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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361-370 订阅

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Efficiency Enhancement of machine learning Approaches through the Impact of Preprocessing Techniques 6

Efficiency Enhancement of Machine Learning Approaches throug...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Gulati, Vineeta Raheja, Neeraj CSE Department Mullana133207 India

ISBN: (纸本)9781665425520

A prediction system is affected by numerous factors. the primary factor is the depiction and traits of instance data. In the presence of unnecessary and redundant data or boisterous and deceptive information, it becomes arduous to discover knowledge during the training phase. One of the persistent challenges in data analytics is identifying and rectifying dirty data. the outcomes will be unreliable decisions and inaccurate analytics if an individual fails to do so. It is known by a large mass of people that the machine learning model performances are affected by the quality of data. Consequently, a significant amount of time is spent by the scientists on cleaning data before model training. Data pre-processing is considered the fundamental stage of ML methods by countless researchers. Nevertheless, only a fraction of works have paid emphasis on the consequences of data processing techniques. this paper addresses the serious impact that issues of data pre-processing can have on the generality performance of an ML algorithm and here all the pre-processing techniques like removal of missing values with all the possible methods, data binning, and data normalization, etc. are discussed. After performing all the pre-processing techniques, divide the dataset into training and testing datasets and then apply machine learning algorithms. It is concluded that to achieve a reliable result or better accuracy data preprocessing plays a very important role and to build a machine learning model we must have deep knowledge of all the pre-processing techniques, also where and how to apply them. © 2021 IEEE.

关键词： Support vector machines

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A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telugu Language 6th

A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telug...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Passi, Kalpdrum Kalakala, Sujay Laurentian University SudburyONP32C6 Canada

ISBN: (纸本)9789811935749

One of the most active areas of research and discussion in the field of natural language processing is the concept of sentimental analysis or the concept of opinion *** has gained a lot of popularity in the past few years and the main cause behind that is the usefulness of the derived *** methods and concepts can analyze the given text and provide results based on the nature of the *** is very useful in terms of business growth as well as customer satisfaction and *** rule-based approach was then used to train a machine learning model using a few parametric classifiers like K-nearest neighbors (KNN), XGBoost, and support vector machines (SVM).the classifiers also fetched a decent accuracy of 81%, 82%, and 78%, respectively, which indicated toward the good performance of the rule-based approach and its effectiveness with error counts of 0.296, 0.288, and 0.252 with TF-IDF and 0.285, 0.285, and 0.234 with bag of *** the process, manual observation was also used to compare the assigned sentiments to the sentence to find the errors in the *** best performance with respect to results was given by SVM classifiers that returned an F1-score of 75%, and the lowest error count of 0.25 which is better among all the *** metrics which were used to judge these classifiers were the F1-scores as well as the mean squared error. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Low Birth Weight Prediction Model Based on Pre-Pregnancy Information

Low Birth Weight Prediction Model Based on Pre-Pregnancy Inf...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Kai Wang Qiaoyun Dai Xueying Yang Yanquan Zhou Xu Ma Yanxu Chen Beijing University of Posts and Telecommunications Artificial Intelligence Institution Beijing China National Center for Human Genetic Resources Institute of Science and Technology of National Health Commission Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

this work developed and validated several machine learning-based risk prediction models for low birth weight (LBW) newborns by using data from the “National Free Preconception Check-up Projects” (NFPCP) and compared their performance. In addition, through model fusion and Permutation Feature Importance (PFI) analysis, the key factors affecting the pregnancy outcome of LBW were determined before and during early pregnancy, and only a small number of examination items in the pre-pregnancy examination data and pregnancy follow-up data were used, and the AUC of the prediction model reached a maximum of 0.7390. It achieves a good prediction effect, and provides a low-cost tool for predicting LBW, and is of great significance for preventing the occurrence of LBW.

