Efficient Learning Research Articles

Enhancing early detection of autistic spectrum disorder in children using machine learning approaches

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

Efficient Reinforcement Learning of Task Planners for Robotic Palletization Through Iterative Action Masking Learning

Efficient Reinforcement Learning of Task Planners for Robotic Palletization Through Iterative Action Masking Learning

Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.

Education management: The role of religious and non-formal schools in increasing the efficiency of children's learning

Education management: The role of religious and non-formal schools in increasing the efficiency of children's learning

Pemanfaatan Multimedia Pembelajaran Interaktif bagi Guru PAUD

The aim of this research is to find out an overview of the use of interactive multimedia learning for PAUD teachers. This research uses the library research method or literature review. Data was obtained through 9 scientific articles related to the use of multimedia, interactive learning, PAUD teachers. Data was analyzed using content analysis. The research results show that the use of interactive multimedia in learning can also increase understanding, positive emotions and behavior of PAUD children during the learning process. In conclusion, multimedia can be useful in increasing interaction between teachers and students in PAUD in the learning process. The implication of this research is to provide an understanding of the importance of improving teacher competence, especially in preparing interesting, innovative, efficient and effective learning media in accordance with the growth and development of early childhood in PAUD.

Business English Teaching with Intelligent Assistance—Research on Case-driven and Artificial Intelligence Application Improvement

This paper explores a new model of business English teaching in an intelligent-assisted environment. Through the application of case-driven and artificial intelligence technology, it aims to improve teaching effectiveness and learning efficiency. The article first analyzes the current status and problems of business English teaching, and then introduces in detail the specific application of case-driven teaching method and artificial intelligence technology in business English teaching. Finally, the effectiveness of this teaching model is verified through empirical research, and the prospects for future research are proposed. Resources.

A Study on the Relationship between Field Cognitive Style and English Reading Strategy Choice

In recent years, China has accelerated its internationalization process and made more and more achievements in cross-border exchanges and cooperation. Learning English is essential for the current generation of college students. English reading is an important means to acquire English language knowledge, understand external information, and improve English language practice skills. Since the 70s of the 20th centuries, many scholars at home and abroad have been conducting a lot of research on the correlation between reading strategies and cognitive styles, but most of them are descriptive analysis, and there are few empirical studies. In this study, 324 first-year students majoring in science and engineering of Beijing Institute of Petrochemical Technology were selected by random sampling method, and the questionnaires of mosaic pattern test (Chinese version) and reading strategies were conducted, and data processing was carried out by SPSS 26.0, and descriptive statistics, independent samples t-test and Pearson correlation analysis were performed. The results show that there are significant differences in the use of reading strategies among field-independent and field-dependent science and engineering freshmen. Among them, field-independent individuals are more inclined to choose metacognitive strategies, and field-dependent individuals are more inclined to choose social/emotional strategies. Teachers should give full consideration to the characteristics of students' cognitive styles in English teaching, scientifically adjust the design of activities, the selection of teaching materials and teaching methods, and provide diversified guidance to help students correctly understand themselves and improve their learning efficiency. Due to the limitation of time and sample size, there are still many shortcomings in this study, but to a certain extent, it reflects the current situation of science and engineering English learners in China, and provides empirical data support for teachers' "teaching" and students' "learning".

Lightweight and efficient deep learning models for fruit detection in orchards

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (4.3×105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.3 \ imes 10^5$$\\end{document}), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.

Effectiveness of a Three-Step Decoding Strategy with Constant Time-Delay to Teach Word Reading to Students with Mild and Moderate Intellectual Disability

The purpose of this study was to examine the effects of a three-step decoding strategy with constant time delay on teaching and acquisition of decoding and word reading skills to four students with mild and moderate intellectual disability and to measure the generalization of those skills to untaught words. A multiple probe design across participants was used to examine the percentage of sounds correctly decoded and words correctly read. The results indicate that all four participants learned to decode and read words using the two strategies. Furthermore, all participants showed an increased learning efficiency in decoding and reading when teaching the second set of similar words. Generalization data showed that only two participants were able to decode and read some of the new words before teaching them, while all participants' ability to decode the letters of the new words was improved. Keywords: Reading, Phonics, Three-step decoding, Time delay, Intellectual disability

