Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140610
Moorthi K, Anju Asokan, Sri Sathya K B, P. Chellammal, K. V., Ravi Rastogi
The ability of humans to effectively communicate through the use of hand signs has a wide range of practical applications. People with speech problems around the world have embraced them due to their intuitive design. Around 1% of Indians are in this group, which is a quite high percentage. It is for this reason that the incorporation of a framework familiar with Indian Sign Language would have such a profoundly positive effect on the lives of the people of India. A median filter is used to an input image to remove unnecessary details and improve clarity. Feature extraction is performed using principal component analysis (PCA), and the YCbCr color space is used for hand segmentation. The model is then trained through Regularized Extreme Learning. Using regularization to achieve peak structural performance for precise prediction, RELMs are a subclass of ELMs. This method exceeds popular alternatives like the support vector machine (SVM), Extreme Learning Machine (ELM), and CNN in terms of accuracy (around 97.8%).
{"title":"Novel Method for Recognizing Sign Language using Regularized Extreme Learning Machine","authors":"Moorthi K, Anju Asokan, Sri Sathya K B, P. Chellammal, K. V., Ravi Rastogi","doi":"10.1109/ICAAIC56838.2023.10140610","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140610","url":null,"abstract":"The ability of humans to effectively communicate through the use of hand signs has a wide range of practical applications. People with speech problems around the world have embraced them due to their intuitive design. Around 1% of Indians are in this group, which is a quite high percentage. It is for this reason that the incorporation of a framework familiar with Indian Sign Language would have such a profoundly positive effect on the lives of the people of India. A median filter is used to an input image to remove unnecessary details and improve clarity. Feature extraction is performed using principal component analysis (PCA), and the YCbCr color space is used for hand segmentation. The model is then trained through Regularized Extreme Learning. Using regularization to achieve peak structural performance for precise prediction, RELMs are a subclass of ELMs. This method exceeds popular alternatives like the support vector machine (SVM), Extreme Learning Machine (ELM), and CNN in terms of accuracy (around 97.8%).","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123768415","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140800
Rishabh Saklani, Karan Purohit, Satvik Vats, Vikrant Sharma, V. Kukreja, S. Yadav
Multi-core processing is extensively used in every sector for its performance efficiency, with the advent of multi-core architecture have to modify the existing primitive algorithms. This study analyses the feasibility of K-mean data-mining technique, which is applied to a hybrid cluster with multi-core programming. The algorithm is developed using Message Passing Interface (MPI) and C programming languages for the parallel processing of the sets and uses the CPU to its maximum power for the hybrid sets. The heterogeneous clusters are confirmed by the usage of MPICH2 (High performance and portability implementation of MPI). examined the algorithm for the huge dataset. The dataset is split into a number of cores and each of the cores estimates the number of dusters on the same dataset interdependent to each other. By this, assert the core processor time for communication is significant for huge datasets. Hence, the same dataset for two different processors takes different times even with identical speed and memory and also with different speeds and access times.
{"title":"Multicore Implementation of K-Means Clustering Algorithm","authors":"Rishabh Saklani, Karan Purohit, Satvik Vats, Vikrant Sharma, V. Kukreja, S. Yadav","doi":"10.1109/ICAAIC56838.2023.10140800","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140800","url":null,"abstract":"Multi-core processing is extensively used in every sector for its performance efficiency, with the advent of multi-core architecture have to modify the existing primitive algorithms. This study analyses the feasibility of K-mean data-mining technique, which is applied to a hybrid cluster with multi-core programming. The algorithm is developed using Message Passing Interface (MPI) and C programming languages for the parallel processing of the sets and uses the CPU to its maximum power for the hybrid sets. The heterogeneous clusters are confirmed by the usage of MPICH2 (High performance and portability implementation of MPI). examined the algorithm for the huge dataset. The dataset is split into a number of cores and each of the cores estimates the number of dusters on the same dataset interdependent to each other. By this, assert the core processor time for communication is significant for huge datasets. Hence, the same dataset for two different processors takes different times even with identical speed and memory and also with different speeds and access times.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130250187","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}
Earthquake is one of the most devastating natural catastrophes, mainly because there is rarely any advance notice and hence little opportunity to react. This makes the issue of earthquake prediction highly crucial for human safety. This paper offers a technique for predicting earthquakes by using normalized artificial neural network (ANN). Exploratory Data Analysis (EDA) is applied on the raw dataset to find outliers and the co-relationship between input features. Then, Feature Engineering is performed to normalize the data and remove all unnecessary features. The training data is fed into the neural network model, which generates certain output. Error is calculated based on actual and generated output. Backpropagation algorithm is applied to minimize the error, after which this data is used to train the model. Finally, the Testing data is fed into the model to calculate accuracy and other performance metrics. The outcomes of several experiments are promising. Accuracy of prediction obtained was 94.3%. Also, the training and testing delay recorded were 2.12 seconds and 3.14 seconds respectively.
