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}
Pub Date : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848137
Disha D N, S. S., Sharada U. Shenoy, Sudesh Rao
bipolar disorder may be an advanced disorder that affects variant individuals across the world. We assume that with the utilization of huge information with machine learning we will facilitate every patient as well as doctors to perform a much better designation of this sickness. Paper aims to use different Machine learning algorithms to predict the variants of bipolar disorder. The prediction model would help the psychiatrists fordiagnosing whether the patients are having a depression or mania episode, or staying in an exceedingly euthymic state. It also aims at developing a prophetic model with an appropriate level of confidence, it's essential to own each associate understanding of the information that's getting used and also thetheory relating to every algorithmic rule that's applied, similarly as having enough information for the algorithms to figure with.
{"title":"Prediction of Bipolar Disorder Using Machine Learning Techniques","authors":"Disha D N, S. S., Sharada U. Shenoy, Sudesh Rao","doi":"10.1109/CONIT55038.2022.9848137","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848137","url":null,"abstract":"bipolar disorder may be an advanced disorder that affects variant individuals across the world. We assume that with the utilization of huge information with machine learning we will facilitate every patient as well as doctors to perform a much better designation of this sickness. Paper aims to use different Machine learning algorithms to predict the variants of bipolar disorder. The prediction model would help the psychiatrists fordiagnosing whether the patients are having a depression or mania episode, or staying in an exceedingly euthymic state. It also aims at developing a prophetic model with an appropriate level of confidence, it's essential to own each associate understanding of the information that's getting used and also thetheory relating to every algorithmic rule that's applied, similarly as having enough information for the algorithms to figure with.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"52 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":"123651346","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/CONIT55038.2022.9847813
M. Chinnaiah, Sanjay Dubey, N. Janardhan, V. Pathi, Nandan K, Anusha M
Agni is a vital component of the body's physiological function. Individuals' physical constitutions depended on summer, fall, winter, spring seasons, age and other factors all influence this. The Prakriti, which is concerned with physical and psychological development, determines the uniqueness of everyone. The Prakriti has an immediate effect on Vata, Pitta and Kapha. This paper proposes the pitta imbalance evaluation using machine learning algorithm. The proposed method provides novelty in analyzing pitta dosha with real time pitta datasets and machine learning algorithms. Vata-pitta prakriti impacts with lifestyle changes which have been evaluated in this proposed method. The Support Vector Machine (SVM) used for evaluation of pitta dosha. Authors taken 152 healthy persons for analyzing their tri-dosha, age group of 18–22 years, 72.4% boys and 27.6% girls.
{"title":"Analysis of Pitta Imbalance in young Indian adult using Machine Learning Algorithm","authors":"M. Chinnaiah, Sanjay Dubey, N. Janardhan, V. Pathi, Nandan K, Anusha M","doi":"10.1109/CONIT55038.2022.9847813","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847813","url":null,"abstract":"Agni is a vital component of the body's physiological function. Individuals' physical constitutions depended on summer, fall, winter, spring seasons, age and other factors all influence this. The Prakriti, which is concerned with physical and psychological development, determines the uniqueness of everyone. The Prakriti has an immediate effect on Vata, Pitta and Kapha. This paper proposes the pitta imbalance evaluation using machine learning algorithm. The proposed method provides novelty in analyzing pitta dosha with real time pitta datasets and machine learning algorithms. Vata-pitta prakriti impacts with lifestyle changes which have been evaluated in this proposed method. The Support Vector Machine (SVM) used for evaluation of pitta dosha. Authors taken 152 healthy persons for analyzing their tri-dosha, age group of 18–22 years, 72.4% boys and 27.6% girls.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"5 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":"123773374","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/CONIT55038.2022.9847720
Karan Bhat, Vaibhavi Ghumare, Siddhesh Khadake, H. Gadade
Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much more simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence more easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives of complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex word in the sentence in account. In this paper, we are proposing a lexical simplifier which is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool which can make use of the wider context of the sentence in both forward and backward direction. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after computation is over.