Advancing Artificial Intelligence: The Potential of Brain-Inspired Architectures and Neuromorphic Computing for Adaptive, Efficient Systems

Brain-inspired AI architecture, also known as neuromorphic computing, seeks to emulate the structure and functionality of the human brain to create more efficient, adaptive, and intelligent systems. Unlike traditional AI models that rely on conventional computing frameworks, brain-inspired architectures leverage neural networks and synapse-like connections to perform computations more similarly to biological brains. This approach offers significant advantages, including lower power consumption, improved learning capabilities, and enhanced problem-solving efficiency, particularly in tasks that require complex pattern recognition and cognitive processes. This paper explores the key components of brain-inspired AI architectures, such as spiking neural networks (SNNs) and neuromorphic hardware, and reviews the latest advancements in this field. We examine their applications across diverse domains, including robotics, autonomous systems, and medical diagnostics, where brain-like adaptability and real-time learning are critical. Additionally, we analyze the challenges associated with scaling these architectures, including hardware constraints and the complexity of accurately mimicking human brain functionality. The potential for combining brain-inspired AI with current machine learning models is also discussed, highlighting future directions for achieving more advanced, efficient, and human-like artificial intelligence systems. This research contributes to the growing body of knowledge on neuromorphic computing and its promise in shaping the future of AI technologies.Moreover, brain-inspired AI architectures have the potential to surpass traditional AI systems in terms of real-time decision-making and learning efficiency, particularly in environments that require adaptive behavior. The use of spiking neural networks (SNNs) in these architectures allows for more biologically plausible models of neuron activity, which can lead to advancements in sensory processing and autonomous decision-making. As neuromorphic hardware continues to evolve, integrating it with existing AI frameworks could enhance both performance and scalability. However, replicating the brain's full complexity remains a significant challenge, particularly in terms of creating energy-efficient hardware capable of supporting large-scale neural networks. Despite these challenges, the development of brain-inspired AI promises to bridge the gap between artificial and human intelligence, offering transformative possibilities for various industries.

A Study of Factors That Affect Chinese Tertiary EFL Learners’ Willingness to Communicate in Class

Classroom interaction characterizes communicative language teaching. Effective classroom interaction promotes second language acquisition (SLA) and serves as an important means of using the language especially for students in a foreign language context. Willingness to communicate (WTC) has received constant attention since its application to the SLA field in the 1990s. Students’ WTC ensures the quantity and quality of classroom interaction. By analyzing the data from an end-of-year questionnaire in an ordinary Chinese college English class, the present study aims to examine Chinese non-English major students’ general attitudes towards classroom communication activities and two major categories of factors, namely Teacher Factors and Task Factors, that influence students’ WTC in English classes from students’ perspective. The results show that the factors affecting WTC in college English classrooms are multidimensional, with Teacher’ s Emotions (being patient, encouraging, guiding, positive, happy, lively, passionate, trust), Teacher’ s Professional qualities (content selection, class organization, pronunciation, wait-time) and Task Content (usefulness, degree of difficulty, interest) having relatively greater effects. At the end of the paper, direction for future research is given. Second/foreign language teachers are advised to conduct further classroom research and actively regulate these factors for a more interactive English class and thus improve students’ class participation and learning efficiency.

An efficient deep learning approach for ultimate bond strength estimations of corroded bar and concrete

