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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

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AgroVision: Advancing Agricultural Insights with Image-Based Deep learning Techniques 3

AgroVision: Advancing Agricultural Insights with Image-Based...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Kanjalkar, Jyoti Kanjalkar, Pramod Matkar, Aakash Jadhav, Akshay Shaikh, Al Hussain Bonde, Anish Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350317022

The field of precision agriculture has been advancing rapidly with the growing availability of data from sensors, drones, and satellites in the past few years. One important aspect of precision agriculture is cropping recommendation, which involves suggesting the best crops to sow in a particular area based on soil and weather conditions. In this paper, a unique method for crop recommendation using soil images and weather data collected from a flutter application is provided. The presented methodology uses convolutional neural networks (CNNs) implemented in TensorFlow Keras to analyze the soil images and weather data and make crop recommendations based on the results. The effectiveness of the proposed approach is demonstrated through extensive experimentation and comparison with existing methods. The findings demonstrate that the suggested solution performs better than existing methods concerning accuracy and efficiency and has the potential to significantly boost agricultural output while decreasing waste. This paper contributes to the growing body of research in precision agriculture and has important implications for sustainable farming practices. © 2023 IEEE.

关键词： convolution Precision agriculture soil sustainable yields

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An Analysis of Proposition Abstraction from Domain Text for Conceptual Plan Mining Using Deep learning Methods 3

An Analysis of Proposition Abstraction from Domain Text for ...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Dev, Sudhanshu Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350343274

Summarization is a perfect example of one of the numerous text mining jobs that may be improved with concept maps. A broad idea map illuminates the connections between the numerous thoughts being mapped. This article describes an evaluation technique designed with tokenization, proposition extraction, and refining in mind. The Deep Tokenizer approach, which is based on deep learning principles, has a processing time of 5 milliseconds and a tokenization accuracy of 98%. The extraction accuracy with the given proposition extractor approach was 95%, and the processing time was 10 milliseconds. The program was modified specifically to extract propositions from tokenized text. The suggested proposition refiner technique was proven to have a refining accuracy of 94% and a processing time of 8 milliseconds. The goal of this strategy was to back up and enhance the ideas derived from the data. Bar charts were used to graphically show the performance statistics of the various algorithms. These findings indicate the techniques' usefulness and reliability in dealing with large text datasets, shedding light on possible applications in natural language processing. © 2023 IEEE.

关键词： Extraction

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PPG Segmentation Using Deep learning 3

PPG Segmentation Using Deep Learning

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Tabbakha, Mohammad Masri, Hassan Al Hammad, Abdullatif Alsulatie, Mohammed Alquran, Hiam Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Centre of Excellence Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

As an indicator regarding cardiovascular health, vascular dynamics, and more, Photoplethysmography (PPG) consists of a baseline with two pulsatile peaks that reflect the blood flow variations. In the absence of critical studies and a precise understanding of PPG signal this study proposes a deep learning approach to predict and define each phase of PPG waveform utilizing Gated recurrent unit (GRU) due to its optimal performance in the short-term dependencies in sequences, the model is composed mainly of GRU layer with 20 hidden units applying tanh and sigmoid activation function, followed by dropout layer and a fully connected layer, afterward a softmax layer was added, we conducted our study on a dataset combined of an accessible Bed-Based Ballistocardiography dataset (BBB), after that, the data was manually labeled. Following up, hyperparameters such as learning rate, batch size, and other parameters were adjusted after numerous trials, which resulted in test accuracy, precision, and recall of 0.875, 0.892, and 0.854, respectively in the used dataset. These promising results might be the first step of more future investigations concerning PPG. © 2023 IEEE.

关键词： Deep learning Gated recurrent unit PPG Segmentation

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learning Method to Detect Anomalies in Sewer Pipe Images Using Object Detection 3

Learning Method to Detect Anomalies in Sewer Pipe Images Usi...

引用

3rd IEEE international conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Igarashi, Ryutaro Ogawa, Tomomi Tokyo Denki University Department of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350337952

Object detection has have been used in a wide range of fields, e.g., manufacturing, construction, and medicine. In this study, we investigated the possibility of applying an object detection method to the sewer pipe inspection task. Currently, many sewer pipes are aging, and there is a shortage of personnel and funding for maintenance work. To address this problem, a system is required to automatically detect sewer pipe anomalies. In a previous study, we classified 15 types of sewer pipe anomalies from a total of 58 types, and we trained using the YOLOv5 object detection method, resulting in an overall accuracy rate of 60%. However, higher accuracy is required for practical application. Thus, in this study, we optimized the YOLOv5 learning process by dividing the sewer pipe images into direct-view and side-view images and by the types of pipes, and we achieved an overall accuracy rate of 70.2% on images categorized according to the types of pipes. © 2023 IEEE.

