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EEG signals based automated epilepsy detection using tunable-Q wavelet transform and Hjorth parameters

EEG signals based automated epilepsy detection using tunable...

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Data Science and Information Technology (DSIT), international conference on

作者： Kun Li Meili Lu School of Information Technology Engineering University of Technology and Education Tianjin China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

In recent years, electroencephalogram (EEG) signals have been increasingly used in the automatic identification of brain diseases. Most of the current EEG-based methods for epilepsy classification focused on single dimensional feature extraction techniques in the time domain or frequency domain, respectively. In this paper,we proposed a joint time-frequency domain feature extraction method using tunable-Q wavelet transform and Hjorth parameters. the tunable-Q wavelet transform is used to decompose the EEG signal into different subbands, the optimal subbands are calculated according to the maximum energy criterion and divided into different frequency ranges, and the Hjorth parameters (activity, mobility, and complexity) are calculated on the optimal self-bands of different frequency ranges, which are applied to the machine learning algorithm to realize the automatic epilepsy detection. the experimental results show that the method has a high classification accuracy, determines the frequency range in which seizures are active and further improves the accuracy of automated seizure detection.

关键词： Wavelet transforms Time-frequency analysis Accuracy Wavelet domain Epilepsy Frequency conversion Wavelet analysis Feature extraction Electroencephalography complexity theory

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Temporal Action Detection with Frequency Attention Mechanism

Temporal Action Detection with Frequency Attention Mechanism

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IEEE international conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Wanghui Wu Tao Lu Jiaming Wang Pan Tang Fangqun Gao Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Wuhan Institute of Technology Wuhan China State Key Laboratory of Power Grid Environmental Protection China Electric Power Research Institute Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

Due to the variability of video length and action duration, the temporal action detection task faces the problem of blurred action boundaries that are difficult to capture accurately. To alleviate this problem, this paper proposes a Frequency Attention Mechanism (FAM) that adaptively models the frequency dependencies between video signal channels, enabling the model to better understand the frequency variations in the video and to handle the complexity of different action durations, thus enhancing the sensitivity and discriminative power of the action boundaries, and still providing powerful action recognition even in long video sequences Capabilities. through comprehensive experimental validation on a series of representative benchmark datasets (e.g. thUMOS14 and ActivityNet1.3), our approach demonstrates significant performance improvement.

关键词： Adaptation models Attention mechanisms Sensitivity Mechatronics Video sequences Benchmark testing Transformers complexity theory Robotics and automation Faces

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Enhanced Investment Prediction via Advanced Deep Learning Ensemble

Enhanced Investment Prediction via Advanced Deep Learning En...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Ningjing Sang Wenxiu Cai Chang Yu Mujie Sui Hao Gong Columbia University New York USA University of San Francisco Harrison USA Independent Researcher Boston USA

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Accurate prediction of investment returns is crucial for financial decision-making. Traditional models often struggle with the complexity and variability inherent in financial data. this article presents an advanced predictive model integrating three deep neural networks, pretraining, and ensemble learning to enhance prediction accuracy. By leveraging a combination of DNNs for handling different complexities of data, and a CNN for efficient feature extraction, our approach significantly outper-forms traditional methods. Our model incorporates sophisticated data handling techniques, including extensive preprocessing and data augmentation, and benefits from pretraining and ensemble methods to improve robustness and performance. this comprehensive framework offers a robust solution for investment return prediction, addressing the shortcomings of existing models and providing enhanced accuracy and reliability.

关键词： Deep learning Accuracy Computer network reliability Predictive models Data models Robustness complexity theory Convolutional neural networks Ensemble learning Investment

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Enhancing Educational Content Matching Using Transformer Models and InfoNCE Loss

Enhancing Educational Content Matching Using Transformer Mod...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Yujian Long Dian Gu Xinrui Li Peiqing Lu Jing Cao Independent Researcher TX USA University of Pennsylvania Philadelphia USA Independent Researcher Austin USA Boston University Maplewood USA Northeastern University Oakland USA

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

the task of matching educational content to specific topics within curricula is crucial for enhancing learning outcomes and ensuring that students receive relevant and coherent learning experiences. this research seeks to develop a precise and efficient model, leveraging a dataset that spans various languages and numerous STEM fields. Our proposed solution integrates a Transformer model with InfoNCE Loss and model distillation techniques to effectively address noise from related content and improve matching accuracy. By incorporating advanced deep learning methods, the model is designed to handle the complexity and variability of educational datasets, outperforming existing models in terms of accuracy and efficiency. this approach provides a robust framework for educational content alignment, significantly benefiting educational practitioners by streamlining the content matching process and enhancing the quality of education delivered to students.

