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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Hashim Elshafie Abdulrahman Saad Alqahtani Azath Mubarakali Adarsh Rag S M. Venkatesh P. Parthasarathy Department of Computer Engineering College of Computer Science King Khalid University Abha Saudi Arabia College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru Karnataka India

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.

关键词： Biosensors Logic gates Sensitivity Tunneling Dielectric constant Performance evaluation Metals Biological system modeling TFETs Electrostatics

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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Basketball Win Percentage Prediction using Ensemble-based Machine Learning

Basketball Win Percentage Prediction using Ensemble-based Ma...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Dhruv Sikka Rajeswari D Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9781665482721

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy.

关键词： Schedules Machine learning algorithms Games Forestry Predictive models Prediction algorithms Reliability

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Cosmology with fast radio bursts in the era of SKA

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science China(Physics,Mechanics & Astronomy) 2023年第12期66卷 37-55页

作者： Ji-Guo Zhang Ze-Wei Zhao Yichao Li Jing-Fei Zhang Di Li Xin Zhang Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Cosmology and Astrophysics(Liaoning) Northeastern UniversityShenyang 110819China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China

We present a forecast of the cosmological parameter estimation using fast radio bursts(FRBs)from the upcoming Square Kilometre Array(SKA),focusing on the issues of dark energy,the Hubble constant,and baryon *** simulate 105and 106localized FRBs from a 10-year SKA observation,and find that:(1)using 106FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe,providing an independent cosmological probe to explore dark energy;(2)combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope,the Hubble constant can be constrained to a sub-percent level,serving as a powerful low-redshift probe;(3)using 106FRB data can constrain the baryon density?bh to a precision of~0.1%.Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem,and help resolve the Hubble tension.

关键词： fast radio burst cosmology dark energy the Hubble constant baryon density Square Kilometre Array

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Testing of Emerging Wireless Sensor Networks Using Radar Signals With Machine Learning Algorithms

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 49-59页

作者： Shitharth Selvarajan Hariprasath Manoharan Adil O. Khadidos Alaa O. Khadidos School of Built Environment Engineering and Computing Leeds Beckett University Leeds U.K. Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia

In this article, machine learning methods are used to assess how well wireless sensor networks transmit and receive radar signals. Measurements are done with labeled and unlabeled datasets where output functions are modified in relation to transmitted input in order to test the transceiver of radar signals. The main contribution in the proposed method is to focus on the possibility of choosing a free space model that transmits the radar signals in wireless sensor networks without any interruptions. Hence, for such type of transmissions, reference time period is selected in order to perform radar signal classification, and at the same time, separation of unnecessary interruptions is reduced using clustering procedures. Since the radar signals can be monitored with automatic transmission techniques, the outcomes are combined with supervised, unsupervised, and reinforcement learning models to increase the effect of transmissions. Therefore, the objective functions are designed with three scenarios where reinforcement learning proves to provide adequate connections for radar signals to all wireless sensor networks at reduced error of 0.3%. In addition, with reinforcement learning, the distance of radar signal transmission is maximized to a level greater than 75% at minimized noise ratio of 0.8%.

关键词： Radar Wireless sensor networks Sensors Spaceborne radar Security Radar measurements Mathematical models

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Automated Assignment Management System: Integrating Email Inbox Parsing, database Updates, and Notification

Automated Assignment Management System: Integrating Email In...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P Varun R.S. Rashika S Kayalvizhi D Preetha P Naveen T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSE Panimalar Engineering College Chennai Department of Computer Science and Engineering Velammal Engineering College Department of Information Technology Alpha College of Engineering Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of CSE School of Engineering & Technology CMR University Bengalunu Karnataka

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This paper presents the design and implementation of an automated assignment management system utilizing a bot interface. The system’ s primary objective is to streamline the process of collecting and tracking student assignments, ensuring timely updates to a central database, and facilitating communication between students and teachers. The bot employs email inbox parsing techniques to identify incoming assignment submissions, extracting relevant data such as student names, assignment details, and submission timestamps. Upon receiving a submission, the bot updates an Excel database with the pertinent information, maintaining an organized record of completed ***, the system incorporates a notification feature to promptly alert students who have not yet submitted their assignments by the deadline. Leveraging email integration, the bot sends personalized notifications reminding students of impending deadlines and urging them to submit their work promptly. Additionally, the bot generates a list of incomplete assignments and forwards it to the respective teacher, providing a consolidated overview of students who require *** automating the assignment management process, this system reduces manual intervention, minimizes administrative burden, and enhances efficiency in monitoring student progress. The integration of email parsing, database updates, and notification functionalities ensures seamless communication and facilitates timely submission of assignments, contributing to improved academic outcomes and instructor-student engagement

关键词： Automation databases Manuals Chatbots Feature extraction data mining Informatics

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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