In the evolving digital landscape, it is crucial to study the dynamics of cyberattacks and defences. This study uses an Evolutionary Game Theory (EGT) framework to investigate the evolutionary dynamics of attacks and defences in cyberspace. We develop a two-population asymmetric game between attacker and defender to capture the essential factors of costs, potential benefits, and the probability of successful defences. Through mathematical analysis and numerical simulations, we find that systems with high defence intensities (at least 80%) show stability with minimal attack frequencies (at most 10%), whereas low-defence environments (lower than 50%) show instability and are vulnerable to attacks. We simulate 100,000 randomly sampled games and observe three key results: (i) the defend and attack equilibrium remains stable in 39.8% of configurations; (ii) increasing the defence success rate from 0.2 to 0.8 reduces the frequency of successful attacks by nearly 50%; and (iii) in over 80% of sampled games the system converges to a stable boundary equilibrium, indicating robust evolutionary dynamics. We validate these outcomes using a public dataset of real-world cyber incidents (2004–2020). Our theoretical findings align with this historical data, demonstrating the interdisciplinary impact, such as fraud detection, risk management and cybersecurity decision-making. Our EGT framework uniquely captures co-evolving attacker-defender populations, achieves stable equilibrium outcomes, and demonstrates robustness through large-scale random game analysis and social welfare evaluation. Overall, our analysis suggests that adaptive cybersecurity strategies based on EGT can improve resource allocation, enhance system resilience, and reduce the overall risk of cyberattacks. By incorporating real-world data, this study demonstrates the applicability of EGT in addressing the evolving nature of cyber threats and the need for secure digital ecosystems through strategic planning and proactive defence measures.

This project develops a blockchain-based web application aimed at verifying pharmaceutical products, a critical step in combating counterfeit drugs. By leveraging Ethereum's Sepolia test network and the power of smart contracts, the system facilitates secure, transparent processes for registering, verifying, and tracking pharmaceutical items. The application combines a PHP-based backend with a JavaScript-powered frontend, seamlessly integrating tools like MetaMask for user authentication and Web3.js to enable blockchain communication. The study underscores the significant advantages blockchain offers over traditional verification methods, particularly in terms of data integrity, transparency, and security. As a result, it meets the CIA triad model's requirements for confidentiality and integrity in information security. The novel features of the project include the use of optimism roll-ups for scalability, two-factor authentication (2FA), and data encryption to address critical challenges often overlooked in similar proposals for blockchainbased authentication systems. The results of the project demonstrate a tangible improvement in supply chain transparency and fraud prevention within the pharmaceutical industry. This work lays a strong foundation for further exploration of decentralised applications, not only in pharmaceutical validation but also across other essential sectors.

Customer clustering is an unsupervised machine-learning approach that groups diverse customers based on shared characteristics. This research focuses on improving customer cluster analysis in the retail sector through an exploration of machine learning techniques, specifically employing the k-means model and predictive algorithms. An Ensemble approach is proposed to gain deeper insights into customer behavior and predict the future actions of new customers within the same cluster assignment. The study evaluates multiple machine learning techniques, utilizing K-fold cross-validation for enhanced model performance, with key metrics including accuracy, precision, recall, and F1-score. Notably, the Extreme Gradient Boosting Classifier excels in Dataset One, while Random Forest outperforms in Dataset Two. The project aims to combine these top-performing ensemble classifiers using the Voting-Soft Classifier for customer classification. The proposed model achieves high precision scores for both datasets, with a particularly promising precision score of 94.57% and an F1-score of 93% for Dataset Two, demonstrating its effectiveness in customer classification.

