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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86325 条记录，以下是231-240 订阅

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Machine Learning-Enabled Examination Feedback System 3

Machine Learning-Enabled Examination Feedback System

引用

3rd ieee international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Sai, Botcha Taraka Rutesha Mekatotti, Anshita Priyadarshini Ojasvi, Nallamala Jahnavi, Adigoppula Danaveni, Pavithra School of Computer Science & Engineering VIT-AP UNIVERSITY Inavolu AP Vijayawada522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science & Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

The project proposes an Examination Feedback System utilizing machine learning to automate the evaluation of subjective answers in educational assessments. The system will analyze student responses and classify them under a number of predefined topics while making sure that the feedback it generates is tailored and specific to the needs of the students. The primary goal is to ensure improved outcomes through enhanced information to students, where improvement is needed, while helping educators identify performance trends. The system is powered by DistilBERT, which is a transformer-based natural language processing model which is known for its efficiency and contextual knowledge. The proposed system differs from previous manual grading systems in that it offers data-driven information while addressing confidentiality issues with a computationally efficient design. Thus, the examination process becomes scalable, accurate, and data-driven in satisfying the demands of upstream education systems with very limited computational infrastructure. © 2025 ieee.

关键词： Natural language processing systems

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Maximizing Utility and Quality in Smart Healthcare with Incentive Driven Hierarchical Federated Learning 17

Maximizing Utility and Quality in Smart Healthcare with Ince...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Singh, Himanshu Patidar, Sourabh Pratap, Ajay Yasumoto, Keiichi Department of Computer Science and Engineering Varanasi India Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331531195

Recent development of federated learning based smart healthcare techniques facilitates distributed processing of patient data while protecting privacy. However, two significant challenges can notably degrade model learning performance. Firstly, patients often show reluctance to participate in model training due to a lack of incentives. Secondly, the quality of updated models can change significantly due to different aspects such as the size and training data quality. The incorporation of low-quality updates can subsequently degrade global model quality in FL. Considering the above scenarios, this paper formulates a utility maximization problem keeping model quality function and budget into deliberation. To solve the formulated problem, this paper proposes association algorithm utilizing Utility Maximization and Payment (UMaxP) algorithm based on matching approach to select Wireless Body Area Networks (WBANs) and hospitals in a way that increases the utility and improves model quality using Hierarchical FL (HFL). In UMaxP, the allocation of patients to hospitals is optimized to maximize both the sum of local model qualities and the overall utility of the patients. The effectiveness of the proposed algorithmic model is demonstrated through theoretical analysis, experimental validation, and comparative evaluation with various methods using real-world data. © 2025 ieee.

关键词： Federated learning

来源：评论

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Advancing Healthcare Through Internet of Things: A Comprehensive Review of Smart Healthcare systems and Their Applications 2

Advancing Healthcare Through Internet of Things: A Comprehen...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Raushan, Rahul Chaudhri, Shiv Nath Suman, Saurabh Kumar, Dhiraj Kumar, Gulshan Kumar, Abhishek Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The Internet of Things (IoT) has turned the healthcare domain in a better direction due to more interconnected and smarter systems that enhance care delivery. Certainly, the IoT-accessible applications in these fields are only possible if devices, sensors, and real-time data are interconnected. Among today's marvels are remote patient monitoring, wearable health trackers, telemedicine, and smart hospital management. Medicine gets more personal, effective, and accessible due to these improvements. However, its development remains unmatched being faced with complexities such as security of personalized data, safety risks resolution, and seamless device-to-system integration. This paper examines the influential role of IoT in advanced healthcare systems, with its virtues, major applications, and implementation obstacles as the subject. The Internet of Things as a mode of health and technology bridging can entirely change how medicines are being practiced and even lead to the improvement of the lives of patients by introducing adaptive and efficient hospital systems. Thus, IoT can be a technology that balances tech and medicine for the betterment of patient treatment outcomes and make the system adaptive and efficient. © 2025 ieee.