{"title":"Web Extension for Lexical Simplification of Text","authors":"Karan Bhat, Vaibhavi Ghumare, Siddhesh Khadake, H. Gadade","doi":"10.1109/CONIT55038.2022.9847720","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847720","url":null,"abstract":"Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much more simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence more easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives of complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex word in the sentence in account. In this paper, we are proposing a lexical simplifier which is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool which can make use of the wider context of the sentence in both forward and backward direction. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after computation is over.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"3 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":"124524666","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/CONIT55038.2022.9847972
Manaswita Datta, Bhawana Rudra
The process of selecting football team players will determines a team's performance. An effective team is made up of a successful group of individual talented players. In general, a football team player selection is a decision made by the club based on the best available information. Club managers and scouts travel to different countries to watch matches and hire the best talent that can help their club to perform better. But for the lower leagues, it becomes difficult to hire the same talents because of strict budget. Here we devise a method so that we can leverage the undervalued players to get selected by the clubs. Clearly the benefit will be in two fold. First, the smaller clubs can get better players at an affordable cost. Second, the bigger clubs can get same performance players at a lower price helping them in cost cutting. We employ novelty detection methods to find out the undervalued players from our data and investigate our method by using five machine learning models. For performance evaluation, the five machine learning models used are support vector machine, Random Forest, Decision Tree, Linear Regression and XGBoost. Here XGboost performed best both for 10 fold cross-validation and external testing with a RMSE of 0.0122 and 0.0107 respectively.
{"title":"An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players","authors":"Manaswita Datta, Bhawana Rudra","doi":"10.1109/CONIT55038.2022.9847972","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847972","url":null,"abstract":"The process of selecting football team players will determines a team's performance. An effective team is made up of a successful group of individual talented players. In general, a football team player selection is a decision made by the club based on the best available information. Club managers and scouts travel to different countries to watch matches and hire the best talent that can help their club to perform better. But for the lower leagues, it becomes difficult to hire the same talents because of strict budget. Here we devise a method so that we can leverage the undervalued players to get selected by the clubs. Clearly the benefit will be in two fold. First, the smaller clubs can get better players at an affordable cost. Second, the bigger clubs can get same performance players at a lower price helping them in cost cutting. We employ novelty detection methods to find out the undervalued players from our data and investigate our method by using five machine learning models. For performance evaluation, the five machine learning models used are support vector machine, Random Forest, Decision Tree, Linear Regression and XGBoost. Here XGboost performed best both for 10 fold cross-validation and external testing with a RMSE of 0.0122 and 0.0107 respectively.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"10 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922034","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/CONIT55038.2022.9848020
A. Chavan, Alok Sahu, Apparna A. Junnarkar
In like manner, the objective of this exploration study is to foster a proficient trust-based security answer for dynamic range detecting in CR-MANETs. Our proposed answer for working on the exhibition of CR-MANETs within the sight of attacks, for example, SSDF and ISSDF is depicted exhaustively in the accompanying area. The primary component of the interaction includes the improvement of SSDF and ISSDF assaults for use in CR-MANETs. Second, the writing survey and recognizable proof of worries connected with the security of CR-MANETs against different sorts of attacks. In the accompanying area, we propose a one of a kind trust-based worldview that can improve the inadequacies of existing methodologies while likewise safeguarding CR-MANETs from SSDF and ISSDF attacks. Leading a presentation examination to demonstrate the adequacy of the proposed model was the last advance in characterizing the review results. We are endeavoring to assemble a trust-based framework in which the trust of PU and SU will be estimated in light of their development designs and different measurements like energy utilization and creation. Alongside a setting mindful circulated trust procedure, the structure is based on a versatility mindful answer for energy proficient SSDF and ISSDF assault location, as well as a setting mindful disseminated trust technique. With the assistance of PU missing and present settings, the SU hubs analyze the dependability of their associations with each other. They will then, at that point, mention objective facts from each other while considering the versatility and energy upsides of SUs.
