Vaagdevi College of Engineering

In this paper, a DM DPDG TFET (Dielectrically modulated Drain pocket Dual gate Tunnel Field Effect Transistor) with an integrated nanocavity intended for biosensing applications is simulated and its performance assessed. The Silvaco Atlas TCAD used to do the simulations. The study compares several metrics for different biomolecules, including SARS COV-2 (Corona virus, K =2.5), Biotin (K =2.63), Protein (K =3.23), MCF-10A (Healthy Cancer cell, K =4.5), Carbohydrates (K =5) and MDA-MB-231 (Breast Cancer cell, K =22). These biomolecules are rendered immobile by a nanocavity is placed near the source end. When biomolecules are immobilized, the dielectric constant (K) of the nanocavities varies, which affects how the electrical properties of the proposed device is modulated. This modulation is tuned to identify the SARS COV-2, Breast cancer cell lines, and etc. To improve performance of the sensor device, the length of the oxide layer and thickness of the nanocavity adjusted in the process of optimization. The proposed Biosensor of its detection method is greatly influenced by the differences in the dielectric characteristics of different cell lines. The sensitivity of the biosensor is assessed in terms of Delta I-on, Delta V-th, Delta g(m) and Delta SS. The MDA-MB-231 (K =22) breast cancer cell line is the sample for which the biosensor shows highest sensitivity with Delta V-th=1.712 V, Delta I-on=0.183 mA/mu m, Delta g(m)=0.581 mA/V- mu m, and Delta SS =25.86 mV/decade. The effect of different cavity occupancy by immobilized cell lines is also investigated. Increase in cavity occupancy amplifies the variance in the performance characteristics of the biosensor. The threshold voltage(Vth) sensitivity of the proposed biosensor is compared to that of existing biosensors, it shows advantages in terms of cost-effectiveness and simplicity of manufacturing in addition to incr...

The incorporation of reduced graphene oxide (rGO) markedly enhances the electrical conductivity, mechanical robustness, and interfacial charge transport characteristics of transition metal sulfides. Owing to their synergistic nanoscale interactions, rGO-integrated metal sulfide composites have emerged as versatile materials for high-performance energy storage and conversion devices. In the present study, a porous reduced graphene oxide/manganese cobalt sulphide (rGO@MnCo₂S₄) hybrid electrode was successfully synthesized through a facile hydrothermal approach. This design strategy aims to improve both the charge storage capacity and long-term cycling stability for supercapacitor applications. Electrochemical evaluation demonstrates that the pristine MCS and rGO@MCS electrodes exhibit specific capacitances of 277 F g⁻¹ and 472 F g⁻¹, respectively, at a current density of 1 A g⁻¹. Moreover, the MCS electrode retains 89

Land cover classification (LCC) from satellite images is crucial in identifying and monitoring natural disasters, including cyclones, earthquakes, floods, and wildfires. Statistics reveal that accurate disaster classification from satellite data can enhance response times by up to 30 % and improve prediction accuracy by approximately 25 %. However, existing methods need more accuracy due to varying image resolutions and difficulty distinguishing between similar land cover types under different disaster conditions. This research proposes a specialized network named the land cover classification network, referred to as LCC-Net, for classifying the land covers from satellite images. This method involves initial image normalization, noise reduction, and enhancing spatial resolution to improve classification performance. Here, an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is implemented to reduce the noise with the super-resolution concept, improving image quality by combining deep learning with adversarial training. The core of LCC-Net employs the Swin Transformer Convolutional Neural Network (ST-CNN), which leverages self-attention mechanisms to capture intricate spatial features and temporal dynamics. The ST-CNN outperforms traditional CNN models by providing a better contextual understanding of land cover variations associated with different disaster scenarios. To further enhance classification accuracy, the Adaptive Moment Estimation (AME) optimizer is utilized for loss minimization, ensuring efficient convergence and improved model robustness. This approach aims to enhance the precision of disaster identification and response strategies across the four fundamental classes: Cyclone, Earthquake, Flood, and Wildfire. The LCC-Net achieved Accuracy (99.999 %), Precision (99.569 %), Recall (99.320 %), and F1-Score (99.270 %). Finally, LCC-Net delivers highly accurate image classification with remarkably fast processing speed, outperforming state-of-the-art approaches.

As the Industrial Internet of Things (IIoT) continues to expand, the need for robust, efficient, and secure communication mechanisms has become critical. Near-Field Communication (NFC) offers a promising solution for IIoT devices that require low-power, short-range, and secure interactions. However, optimizing NFC for industrial environments presents challenges, particularly in terms of security vulnerabilities and communication efficiency. This paper proposes a biologically-inspired approach to enhancing NFC performance in IIoT systems. Drawing from natural processes such as immune responses, swarm intelligence, and neural network communication strategies, we design adaptive algorithms that dynamically adjust communication protocols and security measures based on contextual factors. The proposed solution leverages decentralized decision-making, self-healing mechanisms, and resource-efficient strategies to improve both the security and efficiency of NFC systems in industrial applications. Through simulation and real-world testing, we demonstrate significant improvements in energy consumption, communication reliability, and resistance to cyber threats. Our results highlight the potential of biologically-inspired approaches for optimizing NFC in IIoT environments, paving the way for more resilient, scalable, and intelligent industrial communication systems.

Malware continues to wreak havoc on global digital ecosystems, with companies facing an average financial loss of $4.35 million per data breach in recent years. At the same time, individual users suffer from identity theft, affecting over 1.1 billion personal records annually. Existing malware detection systems often struggle with high latency in centralized cloud environments and fail to generalize across diverse malware variants generated by edge devices. To address these challenges, this work introduces MalwareNet, a novel multiclass malware detection network designed specifically for edge computing environments. MalwareNet innovatively processes data directly on edge devices, enabling real-time detection and classification with minimal latency and enhanced data privacy. The system employs a robust preprocessing pipeline to clean raw data, followed by Independent Component Analysis (ICA) to extract discriminative features while reducing dataset dimensionality. A Hybrid Wrapper-Filter (HWF) feature selection method optimizes feature subsets by integrating wrapper and filter techniques, ensuring compatibility with the chosen machine-learning classifier to maximize classification accuracy. The Extreme Learning Machine (ELM), selected for its rapid training and strong generalization, classifies malware into distinct categories, effectively identifying threats in edge settings. By combining edge-based processing, advanced feature engineering, and efficient classification, MalwareNet offers a scalable and reliable solution, significantly advancing malware detection capabilities for resource-constrained environments and providing a foundation for future adaptive security systems. Experimental evaluations on a large-scale malware dataset demonstrate the effectiveness of the proposed approach with an accuracy of 99.7 %, and F-measure of 99.55 %. The system also achieves high Jaccard index with an increment of 2.63 % in detecting and classifying malware, providing reliable security measures in edge computing environments.