Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088380
S. Naik, Mohammad Arshad
Malicious Web attacks hide behind normal data in irregular organization traffic. It causes internet frustration and obscurity, making it difficult for the Organization Access Framework to maintain identification accuracy and timing. This research examines machine learning and deep reading for unequal network traffic. First, utilise ENN to divide incomparable training sets into solid and simple sets. Next, use KMeans to compress a fancy set's samples to reduce degree. Focus and delete little samples from a nice set, then mix fresh samples to increase the minimal number. A simple set, a compressed set of heavy objects, and several hard sets were merged to produce a new training set. The technique lowers initial training set inconsistencies and improves data for younger students. It helps class dividers learn differences during training and improves design effectiveness. For testing, we used the old NSL-KDD website. We employ random field (RF) and VSM classification models (SVM). Our proposed DSSTE algorithm performs worse than 24 other techniques.
{"title":"Detection of Cyberattack in Network Using Machine Learning","authors":"S. Naik, Mohammad Arshad","doi":"10.1109/ASSIC55218.2022.10088380","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088380","url":null,"abstract":"Malicious Web attacks hide behind normal data in irregular organization traffic. It causes internet frustration and obscurity, making it difficult for the Organization Access Framework to maintain identification accuracy and timing. This research examines machine learning and deep reading for unequal network traffic. First, utilise ENN to divide incomparable training sets into solid and simple sets. Next, use KMeans to compress a fancy set's samples to reduce degree. Focus and delete little samples from a nice set, then mix fresh samples to increase the minimal number. A simple set, a compressed set of heavy objects, and several hard sets were merged to produce a new training set. The technique lowers initial training set inconsistencies and improves data for younger students. It helps class dividers learn differences during training and improves design effectiveness. For testing, we used the old NSL-KDD website. We employ random field (RF) and VSM classification models (SVM). Our proposed DSSTE algorithm performs worse than 24 other techniques.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"3 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120909651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088383
Sahasra Sai Tarun Mandiga, Sai Prabhath Mallavarapu, Jayanth Nayani, R. Mathi, Subramani R
International Diabetes Federation estimates put the number of diabetics in India at 50.8 million in 2010. and it is estimated to rise to 87.0 million by 2030. One of the most common problems associated with Type 2 diabetes is Retinopathy. Diabetic Retinopathy is a kind of visual loss that affects persons between the ages of 20 and 64. Diabetic Retinopathy puts pressure on the eyeball by shattering the natural flow of fluid out of the eye, harming nerves and leading to glaucoma. If it is detected and treated early, we can reduce the risk of visual loss. However, diagnoses by ophthalmologists involve time, effort, and money, and if computer-aided diagnosis techniques aren't used, misdiagnosis can occur. In recent times deep learning has become the most popular method for obtaining high performance in various fields, even in medical image analysis and classification. The purpose of this research is to anticipate diabetic Retinopathy beforehand in order to avoid future eye problems. The proposed deep learning architecture is based on the Mobile Net architecture, a mobile-friendly, lightweight design that was trained and tested on retinal fundus pictures from the Aptos 2019 challenge data set.
