The internet plays important role in the modern society. With the passage of time, internet consumers are increasing. Therefore, the traffic loads during communication between client and its associated server are getting complex. Various networking systems are available to send the information or to receive messages via the internet. Some networking systems are so expensive that they cannot be used for the regular purpose. A user always tries to use that networking system that works on expansion of optimizing the cost. A content delivery network (CDN) also called as content distribution network has been developed to manage better performance between client and list of available servers. This chapter presents the mathematical model to find optimization among client and cache server during delivery of content based on fuzzy logic.

A social network is a structure made up of individuals, groups, or organizations connected by relationships. These relationships can be friendships, family bonds, or professional links. In this study, the focus is on homophily, which means people with similar interests tend to connect. The research analyzes a dataset of 1,000 users from [social media platform] to understand how shared interests affect community formation. Pearson correlation measures interest similarity between users. The Label Propagation Algorithm (LPA) and the Louvain method help detect user communities. The study finds distinct communities, where users are grouped based on common interests. The results show that homophily strongly influences how online communities form. This research provides a simple method to analyze user connections and improve community detection in social networks.

Admission to an appropriate college is an important process. The method of enquiry is an important part of it. As a result, students gather data, analyse it, and attempt to predict which college is best for them to attend. In general, students find it useful to research the trends in fee structures, academic staff numbers, multi-national companies tie up, and placements received by students during that academic year. K-Nearest Neighbors (k-NN) is one of the simplest learning algorithms used in supervised learning approaches. It makes assumptions about the similarity between new cases or data and available cases and places new cases in the category that most closely resembles the available categories. In this paper, the authors have tried to identify or predict the category or class of a particular dataset using the k-NN algorithm.

Objectives: Social network applications such as Facebook, LinkedIn, Twitter, and WhatsApp are becoming an important part of daily communication. These platforms help users to share opinions, information, and feedback with others. Sentiment analysis of such content provides insights into users’ views, emotions, and engagement levels. This paper analyzes student feedback data collected from Kaggle to identify sentiment patterns in social networks. The study also aims to examine whether hybrid models can provide a balanced and interpretable alternative to deep learning models and transformer-based methods. Methods: This study uses several sentiment analysis approaches, including Support Vector Machine (SVM) along with TF-IDF features, Long Short-Term Memory (LSTM) networks, a hybrid TF-IDF–LSTM model, and transformer-based models like BERT and RoBERTa. The analysis is performed on a student feedback dataset consisting of 5,200 cases. The dataset is divided into training and testing sets using 80:20 ratios. Performance is measured using accuracy, precision, recall, F1-score, and a confusion matrix. The models are tested on student feedback data and compared under a unified experimental framework. Findings: In the LSTM model highest accuracy of 0.881 is found whereas in BERT and RoBERTa achieved accuracies of 0.880 each, while TF-IDF with SVM reached 0.865. LSTM and transformer-based models performs better than traditional methods in classification accuracy. In these results it is found that deep learning and transformer-based models give higher accuracy in sentiment classification. The hybrid TF-IDF and LSTM approach shows stable and competitive performance. It also needs less computational resources. So there can be a practical option for sentiment analysis of student feedback on social networks. Novelty: A hybrid sentiment analysis method combining TF-IDF and LSTM is presented. It combines frequency-driven statistical feature extraction and representation of sequential data for sentiment classification. The hybrid model is not designed to perform better than transformer models. The study tried to show hybrid models can be applicable in sentiment analysis of student feedback in social networks. Keywords: Sentiment Analysis, Social Networks, Student Feedback, LSTM, BERT, TF-IDF

Security of information is always a challenging domain for any computer network organization. An organization always sets different types of policies with the course of time so that no information can be leaked. Some external or some internal factors of an organization play important roles in revealing the information. An organization mainly depends on its employees. An employee manages the data and information and there exists some chance among employees to reveal the data. There is need to study and set policies for employees so that no full information can be revealed. Information security management system (ISMS) has collection of different types of policies and procedures for systematically managing organizationally sensitive data. ISMSs have to deal with management of employees of an organization to minimize the risk of revealing information. This chapter studies employee management so that an organization can continue its business securely.