Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118086
Mary Shiba C, Sumit Mishra, S. Sandhya, K. Vidhya, Jaichandran R, G. Manjula
Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not.
{"title":"Covid19 Disease Assessment Using CNN Architecture","authors":"Mary Shiba C, Sumit Mishra, S. Sandhya, K. Vidhya, Jaichandran R, G. Manjula","doi":"10.1109/ICIPTM57143.2023.10118086","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118086","url":null,"abstract":"Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116794277","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118148
Sudhanshu Singh, Sanjeev Kumar, H. Olasiuk, R. K. Revulagadda, N. Vihari
The current study proposes a blockchain-based technology application for managing blood supply chain. Every peer-to-peer transaction in a blockchain that is recorded and stored is suggested to be shared with the entities participating in the transactions. The characteristics of any blockchain used for blood supply chain are that it is immutable, decentralised, consensus-driven and transparent. Being process centric, it would be helpful in managing an efficient blood supply chain while protecting the privacy of the blood donors. Further, the use of Radio frequency identification (RFID) technologies is recommended to prevent errors in blood transfusion as well as improve the quality and productivity of the blood supply chain.
{"title":"A Blockchain Technology Application for Managing Blood Supply Chain","authors":"Sudhanshu Singh, Sanjeev Kumar, H. Olasiuk, R. K. Revulagadda, N. Vihari","doi":"10.1109/ICIPTM57143.2023.10118148","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118148","url":null,"abstract":"The current study proposes a blockchain-based technology application for managing blood supply chain. Every peer-to-peer transaction in a blockchain that is recorded and stored is suggested to be shared with the entities participating in the transactions. The characteristics of any blockchain used for blood supply chain are that it is immutable, decentralised, consensus-driven and transparent. Being process centric, it would be helpful in managing an efficient blood supply chain while protecting the privacy of the blood donors. Further, the use of Radio frequency identification (RFID) technologies is recommended to prevent errors in blood transfusion as well as improve the quality and productivity of the blood supply chain.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735390","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117801
Sudhir Anakal, S. Kar, A. Sangeetha, Sritha P, Dekka Satish, Sajitha. L. P, A. R. Prasad
Temperature increases are a common source of problems in transformers. Temperature increases as load current increases. There are two ways to monitor the rise in load current. Use a potential transformer and a temperature sensor, respectively. Voltage drops and winding temperature rises as load current increases. By measuring these two variables with a voltage sensor and a temperature sensor, the transformer problem can be resolved. The quantity detected by the sensor will be relayed over IoT to the control room in order to inform it of the transformer's state. It operates totally on autopilot. The Node MCU8266 board (which consist of microcontroller and built-in Wi-Fi board) included with the sensor will activate the circuit breaker to protect the transformer when the fault level is high.
{"title":"Transformer Monitoring and Security System Using IoT","authors":"Sudhir Anakal, S. Kar, A. Sangeetha, Sritha P, Dekka Satish, Sajitha. L. P, A. R. Prasad","doi":"10.1109/ICIPTM57143.2023.10117801","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117801","url":null,"abstract":"Temperature increases are a common source of problems in transformers. Temperature increases as load current increases. There are two ways to monitor the rise in load current. Use a potential transformer and a temperature sensor, respectively. Voltage drops and winding temperature rises as load current increases. By measuring these two variables with a voltage sensor and a temperature sensor, the transformer problem can be resolved. The quantity detected by the sensor will be relayed over IoT to the control room in order to inform it of the transformer's state. It operates totally on autopilot. The Node MCU8266 board (which consist of microcontroller and built-in Wi-Fi board) included with the sensor will activate the circuit breaker to protect the transformer when the fault level is high.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132963157","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117580
Mary Getsy, H. Kousar, A. Shanmugam, J. S. Isaac, Nirzar Kulkarni, P. Raja, M. Sudhakar
The Automated Buying Cart, or “Smart Cart,” is a cutting-edge consumer device created to speed up the shopping process for customers! The Automated Shopping Cart collects all the data from the moment a customer removes an item from the shelf of the store until the final bill is generated and ready for final checkout. The time needed to shop and pay is significantly decreased. When speed and efficiency are the main concerns in today's shopping systems, an automated shopping system using Radio Frequency Identification technology emerges as a convergent technology. The evaluation's findings were presented together with suggestions for future prototype development improvements.
