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Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive Learning in Sri Lanka 6

Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Fernando, P.S.D. Amarasekara, H.P.D.E.R. Rathnakumara, M.D.B. Amarakoon, B.W. Rajapaksha, Samantha Krishara, Jenny Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Sri Lanka Institute of Information and Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. This application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. The suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka. © 2024 IEEE.

关键词： Students

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Detection of Autism Spectrum Disorder in Children Using Efficient Pre-Processing and Machine Learning Techniques

Detection of Autism Spectrum Disorder in Children Using Effi...

引用

2023 International Conference on Decision Aid Sciences and Applications, DASA 2023

作者： Sallibi, Anas D. Ali Alheeti, Khattab M. College of Computer and Information Technology University of Anbar Dept. Dept. of Computer Sciences Anbar Iraq College of Computer and Information Technology University of Anbar Dept. of Computer Networking Systems Anbar Iraq

ISBN: (纸本)9798350342055

Early diagnosis of the brain condition known as autism spectrum disorder (ASD) could significantly reduce the high healthcare costs typically involved with treating ASD. Autism's fiscal toll emphasizes the importance of developing easy and effective detection instruments. Therefore, a quick ASD evaluation is essential to helping healthcare professionals and letting people know if they should seek a formal clinical diagnosis. Currently, there are very few autism datasets related to screening accessible, most of which are based on genetics. To expose critical information about autism screening, this work suggests applying a novel machine learning framework that contains vital characteristics and performing predictive analysis utilising a Support vector machine, logistic regression, K-nearest neighbour, and random forest. The main objective of this study is to develop a system for the early detection of children at risk for autism spectrum disorders (ASD). © 2023 IEEE.

关键词： Diagnosis

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets 4

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

引用

4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Ikram, Muhammad Ahmed, Salman Khan Marwat, Safdar Nawaz University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets. © 2023 IEEE.

关键词： Smart power grids

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, Tharindu Thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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Analysis of Arabs Publications In the Field of computer Networks 14

Analysis of Arabs Publications In the Field of Computer Netw...

引用

14th International Conference on Information and Communication systems, ICICS 2023

作者： El-Bashabsheh, Emran Younes, Mutaz Shatnawi, Mohammed Jordan University of Science and Technology Dept. of Computer Information Systems Irbid Jordan Jordan University of Science and Technology Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350307870

Increasing technology led to increasing collaboration between the countries in general, especially the Arabic world. In recent years, many impacts affect the growth of research work in different Arabic countries. In this research, we show the growth of research work and its quality between Arabs and the rest of the world. We collected over 2500 publications and used it to create a new table that contains the intersection between each Arab country and each co-author's continent to show the relationship between each Arabic country with the rest of the world. We divided the data into two periods, before and after 2015, then we used Hue to apply our analysis on the data. We focused on the number of publications and the number of citation for each Arabic country. We noticed the rapid growth in the last period in some Arabic countries such as Jordan, Qatar, United Arab Emirates, and Saudi Arabia. In contrast, the rest of the countries had slow or decreasing growth. © 2023 IEEE.

关键词： Big data

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Development of a Predictive IoT-Based System for Real-Time Air Quality Monitoring and Route Optimization 6

Development of a Predictive IoT-Based System for Real-Time A...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Inthikhaff, M.I.M. Rimas, M.J.M. Kumari, J.M.D. Pasan, M.G.R. Kahandawaarachchi, K.A.D.C.P. Wijesiri, M.P.M. Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517878

Sri Lanka faces significant air pollution challenges, primarily from vehicular emissions, posing serious public health risks and impacting the quality of life. To address this, we developed a real-time air quality monitoring system that visualizes pollution data through an interactive heatmap. Our system integrates gas sensors that measure methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (PM.), connected to an ESP32 board and Wi-Fi module, and strategically deployed around smart buildings. The collected data is transmitted to a central hub using IoT technology and visualized on a mobile app developed with Flutter, featuring a color-coded scheme to represent pollution levels. This allows users to easily identify high-risk areas and select cleaner routes for travel. In testing, we focused on predicting PM. concentrations in Colombo using machine learning models, including Random Forest, XGBoost, and Prophet. Random Forest excelled in PM. prediction, while Prophet was highly effective for CO, demonstrating machine learning's potential in air quality forecasting. The system also includes a MIMI Air device that collects, processes, and transmits air quality data every 30 seconds, alerting users in real-time when pollutants exceed WHO thresholds, providing a cost-effective, reliable solution for public health. © 2024 IEEE.

关键词： Decision trees

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Harnessing Big Data for Economic Transformation in a Digitalized World 3

Harnessing Big Data for Economic Transformation in a Digital...

