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Deep learning Approaches for Epileptic Seizures Recognition based on EEG Signal 46

Deep Learning Approaches for Epileptic Seizures Recognition ...

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46th international conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Recognition system of human actions is a popular research field in healthcare which is essential in identifying abnormal patient activities to estimate their psychological state. Epileptic seizures are a common neurological disorder affecting millions of individuals globally. Early and accurate diagnosis of epilepsy can lead to up to 70% of seizure-free patients. there is a requirement for smart and automated HAR systems to aid clinicians in accurately diagnosing neurological disorders. this study examines the latest deep learning networks for time-series classification to detect epileptic seizures using EEG signal data. To enhance recognition performance, a hybrid residual network called Inception-ResNet is presented for the binary classification of epileptic activities. through thorough analysis, the Inception-ResNet model was observed to provide the best results, with an average accuracy and F1-score of 99.61%. © 2023 IEEE.

关键词： Electroencephalography

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CAPTURING thE MOTION OF EVERY JOINT: 3D HUMAN POSE AND SHAPE ESTIMATION WIth INDEPENDENT TOKENS 11

CAPTURING THE MOTION OF EVERY JOINT: 3D HUMAN POSE AND SHAPE...

引用

11th international conference on learning Representations, ICLR 2023

作者： Yang, Sen Heng, Wen Liu, Gang Luo, Guozhong Yang, Wankou Yu, Gang School of Automation Southeast University China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Tencent PCG China

In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. this task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely on the initialized mean pose and shape as prior estimates and parameter regression with an iterative error feedback manner. In addition, video-based approaches model the overall change over the image-level features to temporally enhance the single-frame feature, but fail to capture the rotational motion at the joint level, and cannot guarantee local temporal consistency. To address these issues, we propose a novel Transformer-based model with a design of independent tokens. First, we introduce three types of tokens independent of the image feature: joint rotation tokens, shape token, and camera token. By progressively interacting with image features through Transformer layers, these tokens learn to encode the prior knowledge of human 3D joint rotations, body shape, and position information from large-scale data, and are updated to estimate SMPL parameters conditioned on a given image. Second, benefiting from the proposed token-based representation, we further use a temporal model to focus on capturing the rotational temporal information of each joint, which is empirically conducive to preventing large jitters in local parts. Despite being conceptually simple, the proposed method attains superior performances on the 3DPW and Human3.6M datasets. Using ResNet-50 and Transformer architectures, it obtains 42.0 mm error on the PA-MPJPE metric of the challenging 3DPW, outperforming state-of-the-art counterparts by a large margin. Code will be publicly available. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Image enhancement

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Differentially Distributed Private Intelligence Security in Cybersecurity Infrastructures 5

Differentially Distributed Private Intelligence Security in ...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Jenifer, P. Geebin, A.R. Brundha, P. Manohar, E. Francis Xavier Engineering College Department of Cse Tirunelveli India

ISBN: (纸本)9781665474672

Novel ways of security against attacks to critical infrastructure are becoming more important as their number and sophistication increase. In order to provide the kind of security that is necessary, one must consider both preventive measures and an understanding of the current state of affairs inside the systems being protected. Researches and institutions have brought security solutions that are both intelligent and selfsufficient, such as the necessity for sophisticated proactive monitoring capability in Intrusion Detection Systems (IDS). this study argues that critical infrastructures need automated IDS solutions and that adding Machine learning (ML) approaches into IDS will be useful for developing dynamically adjustable algorithms that can promptly respond to changing circumstances and threats. To this end, this study assesses the several degrees of automation provided by the IDS and provide a plan for introducing preemptive automation into critical settings. Finally, this study evaluates how well the proposed method stacks up against other methods previously presented in the literature. Machine learning presents difficulties in the field of cybersecurity that must be effectively addressed using sound methodology and theory. Transfer learning, support vector machines, and Bayesian classification are just a few examples of the statistical and machine learning approaches that have been shown to be useful in reducing the effects of cyberattacks. It is crucial to establish smart security systems that can discover hidden patterns and insights from packet headers and then construct a data-driven machine-learning model to avoid these assaults. this study examines the use of machine learning methods to cybersecurity data with the goal of bolstering the safety of existing infrastructures. this article has addressed the current state of cybersecurity threats as well as the deployment of machine learning methods to combat the aforementioned challenges. © 2023 IEEE.

关键词： Cybersecurity

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Lung Disease Classification using X-Ray Imaging with Ensemble learning

Lung Disease Classification using X-Ray Imaging with Ensembl...

