The study evaluates the return on investment (ROI) benefits of transitioning from a traditional reactive construction workflow to a proactive hybrid geotechnical resilience workflow for high-density urban infrastructure projects. This paper addressed the complexities often seen in dense-urban environments characterised by high-moisture basins, hydraulic instability, and substructure compromised by environmental unpredictability, often leading to systemic delays, soil collapse, and material wastage. These complexities are addressed by implementing lean construction principles that focus on mitigating Muda (waste), Mura (unevenness), and Muri (overburden), and on enhancing the project’s predictability and structural safety. The lean strategies to overcome the structural difficulties involved establishing a responsive feedback loop. Firstly, by achieving precise excavation using hydraulic machinery with 3D-GPS guidance kits synchronised with Digital Terrain Modelling (DTM). This helped eliminate the 10% standard manual over-dig typically encountered in traditional depth control. Thereby optimising excavation volumes and reducing redundant soil hauling. Secondly, a real-time monitoring network comprising vibrating-wire piezometers and inclinometers was used to monitor pore-water pressure and soil displacement. This sensor-driven approach enabled a Jidoka (built-in quality) protocol, in which automated alerts for pressure spikes triggered immediate stabilisation measures that helped prevent catastrophic failures that historically stall urban developments. In the study, a comparative performance analysis of a traditional workflow and a lean-integrated workflow demonstrates that the proactive lean-integrated workflow results in a quantifiable reduction in the construction timeline and labour volatility. Specifically, the excavation and the shoring durations were reduced by up to 40% through data-driven execution and Target Value Design (TVD). The findings validate that incorporating digital intelligence during the substructure phases helps achieve a net fiscal recovery of over ₹2.17 crores by preventing rework and resource wastage. By providing a scalable model for geotechnical resilience, this study helps optimise operations and improve ROI for projects in complex urban settings.

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Eye diseases are becoming common in day-to-day life and affecting all age groups of people. The ratio of ophthalmologists to patients suggests the need for an automated technique to detect eye diseases. Conjunctivitis is of many types, including adenoviral conjunctivitis, ocular drug toxic conjunctivitis, pollen-allergic conjunctivitis, bacterial conjunctivitis, and many others. Conjunctivitis can be automatically detected using conventional image processing techniques, but with lower accuracy and precision, and more computational time is required compared to deep learning and AI techniques. This paper presents a novel deep-learning-assisted segmentation technique for the automatic detection of conjunctivitis that overcomes the limitations of conventional methods. The proposed method uses Attention-U-Net++ with Transformer Encoder (Global Context), Swin / ViT CNN +, Transformer Monte-Carlo Dropout Layer Enabled at inference time, Uncertainty-aware and Segmentation Mask + Uncertainty Map, which provides better results with Accuracy= 90.3%, sensitivity= 0.87, specificity= 0.93, precision=0.88, recall=0.87, F1-Score= 0.89, ROC-Auc=0.96.

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Open bore wells pose significant safety risks, especially to children, with many incidents leading to life threatening situations from accidental falls. Traditional rescue methods are often slow, complex, and lack real-time monitoring, which delays decisions and increases danger to victims. To address these issues, this research introduces a smart, cloud-based rescue management system to improve the efficiency and effectiveness of borehole rescues. The system uses sensor-based monitoring and IoT technology for rapid detection and response. A Passive Infrared (PIR) sensor detects movement and confirms the presence of a trapped person, while a gas sensor monitors for hazardous gases. When a victim is detected, a NodeMCU microcontroller processes the data and automatically activates an air pump to maintain safe oxygen levels. A robotic arm with a mechanical gripper assists in the physical rescue. All components connect to a cloud-based IoT platform, enabling real-time data transmission, remote monitoring, and coordinated control by emergency teams. This connectivity improves situational awareness and supports faster, more informed decisions during rescues. The study shows that integrating sensor technology, automation, and cloud-based communication can reduce response times and increase rescue success rates. This research aims to provide a safer, more reliable, and more advanced solution for borewell rescues, ultimately reducing fatalities and improving emergency response outcomes.

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Purpose: The rapid escalation of ransomware and zero-day malware attacks poses a significant challenge to conventional signature-based detection systems, which cannot generalise to previously unseen threats. This study aims to develop a robust, scalable, and behaviour-aware malware-detection framework capable of accurately identifying ransomware and zero-day attacks across heterogeneous computing environments. Design/methodology/approach: A novel multi-stage hybrid detection pipeline is proposed that integrates advanced feature selection, deep sequential learning, attention mechanisms, and ensemble classification. Initially, irrelevant and redundant features are eliminated using correlation thresholding, Chi-square analysis, mutual information, and variance-based ranking. To capture latent behavioral patterns, a hybrid Gated Recurrent Unit Temporal Convolutional Network (GRU-TCN) architecture is employed to model long- and short-term temporal dependencies. These representations are further refined using squeeze-and excitation attention-enhanced TCN blocks. Finally, an XG-Fusion framework that combines GRU encoding, dilated residual TCNs, attention-based feature fusion, and focal loss optimisation is introduced to address class imbalance, with XGBoost serving as a meta-classifier for final decision-making. Findings: Experimental evaluations conducted on multiple benchmark datasets demonstrate that the proposed framework consistently outperforms traditional machine learning and baseline deep learning models. Superior performance is achieved in terms of accuracy, precision, recall, F1 Score, and ROC AUC. The hierarchical and attention-driven architecture effectively abstracts malicious behavioral patterns and enhances generalization to previously unseen malware variants. Originality: This work introduces a novel multi-stage hybrid deep learning architecture that synergistically combines sequential behavioural modelling, attention-enhanced feature learning, and ensemble-based classification. The proposed approach offers a forward-looking and reliable solution for proactive detection of ransomware and zero-day malware threats.

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