Pub Date : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544862
A. Bharambe, Akshaya Arun Chandorkar, Dhanajay Kalbande
Dengue is one amongst the foremost widespread vector borne diseases best-known these days. According to National Institute of Allergy and Infectious Disease (NIAID), Dengue fever has been identified as a threat to public health [1]. More than 33% of the total world population is under risk, together with several cities of Asian nation. In recent years, the utilization of social media (from tweets to Facebook posts) in healthcare has risen tremendously because social media is the platform to point out growing want of patients who are suffering, to attach with one another. Tweets are too short to supply sufficient word occurrences for traditional classification methods to give results reliably. Also, natural language is extremely complicated creating classification of health connected problems difficult. The performance of most conventional classification systems depends on acceptable information illustration and tremendous effort in feature engineering. Deep Learning is new space of machine learning that do automatic feature extraction. In this study, Convolutional Neural Network (CNN) has been used to classify dengue related tweets extracted from twitter into seven multiple classes such as ‘Infected’, ‘Informative’, ‘Vaccination’, ‘News', ‘Awareness', ‘Concern’ and ‘Others'. From Experimental results, Deep Learning algorithm shows increased accuracy when put next to Machine Learning algorithms such as Support Vector Machine (SVM), Naïve Bayes(NB) and Decision Tree Classifier(DT).
{"title":"A Deep Learning Approach for Dengue Tweet Classification","authors":"A. Bharambe, Akshaya Arun Chandorkar, Dhanajay Kalbande","doi":"10.1109/ICIRCA51532.2021.9544862","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544862","url":null,"abstract":"Dengue is one amongst the foremost widespread vector borne diseases best-known these days. According to National Institute of Allergy and Infectious Disease (NIAID), Dengue fever has been identified as a threat to public health [1]. More than 33% of the total world population is under risk, together with several cities of Asian nation. In recent years, the utilization of social media (from tweets to Facebook posts) in healthcare has risen tremendously because social media is the platform to point out growing want of patients who are suffering, to attach with one another. Tweets are too short to supply sufficient word occurrences for traditional classification methods to give results reliably. Also, natural language is extremely complicated creating classification of health connected problems difficult. The performance of most conventional classification systems depends on acceptable information illustration and tremendous effort in feature engineering. Deep Learning is new space of machine learning that do automatic feature extraction. In this study, Convolutional Neural Network (CNN) has been used to classify dengue related tweets extracted from twitter into seven multiple classes such as ‘Infected’, ‘Informative’, ‘Vaccination’, ‘News', ‘Awareness', ‘Concern’ and ‘Others'. From Experimental results, Deep Learning algorithm shows increased accuracy when put next to Machine Learning algorithms such as Support Vector Machine (SVM), Naïve Bayes(NB) and Decision Tree Classifier(DT).","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125692931","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}
Suicide is the 2nd leading cause of death in the world, for those aged 15-24 and about 800,000 victims of suicide yearly (all age), which is about 40 per second. Behavioural health disorder, explicitly depression, are the type of health concerns, not many are aware of. There is no way one can get treatment of something they are not aware of. So, classifying potential health disordered person is the first step towards prevention. Lifestyle is something which defines individual the best. Lifestyle including Income, age group, martial status, child, property owned, alcohol or tobacco consumption, medical expenditure, insurance or other type of investment and many more. Using 76 such kind of attributes, model will predict if the individual is victim of depression or not. The proposed model has used eight mainstream ML calculation methods, namely (Decision tree (DT), Random Forest(RF), Support Vector Machine(SVM), Naïve Bayes(NB), Logistic Regression(LR), XGBoost(XGB), Gradient Boosting Classifier(GBC) and Artificial Neural Network(ANN) to build up the expectation models utilizing a huge dataset (1429 individual's survey), bringing about precise and productive dynamics. By using various strategies and different model, this research work has attempted to get a clear and precise picture. The reason to follow various approaches is that, precise the information, work in a better way and reduce the number of suicide case. The final outcome received was 87.38 percent, which was using Support Vector Machine (SVM).