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intelligent Systems and Embedded Design (ISED), international conference on

作者： Kriti Vij Saloni thakur Aman Sharma Rajni Mohana Computer Science and Engineering Department Jaypee University of Information Technology Solan India Department of Computer Engineering Amity University Punjab India

Lung diseases are regarded as serious health issues in both middle-aged adults and youngsters. they cover a variety of ailments, including asthma, COPD, and other respiratory diseases. For effective treatment and positive patient outcomes, lung illnesses must be detected early and accurately. However, due to the intricacy of the circumstances, physical inspection of X-ray images of chest for lung illness diagnosis can be mistake-prone. automated classification methods can be proposed using Machine learning and Deep learning. We can utilise the advantages of various independent models to increase accuracy and dependability by using ensemble learning. In this study, we utilised the benefits of two popular deep learning models, ResNet and VGG16 to classify x-ray images. the proposed ensemble model helps to multi classify the lung x-ray images and it outperforms the existing available ensemble model with an accuracy of 97.62%.

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Time Series Processing Scheme Based on Deep learning and Spatio-temporal Network Modeling 11th

Time Series Processing Scheme Based on Deep Learning and Spa...

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11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Xu, Zetao Li, Jinlei Wang, Jingzhong Liu, Jia Liu, Yunlong Wang, Yue Liu, Mengdi Feng, Zhebin China Mobile Group Design Institute Co. Ltd. Beijing China China Mobile Communications Group Design Institute Co Shandong China China Mobile Communications Group Design Institute Co Heilongjiang China

ISBN: (纸本)9789819721238

In order to study how massive data with periodic and tidal performance influences identification and processing in present network, this paper proposes a data-processing method combined with deep learning algorithm, XGBoost algorithm and spatio-temporal network model. By collecting a large amount of data from existing network and dealing it with pre-treatment, such as anomaly detection and periodicity detection, this proposed data processing method is capable of weeding out erroneous data and fulfill whole data set. In this way, we are capable of getting some characterized results and forming a result set. XGBoost algorithm and space-temporal network model, can then help with data analysis and providing reference for subsequent research such as feasibility analysis and scheme design. this scheme proposed in this paper is able to work in an effective way, especially when facing problems composed of large amount of data with periodicity. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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Research of Medical Tumor Image Segmentation Algorithm Based on U-Net 5

Research of Medical Tumor Image Segmentation Algorithm Based...

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5th international conference on Advances in Electrical engineering and Computer Applications, AEECA 2024

作者： Zhu, Yaling Zheng, Gang Li, Xiangwei Lanzhou Institute of Technology College of Software Engineering Lanzhou China

ISBN: (纸本)9798350355253

this research aims to optimize and rigorously evaluate the effectiveness of a U-Net-based deep learning model, with a focus on improving the automated segmentation of tumor regions in medical images. It adopts an encoder-decoder architecture augmented with skip connections, empowering the U-Net model to adeptly capture intricate spatial details and features from images, thereby bolstering segmentation precision. Targeting liver CT scans with tumorous regions, the study develops and refines a customized U-Net model, leveraging the 3D-IRCADb dataset for comprehensive training and evaluation. the experimental outcomes underscore the model's exceptional capabilities, exhibiting a minimal loss rate and an accuracy surpassing 98%, underscoring its ability to delineate tumorous areas with remarkable precision. Notably, it achieves a mean Intersection over Union (mIoU) of approximately 83%, and a mean Pixel Accuracy (mPA) of 87%, while maintaining high Precision and Recall rates at around 94% and 98%, respectively. these robust evaluation metrics not only affirm the model's proficiency in segmenting intricate tumor images but also demonstrate its stability and adaptability. Furthermore, the model demonstrates impressive generalization potential, accurately segmenting tumors across diverse CT image sets, underscoring its potential as a versatile tool for medical image processing. In essence, this research, through the optimization of the U-Net deep learning paradigm, introduces an efficient and precise methodology for medical tumor image segmentation. It holds promise for clinical applications and is poised to propel advancements in the realm of medical image analysis, fostering innovation and improved patient care. © 2024 IEEE.

关键词： Image segmentation

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Application of Mathematical Modeling Based on MATLAB in Experimental data Analysis 11th

Application of Mathematical Modeling Based on MATLAB in Expe...

