Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126277
Amruta E Kabade, P. Desai, S. C, Shankar G
Sign Language Recognition(SLR) is a complex gesture recognition problem because of the quick and highly coarticulated motion involved in gestures. This research work focuses on Fingerspelling recognition task, which constitutes 35% of the American Sign Language (ASL). Fingerspelling identifies the word letter by letter. Fingerspelling is used for signing the words which do not have designated ASL signs such as technical terms, content words and proper nouns. In our proposed work for ASL Fingerspelling recognition, we consider ChicagoFSWild dataset which consists of occlusions and images captured in varying illuminations, lighting conditions (in the wild environments). The optical flow is obtained from Lucas-Kanade algorithm, prior is generated, images are resized and cropped with face-roi technique to get the region of interest (ROI). The visual attention mechanism attends to the ROI iteratively. ResNet, pretrained on Imagenet is used for the extraction of spatial features. The Bi-LSTM network with Connectionist Temporal Classification (CTC) is used to predict the sign. It provides the accuracy of 57% on ChicagoFSWild dataset for Fingerspelling recognition task.
{"title":"American Sign Language Fingerspelling Recognition using Attention Model","authors":"Amruta E Kabade, P. Desai, S. C, Shankar G","doi":"10.1109/I2CT57861.2023.10126277","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126277","url":null,"abstract":"Sign Language Recognition(SLR) is a complex gesture recognition problem because of the quick and highly coarticulated motion involved in gestures. This research work focuses on Fingerspelling recognition task, which constitutes 35% of the American Sign Language (ASL). Fingerspelling identifies the word letter by letter. Fingerspelling is used for signing the words which do not have designated ASL signs such as technical terms, content words and proper nouns. In our proposed work for ASL Fingerspelling recognition, we consider ChicagoFSWild dataset which consists of occlusions and images captured in varying illuminations, lighting conditions (in the wild environments). The optical flow is obtained from Lucas-Kanade algorithm, prior is generated, images are resized and cropped with face-roi technique to get the region of interest (ROI). The visual attention mechanism attends to the ROI iteratively. ResNet, pretrained on Imagenet is used for the extraction of spatial features. The Bi-LSTM network with Connectionist Temporal Classification (CTC) is used to predict the sign. It provides the accuracy of 57% on ChicagoFSWild dataset for Fingerspelling recognition task.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125002887","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126473
Thanu Kurian, T. S
A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. The ECG image can be taken by scanning the image using a smart phone. This would be very helpful in small healthcare centers where there are no experts for diagnosis. The proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. The work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset.
{"title":"Deep Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) Images","authors":"Thanu Kurian, T. S","doi":"10.1109/I2CT57861.2023.10126473","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126473","url":null,"abstract":"A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. The ECG image can be taken by scanning the image using a smart phone. This would be very helpful in small healthcare centers where there are no experts for diagnosis. The proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. The work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125181784","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126393
Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil
India is anticipated to overtake China as the third-largest vehicle market in the near future. Vehicle theft, according to data, has increased yearly. But the proportion of cases that the police really resolve is still quite small. It is challenging for police to locate stolen vehicles since they are sometimes carried to locations distant from the scene of the theft. Therefore, a need for an automated system to assist in tracking such cars arises. These issues are what our project tries to fix. The police will receive a tonne of information from this system that they may utilise to solve theft cases. Using the YOLO V3 algorithm and Canny Edge Detection, the identification system will automatically recognize automobile license plate numbers. After a license plate is identified, the following actions are taken: 1. to photograph the license plate. 2. to recognize and divide characters. 3. The time and date are then recorded in a database together with the identifying license plate for further use. 4. In the event that a stolen vehicle is discovered, a thorough report detailing the location and the time the vehicle first appeared is prepared, and police are notified that a match has been made. The method may be applied to increase security and accuracy.
