Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data.

Accurate sales forecasting is vital for balancing demand and supply and enhancing profitability in the retail sector. Deep learning (DL) models have shown promise in this area; however, most either handle temporal or spatial patterns in isolation. Moreover, many studies rely on synthetic datasets or omit critical contextual variables, reducing real-world accuracy. This study proposes a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model for retail sales forecasting using real-world data enhanced with environmental and demographic variables in term of holidays, salary days, protests, and weather conditions. CNNs capture spatial patterns, while LSTMs model temporal dependencies, making the hybrid architecture well-suited for multivariate forecasting tasks. Our model demonstrates a significant improvement in predictive performance, achieving a mean absolute percentage error (MAPE) of 4.16%, outperforming traditional and standalone neural models. By incorporating external factors, the proposed approach enables more reliable forecasting and supports informed decision-making in retail operations.

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.

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.

Software developers face several challenges when creating or maintaining applications, security assurance is one of them. To minimise the occurrence of vulnerabilities, developers utilize various solutions, including static application security testing (SAST) tools. These tools use different analysis techniques to detect application flaws and support various programming languages, frameworks and third-party libraries. It is important to understand their capabilities. To the authors’ knowledge, researchers have not yet addressed the gap in the benchmarking of SAST tools used with Spring framework. Therefore, this research proposes a benchmarking utility that is designed to assess the performance of Spring framework SAST tools. The study is based on action research and consists of several parts: the analysis of existing Spring framework vulnerabilities, the collection and enhancement of benchmarking strategies from similar tools and the development of the utility using the collected data. The study findings are of interest to SAST providers as they would be able to use the benchmark for the evaluation of the detection capabilities of their SAST solution in Spring environment. Moreover, the utility could be used to provide benchmark for future research to compare other SAST tools. Overall, the research contributes to the IT, cyber security and related research fields.