{"title":"An Innovative Shopping System with GSM Based Automation for Physically Challenging and Old Age People","authors":"Mary Getsy, H. Kousar, A. Shanmugam, J. S. Isaac, Nirzar Kulkarni, P. Raja, M. Sudhakar","doi":"10.1109/ICIPTM57143.2023.10117580","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117580","url":null,"abstract":"The Automated Buying Cart, or “Smart Cart,” is a cutting-edge consumer device created to speed up the shopping process for customers! The Automated Shopping Cart collects all the data from the moment a customer removes an item from the shelf of the store until the final bill is generated and ready for final checkout. The time needed to shop and pay is significantly decreased. When speed and efficiency are the main concerns in today's shopping systems, an automated shopping system using Radio Frequency Identification technology emerges as a convergent technology. The evaluation's findings were presented together with suggestions for future prototype development improvements.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131438096","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117927
Kumar Rajesh, Sanjeev Kumar, Kanaujia kumar Binod, H. Olasiuk, N. Vihari
This design explores the analysis and implementation to a reconfigurable harmonic matching technique with Cascode Class-F Power Amplifier (PA) for IoT-enabled healthcare application. The special circuit efficiency and extended digital pre-distortion are taken to select for providing high efficiency and high linearity properties. A compact gallium nitride (GaN) Class F PA is developed as a proof of concept, using a reconfigurable harmonic network to demonstrate its broadband characteristics. A reconfigurable harmonic matching technique is utilized to enhance the gain up to 19.3 dB to the saturation power region. and improve the resonance band operation. The design verified maximum range of output power of 20 to 43dBm at 74.5% and power added efficiency (PAE) of 67.8%. The harmonic balance of fundamental & second balance is showed from −21dBc to − 16dBc. The simulations and calculations are verified.
{"title":"A Novel Class-F Power Amplifier with reconfigurable harmonic matching technique for IoT-enabled healthcare application","authors":"Kumar Rajesh, Sanjeev Kumar, Kanaujia kumar Binod, H. Olasiuk, N. Vihari","doi":"10.1109/ICIPTM57143.2023.10117927","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117927","url":null,"abstract":"This design explores the analysis and implementation to a reconfigurable harmonic matching technique with Cascode Class-F Power Amplifier (PA) for IoT-enabled healthcare application. The special circuit efficiency and extended digital pre-distortion are taken to select for providing high efficiency and high linearity properties. A compact gallium nitride (GaN) Class F PA is developed as a proof of concept, using a reconfigurable harmonic network to demonstrate its broadband characteristics. A reconfigurable harmonic matching technique is utilized to enhance the gain up to 19.3 dB to the saturation power region. and improve the resonance band operation. The design verified maximum range of output power of 20 to 43dBm at 74.5% and power added efficiency (PAE) of 67.8%. The harmonic balance of fundamental & second balance is showed from −21dBc to − 16dBc. The simulations and calculations are verified.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131944774","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118228
Maheep Dwivedi, G. Kumar, R. Singh
A thermal management system based on liquid coolant is proposed for the single 18650-25R cylinder - shaped Lithium-ion battery to preserve operating temperature of a single battery. Result suggests that highest temperature of battery is 299.8918K, a value lower than 313K because the water coolant inflow rate for the presented model is 0.05kg/s. In addition, the effect of the 2% Ag water-based nanofluid is contrasted with that of the water coolant. It can be concluded that the effect of the nanofluid is very similar to that of a water-based coolant.
{"title":"A Numerical study of Thermal Management of single cylindrical LiFePO4 battery","authors":"Maheep Dwivedi, G. Kumar, R. Singh","doi":"10.1109/ICIPTM57143.2023.10118228","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118228","url":null,"abstract":"A thermal management system based on liquid coolant is proposed for the single 18650-25R cylinder - shaped Lithium-ion battery to preserve operating temperature of a single battery. Result suggests that highest temperature of battery is 299.8918K, a value lower than 313K because the water coolant inflow rate for the presented model is 0.05kg/s. In addition, the effect of the 2% Ag water-based nanofluid is contrasted with that of the water coolant. It can be concluded that the effect of the nanofluid is very similar to that of a water-based coolant.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128158479","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117729
Neetima Agarwal, Arpana Kumari
The workplace is going digital with the inclusion of artificial intelligence tools. These tools are deeply reshaping the service industry and influencing customer relationship management. There have been various studies that have shown the correlation between technology and its effect on the organization. Through the study, the effect of AI tools on the EmQ of Customer Care Executives has been analyzed as perceived by the customers. The study highlights the effect of AI tools on the affective and cognitive empathy of the CCE and thus on responsiveness on the job.