{"title":"Context-aware Secure Spectrum Sensing for Cognitive Radio Networks","authors":"A. Chavan, Alok Sahu, Apparna A. Junnarkar","doi":"10.1109/CONIT55038.2022.9848020","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848020","url":null,"abstract":"In like manner, the objective of this exploration study is to foster a proficient trust-based security answer for dynamic range detecting in CR-MANETs. Our proposed answer for working on the exhibition of CR-MANETs within the sight of attacks, for example, SSDF and ISSDF is depicted exhaustively in the accompanying area. The primary component of the interaction includes the improvement of SSDF and ISSDF assaults for use in CR-MANETs. Second, the writing survey and recognizable proof of worries connected with the security of CR-MANETs against different sorts of attacks. In the accompanying area, we propose a one of a kind trust-based worldview that can improve the inadequacies of existing methodologies while likewise safeguarding CR-MANETs from SSDF and ISSDF attacks. Leading a presentation examination to demonstrate the adequacy of the proposed model was the last advance in characterizing the review results. We are endeavoring to assemble a trust-based framework in which the trust of PU and SU will be estimated in light of their development designs and different measurements like energy utilization and creation. Alongside a setting mindful circulated trust procedure, the structure is based on a versatility mindful answer for energy proficient SSDF and ISSDF assault location, as well as a setting mindful disseminated trust technique. With the assistance of PU missing and present settings, the SU hubs analyze the dependability of their associations with each other. They will then, at that point, mention objective facts from each other while considering the versatility and energy upsides of SUs.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"97 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":"128413482","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/CONIT55038.2022.9848267
Harini V, Sairam M V S, Madhu R
A dual-band hexagonal-shaped planar quad element millimeter-wave Multi-Input Multi-Output (MIMO) antenna is proposed for 5G femtocells applications. Initially, a hexagonal-shaped single element is designed and analysis is performed on Rogers R04003 ™ substrate with Er of 3.55 and & = 0.0027 with a thickness of substrate as 0.8mm. Later the single element is repeatedly placed on four sides of the substrate making a quad element MIMO antenna with four different ports. The Proposed antenna is radiating at 27.5GHz with a gain of 4.7 dBi at port1 and port3 and at port2 and port4, the antenna is radiating at dual bands like 28.5GHz and 38.5GHz with average gains of 4.69dBi and 5.5dBi. The antenna has a total efficiency of 95% with MIMO key performance metrics like envelope correlation coefficient and diversity gains as 0.045 and 9.995 at 10dB. Due to the lower perceptivity of tapping by unauthorized persons, MIMO antennas can be easily incorporated in 5G Femtocells.
{"title":"Dual-Band Hexagonal Millimeter Wave MIMO Antenna for 5G Femtocell Implementations","authors":"Harini V, Sairam M V S, Madhu R","doi":"10.1109/CONIT55038.2022.9848267","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848267","url":null,"abstract":"A dual-band hexagonal-shaped planar quad element millimeter-wave Multi-Input Multi-Output (MIMO) antenna is proposed for 5G femtocells applications. Initially, a hexagonal-shaped single element is designed and analysis is performed on Rogers R04003 ™ substrate with Er of 3.55 and & = 0.0027 with a thickness of substrate as 0.8mm. Later the single element is repeatedly placed on four sides of the substrate making a quad element MIMO antenna with four different ports. The Proposed antenna is radiating at 27.5GHz with a gain of 4.7 dBi at port1 and port3 and at port2 and port4, the antenna is radiating at dual bands like 28.5GHz and 38.5GHz with average gains of 4.69dBi and 5.5dBi. The antenna has a total efficiency of 95% with MIMO key performance metrics like envelope correlation coefficient and diversity gains as 0.045 and 9.995 at 10dB. Due to the lower perceptivity of tapping by unauthorized persons, MIMO antennas can be easily incorporated in 5G Femtocells.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"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":"129043307","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/CONIT55038.2022.9847769
Sravankumar Jogunuri, F. T. Josh
Renewable energy integration to the conventional power grid is a challenge and requires an accurate forecasting of power output from the renewable energy sources for ensuring the reliability and grid stability. Many forecasting techniques for different time horizons were developed using different machine learning techniques. In the recent past mostly forecasting techniques based on artificial neural networks were developed. But, looking at the environmental parameters like insolation, temperature, sky clearness index and cloud cover etc., and its variable behavior makes the forecasting more complex. To address., complex and non-linearity issues in many applications, deep neural networks were proved effective and hence an attempt made in this paper forecasting power from solar photovoltaic plant for very short-term durations through deep neural networks model and compared the same with ANN model with only one hidden layer and found significant improved accuracy in deep neural networks.
