arXiv subjects

Akhtar Jamil

Publications and source records attributed to Akhtar Jamil.

5 recordsLinked to original sources

arXiv2026

HQNN-CL: Regime-Aware Hybrid Quantum Neural Network with Continual Learning for Financial Forecasting

Financial time-series forecasting is still difficult owing to the non-stationary nature of the market, regime changes, and catastrophic forgetting, which makes conventional deep learning models hard to adapt in the long term. To achieve uncertainty-aware robust financial forecasting in the face of changing market conditions, this study proposes a Hybrid Quantum-Classical Neural Network with Continual Learning (HQNN-CL). The proposed framework combines Gaussian Hidden Markov Model (HMM) based regime detection, BiLSTM temporal encoding, adaptive-depth parameterized quantum circuits, Elastic Weight Consolidation (EWC), experience replay, and probabilistic forecasting in a single architecture. The framework was tested on four real-time stock datasets: Microsoft, Systems Limited, Teradata and Tata Consultancy Services, and compared with seven state-of-the-art forecasting models, including recurrent and Transformer-based models. The experimental results show that HQNN-CL has outperformed other methods on the Systems Limited (RMSE = 3.0149) and Teradata (RMSE = 0.8909) datasets and is within 0.32% and 0.40% of the best baseline method for Microsoft and Tata Consultancy Services, respectively. The proposed framework also generates well-calibrated probabilistic forecasts within the range of 89.5% to 96.5% empirical coverage rate, has zero catastrophic forgetting (BWT = 0.0000) for all sequential learning tasks, and consistently shows positive forward transfer. Ablation and statistical significance analyses also validate the performance of adaptive quantum feature learning and continual adaptation under heterogeneous market conditions, regime-wise.

cs.LG↗

arXiv2026

CalibCause: A Quality-Gated, Interpretable and Calibrated Deep Learning Framework for Diabetic Retinopathy Grading

Most deep learning systems for diabetic retinopathy (DR) grading are evaluated on a single data source, with limited evidence of cross-site generalisation, uncertainty calibration, and interpretability. We propose CalibCause, a quality-gated mixture-of-experts framework that combines per-expert visual explanations, label-free image-quality conditioning, knowledge distillation, and calibrated confidence with deferral. We evaluated the framework using 51,310 fundus images from EyePACS, DDR, and APTOS 2019, with IDRiD and DeepDRiD for external testing. Evaluation included pooled testing, leave-one-environment-out experiments, paired bootstrap confidence intervals, and local grade-threshold adaptation. On the pooled test split, the label-free distilled student achieved quadratic weighted kappa (QWK) of 0.863 and accuracy of 0.865 with 27.8 million parameters, 52% fewer than its teacher. Across held-out environments, the label-free model achieved mean QWK of 0.741 compared with 0.743 for a plain ConvNeXt-Tiny baseline, while adversarial invariance did not improve cross-site grading accuracy. Refitting grade thresholds using 200 labelled images increased held-out EyePACS QWK from 0.533 to 0.621 for the label-free model. After vector scaling, expected calibration error ranged from 0.025 to 0.036 across CalibCause variants, and deferring the least confident 20% of images increased student accuracy from 0.865 to 0.923 on the remaining images. These findings demonstrate that quality-aware grading, interpretable expert routing, model compression, and calibrated deferral can be integrated without sacrificing cross-site performance relative to a plain baseline, although reliable deployment still requires local validation and adaptation.

eess.IV↗

arXiv2026

Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging

Deep learning shows promise for breast cancer diagnosis, but limited interpretability hinders clinical adoption. We benchmark ten ImageNet-pretrained models (ResNet50, VGG19, InceptionV3, DenseNet121, Xception, MobileNetV2, EfficientNet, ConvNeXt, ViT, and Swin Transformer) for benign-versus-malignant classification on the BreakHis histopathology dataset using a patient-level split, and explain their predictions with Grad-CAM, LIME, Integrated Gradients, and LRP. DenseNet121 performed best, with 99.4% test accuracy and an AUC-ROC of 0.999, and significantly outperformed all other models (p < 0.001). Transformer-based models trailed the CNNs. The explanations consistently highlighted nuclei-rich and glandular regions of diagnostic relevance. Combining transfer learning with explainable AI can thus yield accurate, interpretable tools for breast cancer diagnosis.

cs.CV↗

arXiv2026

SHIFT-JEV: Certified Abstention and Gain-Gated Continual Adaptation for LLM-Based Decision Models under Distribution Shift

Large language models (LLMs) can act as decision models that return a probability distribution over a fixed set of options, but deployed decision models face domain, label-prior, emerging-class and candidate-set shift. We study how such models fail under shift and propose SHIFT-Jev, a monitoring, abstention and continual-adaptation procedure for them. It scores the risk of each decision from uncertainty, format and option-consistency signals, certifies abstention thresholds with Learn-then-Test, monitors the decision stream with drift and label-shift detectors, and accepts a model update only when an expected-gain gate finds a significant held-out improvement without loss on earlier regimes. On a benchmark of ten shifted test environments with Qwen3-0.6B and Qwen3-4B (three seeds each) and Phi-4-mini, fine-tuning with KL anchors and temperature scaling lowers calibration error under shift from 0.289 to 0.087. At a target risk of 10%, a learned uncertainty score with a certified threshold exceeds the target in 3 to 5 of 27 environment-seed tests, against 5 to 15 for a max-probability score with the same certification and 9 to 14 for conformal prediction sets; drift-based routing changes little. A chi-squared test on predicted labels detects 95 to 99% of class-prior shifts that confidence- and embedding-based detectors miss. In continual adaptation, SHIFT-Jev reaches 70.5% accuracy on Qwen3-0.6B, above replay, LwF, EWC and fine-tuning baselines in every seed, whereas on Qwen3-4B its conservative gate leaves it 0.4 to 1.4 points behind them.

cs.LG↗

arXiv2023

Tumor-Centered Patching for Enhanced Medical Image Segmentation

The realm of medical image diagnosis has advanced significantly with the integration of computer-aided diagnosis and surgical systems. However, challenges persist, particularly in achieving precise image segmentation. While deep learning techniques show potential, obstacles like limited resources, slow convergence, and class imbalance impede their effectiveness. Traditional patch-based methods, though common, struggle to capture intricate tumor boundaries and often lead to redundant samples, compromising computational efficiency and feature quality. To tackle these issues, this research introduces an innovative approach centered on the tumor itself for patch-based image analysis. This novel tumor-centered patching method aims to address the class imbalance and boundary deficiencies, enabling focused and accurate tumor segmentation. By aligning patches with the tumor's anatomical context, this technique enhances feature extraction accuracy and reduces computational load. Experimental results demonstrate improved class imbalance, with segmentation scores of 0.78, 0.76, and 0.71 for whole, core, and enhancing tumors, respectively using a lightweight simple U-Net. This approach shows potential for enhancing medical image segmentation and improving computer-aided diagnosis systems.

eess.IV↗