Pub Date : 2022-12-09DOI: 10.1109/ICAC57685.2022.10025101
K. I. Prihan Nimsara, J. Bodaragama, K. A. Roshan Maduwantha, S. Fernando
IoT technology-based process automation that can be applied to a greenhouse leads to making condition management and status monitoring more robust while leading to saving energy and resources. The proposed system which is based on IoT technology and MQTT protocol can set optimal growth conditions for plant and seed growth within the greenhouse. The sensor-based inputs are to be transformed into the processed values based on the defined logic and the standard benchmarks gathered from the local agricultural authorities. The key areas of condition monitoring to be done via temperature, humidity, soil moisture, and lighting can ultimately yield an increased harvest having supported both the plant and seeds-based implementations for multiple types of plants. One of the most important factors to consider is that the farmers can have energy savings through the proposed solution by controlling the actuators in an optimal manner and reducing manual intervention by a considerable amount. The excess usage of electricity by lights and cooling fan usage in the greenhouse can be controlled with real-time data tracking and better analytics. The use of water can be properly maintained for the plants by putting only the required amount will make the soil wet and spraying the required amount to air will make better humidity control. Thus, the real-time condition-based controlling of the actuators leads to making the greenhouse operations more optimal and better utilization of resources and energy which ultimately results in financial benefits for the greenhouse owner. Based on the evaluated power consumption of the greenhouse power usage before and after the system was installed, the newly introduced system can save energy by having optimal control of actuators by performing algorithmic calculations to meet only the required level of weather conditions. This is to be proven experimentally by implementing the proposed system for a defined period of time under the monitoring of energy usage.
{"title":"Energy and Operations Optimization for Effective Greenhouse Management","authors":"K. I. Prihan Nimsara, J. Bodaragama, K. A. Roshan Maduwantha, S. Fernando","doi":"10.1109/ICAC57685.2022.10025101","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025101","url":null,"abstract":"IoT technology-based process automation that can be applied to a greenhouse leads to making condition management and status monitoring more robust while leading to saving energy and resources. The proposed system which is based on IoT technology and MQTT protocol can set optimal growth conditions for plant and seed growth within the greenhouse. The sensor-based inputs are to be transformed into the processed values based on the defined logic and the standard benchmarks gathered from the local agricultural authorities. The key areas of condition monitoring to be done via temperature, humidity, soil moisture, and lighting can ultimately yield an increased harvest having supported both the plant and seeds-based implementations for multiple types of plants. One of the most important factors to consider is that the farmers can have energy savings through the proposed solution by controlling the actuators in an optimal manner and reducing manual intervention by a considerable amount. The excess usage of electricity by lights and cooling fan usage in the greenhouse can be controlled with real-time data tracking and better analytics. The use of water can be properly maintained for the plants by putting only the required amount will make the soil wet and spraying the required amount to air will make better humidity control. Thus, the real-time condition-based controlling of the actuators leads to making the greenhouse operations more optimal and better utilization of resources and energy which ultimately results in financial benefits for the greenhouse owner. Based on the evaluated power consumption of the greenhouse power usage before and after the system was installed, the newly introduced system can save energy by having optimal control of actuators by performing algorithmic calculations to meet only the required level of weather conditions. This is to be proven experimentally by implementing the proposed system for a defined period of time under the monitoring of energy usage.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"11 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":"122308547","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}
The customer journey is a full interaction that a customer has with a business. Every touchpoint of the business is an opportunity to provide good experiences that encourage future opportunities to become customers and consumers to be committed loyal customers through the customer journey. This research paper refers to the student’s journey at university as a customer journey & considers the student’s actions to map the next suitable actions. This paper proposed a machine learning-based novel approach to recommending the suitable next best action for the students based on their past performance at university by using customer journey orchestration and optimization. Customer journey orchestration is the process of coordinating customer experiences in real-time to encourage better engagement with the systems and organization. The journey orchestration of university students is currently a manual flow. The main goal of this research is to convert the manual flow of university journey orchestration into an automated flow. The proposed system orchestrates and optimizes the student journeys at each milestone of the university by recommending the suitable path or next best action as the outcome to help students make a successful path throughout their university journey. This research contributes to achieving the educational goals and professional career goals of university students successfully. Furthermore, from the perspective of the university, this proposed system supports everything to facilitate better directions for the students to complete their studies successfully.
