Sukhjit Singh Sehra

Seerat Kaur, Sukhjit Singh Sehra, Dariush Ebrahimi

Traffic prediction plays a central role in intelligent transportation systems (ITS) by supporting real-time decision-making, congestion management, and long-term planning. However, many existing approaches face practical limitations. Most spatio-temporal models are trained on centralized data, rely on numerical representations, and offer limited explainability. Recent Large Language Model (LLM) methods improve reasoning capabilities but typically assume centralized data availability and do not fully capture the distributed and heterogeneous nature of real-world traffic systems. To address these challenges, this study proposes FedLLM (Federated LLM), a privacy-preserving and distributed framework for explainable multi-horizon short-term traffic flow prediction (15-60 minutes). The framework introduces four key contributions: 1) a Composite Selection Score (CSS) for data-driven freeway selection that captures structural diversity across traffic regions 2) a domain-adapted LLM fine-tuned on structured traffic prompts encoding spatial, temporal, and statistical context 3) FedLLM framework, that enables collaborative training across heterogeneous clients while exchanging only lightweight LoRA adapter parameters, 4) a structured prompt representation that supports contextual reasoning and cross-region generalization. The FedLLM design allows each client to learn from local traffic patterns while contributing to a shared global model through efficient parameter exchange, reducing communication overhead and keeping data private. This setup supports learning under non-IID traffic distributions. Experimental results show that FedLLM achieves improved predictive performance over centralized baselines, while producing structured and explainable outputs. These findings highlight the potential of combining FL with domain-adapted LLMs for scalable, privacy-aware, and explainable traffic prediction.

Kaniz Fatema, Emad A. Mohammed, Sukhjit Singh Sehra

Effective and interpretable classification of medical images is a challenge in computer-aided diagnosis, especially in resource-limited clinical settings. This study introduces spline-based Kolmogorov-Arnold Networks (KANs) for accurate medical image classification with limited, diverse datasets. The models include SBTAYLOR-KAN, integrating B-splines with Taylor series; SBRBF-KAN, combining B-splines with Radial Basis Functions; and SBWAVELET-KAN, embedding B-splines in Morlet wavelet transforms. These approaches leverage spline-based function approximation to capture both local and global nonlinearities. The models were evaluated on brain MRI, chest X-rays, tuberculosis X-rays, and skin lesion images without preprocessing, demonstrating the ability to learn directly from raw data. Extensive experiments, including cross-dataset validation and data reduction analysis, showed strong generalization and stability. SBTAYLOR-KAN achieved up to 98.93% accuracy, with a balanced F1-score, maintaining over 86% accuracy using only 30% of the training data across three datasets. Despite class imbalance in the skin cancer dataset, experiments on both imbalanced and balanced versions showed SBTAYLOR-KAN outperforming other models, achieving 68.22% accuracy. Unlike traditional CNNs, which require millions of parameters (e.g., ResNet50 with 24.18M), SBTAYLOR-KAN achieves comparable performance with just 2,872 trainable parameters, making it more suitable for constrained medical environments. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for interpretability, highlighting relevant regions in medical images. This framework provides a lightweight, interpretable, and generalizable solution for medical image classification, addressing the challenges of limited datasets and data-scarce scenarios in clinical AI applications.

Sumeet Kaur Sehra, Jasneet Kaur, Sukhjit Singh Sehra

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, data mining is used to improve an organization's software process quality, e. g. the accuracy of effort estimations . There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study, three simple preprocessors are taken (none, norm, log) and effort is measured using COCOMO model. Then results obtained from different preprocessors are compared and norm preprocessor proves to be more accurate as compared to other preprocessors.

Gitanjali, Sukhjit Singh Sehra, Jaiteg Singh

Cloud Computing is a set of IT Services that are provided to a customer over a network and these services are delivered by third party provider who owns the infrastructure and reduce the burden at user's end. Nowadays researchers devoted their work access control method to enhance the security on Cloud. RBAC is attractive access model because the number of roles is significantly less hence users can be easily classified according to their roles. The Role-based Access Control (RBAC) model provides efficient way to manage access to information while reducing the cost of security administration and complexity in large networked applications. This paper specify various policies in RBAC on clouds such as migration policy which helps the user to migrate the database schema and roles easily to the Cloud using XML with more security. Restriction policy provide the security enhancement in Role Based Access Model by restricting the number of transaction per user and if the number of transactions will increase the admin will come to know through its monitoring system that unauthorized access has been made and it would be easier to take action against such happening. This paper proposes backup and restoration policy in Role Based Access Model in which if the main cloud is crashed or not working properly then the backup and restoration facility will be available to avoid the lost of important data. In this case chances of loss of data are very less so enhance more security on Cloud Computing.