关键词： Object detection

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Machine learning Approaches for Statistical Analysis of Customer Satisfaction in Service Management 3

Machine Learning Approaches for Statistical Analysis of Cust...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Ramachandran, K.K. Karthick, K.K. Singh, Kuljeet Ramesh, R. Ganesh, Chennakeshi Kumar, Sudarshan DR. G. R. D. College of Science Management/Commerce/International Business India Dr. G. R. Damodaran College of Science Department of Management Science Coimbatore14 India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Business Telangana Warangal India KG Reddy College Of Engineering & Technology Department Of MBA Telangana Moinabad India TMBU Management Faculty University Department of Commerce & Business Administration Bihar Bhagalpur812005 India

ISBN: (纸本)9798350399264

This research paper aims to analyze customer satisfaction data in service management, with a focus on identifying key drivers of customer satisfaction and areas for improvement. The research was conducted using a survey method, where a sample of 1000 customers from different service industries were asked to rate their satisfaction with various aspects of the service provided. The data was collected using a standardized questionnaire and analyzed using statistical methods, including descriptive statistics, inferential statistics, and linear regression analysis. The results of the research indicate that the most important drivers of customer satisfaction in service management are timeliness, competence of staff, and quality of service. The results also reveal that there are significant differences in customer satisfaction across different service industries. The findings of this research will be useful for service managers to identify areas for improvement and to develop strategies to enhance customer satisfaction. © 2023 IEEE.

关键词： Customer satisfaction

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A Lip-Reading Method Based on Bottleneck Attention Module 3

A Lip-Reading Method Based on Bottleneck Attention Module

引用

3rd international conference on Networking Systems of AI, INSAI 2023

作者： Yuan, Wenbo Cao, Yang Li, Jie Zhengzhou University School of Cyber Science and Engineering Zhengzhou China

ISBN: (纸本)9798350344660

Lip-reading aims to recognize speech signals by analyzing the relationship between the speaker's mouth movements and sound signals. The development of deep learning and advances in the field of computer vision have gradually made lip-reading a research area of great interest. Deep learning has become the most effective method for lip reading analysis. Although convolutional neural networks have achieved better results in the field of visual speech recognition, ordinary CNNs have the problem of not being able to focus on the important features in lip reading. To alleviate this problem, we propose a hybrid neural network BAM-ResNet applied to lip reading. Specifically, we integrate the Bottleneck Attention Module (BAM) into the residual network ResNet101, which enhances the network's ability to focus on important features of the input data and suppresses the response to irrelevant features, thus improving the network's generalization ability. Next, we use gated recurrent units (GRUs) to efficiently capture features in the input sequence and output predictions. On the LRW dataset, our model achieves an accuracy of 84.1%, which is an effective lipreading analysis method. © 2023 IEEE.

关键词： Convolutional neural networks

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Comparative Study in Parkinson’s Disease Diagnosis Using Machine learning 1

引用

3rd international conference on "Emerging Trends and Technologies on intelligent Systems" (ETTIS-2023)

作者： Singh, Lakshya Khapre, Shailesh Patle, Ashish International Institute of Information Technology Chhattisgarh Naya Raipur493661 India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

In this paper, we have studied Parkinson's disease which is a multi-dimensional disease and is hard to detect because it involves a variety of dimensions and becomes difficult to diagnose whether a person is suffering from Parkinson's disease or not. It is often also known as pseudo-paralysis;hence, we have taken some key data points to detect it such as typing speed, gait, keystrokes. For proper analysis, we have taken data from the physionet website and optimized the data to fit it with our model to get the results and predictability. We have used the gradient boosting algorithm because it takes consideration of minute data into consideration and does not optimize to leave critical points. The learning rate is 0.01, and the dataset split ratio is 0.7 and 0.3 for testing;data was split randomly. After running the data with the above constraints, we have classified the outputs as a confusion matrix, correlation feature, accuracy, and AUC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Deep learning Approaches for Enhancing Content Retrieval 3