{"title":"A Normalized ANN Model for Earthquake Estimation","authors":"Dibyendu Mehta, Priti Priya Das, Sagnik Ghosh, Sushruta Mishra, A. Alkhayyat, Vandana Sharma","doi":"10.1109/ICAAIC56838.2023.10140242","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140242","url":null,"abstract":"Earthquake is one of the most devastating natural catastrophes, mainly because there is rarely any advance notice and hence little opportunity to react. This makes the issue of earthquake prediction highly crucial for human safety. This paper offers a technique for predicting earthquakes by using normalized artificial neural network (ANN). Exploratory Data Analysis (EDA) is applied on the raw dataset to find outliers and the co-relationship between input features. Then, Feature Engineering is performed to normalize the data and remove all unnecessary features. The training data is fed into the neural network model, which generates certain output. Error is calculated based on actual and generated output. Backpropagation algorithm is applied to minimize the error, after which this data is used to train the model. Finally, the Testing data is fed into the model to calculate accuracy and other performance metrics. The outcomes of several experiments are promising. Accuracy of prediction obtained was 94.3%. Also, the training and testing delay recorded were 2.12 seconds and 3.14 seconds respectively.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"30 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129991512","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140446
Kummari Karthik, Alla Lokesh Reddy, Rithesh Kulkarni, Mohd. Javeed Mehdi
Recent studies say that heart diseases are the major threat to humans. The diagnosis of the disease is obtained by making predictions from the patient's medical details. A minor error in predicting or diagnosis the results of heart related diseases can cause several problems. To address the issue, several researchers used the hospital data or patients' information for data mining and statistical tools for helping the health care system in the diagnosis of heart diseases. For making people aware of heart disease, a prediction model is required for early detection. The prediction model uses the training data and predicts the results by using several machine learning techniques. Using this training data, the testing of the other data is done precisely. In this research, for the prediction of the results from the given data, machine learning algorithms are used for model development. The prediction includes accuracy of each algorithm. By using machine learning techniques, the correlation between various features present in the dataset is also identified in the research while performing the experiment. The framework makes use of 13 features, including ones related to age, gender, obesity, blood pressure, cholesterol, and cp as various attributes to generate the classifiers. Using these features the output of these classifiers reveals that the accuracy of each algorithm and assists in predicting risk factors related to heart diseases and gives which is best suitable technique for producing the best predictions.