{"title":"Effect of Artificial Intelligent on Empathy Quotient (EmQ) and Responsiveness of Customer Care Executive- A Study from Customer's Lenses","authors":"Neetima Agarwal, Arpana Kumari","doi":"10.1109/ICIPTM57143.2023.10117729","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117729","url":null,"abstract":"The workplace is going digital with the inclusion of artificial intelligence tools. These tools are deeply reshaping the service industry and influencing customer relationship management. There have been various studies that have shown the correlation between technology and its effect on the organization. Through the study, the effect of AI tools on the EmQ of Customer Care Executives has been analyzed as perceived by the customers. The study highlights the effect of AI tools on the affective and cognitive empathy of the CCE and thus on responsiveness on the job.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163623","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}
Green technology is critical to reaching the global sustainable development goals. It is critical to understand and analyze why different people adopt green technologies in different ways. Despite the fact that we recognize that numerous factors influence adoption, there is still a general lack of desire to accept new green technologies. This research is an attempt to find the level of knowledge and adoption of green technologies by residents of Delhi and the NCR region. This research advances knowledge of a better understanding of green technology awareness and uptake. The findings are consistent, and people are aware of Green Technologies. The demographic profile of the respondents have a statistically significant influence on the application of these green technologies.
{"title":"Factors Affecting Awareness and Practices of Green Technology","authors":"Vinita Sharma, Tanu Manocha, Seema Garg, Dr. Anchal Luthra, Shivani Dixit, Meghna Sharma","doi":"10.1109/ICIPTM57143.2023.10118326","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118326","url":null,"abstract":"Green technology is critical to reaching the global sustainable development goals. It is critical to understand and analyze why different people adopt green technologies in different ways. Despite the fact that we recognize that numerous factors influence adoption, there is still a general lack of desire to accept new green technologies. This research is an attempt to find the level of knowledge and adoption of green technologies by residents of Delhi and the NCR region. This research advances knowledge of a better understanding of green technology awareness and uptake. The findings are consistent, and people are aware of Green Technologies. The demographic profile of the respondents have a statistically significant influence on the application of these green technologies.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876922","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117823
Manish K. Assudani, Neeraj Sahu, Arulmozhi, A. Saravanan, K. Dhinakaran, Ashok Kumar
COVID-19 is one of the threats that came out of nowhere and literally shook the entire world. Various prediction techniques have been invented in a very short time. This study also develops a Deep Learning (DL) model which can predict the presence of COVID-19 and pneumonia by analyzing the X-ray images of human lungs. From Kaggle, a collection of X-ray images of the lungs is collected. Then, this dataset is preprocessed using two alternative methods. Some of the techniques include image enhancement and picture resizing. The two deep-learning models are then trained using the preprocessed dataset. A few more examples of DL algorithms include MobileNet and Inception-V3. The best model is then selected by validating the learned deep-learning models. As the epochs count increases during training and validation, the accuracy value for both models increases. The value of the loss increases as the number of epochs decreases. During the fourteenth validation period, the model generates a loss value of 0.32 for the MobileNet technique. During the first few training epochs, accuracy is lower, and by the fifteenth, it is close to 0.9. The Inception-V3 method produces a loss value of 0.1452 at the eleventh validation epoch, which is the lowest value. The greatest accuracy value of 0.9697 is obtained after the twelfth cycle of validation. The model that performs better and has lower loss values is then put through one last test. Inception-V3 is therefore selected as the top method for COVID-19 detection. The Inception-V3 system properly predicted each of the normal images and the COVID-19 images in the final test. Regarding pneumonia, it correctly predicted just one image out of 20 that are so small as to be disregarded. When a patient cannot afford to find a doctor for consultation, the DL model created in this work can be utilized as a preliminary test for COVID-19. By including the model created in this study as a backend processor for a website or software application, the study's findings can be updated.
