Research & Publications

Abstract: The increasing vulnerability of high-rise structures to seismic hazards, particularly in developing countries such as Pakistan, necessitates advanced, adaptive, and intelligent structural assessment frameworks. This study developed an AI-assisted seismic performance evaluation model for sustainable high-rise structures incorporating smart material technologies, including shape memory alloys (SMA), fiber-reinforced polymers (FRP), and damping systems. A quantitative simulation-based research design was employed, integrating finite element modeling with machine learning algorithms to predict seismic response parameters such as inter-story drift ratio, base shear, damage index, and energy dissipation capacity. Multiple AI models, including ANN, CNN, LSTM, and hybrid ANN–LSTM architectures, were trained and validated using k-fold cross-validation. The results indicated that the hybrid ANN–LSTM model achieved the highest predictive accuracy (R² = 0.96), outperforming conventional machine learning approaches. Furthermore, structures integrated with smart materials exhibited significant improvements in seismic resilience, including reduced inter-story drift (35.7% reduction), lower damage indices (39.0% reduction), and enhanced energy dissipation capacity (28.1% increase). Sustainability performance also improved substantially in smart material-based systems due to enhanced material efficiency and reduced lifecycle impact. The study concludes that the integration of AI-driven predictive analytics with smart material technologies provides a robust and scalable framework for seismic performance assessment of high-rise structures. This integrated approach enhances structural safety, sustainability, and decision-making capabilities, particularly in seismic-prone urban regions of Pakistan

Abstract: Nowadays, people use the web more consistently and the World Wide Web (WWW) is used as the largest global information media by which the user can write, read or share information throughout the internet. The first version was Web 1.0 which was only static, and the second version is Web 2.0 users can only read, write, and create the data which helps businesses to cover the dynamic data of the users. However, the third version Web 3.0, uses the algorithm that works differently for every user to interpret the individual data and customise the internet for every user. Many companies like YouTube, Netflix, and Spotify use this technology and only share valuable things with users by analysing their data and behaviour. This paper reviewed the papers, which are published in the domain of Web 3.0, and defines how the decentralised web is focused on underlying technologies and developing protocols.

Abstract: The study proposes the use of a stacked Long-Short-Term Memory (LSTM) model to predict the KSE-100 stock exchange trend and provides a comprehensive review of the literature on deep learning models and time series forecasting in the stock market. The study's findings suggest that the stacked LSTM model outperforms other models in terms of prediction accuracy. The study's contribution lies in its approach to improving the accuracy of stock price prediction using deep learning models. The stacked LSTM model architecture is a novel approach that provides better results than other traditional time series forecasting models. Furthermore, the study's use of hyper-parameter optimization techniques demonstrates the importance of model tuning for improving performance intended for accurate time series forecasting in the financial market. The study's results have practical implications for investors, who can use the stacked LSTM model to make informed decisions about buying or selling stocks in the KSE-100. The model's ability to predict stock prices accurately can help investors maximize their profits and minimize their losses. Hence, the proposed stacked LSTM model can effectively predict stock prices in the KSE-100 and can assist investors in making informed decisions in the stock market.

Abstract: Emotion recognition from facial expressions is an important research area in the field of artificial intelligence. In this study, a novel deep-learning model is proposed for emotion recognition from facial expressions. The model uses a combination of modifications in COGMEN (Contextualized GNN-based Multimodal Emotion Recognition) to extract features from facial images and other modalities and capture temporal dependencies between frames. The model was evaluated on the IEMOCAP dataset, achieving an accuracy of 87.8%, outperforming (mostly) existing state-of-the-art methods. Furthermore, the model has a lower computational complexity compared to other methods, making it more practical for real-time applications. A high F1 score was reported for each emotion category, indicating good performance across all classes. The results demonstrate the potential method for improving emotion recognition from multiple modalities (i.e.: Audio, Visual & Text) and have implications for a wide range of applications such as affective computing, human-computer interaction, and mental health monitoring.

Abstract: Breast cancer is the most diagnosed type of cancer as per the data that is collected by the World Health Organization (WHO) within the past few years. Over 600,000 deaths were recorded in 2021 due to breast cancer. Breast cancer screening is done using two-dimensional (2D) and three-dimensional (3D) mammography, but MRIs and Ultrasounds are also used in certain conditions. The diagnosis from the screenings is not always accurate as a practitioner must physically look at the digital images to find any signs of cancer. Approximately, each diagnosis has a variable chance of a false-positive or a false-negative. Many CAD (computer-aided detection) systems have been developed for the assistance of a practitioner with the diagnosis. However, in the past years, Deep Neural Networks (DNN) have seen a spike and the models are being used to aid breast cancer screening. Data shows a possibility of reaching Area under the curve (AUC) values as high as 0.99 under ideal conditions when the training dataset is cleaned of noise and properly pre-processed and in some studies, the accuracy and sensitivity are even compared to that of a practitioner’s, with the DNN model outperforming in numbers across the board. After performing a literature review on similar work, we have trained a model of our own on a publicly available dataset (MIAS) reaching promising results of an AUC of 0.87 and an Accuracy of 0.88 with the initial model built on a DenseNet121 architecture.

Abstract: "PyKids" is an exciting and interactive guidebook designed for young minds to explore the world of computer programming using Python. Written with a fun and enthusiastic approach, this book delivers all the essential concepts and skills required to begin coding in Python.

Abstract: Mammory, which is a mobile and desktop application designed to assist users in detecting potential abnormalities in mammography and ultrasound images. Our project aims to improve the accessibility and accuracy of breast cancer screening by using advanced artificial intelligence (AI) models to analyze images and provide detailed results. Mammory is designed to be user-friendly and accessible to a wide range of users, including both medical professionals and non-medical individuals. The application allows users to upload images from their device and receive results within seconds, with the option to view and download detailed reports. Additionally, the application includes a feature for users to search nearby clinics for further screening and treatment. Throughout the development process, we have employed various software engineering and testing methods to ensure the reliability and accuracy of the application. We have also considered various ethical and privacy concerns and have implemented measures to safeguard the security and confidentiality of user data. We hope that this project will make a significant impact in the field of breast cancer screening and improve the lives of those affected by the disease. We believe that Mammory will help in early detection, which is crucial in increasing the survival rate of breast cancer patients.