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Artificial intelligence in Mathematics Education: A Bibliometric Analysis in Scopus Database (2000–2024) 3rd

Artificial Intelligence in Mathematics Education: A Bibliome...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Son, Le Ngoc Tuyen, Nguyen Thi Thanh Tinh, Phan Thi Tu, Nguyen Xuan Dinh, Nguyen Thi Huyen, Luu Thi Thu Trang, Nguyen Huyen Hung Vuong University Phu Tho Viet Nam

ISBN: (纸本)9783031809422

Artificial intelligence is considered a useful technological solution in mathematics education within the context of the modern world’s educational reforms. In this study, we provide an overview of the development of collaboration networks and research trends in the field of artificial intelligence in mathematics education through the analysis of scientific documents extracted from the Scopus database. We filtered and obtained 431 documents published between 2000 and 2024 that met the research criteria. The analysis results show that research trends in artificial intelligence in mathematics education are quite diverse, focusing on integrating intelligent technologies in teaching mathematics, measuring and evaluating learning outcomes, and improving students interest in mathematics through technological applications. We present the collaboration network among countries, the most cited publications, and influential authors in this research field in our results. However, the study has some limitations as it only extracts documents from the Scopus database, the data filtering process is manual, and the research quality depends on the input data quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

3rd International Conference on Mobile Radio Communications ...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

ISBN: (纸本)9789811979811

The proceedings contain 56 papers. The special focus in this conference is on Mobile Radio Communications and 5G Networks. The topics include: Predictive Analysis of Air Pollutants Using machine Learning;machine Learning Techniques Applied of Land Use—Land Cover (LULC) Image Classification: Research Avenues Challenges with Issues;crime Analysis Using Computer Vision Approach with machine Learning;natural Language Processing Implementation for Sentiment Analysis on Tweets;VLSI Implementation of BCH Encoder with Triple DES Encryption for Baseband Transceiver;design and Implementation of Image De-hazing Using Histogram Equalization;Improved Hybrid Unified Power Flow Controller Using Fractional Order PID Controlled Systems;Optimized Activation Function-Based SAR Ship Detection;framework for Implementation of Smart Driver Assistance System Using Augmented Reality;Power-Efficient Hardware Design of ECC Algorithm on High Performance FPGA;A Creative Domain of Blockchain Application: NFTs;sign Language recognition Using machine Learning;an IoT-Based Health Monitoring System for Stress Detection in Human Beings;ioT-Based Driving pattern Analysis and Engine Sensor Damage Prediction Using Onboard Diagnostics;Solving the Element Detecting Problem in Graphs via Quantum Walk Search Algorithm (QWSA);critical Analysis of Secure Strategies Against Threats on Cloud Platform;pareto Optimal Solution for Fully Fuzzy Bi-criteria Multi-index Bulk Transportation Problem;automatic Candidature Selection by Artificial Natural Language Processing;elimination and Restoring Deduplicated Storage for Multilevel Integrated Approach with Cost Estimation;vulnerability Assessment of Cryptocurrency Wallet and Exchange Websites;greedy Theory Using Improved Performance Prim’s Algorithm, Big Bang Speedup of the Bellman–Ford Algorithm;performance Analysis of High-Speed Optical Communication Systems Under the Impact of Four Wave Mixing.

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Computer Art pattern Creation and P-filling Algorithm 3rd

Computer Art Pattern Creation and P-filling Algorithm

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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： Tang, Weiming Meng, Xianfeng Yunnan College of Business Management Yunnan650106 China

ISBN: (纸本)9783031631351

Expert system (expcrt systcm) has been widely used in the application and research field of artificial intelligence in the world. Many of them, molgen system, which studies molecular genetics, have been put into practical use and made remarkable achievements. At present, one of the topics of in-depth research in this field is to combine expert system with CAD, so that its role is not limited to question and answer consultation, but can form an automatic design system, from which we can further directly obtain production materials such as mechanical drawings, architectural schemes, product design and so on. This is obviously of great economic significance. The computer art pattern creation system developed by us is an exploration in this direction. Computer art pattern creation based on p-filling algorithm. This help these products occupy and expand the domestic and foreign markets. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Product design

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A Novel ML classifier Using Modern Hopfield Neural Networks

A Novel ML classifier Using Modern Hopfield Neural Networks

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Computing and machine intelligence (ICMI), international conference on

作者： Mohamed Abdelkarim Hanan Ahmed Kamal Doaa Shawky Siemens Digital Industries Software Faculty of Engineering Cairo University Zewail City of Science and Technology

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In contrast, the Hopfield Neural Network (HNN) is simpler, being a single-layer, fully connected network that mimics the human brain's associative memory network, making it easy to implement and computationally efficient. Its compatibility with oscillatory neural networks (ONNs) makes it ideal for lightweight machine learning applications in the Internet of Things (IoT) era. Normally, HNN has been primarily used for associative memory aiding in image processing, pattern recognition, and more, but this paper introduces it as a classifier. The proposed HNN classifier is adaptable to various datasets, including images and tabular data, and requires zero training time, making it suitable for resource-limited environments. It represents a significant leap in classification, with the highest accuracy reported for HNN classifiers to the best of our knowl-edge, achieving 96% accuracy on the MNIST dataset, a 36% percentage improvement over previous models.

关键词： Training Associative memory Accuracy MIMICs Hopfield neural networks machine learning pattern recognition

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2022 3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

2022 3rd International Conference on Issues and Challenges i...

