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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 763 条记录，以下是401-410 订阅

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Competitive Sports Data Analyzing Model Based on Machine Learning 3

Competitive Sports Data Analyzing Model Based on Machine Lea...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Luo, Junyao Wang, Zihan Zhang, Junyi Franklin & Marshall College 17603 United States School of Engineering The University of Mississippi Oxford38655 United States University of Electronic Science and Technology of China 610000 China

ISBN: (纸本)9781510661301

Machine learning is an important research area belonging to artificial intelligence technology. Machine learning has potent data processing and prediction ability. This paper uses machine learning to build a data analysis model and validate it with the NBA. This paper consists of two parts in total. The first part uses a clustering algorithm to analyze all teams' high-order data from 2000 to 2021 and classify the teams' styles. The main effect of using the k-means algorithm is to put the data in very different clusters, and in this paper the different clusters are considered to be the different team styles. At the same time, we can determine whether the style of the team has changed. We used the Euclidean distance as the distance formula for k-means. By the test of different k numbers, the result showed that between the numbers 2 to 9, the Silhouette Coefficient performed best when and second when. This paper used to measure the method that can be used to evaluate the influence of different algorithms or operating modes of algorithms on clustering results based on the same original data. For example, when k=2 and k=4. The experimental results show that the team styles in the NBA can be broadly classified into several categories over a long development period. The second part constructs a win prediction model based on the Elo method for the matchup data from 2020 to 2022. To sum up, in this paper, Elo ranking system, the Logistic regression model and three data sets are used to get a detailed prediction of the winning rate of NBA teams in 21-22 seasons. © 2023 SPIE.

关键词： Machine learning

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A sleep scoring application of Ensemble learning algorithms in sleep patient scenario 3

A sleep scoring application of Ensemble learning algorithms ...

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Li, WenJie Liu, YaDong Zhou, JinXia Intelligent Science Department National University of Defend Technology Changsha China Department of Neurology Xiangya Hospital Central South University Changsha China

ISBN: (纸本)9781510657281

Sleep scoring is manually marked by professional neurophysicians, which is a time-consuming and subjective error prone work. An automatic classification algorithm dealing with this task is of great significance to auxiliary medical diagnose. In this paper, data set are collected from the clinical physiological signals of 7 patients from Xianga Hospital. signal preprocessing and feature extraction algorithm combines the advantages of principal component analysis and wavelet decomposition in only single EEG channel. In result, the five fold cross validation on the data set of all 7 patients has achieved an average accuracy of 77.18% and the highest accuracy of 83.24%. In addition, it is also found that the stable and optimal EEG channel is C3-M2, and the extracted 41 wavelet statistical features are not sensitive to signal noise, and it is found that the random forest model has better classification performance. © 2022 SPIE.

关键词： Learning systems

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Feature Extraction from Plant Leaves and Classification of Plant Health Using Machine Learning 3rd

Feature Extraction from Plant Leaves and Classification of P...

引用

3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Abisha, A. Bharathi, N. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus No.1 Jawaharlal Nehru Rd Tamil Nadu Vadapalani India

ISBN: (纸本)9789811908392

Feature extraction (FE) is an important method that contributes dimensionality reduction especially when extracting the features from images. It is the method of converting the input images into a set of features understandable by the system or software. While processing images, it is desirable to extract features that are oriented toward discernment between two or more classes. In this proposed method, two classes are considered as infected and healthy, and data is distributed into six cases. Features are extracted from the plant images using feature descriptors Hu moments, Haralick texture and color histogram. After feature extraction, the images are classified as infected or healthy based on the feature descriptors. The extracted data are trained with the various classifier models such as LR, LDA, KNN, CART and RF, and validation is performed. 10 k cross-validation is used, and the three best algorithms are chosen to classify and predict the model. Among these models, RF, CART and KNN have given better performance, respectively. Finally, accuracy score above 95% and r2 score above 85% is found for various cases, and the results are visualized for all six cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Cluster-Based Probabilistic Neural Networks for Outlier Detection Via Autoencoder Variants 3rd

Cluster-Based Probabilistic Neural Networks for Outlier Dete...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Chander, Bhanu Kumaravelan Department of Computer Science and Engineering Pondicherry University Pondicherry609605 India