Deep Learning Approaches for Enhancing Content Retrieval

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Singh, Shweta Ranganathaswamy, M.K. Renuka, G. Diwakar, Mandar P. Jain, Sandeep Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350319125

Deep learning strategies for content material retrieval have been explored and updated to cope with the want for accurate search effects. This machine up to date is used to date apprehend and examine text-up-to-date content material from sources, up-to-date webpages, or virtual up-todate motion pictures. As the quantity and complexity of online content will increase, so up-to-date have the want for higher up-to-date up to date those resources. Recent advancements in deep gaining knowledge have enabled better content retrieval structures that are up to date, updated correctly, and speedy to understand statistics from one-of-a-kind sources. Such structures include natural language processing (NLP), sentiment analysis, entity reputation, and up-to-date updated reputation. Those deep studying technologies have been used to decorate content material retrieval by enhancing search effects' accuracy, robotically updated extracting applicable statistics, categorizing information, and identifying patterns. Additionally, deep-getting, updated systems are up-to-date and quick to examine large volumes of data, generating and relevant consequences than up-to-date methods. In summary, deep up-to-date techniques can beautify the retrieval system of content material efficiently, enhancing the accuracy and pace of seek effects. © 2023 IEEE.

关键词： categorizing comprehend effectively learning techniques

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Kid Activity Recognition: A Comprehensive study of kid activity recognition with monitoring activity level using YOLOv8s Algorithms 3

Kid Activity Recognition: A Comprehensive study of kid activ...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Bisla, Tiya Shukla, Ritik Dhawan, Mansi Islam, Md. Rahatul Koyasu, Takao Teramoto, Keisuke Kataoka, Yui Horio, Keiichi Dronacharya Group of Institution Dept. of Computer Science & Engineering Greater Noida India Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering Fukuoka Japan Kyushu Women's Junior College Fukuoka Japan Aichi University of Education Aichi Japan Fukuoka Kodomo Junior College Fukuoka Japan

ISBN: (纸本)9798350372120

Recognizing human activities and behavior is a cutting-edge field of research because of the complexity and limited availability of data. Our research involves the use of the trending YOLOv8s deep learning algorithm for the classification of active states (sitting, standing, running, laying, and jumping). The research focuses on the detection of a child's activities in the kindergarten playroom. To train the model, we created the KAR (Kids Action Recognition) dataset using our own sources. We labeled over 6,489 images in the dataset using ***. Each image is annotated with activity labels and the location of the student (in a room or on a mat). This assisted in presenting insights on the student's vitality level and physical involvement over a period of approximately an hour. We analyzed the whole activity and compared it with the times. Standing in the room and mat, standing in the room, and jumping in the room and mat are most of the activities of the kid. The model achieved an impressive overall accuracy of 88.7 %. We conducted all the training in a Google collaborative environment with A-100 GPU specifications. The results demonstrate that the model can identify and classify each activity with high precision. © 2024 IEEE.

关键词： Graphics processing unit

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Design of Biological Sample Tube Recognition and Localization Module for Transit Robots 24

Design of Biological Sample Tube Recognition and Localizatio...

引用

3rd international Symposium on Control engineering and Robotics, ISCER 2024

作者： Sun, Zhaoguo Zhao, Honghua Cao, Baolu Yang, Zhaohui Liu, Zhanjie School of Mechanical Engineering University of Jinan Jinan60103801 China Jinan Yifei Intelligent Technology Co. Jinan China Qingdao Haier Biomedical Co. Ltd Qingdao60109081 China

ISBN: (纸本)9798400709951

To meet the needs of biological sample tube transfer in cold storage under low temperature environment, we design a sample tube detection and localization system by fusion of binocular camera and YOLOv5 deep learning target detection network structure, to obtain the x, y, z coordinates of the sample tube gripping point in the coordinates of the ZED2 binocular camera, which need to be transformed through the connection between the camera coordinate system, the image coordinate system and the pixel coordinate system, and then transformed into the robot arm end gripper coordinate system by known 3D coordinate values through hand-eye calibration. Then the known values of the 3D coordinates are converted to the coordinate system of the gripper at the end of the robot arm by hand-eye calibration. The accuracy of the sample tube detection and localization system was verified with an optical navigator and an optical positioning device, and the average error of the x, y, and z axes detection was less than 3.5 mm, which meets the requirements of sample tube grasping. © 2024 ACM.

关键词： Grippers

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