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3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

ISBN: (纸本)9781665482684

The proceedings contain 60 papers. The topics discussed include: web accessibility challenges for disabled and generation of alt text for images in websites using artificial intelligence;developing ERP success model in Indian manufacturing sector;lane line detection using machine learning;real-time sign language to text and speech translation and hand gesture recognition using the LSTM model;a survey of cloud architectures: confidentiality, contemporary state, and future challenges;several categories of energy-efficient routing protocols, features, and security necessities in WSN: a review;face mask detection using convolution neural network;influence of time on efficiency of Indian capital markets: proposition of a recommendation engine;diet recommendation using predictive learning approaches;identification of fraud transactions using Lightgbm technique;machine learning in student health - a review;comparative analysis of rainfall prediction using machine learning and deep learning techniques;energy cognizant scheduling in three-layer cloud architecture: a systematic review;and investigation of thermographic images of photovoltaic modules using deep learning models.

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Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture 3

Forecast Model for Scheduling an HPC Application on CPU and ...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Chandrashekhar, B.N. Mohan, M. Geetha, V. BMS Institute of Technology & Management Artificial Intelligence and Machine Learning Benguluru India Nitte Meenakshi Institute of Technology Information Science & Engineering Benguluru India Reva University School of Management Bengaluru India

ISBN: (纸本)9798350338607

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases. © 2023 IEEE.

关键词： Forecasting

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Revolutionizing Financial Cloud Services: AI and Blockchain-driven Resource Allocation for Maximized Transaction Speed and Security 3

Revolutionizing Financial Cloud Services: AI and Blockchain-...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kumar, M. Lenin Amanullah, M. Krishnamurthy, Revatthy Suganthi, N. Mohana Kalaiselvan, S.A. Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai Tamil Nadu Avadi India

ISBN: (纸本)9798350369083

In response to the evolving challenges of real-time monitoring in financial cloud environments, we propose a comprehensive solution integrating cutting-edge technologies. Our system employs AI-driven decision-making (ADDM) with a Dynamic Nested Neural Network (DNN) for adaptive resource allocation. This is coupled with a Markov Decision Process (MDP) for probabilistic decision-making, optimizing computational power, storage, and network resources dynamically. Blockchain technology fortifies transaction verification through a decentralized consensus mechanism and cryptographic techniques like RSA. The collaborative synergy of optimized resource allocation and Blockchain security ensures near-instantaneous processing, meeting the demands of a dynamic financial landscape. Our proposed system transforms the financial cloud environment into an adaptive and intelligent system, enhancing efficiency, security, and responsiveness for seamless and reliable real-time transaction processing. The results demonstrate accelerated transaction speeds, resource efficiency, and a robust security framework, addressing critical concerns in the realm of financial transactions. © 2024 IEEE.

关键词： Decentralized finance

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FitTip: Merging Technology and Fitness for Posture Improvement 3

FitTip: Merging Technology and Fitness for Posture Improveme...

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3rd international conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2024

作者： Tembhurnikar, Atharv Sarvankar, Girish Yadav, Gitanjali Vaidya, Rohan Shelar, Aditya Bhage, Kartik Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331505714

Poor form is a primary contributor to exercise-related accidents, which can result in permanent physical handicap and deter people from maintaining a regular fitness regimen. This study suggests FitTip, an intelligent posture identification and correction device that uses computer vision and machine learning, as a means of preventing these kinds of injuries. Using a proprietary dataset of yoga and gym routines, we developed a convolutional neural network (CNN) integrated with the Mediapipe framework to accurately categorize and correct workout positions in real time. Position analysis is used in FitTip' s design to provide real-time feedback and change recommendations. The algorithm was evaluated on a dataset of five workout poses, and the results indicated that it could get 98.73% training and validation accuracy. These findings show how well algorithm works to improve overall fitness results and workout safety. The suggested algorithm offers a framework that is scalable for enhancing posture during activity, and it may eventually be integrated with wearables and fitness applications. It includes a real-time camera feed for pose detection, a key-point detection module, and a feedback algorithm that suggests corrections based on the angles between body joints. This paper also addresses challenges such as limited dataset size and variability in user movements, proposing solutions through enhanced preprocessing and pose analysis techniques. FitTip algorithm makes training safer and more accessible for people of all fitness levels, which adds to the expanding field of fitness technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Common-Mode Voltage Elimination for Dual Two-Level Inverter-Fed Six-Phase PMSM Based on Reference Voltage Decomposition 3

Common-Mode Voltage Elimination for Dual Two-Level Inverter-...

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3rd international conference on Power Systems and Electrical Technology, PSET 2024

作者： Wu, Mingzhe Xu, Xiaona Wang, Kui Pan, Suna Yang, Kehu Li, Yongdong China University of Mining and Technology-Beijing Department of Electrical Engineering Beijing China Beijing Huairou Labortory Department of Hydrogen Technology Beijing China Tsinghua University Department of Electrical Engineering Beijing China China University of Mining and Technology-Beijing Department of Artificial Intelligence Beijing China

ISBN: (纸本)9798350353204

Common-mode voltage (CMV) can greatly reduce the reliability and performance of dual two-level inverters-fed six-phase motor drive systems. To address this issue, a novel CMV elimination scheme based on reference voltage decomposition is proposed in this paper. Firstly, the reference voltage of each phase is decomposed into two virtual reference voltages, resulting in six virtual reference voltages that are inter-related for dual two-level inverters. Then, by comparing each virtual reference voltage with a triangle carrier based on the two-level carrier-based pulse width modulation (PWM), the final PWM voltage of each phase can be obtained from the differences of the corresponding two virtual PWM outputs. With the proposed method, the CMV of six-phase machine can be totally eliminated. Simulations results are presented to verify the proposed method. © 2024 IEEE.

关键词： Permanent magnets

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection Dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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