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3rd International Conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wu, Rutao Xu, Zhiwei Liu, Xuyang Liu, Wenjing Liu, Limin School of Data Science and Application Inner Mongolia University of Technology Huhehaote010000 China Inner Mongolia Autonomous Reg. Software Eng. Serv. Eng. Technology Research Center Based on Big Data Huhehaote010000 China Inner Mongolia Autonomous Region Product Quality Inspection and Research Institute Huhehaote010070 China Haihe Laboratory of Information Technology Application Innovation Tianjin300450 China Institute of Computing Technology of the Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9781510680449

The purpose of data penetration is to achieve timely exchange of data information while ensuring the security of network isolation environment. The current research mainly focuses on protecting data privacy and developing basic data penetration methods for netgap, but these methods generally overlook the adaptive matching of data. Therefore, this article proposes an intelligent netgap data penetration mechanism based on database adaptive matching, which is an end-to-end cross netgap data packet adaptation mechanism. To overcome the problems of overfitting and local optima, XGBoost based on adaptive particle swarm optimization is adopted. By increasing the diversity of subgroups, the adaptive matching effect was improved, and the optimized XGBoost model was fitted to real-time data, with an accuracy increase from 72% to 94%. The experimental results show that this mechanism significantly improves the efficiency of packet adaptive matching while solving the problem of data penetration in the isolated environment of the network gate. The time cost is only 50% of the previous one, which improves the throughput of the system. The optimized XGBoost model is superior to the original model in terms of error and accuracy, and has certain practicality and easy generalization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data privacy

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction 2

Impurity explicit optimal Machine learning and statistical p...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Interpretable Deep Learning for Solar Flare Prediction

Interpretable Deep Learning for Solar Flare Prediction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Vinay Ram Gazula Katherine G. Herbert Yasser Abduallah Jason T. L. Wang Department of Data Science New Jersey Institute of Technology Newark NJ USA School of Computing Montclair State University Montclair NJ USA Department of Computer Science New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

We propose to incorporate three interpretable methods, namely SHAP (SHapley Additive exPlanations), PDP (partial dependence plots) and Anchors, into a deep learning-based model, called SolarFlareNet, for operational flare forecasting. SolarFlareNet takes as input a sample of SHARP (Space-weather HMI Active Region Patches) magnetic parameters and predicts as output whether a solar flare would occur within the next 24 hours. We analyze flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite's X-ray flare catalogs and construct a database of flares with identified active regions in the catalogs. This database, together with the SHARP magnetic parameters, is used to train and test the SolarFlareNet model. Our experimental results describe the use of the three proposed methods (SHAP, PDP, and Anchors) to interpret the SolarFlareNet model and demonstrate the effectiveness of the methods.

关键词： Deep learning Additives Accuracy databases Explainable AI Predictive models Forecasting

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Machine Unlearning Through Fine-Grained Model Parameters Perturbation

arXiv

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arXiv 2024年

作者： Zuo, Zhiwei Tang, Zhuo Li, Kenli Datta, Anwitaman College of Computer Science and Electronic Engineering Hunan University China College of Computing and Data Science Nanyang Technological University Singapore Shenzhen Research Institute Hunan University China Faculty of Computing Engineering & Media De Montfort University Leicester United Kingdom

Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree. © 2024, CC BY.

关键词： Differential privacy

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Inverse Multiobjective Optimization by Generative Model Prompting

Inverse Multiobjective Optimization by Generative Model Prom...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jiao Liu Abhishek Gupta Yew-Soon Ong Puay Siew Tan College of Computing & Data Science Nanyang Technological University (NTU) Singapore School of Mechanical Sciences Indian Institute of Technology Goa India A*STAR Singapore Institute of Manufacturing Technology (SIMTech) Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The integration of multiobjective optimizers with inverse models—that map points on the Pareto front to corresponding nondominated solutions—has drawn attention. These inverse models serve a dual purpose, not only facilitating the generation of candidate solutions during the optimization process, but also offering insights for multiobjective decision-making upon completion of optimization. However, today’s inverse models mainly serve to capture one-to-one mapping relations, restricting them to learn only from nondominated solution samples. As a result, the information embedded in dominated samples is not fully utilized. In this paper, we introduce a novel approach of building conditional inverse generative models (invGMs) from optimization data, making the most of both nondominated and dominated solution samples during training. Different from standard inverse models, decision-makers can query such invGMs with prompts expressed in the form of any desired objective function values, leading them to produce a corresponding solution. Through iterative prompting, invGMs are shown to accelerate the creation of diverse sets of high-quality solutions even during the course of multiobjective optimization runs. Empirical studies on three industrial optimization problems highlight the proposed method’s faster convergence rate and improved inverse modeling accuracy.

