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Humanities and Social Sciences Communications 2024年第1期11卷 1-14页

作者： Zhai, Xuesong Chu, Xiaoyan Wang, Minjuan Tsai, Chin-Chung Liang, Jyh-Chong Spector, Jonathan Michael College of Education Zhejiang University Hangzhou China Hangzhou International Urbanology Research Center & amp Zhejiang Urban Governance Studies Center Hangzhou China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong Learning Design and Technology San Diego State University San Diego CA United States Program of Learning Sciences and Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Department of Learning Technologies University of North Texas Denton TX United States

Stimulated Recall (SR) has long been used in educational settings as an approach of retrospection. However, with the fast growing of digital learning and advanced technologies in educational settings over the past decade, the extent to which stimulated recall has been effectively implemented by researchers remains minimal. This systematic review reveals that SR has been primarily employed to probe the patterns of participants’ thinking, to examine the effects of instructional strategies, and to promote metacognitive level. Notably, SR video stimuli have advanced, and the sources of stimuli have become more diverse, including the incorporation of physiological data. Additionally, researchers have applied various strategies, such as flexible intervals and questioning techniques, in SR interviews. Furthermore, this article discusses the relationships between different SR research items, including stimuli and learning contexts. The review and analysis also demonstrate that stimulated recall may be further enhanced by integrating multiple data sources, applying intelligent algorithms, and incorporating conversational agents enabled by generative artificial intelligence such as ChatGPT. This article provides a comprehensive analysis of SR studies in the realm of education and proposes a promising avenue for researchers to proactively apply stimulated recall in investigating educational issues in the digital era. © The Author(s) 2024.

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Exploring the Influence of Digital Games on Environmental Attitudes and Behaviours Based on the New Ecological Paradigm Scale: A Mixed-Methods Study in India

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Journal of Education for Sustainable Development 2021年第1期15卷 72 - 99页

作者： Shamila Janakiraman Sunnie Lee Watson William R. Watson Daniel P. Shepardson is Visiting Faculty in the Learning Design and Technology Programme Department of Curriculum and Instruction at Purdue University West Lafayette IN USA. is Associate Professor in Learning Design and Technology Purdue University West Lafayette IN USA. E-mail: is Professor of Geo-environmental and Science Education Departments of Learning Design and Technology and Earth Atmospheric and Planetary Sciences Purdue University IN USA. E-mail:

Anthropogenic activities cause environmental degradation-related problems. However, people fail to perform pro-environmental behaviours because they believe that they cannot make a difference or they focus on short-term benefits. Interventions that address specific target groups aimed at breaking barriers and changing behaviours are required. To teach young learners, environmental education using digital games can be a more effective instructional method. This mixed-methods study, conducted among high school students in India, examined differences in pro-environmental attitudes and behaviours between students who played a game called EnerCities and those students who did not play that game. Significant differences existed between the two groups considering the unidimensional and multi-dimensional properties of the New Ecological Paradigm (NEP) scale. Thematic analysis with an inductive approach identified from the interview data: How EnerCities changed participants’ environmental attitudes? This study finds implications for implementing games and using the NEP to examine environmental attitudes of high school students in India.

关键词： Digital games environmental sustainability education pro-environmental attitudes and behaviours New Ecological Paradigm scale mixed-methods study

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Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident

Deep Reinforcement Learning In Autonomous Cars To Preventing...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Rushikesh Thote Aditya Barhate Abhay Tale Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies.

关键词： Road accidents Reviews Decision making Transportation Transforms Deep reinforcement learning Road safety Safety Reliability Stress

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Grammar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Geetha K. Vijaya Subramanian K M Rajesh Kanna P Rajeshkumar G Karthika S Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Department of Computer Science and Design Kongu Engineering College Erode Department of Computer Science and Engineering Shadan College of Engineering and Technology Hyderabad Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Machine Learning Bannari Amman Institue of Technology Sathyamangalam

In the digital era, mos t of the communication is happening through online platforms. English plays a vital role for better understanding. Good grammar helps in framing a better sentence. This demands for the development of an automatic Grammar Error Correction (GEC) tool. Many research works have been initiated to make Grammar Error Correction possible by using various machine learning algorithms. Some of the algorithms are Naive Bayes, BERT, Support Vector Machine, and ULMFIT. Among these algorithms BERT gives a comparatively best accuracy rate. The fundamental goal of this research work is to compare these algorithms with respect to accuracy Here, NLP is used for analyzing the grammatical corrections in the sentence. Further, this study can be extended to overcome the drawbacks faced by the BERT algorithm such as increase in time cons umption for training and cos t.

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Intrusion Detection in Cloud Architecture Using Machine learning 4

Intrusion Detection in Cloud Architecture Using Machine Lear...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Govinda Rajulu, G. Santhoshkumar, R. Venkatesan, D. Kalvikkarasi, S. Santosh Kumar Patra, P. St. Martin's Engineering College Department of Computer Science and Design India St. Martin's Engineering College Department of Artificial Intelligence and Data Science India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE India Podhagai College of Engineering & Technology Professor in Ece Department India St. Martin's Engineering College India

ISBN: (纸本)9781665474368

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ. © 2022 IEEE.

关键词： Cloud computing architecture

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Medical Image Classification Using Thermal Images and Diagnosis of Carcinogen

Medical Image Classification Using Thermal Images and Diagno...

