Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058021
Sarthak Seth, Dhruv C. Mathur
Advancements in mobile data communications require a very robust and reliable system. Future wireless systems requires high bit rate, low bit error rate, maximum transmission with very low power, lower bandwidth etc. The designed system should also be able to combat the effects of noise, co-channel interference (CCI), inter-symbol interference (ISI), spatial correlation and multipath fading effects. These effects deteriorate the performance of system significantly. MIMO system with more than one antenna at the transmitter and the receiver are used to combat all these impairments. Different detection techniques are applied at the receiver for the equalization and reduction of unwanted effects. In this paper, bit error rate (BER) of different equalization algorithms has been derived under the influence of ISI, CCI, and spatial correlation. Different Equalization techniques like ZF, MMSE, ZF-SIC, MMSE-SIC and ML.
{"title":"Performance Characterization of Equalization Techniques in MIMO System under Co-channel Interference and Spatial Correlation","authors":"Sarthak Seth, Dhruv C. Mathur","doi":"10.1109/Confluence47617.2020.9058021","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058021","url":null,"abstract":"Advancements in mobile data communications require a very robust and reliable system. Future wireless systems requires high bit rate, low bit error rate, maximum transmission with very low power, lower bandwidth etc. The designed system should also be able to combat the effects of noise, co-channel interference (CCI), inter-symbol interference (ISI), spatial correlation and multipath fading effects. These effects deteriorate the performance of system significantly. MIMO system with more than one antenna at the transmitter and the receiver are used to combat all these impairments. Different detection techniques are applied at the receiver for the equalization and reduction of unwanted effects. In this paper, bit error rate (BER) of different equalization algorithms has been derived under the influence of ISI, CCI, and spatial correlation. Different Equalization techniques like ZF, MMSE, ZF-SIC, MMSE-SIC and ML.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124908103","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058106
Anmol Uppal, Vipul Sachdeva, Seema Sharma
Online news platforms greatly influence our society and culture in both positive and negative ways. As online media becomes more dependent for source of information, a lot of fake news is posted online, that widespread with people following it without any prior or complete information of event authenticity. Such misinformation has the potential to manipulate public opinions. The exponential growth of fake news propagation have become a great threat to public for news trustworthiness. It has become a compelling issue for which discovering, examining and dealing with fake news has increased in demand. However, with the limited availability of literature on the issue of uncovering fake news, a number of potential methodologies and techniques remains unexplored. The primary aim of this paper is to review existing methodologies, to propose and implement a method for automated deception detection. The proposed methodology uses deep learning in discourse-level structure analysis to formulate the structure that differentiates fake and real news. The baseline model achieved 74% accuracy.
{"title":"Fake news detection using discourse segment structure analysis","authors":"Anmol Uppal, Vipul Sachdeva, Seema Sharma","doi":"10.1109/Confluence47617.2020.9058106","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058106","url":null,"abstract":"Online news platforms greatly influence our society and culture in both positive and negative ways. As online media becomes more dependent for source of information, a lot of fake news is posted online, that widespread with people following it without any prior or complete information of event authenticity. Such misinformation has the potential to manipulate public opinions. The exponential growth of fake news propagation have become a great threat to public for news trustworthiness. It has become a compelling issue for which discovering, examining and dealing with fake news has increased in demand. However, with the limited availability of literature on the issue of uncovering fake news, a number of potential methodologies and techniques remains unexplored. The primary aim of this paper is to review existing methodologies, to propose and implement a method for automated deception detection. The proposed methodology uses deep learning in discourse-level structure analysis to formulate the structure that differentiates fake and real news. The baseline model achieved 74% accuracy.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125163297","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057867
Bedatri Moulik, Anupama Prakash, A. Ganguly
This contribution investigates two different power management optimization techniques to optimally split the power between the engine and accumulator of a parallel hybrid hydraulic vehicle (HHV). The goal is to operate the engine at its most efficient region, keep the accumulator charge within bounds, and reduce the fuel consumption while maintaining the vehicle performance. After deriving the mathematical model of the HHV, a local optimization technique is used to solve the problem in each time step for an urban European drive cycle. Then for the same cycle, the results are compared with a global optimization technique. The global optimization shows a distinct improvement in terms of fuel consumption.