{"title":"Machine Learning Techniques for Prediction of Mental Health","authors":"Tarun Jain, Ashish Jain, Priyank Singh Hada, Horesh Kumar, V. Verma, Aayush Patni","doi":"10.1109/ICIRCA51532.2021.9545061","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9545061","url":null,"abstract":"Suicide is the 2nd leading cause of death in the world, for those aged 15-24 and about 800,000 victims of suicide yearly (all age), which is about 40 per second. Behavioural health disorder, explicitly depression, are the type of health concerns, not many are aware of. There is no way one can get treatment of something they are not aware of. So, classifying potential health disordered person is the first step towards prevention. Lifestyle is something which defines individual the best. Lifestyle including Income, age group, martial status, child, property owned, alcohol or tobacco consumption, medical expenditure, insurance or other type of investment and many more. Using 76 such kind of attributes, model will predict if the individual is victim of depression or not. The proposed model has used eight mainstream ML calculation methods, namely (Decision tree (DT), Random Forest(RF), Support Vector Machine(SVM), Naïve Bayes(NB), Logistic Regression(LR), XGBoost(XGB), Gradient Boosting Classifier(GBC) and Artificial Neural Network(ANN) to build up the expectation models utilizing a huge dataset (1429 individual's survey), bringing about precise and productive dynamics. By using various strategies and different model, this research work has attempted to get a clear and precise picture. The reason to follow various approaches is that, precise the information, work in a better way and reduce the number of suicide case. The final outcome received was 87.38 percent, which was using Support Vector Machine (SVM).","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"462 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131920493","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544607
Manojkumar. K, L. Sujihelen
Crowd behavioural analysis is an interesting and emerging domain in research, with incomplete set of activities, tasks and lack of intermediate cub-processes which are mandated for productive analysis. Since the domain is untapped to a major extent, the research carried out in the domain needs proper stages of operations. A proper taxonomy will direct the futuristic domains in the right track of processes and organization of intermediate tasks. This review paper intends to document the list of stages and processes, data collection, pipelining the sub-tasks, pre-emptive identification of supposed problems during the later stages in detection of crowd emotions and behavioural analysis. Deep learning techniques have been widely deployed to investigate the models of crowd analysis, anomaly detection, and look for meaningful insights and patterns from datasets. The Different models are investigated thoroughly for their respective understanding about the emotional aspects considered in the studies. Emotional characteristics when powered with crowd behavioural analysis and real world entities will deliver a promising solution for crime detections, anomaly detection and ensure a safer environment for nations. Video surveillance tools, datasets from crime datasets and various other factors contributed to the previous research works, models are now being designed to incorporate the best features of these models into one and thus achieve one fruitful model for continuous video analytics.
{"title":"Behavioural Analysis For Prospects In Crowd Emotion Sensing: A Survey","authors":"Manojkumar. K, L. Sujihelen","doi":"10.1109/ICIRCA51532.2021.9544607","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544607","url":null,"abstract":"Crowd behavioural analysis is an interesting and emerging domain in research, with incomplete set of activities, tasks and lack of intermediate cub-processes which are mandated for productive analysis. Since the domain is untapped to a major extent, the research carried out in the domain needs proper stages of operations. A proper taxonomy will direct the futuristic domains in the right track of processes and organization of intermediate tasks. This review paper intends to document the list of stages and processes, data collection, pipelining the sub-tasks, pre-emptive identification of supposed problems during the later stages in detection of crowd emotions and behavioural analysis. Deep learning techniques have been widely deployed to investigate the models of crowd analysis, anomaly detection, and look for meaningful insights and patterns from datasets. The Different models are investigated thoroughly for their respective understanding about the emotional aspects considered in the studies. Emotional characteristics when powered with crowd behavioural analysis and real world entities will deliver a promising solution for crime detections, anomaly detection and ensure a safer environment for nations. Video surveillance tools, datasets from crime datasets and various other factors contributed to the previous research works, models are now being designed to incorporate the best features of these models into one and thus achieve one fruitful model for continuous video analytics.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561779","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9545077
M. Ahmed, Rafeed Rahman, Shahriar Hossain, Shahnewaz Ali Mohammad
The brain tumor is a lethal illness that has endured innumerable individuals. Brain tumor causes abnormal growth of brain tissues, the tissues can be either malignant or non-malignant, but both are capable of causing long term harm and in about 95% cases can cause demise. Utilizing MRI (Magnetic resonance imaging) scans has become one of the meaningful techniques for identifying its existence in the human brain. Subsequent to getting the MRI filters these are physically investigated by experts to determine the presence of a brain tumor in a patient. Various specialists assessing MRI scans may result in outcomes that are not same; this happens because of the variance in forming evaluations from one professional to the next. Furthermore, because MRI scan analysis is a manual procedure, various people might make different mistakes. Based on the interpretations of the experts, two distinct MRI scans performed on the same patient may yield different findings. To make things simpler, reliable, and obtaining acquiring predictable outcomes for both specialists and non-specialists while performing assessment of MRI scans, this research work has presented deep learning strategies in the context of transfer learning models such as ResNet 50, ResNet 152 inception v3, VGG16 and also proposed Conv2d+SVM model to analyze MRI scans and determine the presence of a brain tumor. Also, this research work has utilized a dataset consisting of 253 images and then performed augmentation to increase the amount of data. After training, our model portrayed accuracy of 87.17% and 76.62% for ResNet 50, 99.28% and 88.24% for ResNet 152, 99.28% and 96.08% for inception v3, 99.78 and 86.27% for VGG16 and 92% and 78.3% for Conv2D+SVM in terms of training and validation respectively
