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

共 769 条记录，以下是451-460 订阅

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Expert System for Real Time Arm Movement Recognition Based on Surface EMG signal 1

引用

Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2023

作者： Bhatlawande, Shripad Shilaskar, Swati Kinge, Mahesh Mahajan, Anurag Department of E and TC Engineering VIT Pune411037 India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

this paper presents classification of Electromyography (EMG) signals to distinguish between upper-limb motions. the system can help in the movement of a prosthetic hand. Eight healthy subjects participated in the experiment. Total eight movements are performed on four distinct muscles to capture and analyze the signals. the movements performed are elbow extension, hand close, wrist extension, wrist flexion, hand open, ulnar deviation, elbow flexion and radial deviation. Sixteen statistical features are used to classify data. Sampling frequency used is 1024 Hz. To eliminate powerline frequency equal to 50 Hz, a bandstop filter is utilized. Bandpass filter is used with cut-off frequencies 50 Hz and 160 Hz. An array of 6 classifiers is used such as Support Vector machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors_(KNN), Decision Tree_(DT) and Random Forest_(RF). A voting classifier (VC) is used to explore consensus-based classification and detection. Metrics used are F1 score, accuracy, precision and recall. Average accuracy achieved is 70.36%, average precision is 73.36%, average recall is 70.36% and average F1 score achieved is 69.56%. © 2023, IFIP international Federation for Information processing.

关键词： Expert systems

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A Novel Fractal Geometry Enhanced Microstrip Patch Antenna Design for 5G Connectivity with DGS and machine learning Optimization for Vehicle-to-Vehicle Communication 6th

A Novel Fractal Geometry Enhanced Microstrip Patch Antenna D...

引用

6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Karanam, Raghavendra Kakkar, Deepti Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

ISBN: (纸本)9789819778614

Ensuring robust vehicle connectivity is the most critical significance in the rapidly advancing field of vehicular communication within the 5G framework, which uses the futuristic frequency ranges. Here, we provide a novel proposal that addresses this problem by combining innovative antenna design with state-of-the-art optimization techniques and fractal geometry. Our concept centers on developing a rectangular microstrip patch antenna capable of operating on multiple bands with the operating frequency range of 25–32 GHz. It relies on fractal geometry for multi-band operation support and to obtain improved radiation characteristics. By applying the concept of defective ground structure (DGS) an enhancement in gain and connection is noticed. this innovative design employs Artificial Neural Networks’ (ANNs) techniques for optimizing antenna dimensions with precise alignment. Our innovative method resulted in a gain of 9 dB which improves vehicle communication range and quality. To address the complicated frequency spectrum needs of 5G vehicle communication, fractal geometry integration increases multi-band adaptability. Incorporating DGS not only enhances radiation properties but also guarantees more robust noise reduction, leading to higher signal fidelity. An exciting new direction in antenna engineering and fractal geometry optimization with DGS, ANN sets the stage for unprecedented connection and communication quality gains. By offering more secure, efficient, and technologically advanced solutions in the ever-changing 5G environment, our invention could significantly impact the evolution of vehicle networks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： 5G mobile communication systems

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Application of Image processing Based on Improved Canny Algorithm and Gurukin's theorem 23

Application of Image Processing Based on Improved Canny Algo...

引用

6th international conference on Information Science and Systems, ICISS 2023

作者： Xie, Yufei Yu, Weiwei Song, Shasha Wang, Wenlin Jia, Ying Gao, Xin Wang, Chongyang College of Pharmacy Shenzhen Technology University Shenzhen518118 China School of Automation Wuhan University of Technology Wuhan430070 China Yantai Yuhuangding Hospital Yantai264001 China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen518118 China Daqing Longnan Hospital Daqing163458 China College of Electronic and Information Engineering Tongji University Shanghai200092 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Wuhan China

ISBN: (纸本)9798400708206

Placental volume is an important indicator for evaluating fetal health, and automatic identification of placenta and automatic calculation of its volume is of great significance in clinical practice. Due to the complexity and diversity of placental images, domestic and foreign scholars have proposed a variety of image recognition and segmentation methods. Among them, the convolutional neural network method is widely used, but this method has black box theory, which is difficult to explain and requires a large number of training samples. In this context, this paper proposes an improved Canny algorithm based on meshing and OTSU for edge detection, recognition and segmentation of placental images. the placenta volume was calculated by using Gurukin's theorem, and the calculation results were compared with the results of manual calculation by doctors. the results show that the algorithm model proposed in this paper is convenient to calculate, and the error of manual calculation is within 10%. the feasibility of the method has been verified. Especially when the sample size is not enough to use machine learning algorithms, it provides strong support for clinical decision-making. © 2023 ACM.

