Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118132
Neeti Pal, Omdev Dahiya
Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future.
{"title":"Analysis of Educational Recommender System Techniques for Enhancing Student's Learning Outcomes","authors":"Neeti Pal, Omdev Dahiya","doi":"10.1109/ICIPTM57143.2023.10118132","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118132","url":null,"abstract":"Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129255553","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118353
L. T, Tammineni Sreelatha, S. Chaudhary, R. Mapari, Janardhan Saikumar, Harshal Patil
In this analysis, the brain tumour is described through an experimental diagnostic procedure. For the identification of brain tumours, the magnetic resonance picture is called. 2% of the body weight is absorbed by the human brain. For the MRI-based brain tumour diagnosis, The CT scan image typically favours magnetic resonance images. While in multiple segmentation approaches occur, the water source is helpful to identify the zone of special concern. Of importance. The map of the horizontal and vertical areas reveals a highly successful and realistic approach.
{"title":"Brain tumour edge detection by segmentation technique","authors":"L. T, Tammineni Sreelatha, S. Chaudhary, R. Mapari, Janardhan Saikumar, Harshal Patil","doi":"10.1109/ICIPTM57143.2023.10118353","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118353","url":null,"abstract":"In this analysis, the brain tumour is described through an experimental diagnostic procedure. For the identification of brain tumours, the magnetic resonance picture is called. 2% of the body weight is absorbed by the human brain. For the MRI-based brain tumour diagnosis, The CT scan image typically favours magnetic resonance images. While in multiple segmentation approaches occur, the water source is helpful to identify the zone of special concern. Of importance. The map of the horizontal and vertical areas reveals a highly successful and realistic approach.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128577902","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118329
R. Mohandas, Aishwarya Vh, Akshatha Shree S, A. S, Khushi J
The extension of transportation networks has sped up the speed of our lives. Car crashes cause a significant measure of death, property harm, and inestimable time, which is a significant worldwide medical condition. It is viewed as one of the essential executioners in the advanced world. The plan of a savvy mishap identification, area following, and cautioning framework that can detect mishaps as they happen is shrouded in this article. To find the mishap's definite area, a GPS device is utilized. The Worldwide Framework for Versatile (GSM) module sends a notification message that remembers a connection to the mishap's area for a Google guide to the nearby emergency clinic and police control focus. They can do whatever it may take to facilitate the salvage exertion by visiting the connection to find where the mishap is in the area.
{"title":"Implementation of Accident Detection and Reporting System Using IOT","authors":"R. Mohandas, Aishwarya Vh, Akshatha Shree S, A. S, Khushi J","doi":"10.1109/ICIPTM57143.2023.10118329","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118329","url":null,"abstract":"The extension of transportation networks has sped up the speed of our lives. Car crashes cause a significant measure of death, property harm, and inestimable time, which is a significant worldwide medical condition. It is viewed as one of the essential executioners in the advanced world. The plan of a savvy mishap identification, area following, and cautioning framework that can detect mishaps as they happen is shrouded in this article. To find the mishap's definite area, a GPS device is utilized. The Worldwide Framework for Versatile (GSM) module sends a notification message that remembers a connection to the mishap's area for a Google guide to the nearby emergency clinic and police control focus. They can do whatever it may take to facilitate the salvage exertion by visiting the connection to find where the mishap is in the area.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121380583","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117728
K. V. Ramana, S. Arulkumar, Asmita Marathe, Kedir Beshir, V. Jaiganesh, K. Tamilselvi, M. Sudhakar
Our main focus was on creating a Two directional power converter for a standalone photo-voltaic energy-generating technology as well as technology energy control when a lead-acid energy storage device is used to control the energy supply. We utilized a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of our project as well as the functionality of energy control technologies. The suggested technology was composed of an energy control technology, a lead-acid battery, a bulb, a two-directional buck-boost converter, an utmost power point tracking controller, and these components. An energy control technique was developed to grow the rate at which the Photo-voltaic power producing technology consumed energy. The circuits are intended to charge the battery between upper and lower voltage limits, as well as to continuously check the battery's state of charge and add or release current as necessary. The bidirectional buck-boost converter's ability to function as a charge controller on its own, along with the use of particular BUCK and BOOST converter properties to optimize the home application, is the primary distinction between the method used in the proposed technology and other techniques used in the past. The battery is charged and discharged using a bidirectional dc-dc converter. Measurement data are employed to support the viability of a photovoltaic air conditioning technology.
