Journal of Computing & Biomedical Informatics

BBR v3-Cubic smackdown: A Fairness and Convergence Quantitative Evaluation

Muhammad Ahsan — 2025-09-01

Congestion control has been an important component of TCP for nearly four decades now. Bottleneck Bandwidth and Round-Trip Time (BBR) has brought a major change in the ways the congestion onset can be monitored, and proactive measures can be taken instead of a reactive technique being used in loss-based traditional algorithms, such as Cubic and Reno, for a long time. With the introduction of the latest iteration of BBR, BBR v3, it has been claimed that it has improved its co-existence with Cubic flows with better fairness, but convergence issues within BBR-v3 have been reported. No existing study quantifies BBR v3's fairness with Cubic, and the convergence of BBR v3 for intra-protocol streams needs to be quantified for each stream’s throughput as well. The simulations and emulations generally don’t bring the true picture of the performance of a congestion control algorithm. In this paper, we have evaluated BBR v3 with cubic using our real-time physical testbed using Jain’s Fairness Index to bring a more accurate fairness analysis of BBR v3 and Cubic streams. For convergence, a well-established statistical metric that measures the relative stability of throughput, known as the Coefficient of Variation (CoV), has been calculated for BBR v3/Cubic flows. We used Flent (a FLExible Network Tester) to perform rigorous tests using various pairs of streams in upload, and the results, along with the metadata, have been saved for reproducibility and validation. Our thorough testing on both wired (Ethernet) and wireless (Wi-Fi 4) testbeds confirms that fairness issues between BBR v3 and Cubic streams persist. These issues are particularly serious as the number of streams increases. Similarly, our convergence tests confirm that BBR v3 flows, especially the first stream, obtain a larger share of the bandwidth, and the throughput of each stream remains highly volatile.

Preserving Critical Signals in Magnetic Image Denoising: A Deep Learning Approach with Selective Feature Preservation

Muhammad Umair — 2025-09-01

Noise contamination is a major issue in medical imaging because it affects the clarity of structures and can impact how doctors make diagnoses. To address this, this study introduces a new deep learning method called DenoiseNet. The main goal is to reduce noise without losing important details of the body’s anatomy, which is a challenge with traditional filtering techniques and standard CNN models that often smooth out too much and lose key information. DenoiseNet builds on the U-Net structure by adding spatial attention, channel attention, and residual blocks. These components help the model focus on noisy areas, highlight important features, and ensure that the learning process works smoothly. The model uses residual-attention fusion in the bottleneck, extracts important features in the encoder, and restores clear images in the decoder using skip connections and residual attention blocks. A hybrid loss function that combines MSE and SSIM helps balance pixel accuracy with how realistic the image looks, improving both noise reduction and structure preservation. Hybrid DenoiseNet, incorporating spatial and channel attention along with residual U-Net blocks, achieves a PSNR of 32.27 dB and SSIM of 0.9598. The performance is robust in both our Salt & Pepper noise dataset as well as a semi-synthetic MRI dataset—outperforming both BM3D (31.9 dB, 0.9862) and DnCNN (31.5 dB, 0.8826) under identical test conditions. These qualitative gains are a demonstration of improved noise suppression without loss of structural detail. This approach's strength is its capacity to produce encouraging outcomes even in the early phases of training, exhibiting consistent performance and the possibility of more gains with more time spent training. In comparison to conventional methods, the model gains improved feature representation and better convergence by including attention and residual learning into the U-Net backbone. When taking everything into account, the proposed DenoiseNet demonstrates that merging residual learning with attention mechanisms on a U-Net structure creates a powerful and effective approach for removing noise from medical images. The results show that the model preserves key anatomical details essential for accurate clinical analysis while also effectively reducing noise. These outcomes highlight DenoiseNet's potential as a robust framework that can be further improved and adapted for different types of medical imaging, paving the way for better patient outcomes and more reliable diagnoses.

Privacy-Aware E-Health Data Sharing via Decentralized Blockchain System

Usama Ahmed — 2025-09-01

With the growing sensitivity of personal health information (PHI), ensuring secure and privacy-preserving mechanisms for data exchange has become a critical challenge. Blockchain technology, with its inherent properties of immutability, decentralization, and transparency, shows significant promise in reshaping healthcare data management. Unlike existing single-layer approaches, this paper presents a decentralized blockchain-based multi-layer architecture with trap-door based searchable encryption, designed to enable secure, scalable, and privacy-aware sharing of electronic health records (EHR), consisting of data generation, storage, service, and super service layers. It leverages private and consortium blockchains to preserve data confidentiality and enforce access control through smart contracts. Trapdoor-based searchable encryption enables privacy-preserving queries on encrypted records, ensuring sensitive PHI remains protected yet discoverable by authorized users. Experimental evaluation demonstrates improved access efficiency, reduced cost, and compliance with data privacy regulations. This work highlights blockchain’s transformative role in healthcare by ensuring trust, security, and accessibility.