关键词： Image recognition

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machine Vision Based Transformer Insulation Pad Identification Detection

Machine Vision Based Transformer Insulation Pad Identificati...

引用

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

作者： Shuo Huang Yali Wang Yongfei Xiao Jie Sun Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Addressing the issues of low efficiency, low accuracy, and high cost in traditional transformer insulation pad recognition and detection, A machine vision based contour size detection system for transformer insulation pads was studied and designed. the image of the pad is captured by an industrial camera, the image is analyzed and processed by the vision software OpenCV, the shape is recognized by an improved edge detection algorithm, the minimum outer contour is fitted by the Douglas-Peucker algorithm, and the conversion of the spatial coordinate system is completed by fast camera calibration and pixel conversion, and finally the outer contour recognition and the length and width of different models of transformer insulation pads are realized. the results are displayed through the MFC user interface. the experimental results show that the pad identification and detection system is stable and can guarantee a high accuracy rate and solve various problems in traditional measurement.

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Prediction of Dermatological Disease with machine learning

Prediction of Dermatological Disease with Machine Learning

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Pranchal Rajput Atreyi Pramanik Yerrolla Chanti School of Applied and Life Sciences Division of Research and Innovation Uttaranchal University Dehradun Uttarakhand India School of Computer Science & Artificial Intelligence SR University Warangal Andhra Pradesh India

Dermatological disorders (DD) are widespread health issues in nations worldwide. the dangers of infections are hidden, and may lead to both mental and physical health problems. In severe circumstances, it might even result in skin cancer. therefore, one of the most difficult jobs in medical image processing is to diagnose DD from clinical photographs. Additionally, diagnosing DD manually by medical professionals is time-consuming and arbitrary. therefore, automatic DD prediction is necessary for both patients and dermatologists, which speeds up the planning of therapies. In this piece of writing, we reviewed the different types of DD, procedure uses for the identification and categorization of DD and their early identification by the help of various machine learning (ML) algorithms which gives their specific accuracy rate in which Convolutional Neural Network (CNN) shows the highest accuracy with the accuracy rate 99.05% respectively.

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Big Data Analysis of Accounting Forecasting Based on machine learning 6

Big Data Analysis of Accounting Forecasting Based on Machine...

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Zhai, Wenshuo Wu, Guanlin Bao, Weidong Niu, Liyuan National University of Defense Technology College of Systems Engineering Nudt Changsha Hunan China

ISBN: (纸本)9780738143705

Artificial intelligence, big data and other technologies will bring transformative significance to the traditional accounting and audit process, but the relevant research on the specific application methods of such technologies in the field of accounting and audit is still basically blank. In this paper, we combine 5 million pieces of user behavior information of Ant Financials' Yu Ebao business to predict the company's total daily capital inflow as the goal, and discuss the value of machine learning method in artificial intelligence in accounting prediction and specific implementation methods. In addition, based on the construction of application examples and the comparison between machine learning method and traditional method for accounting prediction, this paper also provides theoretical support for the application of machine learning method in the field of accounting. the results of this study show that machine learning is a very special method to predict the problem compared with traditional accounting, because it transcends the existing accounting information model to some extent. Although there has been a long history of using data to support accounting prediction, the depth, breadth, cost performance and potential of accounting prediction through artificial intelligence machine learning are unprecedented. the fundamental goal of machine learning technologies is to help a wide variety of organizations use modern data sets to gain valuable insights, and this is partly in line with the role that accounting has played in business operations in the past. © 2021 IEEE.