{"title":"An Automated Tool for Student Journey Orchestration & Optimization using Machine Learning","authors":"Ramanayaka D.Y, Liyanagunawardana A.P, E.M.T.K. Ekanayake B, Weerarathna U.U, T.B. Jayasingha, Thusithanjana Thilakarthna","doi":"10.1109/ICAC57685.2022.10025114","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025114","url":null,"abstract":"The customer journey is a full interaction that a customer has with a business. Every touchpoint of the business is an opportunity to provide good experiences that encourage future opportunities to become customers and consumers to be committed loyal customers through the customer journey. This research paper refers to the student’s journey at university as a customer journey & considers the student’s actions to map the next suitable actions. This paper proposed a machine learning-based novel approach to recommending the suitable next best action for the students based on their past performance at university by using customer journey orchestration and optimization. Customer journey orchestration is the process of coordinating customer experiences in real-time to encourage better engagement with the systems and organization. The journey orchestration of university students is currently a manual flow. The main goal of this research is to convert the manual flow of university journey orchestration into an automated flow. The proposed system orchestrates and optimizes the student journeys at each milestone of the university by recommending the suitable path or next best action as the outcome to help students make a successful path throughout their university journey. This research contributes to achieving the educational goals and professional career goals of university students successfully. Furthermore, from the perspective of the university, this proposed system supports everything to facilitate better directions for the students to complete their studies successfully.","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":"122402231","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/ICAC57685.2022.10025147
F.S. Nizer A, R. Iksudha Bhargavi, P. Agalyah, M. Raveendran, Anuththara Kuruppu, Shalini Rupasinghe
Public speaking is the most common form of fear, and everyone feels uneasy with it. Fear of speaking in public is commonly called “glossophobia,” where people are discouraged from speaking in front of people due to embarrassment and rejection. Public speaking anxiety (PSA) is one of the most universal subtypes of anxiety where people fear, lose their confidence, and become uncomfortable physically and mentally. But public speaking is considered important in the educational sector and workplaces, where people get higher opportunities. Therefore, clubs like Toastmasters help people overcome their fear of public speaking and improve their confidence. We are launching the idea of a Smart Monitoring and Reporting Toastmasters System for people to improve their public speaking so they do not need a supervisor or mentor to train them. This smart monitoring system recognizes the candidate through image processing and deep learning. Moreover, this will analyze some features from the candidates’ speeches, such as facial emotion recognition, speech recognition, hand and body gesture recognition, and the candidates’ attire and appearance separately. This system will identify their mistakes and flaws and provide overall feedback to the users on the speech provided by the candidate. By implementing this web application, users can train themselves without a supervisor, and they can improve themselves and gain the confidence to participate in a Toastmasters competition as perfect candidates.
{"title":"ELIZA: Smart Monitoring and Reporting Toast Master System","authors":"F.S. Nizer A, R. Iksudha Bhargavi, P. Agalyah, M. Raveendran, Anuththara Kuruppu, Shalini Rupasinghe","doi":"10.1109/ICAC57685.2022.10025147","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025147","url":null,"abstract":"Public speaking is the most common form of fear, and everyone feels uneasy with it. Fear of speaking in public is commonly called “glossophobia,” where people are discouraged from speaking in front of people due to embarrassment and rejection. Public speaking anxiety (PSA) is one of the most universal subtypes of anxiety where people fear, lose their confidence, and become uncomfortable physically and mentally. But public speaking is considered important in the educational sector and workplaces, where people get higher opportunities. Therefore, clubs like Toastmasters help people overcome their fear of public speaking and improve their confidence. We are launching the idea of a Smart Monitoring and Reporting Toastmasters System for people to improve their public speaking so they do not need a supervisor or mentor to train them. This smart monitoring system recognizes the candidate through image processing and deep learning. Moreover, this will analyze some features from the candidates’ speeches, such as facial emotion recognition, speech recognition, hand and body gesture recognition, and the candidates’ attire and appearance separately. This system will identify their mistakes and flaws and provide overall feedback to the users on the speech provided by the candidate. By implementing this web application, users can train themselves without a supervisor, and they can improve themselves and gain the confidence to participate in a Toastmasters competition as perfect candidates.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"221 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":"122515576","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/ICAC57685.2022.10025129
S.F.M. Abraar, D.T. Thuduhenage, V. Balasubramaniyam, S. Mohanraj, G. Wimalaratne, S. Rajapaksha
In the present world, the IT (Information Technology) industry is so advanced that it has opened many opportunities to communities with numerous roles. Even though the industry is growing day by day and providing more opportunities, it has had serious effects on human well-being. If a person fails to control the demands of work or study, such as tasks with higher complexity, an unmanageable workload, pressure, enduring conflicts within the team, and other physical and emotional demands, it could lead that person to exhaustion, anxiety, and stress. Such factors can affect the health of a person in an extremely negative way. The proposed topic “Smart Diary: Auto generation of diary and Prioritization of Daily Activities for Improved Well-Being” is a solution for people with uncontrolled job demands and busy work schedules. This helps to keep track of day-to-day life activities and review them to make better plans for the future. It also helps the user prioritize their daily tasks and provides suggestions for people who are stressed and showcasing negative emotions based on text analysis.
