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检索条件"主题词=CLASSIFICATION algorithms"

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Development of Software for Automated Marking of Seismic P- and S-waves

Development of Software for Automated Marking of Seismic P- ...

引用

Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Kirill V. Kovbasiuk Aleksandr A. Yakimenko Dept. of Computer Engineering Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The paper presents a novel software solution for the automatic classification and detection of seismic P- and S-waves using neural networks. The research focuses on addressing the limitations of manual methods for seismic wave identification and the need for more reliable and efficient automation. The developed software leverages a modified ResNet34 neural network, trained on real seismic event data from Southern California. The approach involves transforming seismic waveforms into spectrograms using Short-Time Fourier Transform, which are then processed by the neural network for classification. Additionally, a post-processing algorithm filters the output to reduce noise and improve accuracy. Testing on real seismic data reveals the effectiveness of the software in identifying P-waves, achieving a Matthews correlation coefficient of 0.82 when using a band-pass filter. However, S-wave detection proved more challenging, with a maximum coefficient of 0.38 using a high-pass filter. The paper identifies this difficulty as a key area for improvement, suggesting that further refinement of neural network architecture and post-processing techniques could enhance performance. Recommendations for the practical application of the software in seismic data analysis and future research directions are also provided.

关键词： Correlation coefficient Band-pass filters Filters Neural networks Transformers Software classification algorithms Software reliability Spectrogram Testing

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Decomposition of Difficulties in Complex Optimization Problems using a Bilevel Approach

Decomposition of Difficulties in Complex Optimization Proble...

引用

Congress on Evolutionary Computation

作者： Ankur Sinha Dhaval Pujara Hemant Kumar Singh Centre for Data Science and AI Indian Institute of Management Ahmedabad Gujarat India School of Engineering and Technology The University of New South Wales Canberra Australia

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Practical optimization problems may contain differ-ent kinds of difficulties that are often not tractable if one relies on a particular optimization method. Different optimization approaches offer different strengths that are good at tackling one or more difficulty in an optimization problem. For instance, evolutionary algorithms have a niche in handling complexities like discontinuity, non-differentiability, discreteness and non-convexity. However, evolutionary algorithms may get computationally expensive for mathematically well behaved problems with large number of variables for which classical mathematical programming approaches are better suited. In this paper, we demonstrate a decomposition strategy that allows us to synergistically apply two complementary approaches at the same time on a complex optimization problem. Evolutionary algorithms are useful in this context as their flexibility makes pairing with other solution approaches easy. The decomposition idea is a special case of bilevel optimization that separates the difficulties into two levels and assigns different approaches at each level that is better equipped at handling them. We demonstrate the benefits of the proposed decomposition idea on a wide range of test problems.

关键词： Heuristic algorithms Evolutionary computation Benchmark testing Complexity theory classification algorithms Quadratic programming Optimization

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Pointer-Type Meter Recognition Based on Elliptical Bounding Box Detection and Reading Regression

Pointer-Type Meter Recognition Based on Elliptical Bounding ...

引用

Chinese Automation Congress (CAC)

作者： Mengjin Zheng Wenbo Xiang Le Zheng Yaobin Mao School of Nanjing University of Science and Technology Nanjin China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The automatic reading of pointer-type meters plays an important role in routine inspection of power substation. However, considering various factors such as meter types, shooting angles, and lighting conditions, the recognition of the pointer meters in natural environment is still a challenging task. To address this issue, an improved YOLOv5 algorithm that using elliptical bounding box instead of traditional rectangle one is utilized to detect and classify dials, and a deep regression model based on UNet is designed to achieve meter readings. This algorithm has many advantages, such as without the need of pre-processing and post-processing, the effective filtering of background information through elliptical bounding boxes, thus can significantly improve the accuracy of reading recognition. Experimental results demonstrate that, within a reading error of ±5%, the recognition accuracy reaches 87.33%, while within a reading error of 10%, the recognition accuracy reaches 96.54%.

关键词： Meters YOLO Image quality Accuracy Substations Lighting Interference Inspection classification algorithms Meter reading

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A Comprehensive Study on Multi-Task Learning for Domain Generation Algorithm (DGA) Detection

A Comprehensive Study on Multi-Task Learning for Domain Gene...

