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Design and Implementation of Key Modules of English teaching System Based on J2EE 3rd

Design and Implementation of Key Modules of English Teaching...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wang, Huijiao Sun, Yuheng Wu, Jianan Zhao, Wei Cao, Jingjie Wuhan Technology and Business University Hubei430065 China Xi’an Peihua University Institute of Media Shaanxi Xi’an710025 China

ISBN: (纸本)9783031631290

The user management module provides functions such as registration, login, and personal information management to achieve user authentication and personalized settings. The course management module supports course publishing, resource sharing, and homework management, providing rich learning resources and interactive communication platforms. The learning module integrates course content, listening, speaking, reading and writing training, and provides personalized learning recommendations and feedback through multimedia and artificial intelligence technology. The evaluation module achieves English proficiency testing, homework evaluation, and personal progress recording, helping students and teachers to achieve learning effectiveness and teaching management. Through the layered structure and componentization development of J2EE, the system has good scalability and maintainability. The experimental results indicate that the system has significant advantages in improving learning effectiveness and user experience. This study provides important reference and guidance for the design and implementation of English teaching systems.J2EE platform is an open, modular, distributed object-oriented framework for building applications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： teaching

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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th international conference on Soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive learning

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Enhancing Parallelization with OpenMP through Multi-Modal Transformer learning 5

Enhancing Parallelization with OpenMP through Multi-Modal Tr...

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5th international conference on Computer engineering and Application (ICCEA)

作者： Chen, Yuehua Yuan, Huaqiang Hou, Fengyao Hu, Peng Dongguan Univ Technol Dongguan Peoples R China Chinese Acad Sci Inst High Energy Phys Beijing Peoples R China Spallat Neutron Source Sci Ctr Dongguan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The popularity of multicore processors and the rise of High Performance computing as a Service (HPCaaS) have made parallel programming essential to fully utilize the performance of multicore systems. OpenMP, a widely adopted shared-memory parallel programming model, is favored for its ease of use. However, it is still challenging to assist and accelerate automation of its parallelization. Although existing automation tools such as Cetus and DiscoPoP to simplify the parallelization, there are still limitations when dealing with complex data dependencies and control flows. Inspired by the success of deep learning in the field of Natural Language Processing (NLP), this study adopts a Transformer-based model to tackle the problems of automatic parallelization of OpenMP instructions. We propose a novel Transformer-based multimodal model, ParaMP, to improve the accuracy of OpenMP instruction classification. The ParaMP model not only takes into account the sequential features of the code text, but also incorporates the code structural features and enriches the input features of the model by representing the Abstract Syntax Trees (ASTs) corresponding to the codes in the form of binary trees. In addition, we built a BTCode dataset, which contains a large number of C/C++ code snippets and their corresponding simplified AST representations, to provide a basis for model training. Experimental evaluation shows that our model outperforms other existing automated tools and models in key performance metrics such as F1 score and recall. This study shows a significant improvement on the accuracy of OpenMP instruction classification by combining sequential and structural features of code text, which will provide a valuable insight into deep learning techniques to programming tasks.

关键词： component OpenMP Natural Language Processing Abstract Syntax Trees Parallelization

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Student Proficiency: A Review of Comprehension, Skills, and Influence of Machine learning 12th

Student Proficiency: A Review of Comprehension, Skills, and ...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Salonga, Jesus C. Ruiz, John Angelo C. Roño, Nathaniel S. Santiago, Cereneo S. Reyes, Jocelyn L. Cavite State University Silang Campus Cavite Philippines

ISBN: (纸本)9789819601387

This paper examines college students’ computer programming skills, emphasizing the effects of machine learning and logical and critical thinking. The reviewed articles used machine learning methods to evaluate attitudes and learning habits affecting coding understanding. A comparative literature review synthesized information from 3420 results obtained from Google Scholar and other databases, resulting in 21 selected articles. The review was designed to collect students’ understanding of programming computation and their logical and critical thinking skills. The effect of machine learning on their academic development will also be assessed. Priority goes to teacher’s skill development through competent teaching practices and curriculum design to further the knowledge of students. Participating in group activities and constant self-education are the trains that help them think logically and critically, so the abilities of problem-solving meet growth. The application of machine learning allows the teaching to be personalized by recognizing the learner’s style and moving toward the achievement of the content. The paper stresses that new advanced technologies must be employed to boost education programs in the new era of digital ecosystems infographic video stinger interstitial content how to write a conclusion paragraph of a persuasive essay website. Future researchers should explore the effective study of the adaptive learning technologies that are produced by machine learning, which will cover education in a variety of settings and different languages. In addition, the long-term contributions of incorporating cooperative learning and logic exercises in teaching students problem-solving skills and overall programming knowledge will also be worthwhile to investigate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Ensemble Model for Crop Prediction to Handle Extreme Dry and Wet Climate 3

Ensemble Model for Crop Prediction to Handle Extreme Dry and...

