Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025148
W.M.S.K. Wijesinghe, Muditha Tissera
Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as “Class_Label_Suggester” was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for ‘Ground’, 99.53% for ‘School’, 98.55% for ‘Tournament’, and 97.87% for ‘Other’ classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study.
{"title":"Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports","authors":"W.M.S.K. Wijesinghe, Muditha Tissera","doi":"10.1109/ICAC57685.2022.10025148","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025148","url":null,"abstract":"Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as “Class_Label_Suggester” was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for ‘Ground’, 99.53% for ‘School’, 98.55% for ‘Tournament’, and 97.87% for ‘Other’ classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"42 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":"122943221","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/ICAC57685.2022.10025237
K. Silva, Rashmin Induwara, Malshan Wimukthi, Sathsarani Poornika, Udara Srimath S. Samaratunge Arachchillage, Thilini Jayalath
With the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students’ performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students’ focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities.
{"title":"E-tutor: Comprehensive Student Productivity Management System for Education","authors":"K. Silva, Rashmin Induwara, Malshan Wimukthi, Sathsarani Poornika, Udara Srimath S. Samaratunge Arachchillage, Thilini Jayalath","doi":"10.1109/ICAC57685.2022.10025237","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025237","url":null,"abstract":"With the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students’ performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students’ focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"13 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":"122986071","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}
E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.
{"title":"E-Learning Assistive System for Deaf and Mute Students","authors":"Pamaljith Ranasinghe, Kaveen Akash, Lumini Nanayakkara, Hiruni Perera, S. Chandrasiri, Suriyaa Kumari","doi":"10.1109/ICAC57685.2022.10025212","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025212","url":null,"abstract":"E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"64 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":"117335668","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/ICAC57685.2022.10025255
D.M.S.J Dahanaka, Ayesha Wijesooriya, D.S.S Wickramasinghage, G.V.C Bhaggya, S. Harshanath, U. U. Samantha Rajapaksha
In this research paper, we focus on how multitasking robots team up to clean a city. In particular, we consider how they build their team, how they position themselves in their positions, how they work with teams, how they face obstacles along the way, and how to move groups out of control in an emergency. We use a leader-follower strategy here, and we are also tasked with selecting a leader for each group. The leader finds the shortest route to avoid the obstacle by considering the obstacle details such as obstacle location, obstacle width, and destination. The leader decides the best way for the team to go. If the leader wants to change the group, it gives the message to the relevant member. In the event of meeting an obstacle, it changes its shape and moves. A Robot Operating System (ROS) framework was created to perform real-time experiments with ROS-capable mobile robotic TURTLEBOTs to evaluate this control strategy. Simulations performed on a mobile robot team demonstrate the effectiveness of the proposed approach.
{"title":"Group Formation and Communication of Multitasking Multi-Robots for Smart City Cleaning Process","authors":"D.M.S.J Dahanaka, Ayesha Wijesooriya, D.S.S Wickramasinghage, G.V.C Bhaggya, S. Harshanath, U. U. Samantha Rajapaksha","doi":"10.1109/ICAC57685.2022.10025255","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025255","url":null,"abstract":"In this research paper, we focus on how multitasking robots team up to clean a city. In particular, we consider how they build their team, how they position themselves in their positions, how they work with teams, how they face obstacles along the way, and how to move groups out of control in an emergency. We use a leader-follower strategy here, and we are also tasked with selecting a leader for each group. The leader finds the shortest route to avoid the obstacle by considering the obstacle details such as obstacle location, obstacle width, and destination. The leader decides the best way for the team to go. If the leader wants to change the group, it gives the message to the relevant member. In the event of meeting an obstacle, it changes its shape and moves. A Robot Operating System (ROS) framework was created to perform real-time experiments with ROS-capable mobile robotic TURTLEBOTs to evaluate this control strategy. Simulations performed on a mobile robot team demonstrate the effectiveness of the proposed approach.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"58 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":"115529022","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 present era, mental health has become one of the most neglected, yet critically important, factors of our overall well-being. A large number of people are affected by various types of mental illnesses and mental health disorders. Stress, anxiety, and depression are the most common disorders among children and adolescents in Sri Lanka, and their prevalence has increased over the years, likely to require immediate medical attention. In today’s world, mobile phones and applications play an important role in everyone’s life. With the rapid growth of mental illness, mental health-focused apps and websites have gradually increased globally in recent years. This study aims to develop a mobile application that will primarily serve Sri Lankans with mental health problems, helping them identify their levels of stress, anxiety, and depression (ADS) and receiving advice on how to deal with them. This app’s main objective is to support those who are dealing with mental illnesses and raise awareness of them locally using machine learning and image processing techniques. It does this by serving as a constant reminder of how crucial mental health is and how much of an impact it has on daily life. The GSE Scale, DASS 21 scale has been used to find the users’ mental health illness and the severity of each mental health illness such and Anxiety depression and stress. These methods are put to our mobile application using machine learning techniques such as Decision tree and Random Forest classifiers and uses image processing technologies, CNN machine learning algorithm to offer a variety of activities for reliving stress, depression, and anxiety,
