Kakatiya Institute of Technology and Science

Accurate classification of glioma grades from magnetic resonance imaging (MRI) is essential for clinical decision-making in neuro-oncology. Although deep learning performance has been impressive with classical models, they struggle with high-dimensional medical imaging data and generalise poorly beyond their training data, especially in time- and resource-constrained settings. In light of the aforementioned challenges, we propose QuantumMedDx, a hybrid quantum–classical learning framework for classifying gliomas using MRI. The framework combines quantum feature encoding and variational quantum circuits with classical neural inference to improve diagnostic performance. The base model, QImageNet, uses amplitude-based quantum encoding for writing, entanglement-enabled parameterised quantum circuits (EPQCs) as feature extractors, and classical dense layers for classifying HGG and LGG from multimodal MRI slices. We demonstrate the effectiveness of the proposed approach on the BraTS 2021 benchmark dataset using a patient-aware 5-fold cross-validation protocol. Experimental results show that QuantumMedDx achieves accuracies of 94.12

This study investigates the integrated enhancement of concrete performance through Microbially Induced Calcite Precipitation (MICP) and steel fiber reinforcement to improve mechanical properties, durability, and microstructural characteristics. Sixteen concrete mixes including conventional, steel fiber-reinforced (0.8–2

Internet of Things (IoT) enabled wireless sensor networks face severe issues in secure and energy-efficient data routing, which are associated with dynamic topology, limited node energy, non-independent and identically distributed data, and malicious routing. The traditional centralized routing methods have a high communication overhead, a lack of scalability and privacy. In this research, secure routing is developed as a federated multi-agent deep reinforcement Q-learning network to collaboratively optimize energy efficiency, delay, throughput, and routing security. Security is implemented as a reward or penalty for malicious path choice; the abnormal packet drops and unstable links. The potential inference search algorithm is employed to perform cluster formation, and the hybrid human memory golden jackal optimization algorithm is employed to optimize cluster-head selection. federated learning allows learning policies without exchanging raw data, which makes it decentralized, minimizing privacy risks and communication overhead over centralized Deep Reinforcement Learning (DRL). NS3 simulations of 100 nodes indicate that the proposed approach attains 325 kbps throughput, 8 ms delay, 175 J energy consumption and 55

Accurate air pollution prediction needs systems that monitor diverse environmental data, adapt to changing pollution patterns, and ensure secure real-time communication. Existing systems struggle to capture distributed sensor interactions and dynamic environmental variations effectively. To address this problem, the study proposes an Adaptive and Efficient Air Pollution Monitoring Network (AEPM-Net) incorporating Metal Oxide Semiconductor (MOS) nanosensors with an average particle size ranging from 20 to 40 nm for sensitive air-quality monitoring. The system monitors levels of Carbon Monoxide CO , Nitrogen Dioxide NO_2 , Sulfur Dioxide SO_2 , Nitric Oxide NO , Volatile Organic Compound VOC , and Particulate Matter PM , which includes fine particulate matter PM2.5 and coarse particulate matter PM10 , in different locations. The system uses SPECK encryption and additive Lightweight Homomorphic Encryption (LHE) to protect collected pollutant data, while noise filtering and modified Z-score normalization methods help to stabilize the data. The Spatio-Temporal Data Processing Module (ST-DPM) analyzes nanosensor data across time and space by capturing correlations among distributed sensors and environmental variations. The Hierarchical Attention Mechanism (HAM) prioritizes important spatio-temporal features to improve prediction accuracy. The Dynamic Adaptation Module (DAM) adjusts parameters in real time, while the Pollution Level Prediction Module (PLPM) forecasts pollutant levels with uncertainty handling. Over 90 days, the model achieved R² values of 0.973 CO , 0.974 NO , 0.975 NO_2 , 0.978 SO_2 , 0.971 VOC , 0.969 PM2.5 , and 0.967 PM10 . The proposed framework supports environmental sustainability by enabling early detection of hazardous pollutants, improving urban air quality monitoring, and assisting effective pollution control to protect public health.

The pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are critical for transporting enormous fluid volumes (e.g., water) across extended distances. Not only would solving this issue save the nation a great deal of money and resources, but it will also save the environment. However, because of the harsh climatic conditions below earth, current leak detection systems are not enough for monitoring subterranean pipelines. To address these issues, this study suggests a hybrid wireless sensor network for monitoring subterranean pipelines that is based on magnetic induction and time domain reflectometry (TDR). TDR is installed in this instance below a wireless sensor network that is based on MI. TDR significantly reduces the time needed for inspection while accurately locating the leak. Based on MI technology, we provide a wireless sensor network for inexpensive, real-time leak detection in subterranean pipelines. Through the integration of data from several sensor types located within and around subterranean pipes, MISE-PIPE detects leaks. Ad-hoc WSNs are employed in pressure measurement. (WDNs) is a popular subject that has drawn attention from scholars lately. Since leak localisation has a significant influence on the human population and the economy, time and accuracy are essential components. A broad leak localisation technique is proposed using statistical classifiers operating in the residual space. Classifiers are trained using leak data from every node in the network, accounting for demand uncertainty, noise from sensor preservatives, and leak size. After localising and identifying leaks, all monitoring data is sent to the CH using the K-means clustering technique, which performs two vital tasks: optimum clustering, extending the Network Lifetime, and maintaining Quality of Service. The K-Means technique is used to optimise the clustering process. The K-means clustering technique is used to transfer all monitoring data to the CH for the purpose of pipeline leak identification and localisation. Unlike the current underground pipeline monitoring system, our proposed Hybrid TDR-MI-based wireless sensor network allows precise real-time leak identification and localisation.