Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025139
T.R Legrand, K. Bandara, J.A.D Stefania Crishani, L.W.P Uvindu, N.C Amarasena, D. Kasthurirathna
Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.
{"title":"TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates","authors":"T.R Legrand, K. Bandara, J.A.D Stefania Crishani, L.W.P Uvindu, N.C Amarasena, D. Kasthurirathna","doi":"10.1109/ICAC57685.2022.10025139","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025139","url":null,"abstract":"Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125464028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025077
Lasal Sandeepa Hettiarachchi, Senura Vihan Jayadeva, Rusiru Abhisheak Vikum Bandara, Dilmi Palliyaguruge, Udara Srimath S. Samaratunge Arachchillage, D. Kasthurirathna
The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools’ failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to conFigure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes.
{"title":"Expert System for Kubernetes Cluster Autoscaling and Resource Management","authors":"Lasal Sandeepa Hettiarachchi, Senura Vihan Jayadeva, Rusiru Abhisheak Vikum Bandara, Dilmi Palliyaguruge, Udara Srimath S. Samaratunge Arachchillage, D. Kasthurirathna","doi":"10.1109/ICAC57685.2022.10025077","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025077","url":null,"abstract":"The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools’ failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to conFigure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129433877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025200
Manuri Senarathna, K. Pulasinghe, Shyam Reyal
This paper presents a comprehensive guide for creating synthetic voices to support under resourced languages for the MaryTTS platform. Although researchers have extensively contributed in the domain of speech synthesis, the lack of a thorough documentation hinders the voice building process for languages not yet supported by MaryTTS, complicating the implementation process for users with inadequate knowledge in the field of Text-to-Speech (TTS). The step-by-step process discussed in this study is further demonstrated with the creation of a synthetic voice for the Sinhala language, with unit selection as the voice building approach. A Sinhalese voice was generated with an intelligibility score of 91.7% upon evaluation with Diagnostic Rhyme Test (DRT). Comparison with ground truth data proved a close approximation to human speech where the intelligibility score was identified as 97.9%, when tested with the same participants. The Mean Opinion Score (MOS) revealed a naturalness level of 2.993, indicating a moderately high speech quality for the proposed system in comparison with the ideal score of 4.972.
{"title":"Step-by-Step Process of Building Voices for Under Resourced Languages using MARY TTS Platform","authors":"Manuri Senarathna, K. Pulasinghe, Shyam Reyal","doi":"10.1109/ICAC57685.2022.10025200","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025200","url":null,"abstract":"This paper presents a comprehensive guide for creating synthetic voices to support under resourced languages for the MaryTTS platform. Although researchers have extensively contributed in the domain of speech synthesis, the lack of a thorough documentation hinders the voice building process for languages not yet supported by MaryTTS, complicating the implementation process for users with inadequate knowledge in the field of Text-to-Speech (TTS). The step-by-step process discussed in this study is further demonstrated with the creation of a synthetic voice for the Sinhala language, with unit selection as the voice building approach. A Sinhalese voice was generated with an intelligibility score of 91.7% upon evaluation with Diagnostic Rhyme Test (DRT). Comparison with ground truth data proved a close approximation to human speech where the intelligibility score was identified as 97.9%, when tested with the same participants. The Mean Opinion Score (MOS) revealed a naturalness level of 2.993, indicating a moderately high speech quality for the proposed system in comparison with the ideal score of 4.972.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124681007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025191
M. Mahir, M. Hussain, R.A.D.B.S Perera, Y.A.M Upendra, C. J. Wickramarathne, D. Kasthurirathna
Sri Lankan population accounts up to almost one million visually impaired individuals out of which are mostly students and young individuals. As the educational structure for the visually impaired improves with funds, blind schools, and free education, assistance with minute needs for most visually impaired individuals comes at a cost. There are many assistive technologies, such as audio books, screen magnifiers, braille books, and screen readers, prevalent around the island. However, there are several limitations to these technologies, mainly their availability and affordability. In Sri Lanka, many individuals, societies, clubs, and many more are willing to volunteer to help those in need, even those that require physical attention. As much as it is anticipated to aid those in need, there is very little attention to the ways it can be done. Hence, this research provides a way to develop a user-friendly mobile application with assistive software and volunteerism to aid visually impaired students with their daily needs.
