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2024 international conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Chen, Jiayu School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Facial expressions are a challenging area in image recognition, and ensuring the universality of the model in the presence of a large sample size is a difficult point in such research. This article conducts repeatability experiments on two extreme scenarios: high-quality small samples and low-quality large samples, to explore solutions. Through transfer learning, data augmentation, and substantive evaluation of confusion matrices, the article demonstrates the accurate classification of seven facial expressions and the design of a 4-class model with high universality, which is presented in the form of an APP. © 2025 SPIE.

关键词： Deep learning

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An Investigation into machine learning-Based Techniques for Detecting Malicious Cryptomining Activities 3rd

An Investigation into Machine Learning-Based Techniques for ...

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Xie, Renchao Huang, Xuange Tang, Qinqin Jiangsu Future Network Innovation Institute Nanjing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819616978

With the popularity of blockchain concepts and the appreciation of cryptocurrencies, an increasing number of hackers and illegal elements have started to focus on stealing users’ hardware resources for mining activities. In this context, web mining has emerged, which can occupy the computing resources of website visitors to mine cryptocurrencies. However, this method can affect user experience, increase energy consumption, and reduce computer performance, making web mining detection and defense increasingly important. Due to the high degree of obfuscation and complex behavior in web mining code, this paper proposes a mining detection model based on machine learning, which extracts features from multiple dimensions and combines machine learning algorithms to identify web pages with mining behavior. We used four classification algorithms for model training, and through extensive experiments and performance comparison, the Random Forest algorithm was proven to be the most effective [1]. This indicates its high accuracy and suitability for detecting malicious mining activities. Our research not only provides a robust method for identifying malicious web mining but also lays a foundation for future cybersecurity research, offering insights into the application of machine learning techniques in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： blockchain cryptojacking cryptomining detection machine learning

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Dynamic Soft Contrastive learning for Time Series Anomaly Detection

Dynamic Soft Contrastive Learning for Time Series Anomaly De...

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

作者： Song, Yifan Liu, Yu Shu, Shaolong College of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection has been extensively studied in diverse domains. Within the horizon of unsupervised methods, density estimation is regarded as a promising direction. However, the anomalies that distribute closely with normal data in data space impact the desired density estimation results, thereby significantly affecting the discrimination of anomalies. In this work, to tackle this challenge, we propose Dynamic Soft Contrastive learning (DiSCo). DiSCo generates a representation that better captures the underlying correlations within the data distribution, providing a more compact, informative, and suitable description for the subsequent density estimation task. Furthermore, DiSCo employs a dynamic and learnable distance metric to measure similarities between inputs. The learned task-specific distance metric contributes to minimizing the intra-class distance and maximizing the inter-class distance, thereby assisting the generation of better representation. The efficacy of DiSCo is proved through extensive experiments conducted on four common anomaly detection datasets. © 2025 IEEE.

关键词： Anomaly Detection Contrastive learning Normalizing Flow Time Series Unsupervised learning

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Research on high-precision filtering method for standard bottle calibration pressure based on deep learning approaches 5

Research on high-precision filtering method for standard bot...

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5th international conference on Mechanical engineering and Materials, ICMEM 2024

作者： Xiong, Shutao Zhou, Yu Li, Xiuquan Li, Yanjun Zhang, Xuelin Key Laboratory of Electromechanical Equipment Security in Western Complex Environment State Administration for Market Regulation Chongqing401121 China National Compressed Natural Gas Cylinder Quality Inspection and Testing Center Chongqing Spec ial Equipment Inspection and Research Institute Chongqing401121 China

ISBN: (数字)9781510688971

ISBN: (纸本)9781510688964

This paper proposes a high-precision filtering scheme for standard bottle calibration pressure based on Transformer model feature extraction and Kalman filtering methods, which is used to analyze the data of standard bottle pressure over time. Firstly, a Transformer model is employed as a feature extractor to transform the data sequence of standard bottle pressure over time into high-dimensional feature vectors, thereby capturing rich semantic information of the input data. Then, a Kalman filter is designed, using the feature vectors extracted by the Transformer as observation values, combined with state transition equations and observation equations to estimate and filter these feature vectors. By using the feature vectors outputted by the Transformer model as observation values, the Kalman filter can more accurately estimate the state of standard bottle pressure over time by utilizing the information extracted by the Transformer model. Experimental results demonstrate that the calibration pressure data obtained through the proposed method in this paper exhibit higher stability and better meet the requirements of standard bottle calibration. © 2025 SPIE.

关键词： Kalman filters

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Artificial Intelligence and machine learning Approaches in Eco-Design for Additive Manufacturing: A Literature Review

Artificial Intelligence and Machine Learning Approaches in E...

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international Joint conference on Mechanics, Design engineering and Advanced Manufacturing, JCM 2024

作者： Musiari, Francesco Marconi, Marco Villazzi, Nicola Murgese, Luca Mandolini, Marco Gallozzi, Simone Favi, Claudio Università degli Studi della Tuscia Largo dell’Università Viterbo01100 Italy Università degli Studi di Parma Parco Area delle Scienze 181/A Parma43124 Italy Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy

ISBN: (纸本)9783031728280

Additive manufacturing (AM) is currently considered one of the most promising technologies for fostering innovation in the industrial sector, specifically in product design. Considering the non-subtractive nature of AM processes, in first approximation, they all seem to be environmentally sustainable since they lead to a null or at least reduced quantity of scraps. However, the peculiarities of such technologies, such as the specific input materials (e.g., metal powders, polymer filaments) or the low productivity, make the situation more complex, not so homogeneous in all the cases, and strongly dependent on the process and product parameters. In this complex scenario, the present paper aims to investigate the current state of the art regarding adopting Artificial Intelligence (AI) and machine learning (ML) toward the eco-design for AM. Following the PRISMA methodology, a set of 19 scientific papers were analysed. Papers were grouped according to the benefits (improvement of quality and reduction of energy, waste and defects) and design opportunities (definition of shapes and selection of materials) that AI and ML potentially provide. The discussion of the results presents the most relevant research and development opportunities for scholars and companies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart manufacturing

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Proceedings of the 4th international conference on data Science, machine learning and Applications 1

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丛书名： Lecture Notes in Electrical engineering

1000年

作者： Amit Kumar Vinit Kumar Gunjan Yu-Chen Hu Sabrina Senatore

ISBN: (数字)9789819920587

ISBN: (纸本)9789819920570;9789819920600

This book includes peer reviewed articles from the 4th international conference on data Science, machine learning and Applications, 2022, held at the Hyderabad Institute of Technology & Management on 26-27th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of data Science & machine learning offering in-depth information on the latest developments in Artificial Intelligence, machine learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.

关键词： Artificial Intelligence Computational Intelligence IT in Business Mathematical Models of Cognitive Processes and Neural Networks Cyber-physical systems, IoT Professional Computing

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Constructing and Evaluating Predictors for data-Driven PM2.5 Forecasting Models

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international JOURNAL OF ENVIRONMENTAL RESEARCH 2025年第3期19卷 1-15页

作者： He, Ran-Ran Chen, Yu-Qiao Tian, Lei Shan, Lei Sang, Xiao-Shuang Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Anhui Peoples R China Bengbu Bur Meteorol Bengbu 233000 Anhui Peoples R China

data-driven methods for air quality forecasting are beneficial to early-warning of air pollution, especially in small and medium-sized cities. In this study, the Prophet model is used for extracting time-related features, such as trend, annual cycles, and weekly cycles, from the PM2.5 series, and the gradient boosting regression trees (GBM) and long short-term memory networks (LSTM) are used to build forecasting models utilizing predictors including time-related features. All predictors are grouped into time-related features, antecedent air pollutant concentration, and meteorological parameters, and permutation feature importance (PFI) is used for measuring the importance of these feature groups. A series of forecasting experiments on a middle city in China are carried out, and the results are analyzed for a better understanding of the mechanisms of the forecasting skill. The result shows that time-related features extracted by Prophet can enhance the forecasting skill significantly, and this advantage is more apparent when the forecasting horizon is large. PM2.5 time series has short memory and a rapid response to changes in meteorological conditions, and only meteorological conditions in the previous 2 days have useful signals for forecasting. The rapid response and short memory of PM2.5 make LSTM show no advantage over GBM in this case study. We show that feature engineering is important for forecasting PM2.5, and the time-related features and antecedent pollutant concentrations can be seen as proxy variables for some variables that are difficult to obtain, such as pollutant emission and missing meteorological conditions.

关键词： Air quality machine learning Feature engineering data-driven model PM2.5

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Scalable VLSI Architectures for Pattern Matching in Serial data Stream

Scalable VLSI Architectures for Pattern Matching in Serial D...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kong, Byeong Yong Institute of IT Convergence Technology Kongju National University Division of Electrical Electronic and Control Engineering Cheonan Korea Republic of

ISBN: (纸本)9798331510756

In this paper, a pair of regularized and scalable very large scale integration (VLSI) architectures are presented for matching patterns in a serial data stream. One is grounded on a shift register (SR), whereas the other is based on a finite state machine (FSM), both of which are very easy to constitute for arbitrary lengths of pattern. While the architectures for parallel pattern matching have been extensively studied in the literature, those for serially incoming data have been rare. To suggest a guideline for efficient implementation under such circumstances, the pros and cons of the proposed architectures are analyzed in various aspects. © 2025 IEEE.

关键词： Network intrusion

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Research on Travel Mode Identification Based on Trajectory data 11th

Research on Travel Mode Identification Based on Trajectory D...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Zhang, Ruonan Li, Dewei Huang, Yue School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796434

Understanding users’ travel behaviors is an important subject in traffic science, which helps traffic management departments to formulate appropriate traffic control strategies. Travel mode identification is a critical aspect of analyzing users’ travel behaviors, aiming to precisely determine the travel modes of users and promote the sustainable development of urban intelligent transportation systems. In this article, we focus on identification methods for travel modes and propose a multi-scale convolutional neural network model based on channel attention mechanism. Our model efficiently extracts multi-scale sub-features and prioritizes key features. Additionally, to deeply extract effective information from the GPS data, we design 24 time-domain features and frequency-domain features and simplify them using principal component analysis and random forest algorithm. To verify the effectiveness of our method, we design five traditional machine learning models and two shallow deep learning models as comparative models, which are trained and tested on the Geolife dataset. Experimental results show that our approach is superior to the baseline models, achieving a maximum accuracy of 83.09%. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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On Jumping Graph Grammars

On Jumping Graph Grammars

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international conference on Mathematics of Intelligent Computing and data Science, ICMICDS 2022

作者： Vijayakumar, Jayakrishna Mathew, Lisa Mathew, Juby Amal Jyothi College of Engineering Kerala Kottayam Kanjirappally India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram India Amal Jyothi College of Engineering Kerala Kottayam Kanjirappally India

ISBN: (纸本)9789819615049

This paper introduces the notion of jumping graph grammars. In particular it focuses on the non-confluent version. As a graphical counterpart of general jumping grammars, jumping graph grammars have a greater generative power compared to existing graph grammars. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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