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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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HealthChain: A Blockchain-aided Federated Healthcare Management System

HealthChain: A Blockchain-aided Federated Healthcare Managem...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Arnab Mukherjee Raju Halder Joydeep Chandra Shailesh Shrivastava Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India National Informatics Center Patna India

Privacy and integrity of medical records while preserving and sharing them within any healthcare system is of high importance. Recently, India's Ayushman Bharat Digital Mission (ABDM) aims to address various issues of the existing healthcare systems and proposes a federated healthcare system to bring all the players under a common umbrella. However, it falls short on various aspects, including transparency, auditability, and traceability of activities and information within the system. In this proposal, we aim to bridge these existing gaps and propose an extension of the ABDM proposal by incorporating a blockchain layer instrumented with additional features such as traceable consent management, auditable access logs, etc., while keeping in mind its performance and scalability. We present a proof of concept of our proposal based on Hyperledger Fabric and related backend technologies and manifest experimental results to reinforce the practicality of our proposal.

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Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach

arXiv

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arXiv 2024年

作者： Siddhad, Gourav Iwamura, Masakazu Roy, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Sakai Osaka599-8531 Japan

Emotions are crucial in human life, influencing perceptions, relationships, behaviour, and choices. Emotion recognition using Electroencephalography (EEG) in the Brain-computer Interface (BCI) domain presents significant challenges, particularly the need for extensive datasets. This study aims to generate synthetic EEG samples that are similar to real samples but are distinct by augmenting noise to a conditional denoising diffusion probabilistic model, thus addressing the prevalent issue of data scarcity in EEG research. The proposed method is tested on the DEAP dataset, showcasing upto 4.21% improvement in classification performance when using synthetic data. This is higher compared to the traditional GAN-based and DDPM-based approaches. The proposed diffusion-based approach for EEG data generation appears promising in refining the accuracy of emotion recognition systems and marks a notable contribution to EEG-based emotion recognition. Our research further evaluates the effectiveness of state-of-the-art classifiers on EEG data, employing both real and synthetic data with varying noise levels. © 2024, CC BY.

关键词： Emotion Recognition

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Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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An Explainable AI model in the assessment of Multiple Sclerosis using clinical data and Brain MRI lesion texture features *

An Explainable AI model in the assessment of Multiple Sclero...

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IEEE EMBS International Conference on Information technology Applications in Biomedicine (ITAB)

作者： A. Nicolaou M. Pantzaris C. P. Loizou A. Kakas C. S. Pattichis Department of Computer Science University of Cyprus Nicosia Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

Magnetic resonance imaging (MRI) is an essential visualizing tool in the diagnosis and monitoring of Multiple Sclerosis (MS) disease. However, the neurological examinations and the MRI assessments are insufficient to provide personalized treatment to the patients due to the complexity of the disease. This study implemented an explainable artificial intelligence (AI) model with embedded rules to assess MS disease evolution. Clinical data were used including demographic and neurological measurements. Texture features were extracted from manually delineated and normalized brain MRI lesions. Statistical analysis was employed to select the statistically significant texture features and clinical data. Different models using machine learning algorithms were implemented to differentiate the subjects diagnosed with relapsing-remitting MS (RRMS) from the subjects with progressive MS (PMS). Argumentation-based reasoning was performed by modifying the rules extracted from models with the best evaluation results. The findings indicated that the proposed explainable AI model can predict the clinical conditions of MS disease with high accuracy and provide transparent and understandable explanations with high fidelity. Future work will include further clinical data such as medications and investigate the correlation of the texture features and clinical data with the neurological impairment. The proposed model should also be evaluated on more MS *** Relevance— This method can assist clinical experts by providing explainable and interpretable diagnosis in the assessment of MS disease.

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Alapana Generation using Finite State Machines and Generative Adversarial Networks

Alapana Generation using Finite State Machines and Generativ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： Vithushigan Jayatharan Dileeka Alwis Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation.