{"title":"SPAVIS: Mobile Application for Visually Impaired Based on Assistive Software and Volunteerism","authors":"M. Mahir, M. Hussain, R.A.D.B.S Perera, Y.A.M Upendra, C. J. Wickramarathne, D. Kasthurirathna","doi":"10.1109/ICAC57685.2022.10025191","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025191","url":null,"abstract":"Sri Lankan population accounts up to almost one million visually impaired individuals out of which are mostly students and young individuals. As the educational structure for the visually impaired improves with funds, blind schools, and free education, assistance with minute needs for most visually impaired individuals comes at a cost. There are many assistive technologies, such as audio books, screen magnifiers, braille books, and screen readers, prevalent around the island. However, there are several limitations to these technologies, mainly their availability and affordability. In Sri Lanka, many individuals, societies, clubs, and many more are willing to volunteer to help those in need, even those that require physical attention. As much as it is anticipated to aid those in need, there is very little attention to the ways it can be done. Hence, this research provides a way to develop a user-friendly mobile application with assistive software and volunteerism to aid visually impaired students with their daily needs.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122353760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025175
J. G. Jayawickrama, R. Rupasingha
Stress is an emotional or mental state caused by inescapable or demanding situations, known as stressors. Because of the high stress level human are addicted to some illegal or unethical activities and also they try to do different activities to reduce their stress level. Because of that, the detection of human stress levels becomes important today. The major goal of this study is to look into how human stress detection is based on the behaviors during sleep using the ensemble learning algorithm. In the first experiment, five Machine Learning (ML) algorithms were used in the classification level, including Random Forest, Support Vector Machine (SVM), Decision Tree (J4S), Logistic regression, and Naive Bayes. In a second experiment, an ensemble learning algorithm was used with an average probability combination method for the above five algorithms. Based on the experiment results, ensemble learning can classify the data with 94.25% highest accuracy, high precision, recall, f-measure values, and the lowest error rate in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) better than the separate algorithm results.
{"title":"Ensemble Learning Approach to Human Stress Detection Based on Behaviours During the Sleep","authors":"J. G. Jayawickrama, R. Rupasingha","doi":"10.1109/ICAC57685.2022.10025175","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025175","url":null,"abstract":"Stress is an emotional or mental state caused by inescapable or demanding situations, known as stressors. Because of the high stress level human are addicted to some illegal or unethical activities and also they try to do different activities to reduce their stress level. Because of that, the detection of human stress levels becomes important today. The major goal of this study is to look into how human stress detection is based on the behaviors during sleep using the ensemble learning algorithm. In the first experiment, five Machine Learning (ML) algorithms were used in the classification level, including Random Forest, Support Vector Machine (SVM), Decision Tree (J4S), Logistic regression, and Naive Bayes. In a second experiment, an ensemble learning algorithm was used with an average probability combination method for the above five algorithms. Based on the experiment results, ensemble learning can classify the data with 94.25% highest accuracy, high precision, recall, f-measure values, and the lowest error rate in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) better than the separate algorithm results.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126871061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An automated approach for object tracking and gaze estimation via head pose estimation is crucial, to facilitate a range of applications in the domain of -human-computer interfacing, this includes the analysis of head movement with respect to a stimulus in assessing one’s level of attention. While varied approaches for gaze estimation and object tracking exist, their suitability within such applications have not been justified. In order to address this gap, this paper conducts a quantitative comparison of existing models for gaze estimation including Mediapipe and standalone models of Openface and custom head pose estimation with MTCNN face detection; and object detection including models from CSRT object tracker, YOLO object detector, and a custom object detector. The accuracy of the aforementioned models were compared against the annotations of the EYEDIAP dataset, to evaluate their accuracy both relative and non-relative to each other. The analysis revealed that the custom object detector and the Openface models are relatively more accurate than the others when comparing the number of annotations, absolute mean error, and the relationship between x displacement-yaw, and y displacement-pitch, and thereby can be used in combination for gaze tracking tasks.