{"title":"Algorithm Accuracy Verification in Heart Disease Analysis using Machine Learning","authors":"Kummari Karthik, Alla Lokesh Reddy, Rithesh Kulkarni, Mohd. Javeed Mehdi","doi":"10.1109/ICAAIC56838.2023.10140446","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140446","url":null,"abstract":"Recent studies say that heart diseases are the major threat to humans. The diagnosis of the disease is obtained by making predictions from the patient's medical details. A minor error in predicting or diagnosis the results of heart related diseases can cause several problems. To address the issue, several researchers used the hospital data or patients' information for data mining and statistical tools for helping the health care system in the diagnosis of heart diseases. For making people aware of heart disease, a prediction model is required for early detection. The prediction model uses the training data and predicts the results by using several machine learning techniques. Using this training data, the testing of the other data is done precisely. In this research, for the prediction of the results from the given data, machine learning algorithms are used for model development. The prediction includes accuracy of each algorithm. By using machine learning techniques, the correlation between various features present in the dataset is also identified in the research while performing the experiment. The framework makes use of 13 features, including ones related to age, gender, obesity, blood pressure, cholesterol, and cp as various attributes to generate the classifiers. Using these features the output of these classifiers reveals that the accuracy of each algorithm and assists in predicting risk factors related to heart diseases and gives which is best suitable technique for producing the best predictions.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124453266","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141035
K. Manikandan, Kesamreddy Swapna, Narendra Naik J, K. N. Kumar, Kaku Rakesh, Kamisetty Vaishnavi
Economic dispatch for the microgrid (MG) is better adapted to the needs of a system in actual operation in the current scenario because it not only takes into account the scheduling cycle's lowest cost but also coordinates between several distributed generations (DGs) over a long period of time. Due of the unpredictable fluctuations and intervals that wind and solar energy are subject to, the economic dispatch problem is quite challenging to resolve. Intelligent algorithms and multi-objective optimum dispatching systems are acknowledged as excellent strategies for enhancing the economics and environmental friendliness of microgrid applications. The Multi Objective Optimal Dispatching System is developed for microgrids made up of photovoltaic cells (PV), wind turbines (WT), micro turbines (MT), fuel cells (FC), and battery storage (BT). The microgrid's dispatching problems might be solved and its dispatching convergence accuracy., stability., and speed all increased by using optimization techniques.
{"title":"Grey Wolf Optimization Algorithm based Combined Economic and Emission Dispatch Problem","authors":"K. Manikandan, Kesamreddy Swapna, Narendra Naik J, K. N. Kumar, Kaku Rakesh, Kamisetty Vaishnavi","doi":"10.1109/ICAAIC56838.2023.10141035","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141035","url":null,"abstract":"Economic dispatch for the microgrid (MG) is better adapted to the needs of a system in actual operation in the current scenario because it not only takes into account the scheduling cycle's lowest cost but also coordinates between several distributed generations (DGs) over a long period of time. Due of the unpredictable fluctuations and intervals that wind and solar energy are subject to, the economic dispatch problem is quite challenging to resolve. Intelligent algorithms and multi-objective optimum dispatching systems are acknowledged as excellent strategies for enhancing the economics and environmental friendliness of microgrid applications. The Multi Objective Optimal Dispatching System is developed for microgrids made up of photovoltaic cells (PV), wind turbines (WT), micro turbines (MT), fuel cells (FC), and battery storage (BT). The microgrid's dispatching problems might be solved and its dispatching convergence accuracy., stability., and speed all increased by using optimization techniques.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127411307","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140802
S. G., Levin A., Pratheep S., P. S., Ramkumar B., M. R.
Recently, motorcycle accidents are increasing at an unprecedented rate due to the riders who refuse to wear protective gear and riders who are under the influence of alcohol. On the other hand, car accidents are considered as a leading cause of death worldwide. Smart helmet technology can help keep people safe. The primary objective of this research work is to design and develop a wearable technology for monitoring alcohol consumption and preventing accidents. The micro limit button detects when a helmet is worn. The gas detector detects whether the driver's breath has booze. If the rider is intoxicated or not wearing a helmet, the motorbike will not start. A bike will only start if a helmet is worn and there is no evidence of intoxication. When a driver is engaged in a collision, vibration sensors send a warning notification via GPS and GSM modules to the pre-defined contacts.
