Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055275
Farden Ehsan Khan, Ahmed Mahir Ruhan, Rifat Shamsuddin, Faisal Bin Ashraf
Social media use has increased to such levels in recent years that it has transformed into a trend-setting powerhouse, introducing subjects that would have previously remained outside of the public eye. Through people’s shared opinions and responses about a trend on social media, we hope to determine how long it can hold an audience’s attention on its own. We will analyze the sentiment of individuals toward a particular topic using the information gleaned from social media comments. Our work will be based on unreleased films and make predictions about how they will turn out when they are released. In this work, we have processed and examined accumulated reviews about a film to see whether the general public feels positively or negatively about it and to calculate the likelihood that a certain film will be a success. From this, we can infer how the success of a movie or product is influenced by both positive and negative attention before its release.
{"title":"A Machine Learning Approach to Predict Movie Success from Youtube Trailer Comments","authors":"Farden Ehsan Khan, Ahmed Mahir Ruhan, Rifat Shamsuddin, Faisal Bin Ashraf","doi":"10.1109/ICCIT57492.2022.10055275","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055275","url":null,"abstract":"Social media use has increased to such levels in recent years that it has transformed into a trend-setting powerhouse, introducing subjects that would have previously remained outside of the public eye. Through people’s shared opinions and responses about a trend on social media, we hope to determine how long it can hold an audience’s attention on its own. We will analyze the sentiment of individuals toward a particular topic using the information gleaned from social media comments. Our work will be based on unreleased films and make predictions about how they will turn out when they are released. In this work, we have processed and examined accumulated reviews about a film to see whether the general public feels positively or negatively about it and to calculate the likelihood that a certain film will be a success. From this, we can infer how the success of a movie or product is influenced by both positive and negative attention before its release.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126778783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055168
Mebin Rahman Fateha, Md. Saddam Hossain Mukta, M. Hossain, Mahmud Al Islam, Salekul Islam
Nowadays, the increasing number of vehicles and shortage of parking spaces have become an inescapable condition in big cities across the world. Car parking problem is not a new phenomenon, especially in a crowded city such as Dhaka, Bangladesh. Shortage of parking spaces leads to several problems such as road congestion, illegal parking on the streets, and fuel waste in searching for a free parking space. In order to overcome the parking problem, we develop a spatio-temporal based car parking system namely, SlotFinder. We collect the data of 408 buildings those have parking slots from seven different locations. We then cluster these data based on time and locations. Later, we train location wise vacant parking spaces by using stacked Long Short-Term Memory (LSTM) based on their temporal patterns. We also compare our technique with the baseline models and conduct an ablation analysis, which outperforms (lower RMSE and MAE of 0.29 and 0.24, respectively) than that of the previous approaches.
{"title":"SlotFinder: A Spatio-temporal based Car Parking System","authors":"Mebin Rahman Fateha, Md. Saddam Hossain Mukta, M. Hossain, Mahmud Al Islam, Salekul Islam","doi":"10.1109/ICCIT57492.2022.10055168","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055168","url":null,"abstract":"Nowadays, the increasing number of vehicles and shortage of parking spaces have become an inescapable condition in big cities across the world. Car parking problem is not a new phenomenon, especially in a crowded city such as Dhaka, Bangladesh. Shortage of parking spaces leads to several problems such as road congestion, illegal parking on the streets, and fuel waste in searching for a free parking space. In order to overcome the parking problem, we develop a spatio-temporal based car parking system namely, SlotFinder. We collect the data of 408 buildings those have parking slots from seven different locations. We then cluster these data based on time and locations. Later, we train location wise vacant parking spaces by using stacked Long Short-Term Memory (LSTM) based on their temporal patterns. We also compare our technique with the baseline models and conduct an ablation analysis, which outperforms (lower RMSE and MAE of 0.29 and 0.24, respectively) than that of the previous approaches.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"47 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10054742
Md. Tariqul Islam, K. M. Azharul Hasan, Md.Ibrahim Hossen
Classification of emails is an important research issue since a huge number of emails are received every day. Emails become a most objective correspondence with others. Billions of emails that are sent per day all over the world become threatening to people. A spam email can be used to pick up things from our electrical gadgets by forcing or phishing. Besides that, we receive several other less important emails. Bangla emails are very common nowadays that are facing similar problems. But due to less collection of Bangla emails important emails are not correctly classified and the receiver missed them. Considering English or other important languages, there are accessible approaches to distinguishing the emails. In this paper, we propose a classification scheme for emails written in the Bangla language. We create a Bangla email dataset and propose a multilevel classification. We found the distinguished features to classify them. Important machine learning algorithms are used to classify them.