{"title":"Design and Implementation of Renewable Energy Applications Based Bi-Directional Buck-Boost Converter","authors":"K. V. Ramana, S. Arulkumar, Asmita Marathe, Kedir Beshir, V. Jaiganesh, K. Tamilselvi, M. Sudhakar","doi":"10.1109/ICIPTM57143.2023.10117728","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117728","url":null,"abstract":"Our main focus was on creating a Two directional power converter for a standalone photo-voltaic energy-generating technology as well as technology energy control when a lead-acid energy storage device is used to control the energy supply. We utilized a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of our project as well as the functionality of energy control technologies. The suggested technology was composed of an energy control technology, a lead-acid battery, a bulb, a two-directional buck-boost converter, an utmost power point tracking controller, and these components. An energy control technique was developed to grow the rate at which the Photo-voltaic power producing technology consumed energy. The circuits are intended to charge the battery between upper and lower voltage limits, as well as to continuously check the battery's state of charge and add or release current as necessary. The bidirectional buck-boost converter's ability to function as a charge controller on its own, along with the use of particular BUCK and BOOST converter properties to optimize the home application, is the primary distinction between the method used in the proposed technology and other techniques used in the past. The battery is charged and discharged using a bidirectional dc-dc converter. Measurement data are employed to support the viability of a photovoltaic air conditioning technology.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281220","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118181
G. Sidharth, Abijeeth Vasra, S. Sridevi, C. Deisy, M. K. A. A. Khan
Grievance redressal is an indispensable service but involves a lot of issues, which can be resolved if a proper automated application is introduced which involves grievance classification and location fetching mechanism. To arrive at the solution, machine learning techniques can be used, but another major facet of this application is that it should be compatible and transportable. Hence the solution needs to be in the form of a mobile application. The machine learning model must be incorporated into the mobile application. Since mobile phones have minimal computational power to run a model, an architecture which uses minimal resources must be used. MobileNet V2 is an architecture which is specially designed to incorporate Deep learning (DL) algorithm especially Image classification. MobileNet uses minimal computational resources, and interoperability is achieved through Google's Teachable machine learning, which provides a tft lite (TensorFlow Lite) model for our trained dataset and the model can be imported in to the project's asset. Location manager of android's architecture can be used to fetch the user's current latitude and longitude, which can be used by grievance redressal organization to navigate. On achieving this solution, a lot of tedious processes in our existing grievance management system can be automated. Both the public and the government can be benefited and as a result, a lot of data will be in hand which is of prominent importance now a days.
申诉补救是一项不可或缺的服务，但涉及许多问题，如果引入适当的自动化应用程序(包括申诉分类和位置获取机制)，则可以解决这些问题。为了获得解决方案，可以使用机器学习技术，但该应用程序的另一个主要方面是它应该是兼容和可移植的。因此，解决方案需要以移动应用程序的形式出现。机器学习模型必须整合到移动应用程序中。由于移动电话运行模型的计算能力最小，因此必须使用使用最少资源的架构。MobileNet V2是一个专门为融合深度学习(DL)算法尤其是图像分类而设计的架构。MobileNet使用最少的计算资源，互操作性是通过谷歌的可教机器学习实现的，它为我们的训练数据集提供了一个tft lite (TensorFlow lite)模型，该模型可以导入到项目的资产中。android架构的位置管理器可以获取用户当前的经纬度，申诉组织可以使用这些经纬度进行导航。通过实现这个解决方案，我们现有的申诉管理系统中的许多繁琐的过程可以自动化。公众和政府都可以从中受益，因此，大量的数据将掌握在手中，这在现在是非常重要的。
{"title":"Automation of Grievance Registration using Transfer Learning","authors":"G. Sidharth, Abijeeth Vasra, S. Sridevi, C. Deisy, M. K. A. A. Khan","doi":"10.1109/ICIPTM57143.2023.10118181","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118181","url":null,"abstract":"Grievance redressal is an indispensable service but involves a lot of issues, which can be resolved if a proper automated application is introduced which involves grievance classification and location fetching mechanism. To arrive at the solution, machine learning techniques can be used, but another major facet of this application is that it should be compatible and transportable. Hence the solution needs to be in the form of a mobile application. The machine learning model must be incorporated into the mobile application. Since mobile phones have minimal computational power to run a model, an architecture which uses minimal resources must be used. MobileNet V2 is an architecture which is specially designed to incorporate Deep learning (DL) algorithm especially Image classification. MobileNet uses minimal computational resources, and interoperability is achieved through Google's Teachable machine learning, which provides a tft lite (TensorFlow Lite) model for our trained dataset and the model can be imported in to the project's asset. Location manager of android's architecture can be used to fetch the user's current latitude and longitude, which can be used by grievance redressal organization to navigate. On achieving this solution, a lot of tedious processes in our existing grievance management system can be automated. Both the public and the government can be benefited and as a result, a lot of data will be in hand which is of prominent importance now a days.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"455 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315894","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117738
Keshav, Sangeeta Rani
The “Big Data” phrase describes to the management of a wide range of organized and unstructured data with increasing speed and quantity. These datasets are conventional, large, and difficult to maintain. However, these datasets are used within a number of companies to perform various tasks on them as well as for organizational purposes and to provide a summary of the data currently being used. More precise and accurate business judgments can be make as a result of the growing volume of big data, which is now more affordable and available. The objective of this research paper is to demonstrate how to identify and use only the most significant and important data to be used in a follow-up investigation, help other researchers perform additional analysis, take into account only a limited number of data, ensuring that the study will always provide the best results. Although there are other methods and tools to extract data with certain filters. MongoDB uses the NoSQL model as the basis for query processing. To obtain data from a large data collection, query processing is used and it will continue to play an important role in future research and strategies for this work. The behaviour of extraction of data from Big Data and query processing on the bases input parameters that are going to use in Machine Learning. This process also termed as Data Mining which will show the behaviour of mining data from large amount of combine data. This paper show the behaviour implementation of Mongo DB on required parameter and will produce the efficient result.