ISBN: (纸本)9789811908392

Outlier detection in wireless sensor networks (WSNs) is an indispensable issue that plays a significant role in confirming sensor data quality and consistent recognition of exciting events. Since the erroneous and defective sensor data might raise false alarms with wrong conclusions, therefore, it is vital to spot outliers in sensor data proficiently and precisely for comprehensive decision-making. This manuscript proposed a cluster-based probabilistic neural network for outlier detection in WSNs via autoencoder variants. For performance analysis, we analyzed the proposed model with other existing models on real-world datasets. Experimental results show that both DAE-PNN and AE with dropout-PNN outperform all other existing approaches in detection rate and F-score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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Comparative Study On Breast Cancer Classification Using Multiple Convolution Neural Network Architectures 3

Comparative Study On Breast Cancer Classification Using Mult...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Hossinq, Md. Murad Molla, Shourav Javed Mehedi Shamrat, F.M. Samia, Mahsuba Meherunnessa Saha, Rakhi Moni Khater, Ankit Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh Jadavpur University Department of Computer Science and Engineering India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Cancer of the breast is one of the primary causes of mortality of women across the globe. Breast abnormalities may often be diagnosed and classified with the use of ultrasound imaging. To better pinpoint health issues, radiologist diagnosis have been supported by a variety of CAD technologies created in recent decades. Using Automatic Breast Ultrasound (ABUS) scans, this research employed CNN models to differentiate between benign tumor, malignant tumor, and Normal breast tissue. Pre-processing the data is crucial in data classification since it helps the learning model function more effectively. The implemented dataset contains unprocessed image data where classes have data imbalance issue. So, it is important to resolve this imbalance problem. To resolve this issue data augmentation approach is applied on the minority class. Before the learning models are employed to the preprocessed data, the data is first partitioned into two subsets: a training set and a test set. This study's deep learning models for breast ultrasound classification are MobileNetV2, VGG16, AlexNet, ResNet, Inception V3, and Xception. Each model is trained and then evaluated at different epochs to keep track of its progress. The study found that as the epochs go higher, data loss lowers and accuracy rises. The accuracy of MobileNetV2, VGG16, AlexNet, ResNet, Inception V3, and Xception is 98.82%, 94.70%, 95.96%, 93.50%, 92.72%, and 93.65%, respectively. MobileNetV2 outperforms with 98.82% accuracy rate the comparative to other applied models. Finally, breast cancer is classified using the MobileNetV2 model with significant accuracy and precision. © 2022 IEEE.

关键词： Ultrasonic imaging

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd international conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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Generalized Tonic-Clonic Seizure Detection through the Use of Physiological signals 3

Generalized Tonic-Clonic Seizure Detection through the Use o...

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3rd international conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Li, Wen Sheng, Duozheng Wang, Guangming Wang, Gang Xi'an Jiaotong University The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Biomedical Engineering School of Life Science and Technology Xi'an China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Generalized tonic-clonic seizures increase the risk of sudden unexpected death in epilepsy, especially for patients without caregivers around them. There is a need for wearable devices with the ability to detect on-going seizures in an in-home care setting. Video-EEG is the gold standard of seizure detection, but lack of practicability for long-term daily monitoring. Recent years, various studies on wristband that accepted well by patients have achieved high sensitivity but also high false alarm rate (FAR) on seizure detection. To decrease false alarms, we proposed a seizure detection method on multimodal signals related to GTCS, including six-axis accelerometer (ACM), surface electromyography (sEMG) and electrodermal activity (EDA). The method consisted of two classifiers, one for identifying resting state and moving state and the other for identifying seizure state and non-seizure state from segments in moving state, and post-processing consisted of a mean filter and threshold crossing for restraining outliers. In this work, 4 patients were enrolled, and wristband recordings reached 3786 hour with 68 GTCS in total. We achieved a mean sensitivity of 81.69% and FDR of 0.64/24h, and the result was better than using just one signal, ACM, sEMG or EDA. Besides, the FAR of multimodal signals was lower than that of any signal at the same level of sensitivity, which meant that it's a feasible solution to combine the multimodal signals for purpose of less false alarms on the basis of high sensitivity. © 2022 IEEE.

关键词： Errors

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Automated Mapping of Course Outcomes to Program Outcomes using Natural Language processing and Machine Learning 3

Automated Mapping of Course Outcomes to Program Outcomes usi...

