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Redefining ECG Detection using CNN Autoencoder Approach through Deep learning

Redefining ECG Detection using CNN Autoencoder Approach thro...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Singh, Bhanupartap Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ruchira Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Scienceand Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Computer Scienceand Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350378092

This research delves into the application of Convolutional Neural Network (CNN) Autoencoders for anomaly detection in electrocardiogram (ECG) data, leveraging the PTB Diagnostic ECG Database. The dataset comprises 14,552 samples, categorized into normal heartbeats and those affected by cardiac abnormalities. Utilizing Transposed Convolution has significantly boosted the model's performance. This study underscores the critical importance of early detection of cardiac abnormalities and introduces a CNN Autoencoder model designed to efficiently encode and decode ECG data, enabling the identification of irregular patterns. The process involves developing a robust Autoencoder with encoder and decoder components trained to minimize reconstruction errors. Evaluation metrics reveal the model's impressive accuracy (76.93%), precision (55.23%), recall (89.81%), and F1 score (65.40%). This research highlights the pivotal role of deep learning techniques in early-stage anomaly detection in ECG data, demonstrating significant potential for enhancing diagnostic capabilities and improving patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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QubiCML: ML-Powered Real-Time Quantum State Discrimination Enabling Mid-Circuit Measurements 5

QubiCML: ML-Powered Real-Time Quantum State Discrimination E...

引用

2024 international conference on Quantum computing and engineering

作者： Vora, Neel R. Xu, Yilun Hasim, Akel Fruitwala, Neelay Nam Nguyen Liao, Horan Balewski, Jan Rajagopala, Abhi Nowrouzi, Kasra Ji, Qing Whaley, K. Brigitta Siddige, Irfan Phuc Nguyen Huang, Gang Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Univ Massachusetts Amherst MA 01003 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798331541378

This work addresses the critical challenge of accurately and swiftly identifying quantum states in superconducting quantum processors, with a particular focus on qubit (two-level) states. It introduces QubiCML, an innovative FPGA-based system designed for in-situ, real-time quantum state discrimination, which is essential for mid-circuit measurements and the implementation of advanced error correction techniques. By integrating a multi-layer neural network on the FPGA platform, QubiCML achieves exceptional accuracy and low latency in distinguishing quantum states. Traditional methods of quantum state discrimination rely heavily on offline data processing, which is inadequate due to the brief coherence time of quantum states and significant communication delays. QubiCML overcomes these limitations by utilizing in-situ machine learning for state discrimination, enabling real-time state determination in just 54 ns. It optimizes readout RF pulses and digital local oscillator (DLO) signals, significantly enhancing the fidelity of state discrimination. This capability enables mid-circuit measurements (MCM), which we successfully deployed using the QubiCML system. The system has been rigorously evaluated on in-house superconducting quantum processors, demonstrating an impressive average accuracy of 98.5%. QubiCML's modular architecture supports scalability across various qubits, making it adaptable to different quantum computing setups. Its real-time feedback capabilities are particularly beneficial for efficient quantum algorithm development and optimization. This system represents a groundbreaking advancement in the field of quantum computing, offering a robust tool for real-time quantum state discrimination. QubiCML has the potential to become the standard method for state discrimination, providing the quantum computing community with a powerful solution to push the boundaries of current technology.

关键词： In-Situ Machine learning Rapid Feedback for Mid-Circuit Measurements Real-Time Qubit State Discrimination

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Fake News Detection Using Boosting Ensemble Method 14

Fake News Detection Using Boosting Ensemble Method

引用

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

作者： Kaur, Kirandeep Kashyap, Nirbhay Amity School of Engineering and Technology Amity University Computer Science Department Uttar Pradesh Noida201301 India

ISBN: (纸本)9798350344837

Around the globe, Fake news is a raising concern and has several negative repercussions on people, societies, and countries. It has several detrimental effects on society, including disseminating false information and causing confusion, inciting social unrest and division, eroding credibility and confidence, endangering public health, and causing political instability. To protect people from damage, preserve informational accuracy, stop the spread of hate speech, support a vibrant democracy, and maintain journalism's reputation, fake news detection is crucial. In this work, we attempt to tackle this problem by putting forth a method that uses machine learning classification to distinguish between authentic and fraudulent content. Natural language processing and machine learning ensemble approach are taken into consideration for this. In natural language processing, TF-IDF and Word2vec features are used and optimized using wrapper method (via RFE algorithm). Training and testing are done on 'Fake News Detection' dataset from Kaggle. Exploratory examination upon the boosting ensemble method tells that our learners perform better than other options. © 2024 IEEE.