关键词： Deep learning Accuracy Computational modeling Noise Education Transformers complexity theory Information systems

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Classification of Learner Retention using Machine Learning Approaches 7

Classification of Learner Retention using Machine Learning A...

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7th international conference on Research and Innovation in Information Systems, ICRIIS 2021

作者： Amalina Diyana Suhaimi, Nur Binti Kamaruddin, Norshaliza Subramaniam, thirumeni T Amir Sjarif, Nilam Nur Binti Bte Masrom, Maslin Maarop, Nurazean Bte Universiti Teknologi Malaysia Centre for Research and Innovation Open University Malaysia Selangor Malaysia Universiti Teknologi Malaysia Fakulti Teknologi Informatik Razak Kuala Lumpur Malaysia Centre for Research and Innovation Open University Malaysia Selangor Malaysia Universiti Teknologi Malaysia Fakulti Teknologi 7 Informatik Razak Kuala Lumpur Malaysia

ISBN: (纸本)9781665408059

Learner retention issues require a huge commitment from a university as the process of monitoring learners' re-registration status from the beginning of each semester until they graduate can be quite tedious. When the number of learners who re-register for a subsequent semester is low, it not only affects the university's image but also its ranking and reputation in the education sector. therefore, the university must identify, at an early stage, the likelihood of a learner is not retained in the following semester. this study proposed to experiment with the classification methods for solving the issue of learner retention at Open University Malaysia by comparing three Supervised Machine Learning algorithms namely Logistic Regression, Support Vector Machine, and k-Nearest Neighbor. the performance of these algorithms was evaluated based on accuracy, precision, recall, and f-measure. It is determined that Support Vector Machine showed the best accuracy in classifying the learners' retention rate with 80% accuracy. the benefit of performing Machine Learning is that it enables the identification of at-risk learners at the earliest opportunity and therefore implement the earliest interventions to retain them. © 2021 IEEE.

关键词： Nearest neighbor search

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Forest Fire Detection Method Based on Improved YOLOv7

Forest Fire Detection Method Based on Improved YOLOv7

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Advanced algorithms and Control Engineering (ICAACE), international conference on

作者： Peiyuan Shi Xiaopeng Wang College of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou Gansu China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

the proposed method addresses the challenge of real-time and high-precision detection of early fires in complex forest environments, a limitation faced by existing object detection algorithms. the method is based on the improvement of YOLOv7. Firstly, the Coordinate Attention (CA) mechanism is introduced into the backbone network to capture the correlation of features at different spatial positions, enhancing the model's feature extraction capabilities. Additionally, CA is incorporated into the Feature Pyramid Network (FPN) to construct the C-FPN feature fusion network, establishing cross-level connections between shallow and deep networks to shorten the path between shallow and deep features and enhance the retrieval capability of deep network position information. Secondly, the Normalized Wasserstein Distance (NWD) is utilized instead of IoU in label assignment, Non-Maximum Suppression (NMS), and loss functions to improve regression accuracy. Finally, the network structure ELAN is improved to ELAN-P, introducing new partial convolutions to reduce floating-point calculations, redundant computations, and memory access, thereby enhancing the efficiency of the detection model. Experimental results demonstrate that compared to the YOLOv7 algorithm, the proposed method achieves a mean Average Precision (mAP) of 96.3%, a 4.6% improvement over YOLOv7. Additionally, the detection frames per second (FPS) reach 67.7 flames/s, enabling real-time and accurate detection of forest fires in complex environments.

关键词： Training Accuracy Correlation Memory management Process control Forestry Object detection

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ICISSP 2021 - Proceedings of the 7th international conference on Information Systems Security and Privacy

ICISSP 2021 - Proceedings of the 7th International Conferenc...

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7th international conference on Information Systems Security and Privacy, ICISSP 2021

ISBN: (纸本)9789897584916

the proceedings contain 86 papers. the topics discussed include: who stores the private key? an exploratory study about user preferences of key management for blockchain-based applications;utilizing keystroke dynamics as additional security measure to protect account recovery mechanism;on security analysis of periodic systems: expressiveness and complexity;predicting security program effectiveness in bring-your-own-device deployment in organizations;improvement of secure multi-party multiplication of () threshold secret sharing using only servers;a permissioned blockchain-based system for collaborative drug discovery;the comparison of word embedding techniques in RNNs for vulnerability detection;a permissioned blockchain-based system for collaborative drug discovery;and optimizing leak detection in open-source platforms with machine learning techniques.