{"title":"SMART DIARY: Autonomous System for Daily Diary Creation and Prioritization of Daily Activities for Improved Well-Being Using Neural Networks and Machine Learning","authors":"S.F.M. Abraar, D.T. Thuduhenage, V. Balasubramaniyam, S. Mohanraj, G. Wimalaratne, S. Rajapaksha","doi":"10.1109/ICAC57685.2022.10025129","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025129","url":null,"abstract":"In the present world, the IT (Information Technology) industry is so advanced that it has opened many opportunities to communities with numerous roles. Even though the industry is growing day by day and providing more opportunities, it has had serious effects on human well-being. If a person fails to control the demands of work or study, such as tasks with higher complexity, an unmanageable workload, pressure, enduring conflicts within the team, and other physical and emotional demands, it could lead that person to exhaustion, anxiety, and stress. Such factors can affect the health of a person in an extremely negative way. The proposed topic “Smart Diary: Auto generation of diary and Prioritization of Daily Activities for Improved Well-Being” is a solution for people with uncontrolled job demands and busy work schedules. This helps to keep track of day-to-day life activities and review them to make better plans for the future. It also helps the user prioritize their daily tasks and provides suggestions for people who are stressed and showcasing negative emotions based on text analysis.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"83 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":"114260272","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/ICAC57685.2022.10025201
Kajathees Premendran, S.B.D.D. Bopearachchi, Str Senevirathna, Sithpavan Giridaran, K. Archchana, D. Ganegoda, S. Thelijjagoda
As a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.
{"title":"Assistant Zone – Homeschooling Assistance System based on Natural Language Processing","authors":"Kajathees Premendran, S.B.D.D. Bopearachchi, Str Senevirathna, Sithpavan Giridaran, K. Archchana, D. Ganegoda, S. Thelijjagoda","doi":"10.1109/ICAC57685.2022.10025201","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025201","url":null,"abstract":"As a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"90 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":"114327860","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}
Clothing has been one of the basic human needs since ancient times. It is a common thing to try on clothes and consider certain features when buying clothes. With the current pandemic situation, it is risky to wear and buy clothes by physical shopping. Consequently, people do online shopping. Those existing shopping websites are not user-friendly and less reliable as the customers will not have the privilege to purchase the exactly fitting outfit. Therefore, the customer satisfaction level is low with the clothes they have bought through online platforms. Therefore, the aim is to utilize technology to provide a virtual fitting room experience on handheld devices. The objective is to create a customized 3D avatar that represents the customer’s unique body shapes and features, which allows to try on clothes. This avatar is 360 degrees rotatable with pre-defined poses to check what the fit-on looks like. This solution shows whether the clothes are too fit or loose for the customer by showing live wrinkles. The text and voice feedback are generated at the end, which would be helpful for differently-abled people, especially those with vision issues.