{"title":"Mobile Application for Mental Health Using Machine Learning","authors":"E.S Mendis, L.W Kasthuriarachchi, H.P.K.L Samarasinha, Sanvitha Kasthuriarachchi, Samantha Rajapaksa","doi":"10.1109/ICAC57685.2022.10025036","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025036","url":null,"abstract":"In present era, mental health has become one of the most neglected, yet critically important, factors of our overall well-being. A large number of people are affected by various types of mental illnesses and mental health disorders. Stress, anxiety, and depression are the most common disorders among children and adolescents in Sri Lanka, and their prevalence has increased over the years, likely to require immediate medical attention. In today’s world, mobile phones and applications play an important role in everyone’s life. With the rapid growth of mental illness, mental health-focused apps and websites have gradually increased globally in recent years. This study aims to develop a mobile application that will primarily serve Sri Lankans with mental health problems, helping them identify their levels of stress, anxiety, and depression (ADS) and receiving advice on how to deal with them. This app’s main objective is to support those who are dealing with mental illnesses and raise awareness of them locally using machine learning and image processing techniques. It does this by serving as a constant reminder of how crucial mental health is and how much of an impact it has on daily life. The GSE Scale, DASS 21 scale has been used to find the users’ mental health illness and the severity of each mental health illness such and Anxiety depression and stress. These methods are put to our mobile application using machine learning techniques such as Decision tree and Random Forest classifiers and uses image processing technologies, CNN machine learning algorithm to offer a variety of activities for reliving stress, depression, and anxiety,","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"79 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":"115540425","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/ICAC57685.2022.10025261
W. Weerasinghe, K.W.A.M. Somawansha, K.A. Jayanga Chandrasiri, T.M.S.Y.B. Thalagahagedara, K.B.A Bhagyanie Chathurika, N.H.P. Ravi Supunya Swarnakantha
The culture and economy of Sri Lanka heavily depend on agriculture. The All-Island Farmers Federation (AIFF) claims that post-harvest produce loss is a problem that has plagued farmers in all regions of Sri Lanka and occurs both on farms and in commercial locations. The lack of a suitable system to handle produce, such as fruits and vegetables, has been identified as the key problem. The process of sowing seeds to generating the harvest and transporting it to the consumers is an overly complex process. If this process is not correctly identified the demand and supply may not be at equilibrium. Farmers tend to take decisions based on their experiences or from the knowledge gathered from past generations. Over the year environmental factors as well as economic factors have changed, therefore there is a high chance that the decisions taken by farmers might lead to wastage of crops. This research hopes to produce a mobile application for the farmers by considering some factors that affect the wastage in crops and try to provide timely relevant information to minimize the crop wastage by deploying machine learning, one of the advanced technologies in crop prediction.
{"title":"SmartPredi – Development of Agricultural Crop Wastage Reduction System using Machine Learning","authors":"W. Weerasinghe, K.W.A.M. Somawansha, K.A. Jayanga Chandrasiri, T.M.S.Y.B. Thalagahagedara, K.B.A Bhagyanie Chathurika, N.H.P. Ravi Supunya Swarnakantha","doi":"10.1109/ICAC57685.2022.10025261","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025261","url":null,"abstract":"The culture and economy of Sri Lanka heavily depend on agriculture. The All-Island Farmers Federation (AIFF) claims that post-harvest produce loss is a problem that has plagued farmers in all regions of Sri Lanka and occurs both on farms and in commercial locations. The lack of a suitable system to handle produce, such as fruits and vegetables, has been identified as the key problem. The process of sowing seeds to generating the harvest and transporting it to the consumers is an overly complex process. If this process is not correctly identified the demand and supply may not be at equilibrium. Farmers tend to take decisions based on their experiences or from the knowledge gathered from past generations. Over the year environmental factors as well as economic factors have changed, therefore there is a high chance that the decisions taken by farmers might lead to wastage of crops. This research hopes to produce a mobile application for the farmers by considering some factors that affect the wastage in crops and try to provide timely relevant information to minimize the crop wastage by deploying machine learning, one of the advanced technologies in crop prediction.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"38 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":"131288555","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/ICAC57685.2022.10025337
Ravihari Jayasekara, K.A.N.D Kudarachchi, K. Kariyawasam, Dilini Sewwandi Rajapaksha, S.L Jayasinghe, S. Thelijjagoda
The HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.
{"title":"DevFlair: A Framework to Automate the Pre-screening Process of Software Engineering Job Candidates","authors":"Ravihari Jayasekara, K.A.N.D Kudarachchi, K. Kariyawasam, Dilini Sewwandi Rajapaksha, S.L Jayasinghe, S. Thelijjagoda","doi":"10.1109/ICAC57685.2022.10025337","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025337","url":null,"abstract":"The HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"204 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":"114647875","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/ICAC57685.2022.10025327
I.U. Ranaweera, G.K Weerakkody, B. Balasooriya, N. Swarnakantha, U. Rajapaksha
Every person has their way of relaxing and having fun. The most well-liked approach to do it is to own a pet. When most individuals work from home and anxiety levels are high, people have certain restrictions on going outdoors and engaging in activities due to the existing COVID scenario. Consequently, we developed a product called AquaScanner. The problems that come with the aquarium environment can all be handled by our product. Our product primarily consists of an application that can regulate and monitor aquarium tanks by regulating feeding routines, fish disease detection, and water quality monitoring. The AquaScanner focuses on recognizing two significant illnesses, Fin Rot and Fungi bacteria, under the heading of disease identification. Additionally, the product will recommend treatments for the illness and provide two distinct methods for feeding the fish manually and automatically through the application. The AquaScanner can regulate feeding operations. Also, AquaScanner can independently monitor all key water parameters as part of the water quality measurement system. A user-friendly interface connects these three key elements. Owners of aquariums may manage and keep an eye on their beloved aquariums from anywhere in the world.