关键词： Forecasting

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CNN based Leaves Disease Detection in Potato Plant

CNN based Leaves Disease Detection in Potato Plant

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Riya Verma Rahul Mishra Prachi Gupta Pooja Shivani Trivedi Department of Computer Science & Engineering ABES Engineering College Ghaziabad UP

Potatoes are one of the most popular vegetables worldwide, but they are severely affected by potato leaf diseases such as early blight and late blight. Early detection and appropriate action are crucial to prevent substantial financial losses for farmers. In this study, we propose a technology that uses image processing methods to accurately detect and diagnose potato leaf diseases. the presented model employs CNN, a machine learning algorithm that performs better than others, for image classification. the model uses normal and disease-impacted potato leaves to differentiate between normal and abnormal potato leaf properties. After being analysed by the algorithm, the potato leaf is classified as normal or diseased. Our model achieves high precision with a 97% accuracy rate. this technology has the potential to reduce economic losses for potato farmers and improve the efficiency of disease detection in potato crops.

关键词： Plant diseases machine learning algorithms Operating systems Crops Classification algorithms Convolutional neural networks Reliability

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Aspect Based Sentiment Analysis for Amazon Data Products using PAM

Aspect Based Sentiment Analysis for Amazon Data Products usi...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Yuvraj Prakash Dilip Kumar Sharma Dept of Computer Engineering & Applications GLA University Mathura

Recent years have seen a considerable evolution in online shopping, as well as the ability to get text-based client feedback, comments, and recommendations. the sentiment analysis method examines a large collection of text data to determine the customer’s opinion. One method for analyzing customer sentiment that is provided by natural language processing is AbSA. Understanding out precisely exactly consumers feel about a product is the aim of AbSA. In this research, we spoke about aspect-based sentiment analysis, which identifies the sentiment by using the aspect term. To illustrate our framework, we used user reviews for headphones and earphones that were gathered from the Amazon website. We used NLP to preprocess the data, and we then used the Pachinko Allocation model (PAM) to extract the aspect term and polarity from the review. machine learning technique have been explored to evaluate the model.

关键词： Headphones Sentiment analysis Analytical models Computational modeling machine learning Data models Resource management

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A Human-machine Collaborative Video Summarization Framework Using Pupillary Response signals

A Human-Machine Collaborative Video Summarization Framework ...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Haigang Ma Wenyuan Yu Yaoyao Wang Minwei Zhou Nai Ding Jing Zheng Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Zhejiang Lab Zhejiang University Hangzhou China

this paper proposes a novel, human-machine collaborative, pupillary response-based video summarization framework. Considering that humans are the end-users and evaluators of video content, it is natural to establish the links between video features and real-time viewers' attentive response for designing a video summarization framework. In this paper, pupil size, a pupillary response, is introduced as a real-time indicator for assessment of viewers' engagement and attention. Firstly, we augmented the TVSum dataset by replacing manual annotations with attention scores that converted from pupillary size signals to generate the perceptually driven dataset. Secondly, we developed a video summarization framework which uses cues from pupillary size signal of humans to predict frame-level attention scores, and then extracts key shots from videos. On the perception-driven dataset, the average F-measure of the proposed summarization method is 69.71%, while the precision and recall are 69.67% and 69.77%, respectively, which is a significant improvement compared to random summarization. Experimental results initially demonstrated that our modeling approach can learn the dynamic attention mechanism of viewers and apply it to video summarization. In addition, the experimental results validate the effect.

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Intelligent Fault Diagnosis of Rolling Bearing Based on Deep Transfer learning

Intelligent Fault Diagnosis of Rolling Bearing Based on Deep...

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Natural Language processing (ICNLP), international conference on

作者： Lei Fang Yao Liu Xuan Li Jiantao Chang School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Mechano-Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Aiming at the problem of poor effect of traditional machine learning and deep learning fault diagnosis methods in actual industrial scenarios, which is caused by the data distribution difference due to the change of equipment working conditions, an intelligent fault diagnosis method for rolling bearing based on deep transfer learning is proposed. Firstly, the original time domain vibration signal is preprocessed using a sliding window to construct a sample dataset; Secondly, a Deep Residual Shrinkage Network (DRSN) is used to filter out the sample noise and automatically extract the data features; then, the marginal distribution and conditional distribution between the domains are aligned by Maximum Mean Discrepancy (MMD) and Local Maximum Mean Discrepancy (LMMD), and the difference of data feature distribution between different working conditions is weakened; Finally, experimental verification is carried out on two multi-condition rolling bearing datasets. the results show that the proposed method has a good fault diagnosis capability under variable working conditions.

关键词： Fault diagnosis Employee welfare Deep learning Vibrations Transfer learning Noise Rolling bearings Feature extraction Natural language processing Time-domain analysis

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