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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges

Exploring the Impact of Robotics in STEM Education Activitie...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Adil Farooq Syeda Zillay Nain Zukhraf Hafsa Maryam Conrad Attard Mohsin Kamal Dept. of Electrical and Computer Engineering University of Cyprus Cyprus Dept. of Electrical Engineering National University of Computer and Emerging Sciences Pakistan Dept. of Computer Information Systems University of Malta Msida Malta

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges.

关键词： Technological innovation Education Toy manufacturing industry Task analysis Robots Cybernetics STEM

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Enhancing Autism Education: Exploring Interactive Videos and AI Integration for Effective Teaching

Enhancing Autism Education: Exploring Interactive Videos and...

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International Conference on Innovations in Information Technology

作者： Fatima Al Raeesi Mariam K. Al Kuwaiti Jose Berengueres Dept. of Software Engineering United Arab Emirates University Al Ain UAE Dept. of Computer Science & Software Engineering United Arab Emirates University Al Ain UAE

This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the condition and employ tailored instructional strategies, including adapting assignments, aiding those with language difficulties, and utilizing visual aids for better organization and focus. Traditional teacher training methods can be both expensive and time-consuming. In contrast, interactive videos provide a proactive and flexible way to access training content, empowering teachers to engage with the material dynamically and take control of their learning experiences. Future work will explore the integration of AI-driven ChatGPT to offer personalized support and create a dynamic training program, with the goal of improving inclusivity and educational quality in autism settings while benefiting teachers, students, and the education system at large.

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Demonstration of Effectiveness of Nativeness in Stepwise Learning by Performing Sentiment Analysis

Demonstration of Effectiveness of Nativeness in Stepwise Lea...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Al-Mahmud Kazutaka Shimada Dept. of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language; the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it “nativeness.” The experimental result shows that nativeness has an important role in improving performance.

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Optimizing Endotracheal Suctioning Classification: Leveraging Prompt engineering in Machine Learning for Feature Selection

Optimizing Endotracheal Suctioning Classification: Leveragin...

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Activity and Behavior Computing (ABC), International Conference on

作者： Mahera Roksana Islam Anik Mahmud Ferdous Shahera Hossain Md Atiqur Rahman Ahad Fady Alnajjar Dept. of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Dept. of Computer Science and Digital Technologies University of East London London UK Dept. of Computer Science and Software Engineering College of Information Technology UAE University Al Ain UAE

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. This paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. Then the features are passed onto several tuned machine learning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.

关键词： Training Accuracy Computational modeling Machine learning Medical services Predictive models Feature extraction

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Artificial Intelligence for Green Agriculture and Sustainable Resource Management: A Comprehensive Overview

Artificial Intelligence for Green Agriculture and Sustainabl...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Essa Hezzam Dept. of Computer Science University of Petra Amman Jordan Dept. of Computer Science and Information Applied College Taibah University Medina Saudi Arabia Dept. of Information Systems College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Artificial Intelligence (AI) is a revolutionary technology disrupting the process of finding solutions for various sustainability challenges. Specifically, AI is having a significant influence on many sectors, especially agriculture, energy, waste management, and biodiversity conservation. This paper offers a comprehensive overview of AI's applications in sustainable resource management, with a special focus on green agriculture as a case study, in which the application of AI greatly enhances the efficiency of resource use, crop yield, and environmental conservation efforts. This overview describes the potential application of AI to address performance concerns (ethical value, energy use, and data quality deficiency), and proposes the future research directions such as the explainable AI, circular economies, and the integration of AI with the Internet of Things (IoT) to guide researchers and policymakers in exploiting AI to enhance sustainability in key domains.

关键词： Waste management Ethics Explainable AI Data integrity Transforms Resource management Internet of Things Biodiversity Artificial intelligence Sustainable development

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An Implementation of Answer Code Validation Program for Code Writing Problem in Java Programming Learning Assistant System

An Implementation of Answer Code Validation Program for Code...