{"title":"Big Data Query Processing Approach UsingMongoDB","authors":"Keshav, Sangeeta Rani","doi":"10.1109/ICIPTM57143.2023.10117738","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117738","url":null,"abstract":"The “Big Data” phrase describes to the management of a wide range of organized and unstructured data with increasing speed and quantity. These datasets are conventional, large, and difficult to maintain. However, these datasets are used within a number of companies to perform various tasks on them as well as for organizational purposes and to provide a summary of the data currently being used. More precise and accurate business judgments can be make as a result of the growing volume of big data, which is now more affordable and available. The objective of this research paper is to demonstrate how to identify and use only the most significant and important data to be used in a follow-up investigation, help other researchers perform additional analysis, take into account only a limited number of data, ensuring that the study will always provide the best results. Although there are other methods and tools to extract data with certain filters. MongoDB uses the NoSQL model as the basis for query processing. To obtain data from a large data collection, query processing is used and it will continue to play an important role in future research and strategies for this work. The behaviour of extraction of data from Big Data and query processing on the bases input parameters that are going to use in Machine Learning. This process also termed as Data Mining which will show the behaviour of mining data from large amount of combine data. This paper show the behaviour implementation of Mongo DB on required parameter and will produce the efficient result.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131799749","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117875
Abhijith Prabha
OLED displays are very popular among current system designs due various advantages like picture quality, response time, lightweight and lower thickness, etc. Still, one of the major design challenges when dealing with these displays is the amount of power consumed. In battery-operated computing devices, the display consumes almost 50% or more of the total system power. Though many methods are available to reduce power consumption like lowering the refresh rate, panel self-refresh, or adaptive dimming, still designers are looking for more room for optimization. As the design philosophy of mobile devices is directed towards extending the battery life, the methods to save power from the OLED display hold great relevance. This paper surveys the latest research and advancements on the topic.
{"title":"Power Saving and Optimization of OLED Displays for Better System Design: A Survey","authors":"Abhijith Prabha","doi":"10.1109/ICIPTM57143.2023.10117875","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117875","url":null,"abstract":"OLED displays are very popular among current system designs due various advantages like picture quality, response time, lightweight and lower thickness, etc. Still, one of the major design challenges when dealing with these displays is the amount of power consumed. In battery-operated computing devices, the display consumes almost 50% or more of the total system power. Though many methods are available to reduce power consumption like lowering the refresh rate, panel self-refresh, or adaptive dimming, still designers are looking for more room for optimization. As the design philosophy of mobile devices is directed towards extending the battery life, the methods to save power from the OLED display hold great relevance. This paper surveys the latest research and advancements on the topic.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134584161","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118192
R. Raman, T. Inbamalar, N. Pushpalatha, S. Meenakshi, Ashok Kumar, S. Razia, N. Gopinath
Arthritis is a bone disorder that includes swelling and pain in one or more joints. Everyone can develop osteoarthritis, but it grows more common as individuals get older. When arthritis deteriorates over time, it can lead to persistent pain, making it challenging to do daily tasks, and making activities like walking and climbing stairs painful and difficult. If arthritis is correctly identified and treated in its early stages, these consequences can be avoided. The goal of this project is to create two transfer learning models that, by spotting arthritis in its earliest stages, can lower the likelihood of acquiring chronic arthritis. For this purpose, Google served as the source of the images used in this study. After being purchased from Google, the data collection is preprocessed using three different methods. Image scaling, noise reduction, and image enhancement are a few of the pre-processing approaches. The transfer learning models are trained and assessed using this preprocessed dataset. In this work, two distinct transfer learning models are established. The models include SegNet and ENet. On a graph, the outcomes for the performances of both models are displayed. The training data from the first few epochs of the ENet model and SegNet model are also used in the analysis. The models' final accuracy and loss values are then assessed. In the end, it was discovered that the SegNet model had a lower loss value and more accuracy than the other. The model created in this study can be utilised as a preliminary test for arthritis when a person exhibits moderate arthritis symptoms because the final accuracy of the model is higher than or equal to 95%.