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2024 IEEE International Conference on Innovation and Novelty in Engineering and technology, INNOVA 2024

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Srinivas, K. Krishnaiah, Nallam Kumar, K. Laxman Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin’s Engineering College Hyderabad India St. Martin’s Engineering College Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Telangana Secunderabad India Deptartment of Computer Science and Design St. Martin’s Engineering College Hyderabad India

ISBN: (纸本)9798331505134

Lung and Colon (L&C) tumors are lethal sicknesses that can foster in a few organs all the while and, in specific circumstances, jeopardize human existence. Even though it is highly unlikely that these two types of cancer will develop simultaneously, delaying diagnosis significantly increases the likelihood of metastasis between the organs that are affected. To really treat specific sorts of malignant growth, histological determination is fundamental. In the past, doctors had to go through a long and difficult process to look at thermal images and figure out if a patient had cancer;however, this procedure may now be completed much more quickly thanks to the new technology options. Histological images of L&C tumors were classified using a hybrid Deep learning (DL) model with an attention mechanism and a multipath network in this study. To zero in on the main attributes and negligence the less significant ones, a consideration component was utilized. In a multipath network, data travels over a number of channels before each channel is converted and the output from all of the branches is combined. The multipath network is similar to grouped convolution when simplified. The five thermal image categories of the LC25000 dataset were utilized. Among these classes were two for colon disease and three for cellular breakdown in the lungs. The proposed model was compared to a number of well-known DL models, such as ResNet-50, VGG-16, and AlexNet. The proposed approach showed the best exhibition concerning exactness (99.2%), particularity (99.12%), responsiveness (99.28%), accuracy (99.12%), and F1 score (99.2%), as indicated by the exploratory discoveries. © 2024 IEEE.

关键词： Lung cancer

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A Review on Bayesian Methods for Uncertainty Quantification in Machine learning Models Enhancing Predictive Accuracy and Model Interpretability

A Review on Bayesian Methods for Uncertainty Quantification ...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Moses Makuei Jiet Prateek Verma Aahash Kamble Chetan Puri Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research study reviews the statistical fundamentals of machine learning with a focus on Bayesian methods to quantify the uncertainty in model predictions. Bayesian statistics provides a framework for incorporating prior knowledge, updating beliefs, and expressing uncertainty in predictions. This research study will explore Bayesian techniques applied to various aspects of machine learning, including regression, classification, deep learning, and ensemble methods.

关键词： Uncertainty Accuracy Reviews Computational modeling Pipelines Predictive models Probabilistic logic Bayes methods Internet of Things Ensemble learning

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A Review on the Methods of Targeted Drug Delivery Using Nanotechnology

A Review on the Methods of Targeted Drug Delivery Using Nano...

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Engineering and technology (PICET), Parul International Conference on

作者： Mohan Warbhe Prateek Verma Swapnil Gundewar Palash Gourshettiwar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

The use of nanotechnology for targeted drug delivery is a rapidly growing field. Nanotechnology, with its manipulation of materials at the nanoscale, offers novel solutions across various fields, prominently in medicine. Nanotechnology enables precise drug administration to specific sites in targeted drug delivery, ensuring optimal efficacy while minimizing side effects. This approach encompasses passive and active targeting strategies, leveraging biological mechanisms and ligand-mediated interactions for enhanced drug delivery. The current study reviews the techniques of targeted drug delivery using nanotechnology, focusing on various methods of targeted drug delivery, their applications, and strategies for using nanotechnology. The key processes include drug loading, release, and overcoming biological barriers. Nanotechnology also finds applications in imaging, therapeutics, and drug development, promising personalized and precise medical interventions. Although several challenges, such as scalability, drug loading, and biological barriers, persist, the future scope remains promising, with personalized medicine, precision targeting, biomimetic nanoparticles, and advanced imaging integration poised to revolutionize healthcare.

关键词： Drugs Nanoparticles Targeted drug delivery Reviews Precision medicine Biomimetics Loading Medical services Nanotechnology Biomedical imaging

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Adesh Pawar Palash Gourshettiwar Pradnyawant M. Gote Vishal Kumar Choudhary Prajyot Yesankar Shrikant Khadse Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U) Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration.

关键词： Neuroplasticity Transcranial magnetic stimulation Surgery Mental health Machine learning Motors Market research Neurorehabilitation Transcranial direct current stimulation Neurofeedback

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A Review on the Use of AI and Robotics in Agricultural Practices

A Review on the Use of AI and Robotics in Agricultural Pract...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Mohan K. Warbhe Prateek Verma Rajendra Rewatkar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Robotics and artificial intelligence are becoming a part of the agricultural sector in ways bringing a change in it. It is evident that different technologies can help improve the yields, increase sustainability and efficiency in farming. This paper aims at offering a brief on the use of AI and robotics in agriculture; this will include using technologies such as Exact Hybrid Precision Farming, AI in livestock farming, AI-based resource supply chain applications, and others. In this literature review, the ideas of other authors are compiled and compare the concepts of culture as a factor in tackling the issues affecting agriculture and the possibility of utilizing artificial intelligence in the process. The theoretical literature review of the application of AI and robotics in precision farming aims at presenting literature evidence showcasing the use of activities such as predictive analytics, decision support systems, automated crop monitoring, and robotic harvesting. Other discussed issues are threats including high capital investment during the setting up and implementation of the system, replacement of labor by machines, as well as data insecurities.

关键词： Training Costs Bibliographies Government Supply chains Agriculture Artificial intelligence Sustainable development Robots Farming

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