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Deep learning Assisted Readout of In Situ Frequency Tunable Superconducting Qubits 4

Deep Learning Assisted Readout of In Situ Frequency Tunable ...

引用

4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Wang, Wei University of Macau Institute of Applied Physics and Materials Engineering 999078 China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534622

Dispersive readout of superconducting qubits is one of the most critical components of superconducting quantum computing systems. Traditional dispersive readout schemes typically employ digital signal processing technique to demodulate multiple measurements into clouds in the IQ plane, determining the qubit state through projection and threshold analysis. However, when addressing tunable qubits, this method is limited to single-frequency measurements;attempting to measure at different frequencies without adjusting the readout frequency accordingly can compromise readout fidelity. To address this, this paper proposes a deep learning-based readout scheme that can directly train and predict the readout signals corresponding to the ground and excited states of the same superconducting qubit at different frequencies under a fixed readout frequency. A supervised learning approach is utilized to construct a deep learning model based on the multilayer perceptron. The trained model can directly predict the qubit state at different frequencies within a large frequency range. Moreover, the deep learning model can not only distinguish between the ground and excited states of the qubit but also leverage its capability to handle regression problems to test any superposition state of the qubit, which has been verified through Rabi oscillation tests. Experimental results indicate that this approach can effectively discern the state within a 300 MHz tuning range of the qubit frequency, achieving a readout fidelity exceeding 92% under various magnetic flux bias and a Rabi measurement fidelity above 96%. The findings of this study underscore the efficacy of the deep learning scheme in augmenting the convenience of qubit readout processes, concurrently preserving high fidelity, which is imperative for the advancement of quantum computing technologies. © 2024 IEEE.

关键词： Qubits

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Voice Feature based Detection of Parkinson's Disease using Machine learning Approach 15

Voice Feature based Detection of Parkinson's Disease using M...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Akram, Sk. Wasim Kumar, A.P. Siva Department of Computer Science and Engineering Jawaharlal Nehru Technological University Anantapur Andhra Pradesh Anantapuramu India

ISBN: (纸本)9798331300579

At present Parkinson's disease (PD) is the second largest neuro effected disease. Its symptoms resemble daily life activities, which are not identified by the patient. There is no solely technique or measures for detection and confirmation of PD in patients. In this study, it employs that machine learning algorithms plays a major role in identification of PD and in analysis of complex PD representing data types. KNN algorithm is used for this case study to contribute to the growing field of Voice-based analysis that incorporate key features like shimmer, Jitter, frequency to understand the evolution of PD symptoms. This paper presents a comprehensive calculated regression analysis of PD in affected patients. By this study and experimental results, the proposed methodology helps to effectively detect the PD and incorporate into health care for diagnosis purpose. © Grenze Scientific Society, 2024.

关键词： Regression analysis

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Efficient Handoff Detection for Computation Offloading using Machine learning and Bio-Inspired Optimization Techniques in Mobile Edge computing 13

Efficient Handoff Detection for Computation Offloading using...

引用

13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Joshi, Vaishali Patil, Kishor Electronics and Telecommunication Sinhgad Academy of Engineering Kondhwa. Pune India

ISBN: (纸本)9798350305463

With the rise of smartphones and the Internet of Things (IoT), more and more calculations are being performed on mobile devices, which rapidly depletes their power reserves. To save the battery of mobile device offloading the tasks to cloud is one of the solutions but, delay-sensitive applications, such as virtual gaming, augmented reality, etc., are negatively affected by the solution of offloading the task to a centralized cloud. For maximum efficiency in both power consumption and latency, it is desirable to locate cloud resources at the Edge of the mobile Network. Quality of Experience (QoE) is affected if mobility of end user is not addressed. Handoff detection is crucial for resolving mobility issues in mobile edge computing. In this work, we offer a machine learning and bio-inspired optimization-based method for detecting handoffs. © 2024 IEEE.