引用

Annual Conference on Privacy, Security and Trust, PST

作者： Arthur Drichel Ulrike Meyer RWTH Aachen University

ISBN: (数字)9798350367096

ISBN: (纸本)9798350367102

In this work, we perform a comparative evaluation of 21 approaches to multi-task learning (MTL) for the detection of domain generation algorithms (DGAs). To this end, we train and evaluate 2300 classifiers using a combination of 14 different optimization strategies and 6 MTL architectures and compare them statistically with the state of the art. In this context, we propose a novel ResNet backbone, which already surpasses the state of the art on its own, but shines especially in combination with MTL. We evaluate the novel DGA classifiers in a real-world study that avoids temporal and spatial experimental biases to assess whether they generalize well between different networks and are robust over time. Moreover, we analyze the classifiers' capability to detect yet unknown DGAs and discuss their practical application. Our best-performing classifier surpasses the state of the art by over 5.7% in area under the curve (AUC) for practically relevant false-positive rates (FPRs) and exceeds the state of the art by over 7.3% in true-positive rate (TPR) at the same fixed FPR of 0.001 in a real-world setting.

关键词： Training Privacy Multitasking classification algorithms Security Optimization Testing

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Quantum Enhanced AI to Predict the Traffic Flow

Quantum Enhanced AI to Predict the Traffic Flow

引用

Emerging Research in Computational Science (ICERCS), International Conference on

作者： Thenmozhi M Abishake S Dhayal S R Rohit Kanna P R Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering (Autonomous) Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

This research work proposes a quantum traffic flow prediction system based on the difficulty of traffic congestion in cities. The system uses real-time traffic data, details on weather condition, and the behavioral patterns of drivers flexibly by incorporating machine learning along with quantum algorithms as an integrated approach. The data is collected through different data collection instruments namely, traffic sensors, GPS devices, and weather stations, making the data set holistic for analysis. In this model, the basic traditional algorithms of Long Short- Term Memory (LSTM) and Convolutional Neural Networks (CNN) are applied together with quantum-based optimization approaches including the Quantum Genetic Algorithm (QGA) and Learning Vector Quantization (LVQ). The QGA component is involved in the process of feature selection and modeling parameters, LVQ operates on traffic patterns classification. Together, these quantum-enhanced algorithms increase system efficiency and ensure efficient input-output work, especially when working with extensive and variable data arrays. Hence, the present research captures the flow of the model development process and establishes the evaluation of the current model’s efficiency over the conventional prediction models. Outcomes indicate considerable enhancements in predicting novelties and regulating congestion characteristic of the system’s suitability for smart city structures and urban traffic applications.

关键词： Accuracy Vector quantization Computational modeling Predictive models Traffic control Prediction algorithms classification algorithms Convolutional neural networks Meteorology Genetic algorithms

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Transition-Aware Human Activity Recognition using Ensemble Learning and Neural Networks

Transition-Aware Human Activity Recognition using Ensemble L...

引用

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

作者： Thamali Wijewardhana Sahani Goonetilleke Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka Department of Electronic and Telecommunication Engineering University of Moratuwa Moratuwa Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Ubiquitous computing has captivated a significant interest due to its usefulness in various domains. Human activity recognition (HAR) is an important area in ubiquitous computing which identifies human activities using sensor data. In HAR, there is a noticeable lack of focus towards transition aware HAR and most systems have focused only on identifying basic activities. However, for effective HAR, it is necessary to identify transitions accurately. In this paper, we present a novel HAR method which focuses on recognizing both basic activities and transitional activities using accelerometer and gyroscope data. We have implemented a stacked-ensemble model for activity classification using both traditional machine learning algorithms and neural networks. Our method was evaluated using a public dataset and our stacked ensemble model has achieved an overall classification accuracy of 99.74 % with oversampling. Our model accurately identifies both basic and transitional activities.

关键词： Training Artificial neural networks Predictive models Prediction algorithms Ubiquitous computing Robustness classification algorithms

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Design and Application of Spiking Neural Networks Based on LIF Neurons

Design and Application of Spiking Neural Networks Based on L...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Yanxi Wei Fenggang Yao Yuru Kang School of Artificial Intelligence Xi'an Aeronautical Polytechnic Institute Xi'an China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In recent years, brain-like computing has gained significant attention in artificial intelligence, driven by advances in neuroscience and technology. Spiking neural networks (SNNs), inspired by the principles of neuroscience, have emerged as a leading framework in this domain. Unlike traditional artificial neural networks, SNNs simulate temporal dynamics through spiking neurons, enhancing biological interpretability. However, existing research often focuses on single-layer or shallow-layer structures, limiting their applicability to simple classification tasks due to inefficient learning algorithms. To address these challenges, this paper introduces two novel SNN structures based on leaky integrate and fire (LIF) neurons. The first structure combines spike timing-dependent plasticity (STDP) with competitive learning in a two-layer network, improving classification accuracy to 83.179% on the MNIST dataset. The second innovation involves embedding spiking mechanisms into deep models, resulting in two structures, DSNN and convolutional DSNN. These deep SNNs demonstrate enhanced performance in image classification, achieving accuracy rates of 97.67% and 97.09%, respectively, on MNIST and Fashion MNIST datasets. Additionally, the proposed models exhibit shorter training times and increased efficiency, addressing challenges associated with spiking neural networks in deeper applications.