{"title":"Retinal Blindness Detection Due To Diabetes Using MobileNetV2 And SVM","authors":"Sahasra Sai Tarun Mandiga, Sai Prabhath Mallavarapu, Jayanth Nayani, R. Mathi, Subramani R","doi":"10.1109/ASSIC55218.2022.10088383","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088383","url":null,"abstract":"International Diabetes Federation estimates put the number of diabetics in India at 50.8 million in 2010. and it is estimated to rise to 87.0 million by 2030. One of the most common problems associated with Type 2 diabetes is Retinopathy. Diabetic Retinopathy is a kind of visual loss that affects persons between the ages of 20 and 64. Diabetic Retinopathy puts pressure on the eyeball by shattering the natural flow of fluid out of the eye, harming nerves and leading to glaucoma. If it is detected and treated early, we can reduce the risk of visual loss. However, diagnoses by ophthalmologists involve time, effort, and money, and if computer-aided diagnosis techniques aren't used, misdiagnosis can occur. In recent times deep learning has become the most popular method for obtaining high performance in various fields, even in medical image analysis and classification. The purpose of this research is to anticipate diabetic Retinopathy beforehand in order to avoid future eye problems. The proposed deep learning architecture is based on the Mobile Net architecture, a mobile-friendly, lightweight design that was trained and tested on retinal fundus pictures from the Aptos 2019 challenge data set.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088349
E. N. V. Kumari, K. Swetha, Soleti Navya
Currently, an independent car's driving method is chosen based on external criteria (pedestrian crossings, road surfaces, etc.) without considering the car's interior state. “A Driving Decision Approach (DDS) Based on Machine Learning for an Autonomous Vehicle” predicts the proper approach for an autonomous vehicle by searching outside and inside factors. The DDS trains a genetic set of rules that develops an autonomous car's best use method using cloud-based sensor information. The proposed DDS with rules compares to Random Forest and MLP (multilayer perceptron set of rules). Precise DDS beats random forest and MLP. This study compared DDS to MLP and RF neural community models. The DDS had a 5% lower loss rate than conventional car gateways in the study, and it computed Revolutions per minute, speed, direction angle, and converting lanes 40% faster than the MLP and 22% faster than the RF neural networks. DDS provides sensor records to a genetic collection of rules, which chooses the most acceptable value for extra unique prediction.
{"title":"A Driving Decision Strategy (DDS) Based on Machine learning for an autonomous vehicle","authors":"E. N. V. Kumari, K. Swetha, Soleti Navya","doi":"10.1109/ASSIC55218.2022.10088349","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088349","url":null,"abstract":"Currently, an independent car's driving method is chosen based on external criteria (pedestrian crossings, road surfaces, etc.) without considering the car's interior state. “A Driving Decision Approach (DDS) Based on Machine Learning for an Autonomous Vehicle” predicts the proper approach for an autonomous vehicle by searching outside and inside factors. The DDS trains a genetic set of rules that develops an autonomous car's best use method using cloud-based sensor information. The proposed DDS with rules compares to Random Forest and MLP (multilayer perceptron set of rules). Precise DDS beats random forest and MLP. This study compared DDS to MLP and RF neural community models. The DDS had a 5% lower loss rate than conventional car gateways in the study, and it computed Revolutions per minute, speed, direction angle, and converting lanes 40% faster than the MLP and 22% faster than the RF neural networks. DDS provides sensor records to a genetic collection of rules, which chooses the most acceptable value for extra unique prediction.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088289
K. Babu, B. Kumar, S. Prasad, Sreevarsha Maheshwaram, Akhila Yakkali
Fish recognition is the various essential factors of fishery studies applications, where a massive quantity of facts is gathered quickly. Due to bad picture quality, uncontrollable objects, and the environment, in addition to the problems in getting consultant samples, underwater picture popularity poses particular challenges. The primary purpose of this study is to create a supervised feature learning-based fish recognition framework. The required data is provided for further analysis based on medical and fish market usage. The system modules in this work are built using deep neural networks. Neural networks will increase accuracy in a variety of circumstances involving input photographs and targets. Experiments demonstrate that the suggested framework achieves great accuracy while balancing high uncertainty and sophistication on both sides: Public and self-collected underwater fish photos. Finally, the recognized fish type and medicinal uses are called out by utilizing voice instructions on MATLAB plateform.
