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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Hongyi Yu, Han Tang, Xiaoli Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average. Copyright 2024 by the author(s)

关键词： Federated learning

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Depression Prediction of Human Using Advanced Technology and Medical Imaging

Depression Prediction of Human Using Advanced Technology and...

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2022 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Annapoorani, S. Saravanan, P. SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulatur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Depression is a typical condition that impacts 3.8 % in total population, with 5.0 % of adults and 5.7 % of oldaged persons suffering from it. Depression can be dangerous to one's health, especially if it is persistent and has a moderate or severe intensity. Despite the fact that there are effective treatments for mental diseases, as well 75% of developing countries do not obtain them. In this paper, a smart gadget (a mobile phone or a robot) and medical imaging are used to identify depression without the need for clinical advice. By comparing the various elements of input and evaluating their performance, efficient technology can be used to forecast depression and ensure privacy. © 2022 IEEE.

关键词： Deep neural networks

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A Hierarchical Taxonomy For Deep State Space Models

A Hierarchical Taxonomy For Deep State Space Models

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tang, Shiqin Feng, Pengxing Yu, Shujian Dong, Yining Qin, S. Joe Department of Data Science City University of Hong Kong Hong Kong Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Artificial Intelligence Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Computing and Decision Science Lingnan University Hong Kong

ISBN: (纸本)9798350368741

Modeling nonlinear dynamical systems is a challenging task in fields such as speech processing, music generation, and video prediction. This paper introduces a hierarchical framework for Deep State Space Models (DSSMs), categorizing them by their conditional independence properties and Markov assumptions and positioning existing models within this framework, including the Stochastic Recurrent Neural Network (SRNN), Variational Recurrent Neural Network (VRNN), and Recurrent State Space Model (RSSM). We discuss different options for the inference networks and demonstrate how integrating normalizing flows can enhance model flexibility by capturing complex distributions. Our work not only clarifies the relationships among existing models but also paves the way for the development of new, more effective approaches for modeling nonlinear dynamics. In particular, we propose the Autoregressive State Space Model (ArSSM) and evaluate its effectiveness in speech and polyphonic music modeling tasks. © 2025 IEEE.

关键词： Deep State Space Models Dynamical Variational Autoencoders Hierarchical Taxonomy Normalizing Flows

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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EQCNN: Enhanced Remote Sensing Imagery Classification with Circuit-Based Error-Corrected Quantum Convolutional Neural Networks 25

EQCNN: Enhanced Remote Sensing Imagery Classification with C...

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25th International Conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Zaman, Muhammad Kehkashan, Tanzila Akhunzada, Adnan Alaidaros, Hashem Uddin, Mueen Azeem, Muhammad University of Lahore Faculty of Computer Science Punjab Sargodha40100 Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia College of Computing & It University of Doha for Science & Technology Department of Data and Cybersecurity Doha Qatar Dar Al-Hekma University Department of Cybersecurity Jeddah Saudi Arabia

ISBN: (纸本)9798350379037

Quantum information processing is more complex than classical counterpart because of the No-Cloning theorem, decoherence, and issues detecting quantum states. Building a quantum computer without error detection and correction module is challenging. To overcome this challenge, qubit error correction codes are implemented. Inadequate error detection and correction codes have been the downfall of earlier attempts to encode quantum information, leading to disrupted qubit transmission. Qubit information is decohered by circuit noise. Unfortunately, the principal qubit error, phase flip error is not corrected by existing qubit error correction methods. In this study, we proposed Quantum Error Detection and Correction scheme (QEDC) to keep qubit in a coherent state. Used S gates, unitary gates, 10 CNOT gates, and Hadamard gates spanning on 4 physical and 1 logical qubits over the IBM Quantum Experience Machine. We found that the error probability is reduced to 7.91% owing to the efficiency of the quantum information encoding and phase flip control. Furthermore, we proposed Error Corrected Quantum Convolution Neural Network (EQCNN) for Image Classification. EQCNN combat and correct qubit errors that occur during the image processing. EQCNN outperformed over Earth Observatory (EO) dataset, EuroSAT and achieved an impressive results of 99.3% accuracy, 98.9% F1-score, 96.73% precision, and 98.4% recall. © 2024 IEEE.

