Pub Date : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453019
Yunru Bai, Guang Zhang, San Sun
Smart financial policy adjustment system based on multiple game theory and artificial intelligence is designed and implemented in this paper. Financial doud computing is the use of cloud computing model construction principles to interconnect the data centers of various financial institutions and related institutions to form a cloud network which is the core novelty of the proposed framework. In our designed model, the three aspects of the novelties are reflected. (1) The game theory consider the multiple information is studied as the theoretical basis of the data analysis task. (2) Data that organizes, deans, categorizes financial transaction data, and stores it in a certain structure. (3) Model parameters obtained by using the three-time information to establish a model and information obtained by using the model to predict the information. We apply the proposed model into the data colleced and the perfomance shows that compared with the other methods, this one outperforms.
{"title":"Smart financial policy adjustment system based on multiple game theory and artificial intelligence","authors":"Yunru Bai, Guang Zhang, San Sun","doi":"10.1109/ICOEI51242.2021.9453019","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453019","url":null,"abstract":"Smart financial policy adjustment system based on multiple game theory and artificial intelligence is designed and implemented in this paper. Financial doud computing is the use of cloud computing model construction principles to interconnect the data centers of various financial institutions and related institutions to form a cloud network which is the core novelty of the proposed framework. In our designed model, the three aspects of the novelties are reflected. (1) The game theory consider the multiple information is studied as the theoretical basis of the data analysis task. (2) Data that organizes, deans, categorizes financial transaction data, and stores it in a certain structure. (3) Model parameters obtained by using the three-time information to establish a model and information obtained by using the model to predict the information. We apply the proposed model into the data colleced and the perfomance shows that compared with the other methods, this one outperforms.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129530308","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452841
Nurun Nahar Ela, Nusrat Jahan
The educational sector has been proved to be a major sector where improvement measures are a must to develop the system and the curriculum which changes in every few years. Students state that in order to cope with the changing curriculum every now and then, Education is said to be the backbone of a nation. If the students are falling behind that means the nation is falling behind. Therefore, it is necessary to guide student for their betterment which will help us to achieve a strong backbone. Our purpose of the study is to predict the slow learner among the university level learners which is the crucial stage of their study life and the step where they must acquire skills to face the professional life. The proposed study has collected data from the computer science and engineering department students. In order to achieve the better outcome, machine learning algorithms have been applied and finally 98% accuracy has been obtained.
{"title":"Machine Learning based Slow Learner Prediction in Educational Sector","authors":"Nurun Nahar Ela, Nusrat Jahan","doi":"10.1109/ICOEI51242.2021.9452841","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452841","url":null,"abstract":"The educational sector has been proved to be a major sector where improvement measures are a must to develop the system and the curriculum which changes in every few years. Students state that in order to cope with the changing curriculum every now and then, Education is said to be the backbone of a nation. If the students are falling behind that means the nation is falling behind. Therefore, it is necessary to guide student for their betterment which will help us to achieve a strong backbone. Our purpose of the study is to predict the slow learner among the university level learners which is the crucial stage of their study life and the step where they must acquire skills to face the professional life. The proposed study has collected data from the computer science and engineering department students. In order to achieve the better outcome, machine learning algorithms have been applied and finally 98% accuracy has been obtained.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128250334","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452966
R. Niranjana, P. Darney, K. Narayanan, R. Krishnan, A.Vegi Fernando, Y. H. Robinson
This undertaking is expected to have an operational model and overcome any issues in correspondence to help individuals experiencing Blindness and Deafness. Basic human existence is based on correspondence with fellow humans. Humans are designed to be interdependent in some form or another. Correspondence plays a major role for humans to share their views or make a request to another individual. This very essential capability is particularly hard for patrons with some form of disability. By and large, there will probably be some degree of social disengagement for those with incapacities like visual impairment/deafness, utilizing indistinguishable techniques for correspondence from the other individual (for example gesture-based communication or text-to-speech) is certainly going to be a principle factor viable. It is basic to impaired individuals' lives that inability is perceived as a uniformity issue. In this undertaking, we will propose another framework model with an end goal to make the procedure of connection between the handicapped and ordinary individuals a lot simpler. This framework will encourage correspondence among daze and If the typical individual need to speak with(visually impaired and hard of hearing) crippled individual. This framework will change the content language into voice for outwardly debilitated people and voice into content for hard of hearing people For this reason we use text to speech converter in this framework.