关键词： Personalized medicine

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Enhanced Predictive Modeling for Myocardial Infarction Risk Assessment through Multimodal data Integration 3

Enhanced Predictive Modeling for Myocardial Infarction Risk ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Abdulkadar, S. Dharun, S. Kirthivashan, V. Janani, B. K.S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331509675

Since myocardial infarction (MI) continues to be a major cause of death worldwide, precise risk prediction is essential. In order to improve MI risk assessment, this work presents an improved predictive model that makes use of multimodal data, including clinical, genetic, imaging, and lifestyle information. By utilizing sophisticated machine learning methods, specifically the XGBClassifier, the model combines various datasets to reveal intricate patterns, attaining exceptional sensitivity and specificity. Robust reliability testing, a comparison with current approaches, and the use of interpretability tools such as SHAP guarantee that the suggested model provides useful information for clinical decision-making. Addressing dataset biases and extending feature integration to improve generalizability are two potential future steps. © 2025 ieee.

关键词： AI Model and Accuracy Myocardial infarction streamlit metrics Train XGBClassifier

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Efficient Architecture Integrating Wav2Vec 2.0 and Transformer Decoding for Low-Resource Environments 3

Efficient Architecture Integrating Wav2Vec 2.0 and Transform...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Narayanasamy, Kanagaraj Sathish Kumar, L. Saravanan, D. Maragatharajan, M. Department of Computer Science Sivapuram Pudukkottai India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

This article introduces a cutting-edge Automatic Speech Recognition (ASR) architecture that combines a Transformer-based decoder optimised with weak supervision with Wav2Vec 2.0 for self-supervised feature extraction. By using high-level representations of raw audio and optimising with poorly tagged data, the architecture overcomes the difficulties posed by a lack of annotated datasets. Improved transcription accuracy is demonstrated by the model's notable decrease in Word Error Rate (WER), which goes from 15.6% with baseline RNN models to 7.2%. Furthermore, the system's computational efficiency is demonstrated by the 25% reduction in training time. The outcomes demonstrate that the suggested framework is a reliable option for ASR tasks in low-resource environments, attaining a fair compromise between computational cost, data needs, and performance. © 2025 ieee.

关键词： Decoding

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Brain Signal-Based Motion Sickness Classification in Automobile Passengers 13

Brain Signal-Based Motion Sickness Classification in Automob...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Kim, Tae Hun Lee, Hyun Min An, Jinung Interdisciplinary Studies Daegu Gyeongbuk Institute of Science and Technology Korea Republic of

ISBN: (纸本)9798331521929

Motion sickness remains a critical challenge for enhancing passenger comfort, particularly in autonomous vehicles, where non-driving activities are a primary benefit. This study investigates the multi-class classification of motion sickness levels using brain signals measured through 8-channel EEG and 1-channel fNIRS during general road driving scenarios, including motion sickness-inducing sections. A total of four deep learning models (CNN, LSTM, EEGNet, and Conformer) were employed, with the Conformer demonstrating superior performance. The results reveal that specific motion sickness levels, including the critical transition phase from mild to severe motion sickness, can be effectively identified using EEG and fNIRS data. EEG analysis highlighted distinct brain activation patterns across motion sickness levels, while fNIRS demonstrated higher classification accuracy due to its sensitivity to changes in cerebral blood flow caused by accelerations and decelerations. The combined use of EEG and fNIRS achieved the highest accuracy of 78.64%, demonstrating the synergistic potential of multi-modal data. © 2025 ieee.

关键词： Autonomous vehicles Brain signals Deep learning EEG fNIRS Motion sickness

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iDANSE: Iterative data-driven Nonlinear State Estimation of Model-free Hidden Sequences

iDANSE: Iterative Data-driven Nonlinear State Estimation of ...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Hang Ghosh, Anubhab Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350368741

We introduce a model-free hidden sequence (MHS) estimation problem where the task is to estimate a long sequence of 'model-free' process hidden under additive Gaussian noise. To estimate the posterior of the hidden sequence from the noisy observation sequence, we have three main challenges: (a) the process is 'model-free', that means there is no underlying state-space-model (SSM), limiting the use of traditional SSM-informed model-based methods like Kalman filter (KF) and particle filter (PF);(b) only an unlabelled dataset comprised of noisy observation sequences is available as training data, and hence no supervised learning possible;and, (c) the use of sequential ancestral sampling based methods, like dynamical variational autoencoders (DVAEs), results in prohibitive complexity. To address the challenges we adopt a recently established recurrent neural network (RNN)-based method called DANSE (data-driven nonlinear state estimation). We develop an iterative DANSE (iDANSE) in unsupervised learning setup where a set of neural networks (NNs) are used iteratively. The set of NNs refines MHS estimation over the iterations. Using simulations, the performance of iDANSE is demonstrated for a benchmark Lorenz process (a chaotic attractor) and compared with SSM-informed extended KF (EKF) and unscented KF (UKF). © 2025 ieee.

