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Question Answering System using NLP and BERT 3

Question Answering System using NLP and BERT

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Acharya, Shreya Sornalakshmi, K. Paul, Bidisha Singh, Anshul Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

This project proposes a simple and smart question answering system using Named Entity recognition (NER) and Bidirectional Encoder Representations from Transformers (BERT) for communication purposes. Communication is a major factor that eases our day-to-day work. In this smart Question answering system, the system will create a customized dataset for the user based on the answers given by the user. Using NER, only the selective answers are taken, i.e., the most important answers, and then updated in the dataset. Based on that dataset, if a person then asks the system a question, the system will use BERT and CDQA (closed domain question answering) to fetch the best answer for the question asked. The resulting system will be an interactive one. © 2022 IEEE.

关键词： Artificial intelligence

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recognition of Shoulder Exercise Activity Based on EfficientNet Using Smartwatch Inertial Sensors 3

Recognition of Shoulder Exercise Activity Based on Efficient...

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3rd international conference on Big Data Analytics and Practices, IBDAP 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

recognition of human activity is an important research topic due to its potential applications in areas, such as the medical industry and other related fields. Sensor-based human activity recognition (HAR) employing deep learning (DL) techniques has grown in popularity as a result of its immense efficiency in detecting complicated tasks and its low cost in comparison to more traditional machine learning (ML) techniques. More recently, convolutional neural network (CNN) approaches have also been used with sensor-based HAR, and the results have shown promise for improvement. In this study, we applied the CNN-based architecture known as EfficientNet to perform sensor-based HAR. The goal of this research was to apply the EfficientNet architecture to the classification of complicated human activities, such as shoulder workout activities. To evaluate the recognition performance, we used a benchmark HAR dataset called SPARS9x, which collected sensor data from six sports activities. According to the results of the experiments, the EfficientNet-B3 had the best performance overall on the benchmark dataset receiving an F1-score of 98.87%. © 2022 IEEE.

关键词： Network architecture

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Attention and Memory Training System Based on Neural Feedback 3

Attention and Memory Training System Based on Neural Feedbac...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, XiuJun Tang, LiMin Zhang, ZhiLin Wu, Jinglong Li, Qi Changchun University of Science and Technology School of Computer Science and Technology Changchun China Shenzhen Institute of Advanced Technology Chinese Academy of Science Research Center for Medical Artificial Intelligence Shenzhen China Zhongshan Institute of Changchun University of Science and Technology Zhongshan China

ISBN: (纸本)9781665459112

With the rapid development of the society, people's cognitive ability is gradually declining under the pressure of study and work, those declining will bring a great impact on people's daily life, work and study. Therefore, it has become a popular research field on the intervention of early declining cognitive ability. It has been proved that a brain-computer interface (BCI), as a new tool of neural feedback training, can improve the traditional neural feedback method's efficiency. However, due to the weakness of EEG signals, the accuracy of character's recognition in BCI system is still low, and the spelling paradigm is often dominated by the characters output, which makes it difficult for the subjects' attention to continuously focus on the whole experiment process. In order to improve the recognition accuracy and effectively reduce subjects' fatigue as well as increase the concentration of the participant, this study proposed a spelling paradigm that can more effectively stimulate the Event Related Potential (ERP) of the subject. In the end, a cognitive ability training system was designed for daily training. Through the system simulation and preliminary experiments, the average target recognition accuracy increased by 18.75% after the experiment, demonstrating the efficiency and effectiveness of the present cognitive ability training system. © 2022 IEEE.

