The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for people to find similar color images. To obtain similarity matching, the color of the image needs to be identified. This paper aims at various clustering techniques to identify the color of the digital image. Though many clustering techniques exist, this paper focuses on Fuzzy c-Means, Mean-Shift, and a hybrid technique that amalgamates the agglomerative hierarchies and k-Means, known as hKmeans to cluster the intensity of the image. Applying evaluation metrics of Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Homogeneity, Completeness, V-Score, and Peak signal-to-noise ratio it is proven that the results obtained demonstrate the good performance of the proposed technique. Then the color histogram is applied to identify the color and differentiate the color distribution on the original and clustered image.

Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP.

Software-Defined Networking (SDN) offers a paradigm shift in network management, providing increased flexibility and centralized control. However, this centralized architecture introduces unique security challenges. The centralized controller becomes a prime target for attackers, exposing the network to various threats such as direct attacks, unauthorized access, data manipulation, Denial-of-Service (DoS) attacks, and switch vulnerabilities. Furthermore, existing DDoS detection methods in SDN face limitations due to reliance on network topology, incomplete attack type coverage, outdated datasets, and expensive hardware requirements. This dependence on outdated data hinders adaptability to new threats and slows down detection. This research addresses these challenges by proposing a sophisticated hybrid approach integrated within the ONOS controller. This approach combines entropy-based analysis and a machine learning algorithm to enhance the identification of both high-volume and low-volume DDoS attacks through a binary classification task. By leveraging the capabilities of the ONOS controller, the study advances intrusion detection, offering a deeper understanding of network patterns and strengthening resilience against evolving cyber threats. Notably, the results demonstrate outstanding accuracy of up to 97% in detecting and mitigating these threats, underscoring the effectiveness of the proposed methodology. This research contributes significantly to the ongoing discourse on securing SDN environments by proposing a highly effective and adaptable DDoS detection and mitigation approach. This approach addresses the inherent vulnerabilities of SDN while capitalizing on its inherent advantages in flexibility and centralized control.