关键词： Statistical tests

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Towards Precision Agriculture: Harnessing Deep learning for Accurate Plant Disease Diagnosis

Towards Precision Agriculture: Harnessing Deep Learning for ...

引用

2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Senthil Pandi, S. Reshmy, A.K. Vinodh Kumar, S. Kumar, P. Rajalakshmi Engineering College Department of CSE Chennai India Srm Institute Of Science And Technology School Of Computing Department Of Cintel Kattankulathur Chennai India

ISBN: (纸本)9798350352689

To a large extent, food plants are responsible for satisfying the dietary requirements of the world's population. Nevertheless, the danger posed by plant illnesses and diseases is significant, despite the fact that diagnoses are frequently based on the symptoms that may be observed. Studies are currently being conducted to investigate novel ways with the interest of developing more efficient strategies for protecting plants. Recent developments in technology have resulted in the creation of more convenient alternatives to the labor-intensive procedures that have been used in the past. The application of deep learning techniques, which are particularly useful in solving picture categorization issues, has emerged as a potentially fruitful route. The attention-based few-shot learning technique was utilized in this research project to accurately diagnose plant illnesses based on photographs of leaves. The photos were then classified into two distinct groups: healthy and sick. After being trained on a database that contains images of plant leaves, the suggested network model exhibits an impressively high level of accuracy, which is 95.96%. © 2024 IEEE.

关键词： Diagnosis

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Developing Gamification Strategies for international, Interdisciplinary, Team-Based Online Courses in engineering Design 25th

Developing Gamification Strategies for International, Interd...

引用

25th international conference on Interactive Collaborative learning (ICL) / 51st IGIP international conference on engineering Pedagogy

作者： Kessing, David Loewer, Manuel Univ Wuppertal Dept Prod Safety & Qual Engn Wuppertal Germany

ISBN: (纸本)9783031268755;9783031268762

Student motivation is a highly relevant topic, especially since the increased implementation of online courses during the Corona pandemic. The concept of gamification offers a modern approach to the strategic design of courses, among others, concerning the optimal motivation of participants. Based on a case study of the international, interdisciplinary online course of the international Design and engineering EducationAssociation (IDEEA) and the subsequent evaluation with modern digital simulation tools, this research shows how gamification strategies can be sustainably integrated into digital courses to strengthen the motivation and participation of students. The results show a fundamental consistency between the case study and the newly developed simulation model and will therefore be transferred into a process for the general application of gamification in courses for teaching staff at universities.

关键词： Gamification Online course Motivation engineering design

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Exploring the Efficacy of Various Machine learning Approaches for Gold Price Forecasting : Insights and Analysis 7

Exploring the Efficacy of Various Machine Learning Approache...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Narendran, S. Saveetha University Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nanotechnology Chennai India

ISBN: (纸本)9798350372816

The increasing prominence of Artificial Intelligence (AI) or machine learning in data analysis, particularly in Gold price forecasting, is explored in this article. With a specific focus on Autoregressive Integrated Moving Average (ARIMA), Gaussian Process Regression (GPR) Model, Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) Model, and Categorical Boosting (CatBoost), the research delves into AI's transformative impact across various sectors globally, including agriculture, industry, and medicine. Utilizing datasets sourced from Kaggle encompassing daily gold rates from January 2013 to January 2024. The findings highlight the effectiveness of AI techniques in analyzing stock market behavior, providing valuable insights into forecasting methodologies and performance metrics such as Mean Absolute Percentage Error (MAPE), Accuracy, and Mean Absolute Error (MAE) across different algorithms. The analysis is conducted using Python in Google Colab, emphasizing the practical application of AI in enhancing forecasting accuracy and informing investment decisions in the gold market. © 2024 IEEE.

关键词： Adaptive boosting

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Deep learning Approaches for Drowsiness Detection System: A Comprehensive Survey 15

Deep Learning Approaches for Drowsiness Detection System: A ...

引用

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

作者： Mahajan, Pratham Pawar, Om Gaikwad, Tejas Gupta, Deepti JSPM's Rajarshi Shahu College of Engineering Tathawade India

ISBN: (纸本)9798331300579

Road traffic accidents are more likely when drivers are fatigued, which is a serious safety *** address this issue, studies have been done to create sleepiness detecting *** systems track behavioral and physiological changes in drivers that signify tiredness using physiological markers and image processing *** they accurately reflect the driver's physical state, physiological variables like heart rate, pulse, breathing rate, and body temperature serve as trustworthy markers of *** improvements in drowsiness detection technology have the potential to significantly improve driver safety and reduce accidents brought on by driver fatigue. © Grenze Scientific Society, 2024.