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11th International Conference on Information and Education Technology, ICIET 2023

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Mon, Khin Thet Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Okayama University Dept. of Information and Communication Systems Okayama Japan Tokyo University of Agriculture and Technology Dept. of Computer and Information Science Tokyo Japan National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665465489

To assist Java programming learning of novice students, we have developed the web-based Java Programming Learning Assistant System (JPLAS). It provides various exercise problems to cultivate code reading and code writing skills to support studies at various levels. The code writing problem (CWP) asks a student to write a source code to pass the given test code where the correctness is verified by running them on JUnit. Previously, we implemented the answer platform for students to solve CWP assignments with the automatic runs. However, the teacher needs to manually run them one by one to verify a lot of source codes from students. In this paper, we implement the answer code validation program that automatically verifies all the source codes for one test code and reports the number of tests that each code passed with the CSV file. For evaluations, we applied the program to a total of 999 source codes from 12-43 students for a total of 45 test codes that were assigned in the Java programming course in Okayama university, including for final examinations. The application results confirm the validity and effectiveness of the proposal. © 2023 IEEE.

关键词： Students

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Price Optimisation and Management 3

Price Optimisation and Management

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shafkhan, M.T.M Jayasundara, P.R.S.S Kariyapperuma, K.A.D.R.L Lakruwan, H.P.S Rupasinghe, Lakmal Sri Lanka Institute of Information Technology Dept. of Computer and Software Eng Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound judgments. In the process of marketing products, price is the only factor that creates income;everything else is a cost. Guessing at product pricing is a little like throwing darts blindfolded;some will hit something, but it probably will not be the dartboard. Large-scale enterprises throughout the world still depend on Excel sheets with numerous manpower or expensive pricing solutions. Expensive pricing systems are difficult to implement for Medium and Large Sized Enterprises in countries like Sri Lanka. Our goal in this research is to propose an affordable, efficient, easy-To-use and secure solution which can be implemented in Medium and Large Sized Enterprises in Sri Lanka. Manufacturing cost, shipping cost, competitor analysis, customer behaviour are taken as the root factors when deciding the price. The proposed solution includes Machine Learning components which is fed with historical data of these four factors to predict the manufacturing cost, shipping cost, competitor price and customer behavioural factors on a given date and as well as an optimisation component which enables the opportunities to minimise the cost and maximise the profit. The four Machine Learning components are implemented using LSTM, ARIMA, Facebook Prophet and a clustering model. The optimisation model is implemented using linear programming optimise these four components. A user-friendly web application is implemented using MEAN stack with micro service architecture to access this. © 2021 IEEE.

关键词： Cost benefit analysis

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Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery systems

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yeongmin Lee Raihan Muhammad Syahran Mael Spangenberger Huigyu Yang Jiyong Oh Hyunseung Choo Dept. of Artificial Intelligence Dept. of Electrical and Computer Engineering Dept. of Informatics Télécom SudParis Évry France Convergence Research Institute Dept. of AI Systems Engineering Sungkyunkwan University Suwon S. Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services.

关键词： Three-dimensional displays Navigation Scalability Surveillance Handover Autonomous aerial vehicles Trajectory Resource management Vehicle dynamics Optimization

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Quantitative Trends Analysis on Russian Literature Research in Korean Academia: Insights from KCI Data

Quantitative Trends Analysis on Russian Literature Research ...

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International Conference on Platform Technology and Service (PlatCon)

作者： Ten Alexander Iosifovich Sang-Soo Yeo Young-Ae Jung Dept. of Computer Science Sun Moon University Asan South Korea Dept. of Computer Engineering Mokwon University Daejeon South Kora Dept. of AI Software Technology Sun Moon University Asan South Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

This paper presents a quantitative analysis of trends in Russian literature and Korean-Russian translated literature within Korean academia from 1996 to 2024. By analyzing frequency themes and contexts, the study explores evolving research trends, revealing how sociocultural, political, and artistic exchanges between Korea and Russia have influenced each other. Future research using text mining promises to uncover more specific patterns, offering deeper insights into literary and cultural exchanges. This study contributes to understanding the role of literature in expressing and understanding sociopolitical and cultural values in both countries.

关键词： Text mining Translation Statistical analysis Data visualization Curriculum development Market research Cultural differences Indexes

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

关键词： Adversarial machine learning

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