{"title":"Fish Recognition Using Deep Neural Network","authors":"K. Babu, B. Kumar, S. Prasad, Sreevarsha Maheshwaram, Akhila Yakkali","doi":"10.1109/ASSIC55218.2022.10088289","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088289","url":null,"abstract":"Fish recognition is the various essential factors of fishery studies applications, where a massive quantity of facts is gathered quickly. Due to bad picture quality, uncontrollable objects, and the environment, in addition to the problems in getting consultant samples, underwater picture popularity poses particular challenges. The primary purpose of this study is to create a supervised feature learning-based fish recognition framework. The required data is provided for further analysis based on medical and fish market usage. The system modules in this work are built using deep neural networks. Neural networks will increase accuracy in a variety of circumstances involving input photographs and targets. Experiments demonstrate that the suggested framework achieves great accuracy while balancing high uncertainty and sophistication on both sides: Public and self-collected underwater fish photos. Finally, the recognized fish type and medicinal uses are called out by utilizing voice instructions on MATLAB plateform.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116148570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088351
Raju Rollakanti, B. Naresh, Aruna Manjusha, Sudeep Sharma, U. Somanaidu, S. Prasad
The main goal of a coal mine safety system is to be built using things that speak as the data transmission channel. In coal mines, the system monitors and manages a variety of parameters, including light detection, gas leak detection, temperature and humidity conditions, and coal mine fire detection. These sensors are bundled together and put in coal mines. Thing Speak receives and analyses all sensor values in real-time. The gas is monitored regularly here, and if there are any concerns about the gas level, a bell is used to alert the workers. In this configuration, an LDR sensor detects the presence of light. The light comes on automatically and may be controlled using the LED button. An alert notification is sent to the authorized person's mailbox if a fire breaks out in a coal mine. Temperature and humidity levels are regularly checked and displayed on the serial monitor and the thing talk platform. The developed technology is primarily utilized to improve coal mine working conditions and protect workers' safety.
{"title":"Design of IOT based coal mine safety system using LoRa","authors":"Raju Rollakanti, B. Naresh, Aruna Manjusha, Sudeep Sharma, U. Somanaidu, S. Prasad","doi":"10.1109/ASSIC55218.2022.10088351","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088351","url":null,"abstract":"The main goal of a coal mine safety system is to be built using things that speak as the data transmission channel. In coal mines, the system monitors and manages a variety of parameters, including light detection, gas leak detection, temperature and humidity conditions, and coal mine fire detection. These sensors are bundled together and put in coal mines. Thing Speak receives and analyses all sensor values in real-time. The gas is monitored regularly here, and if there are any concerns about the gas level, a bell is used to alert the workers. In this configuration, an LDR sensor detects the presence of light. The light comes on automatically and may be controlled using the LED button. An alert notification is sent to the authorized person's mailbox if a fire breaks out in a coal mine. Temperature and humidity levels are regularly checked and displayed on the serial monitor and the thing talk platform. The developed technology is primarily utilized to improve coal mine working conditions and protect workers' safety.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125058152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088379
S. M, Vijaya Chandra Jadala, S. Pasupuleti, P. Yellamma
Cerebellar Ataxia disease (CA) is one of the neurological diseases that makes the critical health issues in affected patients. For this goal, disease prediction should closely study the premotor stage of Cerebellar Ataxia disease. A novel deep-learning algorithm is used to determine whether a person has Cerebellar Ataxia disease based on promoter traits. In addition to recognizing the CA, we also discuss the feature importance of the Boosting-based CA detection process. The research investigated many tests to detect CA, like Rapid Eye Movement and slow activity movements or wrong movements. The proposed research model is based on a collected dataset, including 195 patients with regular and affected persons. The different images are classified using the various movement factors. This research designed the ResNet50 model, which gives an average accuracy of 87.5%.
