Pub Date : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848085
Ritik Tanwar, Shubham, Shubham Verma, Manoj Kumar
Object Detection in hazy conditions is very challenging as haze significantly degrades the visibility of images limits visibility especially in outdoor settings. Here we introduce an interesting method to deal with haze that is present in images. Before applying any object detection method on the hazy input image, it is needed to be dehaze first and recognised later.For dehazing we have used the an Image Dehazing network known as All-in-One Dehazing Network (AOD-net) which is based on reformulation of atmospheric model and generates clean and clear image through a light-weight CNN and for recognition we have used the third version of famous YOLO i.e. YOLOv3. We test our method on various real time hazy images and compare the object similarity results on hazy image as well as on dehaze image. Along with this we have compared the number of object which are recognised in hazy image and in output clear image.
{"title":"Object Detection using Image Dehazing: A Journey Of Visual Improvement","authors":"Ritik Tanwar, Shubham, Shubham Verma, Manoj Kumar","doi":"10.1109/CONIT55038.2022.9848085","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848085","url":null,"abstract":"Object Detection in hazy conditions is very challenging as haze significantly degrades the visibility of images limits visibility especially in outdoor settings. Here we introduce an interesting method to deal with haze that is present in images. Before applying any object detection method on the hazy input image, it is needed to be dehaze first and recognised later.For dehazing we have used the an Image Dehazing network known as All-in-One Dehazing Network (AOD-net) which is based on reformulation of atmospheric model and generates clean and clear image through a light-weight CNN and for recognition we have used the third version of famous YOLO i.e. YOLOv3. We test our method on various real time hazy images and compare the object similarity results on hazy image as well as on dehaze image. Along with this we have compared the number of object which are recognised in hazy image and in output clear image.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126349404","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847675
Anupama R. Itagi, M. Kappali, S. Karajgi
A standalone DC Microgrid comprising PV as a distributed generator has gained popularity as it gives a promising solution for pollution control and supplies increasing DC loads. The intermittent nature of PV gives rise to challenges in energy management. Hence a system that aids in making appropriate decisions in energy management is essential. In this regard, a system that forecasts solar insolation accurately is imperative to guarantee uninterrupted energy supply to the critical loads. The existing closed loop Artificial Neural Network model developed for predicting solar insolation is costly and complex. Hence, the authors propose an open loop time series Artificial Neural Network model that is simple and economical with comparable accuracy. Bayesian Regularization algorithm is recommended. The model's performance is validated by measuring the Root Mean Square Error and coefficient of Regression.
{"title":"An Open loop time series ANN model for forecasting solar insolation for standalone PV applications","authors":"Anupama R. Itagi, M. Kappali, S. Karajgi","doi":"10.1109/CONIT55038.2022.9847675","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847675","url":null,"abstract":"A standalone DC Microgrid comprising PV as a distributed generator has gained popularity as it gives a promising solution for pollution control and supplies increasing DC loads. The intermittent nature of PV gives rise to challenges in energy management. Hence a system that aids in making appropriate decisions in energy management is essential. In this regard, a system that forecasts solar insolation accurately is imperative to guarantee uninterrupted energy supply to the critical loads. The existing closed loop Artificial Neural Network model developed for predicting solar insolation is costly and complex. Hence, the authors propose an open loop time series Artificial Neural Network model that is simple and economical with comparable accuracy. Bayesian Regularization algorithm is recommended. The model's performance is validated by measuring the Root Mean Square Error and coefficient of Regression.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126553265","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}
“Lung cancer” is one of the most widely found cancers in the world, accounting for 2 million deaths in 2018 alone. It is still the leading cause of cancer worldwide. One of the most routine pathological diagnosis tasks for pathologists is the classification of cancer cells at the histopathological level. Histopathological images allow the pathologists to do an in-depth analysis of the cancer cells. A pathologist must evaluate the microscopic appearance of a “biopsied sample” based on morphological features that have been correlated with patient outcome in order to estimate the severity of a cancer. Since histopathological images provide a better understanding of the grade of the cancer, the dataset used in the articles are histopathological images. The model tries to harness the tremendous power of Artificial Intelligence to identify and classify lung cancer without the help of a pathologist. Knowing that pathologists are facing heavy workloads due to an increasing number of patients struggling with lung cancer, this model would be an appropriate fit for the medical industry. This model could also be used in regions that have a shortage of access to any Pathological center nearby. The output of our model will be the classification of the cancer image into malignant and benign cancer, and in the subsequent step, we hope we will be able to grade the cancer into its corresponding stage. The aim of the article is to do a comparative study between benign and malignant cancer cells.
