Application of Augmented Reality Technology in the Teaching of Literacy at Low Learning Stage by Jing Li

“…Literacy is an important part of primary school Chinese and also an important part of primary school Chinese. At present, literacy education is increasingly valued by teachers. …”
Get full text

Incoherent dictionary learning and sparse representation for single-image rain removal by Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, Xiao LI, Li-zhen MAO

“…The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.…”
Get full text

Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges by Jetli Chung, Jason Teo

“…This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. …”
Get full text

Neurological soft signs in comorbid learning and attention deficit hyperactivity disorders by Runa Uslu, Emine Gül Kapçi, Didem Oztop

“… The present study aimed to examine whether neurological soft signs identified in children with attention deficit hyperactivity disorder (ADHD), learning disorders (LD), comorbid ADHD-LD and children with no known disorders could be grouped and whether these groups of soft signs would differentiate between the clinical groups and the non-clinical group. …”
Get full text

Incoherent dictionary learning and sparse representation for single-image rain removal by Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, Xiao LI, Li-zhen MAO

“…The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.…”
Get full text

Reinforcement Learning for Fail-Operational Systems with Disentangled Dual-Skill Variables by Taewoo Kim, Shiho Kim

“…We present a novel approach to reinforcement learning (RL) specifically designed for fail-operational systems in critical safety applications. …”
Get full text

The Role of Artificial Intelligence in Transforming Language Learning: Opportunities and Ethical Considerations by Adenike A. Akinsemolu, Helen Onyeaka

“…Such innovative tools and applications enhance both language comprehension and production skills among learners, optimizing the outcomes of language teaching and learning. However, the adaptation of AI for language learning presents some ethical challenges related to data privacy, bias in the development and training of algorithms, and the lack of universality in access and usage of AI, which threatens to widen the already extensive inequalities in education. …”
Get full text

Enhanced Distributed Computation for Machine Learning: Coded Strategies and Multidisciplinary Impact by Tian Xiaolin

“…This article explores methods to accelerate distributed computation, focusing on its application in machine learning. It discusses two primary concepts: coded multiplication and data shuffling, along with a non-linear core to Random Access Memory (RAM) approach, presenting new avenues for future research. …”
Get full text

Research on the application of government big data platform based on federated learning by Jianping WU, Chaochao CHEN, Jiahe JIN, Chunming WU

“…At present, the construction of digital government has entered a deepwater area.The government big data platform, as a data base, supports various government information applications.The security and compliance of its private data has been widely concerned by the industry.Federated learning is an important method to effectively solve data silos, and the application of government big data platforms based on federated learning has high research value.Firstly, the current status of government big data platforms and its federated learning application were introduced.Then this paper analyzed three major management challenges involved in the collection, classification and grading and sharing of privacy data on government big data platforms.Further, the problem-solving methods of federated learning based recommendation algorithms and privacy intersection techniques were explored.Finally, summaries and prospects were made for the future application of privacy data on government big data platforms.…”
Get full text

Deep Learning-Based Microorganisms Classification Systems Using Microscopic Images by Baydaa Abdulrazzaq Abed, Dr. Jumana Waleed

“…On the contrary, deep learning models be inclined to hold outstanding performance in speed and accuracy. …”
Get full text

Optimizing Pretrained Autonomous Driving Models Using Deep Reinforcement Learning by Vasileios Kochliaridis, Ioannis Vlahavas

“…Vision-based end-to-end navigation systems have shown impressive capabilities, especially when combined with Imitation Learning (IL) and advanced Deep Learning architectures, such as Transformers. …”
Get full text

Optimizing smart city planning: A deep reinforcement learning framework by Junyoung Park, Jiwoo Baek, Yujae Song

“…This optimization is achieved through strategic construction of smart city components, considering that both the total population and happiness levels are influenced by the interplay between housing, communication, transportation, and healthcare infrastructures, as well as the population ratio. Specifically, we present two distinct formulations of the Markov Decision Process (MDP) for smart city planning to illustrate the practicality of applying reinforcement learning across different scenarios.…”
Get full text

The Impact of GPT Models on Education: Enhancing Learning Outcomes and Addressing Challenges by Yang Nuoya

“…This balanced approach aims to provide a nuanced understanding of the transformative potential of GPT in education, highlighting both its ability to enhance learning outcomes and the challenges it presents for broader implementation.…”
Get full text

Data mining techniques for the study of online learning from an extended approach by José Manuel Sánchez-Sordo

“…On this path the extended mind thesis posits human cognition as a process that occurs in conjunction between biological and non-biological components, furthermore Connectivism is stated as a learning theory for the digital age. Based on such approaches this work presents a summary of a research whose objective was to know how people extend their cognitive processes with the aim of learning through the internet. …”
Get full text

The 7-Day Challenge: Taking Steps Towards a Learning Habit by Thomas Stones

“…Thus, this paper reports on an initial investigation into a seven-day independent learning challenge where learners were set the task of completing five independent learning tasks across a single week with the intention of establishing a habit of studying English. …”
Get full text

Age of machine learning: new trends in autism spectrum disorder prediction by Weihong Xu, Haibei Li, Junwen Li, Min Jin

“…Through the integration of artificial intelligence and the medical field, researchers can establish a machine learning (ML) risk prediction model to estimate the risk of ASD. …”
Get full text

Accurate RFID Trilateration to Learn and Recognize Spatial Activities in Smart Environment by Kevin Bouchard, Dany Fortin-Simard, Sebastien Gaboury, Bruno Bouchard, Abdenour Bouzouane

“…Particularly, the model exploits qualitative spatial reasoning from RFID localization of objects in the smart home to learn and recognize the basic and instrumental activities of daily living of a resident. …”
Get full text

Using Machine Learning Algorithms in Intrusion Detection Systems: A Review by Mazin S. Mohammed, Hasanien Ali Talib

“…This paper explores the unique challenges of IoT environments and presents machine learning (ML) algorithms as powerful solutions for IoT-IDS, encompassing supervised, unsupervised, and semi-supervised learning. …”
Get full text

Student perceptions of an online mathematics curriculum designed for students with learning difficulties by Kristina Higgins, Lindy Crawford, Kelly Silvestri

“… Paper presents research on the perception of technology use including dimensions of computer self-efficacy, motivation, and independence of use of electronic support tools of students with learning difficulties in the context of an online mathematics curriculum. …”
Get full text

Object Ontologies as a Priori Models for Logical-Probabilistic Machine Learning by D. N. Gavrilin, A.V. Mantsivoda

“…Logical-probabilistic machine learning (LPML) is an AI method able to explicitly work with a priori knowledge represented in data models. …”
Get full text