关键词： Physiological models

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Merging approach of pre-trained deep learning models for edge-cloud collaborative inference

Merging approach of pre-trained deep learning models for edg...

引用

2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Yan, Wansong College of Computer and Data Science Fuzhou University Xueyuan Road Minhou Fuzhou350108 China

ISBN: (纸本)9781510685840

Artificial Intelligence (AI)-based Internet of Things (IoT) applications benefit greatly from advanced deep learning models. However, the increasing complexity and resource requirements of deep learning models pose challenges to computational efficiency and deployment on resource-constrained devices. To address these challenges, the paper proposes a merging technique to efficiently merge multiple single-task deep learning models into a unified multi-task model. The proposed method merges models in a serial fashion, potentially replacing various layers with a smaller deep neural sub-network to connect the layers of the previous model and the latter model's layers. This method improves computational efficiency, and can create larger models to perform the functions of the original models with minimal training overhead. The paper designs and implements a merging model that supports end-to-end compressed sensing (CS) sampling and object detection, and the experimental results verify the efficacy of the proposed method in generating fine-tuned multi-task deep neural models with minimal training time and resource costs, making it a cost-efficient solution for edge-cloud collaborative inference. © 2024 SPIE.

关键词： Data modeling Clouds Object detection Education and training Deep learning Performance modeling Data transmission Feature extraction Instrument modeling Sampling rates

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Ensemble Based Precautionary Detection of Parkinson's Disease from Voice Signal 1

Ensemble Based Precautionary Detection of Parkinson's Diseas...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Bhanusree, Yalamanchili Adhvy, Vodnala Sharan Affan, Mohammed Shaji Praneetha, Kanvapuri Sai Sahithi, Katikireddy Sai Computer Science - Data Science VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India

ISBN: (纸本)9798331518943

Parkinson's Disease is a neurological progressive disorder that affects the nervous system of a human being, which effects the parts of the body. Tremors, imbalance, slowed movement, stiffness, difficulty in walking and talking etc. are observed. The reasons being, brain disorder, injuries, toxins certain drugs etc. The disease has no cure however treatment can be offered for relief from symptoms. However early detection can cure the disease in the first stages and extend the life span of the patient. This paper proposes a Ensembled learning model for early detection of Parkinsons disease through voice features. Random forest ensemble models have outperformed the Machine learning and Neural Network models in detecting disease from voice. An accuracy of 94.8% is achieved with this model with proper preprocessing techniques and training. As a future work the proposed model's software prototype can be released for mobile application for cost effective early detection of Parkinsons Disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Adapting Deep learning Transformer Model for Depression-Specific Named Entity Recognition: A Transfer learning Approach 3

Adapting Deep Learning Transformer Model for Depression-Spec...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Pawar, Dipti Phansalkar, Shraddha Department of Computer Science Engineering MIT School of Computing MIT Art DesignTechnology University Pune India

ISBN: (纸本)9798331532420

Named Entity Recognition (NER) is essential in the biomedical domain, particularly in mental health studies focused on disorders like depression. It helps extract structured information from unstructured text, enabling accurate recognition of key entities such as symptoms, disorder, antidepressants, brands, and therapies. This step is crucial for recommending diagnoses, personalizing treatments, and advancing research. Traditional methods, like rule-based or dictionary-driven approaches, often fall short in flexibility and scalability, as they rely heavily on predefined lexicons and corpora. The proposed work presents an advanced deep learning model aimed at improving NER performance on a customized dataset. Our model focuses on identifying critical entities disorders, brands, antidepressants, and therapies by addressing the limitations of conventional techniques. It uses a hybrid approach that combines a BERT-based model, fine-tuned through transfer learning, with a knowledge-based dictionary (MedDRA). This integration allows for enhanced semantic enrichment, improving the identification and categorization of biomedical entities. By leveraging BERT’s contextual capabilities alongside the structured knowledge from MedDRA, the model delivers better accuracy and classification in mental health data. Preliminary results show high confidence levels, with substantial improvements in key metrics like precision, recall, and F1 score, further advancing NER's role in mental health research. © 2024 IEEE.

关键词： Semantics

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