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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是21-30 订阅

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Research on machine learning-Based Fault Diagnosis and Prediction Techniques for Power Transformers 2

Research on Machine Learning-Based Fault Diagnosis and Predi...

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2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Zhang, Shuangying Fu, Lidong Wang, Jiaxing State Grid Liaoning Electric Power Company Limited Economic Research Institute Shenyang China Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

Due to the diversity and heterogeneity of electrical equipment, the operation monitoring systems for power transformers are undergoing diversified development. By utilizing machine learning methods, this study focuses on the prediction and diagnosis of heterogeneous terminal data from power transformers to address the challenges of online monitoring. This research is of significant importance for achieving the interconnection of all components in the power system and maintaining its operational stability. The study adopts machine learning techniques to comprehensively analyze the heterogeneous terminal data of power transformers, considering seven electrical energy parameters and four environmental parameters as the main input variables for the experimental analysis. A weighted least squares support vector machine algorithm based on chemical reaction optimization is proposed for parameter prediction in power transformers. Through a detailed comparative analysis during the simulation process, with a focus on evaluating regression algorithm performance metrics, the simulation results demonstrate that the proposed algorithm can rapidly and accurately predict parameter values for future time intervals. © 2024 IEEE.

关键词： Distribution transformers

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Optimizing Agricultural Productivity: Developing a Smart Crop Recommendation System Using machine learning 2

Optimizing Agricultural Productivity: Developing a Smart Cro...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preprocessing techniques, grid search optimization and the pipeline class are applied in conjunction with the different machine learning algorithm for the crop recommendation. The accuracy and efficiency of the recommendation process enhancement is aimed by applying techniques including data standardization, relative density normalization, and grid search optimization. Leveraging machine learning algorithms within a unified pipeline framework allows for systematic evaluation and comparison of models, ultimately providing farmers with reliable recommendations for crop selection. machine learning algorithms Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector machine, Logistic Regression and Naïve Bayes were applied to predict and optimize agricultural productivity based on the dataset collected from Nagaland state. Among these algorithms, Random Forest outperformed the others, achieving an impressive accuracy of 99.54% in predicting agricultural productivity. © 2024 IEEE.

关键词： Smart agriculture

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2024 IEEE 2nd international conference on Image processing and Computer Applications, ICIPCA 2024

2024 IEEE 2nd International Conference on Image Processing a...

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2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

ISBN: (纸本)9798350360240

The proceedings contain 376 papers. The topics discussed include: analysis of badminton motion trajectory algorithm based on neural network;intelligent insurance actuarial model under machine learning and data mining;research on real-time data transmission and signal processing system of CIM practical training teaching platform based on 5G network;design of systematic financial risk warning system based on integrated classification algorithm;development of a multi-objective optimization framework for submersible localization and search operations;improved thermal dome structure optimization design based on neural network algorithm;transmission line inspection image intelligent diagnosis system;and application of computer artificial intelligence infrared image processing technology in strength detection of building steel structure.

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proceedings of 2024 2nd international conference on signal processing and Intelligent Computing, SPIC 2024

Proceedings of 2024 2nd International Conference on Signal P...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

ISBN: (纸本)9798350368888

The proceedings contain 241 papers. The topics discussed include: image processing based on inter-frame color feature matching for visible light positioning;research on short-term load forecasting method based on ANFIS-BPNN-LSSVM;multi-scale complex terrain wind power output prediction based on multi-feature refinement fusion;research on multi-feature triplet attention based sharpness-aware classification network;optimization and application of the search algorithm for the efficacy of airfield blockade by penetration submunitions;a deep learning approach for rectifying fisheye image;lightweight image deraining network based on dilated depthwise separable convolution and enhanced channel attention;study on the combination of S-growth curves based on ultra-wideband displacement ranging for predicting landslides;and multi-agent reinforcement learning based on cross task information sharing.