引用

international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Valliyammai, C. Chitra, R. Manoj, S. Janani, R. Anna Univ Dept Comp Technol MIT Campus Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The increasing levels of greenhouse gases and climate change, including phenomena such as El Nino, have caused extreme climatic events such as floods and droughts in various parts of the Indian subcontinent, disrupting the cultivable environment for the crops. This unpredictability poses a challenge for the farmers. The proposed alternate crop recommendation system considers temperature, rainfall, soil, and market data to recommend alternate crops that are both sustainable for the climate and fetch higher prices. The proposed system uses stacking regressor with base learners such as Support Vector Regressor (SVR), Linear Regression (LR), and Random Forest Regressor (RFR) for yield prediction. The Gaussian naive Bayesian algorithm is used to calculate the standard precipitation index, which helps in classifying the drought levels. The experimental results show that the proposed system yields 97% accuracy in predicting yields with minimal root mean square error.

关键词： Climate Change Extreme Climate Alternate Crop Recommendation Ensemble learning

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Classification of Sleep Stages Using Single-Channel ECG Signals: A Comparative Analysis of Machine learning and Deep learning Methods 14

Classification of Sleep Stages Using Single-Channel ECG Sign...

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14th IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Ullah, Ahmad Khan, Muhammad Salman College of Engineering Qatar University Department of Computer Science and Engineering Doha Qatar College of Engineering Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350366556

In this paper, we provide an extensive evaluation of machine learning (ML) and deep learning (DL) methods for automatic sleep stage classification using a single-channel electrocardiogram (ECG) signal. To explore ML methods, we extracted 21 heart rate variability (HRV) features using the R-peak detection. These features were used to train and compare the performance of various models such as support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (K-NN), and Naïve Bayes (NB). Furthermore, we analyzed the DL methods to overcome the manual feature extraction. This method involved feeding raw time-series ECG signal to a 1-dimensional convolutional neural network (CNN) and short-time Fourier transform (STFT) based spectrogram to a 2D CNN. The overall evaluation was performed using 15-subject data each from two publicly available datasets. To process the data for analysis, resampling, bandpass filtering, baseline correction, 30-second interval extraction from whole-night ECG recordings, and z-score normalization techniques were applied. We conducted experiments on one dataset and then replicated the experiments on the other dataset to assess the robustness and generalizability of the methods. Through a comparative analysis, the spectrogram-based 2D CNN emerged as the effective method with the highest test accuracy and k-score of 87.65% and 0.82 respectively. This study contributes to ECG-based sleep classification research, highlighting the effectiveness of deep learning for effective classification. © 2024 IEEE.

关键词： Convolutional neural networks

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FMICS: A Robust learning Methodology to Predict Skin Cancer Disease Using Fused Melanoma Image Classification Scheme

FMICS: A Robust Learning Methodology to Predict Skin Cancer ...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bhavani, G. Rama, M. Padma, S. Kumar, P. Praveen Kumar, K. Suresh Rajeshwari, R. R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Qis College of Engineering and Technology Department of It AndhraPradesh Ongole India Sona College of Technology Department of Eee Salem636005 India Qis College of Engineering and Technology Department of Ece AndhraPradesh Ongole India Panimalar Engineering College Department of Mba Chennai India S.A Engineering College Department of Master of Computer Application Chennai India