关键词： Brain computer interface

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A method for estimating pig pose in a breeding scenario 3

A method for estimating pig pose in a breeding scenario

引用

3rd international conference on Optics and machine Vision, ICOMV 2024

作者： Qin, Jia-Jun Mao, Liang Wang, Ji Li, Yue Wang, Lin-Lin Li, Jie College of Mathematics and Computer Science Guangdong Ocean University Guangdong Zhanjiang524088 China Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Guangdong Shenzhen518055 China College of Electronic and Information Engineering Guangdong Ocean University Guangdong Zhanjiang524088 China South China Smart Agriculture Public Research and Development Platform The Ministry of Agriculture and Rural Affairs Guangdong Guangzhou510000 China

ISBN: (纸本)9781510680319

Aiming at the problem of low automation in pig farms, this paper proposes a new pig posture estimation method based on breeding scenarios for intelligent monitoring of pig ***, the video image data of indoor and outdoor scenes in pig breeding scenarios were collected and labeled, and a self-constructed pig posture estimation dataset was built;Secondly,Resnet50, VGG16 and MobileNetV2 were used as the back-bone network, and the three methods based on coordinate regression, heat map and simple coordinate classi-fication were analyzed experimentally, and the Simple Coordinate Classification (SimCC) algorithm with the optimal effect was selected as the extraction method of key points of pigs;Finally, we integrated High Resolution Network(HRNet)and HRFormer, which incorporates Transformer modules, as backbone net-works. They were combined with the SimCC to formulate an effective pig pose estimation framework. The experimental results show that the mAP of HRFormer-SimCC reaches 83.2%, which is an average im-provement of 7.2% over the use of traditional CNN model and 0.4% over the HRNet-SimCC, and the float-ing-point computation and parameter counts of HRFormer-SimCC are only 45.05% and 36.48% of it. This is more suitable to be deployed in breeding environments and provides a theoretical basis for intelligent moni-toring of pig farms. © 2024 SPIE.

关键词： Computer vision

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pattern Mining of Smart Home User Behavior in The Context of The Internet of Things: based on Sensor Networks 3

Pattern Mining of Smart Home User Behavior in The Context of...

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Nie, Bailing School of Network and Communication Nanjing Vocational College of Information Technology Jiangsu Nanjing210023 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

This research study is focused on analyzing the sensor networks based behavior recognition and incident response system of smart home users. The study has mainly considered the existing research results to build a basic incident response framework. The system structure is divided into three levels: situational awareness layer, situational management layer, and situational application layer. At the same time, a complete and practical information model is defined. Having a complete and used information model is the basis for the system to correctly perceive the state of the environment and understand user behavior. In this article, the user's behavior patterns and the meaning of events are defined, and a mechanism for mapping and transforming the original data to the information model is designed. © 2022 IEEE.

关键词： Metadata

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3rd international conference on Artificial intelligence, 2022

3rd International Conference on Artificial Intelligence, 202...

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3rd international conference on Artificial intelligence, 2022

ISBN: (纸本)9789811694226

The proceedings contain 89 papers. The special focus in this conference is on Artificial intelligence. The topics include: Medical Image Segmentation Using Transformer;satellite Online Scheduling Algorithm Based on Proximal Policy;generation Method of Control Strategy for Aircrafts Based on Hierarchical Reinforcement Learning;finding Significant Influencing Factors of Core Quality and Ability Development of Teachers Based on Improved Genetic Algorithm;a New Augmented Method for Processing Video Datasets Based on Deep Neural Network;small-Object Detection with Super Resolution Embedding;graph-Based Anomaly Detection of Wireless Sensor Network;identifying Important Attributes for Secondary School Student Performance Prediction;research on Smart Home System Based on Internet of Things;a Cross-Modal Attention and Multi-task Learning Based Approach for Multi-modal Sentiment Analysis;research on Problem Matching Classification Based on Artificial intelligence Technology;research on the Application of Natural Language Processing in the Virtual Comment’s Classification;Design and Application of Endangered Animal Monitoring System Based on Mobile APP;a Rapid Image Semantic Segment Method Based on Deeplab V3+;analysis and Research on China’s New Energy Vehicles Industry Policy Based on Policy Subjects, Tools and Objectives;research on Semantic Retrieval System of Scientific Literature Based on Deep Learning;research on the Evaluation System Construction of County Innovation Driven Development-Based on Evaluation and Measurement Model;research on the Method of Policy Text Knowledge Mining Based on Neural Network;concept Drift Detection Based on Restricted Boltzmann machine in Multi-class Classification System;Autoencoder-Based Baseline Parameterized by Central Limit Theorem for ICS Cybersecurity;Classroom Speech Emotion recognition Based on Multi-channel Convolution and SEnet Network;LFMCW Milimeter Radar Detection.

