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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是71-80 订阅

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Enhanced Capability on Smart Handheld Devices using On-Device machine learning 6

Enhanced Capability on Smart Handheld Devices using On-Devic...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Ramamurthy, Sivasankar Niranjana, G. Department of Computing Technologies SRM Institute of Science & Technology Chennai India

ISBN: (纸本)9798350398496

the current, most popular method for making edge devices intelligent relies on the cloud, where data is gathered from numerous device sources and uploaded. the developed model is then sent back to the device after being used to train a machine learning model in the cloud. the device then uses this trained model & becomes even more intelligent. Given recent developments and a growth in the number of smart devices with improved hardware capabilities, there is an increasing interest in using machine learning on the edge device itself rather than learning in the cloud. Hardware vendors are promoting AI-enabled chipsets that offer improved processing capabilities better tailored to computer vision, IoT, and machine learning based applications and solutions that benefit end users, which is further fostering this interest. this idea also reduces the overhead of offloading data to the cloud every time & also solves the security concern of user data being offloaded to the cloud. this way an enhanced security will be assured for user data and reduce overall latency for certain time-critical machine learning applications. this research study has surveyed the benefits and challenges of implementing machine learning algorithms on edge devices by paying special attention to how techniques are adapted or designed to execute the resource-constrained devices. © 2023 IEEE.

关键词： Internet of things

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Few-shot learning with Prompting Methods 6

Few-shot Learning with Prompting Methods

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6th international conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Bahrami, Morteza Mansoorizadeh, Muharram Khotanlou, Hassan Bu-Ali Sina University Department of Computer engineering Hamedan Iran

ISBN: (纸本)9798350314557

Today, in natural language processing, labeled data is important, however, getting adequate amount of data is a challenging step. there are many tasks for which it is difficult to obtain the required training data. For example, in machine translation, we need to prepare a lot of data in the target language, so that the work performance is acceptable. We may not be able to collect useful data in the target language. Hence, we need to use few-shot learning. Recently, a method called prompting has evolved, in which text inputs are converted into text with a new structure using a certain format, which has a blank space. Given the prompted text, a pre-trained language model replaces the space with the best word. Prompting can help us in the field of few-shot learning;even in cases where there is no data, i.e. zero-shot learning. Recent works use large language models such as GPT-2 and GPT-3, with the prompting method, performed tasks such as machine translation. these efforts do not use any labeled training data. But these types of models with a massive number of parameters require powerful hardware. Pattern-Exploiting Training (PET) and iterative Pattern-Exploiting Training (iPET) were introduced, which perform fewshot learning using prompting and smaller pre-trained language models such as Bert and Roberta. For example, for the Yahoo text classification dataset, using iPET and Roberta and ten labeled datasets, 70% accuracy has been reached. this paper reviews research works in few-shot learning with a new paradigm in natural language processing, which we dub prompt-based learning or in short, prompting.

关键词： few-shot learning natural language processing prompting prompt-based learning pre-trained language model

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Comparing machine learning Classification Algorithms and Feed Forward Neural Network to Perform Sentiment Analysis 6th

Comparing Machine Learning Classification Algorithms and Fee...

引用

6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Gupta, Abhinav Jindal, Panshul Shukla, Raunak Patil, Hemprasad Yashwant Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819986279

We have utilized a unique dataset of 50,000 review inputs in this research to perform sentiment analysis using a selection of machine learning classification methods. To eliminate potential unnecessary terms in the reviews, the dataset is extensively preprocessed prior to getting employed by any algorithm. In order to get the best results, we evaluated numerous classification techniques, including Logistic Regression, Random Forest, Extra Tree Classifier, Support Vector machine (SVM), and Naïve Bayes. the outputs of all the underlying algorithms are then aggregated by feeding them through a Voting Classification model. Furthermore, to make a clear contrast, we designed a Feed Forward Neural Network Engine as well. the data in the Neural Network is routed via various layers, where it is refined to get the best outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Systematic Review on Real-Time Students Behavior Monitoring using machine learning 6

Systematic Review on Real-Time Students Behavior Monitoring ...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Akash, S. Dinesh, V. Muthamil Selvan, S. Srikanth, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

