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3rd international conference on Human and Artificial Rationalities, HAR 2024

3rd International Conference on Human and Artificial Rationa...

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3rd international conference on Human and Artificial Rationalities, HAR 2024

ISBN: (纸本)9783031845949

The proceedings contain 24 papers. The special focus in this conference is on Human and Artificial Rationalities. The topics include: Data Science and Environmental Sustainability: Mastering the Information and Knowledge;multi-agent Simulation of Violence Emergence in Protests;the Social Exchange Heuristic Operation During a One-Shot Prisoner’s Dilemma Game;development of Intention-Based Moral Judgement in Children and Adolescents with and Without Intellectual Disability;artificialization of intelligence and Embodied Cognition;Consciousness in AI: A Return to Fetishism or Technological Progress?;agent Rationality Under Partially Resolving Uncertainty and the Hurwicz Criterion;expressing Rational Agency Through Pragmatic Meta-vocabularies;the Effect of Initial Letters in Word recognition: A Phonological or Positional Effect ?;coherence as Logical Consistency;degrees of Intuition: Coherence in the New Dual Process Theory;a Tribute to Kahneman and Tversky in the Context of Mathematics Education;effect of Brief Mindfulness Training on Cognitive Rigidity;research Trends in Information Accessibility to Web Content for Adults with Developmental Disabilities: A Literature Review;NAO Robot to Help People with Alzheimer's Manage the Recall of Activities in Prospective Memory;Does Replacing the Experimenter with an Ignorant Student Robot Improve the Success of Children with ASD in the False Belief Task?;enhancing Pharmacology Education: Investigating the Efficacy of Serious Game-Based Learning Through Virtual Simulation;video-Based Analysis of the Mechanical Design Process in a Student Dyadic Activity;critical Thinking and Psychorhetoric for Promoting Environmental Awareness;the Effect of Screens on Children’s Development: Concrete Action Taken in Schools, Closer to Families;can We Really Learn in the Metaverse? A Discussion on Learning Through Immersive Technology;emotion-Enhanced Pain Assessment Protocol.

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Use of Artificial intelligence in Smart City Development 3

Use of Artificial Intelligence in Smart City Development

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

作者： Behera, Nihar Ranjan Tak, Sana Thakur, Gaurav Prabhanjan, Neduri Ibrahim, Waleed Khalid Alazzam, Malik Bader Swiss School of Business and Management Geneva Petit-Lancy1213 Switzerland Amity University Computer Science & Engineering Chhattisgarh India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Engineering Department of Civil Engineering Telangana Warangal India Al-Farahidi University Medical Technical College Baghdad10021 Iraq Ajloun National University Information Technology College Jordan

ISBN: (纸本)9798350399264

A revolutionary concept that is yet in its infancy, artificial intelligence has the potential to, when employed skilfully, provide a vehicle for good change that might elevate supportable adjustments from an asset-effective quality of living viewpoint. This new type of man-made intelligence-based portability, regardless of its machine direction, must be a client-focused innovation that "comprehends"and "fulfils"the human client, the business sectors and society in general. Trust ought to be assembled, and dangers ought to be wiped out, for this change to take off. This essay offers a novel measured commitment that carefully investigates the scarcely concentrated intersection of artificial intelligence, transit, the smart city, and the ramifications for urban prospects that they will have. It addresses a crucial subject, imaginative portability drives alluding to Associated and Independent Vehicles, independent Individual and Automated Flying Vehicles, and mediations that might fill in as empowering advances for transport, like the Web of Things and Actual Web or reflect more extensive changes like Industry 4.0. © 2023 IEEE.

关键词： Industry 4.0

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Ship Detection with Optical Image Based on Attention and Loss Improved YOLO 3

Ship Detection with Optical Image Based on Attention and Los...