关键词： Qubits

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Variational Encoder Based Synthetic Alzheimer's data Generation for Deep Learning, XGBoost and Statistical Survival Analysis 23

Variational Encoder Based Synthetic Alzheimer's Data Generat...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Musto, Henry Stamate, Daniel Stahl, Daniel University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom School of Health Sciences The University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Kings College London Department of Biostatistics London United Kingdom

ISBN: (纸本)9798350374889

Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencoders (VAEs), to generate synthetic datasets that replicate real-world AD data while preserving privacy. These datasets were used to train and compare survival analysis models, including Survival XGBoost, Survival Transformers, and the Cox Proportional Hazards model. Results show that deep learning and boosting models outperform the standard Cox model in predicting the progression of AD, achieving a higher degree of accuracy. Our findings suggest that combining synthetic data with advanced machine learning models can improve predictive capabilities in healthcare research, particularly when real patient data is restricted. © 2024 IEEE.

关键词： Adversarial machine learning

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Speech Emotion Recognition Using GRU and LSTM

Speech Emotion Recognition Using GRU and LSTM

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Sharma, Vriti Raushan, Ritik Verma, Ruchi Gupta, P.K. School of Computing and Electrical Engineering Iit Mandi HP India Jaypee University of Information Technology Department of Cse HP Solan173 234 India School of Computing Mohan Babu University Department of Data Science AP Tirupati517 102 India

ISBN: (纸本)9798350364866

Speech emotion recognition (SER) is a hot topic in research, with applications in human-computer interaction, mental health screening, and customer service. In this research, our objective is to develop an accurate and reliable SER system capable of emotion recognition in speech data. We address important issues such as feature extraction, model selection, data imbalance, robustness, and real-time inference using a labeled dataset of various emotional expressions in voice. To extract complicated shapes from auditory characteristics, our method combines deep learning techniques that include long-short-term memory (LSTM) and gated recurrent units (GRU). We carefully manage the data imbalance to ensure that emotions are fairly represented in the training set, and with the implementation of our functional LSTM and GRU models in the training set, we were able to achieve the precision of 62 % and 90 %, respectively. © 2024 IEEE.

关键词： Long short-term memory

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Implementation of Topic Modeling in the Analysis of Topic Trends in SDGs Goal 6 Research

Implementation of Topic Modeling in the Analysis of Topic Tr...

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2023 International Conference on Electrical Engineering and Informatics, ICEEI 2023

作者： Illia, Fathonah Nooraeni, Rani Suadaa, Lya Hulliyyatus Politeknik Statistika Stis Department of Statistical Computing Jakarta Indonesia Data Science Research Unit Politeknik Statistika Stis Jakarta Indonesia

ISBN: (纸本)9798350381290

SDGs Goal 6 is one of the crucial objectives within the SDGs framework, making fulfilling this goal highly significant as it determines the achievement of other SDGs. As a serious global issue, this goal can be addressed through scientific research and innovation. However, research on this goal is often not well-known, leading to a need for more connection between the academic world and practitioners in implementing available solutions. Consequently, identifying critical pathways through scientific research is essential to attain this goal effectively. One of the expedient approaches is the utilization of topic modeling, which also facilitates the analysis of research topic trends related to SDGs Goal 6. This study employs topic modeling on data sourced from SemanticScholar using the BERTopic method, which captures the comprehensive context of research articles. The research results indicate that studies focusing on SDGs Goal 6 are interconnected with other SDGs. The default BERTopic model, chosen as the optimal model, illustrates that there is a prevailing topic tendency in target 6.3 related to water quality. © 2023 IEEE.

关键词： Water quality

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