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A Comparative Analysis of Feature Extraction algorithms for Augmented Reality Applications 7

A Comparative Analysis of Feature Extraction Algorithms for ...

引用

7th IEEE international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021

作者： Alam, Mir Suhail Morshidi, Malik Arman Gunawan, Teddy Surya Olanrewaju, Rashidah Funke International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia ECE Department Kuala Lumpur53100 Malaysia School of Electrical Engineering and Telecommunications UNSW SydneyNSW2052 Australia

ISBN: (纸本)9781728175225

the algorithms based on image feature detection and matching are critical in the field of computer vision. Feature extraction and matching algorithms are used in many computer vision problems, including object recognition and structure from motion. Each feature detector and descriptor algorithm's computational efficiency and robust performance have a major impact on image matching precision and time utilization. the performance of image matching algorithms that use expensive descriptors for detection and matching is addressed using existing approaches. the algorithm's efficiency is measured by the number of matches found and the number of faults discovered when evaluated against a given pair of images. It also depends on the algorithm that detects the features and matches them in less amount of time. this paper examines and compares the different algorithm (SURF, ORB, BRISK, FAST, KAZE, MINEIGEN, MSER) performances using distinct parameters such as affine transformation, blur, scale, illumination, and rotation. the Oxford dataset is used to assess their robustness and efficiency against the parameters of interest. the time taken to detect features, the time taken to match images, the number of identified feature points, and the total running time is recorded in this study. the quantitative results show that the ORB and SURF algorithms detect and match more features than other algorithms. Furthermore, they are computationally less expensive and robust compared to other algorithms. In addition, the robustness of ORB and SURF is quite high in terms of outliers, and the amount of time taken to match with the reference is also significantly less. However, the efficiency of SURF reduces against blur transformation. FAST is good in detecting corners but lacks efficiency under different transformations. Experiments show that when each algorithm is subjected to numerous alterations, it has its own set of advantages and limitations. © 2021 IEEE.

关键词： Computer vision

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Optimizing Stand-Alone Photovoltaic-Battery System Sizing through Meta-Heuristic Optimization algorithms

Optimizing Stand-Alone Photovoltaic-Battery System Sizing Th...

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IEEE international conference on Control System, Computing and Engineering (ICCSCE)

作者： Iman Zulhakeem Shuhaimi Nur Atharah Kamarzaman Nor Adni Mat Leh Intan Rahayu Ibrahim Centre for Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Malaysia School of Electrical Engineering College of Engineering Universiti Teknologi MARA UiTM Shah Alam Selangor Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Stand-alone photovoltaic (SAPV) systems, comprising PV panels and lithium-ion battery, offer promising solutions for sustainable energy. However, their widespread adoption is hindered by challenges in achieving optimal sizing, which is crucial for enhancing system reliability. Metaheuristic-based artificial intelligence (AI) has emerged as a promising tool for addressing the complexity in PV system sizing. By leveraging advanced algorithms like Particle Swarm Optimization (PSO) and Wild Horse Optimization (WHO), this approach can optimize the system to be more efficient and reliable. this study aims to develop sizing algorithms and find the optimal sizing for system components using data from PV arrays, batteries, controller and inverter. through the analysis, the study demonstrates the effectiveness of WHO in optimizing the system resulting in improving system reliability. By contributing to Sustainable Development Goal 7, this study promotes the adoption of cleaner and more accessible energy technologies.

关键词： Photovoltaic systems Lithium-ion batteries Metaheuristics Control systems Inverters complexity theory Reliability Particle swarm optimization Artificial intelligence Sustainable development

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Lightweight Schemes Fusion for Heatmap-based Human Pose Estimation

Lightweight Schemes Fusion for Heatmap-based Human Pose Esti...

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IEEE conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Sheng-Jhou Lu Hung-Wei Lee Yu-Ming Han Ji-Min Zhou Ying Liu Huang-Chia Shih Department of Electrical Engineering Yuan Ze University Taoyuan City Taiwan Department of Computer Science and Engineering Santa Clara University Santa Clara CA USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

this study introduces a human pose estimation method using heatmap-based techniques combined with lightweight approaches to reduce model parameters. this enhancement results in faster inference and a smaller spatial footprint. Nonetheless, reducing the number of parameters could potentially impact prediction accuracy. Hence, we implemented measures to improve overall accuracy without significantly increasing computational complexity. We aimed to maintain a certain level of accuracy while achieving model acceleration, enabling efficient and effective human body pose prediction and heatmap generation for subsequent computational tasks.

关键词： Heating systems Accuracy Computational modeling Pose estimation Information processing Predictive models Computational efficiency Computational complexity Testing

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