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Identification of EEG Features of Transcranial Electrical Stimulation (tES) Based on eXplainable Artificial intelligence (XAI) 3

Identification of EEG Features of Transcranial Electrical St...

引用

3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Arpaia, Pasquale Ammendola, Lidia Cropano, Maria De Luca, Matteo Calce, Anna Della Gargiulo, Ludovica Lus, Giacomo Maffei, Luigi Malangone, Daniela Moccaldi, Nicola Raimo, Simona Signoriello, Elisabetta De Blasiis, Paolo Naples Italy Multiple Sclerosis Center University of Campania 'Luigi Vanvitelli' Ii Neurological Clinic Naples Italy University of Campania 'Luigi Vanvitelli' Second Division of Neurology Dep. of Advanced Medical and Surgery Science Naples Italy 'Magna Graecia' University of Catanzaro Dep. of Medical and Surgical Sciences Catanzaro Italy University of Basilicata Potenza Italy University of Campania 'Luigi Vanvitelli' Naples Italy

ISBN: (纸本)9798350378009

Electroencephalographic (EEG) features of transcranial Electrical Stimulation (tES) effects in Multiple Sclerosis (MS) patients were identified. machine Learning and eXplainable Artificial intelligence (XAI) algorithms were the used methods for the EEG feature selection. Current tES-based treatments lack of adaptivity to their effects on individuals. Real-time modification of electrical stimulation parameters based on the trend of specific EEG features may represent a new perspective in terms of personalized medicine for MS patients. This preliminary study aimed to identify EEG features reflecting the effect of tES treatment. Ongoing analyses are exploring the correlation between the identified EEG features and the expected clinical outcomes. Five MS patients underwent non-pharmacological treatment combining Theory of Mind (ToM) training with tES or sham treatment. Pre-and post-treatment variation in EEG features were assessed both in Eyes Opened (EO) and Eyes Closed (EC) conditions. In particular, absolute and relative power across six frequency bands, and Posterior Dominant Rhythm (PDR) amplitude and frequency were explored. The Sequential Feature Selection (SFS) algorithm in combination with Support Vector machine (SVM) classifier identified (i) difference of absolute powers in high beta band in T3 channel, (ii) difference of absolute powers in gamma band in T3 channel, and (iii) difference of absolute powers in gamma band in O2 channel in EO condition, and (i) difference of absolute powers in gamma band in T3 channel, (ii) difference of absolute powers in gamma band in C4 channel, and (iii) difference of PDR amplitude in O2 channel in EC condition as the most discriminating tES from sham treatment (67.5 % accuracy in EO and 83.33 % in EC conditions, respectively). The SHapley Additive exPlanations (SHAP) algorithm highlighted PDR amplitudes in O2 and O1 as most informative features. © 2024 IEEE.