{"title":"Classification and Resource Generation for Bangla Emails Based on Machine Learning Algorithms","authors":"Md. Tariqul Islam, K. M. Azharul Hasan, Md.Ibrahim Hossen","doi":"10.1109/ICCIT57492.2022.10054742","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054742","url":null,"abstract":"Classification of emails is an important research issue since a huge number of emails are received every day. Emails become a most objective correspondence with others. Billions of emails that are sent per day all over the world become threatening to people. A spam email can be used to pick up things from our electrical gadgets by forcing or phishing. Besides that, we receive several other less important emails. Bangla emails are very common nowadays that are facing similar problems. But due to less collection of Bangla emails important emails are not correctly classified and the receiver missed them. Considering English or other important languages, there are accessible approaches to distinguishing the emails. In this paper, we propose a classification scheme for emails written in the Bangla language. We create a Bangla email dataset and propose a multilevel classification. We found the distinguished features to classify them. Important machine learning algorithms are used to classify them.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121721372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10054906
Ishraq R. Rahman, Shovito Barua Soumma, Faisal Bin Ashraf
Similar causal relationships can exist between many cancer types, for example, metastatic bladder cancer and secondary lung cancer. This relatedness must therefore be taken into account for the diagnosis to be more accurate. The categorization of cancers can benefit from gene expression studies. In order to categorize cancer tissues with a comparable causal link, the best classifier model is sought after in this research. The CuMiDa dataset is used to obtain the lung and bladder cancer datasets, and parameters are modified to improve accuracy once fewer classifiers are taken into account. According to the experimental findings, Linear SVC achieves the highest accuracy, followed by Logistic Regression and XGBoost.
{"title":"Machine Learning Approaches to Metastasis Bladder and Secondary Pulmonary Cancer Classification Using Gene Expression Data","authors":"Ishraq R. Rahman, Shovito Barua Soumma, Faisal Bin Ashraf","doi":"10.1109/ICCIT57492.2022.10054906","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054906","url":null,"abstract":"Similar causal relationships can exist between many cancer types, for example, metastatic bladder cancer and secondary lung cancer. This relatedness must therefore be taken into account for the diagnosis to be more accurate. The categorization of cancers can benefit from gene expression studies. In order to categorize cancer tissues with a comparable causal link, the best classifier model is sought after in this research. The CuMiDa dataset is used to obtain the lung and bladder cancer datasets, and parameters are modified to improve accuracy once fewer classifiers are taken into account. According to the experimental findings, Linear SVC achieves the highest accuracy, followed by Logistic Regression and XGBoost.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125128568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055126
Mohammad Rakibul Hasan Mahin, Waheed Moonwar, Md. Shamsul Rayhan Chy, Fahim Faisal Rafi, Md. Fahim Shahriar, Dewan Ziaul Karim, Annajiat Alim Rasel
Agriculture has been crucial for centuries. Due to its revenue contribution, agriculture’s importance has grown throughout time. However, some counter factors prohibit us from getting the full benefits of crops. Natural plant diseases are one factor. The main causes of these difficulties are harsh weather and excessive pesticide use, which strain Bangladesh’s economy. To lessen the problem’s severity, an image processing system was created that uses Deep Learning and CNN to classify leaf illnesses. The primary demographic is farmers and others who are willing to tend crops. It was decided to make sure the proposed model is lightweight so that it can be compatible and simple to implement on low-end devices without using up excessive resources. This CNN algorithm predicts the leaf’s status based on the user’s selected images. After constructing CNN, another model is offered, LIME, based on Explainable AI (XAI). XAI helps humans understand AI’s decisions or predictions. After the proposed CNN model diagnoses diseased leaves, the XAI helps us understand why. Conclusively, 99.87%, 99.54%, 99.54% accuracy was found in training, validation and testing respectively after running our models.