Student behavior analysis is an important research area that aims to understand and improve student outcomes. machine learning has emerged as a powerful tool for analyzing student behavior, allowing researchers to identify important patterns and predict future behavior. Further, student behavior analysis can help to identify individual student needs and preferences, allowing for personalized learning experiences that are tailored to the unique needs of each student. this can improve engagement and motivation, and help students reach their full potential. this study provides an overview of various methods for student behavior analysis using machine learning, including clustering, classification, time-series analysis, recommender systems, and natural language processing. Moreover, steps involved in using these methods, including data collection, preprocessing, feature engineering, model training, and model evaluation is elaborated. Furthermore, discussion on the architecture, and ethical considerations for using machine learning in student behavior analysis depicted. Finally, the article highlights the importance of carefully choosing the appropriate method for each research question and considering the potential impact of machine learning on students and society. © 2023 IEEE.

关键词： machine learning

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Using a Screening Risk of Dyslexia Game to Evaluate Students with learning Disabilities through the Use of Language-Independent Content and machine learning 8

Using a Screening Risk of Dyslexia Game to Evaluate Students...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Puritat, Kitti Atsawakornkan, Tippawan Intawong, Kannikar Chiang Mai University Department of Library and Information Science Thailand Banprajan Child Center Chiang Mai Thailand Chiang Mai University Faculty of Public Health Chiang Mai Thailand

ISBN: (纸本)9798350396546

In this paper, we propose a method for global dyslexia screening based on machine learning framework and data collected from a screening risk of dyslexia online game. this research aims to explore students with learning Disabilities. We developed the game's content by analyzing the errors made by students with dyslexia in many languages, as other dyslexia-related criteria include audio and visual processing. Our approach involves gathered data to train predictive machine learning models. Using Random Visual game and Random audio game respectively, the findings achieve effective results. Screening for a dyslexia risk game. Our approach can be used to screen students with disorder and hence aiding a potential early intervention. © 2023 IEEE.

关键词： machine learning

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Implementation of Burmese Language News Classification System by Using SVM and LSTM machine learning Algorithm 6

Implementation of Burmese Language News Classification Syste...

引用

6th IEEE international conference on Computer and Communication Engineering Technology, CCET 2023

作者： Ye, Kyaw Zaw Naing, Ye Myint Naung, Ye Nyein, Khant Phyo Lin, thant Zaw Science and Technology Research Center Pyin Oo Lwin Myanmar

ISBN: (纸本)9798350340686

With the increasing availability of documents in social media and the rapid growth in the number of data exchange systems, the task of automatically categorizing documents has become a key method for organizing information and knowledge retrieval. News classification is the process of sorting text documents into one or more predefined categories or classes of similar documents. the correct classification of electronic documents, online News, blogs, electronic letters and digital libraries requires intellectual analysis of the text, the use of machine learning and methods of a natural language processing to obtain significant information. the article aims to analyze the methods and methodologies that are used in the classification of text documents. the purpose of the work is to classify news in social media that need to be organized and retrieved information using machine learning methods. In this paper, Experiments were done on Myanmar local and international news dataset using TF-IDF feature extraction techniques and the results are compared with performance of Support Vector machine (SVM) machine learning algorithm and long short-term memory (LSTM) recurrent neural network. © 2023 IEEE.

关键词： Classification (of information)

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Rotor Faults diagnosis in PMSMs Based on Branch Current Analysis and machine learning 6

Rotor Faults diagnosis in PMSMs Based on Branch Current Anal...

引用

6th IEEE international Electrical and Energy conference, CIEEC 2023

作者： Yu, Yinquan Gao, Haixi School of Mechatronics and Vehicle Engineering East China Jiaotong University Nanchang330013 China

ISBN: (纸本)9798350346671

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and mixed fault of permanent magnet synchronous motor (PMSM) with slot pole ratio of 3/2 and several times of it, this paper proposes a method to identify the rotor fault based on the combination of branch current analysis and machine learning algorithm. First, in this paper, we select the stator branch current fluctuation in time domain without mutual cancellation as the feature of fault classification. Secondly, after signal preprocessing, feature factors are extracted and several fault feature factors with large differences are selected to construct feature vectors. Finally, genetic algorithm is used to optimize the parameters of support vector machine (SVM), and GA-SVM model is constructed as a classifier for multi fault classification of permanent magnet synchronous motor to classify these three types of faults. the fault classification results show that the proposed method using branch current signal combined with GA-SVM can be effectively diagnose faulty PMSM. © 2023 IEEE.