In today’s job market, finding a suitable job is a complex task that requires innovation to ease the complexity of finding the job. As technology shapes the business landscape, a deeper understanding of the Advance Natural Language Processing (NLP) and using it properly will elevate the solution of finding the match between candidates and employers. In this research, comparative approach between GPT-2 and T5 model has been done to find out the best model for the job matching chatbot. Also, in this chatbot multiple criteria decision making method has been used to find the best job related to the user’s requirement. The research novelty is a chatbot that works based on the Transformer-based Text-to-Text Transfer Transformer (T5) model and compare it to GPT-2 to address the challenges of finding the best job based on job seekers’ preferences and also compare both generated answers to realise the accuracy of each model in job matching chatbot. GPT-2 and T5 both are excel in natural language understanding tasks, enabling us to parse and map user queries to many job preferences, ensuring a comprehensive understanding of user skills, and also they can provide a high rate of accuracy and performance in the natural language tasks. The research’s contribution focuses on preference job matching chatbot application, which effectively bridges the gap between employers and job seekers. Using context-based meanings of specific words and new terms defined in the conversation, the model generates responses based on user input. A seamless connection between job seekers and potential employers is made possible by our approach to Human Resources technology, which serves as a more personalised, effective, and user-friendly job matching system. The model tokenises words and generates test cases based on them. By utilising NLP techniques, it will help to ensure that all scenarios are taken into account.

In this study, we explore deep learning models for single-character Chinese character recognition tasks, with a special focus on two architectures: VGG19 and EfficientNetV2. By improving recognition accuracy with limited computational resources and dataset size, this study aims to address real-world challenges.In the field of image recognition, deep learning has shown excellent ability, especially the importance of convolutional Neural Network (CNN). This study reviews the history of handwritten Chinese character recognition and explores the application of deep learning in various fields such as object detection, image classification, and semantic segmentation. The research methodology incorporates problem analysis, data collection, model construction, training, and result generation. PyTorch is used as the basic framework to implement the model, and strict data preprocessing is conducted to optimise the performance. The user interface and interaction design allow us to show the practical application of the model and encourage user-friendly participation. By applying VGG19 and EfficientNetV2 models in a single-character Chinese character recognition task, we reveal the impact of limited training data and computational constraints on accuracy and performance. We confirm that higher training cycles improve accuracy, but we also note diminishing returns. Meanwhile, the research highlights the exciting potential of deep learning in character recognition tasks and advocates its widespread application in practice. While overcoming computational and data limitations, our study reveals the intricate relationship between model training, accuracy improvement, and practical usability. The experimental results show that the average training result of the EfficientNetV2 model is 94.957322%, and the average training result of the VGG19 model is 95.756285%. This study provides dedicated support for the in-depth research and development of Chinese character recognition and its various application fields.

Stock price forecasting has always been a challenging task due to its high volatility and complexity. Recently, various machine learning models have been employed to improve the accuracy of stock price predictions. This research aims to enhance stock price forecasting by integrating supply chain factors into LSTM models and conducting a comparative performance analysis with other models such as ANN, RNN, and GRU. A comprehensive literature review was conducted to understand the history of machine learning in finance, the role of different models in stock market prediction, the impact of varied factors in stock market prediction, and the challenges and limitations of artificial intelligence in stock forecasting. The methodology involved problem analysis, defining the research purpose, and project design which includes data collection, model building and training, and performance evaluation and validation. The models were trained and assessed on a dataset that incorporated supply chain factors. The experimental results showed that the GRU model outperformed the other models in terms of R2 score, MAE, and MSE. The study contributes to the existing body of knowledge by providing empirical evidence on the importance of incorporating supply chain factors into predictive models and by comparing the performance of different models. The findings have practical implications for investors, analysts, and policymakers who rely on accurate stock price predictions for decision-making.

The admiration of social networks (SNs) and the advent of the Internet of Things (IoT) direct to a new research paradigm called Social IoT (SIoT), where real-world physical objects can form their own SN like the human SN. This effort leads to an immense possibility of unique applications for a smart cognitive society. However, it is still a challenge to explore these applications due to a lack of an adequate SIoT framework, where SIoT nodes can be controlled, managed, and monitored in realtime under a cognitive framework. Hence, in this paper, we propose a framework to create, manage, control, and monitor the SIoT objects intelligently and cognitively in real-time. In our proposed framework, we enable virtual representation of realworld objects known as virtual objects (VOs) and ensure their relationship semantically to compose new services by combining VOs and called composite VOs. Additionally, we identify special skills (e.g., expertise, and/or willingness to help others, etc.) as abstract objects. We also enable real-time interaction by using stream processing techniques. We also evaluated the performance of VO selection to understand the resource consumption and latency during the process.