几个世纪以来，农业一直至关重要。由于其收入贡献，农业的重要性随着时间的推移而增长。然而，一些不利因素使我们无法充分受益于农作物。自然植物病害是一个因素。造成这些困难的主要原因是恶劣的天气和过度使用农药，这给孟加拉国的经济带来了压力。为了减轻问题的严重性，研究人员创建了一个图像处理系统，该系统使用深度学习和CNN对叶子疾病进行分类。主要人口是农民和其他愿意照料作物的人。我们决定确保所提议的模型是轻量级的，以便在低端设备上兼容并易于实现，而不会消耗过多的资源。这个CNN算法根据用户选择的图像来预测叶子的状态。在构建CNN之后，提出了另一个基于Explainable AI (XAI)的模型LIME。XAI帮助人类理解人工智能的决策或预测。在提出的CNN模型诊断出患病叶片后，XAI帮助我们理解原因。模型运行后，训练、验证和测试的准确率分别为99.87%、99.54%和99.54%。
{"title":"Interpretable Disease Classification in Plant Leaves using Deep Convolutional Neural Networks","authors":"Mohammad Rakibul Hasan Mahin, Waheed Moonwar, Md. Shamsul Rayhan Chy, Fahim Faisal Rafi, Md. Fahim Shahriar, Dewan Ziaul Karim, Annajiat Alim Rasel","doi":"10.1109/ICCIT57492.2022.10055126","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055126","url":null,"abstract":"Agriculture has been crucial for centuries. Due to its revenue contribution, agriculture’s importance has grown throughout time. However, some counter factors prohibit us from getting the full benefits of crops. Natural plant diseases are one factor. The main causes of these difficulties are harsh weather and excessive pesticide use, which strain Bangladesh’s economy. To lessen the problem’s severity, an image processing system was created that uses Deep Learning and CNN to classify leaf illnesses. The primary demographic is farmers and others who are willing to tend crops. It was decided to make sure the proposed model is lightweight so that it can be compatible and simple to implement on low-end devices without using up excessive resources. This CNN algorithm predicts the leaf’s status based on the user’s selected images. After constructing CNN, another model is offered, LIME, based on Explainable AI (XAI). XAI helps humans understand AI’s decisions or predictions. After the proposed CNN model diagnoses diseased leaves, the XAI helps us understand why. Conclusively, 99.87%, 99.54%, 99.54% accuracy was found in training, validation and testing respectively after running our models.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122549270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055415
Dewan Md. Shihabul Islam, Niloy Das, M. Uddin
Free space optical (FSO) communication system is a groundbreaking technology in the field of communication systems. Non-orthogonal multiple access (NOMA), on the other hand, currently being used in fifth-generation (5G) wireless systems, is a powerful technique for establishing communication for multiple users using the same time and frequency resources. In this paper, the performances of NOMA and orthogonal multiple access (OMA) in an FSO-based communication system are studied. In the FSO communication system, two base station units are considered to be connected to a central unit for FSO backhauling using an uplink-fixed NOMA scheme. Bit error rate (BER), ergodic capacity, and energy efficiency (EE) performances of the NOMA-based FSO system are studied and compared with an OMA-based FSO system. It is found that the NOMA-based system provides approximately 10% of the ergodic capacity gain and increases EE by 37%-60% for a given BER compared to the OMA-based system.
{"title":"Energy Efficiency Analysis of FSO Backhauled Uplink NOMA System","authors":"Dewan Md. Shihabul Islam, Niloy Das, M. Uddin","doi":"10.1109/ICCIT57492.2022.10055415","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055415","url":null,"abstract":"Free space optical (FSO) communication system is a groundbreaking technology in the field of communication systems. Non-orthogonal multiple access (NOMA), on the other hand, currently being used in fifth-generation (5G) wireless systems, is a powerful technique for establishing communication for multiple users using the same time and frequency resources. In this paper, the performances of NOMA and orthogonal multiple access (OMA) in an FSO-based communication system are studied. In the FSO communication system, two base station units are considered to be connected to a central unit for FSO backhauling using an uplink-fixed NOMA scheme. Bit error rate (BER), ergodic capacity, and energy efficiency (EE) performances of the NOMA-based FSO system are studied and compared with an OMA-based FSO system. It is found that the NOMA-based system provides approximately 10% of the ergodic capacity gain and increases EE by 37%-60% for a given BER compared to the OMA-based system.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121275117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10054999
Annur Tasnim Islam, Sakib Mashrafi Apu, Sudipta Sarker, Syeed Alam Shuvo, Inzamam M. Hasan, Ashraful Alam, Shakib Mahmud Dipto
The formation of altered cells in the human brain constitutes a brain tumor. There are numerous varieties of brain tumors in existence today. According to academics and medical professionals, some brain tumors are curable, while others are deadly. In most cases, brain cancer is identified at a late stage, making recovery difficult. This raises the rate of mortality. If this could be identified in its earliest stages, many lives could be saved. Brain cancers are currently identified by automated processes that use AI algorithms and brain imaging data. In this article, we use Magnetic Resonance Imaging (MRI) data and the fusion of learning models to suggest an effective strategy for detecting brain tumors. The suggested system consists of multiple processes, including preprocessing and classification of brain MRI images, performance analysis and optimization of various deep neural networks, and efficient methodologies. The proposed study allows for a more precise classification of brain cancers. We start by collecting the dataset and classifying it with the VGG16, VGG19, ResNet50, ResNet101, and InceptionV3 architectures. We achieved an accuracy rate of 96.72% for VGG16, 96.17% for ResNet50, and 95.55% for InceptionV3 as a result of our analysis. Using the top three classifiers, we created an ensemble model called EBTDM (Ensembled Brain Tumor Detection Model) and achieved an overall accuracy rate of 98.60%.
