With the proliferation of Internet of Things (IoT) devices across various applications, for example, smart homes, drones, and healthcare, the security vulnerabilities have also increased, necessitating robust network intrusion detection systems (NIDS). This study focuses on the classification of cyberattacks, including denial of service (DoS), worms, and backdoor attacks, from normal network traffic using the UNSW-NB15 dataset. machine learning (ML) and deep learning (DL) models, such as decision tree (DT) classifier, K-nearest-neighbor (KNN) classifier, linear regression, linear support vector machine, logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were employed for both binary and multi-class classification. Data preprocessing involved handling null values, one-hot encoding categorical variables, and normalizing numerical features. Feature selection was performed using the Pearson correlation coefficient method, reducing the dataset attributes significantly. The models demonstrated high accuracy in detecting anomalies, with the RF classifier achieving the highest accuracy of 98.64% for binary classification and notable performance across multi-class classifications. This study underscores the effectiveness of ML techniques in enhancing IoT network security and offers comprehensive insights.

Communication is at the core of all human interactions, whether personal or professional. It is one of the basics for survival in a community. Verbal communication cannot occur without a coherent, mutually recognized language. According to estimates, deaf people make up approximately 7.4% of the disability population in Pakistan. The prevailing technique emphasizes oralism (teaching spoken language). As a result, reducing the gap in communication is crucial amongst the general population with those who have difficulty speaking. The goal is to create an application that evaluates a user's skills as they cycle through each letter of the Urdu alphabet in Pakistan Sign Language (PSL). Furthermore, this work investigates various ML techniques for recognizing hand motions that involve the detection of landmarks. For the vision of the computers, recognizing locations is customary on certain locations on an item for extracting related attributes from it, such as various people's hands. Machine learning techniques are evaluated, for example, Naïve Bayes, K-Nearest Neighbor, AdaBoost, Support Vector Machine, Decision Trees, Logistic Regression, and Random Forest Classifier. All models were trained and tested on a dataset of 10,000 computed hand data expressions. The model was trained using the dataset in CSV format. The project's goal is to make PSL more accessible and improve communication between the deaf and hearing communities. Multiple trials will be carried out, comprising changes to the models' architectural parameters to attain the maximum possible identification accuracy.

The acquisition of a person’s first language is one of their greatest accomplishments. Nevertheless, being fluent in sign language presents challenges for many deaf students who rely on it for communication. Effective communication is essential for both personal and professional interactions and is critical for community engagement. However, the lack of a mutually understood language can be a significant barrier. Estimates indicate that a large portion of New Zealand’s disability population is deaf, with an educational approach predominantly focused on oralism, emphasizing spoken language. This makes it essential to bridge the communication gap between the general public and individuals with speech difficulties. The aim of this project is to develop an application that systematically cycles through each letter and number in New Zealand Sign Language (NZSL), assessing the user’s proficiency. This research investigates various machine learning methods for hand gesture recognition, with a focus on landmark detection. In computer vision, identifying specific points on an object—such as distinct hand landmarks—is a standard approach for feature extraction. Evaluation of this system has been performed using machine learning techniques, including Random Forest (RF) Classifier, k-Nearest Neighbours (KNN), AdaBoost (AB), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), and Logistic Regression (LR). The dataset used for model training and testing consists of approximately 100,000 hand gesture expressions, formatted into a CSV dataset for model training.