{"title":"Image Processing and IoT-based Fish Diseases Identification and Fish Tank Monitoring System","authors":"I.U. Ranaweera, G.K Weerakkody, B. Balasooriya, N. Swarnakantha, U. Rajapaksha","doi":"10.1109/ICAC57685.2022.10025327","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025327","url":null,"abstract":"Every person has their way of relaxing and having fun. The most well-liked approach to do it is to own a pet. When most individuals work from home and anxiety levels are high, people have certain restrictions on going outdoors and engaging in activities due to the existing COVID scenario. Consequently, we developed a product called AquaScanner. The problems that come with the aquarium environment can all be handled by our product. Our product primarily consists of an application that can regulate and monitor aquarium tanks by regulating feeding routines, fish disease detection, and water quality monitoring. The AquaScanner focuses on recognizing two significant illnesses, Fin Rot and Fungi bacteria, under the heading of disease identification. Additionally, the product will recommend treatments for the illness and provide two distinct methods for feeding the fish manually and automatically through the application. The AquaScanner can regulate feeding operations. Also, AquaScanner can independently monitor all key water parameters as part of the water quality measurement system. A user-friendly interface connects these three key elements. Owners of aquariums may manage and keep an eye on their beloved aquariums from anywhere in the world.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"132 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":"124272236","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/ICAC57685.2022.10025103
W.A.I.U. Bandara, K. Kuruppuarachchi, N.N.D. Maduwantha, Udara Srimath S. Samaratunge Arachchillage, Tt Alwis, Thilmi Kuruppu
Pineapple cultivation has higher demand among the farming communities as a growing concern to engender foreign currency and as a means of earning more profit in the export industry of Sri Lanka. As a result, developing a good communication platform among the farming communities, experts and customers has become a key concern and would immensely contribute to its sustainability. According to our observations, key concerns to be addressed and supported by farmers on behalf of decision making to determine net profit for the yield are instructing to remedies for pineapple diseases at the right time, resolving issues during pineapple plantation, and getting guidance from experts in different phases of pineapple cultivation. Generating a product differentiation plan to gain the maximum benefit from the pineapple harvest is another goal that the proposed system would fulfill for farmers. The proposed mobile application solution, the Smart Intelligent Pineapple Farming Assistant Agent (SIPFAA), uses convolution neural networks (CNN) to identify diseases related to pineapples and uses a knowledgebase and chatbot to behave as a human counterpart. Further, a product differentiation plan would provide a sensible approach to gain a profit by analyzing the trends in the market while providing a recommendation system for buyer-seller interactions. As the initiators of applying these technologies to the pineapple domain, higher accuracy and a better harvest are expected through the proposed solution.
{"title":"Smart Intelligent Pineapple Farming Assistant Agent (SIPFAA)","authors":"W.A.I.U. Bandara, K. Kuruppuarachchi, N.N.D. Maduwantha, Udara Srimath S. Samaratunge Arachchillage, Tt Alwis, Thilmi Kuruppu","doi":"10.1109/ICAC57685.2022.10025103","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025103","url":null,"abstract":"Pineapple cultivation has higher demand among the farming communities as a growing concern to engender foreign currency and as a means of earning more profit in the export industry of Sri Lanka. As a result, developing a good communication platform among the farming communities, experts and customers has become a key concern and would immensely contribute to its sustainability. According to our observations, key concerns to be addressed and supported by farmers on behalf of decision making to determine net profit for the yield are instructing to remedies for pineapple diseases at the right time, resolving issues during pineapple plantation, and getting guidance from experts in different phases of pineapple cultivation. Generating a product differentiation plan to gain the maximum benefit from the pineapple harvest is another goal that the proposed system would fulfill for farmers. The proposed mobile application solution, the Smart Intelligent Pineapple Farming Assistant Agent (SIPFAA), uses convolution neural networks (CNN) to identify diseases related to pineapples and uses a knowledgebase and chatbot to behave as a human counterpart. Further, a product differentiation plan would provide a sensible approach to gain a profit by analyzing the trends in the market while providing a recommendation system for buyer-seller interactions. As the initiators of applying these technologies to the pineapple domain, higher accuracy and a better harvest are expected through the proposed solution.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"8 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":"122717517","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/ICAC57685.2022.10025166
Buddhima Attanayaka, D. Nawinna, K. Manathunga, P. Abeygunawardhana
Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.
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