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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Research on Algorithm of Online Merchant Loan P2P Platform Based on data mining 23

Research on Algorithm of Online Merchant Loan P2P Platform B...

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Proceedings of the 3rd international conference on Electronic Information Technology and Smart Agriculture

作者： Zihao Zhu Department of International Politics University of International Relations China

ISBN: (纸本)9798400716775

With the rapid development of Internet finance, P2P (Peer-to-Peer) lending platforms have become a part of the financial market that cannot be ignored. These platforms provide an innovative way to lend money by connecting borrowers and investors. In order to improve the service quality and operational efficiency of the platform, it has become particularly important to use advanced data mining technology to analyze loan applications. This study aims to explore how to use data mining algorithms to improve the credit assessment and risk management of online merchant loan P2P platforms. We conduct experiments using a real data set containing 100,000 loan application records, which is provided by a well-known P2P lending platform and has been made public on the Kaggle platform. Through detailed preprocessing and feature engineering of the dataset, we selected a variety of data mining models for training and evaluation. Experimental results show that some models can accurately predict the repayment status of loans, providing valuable risk assessment information for P2P platforms. This study not only provides scientific methodological support for the risk management of P2P lending platforms, but also lays a solid foundation for future research in this field.

关键词： P2P lending credit assessment data mining feature engineering machine learning model evaluation risk management

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Application of Deep learning in Anomaly Detection and Prediction in Intelligent Operation and Maintenance of data Assets

Application of Deep Learning in Anomaly Detection and Predic...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Taipeng Zhu Min Zhong Ye Liu Yingwei Liang Guangdong Power Grid Company Information Center Guangzhou Guangdong China Meizhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Meizhou Guangdong China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In the intelligent operation and maintenance of data assets, the threshold triggering of network anomaly detection is mainly based on a single parameter, which has a high false positive rate and low efficiency. In order to detect network anomalies efficiently and accurately, an anomaly prediction model based on deep learning is proposed. Firstly, intelligent O&M application scenarios are designed to meet the O&M leaders' requirements for O&M support. Then, a long and short term memory autoencoder model is constructed to reduce the dimensionality of the data and lock the grid where the suspicious anomalies appear to facilitate the extraction of abnormal information. K-means clustering method is used to detect local anomalies, and finally realize anomaly detection and prediction in intelligent operation and maintenance of data asset operations. The results show that the root-mean-square error of anomaly detection is 2.2% and the average absolute error is 24.3% when the data set is 5000. The time of anomaly detection is 350ms, which has advantages and high efficiency.

关键词： Deep learning Training machine learning algorithms Predictive models data models Maintenance Object recognition Anomaly detection Monitoring Long short term memory

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Automated Dynamic Stuttered Speech recognition System and Conversion System using Mel Filter And Enhanced Logistic Regression Model

Automated Dynamic Stuttered Speech Recognition System and Co...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： K. Nirosha N. Venkatesh School of CS&AI SR University Warangal Telengana India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Confidence and communication are both negatively impacted by stuttering, a speech condition that affects more than 70 million people globally. To improve fluency and intelligibility, the Stuttered Speech recognition (SSR) system uses cutting-edge technologies such as the Weighted Mel Frequency Cepstral Coefficient algorithm and machine learning (ML) models to detect and fix speech defects. It can transform text into speech for assured communication and includes noise reduction for clarity. This goes beyond the scope of conventional speech recognition and holds great promise for helping stuttering people lead more fulfilling lives and be more integrated into society at large. The characteristic features of stuttering speech are an abundance of initial repetitions and prolongations. As a result, the stuttering episodes can be categorized using sound analysis. Finding specific stuttering occurrences, such as prolongations and repeats, in stuttered speech using a feature extraction technique is described in this study. To determine how effectively it can identify stuttering prolongations and repetitions, we apply the famous Mel Frequency Cepstral Coefficient (MFCC) feature extraction. Stuttering is a disease of neurodevelopment characterized by a lack of fluency in conventional speech patterns. Prolonged or repeated sounds, syllables, sentences, or phrases are examples of disfluency. Stuttering detection is challenging and complex since it involves pathology, psychology, acoustics, and signal processing, all of which make it an intriguing interdisciplinary domain study challenge. A thorough analysis of stuttering and disfluency classification methods is presented based on acoustic data as well as statistical and machine learning and deep learning approaches. This research is helpful for scholars, academicians and medical experts to identify the limitations in traditional models and to design efficient solutions for stuttered speech classification.