{"title":"Detection of Non-small cell Lung Cancer using Histopathological Images by the approach of Deep Learning","authors":"Dhurka Prasanna P, Janima K Radhakrishnan, Kurapati Sreenivas Aravind, Pranav Nambiar, Nalini Sampath","doi":"10.1109/CONIT55038.2022.9847945","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847945","url":null,"abstract":"“Lung cancer” is one of the most widely found cancers in the world, accounting for 2 million deaths in 2018 alone. It is still the leading cause of cancer worldwide. One of the most routine pathological diagnosis tasks for pathologists is the classification of cancer cells at the histopathological level. Histopathological images allow the pathologists to do an in-depth analysis of the cancer cells. A pathologist must evaluate the microscopic appearance of a “biopsied sample” based on morphological features that have been correlated with patient outcome in order to estimate the severity of a cancer. Since histopathological images provide a better understanding of the grade of the cancer, the dataset used in the articles are histopathological images. The model tries to harness the tremendous power of Artificial Intelligence to identify and classify lung cancer without the help of a pathologist. Knowing that pathologists are facing heavy workloads due to an increasing number of patients struggling with lung cancer, this model would be an appropriate fit for the medical industry. This model could also be used in regions that have a shortage of access to any Pathological center nearby. The output of our model will be the classification of the cancer image into malignant and benign cancer, and in the subsequent step, we hope we will be able to grade the cancer into its corresponding stage. The aim of the article is to do a comparative study between benign and malignant cancer cells.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123020214","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847982
K. Rajalakshmi, K. Ramesh, P. Renjith
Cloud computing plays a vital role in this technical world. Users can access cloud computing services through an internet connection. We store any amount of data in the cloud in various of formats like image, visual, audio, text etc. The main reason for using the cloud is that the user can store data and access it from anywhere and at any time. Storing the data in cloud is very simple but it is very important to notice that they and secure. To keep data, secure in the cloud, several cryptographic algorithms have been introduced. This paper compares and contrasts different cryptographic algorithms that are used to preserve data in the cloud and focus on three major algorithms. To make the analysis and comparison, a simulation program was designed, and the results show that AES is a best method in terms of computation time, memory consumption, and level of security.
{"title":"Comparative Study of Cryptographic Algorithms in cloud storage data security","authors":"K. Rajalakshmi, K. Ramesh, P. Renjith","doi":"10.1109/CONIT55038.2022.9847982","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847982","url":null,"abstract":"Cloud computing plays a vital role in this technical world. Users can access cloud computing services through an internet connection. We store any amount of data in the cloud in various of formats like image, visual, audio, text etc. The main reason for using the cloud is that the user can store data and access it from anywhere and at any time. Storing the data in cloud is very simple but it is very important to notice that they and secure. To keep data, secure in the cloud, several cryptographic algorithms have been introduced. This paper compares and contrasts different cryptographic algorithms that are used to preserve data in the cloud and focus on three major algorithms. To make the analysis and comparison, a simulation program was designed, and the results show that AES is a best method in terms of computation time, memory consumption, and level of security.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"33 2 Pt 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125712655","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}
Stock prediction and historical stock data analysis have been of great interest over the decades. The research is wide from classical deterministic algorithms to machine learning models and techniques along with the supply huge amounts of historical data. Volatility and Market Sentiment are key parameters to account for during the construction of any stock prediction model. Commonly used techniques like the n-moving days average is not responsive to swings in the stocks and the information sent and posted online has made a huge effect on investors' opinions on the market, making these the two optimal parameters of prediction. Hence, we present an automatic pipeline that has 2 modules - N-Observation period momentum strategy to identify potential stocks and then a stock holding module that identifies market sentiment using NLP techniques.