ISBN: (纸本)9798331543617

Skin cancer (SC), particularly melanoma, is a significant health issue due to its high mortality rate when not detected early. This study introduces a novel approach, FMICS: A Robust learning Methodology (Hybrid ANN + XGBoost) for predicting SC through fused melanoma image classification. The methodology integrates Artificial Neural Networks (ANN) for feature extraction and XGBoost for classification, leveraging the strengths of both models. Preprocessing techniques such as image filtering, contrast enhancement, and hair removal were applied to improve image quality. The study utilized dermoscopic images from the PH2 and HAM10000 datasets, containing both benign and malignant lesions. The hybrid model achieved superior performance compared to individual models, with an accuracy of 94.80%, sensitivity of 94.90%, and specificity of 94.70%. Data augmentation techniques, including rotation, scaling, and flipping, were applied to enhance the model's robustness. Evaluation metrics such as the F1-score and ROC-AUC were used to assess the performance. The hybrid approach significantly reduced false negatives, making it highly reliable for melanoma detection. These results suggest that the FMICS methodology offers a promising solution for accurate and efficient SC diagnosis, with potential applications in clinical and mobile environments. © 2024 IEEE.

关键词： Contrastive learning

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Novel System based on Amalgamation of Embedded Systems and Edge AI for Hand Gesture Recognition 2024

Novel System based on Amalgamation of Embedded Systems and E...

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16th international conference on Contemporary computing, IC3 2024

作者： Singh, Gargi Gautam, Saanika Puri, Yugnanda Parikh, Jolly Mishra, Gargi Department of Electronics and Communication Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering New Delhi India

ISBN: (纸本)9798400709722

Hand gesture recognition is highly significant and a natural means of human-computer interaction. This detection is carried out by using the MPU- 6050 sensor, a widely available Inertial Measurement Unit (IMU). MPU-6050 sensor measures the orientation and objects acceleration and is a low-cost, high-performance sensor. NanoEdgeAI studio, a low-power edge computing framework known to process data efficiently, has been employed to implement the accurate machine learning model. In addition to that, the usage of a microprocessor, Nucleo-F401RE, demonstrates the feasibility of deploying robust and responsive hand gesture recognition systems in edge computing scenarios, showcasing the potential for enhanced human-machine interaction. The selected model used for the research is MLP (multilayer perceptron) which is also known for its flexibility and ability. A balanced accuracy a percentage measure of signals correctly identified by the model was found out to be of 84.27%. © 2024 Copyright held by the owner/author(s).

关键词： Embedded systems

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Empirical Evaluation of AI Based Oral Cancer Identification Scheme Using learning Assisted Medical Image Processing Scheme

Empirical Evaluation of AI Based Oral Cancer Identification ...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vaijayanthi, S. Charulatha, G. Murugan, J. Senthil Lakshmi, N. Thenmozhi, E. Khan, S. Mahaboob Rajalakshmi Institute of Technology Department of Ai&ds Chennai India Selvam College of Technology Department of Biomedical Engineering Namakkal India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai India Annamacharya University Department of Mechanical Engineering AndhraPradesh Rajampet India

ISBN: (纸本)9798331543617

Early detection is essential for enhancing patient outcomes, as oral cancer continues to be a substantial health challenge. This manuscript introduces the Optimized Neural learning and Classification Principle (ONLCP), a novel AI-based oral cancer identification scheme that incorporates medical image processing techniques and deep learning. Our method utilizes smart to analyze and classify medical images, thereby enabling the early detection of oral cancer. The efficiency of the proposed scheme is assessed by cross-validating it with the Convolutional Neural Network (CNN). The model is refined by this AI-based logic, which adapts to a variety of image characteristics and improves detection precision, while the ONLCP is employed for feature extraction and initial classification of oral lesions. The experimental results indicate that the AI-based scheme ONLCP obtains a prediction accuracy of 97.86%, with substantial enhancements in sensitivity and specificity when contrasted with conventional methods. The model's continuous development and adaptive learning are enabled by the integration of learning-assisted techniques, resulting in more precise and dependable cancer identification. Our results emphasize the potential of integrating AI with advanced image processing techniques to resolve critical challenges in medical diagnostics. This research has the potential to improve patient care and treatment outcomes by contributing to the growth of more efficient and scalable techniques to early oral cancer identification. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Literary Exploration: A Machine learning Approach to Book Recommendations 7

Enhancing Literary Exploration: A Machine Learning Approach ...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Singh, Kritika Gupta, Sumita Dhir, Saru Gambhir, Sapna Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Uttar Pradesh Noida India Amity University Amity School of Engineering & Technology Department of Information Technology Uttar Pradesh Noida India George Mason University College of Engineering and Computing Department of Computer Science United States

ISBN: (纸本)9798350350067

In today's era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age. © 2024 IEEE.

关键词： Collaborative filtering

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