{"title":"Deep Neural Network based Forecasting of Short-Term Solar Photovoltaic Power output","authors":"Sravankumar Jogunuri, F. T. Josh","doi":"10.1109/CONIT55038.2022.9847769","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847769","url":null,"abstract":"Renewable energy integration to the conventional power grid is a challenge and requires an accurate forecasting of power output from the renewable energy sources for ensuring the reliability and grid stability. Many forecasting techniques for different time horizons were developed using different machine learning techniques. In the recent past mostly forecasting techniques based on artificial neural networks were developed. But, looking at the environmental parameters like insolation, temperature, sky clearness index and cloud cover etc., and its variable behavior makes the forecasting more complex. To address., complex and non-linearity issues in many applications, deep neural networks were proved effective and hence an attempt made in this paper forecasting power from solar photovoltaic plant for very short-term durations through deep neural networks model and compared the same with ANN model with only one hidden layer and found significant improved accuracy in deep neural networks.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"02 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":"129894543","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/CONIT55038.2022.9847701
Akash Trivedi, P. S. Chatterjee
It is extremely difficult to care for animals' health in remote areas, particularly in India. It is too difficult to care for cattle not only in remote areas, but also on large farms with a large number of them. As a result, this paper's primary goal is to develop a health monitoring system capable of routinely monitoring dairy cow health. The monitoring system's goal is to detect various diseases based on behavioural changes and symptoms. We installed different sensors on the cow's body as well as in various locations around the farm to record the dairy cows' behavioural changes. Those sensory readings are sent to the cloud. CARE, our proposed algorithm, will classify possible diseases based on recorded cow behaviour. The proposed algorithm detects cow diseases with high accuracy. This framework was created as part of the smart health monitoring system.
{"title":"CARE: IoT enabled Cow Health Monitoring System","authors":"Akash Trivedi, P. S. Chatterjee","doi":"10.1109/CONIT55038.2022.9847701","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847701","url":null,"abstract":"It is extremely difficult to care for animals' health in remote areas, particularly in India. It is too difficult to care for cattle not only in remote areas, but also on large farms with a large number of them. As a result, this paper's primary goal is to develop a health monitoring system capable of routinely monitoring dairy cow health. The monitoring system's goal is to detect various diseases based on behavioural changes and symptoms. We installed different sensors on the cow's body as well as in various locations around the farm to record the dairy cows' behavioural changes. Those sensory readings are sent to the cloud. CARE, our proposed algorithm, will classify possible diseases based on recorded cow behaviour. The proposed algorithm detects cow diseases with high accuracy. This framework was created as part of the smart health monitoring system.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"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":"130102833","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/CONIT55038.2022.9848131
Rituparna Sinha, Rajat K. Pal, R. K. De
Massively parallel sequencers have enabled genome sequences to be available at a very low cost and price, which opened huge scope on analyzing human genome sequences from different perspectives, thereby the association of diseases with genetic alterations gets further enlightened. However, the sequencing process and alignment of NGS technology based short reads suffer from various sequencing biases which needs to be addressed. In this work, the mappability bias occurring with respect to repeat rich regions of the DNA have been addressed in a novel approach. A model has been designed which considers all non-uniquely mapped reads and performs a pipeline of computations to allocate the reads to an optimal location, due to which the precise detection of breakpoints in the region of duplications and deletions are obtained. In addition, the application of this model for mappability bias correction, prior to the detection of structurally altered regions of the genome, leads to a better sensitivity value.
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