Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025117
W.H.A. Eishan Dinuka, A.Y.S. Wickramasinghe W, W.S. Weerasinghe H, K.P. Karunaratne G, C. Liyanapathirana, L. Rupasinghe
As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users’ feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users’ preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly.
{"title":"Smart Advertising Based on Customer Preferences and Manage the Supermarket","authors":"W.H.A. Eishan Dinuka, A.Y.S. Wickramasinghe W, W.S. Weerasinghe H, K.P. Karunaratne G, C. Liyanapathirana, L. Rupasinghe","doi":"10.1109/ICAC57685.2022.10025117","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025117","url":null,"abstract":"As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users’ feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users’ preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly.","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":"122889170","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/ICAC57685.2022.10025262
C.K. Amarasinghe, R. Pinto, K.N. Sudusinghe
At present, with the advancement of technology, various devices and solutions have been found to aid the visually impaired community (VI Community). Even with the countless technological breakthroughs, yet they face many problems performing the most basic functions in daily life. Identifying the objects, they use daily, identifying a person, whether it’s someone they know or not, and their emotions, and reading a text information displayed anywhere, without the assistance from another person are the basic issues we deal with and try to resolve using a tool consisting of a pair of spectacles with an inbuilt camera that is integrated with a mobile application. The inbuilt camera will capture the image of a text containing label, an object, or a person which will be then detected, analyzed, and recognized and will be converted to Speech using Google TTS engine and produced through the headphones giving the output to the user. Tesseract OCR, YOLO algorithm, and TensorFlow models have been used for each feature of the tool respectively. This tool will be very beneficial to a blind person as it mitigates them the frustration caused by being incapable of performing daily activities without any assistance from another person.
{"title":"Guardian - Smart Assistant Tool for Visually Impaired People","authors":"C.K. Amarasinghe, R. Pinto, K.N. Sudusinghe","doi":"10.1109/ICAC57685.2022.10025262","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025262","url":null,"abstract":"At present, with the advancement of technology, various devices and solutions have been found to aid the visually impaired community (VI Community). Even with the countless technological breakthroughs, yet they face many problems performing the most basic functions in daily life. Identifying the objects, they use daily, identifying a person, whether it’s someone they know or not, and their emotions, and reading a text information displayed anywhere, without the assistance from another person are the basic issues we deal with and try to resolve using a tool consisting of a pair of spectacles with an inbuilt camera that is integrated with a mobile application. The inbuilt camera will capture the image of a text containing label, an object, or a person which will be then detected, analyzed, and recognized and will be converted to Speech using Google TTS engine and produced through the headphones giving the output to the user. Tesseract OCR, YOLO algorithm, and TensorFlow models have been used for each feature of the tool respectively. This tool will be very beneficial to a blind person as it mitigates them the frustration caused by being incapable of performing daily activities without any assistance from another person.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"206 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":"123387447","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}
In films, props are the moving objects that may be seen. Prop masters and art directors’ assistants are responsible for gathering movie-related props. The selection of props is also essential, because the appeal of a film is dependent on its captivating qualities. It is difficult to select without a solid recommendation and a large gallery. Therefore, the suggested platform is a virtual prop house that works as a prop-specific recommendation system and has additional capabilities such as putting previews and history records. The process necessitates a cross-device platform, as the recommendation system functions as a web page, and when it comes to the improved capabilities of positioning previews, it is transitioning to a mobile application with AR Core support. In this study, the recommendations for the props can be made in one of three efficient ways. They are by reading the script and recommending the props using word recognition, recommending props from the available gallery by scanning an image, which can be a screenshot of a movie, and as associated props which makes the prop master’s job easier by suggesting props by grouping and ranking the most prevalently utilized props in the movies that have been screened so far.