{"title":"Qualitative Analysis of Automated Visual Tracking of Objects Through Head Pose Estimation","authors":"Ayeshka Abeysinghe, Isuri Devlini Arachchige, Pradeepa Samarasinghe, Vidushani Dhanawansa, Menan Velayuthan","doi":"10.1109/ICAC57685.2022.10025053","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025053","url":null,"abstract":"An automated approach for object tracking and gaze estimation via head pose estimation is crucial, to facilitate a range of applications in the domain of -human-computer interfacing, this includes the analysis of head movement with respect to a stimulus in assessing one’s level of attention. While varied approaches for gaze estimation and object tracking exist, their suitability within such applications have not been justified. In order to address this gap, this paper conducts a quantitative comparison of existing models for gaze estimation including Mediapipe and standalone models of Openface and custom head pose estimation with MTCNN face detection; and object detection including models from CSRT object tracker, YOLO object detector, and a custom object detector. The accuracy of the aforementioned models were compared against the annotations of the EYEDIAP dataset, to evaluate their accuracy both relative and non-relative to each other. The analysis revealed that the custom object detector and the Openface models are relatively more accurate than the others when comparing the number of annotations, absolute mean error, and the relationship between x displacement-yaw, and y displacement-pitch, and thereby can be used in combination for gaze tracking tasks.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126007084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025124
D. Dassanayake, C.V. Wanigathunga, P.Y. Meeriyagalla, K. Yapa, K.A.P.P. Wickramarathne, Laneesha Rukgahakotuwa
Mental health is a key area of the healthcare sector. While taking care of the physical health of the human body, it is important to pay attention to mental health as well. This project is done to help people maintain their mental health. ‘ SERENITY’ is a web application designed not only for patients but also for doctors. This app works as a virtual assistant for a doctor, and this app helps doctors constantly monitor their patients’ behaviour, as well as SERENITY, which will be able to analyze the emotions of patients individually.
{"title":"Human Behavior Analysis for Psychological Healthcare Sector (Project SERENITY)","authors":"D. Dassanayake, C.V. Wanigathunga, P.Y. Meeriyagalla, K. Yapa, K.A.P.P. Wickramarathne, Laneesha Rukgahakotuwa","doi":"10.1109/ICAC57685.2022.10025124","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025124","url":null,"abstract":"Mental health is a key area of the healthcare sector. While taking care of the physical health of the human body, it is important to pay attention to mental health as well. This project is done to help people maintain their mental health. ‘ SERENITY’ is a web application designed not only for patients but also for doctors. This app works as a virtual assistant for a doctor, and this app helps doctors constantly monitor their patients’ behaviour, as well as SERENITY, which will be able to analyze the emotions of patients individually.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132630298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025325
M. Kothalawala, M.G. Gaveshith K, A.H.D.H. Tharaka, I.A Punchihewa, Disni Sriyaratna
Banana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.
{"title":"Banana Disease Identification Using Machine Learning Based Technologies and Weather-Based Dispersion Analysis","authors":"M. Kothalawala, M.G. Gaveshith K, A.H.D.H. Tharaka, I.A Punchihewa, Disni Sriyaratna","doi":"10.1109/ICAC57685.2022.10025325","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025325","url":null,"abstract":"Banana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129072415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025144
K. Jayakody, Vidushani Dhanawansa, Menan Velayuthan, Pradeepa Samarasinghe
There is a crucial need to screen young children for attention impairments given that the ability of a child to deal with the demands of everyday life is dependent on the development of the child’s attention. Intervention at a young age facilitates the training and enhancement of attention, as young brains are the most responsive to treatment. Sri Lanka, a low-income country, lacks accessible, home-based screening tools which can be used to assess the attention of young children. Moreover, most Sri Lankan parents are not aware of attention impairments. To bridge this gap, this paper proposes an easily accessible, home-based attention assessment tool in the form of a mobile application. The application provides a series of engaging tasks for assessing and training, the aspects of visual attention (focused attention, selective attention, divided attention, sustained attention and shifting attention). The assessments were carefully designed to suit the age and the attention span of the child. The performance analysis performed on the data collected showed the varied responses of children of different ages on different assessments. Clustering was performed in identifying the varying performance levels of typical children and this project will be extended to evaluate atypical child performance.