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THE 2nd CLARITY PREDICTION CHALLENGE: A machine learning CHALLENGE FOR HEARING AID INTELLIGIBILITY PREDICTION 49

THE 2ND CLARITY PREDICTION CHALLENGE: A MACHINE LEARNING CHA...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Barker, Jon Akeroyd, Michael A. Bailey, Will Cox, Trevor J. Culling, John F. Firth, Jennifer Graetzer, Simone Naylor, Graham Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Nottingham Sch Med Nottingham England Univ Salford Acoust Res Ctr Salford Lancs England Cardiff Univ Sch Psychol Cardiff Wales

ISBN: (纸本)9798350344868;9798350344851

This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new approaches for estimating the intelligibility of hearing aid signals that can be used in future hearing aid algorithm development. It extends an earlier round (CPC1, 2022) in a number of critical directions, including a larger dataset coming from new speech intelligibility listening experiments, a greater degree of variability in the test materials, and a design that requires prediction systems to generalise to unseen algorithms and listeners. This paper provides a full description of the new publicly available CPC2 dataset, the CPC2 challenge design, and the baseline systems. The challenge attracted 12 systems from 9 research teams. The systems are reviewed, their performance is analysed and conclusions are presented, with reference to the progress made since the earlier CPC1 challenge. In particular, it is seen how reference-free, non-intrusive systems based on pre-trained large acoustic models can perform well in this context.

关键词： speech-in-noise speech intelligibility hearing aids hearing loss machine learning

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Research on signal processing Methods for Gas Ultrasonic Flowmeters 2

Research on Signal Processing Methods for Gas Ultrasonic Flo...

引用

2nd international conference on machine learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Wan, Yong China Coal Technology and Engineering Group Chongqing Research Institute Chongqing400039 China State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing400039 China

ISBN: (纸本)9798350328530

On the basis of the difficulty in determining the feature point in the signal processing of gas ultrasonic flowmeter,a variable threshold based zero-crossing detection signal processing method is proposed to determine a feature wave of the signal accurately. Thus the propagation time of ultrasonic signal is calculated,and the gas flow rate is measured *** method is realized real time in a signal processing system with DSP and FPGA as dualcore for the gas ultrasonic *** judging the 'step', the error caused by noise interference is eliminated,and the stability of the measurement results is enhanced. With piecewise linear calibration,the non-linear error is corrected,and the measurement accuracy of the system is improved. The experimental results of gas flow calibration verify the effectiveness of the method and the system. © 2023 IEEE.

关键词： Flow of gases

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Fall Detection Using Sound with FPGA signal Analysis and machine learning Integration 2

Fall Detection Using Sound with FPGA Signal Analysis and Mac...

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2nd international conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Andre, Mariano Viegas Ozdemir, Ahmet Turan Kavuncuoglu, Erhan Erciyes University Department of Electric and Electronic Engineering Kayseri Turkey Erciyes University Department of Electric and Electronic Engineering KayseriTR-38039 Turkey Gemerek Vocational School Cumhuriyet University Department of Computer Tecnology SivasTR-58840 Turkey

ISBN: (纸本)9798331505745

Falls represent a considerable risk to the well-being of geriatric populations, frequently resulting in injuries or mortality. In response to this pressing concern, we introduce an innovative fall detection apparatus employing acoustic sensors and FPGA (Field Programmable Gate Array) technology to effectively alleviate these hazards. Our system capitalizes on acoustic phenomena, particularly those associated with footstep sounds and falls, which are gathered through routine activities and video documentation to develop a comprehensive model adept at differentiating between fall incidents and non-fall occurrences. The primary attributes encompass the derivation of Mel-frequency cepstral coefficients (MFCC) from the processed auditory signals. To enhance operational efficacy, an FPGA is utilized to execute all data processing functions, inclusive of Fourier transformations and a threshold-based predictive algorithm. This methodology diminishes dependence on conventional computational systems while preserving elevated processing efficiency. The derived features are subsequently employed to formulate a data classification model utilizing a Convolutional Neural Network (CNN) and Random Forest Tree (RFT), which proficiently discriminates between sounds indicative of falls and those not related to falls. © 2025 IEEE.