{"title":"Brain Tumor Prediction by analyzing MRI using deep learning architectures","authors":"M. Ahmed, Rafeed Rahman, Shahriar Hossain, Shahnewaz Ali Mohammad","doi":"10.1109/ICIRCA51532.2021.9545077","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9545077","url":null,"abstract":"The brain tumor is a lethal illness that has endured innumerable individuals. Brain tumor causes abnormal growth of brain tissues, the tissues can be either malignant or non-malignant, but both are capable of causing long term harm and in about 95% cases can cause demise. Utilizing MRI (Magnetic resonance imaging) scans has become one of the meaningful techniques for identifying its existence in the human brain. Subsequent to getting the MRI filters these are physically investigated by experts to determine the presence of a brain tumor in a patient. Various specialists assessing MRI scans may result in outcomes that are not same; this happens because of the variance in forming evaluations from one professional to the next. Furthermore, because MRI scan analysis is a manual procedure, various people might make different mistakes. Based on the interpretations of the experts, two distinct MRI scans performed on the same patient may yield different findings. To make things simpler, reliable, and obtaining acquiring predictable outcomes for both specialists and non-specialists while performing assessment of MRI scans, this research work has presented deep learning strategies in the context of transfer learning models such as ResNet 50, ResNet 152 inception v3, VGG16 and also proposed Conv2d+SVM model to analyze MRI scans and determine the presence of a brain tumor. Also, this research work has utilized a dataset consisting of 253 images and then performed augmentation to increase the amount of data. After training, our model portrayed accuracy of 87.17% and 76.62% for ResNet 50, 99.28% and 88.24% for ResNet 152, 99.28% and 96.08% for inception v3, 99.78 and 86.27% for VGG16 and 92% and 78.3% for Conv2D+SVM in terms of training and validation respectively","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117142844","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544701
Hejuan Chen
The proposed research study focuses on the leakage location system of the electric vehicle battery pack based on the Wavelet transform. Under the same equalization time, the equalization efficiency of the method that has been tested from the battery pack to the cell to the battery pack is 91.4%, and the overall equalization efficiency of this method is 93.8%. When the battery pack is in a discharging state, the equalization circuit module can complete the migration of the battery pack's power to the battery with the lowest terminal voltage or SOC. With the considerations of the mentioned features, this paper applies the wavelet model to construct the efficient location system. The proposed model is tested on the different scenarios with different data sets. The performance guides us that the accuracy of proposed model is much higher.
{"title":"Leakage Location System of Electric Vehicle Battery Pack Based on Wavelet Transform","authors":"Hejuan Chen","doi":"10.1109/ICIRCA51532.2021.9544701","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544701","url":null,"abstract":"The proposed research study focuses on the leakage location system of the electric vehicle battery pack based on the Wavelet transform. Under the same equalization time, the equalization efficiency of the method that has been tested from the battery pack to the cell to the battery pack is 91.4%, and the overall equalization efficiency of this method is 93.8%. When the battery pack is in a discharging state, the equalization circuit module can complete the migration of the battery pack's power to the battery with the lowest terminal voltage or SOC. With the considerations of the mentioned features, this paper applies the wavelet model to construct the efficient location system. The proposed model is tested on the different scenarios with different data sets. The performance guides us that the accuracy of proposed model is much higher.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116707253","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9545052
J. Ananthi, P. S. H. Jose
Recently, Wireless Body Area Networks (WBAN) have been increasingly significant in healthcare applications. It is derived from the wireless sensor network with biomedical sensors. The Internet of Things (IoT) has a huge impact on how medical data is received and transmitted to the right systems in healthcare applications. Security, fastest delivery, and energy consumption are major concerns in wireless body area networks. This research work focuses on the rapid data transmission between the patient and doctor using Unmanned Aerial Vehicles (UAV). There are five sensors that are analyzed as Heart rate monitoring sensor, Temperature sensor, Human motion sensor, Oximeter sensor, and Blood pressure sensor. For the fastest delivery, the sensed medical data was delivered utilizing unmanned aerial vehicles. This helps the patients in critical/emergencies to communicate the medical information to the doctor safely and securely. The experimental result examines various sensors attached to the Arduino IDE. The obtained results will be transmitted to the patients using unmanned aerial vehicles. These techniques help to improve the fastest communication for emergency condition patients.
