Developed a novel BMFCNet architecture integrating blended multi-level feature extraction for robust detection of Major Depressive Disorder (MDD) from EEG signals. Introduced a Residual-Inception module to effectively capture both low-level (LL) and high-level (HL) discriminative EEG features, enhancing representational capacity. Developed a Constraint Fusion mechanism for adaptive weighting and fusion of LL and HL features, improving feature integration and classification performance. Addressed subjectivity in MDD diagnosis by providing an automated, EEG-based framework that enhances accuracy, reliability, and clinical applicability. Validated the proposed model on benchmark datasets, demonstrating superior performance compared to 16 state-of-the-art methods in terms of accuracy and efficiency.

TinyEyeNet introduces a lightweight CNN for accurate anterior segment eye disease classification Designed for high performance with low computational cost, ideal for real-world deployment Trained and validated on a custom-curated clinical eye image dataset Achieves strong diagnostic accuracy, outperforming conventional deep models Suitable for resource-constrained and portable ophthalmic screening systems Enables faster, scalable, and accessible eye disease detection

This work presents RCAN-RS, an enhanced Residual Channel Attention Network designed for remote sensing image super-resolution. Building upon the RCAN framework, the proposed model incorporates domain-specific improvements, including dual-pooling channel attention, spectral attention, and an edge enhancement module to better preserve spectral information, structural details, and sharp boundaries in satellite imagery. Trained and evaluated on the DOTA dataset with a 2× super-resolution setting, the model achieved strong performance with a PSNR of 34.42 dB, SSIM of 0.9398, EPI of 0.9524, SAM of 0.9830, ERGAS of 6.68, and UQI of 0.9846. These results demonstrate the effectiveness of attention-guided and edge-aware mechanisms for generating high-quality, detail-preserving super-resolved satellite images.

This work proposes an ECA-ResNet-based facial emotion recognition system as a supplementary tool for mental health assessment. The model integrates enhanced channel-spatial attention, residual learning, mixed precision training, and a cosine annealing learning rate scheduler to improve feature extraction, training efficiency, and convergence. Evaluated on the OAHEGA Emotion Recognition Dataset containing six emotional classes (Angry, Surprise, Sad, Ahegao, Happy, and Neutral), the model achieved an overall accuracy of 81%. The attention mechanism enables the network to focus on emotionally significant facial regions, while residual connections enhance feature learning and reduce vanishing gradient issues. The results demonstrate the potential of the proposed framework as a reliable foundation for AI-driven mental health support systems, enabling early emotional screening and more accessible digital psychological care.

1. Multilingual clinical text processing: The framework automatically identifies language, performs transformer-based translation, and normalizes medical semantics to preserve diagnostic meaning. 2. Multimodal feature fusion: It combines clinical text features from FastText and ClinicalBERT with multi-view ultrasound visual features extracted using SwinV2/DINOv2. 3. Automated report generation: A cross-attention fusion module aligns text and image information, followed by a GPT-V2 decoder to generate clinically meaningful medical reports.

This work presents an attention-based CNN–Transformer model for classifying ultrasonic plant stress sounds in noisy agricultural environments. By processing raw 1D audio waveforms, the model preserves fine temporal features, while CNN layers extract local patterns and the Transformer captures long-range dependencies through self-attention. Positional encoding and weighted cross-entropy loss further improve temporal understanding and address class imbalance. The proposed approach outperforms conventional machine learning and CNN-based methods, achieving 79.5% classification accuracy, 82% species-level discrimination accuracy, and 99.5% accuracy in binary stress detection (stressed vs. control). These results highlight its potential for reliable, non-invasive, and real-time plant stress monitoring in precision agriculture.

• Introduces the RAIE Transformer, a lightweight, recency-augmented architecture designed specifically for accurate cricket ball trajectory prediction using short input sequences. • Uses a curated dataset of 4,400 frames from 212 cricket deliveries, manually annotated and preprocessed for reliable spatio-temporal modelling. • Incorporates a novel Recency-Augmented Input Embedding (RAIE) that prioritizes recent motion cues while preserving long-term context to improve prediction accuracy. • Outperforms traditional models such as LSTM, RNN, Base Transformer, Informer, TimeMixer, and Graph Transformer—achieving MSE 3.95, ADE 2.35, and FDE 2.56, the best among all tested models. • Provides a fully automated end-to-end pipeline combining YOLO-based detection, interpolation, and autoregressive prediction—eliminating manual annotation requirements. • Demonstrates strong real-time potential, handling non-linear motion (swing, spin, bounce) with high efficiency and low computational cost, suitable for sports analytics and broadcast applications

• Proposes an AI-assisted otoscope framework for automated ear disease diagnosis • Enables early and accurate detection of common ear conditions from otoscopic images • Utilizes a deep learning–based image analysis pipeline for robust classification • Reduces dependency on specialist expertise, supporting primary healthcare screening • Designed for real-time and portable diagnostic applications • Demonstrates high classification accuracy on clinically relevant datasets • Enhances accessible, scalable, and cost-effective ear healthcare solutions.

