Advances in Artificial Intelligence and Machine Learning

Digitalisation in education and its influence on teaching methods is the focus of this study, which examines the use of ChatGPT in a role-playing game used in the Cloud Computing Engineering Master’s programme at the University of Applied Sciences Burgenland. The aim of the study was to analyse the impact of AI-based simulations on students’ learning experience. Based on Vygotsky’s sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes in simulated business scenarios. The methodological approach included role-playing and qualitative content analysis of 20 student reflections. The findings suggest that ChatGPT enhances students’ engagement, critical thinking, and communication skills, in addition to contributing to the effective application of theoretical knowledge. Furthermore, simulations can contribute to the effective application of theoretical knowledge. The results underscore the significance of adaptive teaching approaches in promoting digital literacy and equipping learners for the digital workplace. The integration of AI into curricula and the need for ongoing innovation in higher education are also emphasised as a means of guaranteeing excellent, future-focused instruction. The findings highlight the potential of AI and ChatGPT in particular, as an innovative cuttingedge educational tool that can both enhance the learning experience and help achieve the Sustainable Development Goals (SDGs) through education.

Rita Stampfl

Barbara Geyer

Marie Deissl-O‘Meara

Igor Ivkic

Revolutionising Role-Playing Games with ChatGPT

用ChatGPT革新角色扮演游戏

Various opinions were made by the researchers to develop an automatic network for Optical Character Recognition (OCR). Still, character recognition in handwritten scripts is an unsolved task. In this paper, two efficient techniques are developed an effective character recognition technique for the handwritten Kannada scripts. The Kannada Character Recognition (KCR) techniques faced several challenges due to the different writing styles of people and the absence of fixed spacing among alphabets, words and lines. Another complication in the KCR model is the absence of large datasets to train the network, and it isn’t easy to write the Kannada script by combining the Kannada alphabets. Therefore, a new handwritten KCR approach is developed to identify the characters from the ancient Kannada scripts. The required Kannada script images are gathered from various online databases. The garnered images are preprocessed and segmented using morphological operation and thresholding. The relevant features from the images are achieved by the geometric feature extraction method. Finally, the characters are recognized by utilizing the Long Short Term Memory (LSTM) network, and the experimental results will be analyzed over the traditional optimization strategies and baseline works to evaluate the efficiency of the proposed network.

Supreetha Patel Tiptur Parashivamurthy

Dr. Sannangi Viswaradhya Rajashekararadhya

Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation

基于长短期记忆与阈值分割的卡纳达文字智能字符识别框架

The numbers of persons who can be enrolled by their iris patterns with no identity collisions is studied in relation to the biometric entropy extracted, and the decision operating threshold. The population size at which identity collision becomes likelier than not, given those variables, defines iris “capacity.” The general solution to this combinatorial problem is derived, in analogy with the well-known “birthday problem.” Its application to unique biometric identification on national population scales is shown, referencing empirical data from US NIST (National Institute of Standards and Technology) trials involving 1.2 trillion (1.2 × 1012) iris comparisons. The entropy of a given person’s two iris patterns suffices for global identity uniqueness.

John Daugman

Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales

理解生物特征熵和虹膜容量：避免国家规模上的身份碰撞

Since CVDs are the leading cause of death worldwide, this article solves the problem of developing a recommendation algorithm for the prevention of cardiovascular diseases (CVDs), as the main component of health recommender systems. To address this issue, a knowledgebased recommendation algorithm was proposed to support self-management of CVD risk factors in adults at home. The proposed algorithm is based on a new user profile and an original multidimensional recommendation model. The user profile includes not only descriptive but also predictive assessments of cardiovascular health based on the SCORE model which are outlined in official guidelines. A multidimensional recommendation model contains target, informational, and explanatory components for higher health lifestyle adherence. We found that ChatGPT provided plausible and understandable explanations of cardiovascular disease risk factors. therefore, these explanations were used in the texts of the recommendations. The main feature of the proposed algorithm is the combination of rule-based logic with the capabilities of a large language model to generate human-like text of multidimensional recommendations. Evaluation of the proposed algorithm showed high user satisfaction with the proposed recommendation algorithm.

Tatiana V. Afanasieva

Pavel V. Platov

Anastasia I. Medvedeva

Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Home

支持成人居家管理心血管疾病风险因素的推荐算法

This paper presents a novel approach in wildfire prediction through the integration of multisource spatiotemporal data, including satellite data, and the application of deep learning techniques. Specifically, we utilize an ensemble model built on transfer learning algorithms to forecast wildfires. The key focus is on understanding the significance of weather sequences, human activities, and specific weather parameters in wildfire prediction. The study encounters challenges in acquiring real-time data for training the network, especially in Moroccan wildlands. The future work intends to develop a global model capable of processing multichannel, multidimensional, and unformatted data sources to enhance our understanding of the future entropy of surface tiles.

Ayoub Jadouli

Chaker El Amrani

Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning

通过多源时空数据、深度学习、集成模型和迁移学习提升野火预测

出版信息	出版商: Shimur Publications，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2021，原创研究文献占比: ，自引率:， Gold OA占比:

Advances in Artificial Intelligence and Machine Learning

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Revolutionising Role-Playing Games with ChatGPT

Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation

Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales

Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Home

Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning

Revolutionising Role-Playing Games with ChatGPT

Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation

Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales

Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Home

Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning

ISSN	2582-9793
h-index	4