关键词： Training Technological innovation Neuroscience Accuracy Limiting Neurons Spiking neural networks Learning (artificial intelligence) classification algorithms Periodic structures

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Application of Machine Learning Book Recommendation Strategy Based on User Reading Preferences in Smart Libraries

Application of Machine Learning Book Recommendation Strategy...

引用

Telecommunications and Power Electronics (TELEPE), International Conference on

作者： Jia Liu Shanghai Urban Construction Vocational College Graphic Information Center Shanghai China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

In the era of mobile social networking, smart libraries can provide personalized recommendation services based on similar content, collaborative filtering, and association rules on micro platforms by creating user reading preferences and utilizing user behavior recognition algorithms, resource reading algorithms, and resource recommendation algorithms. Machine learning (ML) is one of the important branches of artificial intelligence (AI), providing new technological means for analyzing, understanding, and insighting users. By organically integrating ML with the construction of smart libraries and conducting comprehensive analysis of factors such as user preferences, borrowing behavior, and access logs, more targeted and applicable knowledge resource services are provided for user target needs, effectively improving reader satisfaction and service efficiency of book resource information. To improve the satisfaction of readers with library bibliography recommendations, this article proposes a book recommendation system based on ML algorithm. Firstly, extract user reading preferences, then apply ML algorithm to mine the current personalized potential needs of users for books, and finally provide users with personalized intelligent book recommendation services that meet their interests and focus on meeting potential needs. The experimental results show that the system designed in this article can effectively improve user satisfaction.

关键词： Electric potential Machine learning algorithms Social networking (online) Bibliographies Big Data Libraries classification algorithms Telecommunications Protection Recommender systems

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Machine Learning-based Malware Detection on Android Operating System using AdaBoost Algorithm and ReliefF Feature Selection Method

Machine Learning-based Malware Detection on Android Operatin...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Ibadullah Sabrina Aska Amalina Wildanil Ghozi Fauzi Adi Rafrastara Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Technological advancements occur rapidly from year to year. Currently, we experience the benefits of this rapid technological development, including the widespread use of smartphones in our daily lives. However, without robust security measures, smartphones can lead to losses for users and associated individuals. Additionally, as malware evolves, detecting it becomes increasingly challenging. Therefore, AI-based malware detection methods are essential to replace conventional fingerprint-based approaches that struggle to identify new types of malwares. In this research, we explore several stages: data collection, pre-processing, modeling, and evaluation. Our focus is on identifying optimal combinations of pre-processing and modeling methods to achieve high performance in Android malware detection. Specifically, we implement three feature selection methods, such as X 2 (Chi-Square), Information Gain, and ReliefF, in the pre-processing stage. Similarly, in the modeling stage, we employ three classification algorithms, namely: Random Forest, AdaBoost, and Gradient Boosting, to learn patterns from the data. Our most effective solution is the combination of the AdaBoost classifier with the ReliefF feature selection method, achieving 99.4% accuracy and F1-Score.

关键词： Seminars Operating systems Feature extraction Malware Loss measurement Data models classification algorithms Security Smart phones Random forests

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Combination of Empirical Mode Decomposition and Hjorth Parameters for Prediction of Preterm Labor using Electrohysterogram Signals

Combination of Empirical Mode Decomposition and Hjorth Param...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Somayeh Mohammadi Far Matin Beiramvand Mohammad Shahbakhti Piotr Augustyniak Department of Biocybernetics and Biomedical Engineering AGH University of Krakow Krakow Poland Faculty of Information Technology and Communication Tampere University Tampere Finland Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Timely predicting preterm labor is vital for increasing the chance of neonatal survival rates and promoting maternal well-being. In this study, we explore the potential of Hjorth parameters for predicting preterm labor using the electrohysterogram (EHG) signals. Our proposed algorithm begins by decomposing the EHG signals into four modes through empirical mode decomposition. Subsequently, for each mode, we extract Hjorth parameters—activity, mobility, and complexity— and input them into a random forest classifier for discrimination between term and preterm labor. The comparative analysis of these three parameters highlights the superiority of complexity over activity and mobility, resulting in higher accuracy (85% compared to 73% and 77%). These findings underscore the effectiveness of Hjorth parameters in predicting preterm labor using EHG signals.

关键词： Pediatrics Electric potential Empirical mode decomposition Accuracy Prediction algorithms Complexity theory classification algorithms Engineering in medicine and biology Random forests

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