Pub Date : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454231
E. Elbasi, A. Zreikat
As an essential part of the internet of things (IoT), the internet of medical things (IoMT) plays an essential role in the healthcare industry for the timely prediction of diagnosis of diseases to avoid chronic illness. Because of the massive information to be processed by the healthcare industry, some factors such as security, processing power, and accuracy of these information are of great importance for predicting the diagnosis of numerous diseases. To overcome these challenges, machine learning algorithms are used in the literature to increase the accuracy of patient's data. On the other hand, in this research work, patient data is collected from several IoMT devices such as ambulance, medical imaging, wearables, doctor reports, patient history, and labs. All data collected from several sources used in machine learning algorithms to categorize, cluster, and forecast for treatment and diagnoses. The provided results demonstrate that the random forest algorithm gives more than 93% accuracy, and the Hoeffding Tree algorithm gives more than 92% accuracy for patient heart data compared to other suggested algorithms in the literature. Several clustering algorithms are applied such as EM, k-means, density, filtered, and farthest clustering. K-means, filtering, and density algorithms give more reliable clustering results than others.
{"title":"Efficient early prediction and diagnosis of diseases using machine learning algorithms for IoMT data","authors":"E. Elbasi, A. Zreikat","doi":"10.1109/AIIoT52608.2021.9454231","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454231","url":null,"abstract":"As an essential part of the internet of things (IoT), the internet of medical things (IoMT) plays an essential role in the healthcare industry for the timely prediction of diagnosis of diseases to avoid chronic illness. Because of the massive information to be processed by the healthcare industry, some factors such as security, processing power, and accuracy of these information are of great importance for predicting the diagnosis of numerous diseases. To overcome these challenges, machine learning algorithms are used in the literature to increase the accuracy of patient's data. On the other hand, in this research work, patient data is collected from several IoMT devices such as ambulance, medical imaging, wearables, doctor reports, patient history, and labs. All data collected from several sources used in machine learning algorithms to categorize, cluster, and forecast for treatment and diagnoses. The provided results demonstrate that the random forest algorithm gives more than 93% accuracy, and the Hoeffding Tree algorithm gives more than 92% accuracy for patient heart data compared to other suggested algorithms in the literature. Several clustering algorithms are applied such as EM, k-means, density, filtered, and farthest clustering. K-means, filtering, and density algorithms give more reliable clustering results than others.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"239 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133287499","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454170
O. Odeyomi, G. Záruba
This paper addresses the problem of time-varying data distribution in a fully decentralized federated learning setting with budget constraints. Most existing work cover only fixed data distribution in the centralized setting, which is not applicable when the data becomes time-varying, such as in realtime traffic monitoring. More so, a lot of existing work do not address budget constraint problem common in practical federated learning settings. To address these problems, we propose an online Lagrangian descent algorithm. To provide privacy to the local model updates of the clients, local differential privacy is introduced. We show that our algorithm incurs the best regret bound when compared to other similar algorithms, while satisfying the budget constraints in the long term.
{"title":"Differentially-Private Federated Learning with Long-Term Budget Constraints Using Online Lagrangian Descent","authors":"O. Odeyomi, G. Záruba","doi":"10.1109/AIIoT52608.2021.9454170","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454170","url":null,"abstract":"This paper addresses the problem of time-varying data distribution in a fully decentralized federated learning setting with budget constraints. Most existing work cover only fixed data distribution in the centralized setting, which is not applicable when the data becomes time-varying, such as in realtime traffic monitoring. More so, a lot of existing work do not address budget constraint problem common in practical federated learning settings. To address these problems, we propose an online Lagrangian descent algorithm. To provide privacy to the local model updates of the clients, local differential privacy is introduced. We show that our algorithm incurs the best regret bound when compared to other similar algorithms, while satisfying the budget constraints in the long term.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115537814","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454176
HM Tamim, Fahema Sultana, N. Tasneem, Yakut Marzan, Mohammad Monirujjaman Khan
The student monitoring system represents a detailed description of Class Insight. It explains the purpose and the features of the system. It also interprets the interfaces, the working procedures of the system, the constraints under which it will operate and how the system will react to external stimuli. This is a machine learning-based student monitoring system that allows teachers to submit an assessment to students completely paperless. It provides tools for teachers and students to keep track of their assignments, reading materials and other tasks. The application will keep track of the students' face and eye while reading and will update progresses instantly. As a result, instructors can track real-time updates of the tasks. They will also be notified whether it is the student's face or not and how much time they spent on a single page of the reading materials. This will be generated as a report.