关键词： deep neural networks Kalman filter sequential modelling State estimation unsupervised learning

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Leveraging Eager Learning for Retinal Abnormality Classification with Local Binary Pattern Features

Leveraging Eager Learning for Retinal Abnormality Classifica...

引用

2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Silvanus Harianto, Eric Chandra Setiawan, Benny Yuda Prasetyo, Simeon Setyo Purwanto, Eko School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

This study investigates the integration of Local Binary Pattern (LBP) features with eager learning models for the classification of retinal abnormalities, focusing on diseases like Diabetic Retinopathy (DR) and Age-Related Macular Degeneration (AMD). Using the Retinal OCT Images (optical coherence tomography) dataset, we show that LBP is an effective technique for capturing the textural patterns associated with retinal conditions. Among various classifiers, the Random Forest model achieved the highest performance, with superior accuracy, recall, and F1 scores. This highlights the potential of LBP features combined with eager learning for robust and efficient classification of retinal diseases. The approach offers significant advantages for time-sensitive medical applications, supporting early predictions and real-time clinical workflows. Despite promising results, challenges such as addressing false positives and improving precision for overlapping conditions remain. Future work should explore deep learning models, hybrid feature extraction, and optimization strategies to enhance performance. Overall, this research demonstrates the potential for automated retinal disease detection systems, contributing to improved early diagnosis and better management of vision-threatening conditions. © 2025 ieee.

关键词： Local binary pattern

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Decentralization of Cloud Environment Using DES Technique in Medical Image 2

Decentralization of Cloud Environment Using DES Technique in...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Roselinkiruba, R. Deepan, S. Sharmila, L. Poonguzhali, S. Jude Moses Anto Devakanth, J. Kavitha, J. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India S.A.College of Arts & Science India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331505745

Cloud security is an important aspect in centralized data management in public resources. By increasing data processing and service access level, the integrity level be deformed due to data leakage, spoofing, and key leakages because of user behavioral approach to access the data is malpractice to own data. So the owner policy become unauthenticated to access the data rapidly increasing by non-authenticated users. To resolve this problem, propose a Service Level Integrity Proofing (SLIP) Verified data Security. Based on Decentralized ChainLink Provable Advanced Encryption Standard (DCLP-AES) in Cloud Environment, this proposed system verifies each service access policy of user which is relatively posed to aggregate the owner certificate signing at chain-link verification. The verification can be time- stamped after the transaction is recorded into integrity weight. This proposed system improve the security levels based on integrity security signing level to protect the data compared to the other system as well high performance in security. © 2025 ieee.

关键词： Information leakage

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data Fusion Algorithm in Agricultural Internet of Things System 3

Data Fusion Algorithm in Agricultural Internet of Things Sys...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Wang, Fang School of Business Changchun College of Electronic Technology Changchun China

ISBN: (纸本)9798331508456

Agricultural production relies on a large number of sensors to obtain environmental data, and how to effectively integrate this data has become a major challenge in current agricultural IoT applications. Therefore, this article proposes a novel data fusion algorithm. Firstly, the weighted average method is used to preliminarily fuse data from different types of sensors;Then, deep neural networks (DNNs) are used to further extract potential features from the fused data. Finally, the weighting strategy and network model were optimized through experiments. The experimental results indicate that the research method performs well in terms of accuracy, spatiotemporal consistency, and real-time performance. Compared with the weighted average method, this research method reduced the mean square error (MSE) by about 29% and increased the dynamic time warping (DTW) score by about 40.7%. The results verified that the method is practical in improving data quality and suitable for large-scale data processing in agricultural IoT. © 2025 ieee.

关键词： Sensor data fusion

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