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5th International Conference on Advancements in computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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KidSwatch: Children Development Observation System 5

KidSwatch: Children Development Observation System

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5th International Conference on Advancements in computing, ICAC 2023

作者： Oshadhini, P.A.L.N. Siriwardana, K.A.D.H.O. Peshala Ranmini, H.K. Sandeepa, S.R. Weerasinghe, Lokesha Kelegama, Thamali Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The kidSwatch is a mobile application designed specifically for grade 1 children aged six years. It serves as a comprehensive framework for observing and evaluating children's developmental progress. The application focuses on the main components of children's development: Cognitive development, Emotional development, Language development, and Learning development. By analyzing the results obtained from observing these four components, parents can gain valuable insights into their children's development within a matter of minutes. The kidSwatch employs a range of observational techniques, including direct observation, structured interviews, and standardized tests, to gather information on a child's talents, skills, and behaviors. Trained observers proficient in the kidSwatch framework carefully record and analyze the child's interactions, activities, problem-solving abilities, emotional expressions, and social interactions. Ultimately, the kidSwatch empowers parents with a quick and effective means of understanding and assessing their child's development. To satisfy the above features there are a Hand number detection algorithm, letter recognition algorithm, Emotion recognition algorithm, and voice-based answering algorithm They perform an accuracy between 75%-90% on CNN. © 2023 IEEE.

关键词： Emotion Recognition

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Towards Edge computing for 6G Internet of Everything: Challenges and Opportunities 1

Towards Edge Computing for 6G Internet of Everything: Challe...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Azrag, Etidal Universiti Utara Malaysia School of Computing Kedah Malaysia Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen

ISBN: (纸本)9781665472753

The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE. © 2023 IEEE.

关键词： Internet of Everything

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Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

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SN Computer Science 2024年第6期5卷 799页

作者： Imani, Hassan Islam, Md Baharul Faculty of Engineering and Natural Sciences Bahcesehir University Yildiz Ciragan Cd Istanbul Besiktas 34349 Turkey Department of Computing and Software Engineering Florida Gulf Coast University Fort Myers 33965 FL United States

Convolutional Neural Networks (CNNs) have been receiving research attention for Stereoscopic Video Quality Assessment (SVQA) in recent years. Recently, researchers have used 3D CNNs for extracting useful spatial and temporal features from stereo videos and have used them for detecting the reduction in the quality of the stereoscopic videos. To our best knowledge, the concept of transfer learning (TL) has not been well-examined in SVQA. Pretraining and fine-tuning are approaches used in deep neural networks to transform the knowledge learned from other general fields. The previous methods that utilized TL used very heavy 3D ResNet architectures with several layers;therefore, they are very time-consuming. In this paper, we develop a new model for SVQA and use the Inflated 3-Dimensional ConvNet (I3D) network as the backbone feature extractor for our model. We first apply left and right videos to I3D models to extract their features. Then, we apply 3D CNNs to learn quality-aware features from stereo videos. We evaluate our proposed method using LFOVIAS3DPh2 and NAMA3DS1- COSPAD1 SVQA datasets. Extensive experimental studies on two datasets prove that the proposed method correlates with the subjective results. The Root-Mean-Square Error (RMSE) for the NAMA3DS1-COSPAD1 dataset is 0.2454, and the high amount of Linear Correlation Coefficient (LCC) and Spearmen Rank Order Correlation Coefficient (SROCC) values (0.895 and 0.901 respectively) for LFOVIAS3DPh2 dataset show the compatibility of the results with human visual system (HVS). Despite having lighter architecture than the best performing method, the proposed method outperforms most of the methods and overall it is the second best performing method available. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 3 Dimensional convolutional neural networks Disparity Human visual system Motion Objective quality assessment Stereoscopic video

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QSroute : A QoS Aware Routing Scheme for software Defined Networking 8

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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8th IEEE International Conference on software engineering and Computer Systems, ICSECS 2023

作者： Kamarudin, Imran Edzereiq Ameedeen, Mohamed Ariff Umar Ong, Muhamad Idaham Zabidi, Azlee Universiti Malaysia Pahang AL-Sultan Abdullah Faculty of Computing Department of Computer Systems and Networking Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

ISBN: (纸本)9798350310931

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field. © 2023 IEEE.

关键词： Quality of service

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Segmentation Based Approach For Off-line Handwritten Sinhala Word Recognition From Touch Screen Gestures 8

Segmentation Based Approach For Off-line Handwritten Sinhala...

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8th International Multidisciplinary Moratuwa engineering Research Conference, MERCon 2022

作者： Mahesh, Hashan Priyankara, Chathura University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Kelaniya Sri Lanka

ISBN: (数字)9781665487863

ISBN: (纸本)9781665487863

The traditional way of using pen and paper to take notes is getting over by the touch screen devices. These devices provide more options to the users to enhance their productivity while taking notes. The ability to recognize and validate the words written on the touch screens facilitates further capabilities to the users. Hence, in this paper, we describe a segmentation-based approach combined with an n-gram model for the recognition and validation of the Sinhala words written on touch screens. We compare the results of 6 commonly used machine learning models to find the best performing classifier for recognizing individual characters of words. The classifiers are trained to identify 19 different Sinhala characters. Based on the results, Convolution Neural Network (CNN) based word classifier stands ahead of other classifiers. © 2022 IEEE.

关键词： Touch screens

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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