{"title":"Class Insight: A Student Monitoring System with Real-time Updates using Face Detection and Eye Tracking","authors":"HM Tamim, Fahema Sultana, N. Tasneem, Yakut Marzan, Mohammad Monirujjaman Khan","doi":"10.1109/AIIoT52608.2021.9454176","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454176","url":null,"abstract":"The student monitoring system represents a detailed description of Class Insight. It explains the purpose and the features of the system. It also interprets the interfaces, the working procedures of the system, the constraints under which it will operate and how the system will react to external stimuli. This is a machine learning-based student monitoring system that allows teachers to submit an assessment to students completely paperless. It provides tools for teachers and students to keep track of their assignments, reading materials and other tasks. The application will keep track of the students' face and eye while reading and will update progresses instantly. As a result, instructors can track real-time updates of the tasks. They will also be notified whether it is the student's face or not and how much time they spent on a single page of the reading materials. This will be generated as a report.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123168883","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454192
Samira Zad, Maryam Heidari, James H. Jones, Özlem Uzuner
Emotion is a primary aspect of communication and can be expressed in many modalities. Text-Based Emotion Detection (TBED), one of the fastest growing branches of Natural Language Processing (NLP), is the process of classifying syntactic or semantic units of a corpus into a given set of emotion classes proposed by a psychological model. Automatic TBED mechanisms use machine learning approaches to create computational platforms automating the process of extracting emotions. TBED has a wide variety of applications in the area of artificial intelligence: Semantic analysis of documents and public messages related to terrorist attacks (to mitigate risks), automated analysis of historical corpora, and study of product reviews (to assess customer satisfaction). This work reviews the current literature of TBED and the psychological models associated with them.
{"title":"Emotion Detection of Textual Data: An Interdisciplinary Survey","authors":"Samira Zad, Maryam Heidari, James H. Jones, Özlem Uzuner","doi":"10.1109/AIIoT52608.2021.9454192","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454192","url":null,"abstract":"Emotion is a primary aspect of communication and can be expressed in many modalities. Text-Based Emotion Detection (TBED), one of the fastest growing branches of Natural Language Processing (NLP), is the process of classifying syntactic or semantic units of a corpus into a given set of emotion classes proposed by a psychological model. Automatic TBED mechanisms use machine learning approaches to create computational platforms automating the process of extracting emotions. TBED has a wide variety of applications in the area of artificial intelligence: Semantic analysis of documents and public messages related to terrorist attacks (to mitigate risks), automated analysis of historical corpora, and study of product reviews (to assess customer satisfaction). This work reviews the current literature of TBED and the psychological models associated with them.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124766231","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454232
Evariste Twahirwa, Kambombo Mtonga, Desire Ngabo, S. Kumaran
Keeping key air pollutants below the World Health Organization recommended limits is important for combating the ever-increasing deaths resulting from the associated health problems. This is especially true for indoor environments where poor ventilation can magnify the effects of air pollution. Having Knowledge about the level of pollutants in the air would serve as a stepping stone to take mitigation measures. In this work, a domesticated air pollution monitoring system over the LoRa enabled Internet of Things framework is proposed. Two sensors for CO2 and PM2.5that are important for air quality monitoring with compensated weather monitoring capabilities were deployed in the cafeteria kitchen and laboratory room of the University of Rwanda, College of Science and Technology. The sensed parameter readings are then sent to the cloud via LoRaWAN protocol supported gateway that interfaces the sensors and the cloud part of the network. The end users can query the system and access the data together with the analytic information via the developed Web-based user interface dashboard. An analysis of the data over a period of eleven (11) months is carried out and results show high parts per million of CO2of over 800 ppm and PM2.5 concentration of over 100 ppm in the kitchen environment. Whilst a concentration of 500 ppm for CO2and zero ppm for PM2.5 were observed in the laboratory room. Baseline algorithms that facilitate setting of triggers for each sensing node and pushing of notifications for when a measured parameter exceeds a certain threshold value are proposed and implemented.