{"title":"License Plate Recognition for Detecting Stolen Vehicle Using Deep Learning","authors":"Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil","doi":"10.1109/I2CT57861.2023.10126393","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126393","url":null,"abstract":"India is anticipated to overtake China as the third-largest vehicle market in the near future. Vehicle theft, according to data, has increased yearly. But the proportion of cases that the police really resolve is still quite small. It is challenging for police to locate stolen vehicles since they are sometimes carried to locations distant from the scene of the theft. Therefore, a need for an automated system to assist in tracking such cars arises. These issues are what our project tries to fix. The police will receive a tonne of information from this system that they may utilise to solve theft cases. Using the YOLO V3 algorithm and Canny Edge Detection, the identification system will automatically recognize automobile license plate numbers. After a license plate is identified, the following actions are taken: 1. to photograph the license plate. 2. to recognize and divide characters. 3. The time and date are then recorded in a database together with the identifying license plate for further use. 4. In the event that a stolen vehicle is discovered, a thorough report detailing the location and the time the vehicle first appeared is prepared, and police are notified that a match has been made. The method may be applied to increase security and accuracy.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125123463","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126192
Priyanka Bhatele, Dr Mangesh Bedekar
Smartphone/Tablet users are approximately 3 million all over the world. It is likely to increase by several 100 million in the next few years. Around 40% of these users read online. Explicit means of feedback system is strongly based. It provides the most accuracy when rating an online learning application. Increase in the availability of content over the web and high user engagements, has led to the demand of the means that implicitly provide feedback. Implicit feedback relies on understanding the quality of the content based on the user activities performed over the web applications. Less accuracy is the limitation. It needs to stand with a support to provide as strong base as the explicit model does. Clipboard copy operations on the webpage provide an implicit insight to the user intentions. Screen activities like scrolling and pinch to zoom further can statistically be proven the positive indicators of user interest. Smartphone sensors like Gyroscope and Accelerometer silently sense human screen activities and mobile gestures. This review paper is based on the understanding of smartphone sensors and the inferences of user intent through it. The dig is based on various implicit indicators like mobile gestures, smartphone sensors and clipboard copy operations.
{"title":"Survey on Smartphone Sensors and User Intent in Smartphone Usage","authors":"Priyanka Bhatele, Dr Mangesh Bedekar","doi":"10.1109/I2CT57861.2023.10126192","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126192","url":null,"abstract":"Smartphone/Tablet users are approximately 3 million all over the world. It is likely to increase by several 100 million in the next few years. Around 40% of these users read online. Explicit means of feedback system is strongly based. It provides the most accuracy when rating an online learning application. Increase in the availability of content over the web and high user engagements, has led to the demand of the means that implicitly provide feedback. Implicit feedback relies on understanding the quality of the content based on the user activities performed over the web applications. Less accuracy is the limitation. It needs to stand with a support to provide as strong base as the explicit model does. Clipboard copy operations on the webpage provide an implicit insight to the user intentions. Screen activities like scrolling and pinch to zoom further can statistically be proven the positive indicators of user interest. Smartphone sensors like Gyroscope and Accelerometer silently sense human screen activities and mobile gestures. This review paper is based on the understanding of smartphone sensors and the inferences of user intent through it. The dig is based on various implicit indicators like mobile gestures, smartphone sensors and clipboard copy operations.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114583812","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126389
Subhash Mondal, Souptik Dutta, Soumadip Ghosh, Sarbartha Gupta, Dhrubajit Kakati, A. Nag
The thyroid gland plays a significant role in the human body's metabolism, growth, and development. Though it is not a life-threatening disease, a person suffering from thyroid faces many complications in their daily life. Recent trends have shown that women suffer more from thyroid-related diseases than men. The many contributing factors that lead to thyroid disease may be controlled upon early diagnosis stages. Machine learning prediction models help healthcare professionals diagnose thyroid diseases at an initial stage and take measures accordingly. This study deployed initial Sixteen ML models, including six boosting algorithms, on a dataset of 9172 instances with related features. The model performances have been judged through various standard performance metrics. The boosting algorithms showed exceptional results, and Cat Boost (CB) model produced the best accuracy of 95.75%. The hyperparameter tuning performed on boosting models by implementing Randomized Search CV increased the accuracy to 96.19% for CB. The stacking ensemble approach was applied on top of the six boosting tuned models with the CB classifier as the meta-learner. At the same time, the other boosting algorithms were kept as a base learner for the final model prediction. The accuracy of the stack model was impressive, with 95.32% compared with default models, the ROC-AUC at 0.95, and the other results were also promising. The model’s standard deviation was significantly less at 0.57, implying the model’s stability and robustness, and the False Negative (FN) rate reached 1.8%.