{"title":"An Efficient Deep Learning Approach to detect Brain Tumor Using MRI Images","authors":"Annur Tasnim Islam, Sakib Mashrafi Apu, Sudipta Sarker, Syeed Alam Shuvo, Inzamam M. Hasan, Ashraful Alam, Shakib Mahmud Dipto","doi":"10.1109/ICCIT57492.2022.10054999","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054999","url":null,"abstract":"The formation of altered cells in the human brain constitutes a brain tumor. There are numerous varieties of brain tumors in existence today. According to academics and medical professionals, some brain tumors are curable, while others are deadly. In most cases, brain cancer is identified at a late stage, making recovery difficult. This raises the rate of mortality. If this could be identified in its earliest stages, many lives could be saved. Brain cancers are currently identified by automated processes that use AI algorithms and brain imaging data. In this article, we use Magnetic Resonance Imaging (MRI) data and the fusion of learning models to suggest an effective strategy for detecting brain tumors. The suggested system consists of multiple processes, including preprocessing and classification of brain MRI images, performance analysis and optimization of various deep neural networks, and efficient methodologies. The proposed study allows for a more precise classification of brain cancers. We start by collecting the dataset and classifying it with the VGG16, VGG19, ResNet50, ResNet101, and InceptionV3 architectures. We achieved an accuracy rate of 96.72% for VGG16, 96.17% for ResNet50, and 95.55% for InceptionV3 as a result of our analysis. Using the top three classifiers, we created an ensemble model called EBTDM (Ensembled Brain Tumor Detection Model) and achieved an overall accuracy rate of 98.60%.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133751675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10054806
Farhan Ishtiaque, Fazla Rabbi Mashrur, Mohammad Touhidul Islam Miya, Khandoker Mahmudur Rahman, R. Vaidyanathan, S. Anwar, F. Sarker, K. Mamun
Brain-Computer Interface (BCI) technology is used in neuromarketing to learn how consumers respond to marketing stimuli. This helps evaluate the marketing stimuli which is traditionally done using marketing research procedures. BCI-based neuromarketing promises to replace these traditional marketing research procedures which are time-consuming and costly. Although BCI-based neuromarketing has its difficulty as EEG devices are inconvenient for consumer-grade applications. This study is performed to predict consumers’ affective attitude (AA) and purchase intention (PI) toward a product using EEG signals. EEG signals are collected using a single channel consumer-grade EEG device from 4 healthy participants while they are subject to 3 different types of marketing stimuli; product, promotion, and endorsement. Multi-domain features are extracted from the EEG signals after pre-processing. 52 features are selected among those using SVM-based Recursive Feature Elimination. SMOTE algorithm is used to balance out the dataset. Support Vector Machine (SVM) is used to classify positive and negative affective attitude and purchase intention. The model manages to achieve an accuracy of 88.2% for affective attitude and 80.4% for purchase intention proving the viability of consumer-grade BCI devices in neuromarketing.