{"title":"Implementation of IoT and UAV Based WBAN for healthcare applications","authors":"J. Ananthi, P. S. H. Jose","doi":"10.1109/ICIRCA51532.2021.9545052","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9545052","url":null,"abstract":"Recently, Wireless Body Area Networks (WBAN) have been increasingly significant in healthcare applications. It is derived from the wireless sensor network with biomedical sensors. The Internet of Things (IoT) has a huge impact on how medical data is received and transmitted to the right systems in healthcare applications. Security, fastest delivery, and energy consumption are major concerns in wireless body area networks. This research work focuses on the rapid data transmission between the patient and doctor using Unmanned Aerial Vehicles (UAV). There are five sensors that are analyzed as Heart rate monitoring sensor, Temperature sensor, Human motion sensor, Oximeter sensor, and Blood pressure sensor. For the fastest delivery, the sensed medical data was delivered utilizing unmanned aerial vehicles. This helps the patients in critical/emergencies to communicate the medical information to the doctor safely and securely. The experimental result examines various sensors attached to the Arduino IDE. The obtained results will be transmitted to the patients using unmanned aerial vehicles. These techniques help to improve the fastest communication for emergency condition patients.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116977443","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544802
V. Shoba, R. Parameswari
The process of Big data storage has become challenging due to the expansion of extensive data; data providers will offer encrypted data and upload to Big data. However, the data exchange mechanism is unable to accommodate encrypted data. Particularly when a large number of users share the scalable data, the scalability becomes extremely limited. Using a contemporary privacy protection system to solve this issue and ensure the security of encrypted data, as well as partially homomorphic re-encryption and decryption (PHRED). This scheme has the flexibility to share data by ensuring user's privacy with partially trusted Big Data. It can access to strong unforgeable scheme it make the transmuted cipher text have public and private key verification combined identity based Augmented Homomorphic Re Encryption Decryption(AHRED) on paillier crypto System with Laplacian noise filter the performance of the data provider for privacy preserving big data.
{"title":"Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics","authors":"V. Shoba, R. Parameswari","doi":"10.1109/ICIRCA51532.2021.9544802","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544802","url":null,"abstract":"The process of Big data storage has become challenging due to the expansion of extensive data; data providers will offer encrypted data and upload to Big data. However, the data exchange mechanism is unable to accommodate encrypted data. Particularly when a large number of users share the scalable data, the scalability becomes extremely limited. Using a contemporary privacy protection system to solve this issue and ensure the security of encrypted data, as well as partially homomorphic re-encryption and decryption (PHRED). This scheme has the flexibility to share data by ensuring user's privacy with partially trusted Big Data. It can access to strong unforgeable scheme it make the transmuted cipher text have public and private key verification combined identity based Augmented Homomorphic Re Encryption Decryption(AHRED) on paillier crypto System with Laplacian noise filter the performance of the data provider for privacy preserving big data.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115027148","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9545082
Rakesh Kumar, Meenu Gupta, Suyash Shukla, R. Yadav
Diligent Traffic Enforcement is a major problem throughout India, often focusing on corruption and abuse; is the subject of major changes initiated by senior management of all traffic police institutions in India. Therefore, this paper proposes an effective e-challan production strategy using OCR (Optical Character recognition) where the challan ends using the online application. Scanning different number plates and downloading facts from the database and producing E-Challan. The E-Challan is a web platform that provides various types of support for monitoring and managing the traffic penalties and it also helps the users to overcome the problems that they face while paying for their challan during the traffic time. The E-challan Application is the interaction between HD Cameras and drivers with the use of an online platform. The driver who will breach the traffic rule, vehicle's number plate snapshot is captured automatically by the HD Camera located near a traffic light and traffic area through Image Processing technology and Artificial Intelligence, the software will automatically detect the vehicle owner for the penalty and then apply the suitable penalty against the vehicle owner in their account. With the help of this online prototype, the challan system becomes easy for the users by keeping it online.