关键词： Data integration

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Hate Speech Detection in Telugu Language Using Transformer Models and machine learning 2

Hate Speech Detection in Telugu Language Using Transformer M...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Rakhil, M.L. Sirohi, Aman Sriviswa, R. Sachin Kumar, S. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350366570

Hate speech in the social media raises a fundamental concern of social integration and quality individual life. This paper aims at presenting the key importance of hate speech detection in the Telugu language which is a Dravidian language and officially used in the Indian states. Through the use of various transformer models such as BERT, DistilBERT, indicBERT, LaBSE, and MuRIL, the proposed solution offers acceptable accuracy, especially with the MuRIL imprinting an Fl-score of 0.79. The combination of ensemble embedding with machine learning classifiers escalates the performance and exhibits a better position to advance content moderation for only languages in the mainstream multilingual online platforms. This research not only addresses a relevant problem but also helps demonstrate the possibilities of using NLP developments to deal with online hate speech, thus creating less dangerous digital landscapes. The results also underscore the need to maintain the development process and update the further strategies to identify the hate speech persistently due to new tendencies in the interaction within social media platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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Momentum Study Based on CRITIC Evaluation Method and machine learning 2

Momentum Study Based on CRITIC Evaluation Method and Machine...

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2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

作者： Zhou, Jinghua Zhang, Jingruo Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350360240

This paper mainly focuses on the evaluation and prediction of sports competition results, and uses the methods of feature extraction, CRITIC weighted analysis and machine learning algorithm to discuss and analyze. First, after data preprocessing, the dimension of column data is reduced by feature extraction and the evaluation index is obtained. These metrics are then weighted using the CRITIC method to obtain a measure of quantified performance. Secondly, the correlation analysis model is introduced to prove the existence of momentum. Finally, by comparing the performance of XGBoost, Extra Trees and Random Forest algorithm in training and prediction, it is found that XGBoost algorithm has the best performance in AUC of test set, reaching 95.05%. The XGBoost parameters were further optimized by Bayesian optimization, and the signal was reconstructed by combining wavelet analysis to improve the accuracy and robustness of the model. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Cardiovascular Health Monitoring System for Heart Disease Detection Based on machine learning Technique 2

Efficient Cardiovascular Health Monitoring System for Heart ...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kumar, Kottu Santosh Sahoo, Ramesh Chandra Rout, Saroja Kumar Mohammad, Manzoor Jit, Biswa Ranjan Reddy, S. Eswar Kar, Bhabesh Ranjan Department of Information Technology Hyderabad India Manav Rachna International Institute of Research and Studies Faridabad India Hyderabad India School of Professional Studies DRIEMS UNIVERSITY Odisha Cuttack India Bhubaneswar India Cast and Crew LLC Hyderabad India

ISBN: (纸本)9798350366570

Heart diseases are a global leading cause of death, affecting nations universally. Early detection and machine learning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucial to prevent heart attacks. This study aims to identify a machine-learning method that strikes a balance between computational efficiency and prediction accuracy when it comes to heart disease prognosis. This work proposes a model integrating various methods for effective heart disease prediction. Employing efficient data collection, pre-processing, and transformation, our model uses an ensemble approach that includes Six classification machine learning algorithms- SVM, DT, RF, LR, KNN, ANN, and PCA. The study identifies significant heart disease features, predicting the highest risk through machine learning. Experimental outcomes indicate prediction accuracy obtained by SVM, DT, RF, LR, KNN, ANN, and PCA. as a feature selection process are 91%, 90%, 86%, 91%, 88%, and 91%. Collaboration is vital for enhancing algorithm precision in clinical settings. The application of machine learning to cardiovascular health has the potential to transform continuous management, personalized treatment, and early diagnosis. Robust clinical tools for better patient outcomes and public health are ensured by addressing issues like data privacy and model interpretability and by fostering interdisciplinary collaboration. © 2024 IEEE.

关键词： Patient treatment

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