关键词： Pathology machine learning algorithms Signal processing algorithms Psychology Speech recognition Speech enhancement Signal processing Feature extraction Mel frequency cepstral coefficient Medical diagnostic imaging

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Chronic Kidney Disease: data Clustering and Prediction Using Various machine learning Algorithms 3

Chronic Kidney Disease: Data Clustering and Prediction Using...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Dhuriya, Gaurav Saini, Aradhna Johri, Prashant Yadav, Neha Attiuttama G. L. Bajaj Institute of Technology and Management Delhi - NCR Greater Noida India G. L. Bajaj Institute of Technology and Management Delhi - NCR Greater Noida India Galgotias University Greater Noida India

ISBN: (纸本)9798331519582

A major worldwide health concern, chronic kidney disease (CKD) has a high mortality and morbidity rate and can lead to several other illnesses. Since there are no overt signs of infection in the early stages of chronic kidney disease (CKD), patients frequently miss it. Early-stage chronic kidney disease (CKD) patients have a greater chance of receiving prompt therapy to stop the infection from spreading. AI models can successfully help physicians accomplish this aim because of their quick and precise recognition execution. In this work, we propose an AI method for diagnosing chronic kidney disease. The University of California Irvine (UCI) AI collection provided a number of the missing properties in the CKD informative index. KNN ascription, which chooses a limited number of full examples with the most comparable predictions to handle each deficient example's missing data, was used to fill in the gaps. Overall, because patients may miss a few estimations for a variety of reasons, missing qualities are usually seen in clinical settings. Once the inadequate informational index was adequately rounded off, models were constructed using six AI computations: arbitrary woodlands, support vector machine, k-closest neighbor, innocent Bayes classifier, decision tree, and strategic relapse. The choice tree and irregular forests AI models outperformed the others with a 99.00% determination precision. Therefore, it is estimated that this procedure may apply to more complicated clinical data for sickness diagnosis by examining the misconceptions caused by the setup models. © 2025 IEEE.

关键词： Diseases

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GANs Based Conditional Aerial Images Generation for Imbalanced learning 3rd

GANs Based Conditional Aerial Images Generation for Imbalan...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Belderbos, Itzel de Jong, Tim Popa, Mirela Department of Data Science and Knowledge Engineering Faculty of Science and Engineering Maastricht University Maastricht6200 MD Netherlands Statistics Netherlands Heerlen6401 CZ Netherlands

ISBN: (纸本)9783031092817

In this paper, we examine whether we can use Generative Adversarial Networks as an oversampling technique for a largely imbalanced remote sensing dataset containing solar panels, endeavoring a better generalization ability on another geographical location. To this cause, we first analyze the image data by using several clustering methods on latent feature information extracted by a fine-tuned VGG16 network. After that, we use the cluster assignments as auxiliary input for training the GANs. In our experiments we have used three types of GANs: (1) conditional vanilla GANs, (2) conditional Wasserstein GANs, and (3) conditional Self-Attention GANs. The synthetic data generated by each of these GANs is evaluated by both the Fréchet Inception Distance and a comparison of a VGG11-based classification model with and without adding the generated positive images to the original source set. We show that all models are able to generate realistic outputs as well as improving the target performance. Furthermore, using the clusters as a GAN input showed to give a more diversified feature representation, improving stability of learning and lowering the risk of mode collapse. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Applying Deep learning Strategies for Optimizing Real-Time data Analysis in machine learning

Applying Deep Learning Strategies for Optimizing Real-Time D...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Varun Mishra Sudhanshu Dev Akhilendra Pratap Singh Gautham Krishna Geetha B Rutuja S. Kadam Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune India

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization.

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Unsupervised Cell Segmentation in Fluorescence Microscopy Images via Self-supervised learning 3rd

Unsupervised Cell Segmentation in Fluorescence Microscopy I...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Krug, Carola Rohr, Karl Biomedical Computer Vision Group BioQuant IPMB Heidelberg University Heidelberg Germany

ISBN: (纸本)9783031090363

Cell segmentation in microscopy images is challenging particularly when only few or no annotations available. Existing unsupervised deep learning-based segmentation methods rely on large data sets to train large networks, use synthetic training data, pre-trained networks for domain adaptation, or exploit labels to further train pre-trained networks. We propose an unsupervised deep learning method which is trained from scratch by self-supervised learning without requiring any segmentation labels. Our deep neural network generates an attention map and performs the auxiliary task in one network. The segmentation result is directly obtained from the network, and model selection is performed unsupervised based on the behavior of the loss function during training. We applied our approach to two different fluorescence microscopy data sets and achieved generally better or similar results than classical unsupervised segmentation methods. Furthermore, we compared our method to a supervised method trained with a different number of labels, as well as a semi-supervised version of our method where we select the model based on few annotations. © 2022, Springer Nature Switzerland AG.

