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Application of Natural Language in Intelligent Media AI Writing 3

Application of Natural Language in Intelligent Media AI Writ...

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

作者： Mingxuan, Zhang Zichang, Dou Zhengyu, Tian Jingren, Jin Jiahao, Chen School of Software Shenyang University of Technology Liaoning Province Shenyang City110870 China School of Artificial Intelligence Shenyang University of Technology Liaoning Province Shenyang City110870 China

ISBN: (纸本)9798350319125

The application of natural language in intelligent media and artificial intelligence writing is a significant development in the field of artificial intelligence. Natural language processing (NLP) enables machines to understand and interpret human language, enabling them to generate content that is indistinguishable from what humans write. Intelligent media AI writing has many applications, including content creation for websites, social media posts, and news articles. With the help of NLP algorithm, machines can analyze a large amount of data and generate relevant content that meets user needs. One of the most significant advantages of using intelligent media for AI writing is its ability to produce high-quality content faster than humans. Machines can analyze data and generate content in real-time, enabling enterprises to keep up with the fast pace of online marketing. In short, the application of natural language in intelligent media and artificial intelligence writing has completely changed the way we create and consume content online. With the continuous progress of technology, we can look forward to further development in this field, which will continue to affect the way we interact with digital media. This article is based on the media equivalence theory and adopts a research method that combines questionnaire survey and experimental methods to explore the mechanism of the impact of AI writing on news credibility. This article proposes that users perceive the credibility of news written by AI as higher than that written by human journalists. Perceived news bias plays a mediating role, while news types play a moderating role. © 2023 IEEE.

关键词： AI writing Journalism Natural language Smart media

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Artificial intelligence-Based Analysis of Biological Signals and Attention Assessment Methods 24

Artificial Intelligence-Based Analysis of Biological Signals...

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3rd international Symposium on robotics, Artificial intelligence and Information Engineering, RAIIE 2024

作者： Li, Zhijing Li, Hong Jiang, Yiping Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400718311

Biological information processing plays a pivotal role in modern life, with applications ranging from health monitoring to personalized medicine. This study endeavors to delve into a method employing artificial neural networks for the analysis of biological signals, specifically focusing on assessing individual attention levels. By harnessing physiological data such as heart rate variability and skin conductance, coupled with the utilization of artificial neural networks for data analysis and modeling, a more comprehensive understanding of individual attention states can be attained. In the experimental phase, a diverse pool of individuals was tested, and the experimental results were validated using recorded videos. The findings underscore the efficacy of the artificial neural network-based method in assessing individual attention levels, achieving an accuracy rate of 76.9%. This research not only introduces novel methodologies but also opens avenues for leveraging artificial intelligence technology in biological signal analysis and attention assessment. Future research endeavors could delve deeper into exploring additional features of biological signals and enhancing model performance to bolster assessment accuracy and practical applicability. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Artificial neural networks Attention assessment Biological signal analysis Wearable technology

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LiDAR-based human pose estimation with MotionBERT 21

LiDAR-based human pose estimation with MotionBERT

引用

21st IEEE international conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhao, Zichen Zhuang, Chao Li, Jian Sun, Hao Hebei Univ Technol Coll Artificial Intelligence Tianjin Peoples R China Natl Rehabil Aids Res Ctr Beijing Peoples R China

ISBN: (纸本)9798350388084;9798350388077

Human body pose estimation has a wide range of applications in the field of artificial intelligence and is gradually applied in daily life. However, for some specific scenes and people, privacy and security need to be ensured, and devices such as cameras are no longer the optimal choice, so this paper proposes the idea of using LiDAR technology to acquire human body point cloud images, and preprocessing and segmentation to extract a pure human body point cloud model. First, the useful parts around the human body are extracted by preprocessing the point cloud image obtained from LiDAR scanning. Then, a segmentation technique is utilized to separate the human body from the surrounding objects, keeping only the human body part. Next, the excess noise is removed by filtering process to get the pure human body point cloud model. Finally, the motionBert3d model is used to estimate the pose of the preprocessed point cloud image, and the pose estimation points and the 3D pose estimation model of the human body are obtained, which lays the foundation for the subsequent human motion recognition.

关键词： LiDAR point cloud maps image preprocessing human pose estimation motionBert3d modeling

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Preface to Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023) and the 3rd AI + Informetrics (AII2023) at JCDL 2023 4

Preface to Joint Workshop of the 4th Extraction and Evaluati...

