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Application to Enhance the Programming Knowledge of Children

Application to Enhance the Programming Knowledge of Children

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Advancements in Computing (ICAC), International Conference on

作者： Anuga Dissanayake Vishwa Lakpriya Mahendran Sanjeevan Ahamed Fahmi Samanthi E.R. Siriwardana Mihiri Samaraweera Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally.

关键词： Adaptation models Translation Machine learning algorithms Computational modeling Retrieval augmented generation Learning (artificial intelligence) Real-time systems Proposals information technology Programming profession

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture

Multispectral Images and IoT Based Tea Plantation Monitoring...

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Advanced Research in Computing (ICARC), International Conference on

作者： K. P. W. D. V. Kariyawasam P. N. Kahandagamage M. R. R. Fernando J. M. Fernandopulle Vishan Jayasinghearachchi Kapila Dissanayaka Dept. of Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Computer Science Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.

关键词： Scalability Plantations Systems architecture Crops Real-time systems Internet of Things Resource management Reliability Monitoring Stress

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Sentiment Analysis of Customer Feedback and Reviews in E-Commerce Systems

Sentiment Analysis of Customer Feedback and Reviews in E-Com...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Olla Bulkrock Abdallah Qusef Ahmed BaniMustafa Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Natural Language Processing (NLP) is an effective tool for analyzing consumer satisfaction with products and services typically sold by e-businesses. This study explores the use of sentiment analysis to understand customer feedback and ratings using a dataset acquired by Amazon. That dataset covers online purchases over a period of eight years. The study aims to examine the factors that impact sentiment polarity throughout different periods and understand changes in consumer feelings and reviews towards online products and services. The applied method comprehensively studies sentiment, extracting features using the Term Frequency-Inverse Document Frequency (TF-IDF) and other text analysis and data processing procedures. Visualization is also applied to illustrate the trends, patterns, and changes in customer satisfaction over time and per product and line of products. The findings underscore the importance of sentimental research in discerning customer sentiments on online e-commerce platforms and offer practical recommendations for enhancing online and improving customer satisfaction.

关键词： Sentiment analysis Text analysis Reviews Social networking (online) Mood Customer satisfaction Market research Feature extraction Multilingual Electronic commerce

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Data-Driven Decision-Making in IT Projects: A Regression Application in software Estimation

Data-Driven Decision-Making in IT Projects: A Regression App...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Olla Bulkrock Ahmed BaniMustafa Abdallah Qusef Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

IT-related projects are among the most challenging to manage due to their inherent complexity, uncertainty, and the intangibility of their outcomes. However, a data-driven decision-making approach can help tackle these challenges and increase the chance of IT project success. Machine learning techniques can be used to predict uncertain aspects of IT projects, such as the estimation of time, cost, and effort, when applied to historical data collected from previous or similar projects. This approach can allow the project manager to make informed decisions based on facts rather than human judgment or hunch. In this work, we review the potential of data-driven approaches in the management of IT projects, and we demonstrate a use case that applies three regression algorithms, including polynomial regression, linear regression, and partial least squared (PLS), to predict software effort estimation based on the NASA93 public dataset. The best results were achieved using polynomial regression which scored 82% in R 2 , 81% in MAPE, 152 in MAE, 276 in RMSE and 76387 in MSE metrics.

关键词： Measurement Analytical models Reviews Computational modeling Decision making software algorithms Estimation Project management Polynomials software

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning Computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Severity Prediction, and Post Effects

AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Sev...

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International Conference on information Technology Research (ICITR)

作者： Nivasheni B Fernando V.G.S.O. Srihari M Sarah M.I.L. Vishan Jayasinghearachchi Samadhi Chathuranga Rathnayake Dept. of Computer Science Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

This paper aims to examine the applicability of machine learning models for aiding in the diagnosis and management of arboviral illnesses, especially Dengue. The study focuses on three key objectives: Diagnosis of Dengue, prediction of prognosis of severe disease, and the identification of the post dengue effects. Multiple strategies were used to address these challenges. They are feature engineering based multiclass classification of arboviral diseases, enhancement of standard conventional machine learning algorithms for binary classification of severe Dengue and finally MLSOL enhancing ensemble model for drawing future effects of Dengue infections. The comparison of traditional models included both Random Forest, XGBoost, and also ensemble method while applying the feature selection methods of SFA and RFE. This study presents the comparison of traditional individual models and optimal ensemble method for the multiclass classification in arboviral diseases. In the case of binomial classification, the best accuracy level was 0.93 achieved by the Random Forest with RFE feature selection compared to Logistic Regression as well as the Gradient Boosting. Furthermore, the study proposed an MLSOL augmented ensemble approach to handle label imbalance problem in the dataset which in turn substantially enhanced the prediction accuracy. This approach decreased Hamming Loss to 0. 12 and increased the F-measure to 0.82, this has the capability of handling the imbalanced dataset and improving the predictive accuracy and performance. This machine learning framework offers great potential for clinical use in such aspects as early intervention and in effects following Dengue fever. It is meant to help healthcare practitioners and policy makers on how to better prevent and combat Dengue in Sri Lanka.