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3rd international conference on pattern recognition and machine Learning, PRML 2022

作者： Chen, Yao Zhang, Biao Li, Zhen Qiao, Yuanyuan Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Aisino Corporation Beijing China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Object detection is a critical research topic in computer vision. As a subtask of object detection, Ship detection has important research significance. Most ship detection research is based on SAR (Synthetic Aperture Radar) images in the existing research. However, compared with SAR images, optical images have more image feature information, which can assist the algorithm to better learn ship features. In addition, the research of optical image ship detection has more important commercial value. This paper conducts optical image ship detection experiments, applies the YOLO algorithm to the ship detection task, introduces the attention mechanism to transform the residual block in the DarkNet-53 network, and achieves more excellent performance. At the same time, this paper optimizes the recently proposed CIoU loss function which is better than the ln-norm loss function and presents the AIoU loss function on this basis. By increasing the area penalty term, the AIoU loss improves performance while converging speed compared with CIoU loss. It is applied to ship detection in the optical image and compared with GIoU, DIoU, and CIoU loss functions, and it has achieved better results than them in the bounding box regression of ship detection. © 2022 IEEE.

关键词： Object detection

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Towards an Adaptive Learning Process Using Artificial intelligence Technologies 2nd

Towards an Adaptive Learning Process Using Artificial Intell...

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3rd international conference on Digital Technologies and Applications

作者： Lhafra, Fatima Zohra Abdoun, Otman Abdelmalek Essaadi Univ Fac Sci Comp Sci Dept Tetouan Morocco

ISBN: (纸本)9783031298561;9783031298578

Adaptive learning represents a wide field of application of artificial intelligence technologies. The aim of this concept is to cover the different profiles of learners in order to minimize disorientation and increase the rate of engagement and motivation among them. The adaptation is not a single task that can be done alone or is only limited to the assimilation phase. To this end, we have implemented an intelligent and coherent adaptive learning model that accompanies the learner throughout their learning process. The proposed model consists of four phases: identification of learning styles, proposal of adaptive learning activities, recommendation of assessment activities and the implementation of an adaptive remediation strategy. Each phase of this model is based on artificial intelligence technology while respecting the pedagogical context. In order to measure the effectiveness of the proposed model, we set up a real experiment with learners.

关键词： Adaptive e-learning machine learning Evolutionary algorithm Recommendation System

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Ethics in Artificial intelligence: An Analysis of Ethical Issues and Possible Solutions 3

Ethics in Artificial Intelligence: An Analysis of Ethical Is...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Ayub, Zahrah Banday, M. Tariq University of Kashmir Department of Electronics and Instrumentation Technology Srinagar India

ISBN: (纸本)9798350370867

Unprecedented advances in Artificial intelligence and its widespread applications in diverse fields without a sufficient social, ethical, legal, or technological framework to oversee it have sparked concerns about its decisions having ethical and social implications. Identifying and addressing the ethical issues of artificial intelligence has been a topic of great importance to researchers, governments, law enforcement agencies, and corporations, given the growing concerns of individuals, organizations, countries, and society. However, very little progress has been made to address them. These ethical and social issues, such as privacy breaches, bias, discrimination, non-accountability, less transparency, unemployment, promoting inequality, social manipulation, loss of human relationships, and independence, inevitably impact the existing social order and raise ethical concerns. This paper provides an overview and analysis of ethical issues, strategies for dealing with them, and techniques for assessing the ethics associated with artificial intelligence. Additionally, the study underscores the increasing ethical and social ramifications arising from the expanding use of technology in various domains. It also highlights insufficient technical solutions and a fitting framework to tackle these concerns. Ultimately, the paper emphasizes the necessity for thorough research to identify suitable technological solutions to address these ethical challenges. © 2023 IEEE.

关键词： machine learning

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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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DACTransNet: A Hybrid CNN-Transformer Network for Histopathological Image Classification of Pancreatic Cancer 1

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Kou, Yongqing Xia, Cong Jiao, Yiping Zhang, Daoqiang Ge, Rongjun Nanjing Univ Aeronaut & Astronaut Key Lab Brain Machine Intelligence Technol Minist Educ Nanjing 211106 Peoples R China Southeast Univ Zhongda Hosp Dept Radiol Jiangsu Key Lab Mol & Funct ImagingMed Sch Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Nanjing Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Automated and accurate classification of histopathological images of pancreatic cancer can lead to higher survival rates for more pancreatic cancer patients in the clinic. However, there are very scarce existing studies for pancreatic cancer, and the diagnosis of pancreatic cancer remains a challenge for pathologists, especially for well-differentiated pancreatic cancer with a clinical histological pattern similar to that of chronic pancreatitis. We propose a hybrid CNN-Transformer model incorporating deformable atrous spatial pyramids (DACTransNet) to perform automated and accurate classification of histopathological images of pancreatic cancer. We elegantly integrate the powerful local feature extraction capability of CNN for spatial features and the global modeling capability of transformer for abstract patterns. Moreover, we imitate pathologists in the clinic by better integrating deformable convolution and multiscale methods to review histopathology slides in pyramidal format. In addition, a migration learning approach was used to improve the classification accuracy of pancreatic cancer histopathology images. The experimental results show that the proposed method not only has a high classification accuracy (up to 96%), but also its good robustness and generalizability as validated by real clinical datasets from multiple centers. Consequently, we provide an effective tool for the clinical diagnosis of pancreatic cancer.