{"title":"Prop Cone: The Virtual Prop House","authors":"Hiruni Perera, A.A.D.K Weerabahu, B.M Weerasinghe, I.U. Wickramasuriya, Ishara Gamage, Didula Chamara, H.A. Gayan Madushanka","doi":"10.1109/ICAC57685.2022.10025265","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025265","url":null,"abstract":"In films, props are the moving objects that may be seen. Prop masters and art directors’ assistants are responsible for gathering movie-related props. The selection of props is also essential, because the appeal of a film is dependent on its captivating qualities. It is difficult to select without a solid recommendation and a large gallery. Therefore, the suggested platform is a virtual prop house that works as a prop-specific recommendation system and has additional capabilities such as putting previews and history records. The process necessitates a cross-device platform, as the recommendation system functions as a web page, and when it comes to the improved capabilities of positioning previews, it is transitioning to a mobile application with AR Core support. In this study, the recommendations for the props can be made in one of three efficient ways. They are by reading the script and recommending the props using word recognition, recommending props from the available gallery by scanning an image, which can be a screenshot of a movie, and as associated props which makes the prop master’s job easier by suggesting props by grouping and ranking the most prevalently utilized props in the movies that have been screened so far.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"59 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":"124680537","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}
All people expect to live a healthy life. But today about eighty million people a year suffer from non-communicable diseases. Among non-communicable diseases, heart disease and diabetes are at the forefront, and the number of deaths due to heart disease is rising in people with diabetes. Changes in lifestyle, work-related stress and bad food habits, and smoking addiction contribute to the increase in the rate of several heart diseases and diabetes diseases. Therefore, a reliable and accurate system is needed to identify such diseases in time for proper treatment. The methodology proposed in this research is based on Machine learning classification techniques using Random Forest (RF), Logistic Regression, Gradient Boosting, etc. It is an android mobile application. The prognosis process gives a cardiac risk analysis percentage based on the patient’s heart condition and a diabetic risk analysis percentage based on the diabetic condition by the Kaggle dataset. Accordingly, a system was proposed with daily guidelines including calculation of risk level, Exercise recommendation, Meal planner, and stress-releaser. The accuracy of the proposed system was risk calculation of heart at 82,75%, risk calculation of Diabetics at 81.66%, Meal planner at 89.8%, the exercise scheduler Cardiac status prediction at 73.57%, diabetic status prediction at 78.57%, body performance prediction 74.68% and stress release 100%. This system helps to prevent the associated risk levels and keep healthy life.
{"title":"Health Care – A Personalized Guidance for Non-Communicable Diseases","authors":"D.D.T.D Dakshima, K. Seliya Mindula, R.M.D.S. Rathnayake, Sanvitha Kasthuriarachchi, A.K Buddhi Chathuranga, Dilani Lunugalage","doi":"10.1109/ICAC57685.2022.10025109","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025109","url":null,"abstract":"All people expect to live a healthy life. But today about eighty million people a year suffer from non-communicable diseases. Among non-communicable diseases, heart disease and diabetes are at the forefront, and the number of deaths due to heart disease is rising in people with diabetes. Changes in lifestyle, work-related stress and bad food habits, and smoking addiction contribute to the increase in the rate of several heart diseases and diabetes diseases. Therefore, a reliable and accurate system is needed to identify such diseases in time for proper treatment. The methodology proposed in this research is based on Machine learning classification techniques using Random Forest (RF), Logistic Regression, Gradient Boosting, etc. It is an android mobile application. The prognosis process gives a cardiac risk analysis percentage based on the patient’s heart condition and a diabetic risk analysis percentage based on the diabetic condition by the Kaggle dataset. Accordingly, a system was proposed with daily guidelines including calculation of risk level, Exercise recommendation, Meal planner, and stress-releaser. The accuracy of the proposed system was risk calculation of heart at 82,75%, risk calculation of Diabetics at 81.66%, Meal planner at 89.8%, the exercise scheduler Cardiac status prediction at 73.57%, diabetic status prediction at 78.57%, body performance prediction 74.68% and stress release 100%. This system helps to prevent the associated risk levels and keep healthy life.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"2016 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":"127373855","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}
Since the beginning of the millennium, computer technology has been the key area of concern and developing essential programming knowledge and intellectual skills from the young age have proven that they will gain more success in their careers. The ideology behind this research is, the problem with absence of a complete multi-disciplinary and interactive programming application for children between the age of 10 - 15 years, to learn programming concepts with a well-established text-based programming language. There are 4 major approaches in this research. Gamification approach focuses on expressing knowledge about Python programming via a game while concentrating on low perfumers. Collaborative approach aims to deliver a brand-new experience for children by aggregating cooperative methodologies and Artificial Intelligence with learning to enforce mutual learning. This component is based on collaborative sessions which allow a group of students with similar interest to join to learn python programming. Drag-drop approach enables children to learn Python language through videos and will be given basic practice questions after finishing the course. Story telling approach guides children to learn programming concepts step by step using story telling. Focused on storytelling approach and interactivity via voice conversation to learn programming language for children.