Abstract The corrosion behavior of the reinforced concrete structure depends heavily on the interfacial bond behavior between steel and concrete. Over the years, the deterioration of integrated reinforced steel has weakened this bond and potentially led to structural problems. Conventional methods of bond strength evaluation, such as pullout and bond beam tests, is frequently intrusive and tedious. Therefore, there is a growing need for non-intrusive, effective, and reliable forecasting algorithms capable of assessing bond deterioration caused by corrosion. Traditional algorithms for predicting bond strength make it difficult to capture the complex nature of steel-concrete bonds. The present study proposes two different deep learning algorithms, i.e., convolutional neural networks (CNN) and long short-term memory (LSTM), for predicting maximum bond strength in the presence of corrosion. The predictive model is based on a comprehensive dataset comprising 218 datasets from previous studies encompassing diverse input and output variables for predicting the models. The models were trained and tested using the given data to improve early predictions of corrosion-induced bond degradations. The predictive model’s effectiveness was assessed by applying various performance metrics. From this study, the CNN model exhibits higher accuracy and efficiency with mean absolute error, root mean square error, and mean absolute percentage error of 0.25, 0.28, and 95.72, respectively, for predicting ultimate bond strength estimations. The findings of this study provide an accurate and robust prediction model to improve the reliability and safety of the concrete structure by enhancing the residual load-bearing capacity of the concrete structure that has undergone corrosion.

Deep Learning-Based Microscopic Damage Assessment of Fiber-Reinforced Polymer Composites

Fiber-reinforced polymers (FRPs) are increasingly being used as substitutes for traditional metallic materials across various industries due to their exceptional strength-to-weight ratio. However, their orthotropic properties make them prone to multiple forms of damage, posing significant challenges in their design and application. During the design process, FRPs are subjected to various loading conditions to study their microscopic damage behavior, typically assessed through scanning electron microscopy (SEM). While SEM provides detailed insights into fracture surfaces, the manual analysis of these images is labor-intensive, time-consuming, and subject to variability based on the observer’s expertise. To address these limitations, this research proposes a deep learning-based approach for the autonomous microscopic damage assessment of FRPs. Several computationally efficient pre-trained deep learning models, such as DenseNet121, NasNet Mobile, EfficientNet, and MobileNet, were evaluated for their performance in identifying different damage modes autonomously, thus reducing the need for manual interpretation. SEM images of FRPs with five distinct failure modes were used to validate the proposed method. These failure modes include three fiber-based failures such as fiber breakage, fiber pullout, and mixed-mode failure, and two matrix-based failures such as matrix brittle failure and matrix ductile failure. The entire dataset is divided into train, validation, and test sets. Deep learning models were established by training on train and validation sets for five failure modes, while the test set was used as the unseen data to validate the models. The models were assessed using various evaluation metrics on an unseen test dataset. Results indicate that the EfficientNet model achieved the highest accuracy of 97.75% in classifying the failure modes. The findings demonstrate the effectiveness of employing deep learning techniques for microscopic damage assessment, offering a more efficient, consistent, and scalable solution compared to traditional manual analysis.

NGT: Generative AI with Synthesizability Guarantees Discovers MC2R Inhibitors from a Tera-Scale Virtual Screen.

Commercially available, synthesis-on-demand virtual libraries contain upward of trillions of readily synthesizable compounds for drug discovery campaigns. These libraries are a critical resource for rapid cycles of in silico discovery, property optimization and in vitro validation. However, as these libraries continue to grow exponentially in size, traditional search strategies encounter significant limitations. Here we present NeuralGenThesis (NGT), an efficient reinforcement learning approach to generate compounds from ultralarge libraries that satisfy user-specified constraints. Our method first trains a generative model over a virtual library and subsequently trains a normalizing flow to learn a distribution over latent space that decodes constraint-satisfying compounds. NGT allows multiple constraints simultaneously without dictating how molecular properties are calculated. Using NGT, we generated potent and selective inhibitors for the melanocortin-2 receptor (MC2R) from a three trillion compound library. NGT offers a powerful and scalable solution for navigating ultralarge virtual libraries, accelerating drug discovery efforts.

Application of 3D Motion Graphic Design Technology of Object Simulation Human Body in Sports Teaching