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Benchmarking changepoint detection algorithms on cardiac time series

arXiv

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arXiv 2024年

作者： Cakmak, Ayse Selen Reinertsen, Erik Nemati, Shamim Clifford, Gari D. Department of Biomedical Informatics Emory University United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Biomedical Informatics University of California at San Diego United States

The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detec- tion algorithm for a specific task, such as disease classification. Eight key algorithms were compared, and the performance of each algorithm was evaluated as a function of temporal tolerance, noise, and abnormal conduction (ectopy) on realistic artificial cardiovascular time series data. All algorithms were applied to real data (cardiac time series of 22 patients with REM-behavior disorder (RBD) and 15 healthy controls) using the parameters selected on artificial data. Finally, features were derived from the de- tected changepoints to classify RBD patients from healthy controls using a K-Nearest Neighbors approach. On artificial data, Modified Bayesian Changepoint Detection al- gorithm provided superior positive predictive value for state change identification while Recursive Mean Difference Maximization (RMDM) achieved the highest true positive rate. For the classification task, features derived from the RMDM algorithm provided the highest leave one out cross validated accuracy of 0.89 and true positive rate of 0.87. Automatically detected changepoints provide useful information about subject's phys- iological state which cannot be directly observed. However, the choice of change point detection algorithm depends on the nature of the underlying data and the downstream application, such as a classification task. This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application *** Codes 92C30, 92C32, 03H10, 62H30, 68Q07, 8T07, 78-10, 92-10, 62R07, 68T09, 68T10 © 2024, CC BY-NC-SA.

关键词： Time series

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A Review On Breaking the Limits of Time Series Forecasting Algorithms

A Review On Breaking the Limits of Time Series Forecasting A...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Ammar Raneez Torin Wirasingha Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

Time Series (TS) forecasting has stagnated owing to algorithm restrictions, therefore systems developed using these methods can only perform so well. TS remains a challenge despite recent advances in Deep Learning (DL) in Natural Language Processing (NLP) and Reinforcement Learning (RL). This paper reviews the literature on these algorithms, highlights studies using them, and shows their limits. Neural Ordinary Differential Equations (NODEs) with continuous-time and continuous-depth tackle TS forecasting issues. Liquid Time-Constant (LTC) networks, a more advanced and reliable implementation of these NODEs, provides fluidity. We propose a new design that uses the LTC's liquid adaptability and is more adaptable to manage immediate changes. These algorithms are more steady, adaptive, and versatile than DL, which may help overcome its TS forecasting shortcomings.

关键词： Deep learning Liquids Conferences Time series analysis Reinforcement learning Ordinary differential equations Natural language processing

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Apply Combined Model Between VGG16 and LightGBM on Weather Image Recognition

Apply Combined Model Between VGG16 and LightGBM on Weather I...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Tran Quy Nam Irfan Syamsuddin Posts and Telecommunications Institute of Technology Hanoi Vietnam Department of Informatics & Computer Engineering Politeknik Negeri Ujung Pandang Indonesia

This research implements hybrid deep learning network models for weather image classification. The study proposes to apply a combined model, namely VGG16-LightGBM. In its architecture model, a pre-train convolutional neural network (CNN) name as VGG16 is employed for feature extraction of images and the LightGBM algorithm is used to make classification. The results on accuracy of proposed models were compared with four other models, namely Xception, Inception V3,Vgg19, Vgg16 which are all implemented by transfer learning mechanism on the same dataset. The experimental results proved that the VGG16-LightGBM gives the best performance with the highest accuracy of 81,28%, outperforms the transfer learning technique of other 4 pre-train models in the problem of weather image classification.

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An Explainable Artificial Intelligence model in the assessment of Brain MRI Lesions in Multiple Sclerosis using Amplitude Modulation – Frequency Modulation multi-scale feature sets

An Explainable Artificial Intelligence model in the assessme...

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International Conference on Digital Signal Processing (DSP)

作者： Andria Nicolaou Antonis Kakas Constantinos S. Pattichis Marios S. Pattichis Kevin Fotso Christos P. Loizou Marios Pantzaris Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Computer Engineering University of New Mexico USA Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models.

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