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Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents and the 3rd AI + Informetrics, EEKE-AII 2023

作者： Zhang, Chengzhi Zhang, Yi Mayr, Philipp Lu, Wei Suominen, Arho Chen, Haihua Ding, Ying Nanjing University of Science and Technology No. 200 Xiaolinvgwei Nanjing210094 China Australian Artificial Intelligence Institute University of Technology Sydney 15 Broadway UltimoNSW Australia GESIS - Leibniz-Institute for the Social Sciences Unter Sachsenhausen 6-8 Cologne50667 Germany Wuhan University Luojiashan Wuhan430072 China VTT Technical Research Centre of Finland EspooFI-02044 Finland University of North Texas Texas DentonTX76201 United States University of Texas at Austin. AustinTX78712 United States

The Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023;https://***/) and the 3rd AI + Informetrics (AII2023;https://***/) was held at Santa Fe, New Mexico, USA and online, co-located with the ACM/IEEE Joint conference on Digital Libraries (JCDL) 2023. The two workshop series aim to engage the communities in open problems in the extraction and evaluation of knowledge entities from scientific documents and the modeling and applications of AI + Informetrics for broad interests in science of science, science, technology, & innovation, etc. This joint workshop comprises keynote speeches, oral presentations, and poster sessions. The main topics of the proceedings include entity extraction and its applications, along with the integration of Artificial intelligence + Informetrics. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Extraction

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Control of Software-Defined Networks of Unmanned Aerial Vehicles using Distributed Deep Learning 3

Control of Software-Defined Networks of Unmanned Aerial Vehi...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Hauider Abbas, Syed Guru Vimal Kumar, M. Lekha, D. Geethamahalakshmi, G. Sesha Vidhya, S. Deepak, A. Integral University Cse Lucknow India Veltech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Cse India Rmk College of Engineering and Technology Cse India Easwari Engineering College Ramapuram Chennai India Rmk College of Engineering and Technology Ece Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Ece India

ISBN: (纸本)9781665462167

There are a variety of civilian, public, and military applications that might be developed for drones. Because they come equipped with their own communications infrastructure, they may be remotely controlled from a distance. Unmanned Aerial Vehicles (UAVs) are gaining popularity for its utilization in a range of activities due to their low cost, versatility, ease of deployment, and the ability to replace manually-operated aircraft in many situations. These vehicles are capable of performing a wide range of activities, such as monitoring, managing crowds, providing wireless coverage, and surveillance. Unmanned Aerial Vehicles (UAVs), often known as drones have the ability to offer solutions that are not only trustworthy but also economical for addressing a wide range of real-time challenges. With the inherent characteristics such as mobility, flexibility, and compatibility in terms of communications, UAVs are able to provide a wide range of services. The ability to monitor a particular area and the flexibility to react to changing demands for services proves the effectiveness of deploying Unmanned Aerial Vehicles (UAVs). As a result, deep learning, also known as DL, is utilized in an increasingly broad manner to overcome the challenges that UAVs face in terms of connectivity and resource utilization. © 2023 IEEE.

关键词： Drones

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Neural Network-Based Adaptive Tracking Control for Marine Lifting Arm System 17th

Neural Network-Based Adaptive Tracking Control for Marine Li...

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17th international conference on Intelligent robotics and applications

作者： Wang, Kai Ma, Xin He, Xin Zhang, Lei Yang, Xue Shandong Univ Minist Educ Engn Res Ctr Intelligent Unmanned Syst Ctr RobotSch Control Sci & Engn 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Marine Grp Ltd 7000 Jingshi Rd Jinan 250022 Peoples R China Shandong Marine Grp Technol Co Ltd 300 Changjiang Rd Yantai 264006 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

The marine lifting arm system is a novel type of lifting equipment for the dismantling of large offshore platforms. In practical application, the marine lifting arm system is commonly subject to unexpected disturbances such as persistent vessel-induced disturbances, uncertain dynamics, and physical constraints, posing significant challenges for controller system design. This paper proposes a neural network (NN) based adaptive tracking control method with state constraints for the marine lifting arm system to effectively compensate for vessel motions induced by wave disturbances. First, the dynamic model of a marine lifting system is established by employing Lagrange's method. Subsequently, a nonlinear controller is developed based on the dynamic model to compensate for 3-DOF vessel motions, including heave, roll, and pitch motion. By constructing constrained terms, the nonlinear controller effectively restricts the lifting arm's motion within the predefined range, ensuring the safety of lifting operations even under adverse sea conditions. The neural network is employed to estimate parameter/structure uncertainties and the nonlinear input dead zones for lifting arm systems. In particular, the designed update law of the neural network considers physical constraints, thereby further ensuring that the lifting arm operates within the safe range. The theoretical stability of the proposed control system is rigorously proven using the Lyapunov technique. Finally, the practicality and effectiveness of the proposed method are verified by the hardware experiments on a self-built offshore lifting system.