{"title":"Deep Learning analysis using ResNet for Early Detection of Cerebellar Ataxia Disease","authors":"S. M, Vijaya Chandra Jadala, S. Pasupuleti, P. Yellamma","doi":"10.1109/ASSIC55218.2022.10088379","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088379","url":null,"abstract":"Cerebellar Ataxia disease (CA) is one of the neurological diseases that makes the critical health issues in affected patients. For this goal, disease prediction should closely study the premotor stage of Cerebellar Ataxia disease. A novel deep-learning algorithm is used to determine whether a person has Cerebellar Ataxia disease based on promoter traits. In addition to recognizing the CA, we also discuss the feature importance of the Boosting-based CA detection process. The research investigated many tests to detect CA, like Rapid Eye Movement and slow activity movements or wrong movements. The proposed research model is based on a collected dataset, including 195 patients with regular and affected persons. The different images are classified using the various movement factors. This research designed the ResNet50 model, which gives an average accuracy of 87.5%.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127146210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088305
Minakshi Roy, Akash Kumar Bhoi, Kalpana Sharma
One of the most important research fields in the recent digital era is student career prediction. Choosing a career is critical for college students in the planning phase of life. However, accurately forecasting their career choice is challenging because of the diversity of each person's aspirations and ideas. Traditionally, various survey methodologies have been used to forecast a student's future career. However, those methods take significant time to predict the result. In today's digitized world, various computational approaches are utilized to forecast outcomes in various domains. Using computing ideas such as Machine Learning (ML), students' professional choices can also be predicted. Compared to traditional procedures, it takes less time and yields better results. In this research paper, the prediction of the student's career is made using ADABOOST, Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) approaches. The dataset is trained and tested with the four algorithms, and it was observed that SVM had given maximum accuracy with 98 percent, and next to the ADABOOST with 88 percent accuracy.
{"title":"Multimodal Machine Learning approaches for Career Prediction","authors":"Minakshi Roy, Akash Kumar Bhoi, Kalpana Sharma","doi":"10.1109/ASSIC55218.2022.10088305","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088305","url":null,"abstract":"One of the most important research fields in the recent digital era is student career prediction. Choosing a career is critical for college students in the planning phase of life. However, accurately forecasting their career choice is challenging because of the diversity of each person's aspirations and ideas. Traditionally, various survey methodologies have been used to forecast a student's future career. However, those methods take significant time to predict the result. In today's digitized world, various computational approaches are utilized to forecast outcomes in various domains. Using computing ideas such as Machine Learning (ML), students' professional choices can also be predicted. Compared to traditional procedures, it takes less time and yields better results. In this research paper, the prediction of the student's career is made using ADABOOST, Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) approaches. The dataset is trained and tested with the four algorithms, and it was observed that SVM had given maximum accuracy with 98 percent, and next to the ADABOOST with 88 percent accuracy.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121789404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088341
I. D. Muraina, Abdulwahab Folorunso Atanda, Abdulrauf Garba Sharifai, Usman Alhaji Abdurrahman, A. Umar
The benefits of digital transformation has largely been felt in almost every part of professions with inclusion of healthcare industry. Healthcare industry is known to be reached with pool of data which could be historical in nature, thus requires to be kept in a secured and reliable locations. Cloud platform has been used to make data and information available for the users in distributed locations, while many methods and approaches have been provided to preserve the sanctity of a platform. However, less or no study has been conducted on the use of hashing algorithm, which has been proven reliable in protecting the data in an online domain. The objective of this study is to explore the capacity of hashing algorithm towards securing the healthcare data repository in the cloud. The study designs a cloud-based procedural model in form of flowchart to protect the healthcare data repository by using the concept of hash algorithm as basis. Therefore, the model was validated and represented by Pseudocode which shows the reliability of the designed procedural model. Hence, the use of hashing algorithm in protecting the healthcare data repository would assist the healthcare industries in strengthening the curation of data in the cloud system.