引用

3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Tarigan, Riswan Efendi Triyono Mahyuni Agustian, Harry Nurfaizi, Rifky Zahra, Achani Rahmania Az Pelita Harapan University Dept. of Information Systems Tangerang Indonesia University of Raharja Dept. of Information Technology Tangerang Indonesia University of Mataram Dept. of Teacher Training and Education Science Mataram Indonesia Sesindo Dept. of Computer Systems Tangerang Indonesia Bosindo Dept. of Information Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Information Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

Air is essential to life and health, with oxygen (20.94%) and the ozone layer protecting against ultraviolet radiation. Depletion of oxygen reduces the ozone layer, increasing UV exposure. Good air quality is crucial for living organisms. In 2022, Indonesia's PM 2.5 pollution ranked 26th worst globally, exceeding WHO standards, yet awareness and trust in air quality devices remain low. This study aims to implement and evaluate an AI-based machine learning system to predict the Air Quality Index (AQI) in healthcare services. Using Structural Equation Modeling with Partial Least Square analysis via SmartPLS 4.0, the research involves 120 participants and historical air quality data. Comparative analysis of machine learning algorithms assesses accuracy, robustness, and efficiency, highlighting their potential in forecasting AQI and its implications for public health and urban planning. The study demonstrates the effectiveness of these algorithms in predicting AQI, with a significant value of 0.874 for forecasting accuracy. © 2024 IEEE.

关键词： Ozone layer

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Exploring Key Factors Driving QR Payment Adoption in Digital Banking in Indonesia 12

Exploring Key Factors Driving QR Payment Adoption in Digital...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Ruisli, Marchesa Hardini, Marviola Ayu Sanjaya, Yulia Putri Padeli Agustian, Harry University of Raharja Dept. of Information Technology Tangerang Indonesia University of Raharja Dept. of Retail Management Tangerang Indonesia University of Raharja Dept. of Computer Systems Tangerang Indonesia

ISBN: (纸本)9798331528799

Technological progress within the banking industry has facilitated the extensive adoption of digital payment solutions, particularly those utilizing QR codes. Although these methods offer convenience and efficiency, significant hurdles remain in securing widespread consumer endorsement. This study analyzes factors influencing the intention to adopt QR payments, focusing on Perceived Ease of Use, Perceived Usefulness, technology Readiness, Trust in technology, and Behavioral Intention. Using Structural Equation Modeling (SEM) with SmartPLS, the study surveyed 400 digital banking users in Indonesia. Results reveal that perceived ease of use and usefulness significantly impact behavioral intention to adopt QR payments. Additionally, tech-nology readiness and trust in technology are critical drivers of adoption. The findings emphasize that enhancing QR payment adoption requires not only technological advancements but also ensuring user-friendliness and trust, which are statistically signif-icant factors in driving adoption, as demonstrated by the p-value © 2024 IEEE.

关键词： Electronic money

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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Integrating technology and Environmental Policy for Effective Air Quality Monitoring in Indonesia 3

Integrating Technology and Environmental Policy for Effectiv...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Sanni, Mohammad Ifran Pramudya, Reza Dani Hery Jamaludin, Dieng Asep Sihotang, Sondang Visiana Hikam, Ihsan Nuril University of Raharja Dept. of Computer Systems Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Information Technology Tangerang Indonesia Pelita Harapan University Dept. of Information Systems Tangerang Indonesia Bina Bangsa University Dept. of Information Technology Serang Indonesia Pandawan Sejahtera Indonesia Dept. of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

Air quality monitoring is crucial for protecting ecosystems and public health, especially in Indonesia, a country with unique geographical and socio-economic characteristics facing complex environmental challenges. This study examines the multifaceted interactions of various factors affecting Indonesia's air quality monitoring system, including industrial emissions, vehicular traffic, agricultural activities, and forest fires. Geographic factors like topography, wind patterns, and regional diversity significantly influence pollutant distribution and concentrations. Social and economic dimensions, such as rapid industrialization, urbanization, and population growth, also impact energy consumption patterns and air quality. This research employs a comprehensive methodological approach, combining literature review, data analysis, and the SmartPLS analytical model to understand these complexities. Data were collected from air monitoring agencies and scientific sources, focusing on variables such as industrial activity levels, traffic density, meteorological conditions, urbanization, and health impacts. The SmartPLS model was utilized to analyze complex cause- and-effect relationships between these variables. Results highlight significant variations in air quality across different regions and times, influenced by both environmental and human factors. Advanced sensor technologies and remote monitoring systems are identified as potential solutions to enhance monitoring coverage and accuracy, especially in remote areas. Collaboration between government, academia, and the private sector is emphasized as vital for developing a sustainable and effective air quality monitoring infrastructure. The study provides actionable recommendations for improving Indonesia's air quality monitoring system, aiming to foster a cleaner and healthier environment. Enhanced monitoring can help identify and mitigate air quality issues, ensuring better public health outcomes and environmental protec

关键词： Air quality

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