关键词： Fluorescence microscopy

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Navigating Emotion in Code-Mixed Languages: Performance of ML and DL Models on Hindi-English Text

Navigating Emotion in Code-Mixed Languages: Performance of M...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Mahajan, Rutal More, Anjali S. Shah, Unnati Computer Science Department Utica University New York13502 United States Pune412115 India

With the rapid growth of internet usage, text data offers unprecedented insights into user behavior. This study addresses sentiment analysis and emotion recognition in Hindi-English code-mixed text - an underexplored area despite its significance in a multilingual country like India. Existing datasets primarily focus on single-label emotions, overlooking the complexity of mixed emotional states commonly observed in real-world scenarios. To address this gap, a novel multilabel dataset of 13,000 YouTube comments was developed, with 5,720 samples annotated to capture both single and mixed emotions. This dataset facilitates both coarse-grained (positive/negative) and fine-grained emotion recognition tasks, providing a versatile resource for research. The study evaluates several machine learning and deep learning models, including Logistic Regression, SVM, Naive Bayes, Random Forest, LSTM, BiLSTM, GRU, and CNN. Among these, SVM achieves the highest accuracy of 81.40% with an F1 score of 80.39%. In contrast, deep learning models like LSTM and GRU show lower performance, with an accuracy of 69.57% and an F1 score of 57.09%, indicating difficulties in detecting nuanced emotions such as joy and trust. This work offers a foundation for developing emotion recognition models that can effectively handle both single and mixed emotional states, reflecting real-world complexities. Future efforts will explore advanced architectures and extend this approach to other code-mixed languages. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep learning

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Deep learning for Fast Segmentation of E-waste Devices’ Inner Parts in a Recycling Scenario 3rd

Deep Learning for Fast Segmentation of E-waste Devices’ I...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Rojas, Cristof Rodríguez-Sánchez, Antonio Renaudo, Erwan Faculty of Mathematics Computer Science and Physics University of Innsbruck Innsbruck6020 Austria

ISBN: (纸本)9783031090363

Recycling obsolete electronic devices (E-waste) is a dangerous task for human workers. Automated E-waste recycling is an area of great interest but challenging for current robotic applications. We focus on the problem of segmenting inner parts of E-waste devices into manipulable elements. First, we extend a dataset of hard-drive disk (HDD) components with labelled occluded and non-occluded points of view of the parts, in order to increase the diversity and the quality of the learning data with different angles. We then perform an extensive evaluation with three different state-of-the-art models, namely CenterMask, BlendMask and SOLOv2 (including variants) and two types of metrics: the average precision as well as the frame rate. Our results show that instance segmentation using state-of-the-art deep learning methods can precisely detect complex shapes along with their boundaries, as well as being suited for fast tracking of parts in a robotic recycling system. © 2022, Springer Nature Switzerland AG.

关键词： Recycling

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Deep learning-Based Speech Enhancement for Robust Speech recognition in Noisy Environments 3

Deep Learning-Based Speech Enhancement for Robust Speech Rec...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Sowmya, C.S. Das, Neeraj Sharma, Divya Mondal, Sanjoy Soni, Ishika Naveen Kumar, V. Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

It is a very important problem since voice-controlled devices and speech-to-text transcription are only two examples of how automatic recognition of spoken language in noisy environments may impact our lives. Still, speech recognition systems may only sometimes work effectively in background noise, which can result in errors and misinterpretations. This problem dealt with the deep learning model-based speech enhancement techniques for robust ASR in adverse environments. These methods employ deep learning to learn and deny the noisy speech signals, rendering it a better-suited input for recognition in a typical ASR system. Deep learning-based speech enhancement is a machine-learning system using a huge, loud, clean speech data dataset to train DNN. So, the network is trained to find a mapping from some noisy speech signals to ideally clean versions of these speech sounds by removing its background noise. This spectral representation of speech will now have reduced noise, which the ASR much more easily recognizes. These methods have significantly improved against traditional speech enhancement techniques in demising, eventually improving recognition performance. They can turn in high noise levels and are resilient against acoustics drifts. This makes them well-suited to real-world applications when noise levels can vary. © 2025 IEEE.

关键词： Speech enhancement

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