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The impact of toxic trolling comments on anti-vaccine YouTube videos (vol 14, 5088, 2024)

SCIENTIFIC REPORTS 2024年第1期14卷 1-9页

作者： Miyazaki, Kunihiro Uchiba, Takayuki Kwak, Haewoon An, Jisun Sasahara, Kazutoshi Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington IN USA Sugakubunka Co. Ltd. Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan

Anti-vaccine trolling on video-hosting websites hinders efforts to increase vaccination rates by using toxic language and threatening claims to intimidate people and promote vaccine hesitancy. However, there is a shortage of research investigating the effects of toxic messages on these platforms. This study focused on YouTube anti-vaccine videos and examined the relationship between toxicity and fear in the comment section of these videos. We discovered that highly liked toxic comments were associated with a significant level of fear in subsequent comments. Moreover, we found complex patterns of contagion between toxicity and fear in the comments. These findings suggest that initial troll comments can evoke negative emotions in viewers, potentially fueling vaccine hesitancy. Our research bears essential implications for managing public health messaging and online communities, particularly in moderating fear-mongering messages about vaccines on social media.

关键词： Emotional contagion Fearful comment Toxic comment Vaccine hesitancy YouTube videos

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Bioinspired Polarized Skylight Orientation Determination Artificial Neural Network

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Journal of Bionic engineering 2023年第3期20卷 1141-1152页

作者： Huaju Liang Hongyang Bai Ke Hu Xinbo Lv School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing210094China Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information TechnologyJiaxing314001China Guidance Radar General Department Xi’an Electronic Engineering Research InstituteXi’an710100China School of Automation Nanjing University of Science and TechnologyNanjing210094China

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.

关键词： Polarized light navigation Orientation determination Artificial neural network

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Thermal conductivity of GeTe crystals based on machine learning potentials

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Chinese Physics B 2024年第4期33卷 104-107页

作者：张健张昊春李伟峰张刚 School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore School of Physics&State Key Laboratory of Crystal Materials Shandong UniversityJinan 250100China

GeTe has attracted extensive research interest for thermoelectric *** this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a *** phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher ***,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP ***,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP *** lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic *** study provides a theoretical tool for the study of the thermal conductivity of GeTe.

关键词： machine learning potentials thermal conductivity molecular dynamics

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The Application of Virtual Reality Using Kinect Sensor in Biomedical and Healthcare Environment: A Review 1

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4th International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2023

作者： Candra, Henry Yuniati, Umi Chai, Rifai Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9789819714636

ISBN: (纸本)9789819714629

Virtual Reality (VR) and the Kinect sensor, as promising tools in biomedical research, offers diverse applications in medical education, rehabilitation, diagnostics, and health research. The problem statement highlights the demand for innovative solutions and introduces VR and Kinect as potential transformative technologies. This review analyzes the importance of these technologies, their contributions, and future potential. It stands out by evaluating various Kinect-based systems in medical settings. By highlighting distinct features, advancements, and limitations, it provides guidance for future research. Relevant literature was gathered from databases such as Google Scholar, IEEE Xplore, and PubMed. The results showcase a wide range of applications, including patient autonomy, stroke rehabilitation, diagnostics, and monitoring. Despite challenges in accurate movement tracking, integration into clinical settings, and limited generalizability of findings due to small sample sizes, VR and Kinect show potential for revolutionizing healthcare delivery and improving patient outcomes. Their adaptability, affordability, and immersive nature of these technologies offer promising avenues for personalized interventions, remote healthcare, training, and enhanced patient engagement. As these technologies evolve, continued research and development are crucial to optimize their impact in shaping the future of healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual reality

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Application of the Simplified Aquila Optimizer with the Multiple Updating Principle in Unmanned Aerial Vehicle Path Planning 8

Application of the Simplified Aquila Optimizer with the Mult...