In the current era, the rapid development of computer technology has brought unprecedented opportunities for innovation in physical education teaching. The introduction of computer technology not only improves the efficiency of physical education teaching but also provides more accurate and scientific means for sports training. In response to this trend, this paper proposes an innovative human body model design method and explores in detail the key implementation techniques of computer simulation of 3D motion maps, aiming to further optimize and improve the quality of physical education teaching. The design of human body models plays a crucial role in physical education teaching and training, as it can help students and teachers better understand the mechanisms and principles of human movement. By introducing computer technology, this paper can construct a more precise, dynamic, and realistic human body model that conforms to the actual motion situation. This model can simulate various movement states of the human body, providing students with an intuitive and vivid learning experience. In the classroom, teachers can use projection or virtual reality devices to display 3D motion models, allowing students to intuitively see the details and changes of motion movements, deepening their understanding of the essentials of the movements. Teachers can demonstrate different movements in real time by operating models, and explain the technical points and precautions of each movement. Students can observe the motion trajectory and posture changes of the model from multiple perspectives to improve learning efficiency. The system generates personalized feedback reports based on students’ movement data, pointing out the problems and improvement directions that students have during the exercise process. Teachers can provide more accurate guidance to students based on these feedbacks, helping them correct incorrect movements and improve their skill levels.

Advancements and Application of Artificial Intelligence Technologies in Education

In the field of education, information technology has been widely used in teaching and learning activities, bringing more efficient learning process for teachers as well as students. However, there are still some problems. The rapid development of Artificial Intelligence (AI) technology has brought a positive effect on the change of education technology. In this paper, the author did some investigation on the research of AI in education. The results of different researchers are divided into three parts in terms of application scenarios. The first part is student performance prediction. In this part, the author has categorized into machine learning and deep learning based on the kind of methods utilized. The second part is student data analysis, which can be functionally categorized into two directions, facial recognition and text analysis, based on the direction of the research. In the third part, the author summarizes some cases of AI-assisted teaching that are closer to actual teaching activities. These cases present solutions to the current deficiencies in the field of education, proving the efficiency of AI. Based on extensive research on many papers, the author suggests that AI currently faces difficult challenges in terms of interpretability, applicability, and safety. The application of AI in education can be improved in the future through technologies such as Open Learner Models (OLMs), expert system, and transfer learning. This paper provides an overview reference for research in this area.

Feasibility of novel robot-assisted, remotely-performed echocardiographic examination

Abstract Background Access to echocardiography services outside metropolitan areas is persistently limited. We sought to determine the feasibility and efficiency of performing remotely-controlled, semi-automated robot-assisted echocardiographic examinations (robot-assisted echo) by cardiac sonographers as a potential avenue to provide standard echocardiographic specialist services to remote and rural areas as well as permit expedited diagnoses enabling earlier management of disease. Methods Two accredited cardiac sonographers (Cardiac Investigations Unit) undertook 6 hours virtual training, and 10 hours hands-on robot-assisted echo simulation training; using a novel, semi-automated robotic arm system to remotely manipulate an ultrasound probe to acquire echocardiographic images. Sonographers then performed remotely-performed robot-assisted echo examinations on staff volunteers, and consenting inpatients in whom echocardiography was clinically indicated. Cameras, probe pressure sensors and an ultrasound platform remote console were utilised to support remote operation. Traditional echocardiography (manipulation of probe by hand) was also performed as a reference. Feasibility to obtain a comprehensive robot-assisted echo was compared to traditional echocardiography. Sonographer learning curve and efficiency (image acquisition time and offline measurement time) was also investigated. Results Total cohort was n=78 (age 51±15yrs; 57% male) with 63% patients. All participants had robot-assisted echo and traditional echocardiograms performed within 72hrs. Average feasibility (%) of robot-assisted echo as (a) 11 averaged assessments and (b) 38 averaged measures was 92±9 (a) and 86±11 (b), compared to (a) 99±1 and (b) 97±7 by traditional examination (all respectively). Sonographer learning curve was identified via 1:1 block subcohort analysis, with averaged assessment feasibility (n=33:31) being 86±19 (first block) to 98±3 (last block) and averaged measurement feasibility (n=32:31) being 77±11 (first block) to 92±7 (last block), all respectively. In the staff participant cohort (n=31), average image acquisition efficiency in robot-assisted echo was poorer than that of traditional examination (47+9 minutes compared to 19+2 minutes; p<0.0001), respectively. Average efficiency of offline measurement time was 13+7 minutes by robot-assisted echo; comparable (p=0.33) to traditional examination at 12+6 minutes. There was no learning curve effect on image acquisition efficiency; 42+10 vs 51+7 minutes (1:1; n=16:15; p=0.007). Conclusion Remotely-performed, robot-assisted echo using a commercially-available ultrasound platform performed by briefly-trained, cardiac sonographers is feasible, with reasonable efficiency even though less efficient in image acquisition compared to traditional echocardiograms. Further investigation of performance as a remote service is warranted in larger, clinically-indicated populations.

