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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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automated Detection of Pain Across Varied Intensity Levels Through the Fusion of CNN and Random Forest 3

Automated Detection of Pain Across Varied Intensity Levels T...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Dogra, Ayush Bhattacherjee, Abhishek Singh Brar, Tejinder Pal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Cgc Landran Department of Computer Applications Mohali India

ISBN: (纸本)9798350342338

An important part of preserving well-being is addressing pain, a complex human experience. In the medical field, accurate pain assessment is still difficult, especially when verbal communication is restricted. In response, by utilizing state-of-the-art technology, this research offers a strong and automated approach to pain identification. Feature extraction is done using Convolutional Neural Networks (CNNs), face expression analysis is done using deep learning techniques, and classification is done using Random Forest. When image preprocessing techniques are incorporated, data consistency and quality are improved, leading to consistent outcomes for a range of pain intensities. The system's ability to discriminate between various pain intensities, ranging from minor discomfort to excruciating anguish, is demonstrated by a carefully selected dataset. The accuracy and generalization abilities of the model are demonstrated by performance metrics such as precision, recall, and F1-Score. This study adds to the growing body of research on applications of computer vision in healthcare as well as the area of pain assessment. Promising developments in patient care, early intervention, and the comprehension of pain manifestations in many clinical contexts can be attributed to the computerized pain detection system. © 2023 IEEE.

关键词： CNN emotion detection image processing medical technology pain detection random forest

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VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments 3

VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Cod...

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3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Kumaresan, Prasanna Kumar Ponnusamy, Kishore Kumar Shanmugavadivel, Kogilavani Navaneethakrishnan, Subalalitha Chinnaudayar Priyadharshini, Ruba Chakravarthi, Bharathi Raja Insight SFI Research Centre for Data Analytics University of Galway Ireland Guru Nanak College Tamil Nadu Chennai India Kongu Engineering College Tamil Nadu India Department of Computer Science & Engineering SRM Institute of Science and Technology Tamil Nadu India Gandhigram Rural Institute-Deemed to be University Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve weighted F1 scores of 0.86 on the test set and 0.86 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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Vehicle detection in UAV traffic videos using GAN online augmentation: a transfer learning approach 3

Vehicle detection in UAV traffic videos using GAN online aug...

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3rd international conference on Computer Vision and data Mining, ICCVDM 2022

作者： Du, YingJie Yi, YuWei Guo, He Tian, XiangJie Tianjin University of Technology and Education School of Automobile and Transportation Tianjin300222 China

ISBN: (纸本)9781510661363

Low resolution object detection could be challenging. In this paper, we proposed a GAN-based real-time data augmentation algorithm for the transfer learning task of UAV vehicle detection from ImageNet, with improvements including using FocalLoss to replace the cross-entropy loss commonly used in the industry, as well as redesigning the target detection Head combination to improve the model's detection accuracy by 4% over original YOLOv5 model. We make it feasible for deployments on UAV-carried ARM systems. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Unmanned aerial vehicles (UAV)

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Discharging State Recognition Method of intelligent Ring Network Cabinet Based on Audio Signal Spectrum Analysis 3

Discharging State Recognition Method of Intelligent Ring Net...

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3rd international conference on Image Processing and intelligent Control, IPIC 2023

作者： Zhang, Mingming Hu, Jin Li, Wenjun Guangdong Grid Co Shaoguan Nanxiong Power Supply Guangdong Shaoguan China

ISBN: (纸本)9781510668140

The conventional discharge state identification method mainly focuses on partial identification. The field identification environment is subject to various interference signals from Getang, resulting in poor performance of the ring main unit discharge state identification. Therefore, an intelligent ring network cabinet discharge state recognition method based on audio signal spectrum analysis is designed. Collect the partial discharge data of the intelligent ring network cabinet, and extract the characteristics of the partial discharge of the intelligent ring network cabinet. Based on the audio signal spectrum analysis, the partial discharge noise signal of the ring main unit is processed, and the discharge noise signal is filtered to ensure accurate identification of the discharge signal. By means of comparative experiments, it is verified that the recognition effect of this method is better and can be applied to real life. © 2023 SPIE.