{"title":"CodeJr: Comprehensive Programming Application for Children","authors":"M.D.C. Muthuthanthirige J, Illangasinghe U.P, Illangasinghe D.N, I. Halgaswatta, Uthpala Samarakoon, N.C Amarasena","doi":"10.1109/ICAC57685.2022.10025092","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025092","url":null,"abstract":"Since the beginning of the millennium, computer technology has been the key area of concern and developing essential programming knowledge and intellectual skills from the young age have proven that they will gain more success in their careers. The ideology behind this research is, the problem with absence of a complete multi-disciplinary and interactive programming application for children between the age of 10 - 15 years, to learn programming concepts with a well-established text-based programming language. There are 4 major approaches in this research. Gamification approach focuses on expressing knowledge about Python programming via a game while concentrating on low perfumers. Collaborative approach aims to deliver a brand-new experience for children by aggregating cooperative methodologies and Artificial Intelligence with learning to enforce mutual learning. This component is based on collaborative sessions which allow a group of students with similar interest to join to learn python programming. Drag-drop approach enables children to learn Python language through videos and will be given basic practice questions after finishing the course. Story telling approach guides children to learn programming concepts step by step using story telling. Focused on storytelling approach and interactivity via voice conversation to learn programming language for children.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"4 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":"127216994","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/ICAC57685.2022.10025087
A. Jayawardena, Kasuni Ganegoda, Sakuni Imbulana, Gavin Gunapala, N. Kodagoda, Thilini Jayasinghe
This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries; leaf disease identification; cover crop identification; and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology.
{"title":"Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka.","authors":"A. Jayawardena, Kasuni Ganegoda, Sakuni Imbulana, Gavin Gunapala, N. Kodagoda, Thilini Jayasinghe","doi":"10.1109/ICAC57685.2022.10025087","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025087","url":null,"abstract":"This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries; leaf disease identification; cover crop identification; and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"28 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":"121117957","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/ICAC57685.2022.10025174
M.A.P Jayawardena, M.H.F.M Mahadi Hassan, M.I.A Aflal, W.A.H Weerathunga, S. Harshanath, U. U. Samantha Rajapaksha
In today’s world, it is very common among children to use a smartphone or a handheld digital device such as a tablet to entertain themselves and as a medium of socializing with people easily. The COVID-19 pandemic forced many people to stay in their homes and rely on these digital devices to do their day-to-day work and communication. The latter caused the increase in reliance on digital devices to acquire information about the outside world and as a source of entertainment. This new tendency increased the likelihood of children being exposed to pornography, cyberbullying, cyberstalking, excessive gaming, sexting, and behavioral traits related to narcissism. These habits caused many children to develop psychological and physiological illnesses, which affected them in the short term and, for some, which affected them and their families in the long run, such as suicide. Our research proposes to constantly monitor behavioral patterns such as this, notify the relevant individuals, and prevent the children from being prone to such ill fates. According to the findings, using machine learning and natural language processing, sexting, phonographic words, and cyberbullying can all be recognized with pinpoint accuracy. Also, by using two machine learning models, depression and anxiety are detected with an accuracy of 0.84 and 0.86. To prevent and analyze computer vision syndrome caused by improper face-screen distance. An image processing-based algorithm is used to measure the distance from face to screen, and results are narrowed down to an accuracy of 1 inch.