关键词： Marine lifting system Tracking control Nonlinear controller State constraint

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Dynamic Task Subspace Ensemble for Class-Incremental Learning 1

引用

3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Zhang, Weile He, Yuanjian Cong, Yulai Sun Yat Sen Univ Shenzhen 518107 Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Deep-learning models are expected to continually learn new concepts without forgetting old ones in real-world applications with shifting data distributions. However, the notorious catastrophic forgetting often occurs. Recently, methods based on task subspace modeling have been developed to address this issue by gradually adding new subspaces to learn new concepts. In this paper, we reveal that such task-subspace-modeling methods may suffer from the inter-task confusion issue, leading to degraded performance in challenging class-incremental learning settings. Concerning addressing the forgetting issue of deep learning models, we propose a two-stage framework called Dynamic tAsk Subspace Ensemble (DASE), the first stage of which involves the dynamic expansion of the extractor network for memory efficiency, while the second stage delivers dynamic learning and aggregation of diverse features. To further enhance the discriminative capacity of the aggregated features for both historical and new classes, we also introduce new feature-enhancement techniques. Experimental results demonstrate that our method achieves state-of-the-art CIL performance on natural image datasets (CIFAR-100 and ImageNet) and Synthetic Aperture Radar image datasets (MSTAR and OpenSARShip).

关键词： class-incremental learning inter-task confusion dynamic task subspace ensemble memory-efficient

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Deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets 3rd

Deep Learning and Machine Learning Techniques Applied to Spe...

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3rd international conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Manfron, Enrico Teixeira, Joao Paulo Minetto, Rodrigo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Associate Lab Sustainabil & Technol SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

In this study, we explore the capabilities of speaker recognition technology for biometric authentication developing speaker recognition-based access control systems and serving as a resource for future research and improvements in secure and efficient speaker identification solutions. We focused on developing and evaluating machine learning and deep learning models for speaker identification. The models were trained and tested on private datasets with 32 speakers and public datasets with 1251 to 6112 speakers. The Gaussian Mixture Model performed well with our private datasets, with 93,10%, and 95% accuracy in correctly identifying the speakers. The Multilayer Perceptron achieved a peak accuracy of 93.33% on the Framed Trim private dataset. The VGGM model, after initial training on larger datasets, achieved an accuracy of 90.34% and 98.33% on our private datasets. At last, the model ResNet50 slightly outperformed the other models on two versions of our private dataset, achieving accuracies of 97.93% and 100%.

关键词： Speaker Identification Convolutional Neural Network Deep Learning

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robotics Arm Controller with Fuel Cell-Based Stepper Motor Drive Using Double-Ended Forward Converter 3rd

Robotics Arm Controller with Fuel Cell-Based Stepper Motor D...

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3rd international conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2024

作者： Kanchana, K. Chintamani, B. Kavitha, S. Priya, R. Karpaga Sinthia, P. Malathi, M. Department of Electrical and Electronics Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819737444

robotics is a rapidly evolving technology essential in many industries and a significant pillar for all the following technologies. Selecting the best motor for the ideal robot is a problematic issue if one is developing a robot, especially for industry. Several criteria for arms control, location, and angular and linear movements must be considered when choosing electrical motors for industrial robots. Choosing the suitable motor is a crucial decision in the design of robots that requires careful consideration of factors such as the robot’s weight, wheel size, and intended use. Torque, acceleration, and speed must all be considered during the design stage. Industrial robots typically use a range of motor types for various functions. The proposed work uses a stepper motor for high-torque robotic applications and more precise angular movement. The double-ended forward converter is used to operate the stepper motor. For current regulation, loss minimization, and low-power applications, the suggested converter is strongly recommended. This converter increases functionality and has buck. The technology’s source is also mentioned: fuel cells. A mathematical model of a double-ended forward converter is presented in this paper. The suggested converter’s prototype model’s outcomes are compared and verified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Stepping motors

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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