关键词： Training Accuracy Inverse problems Decision making Linear programming data models Iterative methods

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A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective

arXiv

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arXiv 2024年

作者： Liu, Chenyu Zhou, Xinliang Wu, Yihao Ding, Yi Zhai, Liming Wang, Kun Jia, Ziyu Liu, Yang College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Central China Normal University China University of Science and Technology of China China Institute of Automation Chinese Academy of Sciences China

Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature of emotions is a physiological and psychological state change in response to brain region connectivity, making emotion recognition focus more on the dependency between brain regions instead of specific brain regions. A significant trend is the application of graphs to encapsulate such dependency as dynamic functional connections between nodes across temporal and spatial dimensions. Concurrently, the neuroscientific underpinnings behind this dependency endow the application of graphs in this field with a distinctive significance. However, there is neither a comprehensive review nor a tutorial for constructing emotion-relevant graphs in EEG-based emotion recognition. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of graph-related methods in this field from a methodological perspective. We propose a unified framework for graph applications in this field and categorize these methods on this basis. Finally, based on previous studies, we also present several open challenges and future directions in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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PRISM: PRIVACY-PRESERVING INTER-SITE MRI HARMONIZATION VIA DISENTANGLED REPRESENTATION LEARNING

arXiv

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arXiv 2024年

作者： Galada, Sarang Halder, Tanurima Deo, Kunal Krish, Ram P. Jadhav, Kshitij Trust Lab Indian Institute of Technology Bombay India Koita Centre for Digital Health Indian Institute of Technology Bombay India School of Computing and Data Science Sai University Chennai India

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM’s effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://***/saranggalada/PRISM Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Novel Pipeline for Compressing Image Buffers in Remote Education Video Conferencing using Harris Corner Detection and Pixel Map Array

A Novel Pipeline for Compressing Image Buffers in Remote Edu...

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International Conference on Electronics, Communication and computing Technologies (ICECCT)

作者： Gita Alekhya Paul Ansh Sharma Yashvardhan Jagnani Abhishek Saxena P Supraja Networking and Communications Department SRM Institute of Science and Technology Chennai India Computing Technologies Department SRM Institute of Science and Technology Chennai India Data Science and Business Systems Department SRM Institute of Science and Technology Chennai India

The COVID-19 pandemic has compelled educational institutions worldwide to shift to remote online education. Addressing the growing trend, an Oxford University Press report titled ‘Addressing the Deepening Digital Divide’ states that poor digital access is the most significant barrier to digital learning according to 68 percent of academicians. Students in many remote parts of India frequently have access to limited bandwidth internet, which is insufficient for the modern standards of network-hogging online video conference software solutions. This paper provides an algorithmic compression of image buffers to aid low-cost remote online video education. This compression can be done by translating the teacher's blackboard images to pixel arrays projected on a canvas on the student's dashboard while the instructor constantly communicates via real-time voice. The image is first converted to grayscale and dilated with a square kernel. Using Harris Corner Detector, probable board corners are identified and compared to a geometrical center of the points and the corners recovered by cornerSubPix. An adaptive threshold is employed, distinguishing the board's contents from the backdrop on the cropped picture based on the recovered points. The pixel-mapped array is then transmitted to the students through the webRTC real-time protocol, which includes support for two-way audio, allowing the teacher to deliver lectures. Using Canvas API on the application front-end, the array is projected onto the student's device as a dot matrix display. This paper has achieved an effective rate in the video transmission format, aiding online remote education on low-bandwidth network devices.

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ChronoFact: Timeline-based Temporal Fact Verification

arXiv

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arXiv 2024年

作者： Barik, Anab Maulana Hsu, Wynne Lee, Mong Li School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Centre for Trusted Internet and Community National University of Singapore Singapore

Temporal claims, often riddled with inaccuracies, are a significant challenge in the digital misinformation landscape. Fact-checking systems that can accurately verify such claims are crucial for combating misinformation. Current systems struggle with the complexities of evaluating the accuracy of these claims, especially when they include multiple, overlapping, or recurring events. This work introduces a novel timeline-based fact verification framework that identify events from both claim and evidences and organize them into their respective chronological timelines. The framework systematically examines the relationships between the events in both claim and evidence to predict the veracity of each claim event and their chronological accuracy. This allows us to accurately determine the overall veracity of the claim. We also introduce a new dataset of complex temporal claims involving timeline-based reasoning for the training and evaluation of our proposed framework. Experimental results demonstrate the effectiveness of our approach in handling the intricacies of temporal claim verification. © 2024, CC BY.

关键词： Spatio-temporal data

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An Enhanced CNN Model for Motor Imagery-Based Brain Computer Interface

An Enhanced CNN Model for Motor Imagery-Based Brain Computer...

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International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

作者： Balasubramanian Prabhu Kavin Jeeva Selvaraj Velliangiri Sarveshwaran Prakash B Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu India Department of Computational Intelligence SRM Institute of Science and Technology Tamilnadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

An essential Brain-Computer Interface (BCI) paradigm, Motor Imagery (MI) based BCI calls for robust classifiers. In recent years, deep learning technology has been widely adopted, and numerous attempts have been made to apply it to the field of BCI. As much progress has been made in improving the accuracy of deep learning-based MI-BCI systems in comparison to more conventional methods, there is still a significant challenge in accurately interpreting the deep learning models. This study offers an Optimized Complex Structure based 1-D Convolutional Neural Network (OCS-1D-CNN) model for automatic detection using MI image classification to overcome these concerns. Enhancing its properties for distinguishing normal conditions and other levels, 2D maps can be turned into feature signals using two-round 1D convolutional procedures in the layer. Gray Relational Analysis (GRA) investigation classifiers are used to screen the different layers in the classification layer due to their simple mathematical operation. The Artificial Jellyfish Search Algorithm (AJSO) finds the best combination of summation layer weight and GRA weight. Using two big, publicly available databases containing data on 42 and 62 people, the suggested model is put through its paces. Based on the findings, the models outperformed conventional methods.

关键词： Deep learning Biological system modeling Computational modeling Imaging Brain modeling Nonhomogeneous media Mathematical models

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