{"title":"Monitoring System for Underage Smart Phone Users","authors":"M.A.P Jayawardena, M.H.F.M Mahadi Hassan, M.I.A Aflal, W.A.H Weerathunga, S. Harshanath, U. U. Samantha Rajapaksha","doi":"10.1109/ICAC57685.2022.10025174","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025174","url":null,"abstract":"In today’s world, it is very common among children to use a smartphone or a handheld digital device such as a tablet to entertain themselves and as a medium of socializing with people easily. The COVID-19 pandemic forced many people to stay in their homes and rely on these digital devices to do their day-to-day work and communication. The latter caused the increase in reliance on digital devices to acquire information about the outside world and as a source of entertainment. This new tendency increased the likelihood of children being exposed to pornography, cyberbullying, cyberstalking, excessive gaming, sexting, and behavioral traits related to narcissism. These habits caused many children to develop psychological and physiological illnesses, which affected them in the short term and, for some, which affected them and their families in the long run, such as suicide. Our research proposes to constantly monitor behavioral patterns such as this, notify the relevant individuals, and prevent the children from being prone to such ill fates. According to the findings, using machine learning and natural language processing, sexting, phonographic words, and cyberbullying can all be recognized with pinpoint accuracy. Also, by using two machine learning models, depression and anxiety are detected with an accuracy of 0.84 and 0.86. To prevent and analyze computer vision syndrome caused by improper face-screen distance. An image processing-based algorithm is used to measure the distance from face to screen, and results are narrowed down to an accuracy of 1 inch.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"7 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":"121970570","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/ICAC57685.2022.10025139
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/ICAC57685.2022.10025115
K. Perera, M. Pathirana, P. G. R. C. Wijayarathna, N.C Amarasena, C. D. Hewabathma
Prehistoric stone tools can be considered one of the oldest artifacts created by ancient humans. Lithic archeology’s study of stone tools provides important information about early humans’ technologies, agility, and mental and innovative abilities. A vital issue in lithic archeology is the identification and analysis of stone tools found at the excavation sites. Archeologists need to observe and analyze a stone tool under different aspects for a long time to verify whether it is a stone tool or a geofact, the techniques used to create it, and identify its rough relative date and functional value. This can be challenging for amateur scholars studying archeology since it requires a lot of experience and time to identify by a glance. As a solution, ‘Stonelia,’ a mobile-based android application, can be introduced to identify and analyze stone tools. The images captured through the mobile app are preprocessed using image processing. Using Convolutional Neural Network models identifies the stone artifact from a geofact, the mineral type, the rough relative date, techniques used to create the stone artifact, and its functional value. This mobile application provides prompt identification and analysis of stone artifacts within a short time and with higher accuracy.
{"title":"“Stonelia” – Prehistoric Stone Tool Identification Android App for Archaeological Researchers","authors":"K. Perera, M. Pathirana, P. G. R. C. Wijayarathna, N.C Amarasena, C. D. Hewabathma","doi":"10.1109/ICAC57685.2022.10025115","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025115","url":null,"abstract":"Prehistoric stone tools can be considered one of the oldest artifacts created by ancient humans. Lithic archeology’s study of stone tools provides important information about early humans’ technologies, agility, and mental and innovative abilities. A vital issue in lithic archeology is the identification and analysis of stone tools found at the excavation sites. Archeologists need to observe and analyze a stone tool under different aspects for a long time to verify whether it is a stone tool or a geofact, the techniques used to create it, and identify its rough relative date and functional value. This can be challenging for amateur scholars studying archeology since it requires a lot of experience and time to identify by a glance. As a solution, ‘Stonelia,’ a mobile-based android application, can be introduced to identify and analyze stone tools. The images captured through the mobile app are preprocessed using image processing. Using Convolutional Neural Network models identifies the stone artifact from a geofact, the mineral type, the rough relative date, techniques used to create the stone artifact, and its functional value. This mobile application provides prompt identification and analysis of stone artifacts within a short time and with higher accuracy.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"40 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":"126112498","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/ICAC57685.2022.10025150
Salitha Ekanayaka, Akash Anawaratne, Taneesha Ayeshmanthi, Menaka Dilanka, N. S. Aratchige, J. Wijekoon, Dilani Lunugalage
The coconut plant plays a significant role in the Sri Lankan domestic and export industries. It is a major livelihood crop of which more than 65% is consumed locally. However, most coconut trees suffer from various pest and disease outbreaks, which have an impact on the economy of coconut production. Out of them, infestations of Whiteflies, Plesispa Beetle, and Red Palm Weevil are destructive to the coconut plant at different stages, so early detection of those infections is a major task. To this end, the paper describes an IoT-based prediction system for detecting and classifying infections in the coconut industry.; Internet of Things (IoT), image processing, audio processing, and deep learning were used as techniques to utilize for the detection of those infestations. Audio and Image-capturing devices are developed to collect audio and image data. Additionally, there’s a knowledge dissemination system to identify the main coconut pests in Sri Lanka and share this knowledge with farmers. With the audio and image datasets gathered from the mentioned diseases, performance evaluation of the Deep Learning (DL) models revealed that the accuracy of the identifications of Red Palm Weevil infestation Plesispa beetle and Whitefly infestations is 88, 96, and 98% respectively.
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