{"title":"Smart Helmet for Vehicles using IoT for Accident Avoidance","authors":"S. G., Levin A., Pratheep S., P. S., Ramkumar B., M. R.","doi":"10.1109/ICAAIC56838.2023.10140802","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140802","url":null,"abstract":"Recently, motorcycle accidents are increasing at an unprecedented rate due to the riders who refuse to wear protective gear and riders who are under the influence of alcohol. On the other hand, car accidents are considered as a leading cause of death worldwide. Smart helmet technology can help keep people safe. The primary objective of this research work is to design and develop a wearable technology for monitoring alcohol consumption and preventing accidents. The micro limit button detects when a helmet is worn. The gas detector detects whether the driver's breath has booze. If the rider is intoxicated or not wearing a helmet, the motorbike will not start. A bike will only start if a helmet is worn and there is no evidence of intoxication. When a driver is engaged in a collision, vibration sensors send a warning notification via GPS and GSM modules to the pre-defined contacts.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121948579","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140287
Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan
The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.
{"title":"Customer Churn Prediction using Machine Learning: Subcription Renewal on OTT Platforms","authors":"Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan","doi":"10.1109/ICAAIC56838.2023.10140287","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140287","url":null,"abstract":"The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122201174","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}
Named Entity Recognition (NER) is a fundamental task in NLP that is used to locate the key information in text and is primarily applied in conversational and search systems. In commercial applications, NER or comparable slot filling methods have been widely deployed for popular languages. NER is utilized in applications such as human assets, client benefit, substance classification, and the scholarly community. This research study focuses on identifying name entities for low-resource Indian languages that are closely related, like Hindi and Marathi. This study uses various adaptations of BERT such as baseBERT, AlBERT, and RoBERTa to train a supervised NER model. The, compares multilingual models with monolingual models and establish a baseline. The results show the assisting capabilities of the Hindi and Marathi languages for the NER task. Also, the results show that the models trained using multiple languages perform better than a single language. However, this research study also observe that blind mixing of all datasets doesn't necessarily provide improvements and data selection methods may be required.
{"title":"Enhancing Low Resource NER using Assisting Language and Transfer Learning","authors":"Maithili Sabane, Aparna Ranade, Onkar Litake, Parth Patil, Raviraj Joshi, Dipali Kadam","doi":"10.1109/ICAAIC56838.2023.10141204","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141204","url":null,"abstract":"Named Entity Recognition (NER) is a fundamental task in NLP that is used to locate the key information in text and is primarily applied in conversational and search systems. In commercial applications, NER or comparable slot filling methods have been widely deployed for popular languages. NER is utilized in applications such as human assets, client benefit, substance classification, and the scholarly community. This research study focuses on identifying name entities for low-resource Indian languages that are closely related, like Hindi and Marathi. This study uses various adaptations of BERT such as baseBERT, AlBERT, and RoBERTa to train a supervised NER model. The, compares multilingual models with monolingual models and establish a baseline. The results show the assisting capabilities of the Hindi and Marathi languages for the NER task. Also, the results show that the models trained using multiple languages perform better than a single language. However, this research study also observe that blind mixing of all datasets doesn't necessarily provide improvements and data selection methods may be required.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125806730","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141041
Rajeswari Harini Bikkasani, Dhanya M, Veena S V
Manipur is one of the states in the northeastern part of India that has experienced a tremendous change in forest cover and a rapid increase in urbanization. Over the past two decades, this region has relatively changed in land surface features mostly due to anthropogenic activities. Detection of the changes in the land use land cover features helps to ensure sustainable development of the region. To achieve this goal, the present study uses remotely sensed data from sentinel-2A and Landsat 7,8 platforms for the period from the year 2000 to 2022. Machine learning algorithms have been proven to be useful in mapping the various land cover features. Here the land uses land cover (LULC) features are classified into six categories namely dense forest, open forest, agriculture, built-up area, water body, and barren land using random forest method. The classification method yielded an overall accuracy(OA) of 94.5, 93.32, 93.58, and 94.61% and a kappa coefficient index of 0.912, 0.925, 0.914, and 0.938 for 2000, 2008, 2016, and 2022 respectively. The results of the study indicate that the forest cover over Manipur has decreased significantly over recent years.