{"title":"Prolific Sensor Glove based Communication Device for the Disabled","authors":"R. Niranjana, P. Darney, K. Narayanan, R. Krishnan, A.Vegi Fernando, Y. H. Robinson","doi":"10.1109/ICOEI51242.2021.9452966","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452966","url":null,"abstract":"This undertaking is expected to have an operational model and overcome any issues in correspondence to help individuals experiencing Blindness and Deafness. Basic human existence is based on correspondence with fellow humans. Humans are designed to be interdependent in some form or another. Correspondence plays a major role for humans to share their views or make a request to another individual. This very essential capability is particularly hard for patrons with some form of disability. By and large, there will probably be some degree of social disengagement for those with incapacities like visual impairment/deafness, utilizing indistinguishable techniques for correspondence from the other individual (for example gesture-based communication or text-to-speech) is certainly going to be a principle factor viable. It is basic to impaired individuals' lives that inability is perceived as a uniformity issue. In this undertaking, we will propose another framework model with an end goal to make the procedure of connection between the handicapped and ordinary individuals a lot simpler. This framework will encourage correspondence among daze and If the typical individual need to speak with(visually impaired and hard of hearing) crippled individual. This framework will change the content language into voice for outwardly debilitated people and voice into content for hard of hearing people For this reason we use text to speech converter in this framework.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123843929","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452938
K. R. Teja, T. Kumar
Drowsiness has become the biggest problem in the peoples life which results to ineffective work or traffic accidents. There will be huge loss to the property as well as to the lives of the people due to drowsy driving. Therefore, a real-time smart drivers' drowsiness detection system is developed using DNN. The main aim of the project is to detect and analyze the face structure and objects in the frame. Viola Jones and YOLO algorithms are used for detection of face and objects in the frame respectively. Once the face and object gets detected then the movement in the eye is analyzed. PERCLOS is used for calculation of Eye Aspect Ratio (EAR). When the EAR value is less than the threshold value then alert is given to the driver similarly alert will be triggered when there is an object in the frame by using YOLO algorithm. The real-time experimental results shows that the proposed method is highly accurate and advanced in detection of drowsiness and identification of objects in the frame.
{"title":"Real-Time Smart Drivers Drowsiness Detection Using DNN","authors":"K. R. Teja, T. Kumar","doi":"10.1109/ICOEI51242.2021.9452938","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452938","url":null,"abstract":"Drowsiness has become the biggest problem in the peoples life which results to ineffective work or traffic accidents. There will be huge loss to the property as well as to the lives of the people due to drowsy driving. Therefore, a real-time smart drivers' drowsiness detection system is developed using DNN. The main aim of the project is to detect and analyze the face structure and objects in the frame. Viola Jones and YOLO algorithms are used for detection of face and objects in the frame respectively. Once the face and object gets detected then the movement in the eye is analyzed. PERCLOS is used for calculation of Eye Aspect Ratio (EAR). When the EAR value is less than the threshold value then alert is given to the driver similarly alert will be triggered when there is an object in the frame by using YOLO algorithm. The real-time experimental results shows that the proposed method is highly accurate and advanced in detection of drowsiness and identification of objects in the frame.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123591718","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453043
Nitika Goenka, Shamik Tiwari
Alzheimer's disease is a progressive brain disorder, which over a period leads to loss of memory due to the formation of mainly two types of lesions being senile plaques and neurofibrillary tangles. Alzheimer's detection at an early stage thus becomes of paramount importance to lessen the loss of cognitive, other memory since this disease cannot be reversed, and no cure is available until now. This study has put forward a 3-Dimensional Convolutional neural network (3D-CNN) framework for binary classification of Alzheimer disease as Healthy Control (HC) and Alzheimer Disease Control (AD) using the pre-processed volumetric T1 weighted Magnetic Resonance Images obtained from the MIRIAD dataset. The pre-processing pipeline applied on the MRI Images obtained from the MIRIAD dataset is bias correction, skull stripping, and registration. This research also highlights the broad areas for future research on multimodal and multiclass Alzheimer detection.
{"title":"Volumetric Convolutional Neural Network for Alzheimer Detection","authors":"Nitika Goenka, Shamik Tiwari","doi":"10.1109/ICOEI51242.2021.9453043","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453043","url":null,"abstract":"Alzheimer's disease is a progressive brain disorder, which over a period leads to loss of memory due to the formation of mainly two types of lesions being senile plaques and neurofibrillary tangles. Alzheimer's detection at an early stage thus becomes of paramount importance to lessen the loss of cognitive, other memory since this disease cannot be reversed, and no cure is available until now. This study has put forward a 3-Dimensional Convolutional neural network (3D-CNN) framework for binary classification of Alzheimer disease as Healthy Control (HC) and Alzheimer Disease Control (AD) using the pre-processed volumetric T1 weighted Magnetic Resonance Images obtained from the MIRIAD dataset. The pre-processing pipeline applied on the MRI Images obtained from the MIRIAD dataset is bias correction, skull stripping, and registration. This research also highlights the broad areas for future research on multimodal and multiclass Alzheimer detection.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121451148","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453014
A. Datta, Bhakti Kolwadkar, V. Ingale
Cardiac pacemaker is a device that demands very high accuracy alongside sophisticated performance. In this work we have tried to apply the various recent and state of the art actor-critic, policy gradient, both on-policy and off-policy Algorithms for the continuous control of the artificial cardiac pacemaker. We owe this work also due to the recent development of MATLAB® integration with the Reinforcement Learning toolbox in MATLAB® which combines low level RL algorithm tuning down to each and every hyperparameter and the high level model based control and electrical engineering tool that is Simulink®.