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3rd IEEE Applied signal processing conference, ASPCON 2023

作者： Sengupta, Souvik Das, Asit Kumar Aliah University Department of Cse Kolkata India Iiest Department of Cst Howrah India

ISBN: (纸本)9798350325881

Curriculum mapping is an essential academic requirement in outcome-based education (OBE) for successfully implementing the academic program. It is a monitoring tool for course coordinators/instructors to align their curricula according to corresponding program outcomes. This mapping is commonly represented by a two-dimensional matrix depicting the relationship between the Course Outcomes (CO) and Program Outcomes (PO), popularly known as CO-PO mapping. This mapping is a challenging task, even for experienced teachers, as it entails knowledge of the standards and practical aspects of effective mapping, which are not well-documented or readily available. Consequently, this paper aims to tackle this challenge by proposing an AI-based automated system for curriculum mapping, which allows teachers to inspect the CO-PO mappings, identify shortcomings, and recommending suggestions for enhancing curriculum mapping. A dataset is prepared by collecting data from publicly available CO-PO mapped curriculums of the computer science Engineering program of seven Indian Universities. Since one CO can be mapped to multiple POs, this work employs machine learning and deep learning models with NLP to solve this multi-label classification task. An empirical study is performed to compare the efficiency of different feature extraction and classification methods. The results indicate that the BERT-based model outperforms the other models in the accuracy of mapping. © 2023 IEEE.

关键词： Mapping

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A New Machine Learning Algorithm for Users' Movie Recommendation 3

A New Machine Learning Algorithm for Users' Movie Recommenda...

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2022 3rd international conference on computer Information and Big Data Applications, CIBDA 2022

作者： Liu, Zhongyuan Wang, Xuefei Zhu, Hongzheng Faculty of rail transit Wuxi University Wuxi214105 China Shanghai Fuxing Senior High School Shanghai200434 China College of computer and information science Faculty of automation Southwest University Chongqing400700 China

ISBN: (纸本)9783800758760

In this information era, even a movie lover may feel overwhelmed when facing numerous movies on the movie website. Therefore, an efficient movie recommendation system is needed. In this paper, two different recommendation systems were compared, based on the user-based collaborative filtering and the content-based collaborative filtering respectively. Basically, several relevant studies were referred to, and a huge dataset was used from MovieLens. After pre-processing the data, where the data was transformed into matrixes of each user's rating and the corresponding movie (the rating matrix represented the eigenvector of each user), cosine similarity was used to obtain the similarity relationship between users by calculating the cosine similarity between those eigenvectors. Then, K-NearestNeighbor (KNN) algorithm was used to select a certain number of users having the highest similarity with the target, based on which predicted how the target would rate a movie and recommended the movie with the highest predicted score to the target. In the part of using the content-based collaborative filtering, films in each category were ranked based on the average and the number of the scores and the user's favorite movie types were obtained by the user-movie genre preference matrix based on the scores that users gave to each type of movies. The movies with the highest score in each target user's favorite movie type would be recommended to the target. Experimental outcomes indicate that both algorithms generally meet the needs of users despite of the differences in the results. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Collaborative filtering

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Prosody Prediction with Discriminative Representation Method 3

Prosody Prediction with Discriminative Representation Method

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3rd international conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Zhang, Jipeng Yilahun, Hankiz Feng, Xiaoqin Chen, Yunlin Yang, Xipeng Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing China Xinjiang Key Laboratory of Multilingual Information Technology China Mobvoi Ai Lab Suzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Rhythm affects the naturalness and intelligibility of Text-To-Speech (TTS). However, rhythm prediction remains a great challenge, usually in two aspects: 1) the united annotation is a relatively difficult task, which depends on expert's experience. 2) traditional methods based on conditional random field (CRF), which heavily rely on feature engineering, such as word segmentation, part of speech(pos) etc. For above problems, we propose a method to reduce the dependency for united annotation data and conduct the joint experiment which use one unified model on independent data. Meanwhile, we also propose an algorithm of Layer Look Up Table (LLUT): use an embedding layer to learn a discriminative representation for different level of prosody data without any feature engineering. By using this method, the classifier can share the parameters and predict for different prosody level separately, which reduces the number of trainable model parameters. In order to better represent the input text, we use the pre-training model, like BERT, to provide the semantic information. Our experiment shows that the method of LLUT, is better able to acquire the discriminative meaning of different prosody levels. And also, our algorithm is proved to be general for sequence annotation tasks thus we can do extra task, like polyphone-prosody prediction. © 2022 IEEE.

关键词： Forecasting

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