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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Zhao, Juan Liu, Yuan-Man Gao, Zheng-Ming School of Computer Engineering Insititue of Intelligent Computing Technology Jingchu University of Technology China School of Mechanical Engineering Jingchu University of Technology China Institute of Intelligent Computing Technology Hubei Engineering Research Center for Specialty Flowers Biological Breeding Schol of Computer Engineering Jingchu University of Technology China

ISBN: (纸本)9781665456128

The unmanned aerial vehicle (UAV) had been popular around the world and the peasant began to use UAVs to transport bags of crop, spray pesticides or herbicides manually causing the uncertainty and difficulty in path planning. Inspired by the simplified form of expressions and better performance of the Aquila optimizer (AO), a simplified AO with multiple updating principle (SAOMUP) was proposed to improve the capability of the AO algorithm and applied in UAV path planning in this paper. Simulation experiments were carried out to test and verify the capability of the proposed algorithm. And finally, the SAOMUP was applied in UAV path planning for transportation over some mountain field with trees or stones being simulated by cylinders, and better results were achieved. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Santhoshkumar, S.P. Karthika, R. Vijayakumar, N. Gajalakshmi, S. Jayanthi, K. Hariharasudhan, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (纸本)9798331509828

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields. © 2025 IEEE.

关键词： Expert systems

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Waveform based speech coding using nonlinear predictive techniques: a systematic review

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International Journal of Speech technology 2023年第4期26卷 1031-1059页

作者： Sheferaw, Gebremichael Kibret Mwangi, Waweru Kimwele, Michael Mamuye, Adane School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Juja Nairobi Nairobi62000 Kenya School of Information Technology and Engineering Addis Ababa University Institute of Technology 5Kilo Addis-Ababa Addis-Ababa Ethiopia

Speech coding is a technique that compresses speech signals into a smaller digital form, making it easier to transmit or store, while still maintaining the quality and intelligibility of the speech. The review aimed to identify and analyses the most effective waveform-based nonlinear speech coding prediction techniques, including the use of neural networks and polynomial filters. The study analyzed 29 publications from 2000 to 2023 and found that neural network-based models are widely used for speech compression, with RNN topologies being favored due to their ability to introduce nonlinearity and nonstationary. While nonlinear adaptive speech prediction techniques have been explored for speech coding, further research is needed to optimize the adaptive algorithms used in these models. The review also identified a need for future research to address quality performance and computational cost, and suggested further exploration of RNN predictor models. The methodology used in this study involved a computer science approach that follows three main phases: planning, conducting, and reporting. Six different stages were followed, including determining research questions, defining research approach, study selection criteria, quality measurement criteria, data extraction strategy, and synthesizing extracted data. Overall, this study highlights the need for continued research in the development and improvement of neural network-based speech compression models. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Forecasting

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

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International Journal of Hybrid Intelligent Systems 2024年第3期20卷 207-221页

作者： Subbiah, Priyanga Tyagi, Amit Kumar Krishnaraj, N. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperfor

关键词： deep learning disease detection enhanced OPTICS algorithm Image processing image segmentation infection severity tomato plant diseases transfer learning

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Classification and Segmentation of Brain MR Images Using Deep Neural Networks and Fuzzy C-Mean Clustering 1

Classification and Segmentation of Brain MR Images Using Dee...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Asthana, Pallavi Kamboj, Deepika School of Computing MIT Art Design and Technology University CSE Department Pune India School of Engineering The NorthCap University Multidisciplinary Engineering Department Gurugram India

ISBN: (纸本)9798331518943

Brain tumor arises mainly due to the tissue's abnormal growth inside the brain. They are dangerous as compared to other kinds of tumors because of their aggressively fast-spreading nature leading to a lower survival rate in the patients. Therefore, detecting and segmenting brain tumors from brain MR images helps radiologists in the early treatment planning of patients. The proposed work uses deep neural networks to classify and a fuzzy C-mean clustering algorithm to segment. First, classification is done to detect whether the image is normal or has a tumor. Next, segmentation is done for the MR images with tumors. The evaluated results are also compared with the recently published work. © 2024 IEEE.

关键词： Deep neural networks

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