关键词： Spectrum analysis

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3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

3rd International Conference on Computer Vision, High Perfor...

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3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

ISBN: (纸本)9789819966899

The proceedings contain 43 papers. The special focus in this conference is on Computer Vision, High Performance Computing, Smart Devices, and Networks. The topics include: Machine learning-Based Path Loss Estimation Model for a 2.4 GHz ZigBee Network;Comparative Analysis of CNN Models with Vision Transformer on Lung Infection Classification;classification of Alzheimer’s Disease Using Stacking-Based Ensemble and Transfer learning;heart Device for Expectation of Coronary Illness Utilizing Internet of Things;Parallel Programming in the Hybrid Model on the HPC Clusters;an Extensive Study of Frequent Mining Algorithms for Colossal Patterns;a Review Paper on Progressive Approach to Reduce Context Switching in Round Robin Scheduling Algorithm;Syn Flood DDoS Attack Detection with Different Multilayer Perceptron Optimization Techniques Using Uncorrelated Feature Subsets Selected by Different Correlation Methods;Ensemble Model Detection of COVID-19 from Chest X-Ray Images;Evolutionary, Protein–Protein Interaction (PPI), and Domain–Domain Analyses in Huntington’s Disease;Transfer learning-Based Effective Facial Emotion Recognition Using Contrast Limited Adaptive Histogram Equalization (CLAHE);transformer Model for Human Activity Recognition Using IoT Wearables;design and Development of a Chatbot for Personalized learning in Higher Education;performance Evaluation of Concentric Hexagonal Array for Smart Antenna Applications;a Comprehensive Study on Bridge Detection and Extraction Techniques;vegetation Change Detection of Multispectral Satellite Images Using Remote Sensing;performance Evaluation of Neural Networks-Based Virtual Machine Placement Algorithm for Server Consolidation in Cloud data Centres;low-Resource Indic Languages Translation Using Multilingual Approaches;DCC: A Cascade-Based Approach to Detect Communities in Social Networks;an Improved Way to Implement Round Robin Scheduling Algorithm.

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Design and Implementation of Smart Agriculture Management intelligent Things Using NB-IOT 3

Design and Implementation of Smart Agriculture Management In...

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3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Senthil, S. Kumar, M.Rajesh Kumar, R.Sarath Jabakumar, A.Kingsly Madhan, K. Mailam Engineering College Department of Mechanical Engineering India B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering India Sri Krishna College of Engineering and Technology Department of ECE Coimbatore India Christ the King Engineering College Department of ECE Coimbatore India Mailam Engineering College Department of CSE India

ISBN: (纸本)9781665456647

Agriculture is very important since our early daysand we can call India as Land of Agriculture. More than 60 to 70 % of Indian population are farmers. This field contributesover 20% of our Gross Domestic Product. So that we are making the agriculture to be smart rather than Hard using Internet of Things. The main objective of these smart devices using NB-IOT makes this field not to be in backlogs. Hence we want to make this field to be more active by using some Internet of things and Machine learning approaches. Hence we are designing an intelligent Agriculture Management System using Narrow Band IOT which helps farmers to improve their productivity with latest innovative reforms. Our NB-IOT method uses multiple sensors and considers various environmental factors without wasting resources by using a innovative techniques called precision agriculture. All the data what we have gathered and theresults will be displayed as Graphs. © 2022 IEEE.

关键词： intelligent systems

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Research and development of cement clinker production data mining system 3

Research and development of cement clinker production data m...