{"title":"Method and Apparatus for Stock Performance Prediction Using Momentum Strategy along with Social Feedback","authors":"Vishu Agarwal, Madhusudan L, HarshaVardhan Babu Namburi","doi":"10.1109/CONIT55038.2022.9848364","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848364","url":null,"abstract":"Stock prediction and historical stock data analysis have been of great interest over the decades. The research is wide from classical deterministic algorithms to machine learning models and techniques along with the supply huge amounts of historical data. Volatility and Market Sentiment are key parameters to account for during the construction of any stock prediction model. Commonly used techniques like the n-moving days average is not responsive to swings in the stocks and the information sent and posted online has made a huge effect on investors' opinions on the market, making these the two optimal parameters of prediction. Hence, we present an automatic pipeline that has 2 modules - N-Observation period momentum strategy to identify potential stocks and then a stock holding module that identifies market sentiment using NLP techniques.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116053464","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847694
Govind Kumar Pandey, T. Rao, Shyamal Mondal
In this research, a Graphene based octagonal short-angular circular patch (OSACP) 2x2 multi-input-multi-output (MIMO) antenna is designed for Terahertz (THz) short-range communications. Graphene, as a 2D material, has gained momentum for the realisation of the plasmonic THz antennas due to its unique electromagnetic properties, can fetch the property of surface plasmon polariton (SPP) at THz regime. The graphene layers of thickness 0.01 um have been placed on a Silicon Nitride (Si3N4) substrate of thickness 15 um to increase the efficiency of MIMO antenna. The radiation characteristics of the proposed antenna model are compared to those of a Si3N4 substrate structure based microstrip patch antennas using FDTD technique with a cross-sectional area of 1232 × 1232 um2. It exhibits the return loss (S11) better than -10 dB over the frequency range of 0.259 - 0.324 THz.
{"title":"Design and Analysis of Graphene based Octagonal Short-angular Circular Patch MIMO Antenna for Terahertz Communications","authors":"Govind Kumar Pandey, T. Rao, Shyamal Mondal","doi":"10.1109/CONIT55038.2022.9847694","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847694","url":null,"abstract":"In this research, a Graphene based octagonal short-angular circular patch (OSACP) 2x2 multi-input-multi-output (MIMO) antenna is designed for Terahertz (THz) short-range communications. Graphene, as a 2D material, has gained momentum for the realisation of the plasmonic THz antennas due to its unique electromagnetic properties, can fetch the property of surface plasmon polariton (SPP) at THz regime. The graphene layers of thickness 0.01 um have been placed on a Silicon Nitride (Si3N4) substrate of thickness 15 um to increase the efficiency of MIMO antenna. The radiation characteristics of the proposed antenna model are compared to those of a Si3N4 substrate structure based microstrip patch antennas using FDTD technique with a cross-sectional area of 1232 × 1232 um2. It exhibits the return loss (S11) better than -10 dB over the frequency range of 0.259 - 0.324 THz.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132824460","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848093
Shivangi Singh, Reshma Rajan, S. Nandini, D. Ramesh, C. Prathibhamol
Over the last decade, social networking sites have become the most frequent way to connect online, which has led to the rise of underlying friend recommendation structure in social networks which suggests friends to users. Most existing friend recommendation frameworks, unfortunately, merely take into account the number of mutual friends, geo-location, mutual interests and other factors when recommending one person as a friend to another. Meanwhile, a number of recent research have demonstrated the value of deep learning and neural networks in the areas of recommendation systems, as well as recent improvements in the field of recommendation employing various deep learning variations. Thus, in this paper, a personalized friend recommendation system based on a hybrid model that combines link prediction (which is a widely used traditional method in most social media platforms and follows the friend-of-friend approach) with a neural network model for added accuracy and efficiency, is discussed.