{"title":"Transfer Learning for Hand Arthritis Prediction from X-Ray Images","authors":"R. Raman, T. Inbamalar, N. Pushpalatha, S. Meenakshi, Ashok Kumar, S. Razia, N. Gopinath","doi":"10.1109/ICIPTM57143.2023.10118192","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118192","url":null,"abstract":"Arthritis is a bone disorder that includes swelling and pain in one or more joints. Everyone can develop osteoarthritis, but it grows more common as individuals get older. When arthritis deteriorates over time, it can lead to persistent pain, making it challenging to do daily tasks, and making activities like walking and climbing stairs painful and difficult. If arthritis is correctly identified and treated in its early stages, these consequences can be avoided. The goal of this project is to create two transfer learning models that, by spotting arthritis in its earliest stages, can lower the likelihood of acquiring chronic arthritis. For this purpose, Google served as the source of the images used in this study. After being purchased from Google, the data collection is preprocessed using three different methods. Image scaling, noise reduction, and image enhancement are a few of the pre-processing approaches. The transfer learning models are trained and assessed using this preprocessed dataset. In this work, two distinct transfer learning models are established. The models include SegNet and ENet. On a graph, the outcomes for the performances of both models are displayed. The training data from the first few epochs of the ENet model and SegNet model are also used in the analysis. The models' final accuracy and loss values are then assessed. In the end, it was discovered that the SegNet model had a lower loss value and more accuracy than the other. The model created in this study can be utilised as a preliminary test for arthritis when a person exhibits moderate arthritis symptoms because the final accuracy of the model is higher than or equal to 95%.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133229986","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 Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.
{"title":"Applications of Genetic Algorithm with Integrated Machine Learning","authors":"Arman Raj, Avneesh Kumar, Vandana Sharma, S. Rani, Ankit Kumar Shanu, Tanya Singh","doi":"10.1109/ICIPTM57143.2023.10118328","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118328","url":null,"abstract":"The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125618832","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118290
Narendra Kumar Jha, P. Shukla
Plants are one of the core components of human life and its surrounding environment. Various diseases not only diminish the ecological relevance of plants and the products they produce but also have an impact on their economic value. The main goal of this study is to identify plant kinds and design a suitable and effective method for judging a plant's healthiness based on pictures of its leaves in order to give a workable system for an instant and economical solution to this problem. The analysis of both biotic and abiotic elements that affect a plant's general health is known as plant pathology. Farmers must identify the issue quickly in order to take appropriate measures and stop additional losses. Consequently, it is recommended for this study to use a Deep Convolutional Neural Network (DCNN) to categorise damaged leaves. A genuine dataset of 4503 images of the 12 diverse tree leaves that were gathered at the “Shri Mata Vaishno Devi University in Katra, J&K, India”, is used to verify this work. Both healthy and unhealthy leaf photos are taken in the dataset that belongs to the 12 different plants type. The result found after testing the model was quite good. The suggested DCNN model will have greater classification accuracy and able to classify the plant type of the leaves as well as the model can also predict whether the leaf is healthy or unhealthy.
植物是人类生命及其周围环境的核心组成部分之一。各种病害不仅降低了植物及其产品的生态价值，而且影响了它们的经济价值。本研究的主要目的是对植物种类进行识别，并设计一种适合的、有效的基于叶片图片的植物健康状况判断方法，从而为快速、经济地解决这一问题提供一个可行的系统。对影响植物整体健康的生物和非生物元素的分析被称为植物病理学。农民必须迅速发现问题，以便采取适当的措施，防止更多的损失。因此，本研究建议使用深度卷积神经网络(DCNN)对受损叶片进行分类。一个真实的数据集，包含4503张12种不同树叶的图像，这些图像是在“印度查查克什米尔Katra的Shri Mata Vaishno Devi大学”收集的，用于验证这项工作。健康和不健康的叶子照片都在属于12种不同植物类型的数据集中拍摄。经过测试，发现该模型效果良好。建议的DCNN模型具有更高的分类精度，能够对叶子的植物类型进行分类，并且可以预测叶子是健康的还是不健康的。
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