{"title":"Mobile-Based Analysis of Visual Attention in Young Children","authors":"K. Jayakody, Vidushani Dhanawansa, Menan Velayuthan, Pradeepa Samarasinghe","doi":"10.1109/ICAC57685.2022.10025144","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025144","url":null,"abstract":"There is a crucial need to screen young children for attention impairments given that the ability of a child to deal with the demands of everyday life is dependent on the development of the child’s attention. Intervention at a young age facilitates the training and enhancement of attention, as young brains are the most responsive to treatment. Sri Lanka, a low-income country, lacks accessible, home-based screening tools which can be used to assess the attention of young children. Moreover, most Sri Lankan parents are not aware of attention impairments. To bridge this gap, this paper proposes an easily accessible, home-based attention assessment tool in the form of a mobile application. The application provides a series of engaging tasks for assessing and training, the aspects of visual attention (focused attention, selective attention, divided attention, sustained attention and shifting attention). The assessments were carefully designed to suit the age and the attention span of the child. The performance analysis performed on the data collected showed the varied responses of children of different ages on different assessments. Clustering was performed in identifying the varying performance levels of typical children and this project will be extended to evaluate atypical child performance.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131282746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Providing proper care for children with attention difficulty disorder is crucial, one way to ensure this is early identification of these disorders. In Sri Lanka, a developing country, it is difficult to find resources such as clinics, clinical expertise, and other resources which are essential for diagnosis. The absence of these apparatuses risks the mental well-being of the child as well as access to help. Hence a need arises to develop an automated social attention evaluation system. This will serve as the first line of diagnosis and help the parents/guardians secure the help required from an early age for the child. To the best of the authors’ knowledge, no solution of this nature is readily available for the Sri Lankan community so far. Keeping the low-income bracket of the country in mind, we propose a solution that can be easily deployed even on a cheap mobile/tablet-like device. It is difficult to perform these evaluations for children in similar settings as adults, as they are easily distracted. Therefore, care must be taken to grab the child’s attention throughout the evaluation process. In this research, we developed applications for children at different levels and each level assesses child attention between social objects and non-social objects through a child-friendly game, as they have sufficient visual stimuli to hold the child’s attention. In this study we investigated the screen time spent by the child, the attention of the child on different categories of images (High Autism Interested or Low Autism Interested images), and the switching patterns of the attention between these images. Only typical children were evaluated for this research due to the pandemic situation as well as other internal problems in the country. This system will test and evaluate atypical children in our future work.
{"title":"Automated Child Social Attention Evaluation","authors":"Kasuni Sandunika Wasala, Vidushani Dhanawansa, Menan Velayuthan, Pradeepa Samarasinghe","doi":"10.1109/ICAC57685.2022.10025297","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025297","url":null,"abstract":"Providing proper care for children with attention difficulty disorder is crucial, one way to ensure this is early identification of these disorders. In Sri Lanka, a developing country, it is difficult to find resources such as clinics, clinical expertise, and other resources which are essential for diagnosis. The absence of these apparatuses risks the mental well-being of the child as well as access to help. Hence a need arises to develop an automated social attention evaluation system. This will serve as the first line of diagnosis and help the parents/guardians secure the help required from an early age for the child. To the best of the authors’ knowledge, no solution of this nature is readily available for the Sri Lankan community so far. Keeping the low-income bracket of the country in mind, we propose a solution that can be easily deployed even on a cheap mobile/tablet-like device. It is difficult to perform these evaluations for children in similar settings as adults, as they are easily distracted. Therefore, care must be taken to grab the child’s attention throughout the evaluation process. In this research, we developed applications for children at different levels and each level assesses child attention between social objects and non-social objects through a child-friendly game, as they have sufficient visual stimuli to hold the child’s attention. In this study we investigated the screen time spent by the child, the attention of the child on different categories of images (High Autism Interested or Low Autism Interested images), and the switching patterns of the attention between these images. Only typical children were evaluated for this research due to the pandemic situation as well as other internal problems in the country. This system will test and evaluate atypical children in our future work.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131358517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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