{"title":"Artificial Cardiac Pacemaker Control design using Deep Reinforcement learning: A Continuous Control Approach","authors":"A. Datta, Bhakti Kolwadkar, V. Ingale","doi":"10.1109/ICOEI51242.2021.9453014","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453014","url":null,"abstract":"Cardiac pacemaker is a device that demands very high accuracy alongside sophisticated performance. In this work we have tried to apply the various recent and state of the art actor-critic, policy gradient, both on-policy and off-policy Algorithms for the continuous control of the artificial cardiac pacemaker. We owe this work also due to the recent development of MATLAB® integration with the Reinforcement Learning toolbox in MATLAB® which combines low level RL algorithm tuning down to each and every hyperparameter and the high level model based control and electrical engineering tool that is Simulink®.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686205","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452825
M. Neha, M. S. Nair
Online Social Networking sites have become a well-known way for web surfers to connect and meet. Twitter got to be a well-known micro blogging site that clients post and associate with messages known as tweets. As this networking site gains its popularity, spammers target Twitter to spread spam posts. Hence, several spam detection techniques have been proposed by analysts to create Twitter a spam-free stage. Be that as it may, the accessible machine learning algorithms cannot effectively distin- guish spammers on Twitter because of reasonable information controls by unsolicited clients to elude spam discovery. As a result, here, we present an incipient approach predicated on a deep learning technique that leverages a text-predicated feature to detect spammers. A novel architecture that contains a one-dimensional dimension reduction inception module stacked with LSTM along with an attention layer is introduced here. Within the proposed model, the inception module extricates the features from the vectors after GloVe word embedding, and then LSTM is utilized to get the context representations. An Attention layer is also used in this model to give attention to the data outputted from LSTM module. At long last, the sigmoid classifier is utilized to classify the labels as spam or ham. Here, the execution of our proposed model is compared with four machine learning-based and two deep learning-based approaches, exhibiting our approach acquired the best results with an F1-score of 95.74, accuracy of 95.75, and precision of 95.58.
{"title":"A Novel Twitter Spam Detection Technique by Integrating Inception Network with Attention based LSTM","authors":"M. Neha, M. S. Nair","doi":"10.1109/ICOEI51242.2021.9452825","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452825","url":null,"abstract":"Online Social Networking sites have become a well-known way for web surfers to connect and meet. Twitter got to be a well-known micro blogging site that clients post and associate with messages known as tweets. As this networking site gains its popularity, spammers target Twitter to spread spam posts. Hence, several spam detection techniques have been proposed by analysts to create Twitter a spam-free stage. Be that as it may, the accessible machine learning algorithms cannot effectively distin- guish spammers on Twitter because of reasonable information controls by unsolicited clients to elude spam discovery. As a result, here, we present an incipient approach predicated on a deep learning technique that leverages a text-predicated feature to detect spammers. A novel architecture that contains a one-dimensional dimension reduction inception module stacked with LSTM along with an attention layer is introduced here. Within the proposed model, the inception module extricates the features from the vectors after GloVe word embedding, and then LSTM is utilized to get the context representations. An Attention layer is also used in this model to give attention to the data outputted from LSTM module. At long last, the sigmoid classifier is utilized to classify the labels as spam or ham. Here, the execution of our proposed model is compared with four machine learning-based and two deep learning-based approaches, exhibiting our approach acquired the best results with an F1-score of 95.74, accuracy of 95.75, and precision of 95.58.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114773236","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452855
S. Soni, Kuldeep Vashishtha, Chandra Bhandubey
To predict the future weather condition, the probability that the weather on the day of consideration will be least same as the previous day forecast but the chances of it becoming similar in the next two weeks are high. So, processing the weather data of two weeks from the last year slide window is required to choose a size equal to a week. Every quick window week coincides with the current year. Furthermore, the prediction is done based on a window algorithm slide. The results of the method suggest that, the utilization of proposed method to forecast the weather is effective with an average accuracy of 94.2%. Whereas, the radar remote-sensing arena is one of the most exciting and creative future technological enhancements for PWS. Also, the next-generation radar systems (dual-polarization radar, phased-array radar) will enhance the extreme weather detection, rainfall forecasts, and winter weather warnings, and at the same time it will improve the lead time for severe weather threats including tornadoes and heavy rain/flash flood events.