{"title":"Protecting the Cloud-Based Healthcare Data Repository: Overview of Hashing Algorithm","authors":"I. D. Muraina, Abdulwahab Folorunso Atanda, Abdulrauf Garba Sharifai, Usman Alhaji Abdurrahman, A. Umar","doi":"10.1109/ASSIC55218.2022.10088341","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088341","url":null,"abstract":"The benefits of digital transformation has largely been felt in almost every part of professions with inclusion of healthcare industry. Healthcare industry is known to be reached with pool of data which could be historical in nature, thus requires to be kept in a secured and reliable locations. Cloud platform has been used to make data and information available for the users in distributed locations, while many methods and approaches have been provided to preserve the sanctity of a platform. However, less or no study has been conducted on the use of hashing algorithm, which has been proven reliable in protecting the data in an online domain. The objective of this study is to explore the capacity of hashing algorithm towards securing the healthcare data repository in the cloud. The study designs a cloud-based procedural model in form of flowchart to protect the healthcare data repository by using the concept of hash algorithm as basis. Therefore, the model was validated and represented by Pseudocode which shows the reliability of the designed procedural model. Hence, the use of hashing algorithm in protecting the healthcare data repository would assist the healthcare industries in strengthening the curation of data in the cloud system.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088345
M. Sailaja, K. Harika, B. Sridhar, Rajan Singh, V. Charitha, Koppula Srinivas Rao
Over the last few years deep neural network made image captioning conceivable. Image caption generator provides an appropriate title for an applied input image based on the dataset. The present work proposes a model based on deep learning and utilizes it to generate caption for the input image. The model takes an image as input and frame the sentence related to the given input image by using some algorithms like CNN and LSTM. This CNN model is used to identify the objects that are present in the image and Long Short-Term Memory (LSTM) model will not only generate the sentence but summarize the text and generate the caption that is suitable for the project. So, the proposed model mainly focuses on identify the objects and generating the most appropriate title for the input images.
{"title":"Image Caption Generator using Deep Learning","authors":"M. Sailaja, K. Harika, B. Sridhar, Rajan Singh, V. Charitha, Koppula Srinivas Rao","doi":"10.1109/ASSIC55218.2022.10088345","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088345","url":null,"abstract":"Over the last few years deep neural network made image captioning conceivable. Image caption generator provides an appropriate title for an applied input image based on the dataset. The present work proposes a model based on deep learning and utilizes it to generate caption for the input image. The model takes an image as input and frame the sentence related to the given input image by using some algorithms like CNN and LSTM. This CNN model is used to identify the objects that are present in the image and Long Short-Term Memory (LSTM) model will not only generate the sentence but summarize the text and generate the caption that is suitable for the project. So, the proposed model mainly focuses on identify the objects and generating the most appropriate title for the input images.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129911214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088324
V. Hema, A. Sangeetha, Soleti Navya, Ch. Nimisha Chowdary
A helmet is a protecting gear worn to guard the head from wounds and tears. A smart helmet can provide more protection by dividing its system into 3 parts: helmet circuit, automobile circuit and a message alert system. The helmet circuit has transmitter, impact switch, alcohol detection sensor and a button. The automobile circuit has arduino, GSM and GPS modules, buzzer system, receiver, relay. The helmet is worn or not segment is checked by sending message from helmet circuit to the automobile circuit. The auto mobile circuit verifies the status to begin the engine or not. Impact switch works to sense an abrupt force which helps to detect an accident. If accident is detected, message alert circuit sends the accident position automatically to the police and emergency contact number through GSM and GPS.
{"title":"Smart Helmet and Accident Identification System","authors":"V. Hema, A. Sangeetha, Soleti Navya, Ch. Nimisha Chowdary","doi":"10.1109/ASSIC55218.2022.10088324","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088324","url":null,"abstract":"A helmet is a protecting gear worn to guard the head from wounds and tears. A smart helmet can provide more protection by dividing its system into 3 parts: helmet circuit, automobile circuit and a message alert system. The helmet circuit has transmitter, impact switch, alcohol detection sensor and a button. The automobile circuit has arduino, GSM and GPS modules, buzzer system, receiver, relay. The helmet is worn or not segment is checked by sending message from helmet circuit to the automobile circuit. The auto mobile circuit verifies the status to begin the engine or not. Impact switch works to sense an abrupt force which helps to detect an accident. If accident is detected, message alert circuit sends the accident position automatically to the police and emergency contact number through GSM and GPS.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133658438","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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