{"title":"COVID-19 Detection on X-Ray Image Using Deep Learning","authors":"Manish K. Assudani, Neeraj Sahu, Arulmozhi, A. Saravanan, K. Dhinakaran, Ashok Kumar","doi":"10.1109/ICIPTM57143.2023.10117823","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117823","url":null,"abstract":"COVID-19 is one of the threats that came out of nowhere and literally shook the entire world. Various prediction techniques have been invented in a very short time. This study also develops a Deep Learning (DL) model which can predict the presence of COVID-19 and pneumonia by analyzing the X-ray images of human lungs. From Kaggle, a collection of X-ray images of the lungs is collected. Then, this dataset is preprocessed using two alternative methods. Some of the techniques include image enhancement and picture resizing. The two deep-learning models are then trained using the preprocessed dataset. A few more examples of DL algorithms include MobileNet and Inception-V3. The best model is then selected by validating the learned deep-learning models. As the epochs count increases during training and validation, the accuracy value for both models increases. The value of the loss increases as the number of epochs decreases. During the fourteenth validation period, the model generates a loss value of 0.32 for the MobileNet technique. During the first few training epochs, accuracy is lower, and by the fifteenth, it is close to 0.9. The Inception-V3 method produces a loss value of 0.1452 at the eleventh validation epoch, which is the lowest value. The greatest accuracy value of 0.9697 is obtained after the twelfth cycle of validation. The model that performs better and has lower loss values is then put through one last test. Inception-V3 is therefore selected as the top method for COVID-19 detection. The Inception-V3 system properly predicted each of the normal images and the COVID-19 images in the final test. Regarding pneumonia, it correctly predicted just one image out of 20 that are so small as to be disregarded. When a patient cannot afford to find a doctor for consultation, the DL model created in this work can be utilized as a preliminary test for COVID-19. By including the model created in this study as a backend processor for a website or software application, the study's findings can be updated.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114966949","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118162
Arman Raj, Vandana Sharma, S. Rani, B. Balusamy, Ankit Kumar Shanu, A. Alkhayyat
Big Data has influenced almost every sector such as banking, agriculture, Healthcare, Manufacturing and Natural Resources, Government, Communication, Entertainment Industry, Insurance and Education. Moreover, applications of Big Data especially in education sector have been exponentially increased. Big Data is currently a buzzword in both educational sector with the term being used to describe a wide range of concepts, ranging from extricating data from outside sources, storing and properly managing it and to processing it such data with inquisitive methods and tools. Big Data has significantly helped to improve Technology Enabled Learning (TEL) and Outcome Based Education (OBE). With the proliferation in these, AI-based Ed-Tech tools in Big Data, TEL has able to elongate and enhanced personalized learning. The numerous challenges faced by Ed-tech tools are data privacy issues, data quality issues, data storage issues and data analysis issues. In this paper, authors have presented a comprehensive review on AI based Ed-Tech tools using Big Data on parameters like size limit, data loading, Type of Data, user-interface and features.
{"title":"Revealing AI-Based Ed-Tech Tools Using Big Data","authors":"Arman Raj, Vandana Sharma, S. Rani, B. Balusamy, Ankit Kumar Shanu, A. Alkhayyat","doi":"10.1109/ICIPTM57143.2023.10118162","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118162","url":null,"abstract":"Big Data has influenced almost every sector such as banking, agriculture, Healthcare, Manufacturing and Natural Resources, Government, Communication, Entertainment Industry, Insurance and Education. Moreover, applications of Big Data especially in education sector have been exponentially increased. Big Data is currently a buzzword in both educational sector with the term being used to describe a wide range of concepts, ranging from extricating data from outside sources, storing and properly managing it and to processing it such data with inquisitive methods and tools. Big Data has significantly helped to improve Technology Enabled Learning (TEL) and Outcome Based Education (OBE). With the proliferation in these, AI-based Ed-Tech tools in Big Data, TEL has able to elongate and enhanced personalized learning. The numerous challenges faced by Ed-tech tools are data privacy issues, data quality issues, data storage issues and data analysis issues. In this paper, authors have presented a comprehensive review on AI based Ed-Tech tools using Big Data on parameters like size limit, data loading, Type of Data, user-interface and features.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130397626","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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