{"title":"Thyroid Disease Prediction Model on Boosting-based Stacking Ensemble Approach","authors":"Subhash Mondal, Souptik Dutta, Soumadip Ghosh, Sarbartha Gupta, Dhrubajit Kakati, A. Nag","doi":"10.1109/I2CT57861.2023.10126389","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126389","url":null,"abstract":"The thyroid gland plays a significant role in the human body's metabolism, growth, and development. Though it is not a life-threatening disease, a person suffering from thyroid faces many complications in their daily life. Recent trends have shown that women suffer more from thyroid-related diseases than men. The many contributing factors that lead to thyroid disease may be controlled upon early diagnosis stages. Machine learning prediction models help healthcare professionals diagnose thyroid diseases at an initial stage and take measures accordingly. This study deployed initial Sixteen ML models, including six boosting algorithms, on a dataset of 9172 instances with related features. The model performances have been judged through various standard performance metrics. The boosting algorithms showed exceptional results, and Cat Boost (CB) model produced the best accuracy of 95.75%. The hyperparameter tuning performed on boosting models by implementing Randomized Search CV increased the accuracy to 96.19% for CB. The stacking ensemble approach was applied on top of the six boosting tuned models with the CB classifier as the meta-learner. At the same time, the other boosting algorithms were kept as a base learner for the final model prediction. The accuracy of the stack model was impressive, with 95.32% compared with default models, the ROC-AUC at 0.95, and the other results were also promising. The model’s standard deviation was significantly less at 0.57, implying the model’s stability and robustness, and the False Negative (FN) rate reached 1.8%.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123849999","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126472
Janani T, Nandhini Jagadeesan, Shivangi Pandey, Divya B
Wheelchairs are the most prominently used assistive devices. They are used for different kinds of disabilities which can include entire lower body paralysis, multiple sclerosis, or for elderly people who have degenerated mobility. This work attempts to enhance the life’s quality of people with locomotive disabilities by providing automotive control to the wheelchair using the non-invasive Brain Computer Interface (BCI) module instead of applying manual force. The EEG signals are processed and converted into mental command by the NeuroSky MindWave headset. The system acquires and analyzes the alpha and beta waves produced by the brain to determine the attention and meditation level of the user along with eye blinks being recognized as disruption to the signal. These parameters are used to frame an algorithm and command the movements of the wheelchair which are forward, backward, left, and right.
{"title":"Design and Development of A Brain Computer Interface Controlled Wheelchair Prototype","authors":"Janani T, Nandhini Jagadeesan, Shivangi Pandey, Divya B","doi":"10.1109/I2CT57861.2023.10126472","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126472","url":null,"abstract":"Wheelchairs are the most prominently used assistive devices. They are used for different kinds of disabilities which can include entire lower body paralysis, multiple sclerosis, or for elderly people who have degenerated mobility. This work attempts to enhance the life’s quality of people with locomotive disabilities by providing automotive control to the wheelchair using the non-invasive Brain Computer Interface (BCI) module instead of applying manual force. The EEG signals are processed and converted into mental command by the NeuroSky MindWave headset. The system acquires and analyzes the alpha and beta waves produced by the brain to determine the attention and meditation level of the user along with eye blinks being recognized as disruption to the signal. These parameters are used to frame an algorithm and command the movements of the wheelchair which are forward, backward, left, and right.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111504","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126493
Richa Tengshe, Eisha Akanksha
The IoT has brought a digital revolution in connecting vast number of heterogeneous devices together through wireless communication. Definitely it brings a comfort and convenience to the people’s life but on the counterpart the security, privacy and information leakage has become a prime concern specially in the area of finance, trading and healthcare. By the rapid growth of the market low power wide area network technologies have become the area of interest. Narrow band IoT (NB-IoT) and Long range (LoRa) are quite efficient in providing indoor and outdoor coverage with low data rate. Unlicensed LoRa supports a long-range coverage with longer battery life, cost, capacity. While, licensed NB-IoT benefits in terms of latency, reliability, QoS and range. Both the protocols are encapsulated with cryptographic algorithms to provide the secure communication. But still are vulnerable to a wide range of attacks. In this paper network architecture, vulnerabilities, possible security breaches and counter solutions of NB-IoT and LoRa are discussed.