{"title":"A LoRa enabled IoT-based Air Quality Monitoring System for Smart City","authors":"Evariste Twahirwa, Kambombo Mtonga, Desire Ngabo, S. Kumaran","doi":"10.1109/AIIoT52608.2021.9454232","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454232","url":null,"abstract":"Keeping key air pollutants below the World Health Organization recommended limits is important for combating the ever-increasing deaths resulting from the associated health problems. This is especially true for indoor environments where poor ventilation can magnify the effects of air pollution. Having Knowledge about the level of pollutants in the air would serve as a stepping stone to take mitigation measures. In this work, a domesticated air pollution monitoring system over the LoRa enabled Internet of Things framework is proposed. Two sensors for CO2 and PM2.5that are important for air quality monitoring with compensated weather monitoring capabilities were deployed in the cafeteria kitchen and laboratory room of the University of Rwanda, College of Science and Technology. The sensed parameter readings are then sent to the cloud via LoRaWAN protocol supported gateway that interfaces the sensors and the cloud part of the network. The end users can query the system and access the data together with the analytic information via the developed Web-based user interface dashboard. An analysis of the data over a period of eleven (11) months is carried out and results show high parts per million of CO2of over 800 ppm and PM2.5 concentration of over 100 ppm in the kitchen environment. Whilst a concentration of 500 ppm for CO2and zero ppm for PM2.5 were observed in the laboratory room. Baseline algorithms that facilitate setting of triggers for each sensing node and pushing of notifications for when a measured parameter exceeds a certain threshold value are proposed and implemented.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127262498","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454227
M. Mahmud, Faria Soroni, Mohammad Monirujjaman Khan
It is difficult to keep track of one's all medical records. When a person suddenly falls sick, he/she may not have his/her medical documents with him. Hence, we decided to develop a project which is a medical app for both android and iOS that will always give access to a user”s medical records and history. So that the user can effortlessly manage and share them between facilities. This is especially convenient when someone is seeing a specialist and managing a lot of documents. Users can store and update their vitals, medical reports, newly assigned medications basically the entire medical history that is needed to diagnose a patient. For building the app React-native and Firebase database is used as a cloud alongside Redux for store management of the app. Health passport is designed to be a universal patient engagement platform that improves care for the health and encourages the user to take a more active role in their medical life by not doing the same test again and again for different facilities and thus saving money. Overall, the app is a middleman between patients and healthcare providers. This can be very helpful in real emergencies. With proper implementation, it can play a very important role in a person”s medical life.
{"title":"Development of a Mobile Application for Patient's Medical Record and History","authors":"M. Mahmud, Faria Soroni, Mohammad Monirujjaman Khan","doi":"10.1109/AIIoT52608.2021.9454227","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454227","url":null,"abstract":"It is difficult to keep track of one's all medical records. When a person suddenly falls sick, he/she may not have his/her medical documents with him. Hence, we decided to develop a project which is a medical app for both android and iOS that will always give access to a user”s medical records and history. So that the user can effortlessly manage and share them between facilities. This is especially convenient when someone is seeing a specialist and managing a lot of documents. Users can store and update their vitals, medical reports, newly assigned medications basically the entire medical history that is needed to diagnose a patient. For building the app React-native and Firebase database is used as a cloud alongside Redux for store management of the app. Health passport is designed to be a universal patient engagement platform that improves care for the health and encourages the user to take a more active role in their medical life by not doing the same test again and again for different facilities and thus saving money. Overall, the app is a middleman between patients and healthcare providers. This can be very helpful in real emergencies. With proper implementation, it can play a very important role in a person”s medical life.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127060246","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454250
Shreehar Joshi, Eman Abdelfattah
The reviews that are present in different forms on the Internet can provide valuable insights into the opinions of the users that are spread across a wide range of geographical space in the most time and cost-efficient manner. This information can be used to improve the quality or assess the efficiency of a product over any given domain. In this research, analysis of the users' online reviews within the field of pharmaceuticals is presented. These reviews consist primarily of the information regarding the usefulness of drugs or the side effects they have caused. As much as it is important to find a measure of the efficiency of a drug, it is also essential to determine the medical condition for which the drug is manifesting its effects, be it positive or negative. In this research, six different supervised machine learning classifiers are deployed to find the most efficient model to predict the medical condition based on the users' reviews. The classifiers used are as follows: Multinomial Naive Bayes, Multinomial Logistic Regression, Linear Support Vector Classifier (SVC), Decision Trees, Extra Trees, and Random Forests. The results demonstrate that among all the classifiers used, Linear SVC proved to be the most efficient when considering its Precision, Recall, F1score and the time it takes to train and test on the given data.
