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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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Sarcasm detection in product reviews using textual entailment approach 23

Sarcasm detection in product reviews using textual entailmen...

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Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Shubham Sinha Tarun Vijeta Pratik Kishor Kubde Ayush Pradeep Gajbhiye Mansi Anup Radke Christopher Jones Department of Computer Science and Engineering Visvesvaraya National Institute of Technology India School of Computer Science and Informatics Cardiff University UK

ISBN: (纸本)9798400709227

Sarcasm is a form of sentiment characterized by the use of words that express the opposite of what is meant. Sarcasm detection has applications in multiple domains ranging from sentiment analysis in product reviews to user feedback, and online forums. Sarcasm detection is important to understand user opinions and intentions in areas such as sentiment-based classification and opinion mining. This can result in better product development and customer service. Sarcasm detection can be a challenging task because sarcastic sentences may use positive expressions to convey negative meanings or may use negative sentences to convey positive meanings. Also, sarcastic sentences form a very small component of the entire communication. The increasing use of sarcasm in various social media such as Twitter, Reddit, Amazon product reviews, etc. has highlighted the importance of detecting and understanding sarcasm in various contexts. Sarcasm detection is a challenging problem for NLP systems that often rely on statistical models for performing sentiment analysis. In this research, the focus is on the use of a textual entailment approach for detecting sarcasm. Textual entailment is a natural language inference task that involves determining whether one text (hypothesis) can be derived from another text (premise). The underlying assumption behind this approach is that - if there is a contradiction between the premise and hypothesis, we can say that the hypothesis is sarcastic. To test our approach, an annotated corpus of 3000 product reviews was developed methodically from the Amazon Reviews dataset and tested using the textual entailment approach. The proposed approach achieved an F1 score of 0.76 on this dataset. The result is better than the baseline considered which is the BERT binary classifier which gives an F1 score of 0.48 on the same dataset.

关键词： Hypothesis Natural Language Inference Premise Sarcasm Detection Textual Entailment

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Data Science System of Predicting and Detecting the Chikungunya Virus using Ensemble Techniques 4

Data Science System of Predicting and Detecting the Chikungu...

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4th International Conference on Inventive research in Computing Applications, ICIRCA 2022

作者： Reddy, Bommireddy Vijay Kumar Patel, Shail Singh, Anayna Singh, Nishant Ranjit, Sasanapuri Dantkale, Shreyas National Institute of Technology Srinagar Department Computer Science and Engineering Jammu and Kashmir India Vellore Institute of Technology Department Computer Science and Engineering Vellore India Niit University Department Computer Science and Engineering Neemrana India University of Delhi Department of Institute of Informatics and Communication Delhi India Pes University Department of Electronics and Communication Engineering Bangalore India Vishwakarma Institute of Technology Department of Electronics and Communication Engineering Pune India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

The ailment known as chikungunya fever is brought on by a virus that mosquitoes disseminate. Due to the infection's excruciating joint and muscular discomfort, the term translates to 'bends you up' or 'stooped walk.' The abrupt onset of a high temperature, headache, exhaustion, rash, nausea, and red eyes are other symptoms. Chikungunya symptoms frequently show up 2 to 7 days after being bitten by an infected mosquito. There isn't a vaccination yet. Additionally, there is no cure for the illness. However, fatalities or major complications with chikungunya are uncommon. Many people recover in a matter of weeks. However, some people experience joint and muscular discomfort that can linger for months or even years after they have been exposed to the virus. After one to two weeks, a lot of people are free of further problems. Others, though, can experience joint and muscular discomfort for years. This kind of arthritis is known as chronic chikungunya. At least 40persons who contract the virus are affected. Chikungunya is seldom fatal. But for certain people, the infection can lead to serious issues. Older persons, those with chronic illnesses like diabetes or high blood pressure, small children, and pregnant women who could pass the virus to their unborn children are at increased risk. Serious issues with the eyes, heart, and nerves can be complications. Once-infected individuals are likely to be resistant to subsequent infections. The main aim of the authors is to use the ensemble technqiue to find a novel approach and to compare the ensemble techniques. © 2022 IEEE.

关键词： Viruses

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Early Detection of Interproximal Caries using YOLOv8 Convolutional Neural Network on Bitewing Radiographs 5

Early Detection of Interproximal Caries using YOLOv8 Convolu...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Mahizha, Soundar Ida Annrose, Joseph Angelo, Jeyebalaji Mano Christaine Shyni, Israel Domilin St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Sri Ramachandra Institute of Higher Education and Research Faculty of Dentistry Chennai India Dmi College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350389609

Early childhood caries is a common oral health issue in both developed and developing countries. Identifying caries lesions early can help in implementing minimally invasive treatment plans, preserving the remaining tooth structure. Interproximal lesions are typically found between the adjacent surfaces of two teeth. The first step is visual inspection, followed by examination using dental probes to detect the dental caries. Usually, radiographic examination is done for early detection of dental caries. This process is time-consuming. In recent years, AI has been used to detect early caries from X-ray images. In dentistry, proximal caries is difficult to identify from periapical and panoramic radiographs, so Bitewing Radiographs (BWR) are used to detect the interproximal caries. Since the X-ray source is parallel to the sensor, Bitewing Radiographs has least distortion and help in the detection of early proximal caries. Several studies have shown that Bitewing Radiographs have high sensitivity in detecting interproximal caries occurring in existing restorations compared to visual clinical detection. The main objective of this study is to detect dental caries and classify them into various classes enamel, dentin and restorations. In this study, a dataset was collected from Mendeley data and Kaggle. The caries classification utilized a Deep Convolutional Neural Network. A YOLOv8 algorithm was developed based on the severity of the lesions observed in bitewing digital radiographs, enabling convenient and reliable detection of these lesions. The performance of these classifications is evaluated. This study aimed to evaluate the performance of the deep neural network for detecting interproximal caries lesions on Bitewing Radiographs (BWR). Accurate detection and diagnosis of dental caries are crucial for reducing the cost of oral health management and improving the likelihood of preserving natural teeth, leading to better patient outcomes. © 2024 IEEE.

关键词： Boiling water reactors

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

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 gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points 6

An Innovative Way to Increase the Speed and Efficiency of Re...

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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Ray, Ryan Saptarshi Das, Debendranath Sengupta, Aritro Kumar, Pankaj Sahani, Arun Kumar Govt. College of Engineering and Ceramic Technology Information Technology Kolkata India Indian Statistical Institute Cryptology and Security Research Unit Kolkata India Lingaya's Vidyapeeth Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350306613

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form. © 2024 IEEE.

关键词： Elliptic Curve Cryptography Parallel Processing Points Mapping Reverse Mapping Thread Programming

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