关键词： Computation offloading

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Comparison of Deep learning Methods for Detecting Brain Tumor 14

Comparison of Deep Learning Methods for Detecting Brain Tumo...

引用

14th international conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Bedi, Kiran Preet Jadon, Jitendra Singh Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Noida India

ISBN: (纸本)9798350344837

The intricate nature of brain tumors and their resemblance to normal brain tissues present significant challenges in effectively distinguishing and categorizing them through traditional medical image processing methods. Brain tumors pose a considerable global health risk, often leading to inaccurate diagnoses and prognoses, even with human assistance. Recent advancements in deep learning have significantly enhanced the accuracy of brain tumor detection and classification using magnetic resonance imaging (MRI) of the brain. In this research, we provide an illustrative approach for the classification of brain tumors, employing transfer learning and convolutional neural networks (CNNs). We explore several CNN architectures, including MobileNet-V2, ResNet, and Xception. Notably, MobileNet-V2 emerged as the most effective model, achieving an impressive Fl score of 98.45%. The accuracy of brain tumor categorization is pivotal for the effective evaluation of tumors and the formulation of appropriate treatment strategies. MRI is a preferred imaging modality due to its superior image quality and safety, as it does not involve ionizing radiation, unlike other imaging techniques. The integration of computational intelligence, particularly deep learning (DL), has revolutionized the diagnosis and categorization of complex clinical conditions like brain tumors. Deep learning has consistently demonstrated outstanding performance in the partitioning and classification of brain tumors. In this paper, we introduce an AI-driven method for the classification of brain tumors into benign and malignant categories using deep learning algorithms. To conduct this study, publicly available datasets comprising brain images are utilized, subject to in-depth analysis and assessment to enhance the accuracy of brain tumor classification © 2024 IEEE.

关键词： Image classification

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Grid Search Hyperparameters Tuning with Supervised Machine learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

引用

2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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Extraction Method of Disturbance Elements of Urban Emergencies Based on Large Language Model

Extraction Method of Disturbance Elements of Urban Emergenci...

引用

2024 international conference on Intelligent computing and Data Mining, ICDM 2024

作者： Xu, Jian Dang, Anrong Chen, Yang Zhao, Mingyu Sun, Bijiao Wang, Hua Xie, Zhuokun Tsinghua University School of Architecture Beijing China State Key Laboratory of Geo-Information Engineering Xian China

ISBN: (纸本)9798331506261

The general knowledge extraction model lacks the knowledge learning of such as urban emergencies, and the precision of knowledge extraction is insufficient. In this paper, a large language model (LLM) based method for urban disturbance elements extraction (UDEE-LLM) is proposed. The method deploys the local large language model through LangChain and integrates the domain knowledge of urban disturbance, fine-tuning the large language model based on the prompting learning, designing the heterogeneous entity alignment strategy, and optimizing the entity extraction results. The results show that UDEE-LLM can extract and classify the information of urban disturbance elements. The coupling relationship of urban disturbance elements is analyzed, and the potential relationship between mode, object and influence of urban disturbance is dug out, which has important reference for improving urban resilience. © 2024 IEEE.

关键词： Digital elevation model

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Evaluating Teachers' Performance through Aspect-Based Sentiment Analysis 9

Evaluating Teachers' Performance through Aspect-Based Sentim...

引用

9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Bhowmik, Abhijit Noor, Noorhuzaimi Mohd Mazid-Ul-Haque, Md. Miah, Md Saef Ullah Karmaker, Debajyoti Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Malaysia American International Univeristy-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350394474

This research demonstrates a novel approach for evaluating teacher performance by conducting aspect-based sentiment analysis (ABSA) on student feedback. A large dataset of over 2 million student comments about teachers is analyzed using cutting-edge natural language processing and customized deep learning techniques. The methodology involves identifying positive, negative and neutral aspects of teaching using a BiLSTM model. Rigorous preprocessing, domain adaptation, and performance metrics ensure a robust and objective evaluation. The granular, nuanced insights obtained through this aspect-level sentiment analysis enable educational institutions to provide targeted and unbiased feedback to teachers on their strengths and areas needing improvement. Moreover, this work lays the foundation for detecting potentially fraudulent reviews in academic settings - a crucial capability for safeguarding assessment integrity. The detailed aspect-based analysis methodology presented here significantly advances subjective and holistic evaluation practices. This research has far-reaching implications for enriching teacher development while upholding the credibility of performance assessments through sentiment analysis innovations. © 2024 IEEE.