关键词： Electroencephalography

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Action recognition based on temporal skeleton analysis: a new method for IoT scenarios 3

Action recognition based on temporal skeleton analysis: a ne...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Huang, Wenshan Wan, Boxin Xiong, Qingyun Guo, Junqi Beijing Normal University School of Artificial Intelligence Beijing China

ISBN: (纸本)9781665462006

With the rapid development of the Internet of Things (IoT) and the enhancement of the computing power of embedded devices, research on the security monitoring of human beings under the IoT framework is attaching greater importance. However, the current person action recognition algorithms still suffer from low accuracy or high computational cost in complex scenarios. To solve these problems, we combined the Fast-ReID model with HRNet to extract and follow the person's nodes in real time under multiple target states. Then, we replaced the base convolutional module of HRNet with a depthwise separable convolutional layer and reduced the depth of the network to further reduce the computational complexity of the network. At the end of the system, we introduced 3D separable convolution as our action classification module. The multi-model combination of person re-identification (ReID) and action recognition system is suitable for deployment in edge computing devices. The system enables efficient and accurate real-time person ID tagging, skeleton detection, and action recognition, allowing continuous and stable tagging and recognition of all targets in complex situations. In addition, we also proposed a dynamic skeleton data automatic generation algorithm, which could obtain high-quality dynamic skeleton data stably and efficiently. © 2022 IEEE.

关键词： Convolution

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Data Urbanity: Smart City Evolution Through IoT and Data Science 3

Data Urbanity: Smart City Evolution Through IoT and Data Sci...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Singh, Ajay Kumar, Manni Chandigarh University Department.of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350343632

The rapid increase in population density in urban cities has created a demand for suitable infrastructure and services to meet the needs of urban residents. To address this demand, urban planners and developers are turning to data science for assistance in creating smart city solutions. The Internet of Things (IoT) has played a significant role in the application of data science in urban planning, as IoT devices such as sensors and smart devices provide vast amounts of data that can be used to gain insights into the operations of the city and make data-driven decisions to enhance its productivity, efficiency, and overall quality of life. This paper provides a general overview of data science in urban planning for smart city development. It highlights the importance of data science in urban planning and the role it plays in creating smarter, more effective cities. By applying data analytics, machine learning, and other data science approaches, urban planners can gain insights into various aspects of urban life, including traffic patterns, energy usage, and public safety. These insights can then be used to develop strategies that improve traffic flow, reduce energy waste, and enhance overall quality of life in cities. The study aims to describe the function of data science in urban planning, examine case studies and best practices, discuss the challenges and limitations associated with applying data science in urban planning, and provide suggestions for further research and advancements in the field. By investigating how data science can be effectively used in urban planning for smart city development, this study contributes to the growing body of knowledge on creating more sustainable and livable cities. © 2023 IEEE.

关键词： Artificial intelligence Biomarkers cardiovascular disease Computed Tomography (CT) Data analysis Deep learning Diagnostic methods Early detection Electrocardiogram (ECG/EKG) Health informatics heart disease machine learning Magnetic Resonance Imaging (MRI) Medical imaging Predictive modeling Remote monitoring Risk assessment Signal processing Ultrasound Wearable devices

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Physical Exercise Classification from Body Keypoints Using machine Learning Techniques

Physical Exercise Classification from Body Keypoints Using M...

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Applied Artificial intelligence and Computing (ICAAIC), international conference on

作者： Aadhila Rahman Aldrin Saji Avelin Teresa Divya R Nair S Saritha Department of Computer Science and Engineering Rajagiri School of Engineering and Technology Rajagiri Valley Kochi Kerala India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In today’s world, the importance of physical workout and exercise is being realized by the human community. However, there are a lot of challenges faced by people, in terms of convenience and cost, as they need a trainer to monitor their workouts. This research work aims to alleviate this challenge and proposes a methodology that automates the physical workout monitoring process. The proposed framework can take live video streams and identify the type of exercises performed by a person using machine learning techniques. This task is achieved by identifying keypoints from the human pose caught in the live video and classifies it to a physical exercise. The methodology also offers utilities like counter and feedback for physical exercises classified. A comparative evaluation of different machine learning techniques is experimented for physical exercise classification. It is observed from the evaluation that the Random Forest technique gives significant values in terms of precision 89% and recall 89%, as well as in F1-score with 89%.

关键词： Technological innovation Computational modeling machine learning Learning (artificial intelligence) Streaming media Object recognition Task analysis

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