{"title":"Deep Learning based Weather Forecast: A Prediction","authors":"S. Soni, Kuldeep Vashishtha, Chandra Bhandubey","doi":"10.1109/ICOEI51242.2021.9452855","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452855","url":null,"abstract":"To predict the future weather condition, the probability that the weather on the day of consideration will be least same as the previous day forecast but the chances of it becoming similar in the next two weeks are high. So, processing the weather data of two weeks from the last year slide window is required to choose a size equal to a week. Every quick window week coincides with the current year. Furthermore, the prediction is done based on a window algorithm slide. The results of the method suggest that, the utilization of proposed method to forecast the weather is effective with an average accuracy of 94.2%. Whereas, the radar remote-sensing arena is one of the most exciting and creative future technological enhancements for PWS. Also, the next-generation radar systems (dual-polarization radar, phased-array radar) will enhance the extreme weather detection, rainfall forecasts, and winter weather warnings, and at the same time it will improve the lead time for severe weather threats including tornadoes and heavy rain/flash flood events.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125635474","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453020
R. Devi, R. Sarumitraa, B. Sushmitha, J. Vishnupriya, S. Surya
The population of the nation is exponentially increasing and there needs more care and attention for elder people. The proposed system provides an immediate health care in case of any emergency situation observed in the healthcare parameters of the patients. For example sugar level, blood pressure, oxygen level, rate of heart beat, temperature and other essential parameters recorded by the smart monitoring system. It provides an interface between the patients and the emergency team in hospitals by intimating the critical condition of the patients in order to treat them immediately.
{"title":"Social Robot in Health Care Monitoring","authors":"R. Devi, R. Sarumitraa, B. Sushmitha, J. Vishnupriya, S. Surya","doi":"10.1109/ICOEI51242.2021.9453020","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453020","url":null,"abstract":"The population of the nation is exponentially increasing and there needs more care and attention for elder people. The proposed system provides an immediate health care in case of any emergency situation observed in the healthcare parameters of the patients. For example sugar level, blood pressure, oxygen level, rate of heart beat, temperature and other essential parameters recorded by the smart monitoring system. It provides an interface between the patients and the emergency team in hospitals by intimating the critical condition of the patients in order to treat them immediately.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121910600","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452965
R. Vignesh, D. Deepa, Suja Cherukullapurath Mana, B. Samhitha, A. T
Genes are the basis of tumor formations around the body, which is better known as cancer. They inhibit basic processes such as cell death (apoptosis) and promote cell division to an unhealthy extent. The expression of every gene provides a baseline to know the progress of cancer from the organ or tissue it originated from along with its approximated course of action. The analysis of such gene expression values using traditional machine learning methods provide a higher efficiency and accuracy in finding relationships between genes and also it may serve as a future for diagnosing the cancer by using these values. The main challenge is to use the bases that are created to efficiently compute the highly effective genes to treat specific types of cancer by using their expression values and thus, raise the question of a potential relationship between them for each type. A Random Forest Model has been used to perform Feature Selection over the dataset in order to extract the important features (i.e.) the most influential genes. They are then visualized by using traditional packages in Python (i.e. Scikit-plot, Matplotlib, Seaborn) and using a data visualization tool called Tableau to project the result of the analysis.
{"title":"Gene Expression Analysis on Cancer Dataset","authors":"R. Vignesh, D. Deepa, Suja Cherukullapurath Mana, B. Samhitha, A. T","doi":"10.1109/ICOEI51242.2021.9452965","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452965","url":null,"abstract":"Genes are the basis of tumor formations around the body, which is better known as cancer. They inhibit basic processes such as cell death (apoptosis) and promote cell division to an unhealthy extent. The expression of every gene provides a baseline to know the progress of cancer from the organ or tissue it originated from along with its approximated course of action. The analysis of such gene expression values using traditional machine learning methods provide a higher efficiency and accuracy in finding relationships between genes and also it may serve as a future for diagnosing the cancer by using these values. The main challenge is to use the bases that are created to efficiently compute the highly effective genes to treat specific types of cancer by using their expression values and thus, raise the question of a potential relationship between them for each type. A Random Forest Model has been used to perform Feature Selection over the dataset in order to extract the important features (i.e.) the most influential genes. They are then visualized by using traditional packages in Python (i.e. Scikit-plot, Matplotlib, Seaborn) and using a data visualization tool called Tableau to project the result of the analysis.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115832376","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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