14 research outputs found
Ensuring Security Using Core Based Routing Algorithm In Wireless Sensor Networks
Get PDFWireless sensor networks (WSNs) circulate hundreds to thousands of modest miniaturized scale sensor hubs in their locales and these hubs are vital parts of Internet of Things (IoT). In WSN-helped IoT, the hubs are asset obliged from multiple points of view, for example, stockpiling assets, figuring assets, vitality assets, et cetera. Powerful steering conventions are required to keep up a long system lifetime and accomplish higher vitality use. To upgrade WSNs for secured data transmission both at group head and base station data aggregation is required. Data aggregation is performed in each switch while sending data. The life time of sensor arranges lessens in view of utilizing vitality wasteful hubs for data aggregation. Thus aggregation process in WSN ought to be upgraded in vitality proficient way. ESCR will enhance the performance of the system with good potential. Therefore ensuring security using core based routing (ESCR) algorithm in WSNs is proposed. Simulation results show that ESCR perform better than the existing algorithms
VLSI Architecture Based Advanced Encryption Standard (AES) With Optimized Inverse Mix-Column and X-Time Multiplication Process
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Iot based intelligent route selection of wastage segregation for smart cities using solar energy
No full textIMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION
Get PDFAccurate and reliable diagnosis is critical for effective treatment planning for brain cancer. Recent advancements in deep learning have significantly enhanced diagnostic capabilities, but challenges persist in optimizing model performance for diverse and complex datasets. This study investigates the application of Polyak-Ruppert Optimization (PRO) to improve the prediction accuracy of conventional deep learning models for brain cancer diagnosis. Utilizing the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database, the proposed framework incorporates the advanced PRO technique to stabilize training and enhance generalization. The PRO’s impacts on convergence rates, model robustness, and predictive accuracy across multiple cancer types are analyzed. Experimental results demonstrate that VGG and ResNet models employing the PRO technique outperform the conventional architectures such as VGG and ResNet in classification metrics such as accuracy, sensitivity, and specificity. The potential of advanced optimization strategies such as PRO to refine deep learning applications in oncology paves the way for more accurate, efficient, and interpretable diagnostic systems
A Survey on the Impact of DDoS Attacks in Cloud Computing: Prevention, Detection and Mitigation Techniques
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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate
No full textThis study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of , outperforming configurations without overlap ( ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection
Security challenges in internet of things: Distributed denial of service attack detection using support vector machine‐based expert systems
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