{"title":"Elegant Fit-On – Virtual Fitting Room on Handheld Devices","authors":"R.R.N.P.A.B.W.M.S.R Galagoda, E.H.N.L. Gunarathne, K.A.D. Maheshi Purnima, H.P.C.S. Wickramarathna, Shyam Reyal, S. Siriwardana","doi":"10.1109/ICAC57685.2022.10025242","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025242","url":null,"abstract":"Clothing has been one of the basic human needs since ancient times. It is a common thing to try on clothes and consider certain features when buying clothes. With the current pandemic situation, it is risky to wear and buy clothes by physical shopping. Consequently, people do online shopping. Those existing shopping websites are not user-friendly and less reliable as the customers will not have the privilege to purchase the exactly fitting outfit. Therefore, the customer satisfaction level is low with the clothes they have bought through online platforms. Therefore, the aim is to utilize technology to provide a virtual fitting room experience on handheld devices. The objective is to create a customized 3D avatar that represents the customer’s unique body shapes and features, which allows to try on clothes. This avatar is 360 degrees rotatable with pre-defined poses to check what the fit-on looks like. This solution shows whether the clothes are too fit or loose for the customer by showing live wrinkles. The text and voice feedback are generated at the end, which would be helpful for differently-abled people, especially those with vision issues.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"85 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":"121752728","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/ICAC57685.2022.10025039
Shehani Fernando, Nethmi Jayaweera, Sandini Pitawala, R. Kaushalya, Pasangi Ratnayake, S. Siriwardana
Ornamental Fish Industry continues to be one of the fastest growing sectors worldwide. Healthy fish production at aquariums requires intensive care and ensures a stable and an optimum production environment inside the fish tanks, which is a challenging task. Unfortunately, due to the limitations in fish industry, productivity of well-developed, healthy fish has drastically depreciated. Limited skills and knowledge of aquarists have been a challenging task which has led to inaccurate predictions on certain factors such as quantification and length of estimation, amounts and types of fish food and servicing the filters at proper time intervals. Existing aquariums depend on the experience and availability of the aquarists, which can be a challenging process in real life. Developing a system to regulate these major concerns is a prominent solution. This research is done to propose an automated method, with the help of several fish aquariums and existing research papers, to encounter the mentioned major concerns which affects the aquarists and other stakeholders.
{"title":"Smart Caring System for Ornamental Fish","authors":"Shehani Fernando, Nethmi Jayaweera, Sandini Pitawala, R. Kaushalya, Pasangi Ratnayake, S. Siriwardana","doi":"10.1109/ICAC57685.2022.10025039","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025039","url":null,"abstract":"Ornamental Fish Industry continues to be one of the fastest growing sectors worldwide. Healthy fish production at aquariums requires intensive care and ensures a stable and an optimum production environment inside the fish tanks, which is a challenging task. Unfortunately, due to the limitations in fish industry, productivity of well-developed, healthy fish has drastically depreciated. Limited skills and knowledge of aquarists have been a challenging task which has led to inaccurate predictions on certain factors such as quantification and length of estimation, amounts and types of fish food and servicing the filters at proper time intervals. Existing aquariums depend on the experience and availability of the aquarists, which can be a challenging process in real life. Developing a system to regulate these major concerns is a prominent solution. This research is done to propose an automated method, with the help of several fish aquariums and existing research papers, to encounter the mentioned major concerns which affects the aquarists and other stakeholders.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"6 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":"115910548","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/ICAC57685.2022.10025123
Mathanika Mannavarasan, Vishakanan Sivarajah, A. Gamage, S. Chandrasiri
Carbon emission reduction is a worldwide priority. Businesses that refuse to change will face problems in the future. Reduced greenhouse gas emissions should be a key priority for every large, medium, or small firm. Governments also enforce many rules to control GHG emissions. Companies, on the other hand, tend to limit their carbon emissions. Collecting and keeping emission factors is a vital responsibility for every firm. A single business analyst (BA) or a small BA team is generally in charge of this. Collecting data about emission activities from various sources is a time-consuming effort for a business analyst, and it can sometimes be inaccurate. They usually capture emission data after the emission process has been finished for a more extended period, and most of these procedures are done manually. Therefore, there will be no real-time data on the organization’s emissions and no real-time data on the organization’s emissions. The solution of text input is implemented in a mobile application that takes the emission details from the employee’s text. From the text emission factors, named entity recognition techniques will be extracted. The extracted factors will be forwarded to the search system to search for emission factors and provide ranked results.