{"title":"E-Challan Automation for RTO using OCR","authors":"Rakesh Kumar, Meenu Gupta, Suyash Shukla, R. Yadav","doi":"10.1109/ICIRCA51532.2021.9545082","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9545082","url":null,"abstract":"Diligent Traffic Enforcement is a major problem throughout India, often focusing on corruption and abuse; is the subject of major changes initiated by senior management of all traffic police institutions in India. Therefore, this paper proposes an effective e-challan production strategy using OCR (Optical Character recognition) where the challan ends using the online application. Scanning different number plates and downloading facts from the database and producing E-Challan. The E-Challan is a web platform that provides various types of support for monitoring and managing the traffic penalties and it also helps the users to overcome the problems that they face while paying for their challan during the traffic time. The E-challan Application is the interaction between HD Cameras and drivers with the use of an online platform. The driver who will breach the traffic rule, vehicle's number plate snapshot is captured automatically by the HD Camera located near a traffic light and traffic area through Image Processing technology and Artificial Intelligence, the software will automatically detect the vehicle owner for the penalty and then apply the suitable penalty against the vehicle owner in their account. With the help of this online prototype, the challan system becomes easy for the users by keeping it online.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115623868","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544792
C. Z. Basha, D. P. K. Reddy, S. Chand, Azmira Krishna
Augmented reality experience enables us to view real-world objects in 3D by overlapping real-world objects with digital 3d objects to provide a much-enhanced user experience. This paper explains and presents the ways to construct a 3D (3 Dimension) asset of the real world like cities, monuments, and other objects using blender and then the3D digital asset will be incorporated into our application. So that whenever our marker scans the object i.e., the 3D asset gets overlayed. The main idea of this concept is to experience real-world objects in the absence of real-world objects. Let us say that, a person wants to see the Eiffel tower and he/she searches it on Google. Now, the person could only see the images of Eiffel tower. To experience it in 3D he/she can use Google earth but it does not provide an original 3D experience. So, this is the point where augmented reality enters into the scene. The proposed research work has created a 3D asset by using a blender tool. Now, that asset will be imported and applied it to a marker. Whenever, this marker is scanned by using our application, the 3D effect of Eiffel tower will be overlayed on the screen. Augmented Reality is overlapping real-world objects with 3d objects. The main objective of augmented reality is that users cannot notice the discrepancy between augmented objects and real-world objects. AR is a wholly distinct technology, which helps our daily living and many other experiences so improved. It uses our most common hardware such as mobiles, cameras, etc. This makes this technology very beneficial and effortless to use. It is also a lot more different from VR in terms of hardware. But most of the purpose is the same.
{"title":"Augmented Reality Experience for Real-World Objects, Monuments, and Cities","authors":"C. Z. Basha, D. P. K. Reddy, S. Chand, Azmira Krishna","doi":"10.1109/ICIRCA51532.2021.9544792","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544792","url":null,"abstract":"Augmented reality experience enables us to view real-world objects in 3D by overlapping real-world objects with digital 3d objects to provide a much-enhanced user experience. This paper explains and presents the ways to construct a 3D (3 Dimension) asset of the real world like cities, monuments, and other objects using blender and then the3D digital asset will be incorporated into our application. So that whenever our marker scans the object i.e., the 3D asset gets overlayed. The main idea of this concept is to experience real-world objects in the absence of real-world objects. Let us say that, a person wants to see the Eiffel tower and he/she searches it on Google. Now, the person could only see the images of Eiffel tower. To experience it in 3D he/she can use Google earth but it does not provide an original 3D experience. So, this is the point where augmented reality enters into the scene. The proposed research work has created a 3D asset by using a blender tool. Now, that asset will be imported and applied it to a marker. Whenever, this marker is scanned by using our application, the 3D effect of Eiffel tower will be overlayed on the screen. Augmented Reality is overlapping real-world objects with 3d objects. The main objective of augmented reality is that users cannot notice the discrepancy between augmented objects and real-world objects. AR is a wholly distinct technology, which helps our daily living and many other experiences so improved. It uses our most common hardware such as mobiles, cameras, etc. This makes this technology very beneficial and effortless to use. It is also a lot more different from VR in terms of hardware. But most of the purpose is the same.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115643015","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9545023
Karthi S P, A. A, D. S, Guru K, Hariram S
Traditional notice board, is widely used in many places, where there are abundant amount of people either working at the particular places or people who visit those public places like universities, institutions, bus stand, railway station, hospitals etc. Here, the existing ordinary notice board is enhanced into a multi-featured board as well as a smart notice board which alerts the people whenever a place catches fire i.e it acts as a fire alarming system and a special feature is that it transmits the audio message spontaneously, spoken by the user, more precisely an authorized user which requires an authentication to use the particular smart notice board i.e it requires the authentication in a form of password in text form. Here microcontroller and GSM models have been used for transferring the message to the audiences.
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