Mohammad Zubair Khan

Cbam-augmented ResNet for high-accuracy grape leaf disease detection

Learning-driven MIMO channel estimation using a residual U-Net-BiLSTM-attention hybrid model.

Channel estimation provides the channel state information (CSI) required for coherent detection and precoding in multiple-input multiple-output (MIMO) systems. Accurate CSI is particularly critical under strong noise, limited pilot overhead and model mismatch, where conventional estimators often exhibit significant performance degradation. This work introduces a hybrid residual U-Net-bidirectional long short-term memory with attention (ResUNet-BiLSTM-Attention) channel estimator that learns a nonlinear mapping from noisy pilot observations to MIMO channel coefficients. The architecture combines a ResUNet encoder-decoder for multi-scale spatial feature extraction, a BiLSTM module for capturing structured dependencies in the unfolded feature sequence and a self-attention layer that emphasizes globally informative channel components before reconstruction. The model is trained on a synthetically generated [Formula: see text] MIMO dataset with 20, 000 training and 2, 000 validation samples over a wide SNR range, using a normalized mean square error (NMSE) loss for stable convergence. Extensive simulations show that the proposed estimator consistently outperforms both conventional and learning-based baselines. The comparison includes LS, LMMSE, orthogonal matching pursuit (OMP), simultaneous OMP (SOMP), beamspace-based dynamic support detection with windowing (BSP-DSDW), CNN-CE, U-Net-CE, and lightweight attention-based CE. At 25 dB SNR, it attains an NMSE of about [Formula: see text] dB, corresponding to an NMSE gain of about 9-10 dB over BSP-DSDW and a clear improvement over the added learning-based baselines. A module-wise ablation study further verifies the individual contribution of the ResUNet, BiLSTM, and attention blocks, while the runtime evaluation is conducted under a common GPU-enabled benchmarking setup using repeated inference trials. Additional studies on training set size, different user channels, computational complexity, parameter count, FLOPs, memory requirement, inference latency, pilot length, noise factor, and spatial correlation further confirm the robustness and practical feasibility of the proposed design. With a fixed computational structure, the proposed estimator achieves an observed inference latency range of approximately 2.5-4.0 ms per channel realization under the considered compact MIMO setup.

AAC-HABSA: An Adaptive Aspect Conditioning Framework for Interpretable and Robust Aspect-Based Sentiment Analysis

: Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect Conditioning Layer (AACL) that injects aspect information prior to transformer-based contextual encoding. The proposed framework follows a structured pipeline comprising tokenization, token and positional embeddings, AACL, transformer encoding, BiLSTM refinement, aspect-guided attention, fully connected projection, and focal loss optimization. By conditioning token representations before contextual encoding, the framework enables aspect-aware contextual learning that better captures sentiment-relevant semantic dependencies. Subsequent sequential refinement and attention-based reasoning further enhance sentiment polarity alignment while improving model interpretability. To evaluate the proposed approach, a robust ABSA dataset containing approximately 10,000 recent reviews annotated across five sentiment intensity levels was developed. Extensive experiments demonstrate that HABSA and AAC-HABSA consistently outperform transformer-only and conventional hybrid baselines in terms of accuracy, macro-F1 score, robustness, and attention-based interpretability. The proposed framework provides a computationally efficient, mathematically coherent, and interpretable solution for fine-grained sentiment analysis. By strengthening aspect-aware representation learning within transformer and LLM-oriented sentiment analysis pipelines, this work contributes to the development of scalable and deployable AI-driven opinion analytics systems across real-world domains.