{"title":"Emission Activity Parts Extraction using custom Named Entity Recognition","authors":"Mathanika Mannavarasan, Vishakanan Sivarajah, A. Gamage, S. Chandrasiri","doi":"10.1109/ICAC57685.2022.10025123","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025123","url":null,"abstract":"Carbon emission reduction is a worldwide priority. Businesses that refuse to change will face problems in the future. Reduced greenhouse gas emissions should be a key priority for every large, medium, or small firm. Governments also enforce many rules to control GHG emissions. Companies, on the other hand, tend to limit their carbon emissions. Collecting and keeping emission factors is a vital responsibility for every firm. A single business analyst (BA) or a small BA team is generally in charge of this. Collecting data about emission activities from various sources is a time-consuming effort for a business analyst, and it can sometimes be inaccurate. They usually capture emission data after the emission process has been finished for a more extended period, and most of these procedures are done manually. Therefore, there will be no real-time data on the organization’s emissions and no real-time data on the organization’s emissions. The solution of text input is implemented in a mobile application that takes the emission details from the employee’s text. From the text emission factors, named entity recognition techniques will be extracted. The extracted factors will be forwarded to the search system to search for emission factors and provide ranked results.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"10 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113932473","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/ICAC57685.2022.10025134
Sachini Sumeera, Nipun Pesala, Maleesha Thilani, A. Gamage, P. Bandara
The fisheries industry is vital to the Sri Lankan economy because it provides a living for more than 2.5 million coastal communities and meets more than half of the country’s animal protein needs. Today, the fishery community in Sri Lanka is facing several grant problems. Among them, not getting a decent fish price for their harvesting, the inability to identify diseases in shrimp cages in the early stages, and the inability to identify fish species by observing their external appearance. This research developed a prototype mobile application “Malu Malu” to avoid the above-mentioned problems. It facilitates to the prediction of market fish prices, identifying shrimp diseases in their early stages, and identifying fish species by observing their external appearance. The proposed predictive models of the “Malu Malu” contains three main models developed using inseption V3 Convolutional Neural Network (CNN) model for image classification and Linear Regression is used for creating a model for predictions. The experimental results of these models showed above 85% of accuracy.
{"title":"Analyzing Fisheries Market, Shrimp Farming & Identifying Fish Species using Image Processing","authors":"Sachini Sumeera, Nipun Pesala, Maleesha Thilani, A. Gamage, P. Bandara","doi":"10.1109/ICAC57685.2022.10025134","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025134","url":null,"abstract":"The fisheries industry is vital to the Sri Lankan economy because it provides a living for more than 2.5 million coastal communities and meets more than half of the country’s animal protein needs. Today, the fishery community in Sri Lanka is facing several grant problems. Among them, not getting a decent fish price for their harvesting, the inability to identify diseases in shrimp cages in the early stages, and the inability to identify fish species by observing their external appearance. This research developed a prototype mobile application “Malu Malu” to avoid the above-mentioned problems. It facilitates to the prediction of market fish prices, identifying shrimp diseases in their early stages, and identifying fish species by observing their external appearance. The proposed predictive models of the “Malu Malu” contains three main models developed using inseption V3 Convolutional Neural Network (CNN) model for image classification and Linear Regression is used for creating a model for predictions. The experimental results of these models showed above 85% of accuracy.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"194 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":"134040592","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/ICAC57685.2022.10025338
M.A.A. Udara, D.G. Wimalki Dilshani, M.S.W. Mahalekam, V.Y. Wickramaarachchi, J. Krishara, D. Wijendra
In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.
{"title":"Face Skin Disease Detection and Community based Doctor Recommendation System","authors":"M.A.A. Udara, D.G. Wimalki Dilshani, M.S.W. Mahalekam, V.Y. Wickramaarachchi, J. Krishara, D. Wijendra","doi":"10.1109/ICAC57685.2022.10025338","DOIUrl":"https://doi.org/10.1109/ICAC57685.2022.10025338","url":null,"abstract":"In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.","PeriodicalId":292397,"journal":{"name":"2022 4th International Conference on Advancements in Computing (ICAC)","volume":"66 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":"131646595","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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