关键词： Support vector machines

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Comparative Study of Various machine learning Algorithms for American Sign Language Recognition 1

引用

6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Malhan, Prabhat Gusain, Surbhi Raturi, Shashank Singh, Prabhdeep CS/IT Graphic Era University Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

this research paper presents a comparative study on various machine learning algorithms for sign language detection. the objective of this study is to find the sign language identification method that is most accurate and effective for usage in real-time applications. the Residual Network (ResNet), Artificial Neural Network (ANN), Convolutional Neural Networks (CNNs), VGG16, and MobileNet are five well-known machine learning methods whose performance is compared. the dataset utilized in this study comprises camera-captured sign language motions made by diverse people. We assess each algorithm’s performance using a variety of parameters, including accuracy. According to our findings, VGG16 performs better than the other four algorithms, with an accuracy rate of 99%. therefore, we conclude that VGG16 is the most suitable algorithm for sign language detection, which can be used in real-time applications for the deaf and hearing-impaired community. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Early Detection of Heart Attack Using machine learning 6th

Early Detection of Heart Attack Using Machine Learning

引用

6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Kasthuri, N. Ramyea, R. Saravanan, Avinash Vuliya Hariprasath, V. Shree, M. Deevitha Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9789819986279

Cardiovascular diseases are a group of conditions that harm the heart and blood vessels. these illnesses may have an impact on one or more regions of the heart and blood vessels. A person may or may not have symptoms. the leading cause of mortality worldwide over the past several decades has been cardiovascular disease (CVD), which has become the most serious illness worldwide, not only in India. It has been demonstrated that machine learning is useful with the decision-making process and in making predictions from the vast data gathered by the healthcare industry. So, in this paper, a faster method is used to predict heart attack efficiently. Random forest algorithm is used in this method;the attributes are selected at random and trained. Using the trained data, the test data will be compared along with the trained data to predict the result. As the attributes are selected at random to train the data, the performance will increase. A voting is calculated for each decision tree in the random forest algorithm to find out the most significant result. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Weak signal Detection Method Based on Bayesian Neural Network 6

Weak Signal Detection Method Based on Bayesian Neural Networ...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhao, Xiao Wang, Lihui Chen, Zhenjia Shao, Po Chen, Ran Hainan University School of Information and Communication Engineering Haikou570228 China Hainan University Collaborative Innovation Center of Information Technology Department of Was the School of Information and Communication Engineering Haikou570228 China

ISBN: (纸本)9798350325485

With the development of the Times, the high efficiency and privacy of modern communication become increasingly important. How to distinguish weak signal from noise in the case of insufficient prior information becomes particularly important. the traditional modulation recognition process requires manual extraction of signal features, and the process is complicated and the unstable factors are too large, which cannot meet the requirements of high precision and high time of modern communication. With the development of deep learning, machine learning has made great breakthroughs in the direction of radio signal processing due to its obvious classification and decision advantages, and signal recognition has a higher accuracy than before. More and more researches begin to use machine learning method to extract features directly from the original data of communication signals, which ensures the real-Time and robustness of automatic modulation recognition of communication signals. Naive Bayes is one of the classical algorithms of machine learning and one of the few classification algorithms based on probability theory. this method is still effective in the case of less data and can deal with multiple kinds of problems. Based on Bayesian network algorithm, this paper studies the weak signal identification method of wireless telecommunication below 0db. Aiming at the problem that the generalization ability of traditional model is insufficient, this paper proposes a weak signal recognition method based on Bayesian network. the experimental results show that the recognition rate of this method is 40 percent higher than that of different Bayesian neural networks in the recognition of radio weak signals, which improves the recognition ability of the whole recognition model for weak signals not in training concentration. © 2023 IEEE.

关键词： signal detection

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