This 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...
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This 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 $2.5 \times 10$ 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 $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.
One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoli...
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This research investigates the application of state-of-the-art machine learning strategies, including deep learning, clustering, and reinforcement learning, to better understand user behavior in the banking sector. Th...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
This research investigates the application of state-of-the-art machine learning strategies, including deep learning, clustering, and reinforcement learning, to better understand user behavior in the banking sector. This strategy enriches personalisation, boosts customer engagement, and enhances operational efficiencies. Advanced neural network designs, including Recurrent Neural Networks (RNNs) and transformer architectures, are deployed to interpret transactional and communicative data. Various clustering methods, such as K-Means, DBSCAN, and Hierarchical Clustering, are utilised to classify users by their behavioral characteristics. Moreover, techniques like Deep Q-learning and multi-agent reinforcement learning are employed to adapt dynamically to user actions, thereby maximizing satisfaction. The research further highlights essential ethical issues concerning bias minimization, privacy protection, and the interpretability of models, ensuring the ethical deployment of machine learning practices. Through empirical validation, the suggested framework shows notable improvements in predicting defaults, refining marketing approaches, and increasing adoption rates. These findings underscore the revolutionary influence of machine learning in profiling user behavior, hinting at future developments in federated learning and quantum computing.
This study highlights the rapid advancements in artificial intelligence (AI) and machine learning (ML), which have transformed the financial sector by enhancing personalised user interactions and customer engagement. ...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
This study highlights the rapid advancements in artificial intelligence (AI) and machine learning (ML), which have transformed the financial sector by enhancing personalised user interactions and customer engagement. An innovative AI-driven framework was proposed to deliver real-time, context-aware, and highly personalised responses for Digital Banking users. The system architecture employs advanced ML models, natural language processing (NLP) techniques, and modern front-end technologies such as React and GraphQL to address the challenges related to data silos, computational demand, and data privacy. ML models such as user segmentation, recommendation, and NLP models work together to analyse user behaviour, predict needs, and generate human-like responses. Utilising React, GraphQL subscriptions, and Edge AI, the front-end layer ensures a seamless and responsive user experience. The experimental results showed a 120% increase in click-through rates, a 180% increase in follow-up actions, and a 70% reduction in average response time, demonstrating significant improvements in user engagement. User satisfaction, measured by the Net Promoter Score, was significantly higher for the personalised system (8.5) than for the traditional system (5.2). The proposed methodology lays the foundation for an agile trust-based financial environment. Future enhancements will focus on explainable AI, multimodal interactions, and blockchain integration to boost transparency, engagement, and data privacy.
This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algori...
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The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspe...
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This paper describes a streaming audio-to-MIDI transcription method that can sequentially translate a piano recording into a sequence of note-on and note-off events. The sequence-to-sequence learning nature of this ta...
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Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and ti...
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