关键词： Pregnancy Pediatrics Analytical models Computational modeling Prevention and mitigation machine learning Predictive models signal processing Data models Reliability

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Bridge Crack Detection System Design

Bridge Crack Detection System Design

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Qiang Liu Jing Feng Peng Zhao Dandan Fu Department of Electronics Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Aiming at the low efficiency of traditional bridge crack detection and the problem of certain safety factors, this paper designs a bridge crack detection system based on machine learning. the system firstly carries out crack data collection and pre-processing, crack processing, classification and then crack parameter measurement, and finally integrates all the above steps into the GUI software interface. the whole system realizes the detection of bridge cracks and the display of parameters, and also gives the health condition of the bridge and suggestions. the design not only improves the efficiency of bridge crack detection, but also lays a good research foundation for practical engineering applications.

关键词： Bridges Deep learning Accuracy Area measurement signal processing algorithms Length measurement Time measurement System analysis and design Graphical user interfaces Diseases

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Extreme-learning-machine-based architecture to assess the reliability of the integrated energy systems

Extreme-learning-machine-based architecture to assess the re...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Xianxu Huo Qingbiao Wang Chenyang Zhao Yi Ding Bingchen Zhang Electric Power Research Institute of State Grid Tianjin Electric Power Company Tianjin China Baodi Power Supply Branch State Grid Tianjin Electric Power Company Tianjin China State Grid Tianjin Electric Power Company Tianjin China Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China

the global energy crisis has contributed to the development of integrated energy systems (IES). IES enables the coupling, conversion, and utilisation of multiple energy, such as electricity, heat and cooling. As renewable energy penetration increases, the precariousness of renewable energy in IES causes risks to the reliability of energy supply. Traditional optimal energy flow (OEF) models become complex and computationally intensive. To solve this problem, a machine-learning-based method is proposed instead of the traditional OEF model. the method obtains the model optimal coding parameters by extreme learning machine (ELM) model, establishes the nonlinear mapping relationship between the system operation state and the optimal load reduction, and realizes the high accuracy and fast solution of the reliability indices. In this article, simulation experiments are conducted on the integrated community energy system (ICES) to test the advantage and practicality of the approach in this paper. Compared with the model-driven approach, the method in this paper greatly improves the efficiency of reliability assessment and has an accuracy rate of over 95%, which is better than random forest, back propagation neural network and autoencoder.

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A Defect Detection Algorithm of Round Tablets Based on Image Information

A Defect Detection Algorithm of Round Tablets Based on Image...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Lianqi Zhang Rui Zhou Yuan Sun Xiaojin Chen Zhiwang Yang College of Electrical Engineering and Automation Xiamen University of Technology Xiamen Fujian China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen Fujian China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

this paper presents a novel circular tablet defect detection algorithm based on machine learning techniques to address the challenge of insufficient data for training samples. Utilizing cameras, lenses, and light sources, we establish an image acquisition hardware system for capturing tablet images, and on the basis of image preprocessing, the designed defect detection algorithm is used for detection, forming a complete and efficient round tablet defect detection process. the experimental results have been demonstrated that the performance of the sample missed detection rate was 0.2%, the false detection rate is 0.6%, and the total error rate is 0.8%, all lower than 1%. the proposed detection algorithm does not require sample training and can be well applied to the identification of defects such as abnormal tablet circular areas and foreign matter on the surface of tablets.

