick technical breakthroughs, promise unmatched speed, dependability, and connection. With this ground-breaking innovation, it is crucial to have strong authentication and security processes to preserve critical data and shield consumers from any dangers. We thoroughly analyze authentication and security mechanisms created expressly for 5G networks in this study. We examine this sophisticated networking paradigm’s numerous difficulties and weaknesses, emphasizing the requirement for rigorous security measures. We also look at encryption algorithms essential for preserving the availability, confidentiality, and integrity of data transferred across 5G networks. Additionally, we go through cutting-edge technologies and standards like network slicing, edge computing, and virtualization that solve the security issues unique to 5G networks. We examine how they could affect authentication and security processes, highlighting both the opportunities and difficulties they present.