In this paper, we conducted a study on Financial Recommender Systems using tailored prediction and recommendation models. As financial debt and illiteracy rise across Canada, especially among Generation Z, there is a growing need for intelligent and accessible financial guidance. To address this, we synthesize a realistic dataset of 100 simulated Canadian users, we model financial behaviour using demographics, savings, debt, and expense data. Our system integrates multiple components: Isolation Forest for anomaly detection, ARIMA and LSTM for expense forecasting and a rule-based recommendation system (RecSys) to generate personalized financial advice. These elements form a unified pipeline that identifies abnormal spending, forecasts future expenses and delivers risk-aligned recommendations. Experimental results show that our hybrid forecasting model effectively predicts future financial behaviour, while the recommendation engine generates user-specific plans with heuristic scoring for priority labelling. Although this paper uses synthetic data and a rule-based RecSys, it provides proof of concept for scalable, practical applications. Future work includes real-user testing, dynamic LLM integration with an API key, and full-stack deployment.
With rapid advancements in Large Language Model (LLM) technology and the emergence of open-source implementations for running these models, researchers have increasingly turned to LLMs to enhance the capabilities of traditional Machine Learning methods, especially within the purview of product recommendation system. In a paper by Sun et al, they implemented a novel framework which used an LLM integrated with collaborative filtering to determine product recommendations [1]. But this approach relies on a decoder to generate final product recommendations and ranking. Other researchers focused on comparing Encoder and Decoder architectures showed that a fine-tuned encoder can significantly outperform decoder architectures in language understanding tasks [2]. Thus, we propose a novel framework for product recommendation which incorporates both the real world context knowledge and reasoning capabilities of decoder architectures coupled with the superior language capabilities of encoder architectures. Siamese Dual Encoders (SDA) have long been used for tasks including Question-Answering, sentiment analysis, and generating recommendations [3]. By preprocessing product review data using a reasoning based LLM, and then using the processed data to train a BERT-based Siamese Dual Encoder, we aim to demonstrate this novel method which yields better results in terms of more accurate recommendations. This paper will show the positive impact of data preprocessing using LLMs to incorporate real world context and reasoning capabilities, while also integrating the ranking and classification capabilities of a BERT-based encoder.
This paper presents a novel approach to intrusion detection by integrating traditional signature-based methods with the contextual understanding capabilities of the GPT-2 Large Language Model (LLM). As cyber threats become increasingly sophisticated, particularly in distributed, heterogeneous, and resource-constrained environments such as those enabled by the Internet of Things (IoT), the need for dynamic and adaptive Intrusion Detection Systems (IDSs) becomes increasingly urgent. While traditional methods remain effective for detecting known threats, they often fail to recognize new and evolving attack patterns. In contrast, GPT-2 excels at processing unstructured data and identifying complex semantic relationships, making it well-suited to uncovering subtle, zero-day attack vectors. We propose a hybrid IDS framework that merges the robustness of signature-based techniques with the adaptability of GPT-2-driven semantic analysis. Experimental evaluations on a representative intrusion dataset demonstrate that our model enhances detection accuracy by 6.3%, reduces false positives by 9.0%, and maintains near real-time responsiveness. These results affirm the potential of language model integration to build intelligent, scalable, and resilient cybersecurity defences suited for modern connected environments.
Critical infrastructure systems, including energy grids, healthcare facilities, transportation networks, and water distribution systems, are pivotal to societal stability and economic resilience. However, the increasing interconnectivity of these systems exposes them to various cyber threats, including ransomware, Denial-of-Service (DoS) attacks, and Advanced Persistent Threats (APTs). This paper examines cybersecurity vulnerabilities in critical infrastructure, highlighting the threat landscape, attack vectors, and the role of Artificial Intelligence (AI) in mitigating these risks. We propose a hybrid AI-driven cybersecurity framework to enhance real-time vulnerability detection, threat modelling, and automated remediation. This study also addresses the complexities of adversarial AI, regulatory compliance, and integration. Our findings provide actionable insights to strengthen the security and resilience of critical infrastructure systems against emerging cyber threats.
With the rapid evolution of artificial intelligence (AI), face forgery videos have proliferated, posing significant societal challenges. Traditional detection methods struggle with poor generalization and cross-database accuracy, unable to address subtle features and variations in face images across scales and compression levels. This paper reviews current face forgery detection methods, identifying key limitations. It introduces a novel model enhancing features through knowledge distillation, optimizing generalization and robustness via a unique loss function and temperature adjustment strategy. Additionally, a Discrete Cosine Transform with multi-scale and multi-compression capabilities (DCTMS) is integrated, enriching texture and detail capture. Experimental results on deepfake datasets demonstrate the efficacy of the proposed methods, achieving high detection accuracy and robustness across diverse scenarios, including cross-database experiments. This study contributes valuable insights and techniques to advance the field of face forgery detection, addressing risks associated with manipulated video content.