{"title":"Friend Recommendation System in a Social Network based on Link Prediction Framework using Deep Neural Network","authors":"Shivangi Singh, Reshma Rajan, S. Nandini, D. Ramesh, C. Prathibhamol","doi":"10.1109/CONIT55038.2022.9848093","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848093","url":null,"abstract":"Over the last decade, social networking sites have become the most frequent way to connect online, which has led to the rise of underlying friend recommendation structure in social networks which suggests friends to users. Most existing friend recommendation frameworks, unfortunately, merely take into account the number of mutual friends, geo-location, mutual interests and other factors when recommending one person as a friend to another. Meanwhile, a number of recent research have demonstrated the value of deep learning and neural networks in the areas of recommendation systems, as well as recent improvements in the field of recommendation employing various deep learning variations. Thus, in this paper, a personalized friend recommendation system based on a hybrid model that combines link prediction (which is a widely used traditional method in most social media platforms and follows the friend-of-friend approach) with a neural network model for added accuracy and efficiency, is discussed.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"328 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133265575","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848327
R. Mapari, H. Tiwari, K. Bhangale, N. Jagtap, Kunal Gujar, Yash Sarode, Akash Mahajan
In a developing country like India with agriculture as its backbone it has many problems including small landholdings, excessive use of pesticides and harmful chemicals used in place of natural nutrients, etc. These days, consumers are also in demand of a healthier diet such as chemical-free grown plants that are rich in nutrients. Our study fulfills the above requirements for such purpose we have suggested Vertical farming using hydroponics in which vertical farming solves the problem of a small amount of land available for agriculture and Hydroponics helps us go organic. Since the study is performed in a controlled environment the nutrients provided to plants while providing adequate nutrients while using the minimum amount of water such conditions are used and monitoring the plant growth gives us data which can be referred in future for optimum growth of the plants.
{"title":"IOT Based Vertical Farming Using Hydroponics for Spectrum Management & Crop Quality Control","authors":"R. Mapari, H. Tiwari, K. Bhangale, N. Jagtap, Kunal Gujar, Yash Sarode, Akash Mahajan","doi":"10.1109/CONIT55038.2022.9848327","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848327","url":null,"abstract":"In a developing country like India with agriculture as its backbone it has many problems including small landholdings, excessive use of pesticides and harmful chemicals used in place of natural nutrients, etc. These days, consumers are also in demand of a healthier diet such as chemical-free grown plants that are rich in nutrients. Our study fulfills the above requirements for such purpose we have suggested Vertical farming using hydroponics in which vertical farming solves the problem of a small amount of land available for agriculture and Hydroponics helps us go organic. Since the study is performed in a controlled environment the nutrients provided to plants while providing adequate nutrients while using the minimum amount of water such conditions are used and monitoring the plant growth gives us data which can be referred in future for optimum growth of the plants.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133849607","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847732
N. Kumar, Shubha Manohar, M. Shrutiya, Tejaswini Amaresh, Kritika Kapoor
In the present scenario, it has become a major necessity to create easy-to-access, easily-understandable and less time-consuming resources/tutorials. Our application proposes to create a full-fledged tutorial, provided an input in the form of a text file (txt/pdf). We have adopted a generator-subscriber model with role specific duties. The generator is provided with the option to generate tutorials and the subscriber can view/access the tutorials. The input provided by the generator is analysed to identify headings, subheadings and paragraphs as a hierarchy. Additionally, the uploaded material is summarised and a concise presentation with audio voiceover, to enhance comprehension of the user, is presented for quick learning. Furthermore, to ensure user interaction, assessments in the form of multiple-choice questions are dynamically created from the uploaded material. The scores of assessments are instantly provided and the subscriber's performance is recorded to show progress. The application is further extended to handle multimedia input in the form of images. In order to increase the usability of the application and make it accessible to a wider population, language agnosticism has been attempted. The features of language agnosticism are currently concentrated on Indian languages (Kannada and Hindi). A complete user-friendly web interface with a catalogue of automatically generated tutorials is provided for easy use of all the features and hassle-free learning.