关键词： Pancreatic cancer Histopathological image Transformer

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Research on deep learning-based facial expression recognition and its application in online learning state monitoring 3

Research on deep learning-based facial expression recognitio...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wang, Qisheng Yan, Xiaofei Wang, Yanqiu College of Information Science and Engineering Zaozhuang University Zaozhuang277160 China

ISBN: (纸本)9781510668522

This paper aims to research and develop a facial expression recognition algorithm based on convolutional neural networks (CNN) and apply it to a real-Time facial expression recognition system. Firstly, we introduce the Local Binary pattern (LBP) and CNN as commonly used facial recognition algorithms, discussing their characteristics and application areas. We then describe the experimental process in detail, conducting emotion recognition experiments using the FER2013 dataset and performing data preprocessing and augmentation steps. We design a CNN-based model architecture and set the parameters of the model. In the results and analysis section, we compare the performance of our model with other baseline models and discuss possible directions for improvement. Finally, we conclude that our algorithm achieves high accuracy in the experiments and provide prospects for future research and applications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Local binary pattern

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Hardware acceleration design and implementation of YOLOv3-tiny based on ZCU102 platform 3

Hardware acceleration design and implementation of YOLOv3-ti...

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3rd international conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Zhao, Yixuan Shi, Tianjie Liu, Feiyang Tian, Jing Xi'an Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9781510680449

Convolutional Neural Networks (CNNs) have been widely applied in the field of image recognition. To meet the extensive computational demands of CNNs, GPUs or other intelligent computing hardware are commonly employed for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. This paper provides a detailed introduction to Xilinx's FPGA development tool software, Vitis AI, designed for artificial intelligence, along with the process of generating and implementing FPGA hardware logic for deep learning algorithms. The functionality and performance were verified through FPGA experimental platforms. We used a 16-bit fixed-point method to segment neural network tasks and preprocess data, thereby reducing hardware resource consumption. By employing the ZCU102 development platform to accelerate the YOLOv3-Tiny algorithm, we achieved target object detection and recognition. The power consumption was only 9.9% of GPU Quadro K6000, and compared with other YOLOv3-tiny implementations on ZCU102, the detection speed was increased by 5.7 times. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computation theory

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Application and Research of Artificial intelligence Embedded Service 3

Application and Research of Artificial Intelligence Embedded...

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3rd international conference on Artificial intelligence, Robotics and Control (AIRC)

作者： Li, Jingjing Li, Qingda Heilongjiang Bayi Agr Univ Lib Daqing Peoples R China Heilongjiang Bayi Agr Univ Daqing Peoples R China

ISBN: (数字)9781665459471

ISBN: (纸本)9781665459471

With the rapid development of artificial intelligence, the library relies on the massive knowledge data to integrate the user behavior data, and continuously upgrade the library industry chain, the new form, the new service will bring the new experience for the user. The artificial intelligence technology based on machine learning and deep learning will promote the change of embedded service, upgrade the intelligence of embedded service physical scene in library, and advance the intelligence of information resources and virtual space. In the embedded service dimension, the artificial intelligence technology will promote the intelligent service supply of the library, the intelligent library will provide the "A I cloud processing" resource service for the user, the whole domain, the interactive scene service, the human-machine high-efficiency Coordinated Joint Service;In the dimension of user experience, artificial intelligence technology will bring a variety of user experience to the library. Taking the Library of Agricultural University as an example, this paper probes into the characteristics and modes of embedded service, puts forward the application of artificial intelligence in embedded service, and provides beneficial exploration for exerting the value of artificial intelligence in library service.

关键词： cloud processing library embedded service SWOT model

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