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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3332 条记录，以下是121-130 订阅

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Weighted fusion algorithm for unbalanced power big data based on improved integrated learning 4

Weighted fusion algorithm for unbalanced power big data base...

引用

4th international conference on Electrical Engineering and Control Science, IC2ECS 2024

作者： Wang, Xi Liu, Chao Cao, Yuan Yuan, Limin Han, Mo Zhao, Yaxin Shen, Xiaoxian Liu, Shuxiu Xing, Conghui State Grid Jilin Information & Telecommunication Company Jilin Province ChangChun130062 China Crrc Changchun Railway Vehicles Co. Ltd Jilin Province ChangChun130062 China State Grid Info-Telecom Great Power Science And Technology Co. Ltd. Fujian Province Fuzhou350003 China

ISBN: (纸本)9798331510107

With the development of the society, the electricity consumption in each region increases dramatically, which brings serious challenges for data analysis and model construction due to the complexity and imbalance of electric power big data. Aiming at the problem of low classification performance of traditional methods in dealing with unbalanced power data, this paper proposes a weighted fusion algorithm for unbalanced power big data based on improved integrated learning. First, the existing integrated learning algorithm is optimized by combining the sample weight adjustment and the category distribution perception mechanism to improve the recognition ability of samples from a few categories. Second, a model fusion method based on the weighting strategy is designed, which improves the overall classification performance. Experimental results show that the algorithm proposed in this paper exhibits better precision, recall, and F1 scores on several real power big data sets compared with traditional methods, especially in minority class recognition. © 2024 IEEE.

关键词： Adversarial machine learning

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Proceedings - 2024 4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

Proceedings - 2024 4th International Signal Processing, Comm...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

ISBN: (纸本)9798331528676

the proceedings contain 186 papers. the topics discussed include: research on the model of improving engineering safety management decision support system using data mining technology;research on international trade settlement and tracking system based on blockchain technology;research on test data analysis and processing technology based on regression analysis;design and implementation of power data security and compliance management framework;development of an enterprise knowledge base system based on Elasticsearch;improved sine cosine algorithm for estimating parameters of sine signals;research and design of a fault detection system for high-band wireless communication networks using machine learning;artificial noise-like covert communication with low power consumption and low complexity;and research on film and television multimedia fusion analysis technology.

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FedCOA: An Efficient Cluster Optimization for Federated learning 4

FedCOA: An Efficient Cluster Optimization for Federated Lear...

引用

4th international conference on Computer Vision and data mining, ICCVDM 2023

作者： Jin, Wenxiang Zhang, Jianfei School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781510674448

Federated learning is a novel distributed machine learning framework based on data privacy protection. In practical applications, there are often significant differences in data distribution among different clients in federated learning, which can lead to a decrease in the performance of federated learning models. this phenomenon is caused by the problem of independent and identically distributed data. To address the issue of independent and identically distributed data, this paper proposes a novel federated learning algorithm called FedCOA(Federated Cluster Optimization Algorithm). the FedCOA algorithm iteratively partitions the clients into different client clusters based on the similarity of their data distributions. Clients with similar data distributions within a cluster collaborate with each other, thereby enhancing model generalization and reducing overfitting. In the simulation experiments, under the same dataset, the FedCOA algorithm achieves an approximately 10% improvement in accuracy compared to the FedAvg algorithm, and 2% improvement compared to the CFL algorithm. Additionally, this paper conducts comparative experiments under different degrees of Non-Independent and Identically Distributed data distribution. the experimental results demonstrate that FedCOA can enhance model accuracy while effectively addressing issues such as the decrease in model accuracy and the inability of the model to converge caused by Non-Independent and Identically Distributed data distribution. © 2024 SPIE.

关键词： machine learning

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A Novel Procedure for Recognising Human Activity Using machine learning 4

A Novel Procedure for Recognising Human Activity Using Machi...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Singh, Neelam Bhatt, Mansi Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350300888

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity recognition (HAR) is one of these sensors' applications. Given the dependence on general machine learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. the study focuses on Human Activity recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used. © 2023 IEEE.

关键词： Long short-term memory

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Enhancing Symbol recognition Accuracy and Efficiency through machine learning Approach 4

Enhancing Symbol Recognition Accuracy and Efficiency Through...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Singh, Harkirat Dixit, Prachiv Singh, Gurwinder Chandigarh University Department of AIT-CSE Punjab India

ISBN: (纸本)9798350306118

the recognition of symbols holds significant importance in a diverse array of applications, including but not limited to visual character recognition, object recognition, and data analysis. this paper's main aim is to improve the precision and effectiveness of symbol identification by employing a machine learning methodology. In this discourse, we delve into the obstacles encountered in the realm of symbol identification, encompassing issues such as the presence of diverse shapes, potential confusion between symbols that bear resemblance, the interference caused by image noise, the scarcity of available training data, and the computational intricacy involved. In order to tackle these issues, we suggest employing machine learning methods, particularly deep learning models, to enhance the performance of symbol identification. In this study, we investigate various methodologies, including data augmentation, image pre-processing, and the incorporation of extensive training datasets. Furthermore, we examine the hardware and software modifications that enable the optimisation of symbol recognition processing. the effectiveness of the proposed methodology in enhancing accuracy and efficiency in symbol identification tasks is substantiated through rigorous experimentation and evaluation. the results reported in this study make a valuable contribution to the progress of symbol identification methodologies, hence facilitating the development of more resilient and dependable systems across diverse fields. © 2023 IEEE.