{"title":"BCI-based Consumers’ Preference Prediction using Single Channel Commercial EEG Device","authors":"Farhan Ishtiaque, Fazla Rabbi Mashrur, Mohammad Touhidul Islam Miya, Khandoker Mahmudur Rahman, R. Vaidyanathan, S. Anwar, F. Sarker, K. Mamun","doi":"10.1109/ICCIT57492.2022.10054806","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054806","url":null,"abstract":"Brain-Computer Interface (BCI) technology is used in neuromarketing to learn how consumers respond to marketing stimuli. This helps evaluate the marketing stimuli which is traditionally done using marketing research procedures. BCI-based neuromarketing promises to replace these traditional marketing research procedures which are time-consuming and costly. Although BCI-based neuromarketing has its difficulty as EEG devices are inconvenient for consumer-grade applications. This study is performed to predict consumers’ affective attitude (AA) and purchase intention (PI) toward a product using EEG signals. EEG signals are collected using a single channel consumer-grade EEG device from 4 healthy participants while they are subject to 3 different types of marketing stimuli; product, promotion, and endorsement. Multi-domain features are extracted from the EEG signals after pre-processing. 52 features are selected among those using SVM-based Recursive Feature Elimination. SMOTE algorithm is used to balance out the dataset. Support Vector Machine (SVM) is used to classify positive and negative affective attitude and purchase intention. The model manages to achieve an accuracy of 88.2% for affective attitude and 80.4% for purchase intention proving the viability of consumer-grade BCI devices in neuromarketing.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131936598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055366
Sadman Jahin, Md Moniruzzaman, Fahmeed Mahmud Alvee, Inzamum Ul Haque, K. Kalpoma
In this work, first, we created an electronic stethoscope (e-Stethoscope) of very low cost that converts the acoustic sound waves obtained through the chest piece into electrical signals and can amplify heart murmurs and noises created by the heart valves. This paper presents an effective way of predicting heart diseases based on heart sounds produced by this e-stethoscope. Our prediction system collects heart sounds from patients using this e-stethoscope and then analyzes them to predict the disease by running various Machine-learning and Deep-learning models like KNN, SVM, Decision Tree, Random Forest, MLP Classifier, ANN, 1D CNN, 2D CNN, etc. We analyzed the results through the 3 datasets, Physionet, Pascal, and Our Collected Heart Dataset. MLP classifier and ANN both performed well on our dataset. A modern heart sound database platform is developed to impact the telemedicine sector worldwide. This telemedicine service may help to cut costs and travel time massively.
{"title":"A Modern Approach to AI Assistant for Heart Disease Detection by Heart Sound through created e-Stethoscope","authors":"Sadman Jahin, Md Moniruzzaman, Fahmeed Mahmud Alvee, Inzamum Ul Haque, K. Kalpoma","doi":"10.1109/ICCIT57492.2022.10055366","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055366","url":null,"abstract":"In this work, first, we created an electronic stethoscope (e-Stethoscope) of very low cost that converts the acoustic sound waves obtained through the chest piece into electrical signals and can amplify heart murmurs and noises created by the heart valves. This paper presents an effective way of predicting heart diseases based on heart sounds produced by this e-stethoscope. Our prediction system collects heart sounds from patients using this e-stethoscope and then analyzes them to predict the disease by running various Machine-learning and Deep-learning models like KNN, SVM, Decision Tree, Random Forest, MLP Classifier, ANN, 1D CNN, 2D CNN, etc. We analyzed the results through the 3 datasets, Physionet, Pascal, and Our Collected Heart Dataset. MLP classifier and ANN both performed well on our dataset. A modern heart sound database platform is developed to impact the telemedicine sector worldwide. This telemedicine service may help to cut costs and travel time massively.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055902
Senjuti Rahman, M. Hasan, A. K. Sarkar
Heart plays a crucial role in all forms of life. Heart-related disorders demand higher precision, consistency, and accuracy in diagnosis and prognosis because even a small mistake might lead to death. Heart-related deaths are common, and the number of these deaths is rising rapidly day by day. Heart disease (HD) prediction with an acceptable level of accuracy is attainable by using cutting-edge machine learning (ML) and deep learning (DL) algorithms. Making an accurate model using these algorithms can predict and categorize cardiovascular illness with high accuracy and reduce medical testing and human intervention. In this study an assessment between ML and DL was carried out to improve classification models for heart disease prediction based on related performance metrics (Accuracy, Precision, Recall, F-1 score, and AUC curve) using a benchmark dataset from UCI machine learning databases of heart disease. which consists of 14 different heart disease-related features. Extreme Gradient Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, CatBoost, Gradient Boosting, Random Forest, Ridge, Decision Tree, Logistic Regression, K Neighbors, SVM-Linear Kernel, Naive Bayes, and deep neural networks, DNN3(3-layer network) and DNN4(4-layer network) are just a few of the classification models that are successfully used in this work for classification tasks. The highest classification accuracy was attained with the Extreme Gradient Boosting classifier (81.10%) (among the machine learning classifiers). The three layer deep neural network (DNN3) among deep learning approaches has provided the best accuracy of 85.41% when using selected features as input. The gathered results showed that deep neural networks outperformed machine learning techniques.
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