关键词： Sentiment analysis

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Modern Trends in Deep learning for Fruit Quality Analysis of Hyperspectral Images: A Review 14

Modern Trends in Deep Learning for Fruit Quality Analysis of...

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Lanke, Neha Chandak, M.B. Computer Science and Engineering Department Shri Ramdeobaba College of Engineering and Management Nagpur India

Non-destructive testing techniques for fruit quality analysis have gained importance over the years. Hyperspectral image processing is one of the important non-destructive, cost-effective, rapid, and reliable quality testing techniques that provide both spatial as well as spectral information. Due to the automatic feature learning ability, deep learning is one of the most promising and powerful techniques with better classification accuracy for fruit applications. This paper starts by giving an overview of the quality concept of fruits and vegetables followed by fundamentals of hyperspectral imaging, and its potential applications. Then machine learning and deep learning techniques are highlighted followed by recent research work to illustrate the potential of hyperspectral imaging for fruit and vegetable quality analysis. Lastly, the research gap and future considerations are discussed to improve the analysis and applicability of hyperspectral imaging using deep learning for the quality assessment of fruits, and vegetables. © Grenze Scientific Society, 2023.

关键词： Hyperspectral imaging

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Research on teaching Mode and Platform Construction of Intelligent Manufacturing Practical Training Based on Digital Twins Under the Background of New engineering 13

Research on Teaching Mode and Platform Construction of Intel...

引用

13th international conference on Educational and Information Technology, ICEIT 2024

作者： Cao, Jianhua Jiang, Zhigang Hu, Wei Xia, Xuhui Liu, Xiang Liu, Xiaoqin Bao, Xiong Wuhan University of Science and Technology Engineering Training Center Wuhan China Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Wuhan China

ISBN: (纸本)9798350372663

Aiming at the issues of high risks, high costs, and low efficiency in the training and teaching process of intelligent manufacturing, which seriously affect the quality of talent cultivation in intelligent manufacturing. By adopting digital twin technology, the intelligent manufacturing production line is mapped to virtual space, and a two-stage practical training teaching model that integrates virtual space simulation operations and physical space actual operations is constructed. And an intelligent manufacturing digital twin teaching platform was established to support the teaching model. The safe, efficient and economical operation of intelligent manufacturing practical training and teaching has been achieved. © 2024 IEEE.

关键词： E-learning

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AI-K12 Connect: Shaping Tomorrow's Scholars 2

AI-K12 Connect: Shaping Tomorrow's Scholars

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2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Singhal, Aditya Singh, Ritu Jansi, K.R. SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Artificial Intelligence, or A.I., is a quickly developing technology that might completely transform the K-12 educational system. Studies have explored its relationship with performance, behavior, teaching, and student selection, with 24 subcategories. AI can address complex issues such as purpose, feelings, morality, and behavior. With the advancement of individual devices and computer systems, AI has become prevalent in various industries. Integrating AI technologies into education could make learning simpler and more effective, leading to more effective teachers and higher-quality learning opportunities for students. However, challenges like ethical and privacy problems and the requirement for effective training and professional development for educators must be addressed. Despite these challenges, The involvement of AI in K-12 education include personalized learning, improved student outcomes, and increased efficiency. As technology keeps progressing, it is crucial to continuously find its probable potential in K-12 education. © 2024 IEEE.

关键词： Students

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