关键词： Training Visualization machine learning algorithms Error analysis signal processing algorithms signal processing Feature extraction Production facilities Image preprocessing Defect detection

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Real Time Plant Disease Detection Model using Deep learning 6

Real Time Plant Disease Detection Model using Deep Learning

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Ashish Aswal, Upendra Singh Rana, Ajay Vani, V Divya Sankhyan, Akhil Shekhar Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Graphic Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Uttar Pradesh Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Uttar Pradesh Greater Noida India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Convolutional neural networks (CNNs) have had remarkable success in classifying a variety of plant diseases through deep learning. However, just a few researches have shed light on the inference process, leaving it as an unsolvable mystery. In addition to guaranteeing the learnt feature's dependability, revealing the CNN to extract it in an understandable form enables human intervention-based verification of the model's veracity and the training dataset. Using a CNN that had been trained using a public ally accessible collection of images depicting plant diseases, several neuron-wise and layer-wise visualisation techniques were used in this study. We demonstrated that neural networks can, when diagnosing an illness, capture the hues and textures of lesions particular to that disease, which is similar to human judgement. the most critical aspect of agriculture is striking a balance between produce and population. Due to a variety of issues, including natural disasters, unforeseen rainfall, nutrient deficiencies in the soil, etc., the majority of farmers failed to produce and balance the crops. the main issue, however, is pest infection, which is the root of all the issues. To learn about plant diseases, several researchers employed a variety of methods. Convolutional neural network-based deep learning techniques are frequently utilised to solve image-oriented problems. A powerful and successful method for image analysis is the CNN (ConvNet) neural network model of deep learning. In this study, various models for plant disease detection with CNN are compared. the research report concludes by summarising its findings, identifying its limitations, and making recommendations for classification © 2023 IEEE.

关键词： classification deep learning image processing machine learning

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GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification

GP-Net: Global Perceptual Network with Adaptive Gradient Cli...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaohua Wang Jie Yang Min Hu Xia Wu Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. the main module enhances the weighting of objects by showing the interdependencies between modelled channels. the background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis.

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Prophetic Analysis of Bitcoin price using machine learning Approaches 6

Prophetic Analysis of Bitcoin price using Machine Learning A...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Kaushal, Rajesh Kumar Kumar, Naveen Institute Of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9781665425520

the most significant disturbance now affecting all economies and financial institutions is the digital transformation of economies. the world's economy and financial institutions are digitizing at an unprecedented rate. Bitcoin is a devolved crypto-currency, or digital asset, that uses blockchain technology to expedite peer-to-peer financial transactions. Price volatility is one of the primary issues with decentralized cryptocurrencies, highlighting the need of examining the underlying price mechanism. Additionally, Bitcoin prices display non-stationary behaviour, meaning that their statistical distribution fluctuates over time. Bitcoin prices are stochastic, and no one set of characteristics can be used to forecast them completely. Nonetheless, academics have demonstrated varying degrees of effectiveness in estimating Bitcoin values using various feature sets. this article explains how to forecast Bitcoin price movements and prices using machine learning approaches. We intend to apply ARIMA, Facebook Prophet and XGBoost techniques for price prediction. © 2021 IEEE.

关键词： Blockchain

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machine learning Approaches for Adaptive signal processing in 6G Networks

Machine Learning Approaches for Adaptive Signal Processing i...

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Computing, Power and Communication Technologies (IC2PCT), IEEE international conference on

作者： R. Shobarani Sakthi Kumaresh P. Dhivya M.J. Bharathi S. Srividhya Santhi Department of CSE Dr. M.G.R Educational and Research Institute Chennai India Dept of Information Technology M. O. P. Vaishnav College for Women Chennai India Department of Computer Science St. Thomas College of Arts and Science Chennai India Department of Computer Applications Dr.M.G.R Educational and Research Institute Chennai India

Due to the very rapid growth of wireless communication networks, the arrival of the 6th Generation (6G) signals the beginning of groundbreaking experiences and new opportunities. the success of 6G networks hinges on their capacity to adapt to complicated and constantly evolving communication environments. this study investigates the critical role of ML in enabling adaptive signal processing in 6G networks. In conclusion, our study proves that ML is crucial for adaptive signal processing in 6G networks and suggests that it could impact the design of future wireless communication systems. the advent of 6G networks, enabled by machine learning, will usher in a new age of connectedness and innovation by satisfying the need for very fast, extremely reliable, and extremely efficient communication.

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