关键词： Object recognition

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Chronic Disease Symptom Checker with NLP 4

Chronic Disease Symptom Checker with NLP

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Vinoth Kumar, P Selvam, Arul N. Soundharajan, P. St.Joseph's Institue of Technology Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331523923

Chronic disease remains a critical global health challenge due to lifestyle and environmental changes, leading to increased morbidity and mortality. this paper presents an intelligent symptom checker utilizing Natural Language Processing (NLP) to assess probable chronic diseases based on user-reported symptoms. By analyzing free-text symptom descriptions, the system maps them to potential medical conditions using Named Entity recognition (NER)and a rule-based approach. the methodology involves extensive medical data collection, pre-processing, and training of machine learning models to recognize symptom-disease patterns. the checker's backend integrates an NLP model with a user-friendly interface, providing real-time assessments of probable diseases with confidence levels. the system's accuracy is validated through preclinical tests and user studies, comparing outputs with professional diagnoses. Ethical considerations, including patient privacy and diagnostic reliability, are also addressed. With potential applications in early detection and healthcare support, this system aims to enhance disease management and reduce healthcare burdens. the key challenges include NLP accuracy, data privacy, and integration with existing healthcare systems, which are addressed through robust preprocessing, security measures, and model optimization techniques © 2025 IEEE.

关键词： Hospital data processing

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License Plate recognition Using Federated learning 4

License Plate Recognition Using Federated Learning

引用

4th international conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Prajwal, B.S. Madagaonkar, Suraj B. Makkithaya, Krishnamoorthi Manipal Institute of Technology Manipal Manipal Academy of Higher Education CSE Department Karnataka Manipal576104 India

ISBN: (纸本)9798350375466

Federated learning is a decentralized approach in machine learning where models are trained locally on distributed data across various devices or servers. Only model updates, not the raw data, are sent to a central server for aggregation, ensuring privacy and security. this method supports collaborative learning without centralizing sensitive information. Vehicle plate images are gathered, and a Federated learning algorithm processes them. the primary aim of this study is to improve the privacy of License Plate recognition systems. the rationale for using License Plate recognition technology lies in its potential to enhance security through access control and prevent unauthorized entries. Additionally, License Plate recognition helps in the quick identification of stolen vehicles, leading to prompt response actions, and enhances traffic flow, reducing congestion and optimizing efficiency. Federated learning ensures secure data transmission, strengthening system reliability. the methodology includes collecting standardized image data from cameras, enhancing image quality, and extracting alphanumeric characters from license plates. the Federated learning algorithm maintains data decentralization, boosting privacy. A thorough evaluation, adherence to data protection regulations, and comprehensive documentation ensure system accuracy and transparency. Expected results suggest this integrated system offers a comprehensive solution to urban traffic issues. By utilizing image-based technology, it gathers essential data, improving traffic management efficiency and cost-effectiveness. the License Plate recognition algorithm effectively detects license plates. Overall, this project presents a holistic approach to enhancing road safety and traffic management. By focusing on license plate detection, it addresses key urban traffic challenges. the integration of segmentation and advanced algorithms aims to create safer roadways and improve the well-being of urban communities. © 2

关键词： Traffic congestion

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Deep Reinforcement learning for Exact Combinatorial Optimization: learning to Branch 26

Deep Reinforcement Learning for Exact Combinatorial Optimiza...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Tianyu Banitalebi-Dehkordi, Amin Zhang, Yong Univ Alberta Edmonton AB Canada Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machine learning and heuristics based methods.

关键词： Deep learning Systematics Monte Carlo methods NP-hard problem Input variables Reinforcement learning pattern recognition

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Enhancing Engineering Education: the Role of Artificial Intelligence in Personalizing learning and Outcomes 4

Enhancing Engineering Education: The Role of Artificial Inte...

引用

4th international conference on Big data Engineering and Education, BDEE 2024

作者： Furman, Gabriela Dorfman Shenkar College of Engineering Design and Art Ramat-Gan Department of Electrical & Electronic Engineering Israel

ISBN: (纸本)9798350388268

the emergence of artificial intelligence (AI) in educational contexts presents transformative potential for higher engineering education. this paper conducts a comprehensive literature review to explore the role of AI-driven analytics such as personalized learning pathways and adaptive assessment techniques, in personalizing learning experiences and improving educational outcomes for engineering students. through a systematic analysis of existing research, we identify how AI technologies - such as machine learning algorithms, data mining, and natural language processing - can be utilized to tailor educational content, improve learning engagement, and optimize curriculum design. A synthesis of the findings highlights the significant benefits of AI in identifying diverse learning styles, predicting academic performance, and providing real-time feedback, thus fostering a more individualized and effective learning environment. In addition, this article discusses the challenges and ethical considerations associated with implementing AI in education, including data privacy, algorithmic bias, and the need for human oversight to ensure ethical AI deployment. Building upon insights gleaned from a thorough review of published literature, we propose a conceptual framework for integrating AI into engineering education, with the aim of equipping the students with the skills and knowledge required to navigate the complexity of the modern engineering landscape. this theoretical exploration underscores the transformative potential of AI in engineering education and sets a foundation for future empirical research to further investigate and expand upon these benefits and strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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mining and recognition of English learning patterns of Mobile Users Based on Intelligent Algorithm 4

Mining and Recognition of English Learning Patterns of Mobil...

引用

4th international conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022

作者： Li, Jingtai School of Foreign Languages Jiaying University Guangdong Meizhou China

ISBN: (纸本)9781665456357

the latest research results show that activity recognition based on frequent pattern mining is the main method. Its advantages are that the activity patterns obtained through learning mining are comprehensive and accurate, and the recognition effect of activities in the corresponding environment is good. Besides, some activities that can only be carried out in a specific environment can be identified. this study attempts to use data mining technology to help foreign language education systems to further transform massive data, so as to mine more valuable content, helping educators to further tap teaching potential, and improving the efficiency of teaching resources utilization. © 2022 IEEE.

关键词： data mining

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