Autistic Traits, Communicative Efficiency, and Social Biases Shape Language Learning in Autistic and Allistic Learners.

There is ample evidence that individual-level cognitive mechanisms active during language learning and use can contribute to the evolution of language. For example, experimental work suggests that learners will reduce case marking in a language where grammatical roles are reliably indicated by fixed word order, a correlation found robustly in the languages of the world. However, such research often assumes homogeneity among language learners and users, or at least does not dig into individual differences in behavior. Yet, it is increasingly clear that language users vary in a large number of ways: in culture, in demographics, and-critically for present purposes-in terms of cognitive diversity. Here, we explore how neurodiversity impacts behavior in an experimental task similar to the one summarized above, and how this behavior interacts with social pressures. We find both similarities and differences between autistic and nonautistic English-speaking individuals, suggesting that neurodiversity can impact language change in the lab. This, in turn, highlights the potential for future research on the role of neurodivergent populations in language evolution more generally.

Parkinson’s Disease Detection by Using Machine Learning Method based on Local Classification on Class Boundary

Parkinson’s disease (PD) detection has long been an important task in medical intelligence. Recognition methods based on speech signals show great potential in Parkinson’s disease diagnosis. In this paper, based on an efficient machine learning method for Parkinson’s disease detection, we take the use of test data incorporates an efficient Secure Two-Party Computing (S2PC) protocol to protect the privacy of patients. We present two key components, the secure use of data and a local classification methodology, including the description of class boundaries. We conducted experiments on two datasets to validate our proposed method, and the results show well data security protection ability compared to some more sophisticated methods. And the performance of Local Classification on Class Boundary(LCCB) and Hyperplane K-Nearest Neighbor(HKNN) is significantly better than that of both Support Vector Machines(SVM) and Random Forest(RF). When the number of selected features is from 400 to 500, HKNN and LCCB are roughly equal where the accuracy of HKNN is 95.2%, and LCCB has the rate of 94.7%. Then we use Multi-Cluster Feature Selection(MCFS) to analyze and select the important features from D2 dataset. It shows that even if only two features are selected, the boundaries of the two categories are also clear and easy to distinguish.

Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study.

Artificial intelligence (AI) chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future health care providers, play a crucial role in shaping the future of health care. However, little is known about the utilization of, perceptions on, and intention to use AI chatbots among medical students in China. This study aims to explore the utilization of, perceptions on, and intention to use generative AI chatbots among medical students in China, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. By conducting a national cross-sectional survey, we sought to identify the key determinants that influence medical students' acceptance of AI chatbots, thereby providing a basis for enhancing their integration into medical education. Understanding these factors is crucial for educators, policy makers, and technology developers to design and implement effective AI-driven educational tools that align with the needs and expectations of future health care professionals. A web-based electronic survey questionnaire was developed and distributed via social media to medical students across the country. The UTAUT was used as a theoretical framework to design the questionnaire and analyze the data. The relationship between behavioral intention to use AI chatbots and UTAUT predictors was examined using multivariable regression. A total of 693 participants were from 57 universities covering 21 provinces or municipalities in China. Only a minority (199/693, 28.72%) reported using AI chatbots for studying, with ChatGPT (129/693, 18.61%) being the most commonly used. Most of the participants used AI chatbots for quickly obtaining medical information and knowledge (631/693, 91.05%) and increasing learning efficiency (594/693, 85.71%). Utilization behavior, social influence, facilitating conditions, perceived risk, and personal innovativeness showed significant positive associations with the behavioral intention to use AI chatbots (all P values were <.05). Chinese medical students hold positive perceptions toward and high intentions to use AI chatbots, but there are gaps between intention and actual adoption. This highlights the need for strategies to improve access, training, and support and provide peer usage examples to fully harness the potential benefits of chatbot technology.