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11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Lin, Danfeng Shandong Transport Vocational College Shandong Weifang China

In order to serve the "Manufacturing Power", adapt to the new scientific and technological revolution and industrial change, and support the innovation-driven development, the Ministry of Education (MOE) has been vigorously promoting the construction of the "new engineering discipline", exploring a new engineering education model in China, and serving the construction of the "Education Power". Education Power". New engineering is an important initiative for Chinese universities to realise the principle of cultivating innovative talents, and it is also a major development trend and choice for the development of Chinese higher education. In the rapid development of education today, the integration of modern information technology into classroom experiments is essential to improve the teaching effect. this paper discusses the application of MATLAB in mathematical modelling to analyse experimental data, especially in physics experiments. Using MATLAB, students can deepen their understanding of mathematical concepts and physical principles through visualisation and interactive programming. In this study, MATLAB was used to assist teaching and learning, and a comparison experiment was conducted between a control group and an experimental group. the results show that MATLAB can significantly improve students' understanding and retention of knowledge, stimulate learning interest and enhance modelling ability. Analysis of the experimental data showed that the average score of students in the MATLAB-assisted class was 71.06, which was significantly higher than that of the traditional class (65.64), highlighting the effectiveness of MATLAB in enhancing students' understanding and retention of physics concepts. However, some challenges such as increased complexity and additional learning burden were also identified. the paper concludes with recommendations for the effective integration of MATLAB into the curriculum, emphasising its role as an aid to traditional physics teaching

关键词： MATLAB

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Research on Financial Fraud Identification Model Based on Machine learning Algorithms 11

Research on Financial Fraud Identification Model Based on Ma...

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11th international Symposium on Project Management, ISPM 2023

作者： Lin, Huijuan Zhang, Jingkun Wen, Xiaohao Beijing Institute of Technology Guangdong Zhuhai China Teachers college for Vocational and Technical Education Guangxi Normal University Guilin China

ISBN: (纸本)9781713877134

this research aims to construct a financial fraud identification model based on machine learning algorithms. the study analyzes the characteristics and patterns of financial fraud and utilizes the advantages and characteristics of the BP neural network to construct a financial fraud prediction model suitable for the new retail industry. Techniques such as feature selection analysis are adopted to screen and analyze key indicators and construct a financial statement fraud recognition model, which is then verified. the study utilizes 2990 original financial data and compares the traditional back-propagation function with the binary cross-entropy loss function more suitable for binary classification problems. the AUC-ROC curve is used as the evaluation standard, and the results show that the binary cross-entropy loss function is 16.57% more accurate with an AUC value increased by 0.253 compared to the traditional neural network. the accuracy of the model constructed in this paper is 84.43%. the research results have practical application value and theoretical significance in identifying financial fraud behavior, improving the financial management level of the new retail industry, enhancing the financial integrity of companies, and maintaining the economic order of society. © (2023) by Aussino Academic Publishing House All rights reserved.

关键词： Backpropagation

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An intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th international conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

the creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine learning (ML) algorithms, particularly optimization techniques. the widespread use of electronic devices has sparked a strong desire to incorporate the Internet of things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. the objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). this paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. the implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. the use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. the findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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A Comparative Study of Log-Based Anomaly Detection Methods in Real-World System Logs 10

A Comparative Study of Log-Based Anomaly Detection Methods i...

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10th international conference on Internet of things, Big data and Security, IoTBDS 2025

作者： Nipa, Nadira Anjum Bouguila, Nizar Patterson, Zachary Concordia Institute for Information and Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9789897587504

the reliability and security of today’s smart and autonomous systems increasingly rely on effective anomaly detection capabilities. Logs generated by intelligent devices during runtime offer valuable insights for monitoring and troubleshooting. Nonetheless, the enormous quantity and complexity of logs produced by contemporary systems render manual anomaly inspection impractical, error-prone, and laborious. In response to this, a variety of automated methods for log-based anomaly detection have been developed. However, many current methods are evaluated in controlled environments with set assumptions and frequently depend on publicly available datasets. In contrast, real-world system logs present greater complexity, lack of labels, and noise, creating substantial challenges when applying these methods directly in industrial settings. this work explores and adapts existing machine learning and deep learning techniques for anomaly detection to function on real-world system logs produced by an intelligent autonomous display device. We conduct a comparative analysis of these methods, evaluating their effectiveness in detecting anomalies through various metrics and efficiency measures. Our findings emphasize the most efficient approach for detecting anomalies within this specific system, enabling proactive maintenance and enhancing overall system reliability. Our work provides valuable insights and directions for adopting log-based anomaly detection models in future research, particularly in industrial applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Industrial research

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