关键词： Accuracy Machine learning algorithms Medical services Feature extraction Ensemble learning Object recognition Prognostics and health management Viruses (medical) Random forests Standards

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Design and Analysis of Automated Machine Learning (AutoML) in PowerBI Application Using PyCaret

Design and Analysis of Automated Machine Learning (AutoML) i...

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2022 International Conference of Science and information Technology in Smart Administration, ICSINTESA 2022

作者： Sihombing, Denny Jean Cross Dexius, Jawangi Unedo Manurung, Jonson Aritonang, Mendarissan Adinata, Harni Seven Atma Jaya Catholic University of Indonesia Dept. Information System Jakarta Indonesia Stmik Pelita Nusantara Dept. Software Engineering Medan Indonesia Universitas Methodist Indonesia Dept. Informatics Management Medan Indonesia Universitas Sam Ratulangi Dept. Informatics Engineering Manado Indonesia

ISBN: (纸本)9781665472883

Massively using conventional machine learning requires many people to understand the process. However, the design of an automatic machine learning system using the Pycaret Library can make it easier for users by reducing some of the operations of machine learning in general. This automated machine learning design is managed in the Power BI application so that the display form can be better understood and easily visualized. By carrying out several method stages in the form of literature studies, data collection, designing classification methods using conventional machine learning and AutoML in Power BI, analysis of results, and reporting/publication. The result of comparison of machine learning and Auto ML: In machine learning, 98 lines of line code are used, while in AutoML, eight lines of code are used. Another advantage derived from using AutoML in PowerBI is the feature to be able to visualize processed datasets so that they can obtain information in the form of diagrams, charts, and others. In testing using the breast cancer dataset, the use of Machine Learning is considered better based on the score obtained. Machine Learning received the highest Accuracy using GaussianNB is 0.94. In testing using the Diabetes dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using CatBoost Model is 0.777. In testing using the heart failure dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using Random Forest Model with a score of 0.85. © 2022 IEEE.

关键词： Statistical tests

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Concentration of Power and Participation in Online Governance: the Ecosystem of Decentralized Autonomous Organizations 24

Concentration of Power and Participation in Online Governanc...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Peña-Calvin, Andrea Arroyo, Javier Schwartz, Andrew Hassan, Samer Dept. Software Engineering and Artificial Intelligence Universidad Complutense de Madrid Madrid Spain Institute of Knowledge Technology Universidad Complutense de Madrid Madrid Spain Dept. Information Science University of Colorado Boulder BoulderCO United States Spain Berkman Klein Center Harvard University CambridgeMA United States

ISBN: (纸本)9798400701726

Blockchain technology enables a new form of online community: Decentralized Autonomous Organizations (DAOs), where members typically vote on proposals using tokens. Enthusiasts claim DAOs provide new opportunities for openness, horizontality, and democratization. However, this phenomenon is still under research, especially given the lack of quantitative studies. This paper presents the first census-like quantitative analysis of the whole ecosystem of DAOs, including 30K DAO communities on the main DAO platforms. This enables us to provide insights into the allegedly "democratic" nature of DAOs, building metrics concerning their lifespan, participation, and power concentration. Most DAOs have a short lifespan and low participation. There is also a positive correlation between community size and voting power concentration. Like other online communities, DAOs seem to follow the iron law: becoming increasingly oligarchic as they grow. Still, a significant amount of DAOs of varying sizes defy this idea by being egalitarian by design. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering Systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

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

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks Computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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Multi-Agent AMIX-DAPG of Dual-Arm Robot for Long Horizon Lifecare Tasks

Multi-Agent AMIX-DAPG of Dual-Arm Robot for Long Horizon Lif...

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7th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2025

作者： Jung, Hwanseok Oh, Ji-Heon Jaramillo, Ismael Espinoza Jung, Danbi Lee, Won Hee Kim, Tae-Seong Kyung Hee University Dept. of Electronics and Information Convergence Engineering Yong-In Korea Republic of Kyung Hee University Dept. of Software Convergence Yong-In Korea Republic of

ISBN: (纸本)9798331507022

This work introduces a multi-agent reinforcement learning (RL) intelligence capable of performing long-horizon (LH) lifecare tasks with a dual-arm robot. Our proposed intelligence is based on multi-agent reinforcement learning (MARL) under the principles of Centralized Training and Decentralized Execution (CTDE). In general, MARL methods based on Q-learning require extensive training time and exhibit a low convergence rate, limiting their effectiveness in dual-arm applications. We propose AMIX-DAPG which combines an Advantage Function MIXing Network (AMIX) and Demo Augmented Policy Gradient (DAPG) policy to overcome these limitations. Under centralized training, the AMIX network computes the total advantage function, reducing computational cost compared to Q-learning and integrating the states of both arms, making it possible cooperative tasks learning. Under decentralized execution, the DAPG policy is updated to maximize the total advantage function. We have trained our AMIX-DAPG to perform dual-arm LH lifecare tasks, such as serving water from a dispenser and storage an object in a drawer. Our AMIX-DAPG completes these LH lifecare tasks with an average success rate of 92.7%, whereas the conventional MARL intelligence only achieves 82.3%. © 2025 IEEE.

关键词： Reinforcement learning

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