{"title":"Automatic Tutorial Generation from Input Text File using Natural Language Processing","authors":"N. Kumar, Shubha Manohar, M. Shrutiya, Tejaswini Amaresh, Kritika Kapoor","doi":"10.1109/CONIT55038.2022.9847732","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847732","url":null,"abstract":"In the present scenario, it has become a major necessity to create easy-to-access, easily-understandable and less time-consuming resources/tutorials. Our application proposes to create a full-fledged tutorial, provided an input in the form of a text file (txt/pdf). We have adopted a generator-subscriber model with role specific duties. The generator is provided with the option to generate tutorials and the subscriber can view/access the tutorials. The input provided by the generator is analysed to identify headings, subheadings and paragraphs as a hierarchy. Additionally, the uploaded material is summarised and a concise presentation with audio voiceover, to enhance comprehension of the user, is presented for quick learning. Furthermore, to ensure user interaction, assessments in the form of multiple-choice questions are dynamically created from the uploaded material. The scores of assessments are instantly provided and the subscriber's performance is recorded to show progress. The application is further extended to handle multimedia input in the form of images. In order to increase the usability of the application and make it accessible to a wider population, language agnosticism has been attempted. The features of language agnosticism are currently concentrated on Indian languages (Kannada and Hindi). A complete user-friendly web interface with a catalogue of automatically generated tutorials is provided for easy use of all the features and hassle-free learning.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885842","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848060
M. Sobhana, Krishna Rohith Vemulapalli, Lahari Appala, Neelima Narra
Orthodontics is a specialized dental profession that specializes in diagnosing, preventing, and correcting teeth and jawbone, as well as biting patterns. The irregular shape of the teeth and jaws is very common. About 50% of the population of the developed world, according to the American Association of Orthodontics, has malocclusions heavy enough to benefit from orthopedic treatment. Cephalometries is often used by dentists as a tool for diagnosis and treatment planning and evaluation. Cephalometries aids in orthodontic diagnostics by empowering the study of skeletal structures, teeth, and soft tissues of the craniofacial region. Cephalometric analysis has many applications, including diagnostics, the definition of face measurement patterns, planning of orthodontic and orthognathic treatments, monitoring changes due to ageing or treatment and prediction of orthodontic and orthognathic treatment outcomes. Machine-based software is the only solution to reduce the dentist's work of planning and evaluation. Although some software are available for cephalometric analysis but, they are expensive and not easy to use as it requires heavy hardware-based tools such as laser guns and cephalostats. The proposed model uses a decision tree to develop a diagnostic program based on the data of previous patients assigned to it. This model is implemented using python language libraries such as Tkinter, Opencv, Sklearn, PIL and Pandas.
正畸是一门专业的牙科专业，专门诊断，预防和纠正牙齿和颌骨，以及咬痕模式。牙齿和下颚形状不规则是很常见的。根据美国正畸协会(American Association of Orthodontics)的数据，发达国家约有50%的人口有严重的错颌，足以从矫形治疗中受益。颅测术经常被牙医用作诊断、治疗计划和评估的工具。颅面测量通过增强骨骼结构、牙齿和颅面区域软组织的研究，有助于正畸诊断。颅面测量分析有许多应用，包括诊断，面部测量模式的定义，正畸和正颌治疗的计划，监测由于衰老或治疗引起的变化以及预测正畸和正颌治疗结果。基于机器的软件是减少牙医计划和评估工作的唯一解决方案。虽然有一些软件可用于头部测量分析，但它们价格昂贵且不容易使用，因为它需要重型硬件工具，如激光枪和定位仪。提出的模型使用决策树来开发诊断程序，该程序基于分配给它的先前患者的数据。该模型是使用Tkinter、Opencv、Sklearn、PIL和Pandas等python语言库实现的。
{"title":"Automatic Cephalometric Analysis using Machine Learning","authors":"M. Sobhana, Krishna Rohith Vemulapalli, Lahari Appala, Neelima Narra","doi":"10.1109/CONIT55038.2022.9848060","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848060","url":null,"abstract":"Orthodontics is a specialized dental profession that specializes in diagnosing, preventing, and correcting teeth and jawbone, as well as biting patterns. The irregular shape of the teeth and jaws is very common. About 50% of the population of the developed world, according to the American Association of Orthodontics, has malocclusions heavy enough to benefit from orthopedic treatment. Cephalometries is often used by dentists as a tool for diagnosis and treatment planning and evaluation. Cephalometries aids in orthodontic diagnostics by empowering the study of skeletal structures, teeth, and soft tissues of the craniofacial region. Cephalometric analysis has many applications, including diagnostics, the definition of face measurement patterns, planning of orthodontic and orthognathic treatments, monitoring changes due to ageing or treatment and prediction of orthodontic and orthognathic treatment outcomes. Machine-based software is the only solution to reduce the dentist's work of planning and evaluation. Although some software are available for cephalometric analysis but, they are expensive and not easy to use as it requires heavy hardware-based tools such as laser guns and cephalostats. The proposed model uses a decision tree to develop a diagnostic program based on the data of previous patients assigned to it. This model is implemented using python language libraries such as Tkinter, Opencv, Sklearn, PIL and Pandas.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586764","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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