{"title":"Security in LP-WAN Technologies: Challenges and Solutions","authors":"Richa Tengshe, Eisha Akanksha","doi":"10.1109/I2CT57861.2023.10126493","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126493","url":null,"abstract":"The IoT has brought a digital revolution in connecting vast number of heterogeneous devices together through wireless communication. Definitely it brings a comfort and convenience to the people’s life but on the counterpart the security, privacy and information leakage has become a prime concern specially in the area of finance, trading and healthcare. By the rapid growth of the market low power wide area network technologies have become the area of interest. Narrow band IoT (NB-IoT) and Long range (LoRa) are quite efficient in providing indoor and outdoor coverage with low data rate. Unlicensed LoRa supports a long-range coverage with longer battery life, cost, capacity. While, licensed NB-IoT benefits in terms of latency, reliability, QoS and range. Both the protocols are encapsulated with cryptographic algorithms to provide the secure communication. But still are vulnerable to a wide range of attacks. In this paper network architecture, vulnerabilities, possible security breaches and counter solutions of NB-IoT and LoRa are discussed.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130583458","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126315
Arghadeep Pal, A. Banerjee, A. Lahiri
For electrical insulation purposes, polymer composite materials have gained huge attention. Their performance improves especially if they are embodied with nano/micro fillers. In this study, Cross-Linked Polyethylene (XLPE) has been incorporated with Copper Oxide (CuO) nanoparticles, and their electrical characterization has mainly been highlighted.
{"title":"Electrical Characterization of XLPE Embodied With Copper Oxide Nanoparticles","authors":"Arghadeep Pal, A. Banerjee, A. Lahiri","doi":"10.1109/I2CT57861.2023.10126315","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126315","url":null,"abstract":"For electrical insulation purposes, polymer composite materials have gained huge attention. Their performance improves especially if they are embodied with nano/micro fillers. In this study, Cross-Linked Polyethylene (XLPE) has been incorporated with Copper Oxide (CuO) nanoparticles, and their electrical characterization has mainly been highlighted.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130689907","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126215
Tony Alwin
High sensitivity strain sensor using Polymer Fiber Bragg Grating(P-FBG) is presented. An enhancement in strain sensitivity with an increase in the length of polymer FBG is simulated and demonstrated. The strain sensitivity increased from 1.39 to 5.15 pm/μɛ with the change in grating length from 26mm to100mm.Further, the strain sensitivity is increased by placing a polarization rotator in one arm of strain sensor.
{"title":"High sensitivity strain sensor based on Polymer Fiber Bragg Grating","authors":"Tony Alwin","doi":"10.1109/I2CT57861.2023.10126215","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126215","url":null,"abstract":"High sensitivity strain sensor using Polymer Fiber Bragg Grating(P-FBG) is presented. An enhancement in strain sensitivity with an increase in the length of polymer FBG is simulated and demonstrated. The strain sensitivity increased from 1.39 to 5.15 pm/μɛ with the change in grating length from 26mm to100mm.Further, the strain sensitivity is increased by placing a polarization rotator in one arm of strain sensor.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867187","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126248
J. Baikerikar, Nilesh Ghavate, Vaishali Kavathekar, Allen Kodiyan
The Intelligent Medicine box is an effective health-care product that is implemented using a physically inexpensive medicine box powered by IoT devices and an application powered by Android operating system. The Android application is used to start a new medication and store the treatment details along with the medicine history. This application also provides an effective platform for the user to schedule an appointment with the doctor seamlessly. In addition to this the android application has an inbuilt prescription which will be beneficial in times of pandemic. The user can also add custom treatment plan if necessary. The medicine box alerts the user at the correct time to take the medicine. The box produces audio and illuminates the correct container number, thus making it fool proof and prevents the user from taking the wrong medicine. The Intelligent medicine box proposed by us is a very effective solution in the Health care sector and will reduce the care giver’s burden.
{"title":"Intelligent Medicine Box for COVID like Pandemic","authors":"J. Baikerikar, Nilesh Ghavate, Vaishali Kavathekar, Allen Kodiyan","doi":"10.1109/I2CT57861.2023.10126248","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126248","url":null,"abstract":"The Intelligent Medicine box is an effective health-care product that is implemented using a physically inexpensive medicine box powered by IoT devices and an application powered by Android operating system. The Android application is used to start a new medication and store the treatment details along with the medicine history. This application also provides an effective platform for the user to schedule an appointment with the doctor seamlessly. In addition to this the android application has an inbuilt prescription which will be beneficial in times of pandemic. The user can also add custom treatment plan if necessary. The medicine box alerts the user at the correct time to take the medicine. The box produces audio and illuminates the correct container number, thus making it fool proof and prevents the user from taking the wrong medicine. The Intelligent medicine box proposed by us is a very effective solution in the Health care sector and will reduce the care giver’s burden.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129599338","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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