The Advanced AI Techniques for Deepfake Audio Detection

Sheraz Riaz — 2025-09-01

Sharing and retention of information is critical in the growth of the society especially in the current world of technology. As much as technology has led to the revolutionization of sharing knowledge and information, it has also come with challenges, like misinformation. A recent issue of concern is the very persuasive audio deepfakes, artificially created audio clips that are meant to sound like real people. This is highly threatening especially in the professions such as journalism and in the social media when reliability is highly valued. To resolve this problem, Developed Sonic Sleuth, a new tool to detect audio deepfakes. It is based on state-of-the-art deep learning approaches that are able to discriminate between authentic and synthetic audio correctly by means of a custom convolutional neural network (CNN). An elaborate dataset, ASVspoof 2021, which included real and synthetic audio was employed to perform an intensive test. The model was able to perform impressively with no less than 97.27 percent accuracy by incorporating the background noise and the diversity of language. The purposed model gives better accuracy as compared to existing model.

A Predictive Model and Performance Evaluation in Mathematics for Primary Education

Marvi Abro — 2025-09-01

This study investigates the application of predictive modelling to assess and forecast students’ academic performance in primary mathematics education. Four regression techniques, Linear Regression, Decision Tree Regression, Random Forest Regression, and K-Nearest Neighbours Regression, were implemented and comparatively evaluated. Model performance was measured using Mean Squared Error (MSE) as the primary metric. Results indicate that Linear Regression achieved the lowest MSE (1.33), establishing a strong predictive baseline. Although Decision Tree Regression effectively captured non-linear patterns, it yielded a substantially higher MSE (62.38), highlighting the risk of overfitting. Random Forest Regression improved generalization by aggregating multiple decision trees, achieving an MSE of 25.21. Meanwhile, K-Nearest Neighbours Regression provided localized predictive accuracy with a competitive MSE of 19.29. Collectively, these findings contribute to the growing body of research on data-driven approaches in education, providing practical insights for educators and policymakers to leverage predictive analytics and enhance learning outcomes in primary mathematics.

Predictive Analysis on Project Management Success through AI

Muhammad Hamid Qureshi — 2025-09-01

The modern business landscape is evolving rapidly, and each project comes with its own set of challenges and complexities. This calls for new and more inventive methods of handling such projects, as this area of work is becoming increasingly intricate and fluid. This study is centered on the predictive use of AI (Artificial Intelligence) technologies like ML (Machine Learning) and LLMs (Large Language Models) to ensure more effective project management at each of the ten PMBOK® knowledge areas. Merging the qualitative feedback from senior project managers and the quantitative KPIs—budget variance, schedule adherence, stakeholder satisfaction, and risk response time—from 84 organizations in construction, pharma, IT, finance, and manufacturing based in the EU, UK, USA, and Middle East provides richer insights. The analysis demonstrates how advanced AI tools, from predictive analytics to intelligent chatbots, streamline a project’s life cycle by enhancing efficiency, acuity, and overall decision-making. Predictive AI is demonstrated to bolster schedule and risk management as well as stakeholder interaction. Traditional metrics such as schedule creation and risk detection indicate a significant improvement for AI-supported projects, with 50% and 25% improvement respectively, as well as 30% higher stakeholder satisfaction.

A Multidomain Virtual Framework for Sacral Neuromodulation: Integration of CT-Based Anatomical Modeling, Electrode Placement Optimization, and Closed-Loop Device Simulation

Muzamil Ahmed — 2025-09-01

Sacral neuromodulation (SNM) has emerged as an effective third-line therapy for overactive bladder (OAB), fecal incontinence (FI), neurogenic lower urinary tract dysfunction (NLUTD), and nonobstructive urinary retention. However, challenges remain in lead placement accuracy, stimulation efficiency, and device longevity. In this work, we present a comprehensive virtual framework that integrates medical imaging, 3D anatomical modeling, Multiphysics simulation, and system-level instrumentation to optimize SNM therapy. A pelvic CT scan was segmented using 3D Slicer to reconstruct patient-specific anatomy of the sacral plexus and bladder. The reconstructed model was imported into Fusion 360, where realistic 3D geometries were developed and five distinct electrode placements were virtually designed. COMSOL Multiphysics was employed to analyze electric field distribution, current density, and activation zones, enabling objective quantification of placement efficacy. Additionally, a complete instrumentation framework was simulated, including wireless power transfer, microcontroller-based stimulation control, rectification, and closed-loop feedback from bladder sensors. Results indicated that electrode placements within 3 mm of the sacral plexus and an insertion angle of 35–40° achieved superior response scores and minimized revision risk. The integration of anatomical modeling with device-level circuit simulation highlights a pathway toward patient-specific, adaptive, and energy-efficient neuromodulation. This multi-domain approach enhances the translational potential of SNM, offering insights into both clinical efficacy and engineering feasibility.