• Proposes a hybrid activation function (ReLU + ELU) to overcome limitations of existing functions. • Addresses issues like dying ReLU and vanishing gradients. • Demonstrates improved performance on CNN and MLP models using MNIST & FashionMNIST datasets. • Shows better convergence and learning efficiency compared to standard activation functions.

• Proposes SARANet, a self-attentive multi-scale residual attention network for Braille recognition. • Combines R-CBAM (channel & spatial attention) and Compact Self-Attention (CSA). • Captures fine-grained details and long-range dependencies in Braille images. • Works on Arabic Braille dataset with 32 character classes. • Achieves 95.29% accuracy and F1-score of 0.952. • Improves optical Braille recognition (OBR) performance. • Supports assistive technology for visually impaired individuals.

Developed SpineDeep-Net, a novel deep learning framework integrating self-attention with multi-layer CNNs for automated selection of diagnostically relevant lumbar MRI disc slices. Eliminated manual slice selection by automatically identifying optimal transverse mid-height slices of L3/L4, L4/L5, and L5/S1 IVDs, improving efficiency and reducing human error. Introduced a dual self-attention mechanism, enabling enhanced long-range spatial dependency modeling and precise localization of subtle intervertebral disc variations. Enhanced feature representation through dynamic feature recalibration and strengthened inter-feature dependencies, improving discriminative learning for lumbar spine analysis. Demonstrated superior performance on experimental evaluation, achieving 96.83% accuracy and 98.41% specificity, outperforming state-of-the-art methods and supporting scalable clinical deployment.

• Proposes GAT-TNBC-Net, a dual-branch attention-based model for TNBC subtype classification. • Combines Lightweight Attentive Head Network (LAHN) and Graph Attention Network (GAT). • Captures both global gene dependencies and structural gene relationships. • Uses multi-head attention fusion for integrating tabular and graph features. • Works on high-dimensional RNA-seq data (55,662 genes per sample). • Achieves 97.22% test accuracy and 92.22% cross-validation accuracy. • Supports personalized treatment planning with interpretable predictions.

• Lightweight multi-attention CNN designed for real-time knot classification. • Achieves 95.16% test accuracy, outperforming all benchmark models. • Uses Ghost, SE, and CBAM modules with stochastic depth for efficiency. • Computationally efficient at 1.03 GFLOPs with 59 ms inference time. • Grad-CAM explainability highlights knot-specific visual cues for trust and transparency. • Robust performance across lighting, background, and rope tension variations.

Developed RGTFormer, a novel hybrid deep learning architecture integrating a categorical gated transformer with a Relational Graph Convolutional Network (RGCN) for mutation-driven drug resistance prediction in TB. Leveraged multi-modal mutation features, incorporating both genomic sequence and structural characteristics from six key drug-resistance genes to enhance predictive capability. Modeled inter-mutation dependencies using RGCN, enabling effective capture of relational information among mutations, while the transformer captures complex feature interactions. Demonstrated superior predictive performance, achieving 98.67% test accuracy and 97.15% cross-validation accuracy, outperforming conventional ML and DL baselines. Validated model effectiveness through ablation studies, highlighting the contribution of gated attention and graph-based learning, and providing an interpretable framework for clinically relevant resistance prediction and personalized TB treatment.

• Next-Gen AI Safety: SalienNext uses an upgraded ConvNeXt + CBAM attention design to spot drowsy drivers with far higher accuracy than traditional vision models. • Ultra-Sharp Eye Detection: Multi-stage attention zooms into subtle eye and facial cues, enabling the system to detect even minor signs of fatigue. • State-of-the-Art Accuracy: Achieves an impressive 99% accuracy on the MRL Eye Dataset, outperforming leading deep learning models in the field. • Lightweight & Real-Time Ready: Optimized for speed and efficiency, making it deployable on edge devices inside vehicles. • Smart Attention Fusion: Combines low-level details and high-level semantics for a clearer understanding of drowsiness signals. • Designed for Safer Roads: Built to support next-gen ADAS and intelligent transportation systems, targeting reduced accidents and enhanced driver monitoring.