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3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Wang, Zijian Jing, Shaohong University of Jinan College of Automation and Electrical Engineering Shandong China

ISBN: (纸本)9781665490788

Take the production process parameters and quality data of cement clinker as the research object. A set of methods for cleaning, processing and analyzing the data of cement production process parameters is proposed, and a data mining model of cement production process parameters is expected to be established;Optimize the discretization method based on value division, form a new discretization method by introducing the knowledge of cement field, and apply it to the processing of cement clinker production data;According to the characteristics of cement clinker production data, Apriori algorithm is improved, and the parameters in the cement production process are analyzed by association rules using the improved Apriori algorithm to obtain some instructive analysis results. Based on the analysis of the category and characteristics of process data, aiming at the problems of quality data availability and analysis difficulties, a mining algorithm is designed. On the one hand, the massive production process data generated by cement enterprises are filtered by features. On the other hand, when any process data is needed, the quality data that meets the needs can be quickly selected from the characteristic production process data to provide accurate data support for the intelligent control of cement enterprises. © 2023 IEEE.

关键词： data mining

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UDS for data Communication in Temperature Monitoring 3rd

UDS for Data Communication in Temperature Monitoring

引用

3rd international conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Mallikarjun, B.C. Prathima, H.S. Department of ETE Siddaganga Institute of Technology Karnataka Tumkur India

ISBN: (纸本)9789819784288

Electronic Control units (ECUs) are computer systems which are capable to process this data and send signals to the various systems in the vehicle to modify their behavior in order to optimize performance and ensure safety. Maintaining appropriate operating temperatures within ECUs is essential for preventing overheating, which can lead to system malfunction or failure. Overheating can cause the components of an ECU to degrade over time, leading to decreased performance, increased energy consumption, and potential system failure. This proposed work provides a temperature monitoring solution, i.e., Unified Diagnostic Service (UDS), that enhances the overall functionality of ECUs and contributes to vehicle safety and performance. The temperature monitoring solution would enable the ECUs to measure and monitor the temperature of the vehicles components and systems, which would help to reduce the risk of malfunctions and overheating, which can lead to catastrophic failures and safety risks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Catastrophic Electronic control units Temperature monitoring

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Detection and Classification of Power Quality Disturbances Using Variational Mode Decomposition and Deep learning Networks 3rd

Detection and Classification of Power Quality Disturbances U...

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3rd international conference on Flexible Electronics for Electrical Vehicle, FlexEV 2022

作者： Vijaya Bhargava Phani Dutt, P. Balaga, Harish Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819947942

Nowadays, electronic gadgets become part of everyone’s life, and as a drawback, it injects power quality disturbance (PQD) into the power system, which results in maloperation of the device, affects system stability, and reduces the life of the machine. It is somewhat difficult to detect and identify what type of power quality disturbance is occurred in the power system network. Due to the data complexity, it’s highly difficult to separate PQDs data from other data like normal system data and data during faults. In this paper, power quality disturbance data is generated as per IEEE standards and this data is processed by the variational mode decomposition (VMD) to extract hidden features from the PQD signals. These features are transformed into 3D image vectors, which are used to train the convolutional neural network (CNN). In this work, the study is made on VMD parted with various pre-trained CNNs and compared their results with developed CNN models, to find the optimum classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

关键词： Convolutional neural network (CNN) Power quality disturbance (PQDs) Variational mode decomposition (VMD)

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A Comparative Analysis for the Detection of Hit Rate of Popular Music Videos in Social Network using Logistic Regression over Support Vector Machine Algorithm 3

A Comparative Analysis for the Detection of Hit Rate of Popu...

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3rd international conference on intelligent engineering and Management, ICIEM 2022

作者： Reddy, Mekala Bharat Kumar Vani, B. Babu, C. Nelson Kennedy Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The objective of the work is to improve the innovative music videos popularity prediction in social networks based on hit rate using machine learning algorithms like Logistic Regression algorithm and Support Vector Machine (SVM) algorithm. Materials and Methods: SVM algorithm and Logistic Regression algorithm with sample size N = 10 was iterated 20 times for predicting innovative music videos popularity on social media. Result: The support vector machine algorithm produces a better accuracy of (87.6%) than logistic regression classifier (81.6%). The significance (p © 2022 IEEE.

关键词： Support vector machines

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