{"title":"Analysis of Long-term Changes for Land Use and Land Cover using Machine Learning: A case study","authors":"Rajeswari Harini Bikkasani, Dhanya M, Veena S V","doi":"10.1109/ICAAIC56838.2023.10141041","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141041","url":null,"abstract":"Manipur is one of the states in the northeastern part of India that has experienced a tremendous change in forest cover and a rapid increase in urbanization. Over the past two decades, this region has relatively changed in land surface features mostly due to anthropogenic activities. Detection of the changes in the land use land cover features helps to ensure sustainable development of the region. To achieve this goal, the present study uses remotely sensed data from sentinel-2A and Landsat 7,8 platforms for the period from the year 2000 to 2022. Machine learning algorithms have been proven to be useful in mapping the various land cover features. Here the land uses land cover (LULC) features are classified into six categories namely dense forest, open forest, agriculture, built-up area, water body, and barren land using random forest method. The classification method yielded an overall accuracy(OA) of 94.5, 93.32, 93.58, and 94.61% and a kappa coefficient index of 0.912, 0.925, 0.914, and 0.938 for 2000, 2008, 2016, and 2022 respectively. The results of the study indicate that the forest cover over Manipur has decreased significantly over recent years.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138977","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141311
Surendra Mahajan, Aakanksha Bharat Tonpe, Chaitrali Deepak Botkar, Shruti Subhash Salunkhe, V. V. Patil
Coal mines are generally deprived of technological advances. Most of the tasks carried out in coal mines are still manual which leads to many inefficiencies and malpractices. One such scenario is near the entry gate of the coal mine. The system for authenticating the trucks entering the coal mine involves a human intervention to some extent. Truck drivers often change the numberplate of trucks for malicious purposes and forgery. This may lead to coal theft. Hence, it is necessary to ensure that only the authentic truck enters the mine and only a genuine person is driving it. Besides verifying the authenticity of the driver, road condition monitoring including detecting and recognizing traffic signs is an important aspect of coal transportation. This article exhibits a comprehensive system consisting of gate pass automation using face detection and number plate recognition. Integrating real-time traffic analysis in coal-carrying trucks will provide a safe driving experience. A functionality for detecting and recognizing traffic signs and conveying the same to the truck driver using a voice assistant is proposed for providing additional safety. All the data collected by the system in real-time will be stored on the cloud for proofreading. A user interface showing real-time statistics can be provided to concerned authorities for ease of monitoring. Also, the proposed system is versatile and can be used in any other industry involving the transport of goods.
{"title":"A Comprehensive System for Coal Mines with Vehicle Gate Pass Automation using Face Detection, Truck Number Plate Recognition, and Road Conditions Monitoring","authors":"Surendra Mahajan, Aakanksha Bharat Tonpe, Chaitrali Deepak Botkar, Shruti Subhash Salunkhe, V. V. Patil","doi":"10.1109/ICAAIC56838.2023.10141311","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141311","url":null,"abstract":"Coal mines are generally deprived of technological advances. Most of the tasks carried out in coal mines are still manual which leads to many inefficiencies and malpractices. One such scenario is near the entry gate of the coal mine. The system for authenticating the trucks entering the coal mine involves a human intervention to some extent. Truck drivers often change the numberplate of trucks for malicious purposes and forgery. This may lead to coal theft. Hence, it is necessary to ensure that only the authentic truck enters the mine and only a genuine person is driving it. Besides verifying the authenticity of the driver, road condition monitoring including detecting and recognizing traffic signs is an important aspect of coal transportation. This article exhibits a comprehensive system consisting of gate pass automation using face detection and number plate recognition. Integrating real-time traffic analysis in coal-carrying trucks will provide a safe driving experience. A functionality for detecting and recognizing traffic signs and conveying the same to the truck driver using a voice assistant is proposed for providing additional safety. All the data collected by the system in real-time will be stored on the cloud for proofreading. A user interface showing real-time statistics can be provided to concerned authorities for ease of monitoring. Also, the proposed system is versatile and can be used in any other industry involving the transport of goods.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126511467","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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