{"title":"Multi-Class Text Classification Using Machine Learning Models for Online Drug Reviews","authors":"Shreehar Joshi, Eman Abdelfattah","doi":"10.1109/AIIoT52608.2021.9454250","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454250","url":null,"abstract":"The reviews that are present in different forms on the Internet can provide valuable insights into the opinions of the users that are spread across a wide range of geographical space in the most time and cost-efficient manner. This information can be used to improve the quality or assess the efficiency of a product over any given domain. In this research, analysis of the users' online reviews within the field of pharmaceuticals is presented. These reviews consist primarily of the information regarding the usefulness of drugs or the side effects they have caused. As much as it is important to find a measure of the efficiency of a drug, it is also essential to determine the medical condition for which the drug is manifesting its effects, be it positive or negative. In this research, six different supervised machine learning classifiers are deployed to find the most efficient model to predict the medical condition based on the users' reviews. The classifiers used are as follows: Multinomial Naive Bayes, Multinomial Logistic Regression, Linear Support Vector Classifier (SVC), Decision Trees, Extra Trees, and Random Forests. The results demonstrate that among all the classifiers used, Linear SVC proved to be the most efficient when considering its Precision, Recall, F1score and the time it takes to train and test on the given data.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126922283","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454169
Samira Zad, Maryam Heidari, James H. Jones, Özlem Uzuner
Text mining is one of the branches of data mining and refers to as the computing process of finding new patterns and relations among datasets which appear not to be related. Data mining is an interdisciplinary field which uses statistics, artificial intelligence, and database systems to generate new tools for discovering patterns among datasets. Similarly, when dealing with textual data, we need to use various methods in different branches of computer science (e.g. linguistics) and statistics. This study reviews the techniques of text-based sentiment analysis pipeline including preprocessing, aspect extraction, feature selection, and classification techniques used by scholars recently. It also surveys different applications of semantic analysis in the context of social media, marketing, and product reviews.
{"title":"A Survey on Concept-Level Sentiment Analysis Techniques of Textual Data","authors":"Samira Zad, Maryam Heidari, James H. Jones, Özlem Uzuner","doi":"10.1109/AIIoT52608.2021.9454169","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454169","url":null,"abstract":"Text mining is one of the branches of data mining and refers to as the computing process of finding new patterns and relations among datasets which appear not to be related. Data mining is an interdisciplinary field which uses statistics, artificial intelligence, and database systems to generate new tools for discovering patterns among datasets. Similarly, when dealing with textual data, we need to use various methods in different branches of computer science (e.g. linguistics) and statistics. This study reviews the techniques of text-based sentiment analysis pipeline including preprocessing, aspect extraction, feature selection, and classification techniques used by scholars recently. It also surveys different applications of semantic analysis in the context of social media, marketing, and product reviews.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125706478","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454182
Narayana Darapaneni, Nandan Garimella, Santhosh Vadlamani, Sulekha Dileep, S. Manchala, A. Paduri, Dinakar Komanduri, Prajwal Nagisetti
Our work delves into the analysis of temperature time-series data, deployment of forecasting models, and their effectiveness in predicting food temperature based on historical data. The temperature of several food items recorded over a period of three months has been utilized for this purpose. Multiple Machine Learning models and their effectiveness in predicting food temperature have been analyzed. The results of these findings are discussed herein during the conclusion.
{"title":"Food Temperature Analysis and Forecasting","authors":"Narayana Darapaneni, Nandan Garimella, Santhosh Vadlamani, Sulekha Dileep, S. Manchala, A. Paduri, Dinakar Komanduri, Prajwal Nagisetti","doi":"10.1109/AIIoT52608.2021.9454182","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454182","url":null,"abstract":"Our work delves into the analysis of temperature time-series data, deployment of forecasting models, and their effectiveness in predicting food temperature based on historical data. The temperature of several food items recorded over a period of three months has been utilized for this purpose. Multiple Machine Learning models and their effectiveness in predicting food temperature have been analyzed. The results of these findings are discussed herein during the conclusion.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123446776","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454243
Faria Soroni, M. Mahmud, Sajal Chowdhury, Mohammad Monirujjaman Khan
This paper presents the implementation of an online fashion rental service. It can be categorized as a web-based platform where people can rent things without any hassle. It uses a very efficient way to promote hiring exclusive dresses and accessories to people who does not have the means to own expensive products or are less interested in owning for various reasons. The system is designed in such a way that takes care the needs of the users and capable of providing accurate information about the products and an uncomplicated payment system.
{"title":"RentBd-An Exclusive Fashion Rental Service","authors":"Faria Soroni, M. Mahmud, Sajal Chowdhury, Mohammad Monirujjaman Khan","doi":"10.1109/AIIoT52608.2021.9454243","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454243","url":null,"abstract":"This paper presents the implementation of an online fashion rental service. It can be categorized as a web-based platform where people can rent things without any hassle. It uses a very efficient way to promote hiring exclusive dresses and accessories to people who does not have the means to own expensive products or are less interested in owning for various reasons. The system is designed in such a way that takes care the needs of the users and capable of providing accurate information about the products and an uncomplicated payment system.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414960","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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