{"title":"Improvement in fuel economy of hybrid hydraulic powertrain by conducting a comparative study of two different optimization strategies","authors":"Bedatri Moulik, Anupama Prakash, A. Ganguly","doi":"10.1109/Confluence47617.2020.9057867","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057867","url":null,"abstract":"This contribution investigates two different power management optimization techniques to optimally split the power between the engine and accumulator of a parallel hybrid hydraulic vehicle (HHV). The goal is to operate the engine at its most efficient region, keep the accumulator charge within bounds, and reduce the fuel consumption while maintaining the vehicle performance. After deriving the mathematical model of the HHV, a local optimization technique is used to solve the problem in each time step for an urban European drive cycle. Then for the same cycle, the results are compared with a global optimization technique. The global optimization shows a distinct improvement in terms of fuel consumption.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127021334","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058000
Vanshika Nehra, Renuka Nagpal, Rajni Sehgal
The term “Collective” is just not restricted to the human beings but can also be referred to the organisms such as flock of birds, swarm of bees, colony of bats etc. In computer environments, the term may also refer to groups of virtual artificially intelligent agents. Most generally it can applicable to the workings of the entire planet or universe as smart organization whose intelligence is supplied and manifested through the entities within it. Collective Intelligence is a no new terms infact it’s been used from several decades now but what’s new is the emergence of computer technology which makes it a new and one of the most promising application of it used in a variety of field. Machine learning and Artificial Intelligence are making an enormous buzz around the world. The plenty of utilizations in Artificial Intelligence have changed the substance of innovation. This paper would give an overview of the promising future aspects and researches in the field of Collective Intelligence in brief. We need to concentrate on the elements that guide collective intelligence if we really want to optimize our groups for excellent cooperation. We need to concentrate on personality characteristics that are not so simple to follow, yet they are critical to the long-term achievement of organizations, such as intellect, consciousness, compassion, empathy, and regard. In this paper along with the definition of the Collective Intelligence, it would be measured, compared with individual intelligence and its applications are studied in brief.
{"title":"Collective Intelligence: When, Where and Why","authors":"Vanshika Nehra, Renuka Nagpal, Rajni Sehgal","doi":"10.1109/Confluence47617.2020.9058000","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058000","url":null,"abstract":"The term “Collective” is just not restricted to the human beings but can also be referred to the organisms such as flock of birds, swarm of bees, colony of bats etc. In computer environments, the term may also refer to groups of virtual artificially intelligent agents. Most generally it can applicable to the workings of the entire planet or universe as smart organization whose intelligence is supplied and manifested through the entities within it. Collective Intelligence is a no new terms infact it’s been used from several decades now but what’s new is the emergence of computer technology which makes it a new and one of the most promising application of it used in a variety of field. Machine learning and Artificial Intelligence are making an enormous buzz around the world. The plenty of utilizations in Artificial Intelligence have changed the substance of innovation. This paper would give an overview of the promising future aspects and researches in the field of Collective Intelligence in brief. We need to concentrate on the elements that guide collective intelligence if we really want to optimize our groups for excellent cooperation. We need to concentrate on personality characteristics that are not so simple to follow, yet they are critical to the long-term achievement of organizations, such as intellect, consciousness, compassion, empathy, and regard. In this paper along with the definition of the Collective Intelligence, it would be measured, compared with individual intelligence and its applications are studied in brief.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133219786","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057939
H. S. Saraswathi, Mohammed Rafi, K. G. Manjunath, A. Shankar
malignant growth is an irregular development of cell tissue. Pancreatic disease is one of the observable reasons for death around the world. Pancreatic malignant growth starts in the tissues of pancreas. The pancreas secretes proteins that helps the processing and hormones that directs the breakdown of sugars. Pancreatic malignancy is usually detected in the later stages, spreads rapidly and has a poor prediction. In this paper we have made an attempt to discuss various artificial intelligence methods to detect pancreatic cancer and proposing new AI method to spot subtle patterns and provide accurate information to pathologist.
{"title":"Review on computer aided diagnosis of pancreatic cancer using Artificial Intelligence System","authors":"H. S. Saraswathi, Mohammed Rafi, K. G. Manjunath, A. Shankar","doi":"10.1109/Confluence47617.2020.9057939","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057939","url":null,"abstract":"malignant growth is an irregular development of cell tissue. Pancreatic disease is one of the observable reasons for death around the world. Pancreatic malignant growth starts in the tissues of pancreas. The pancreas secretes proteins that helps the processing and hormones that directs the breakdown of sugars. Pancreatic malignancy is usually detected in the later stages, spreads rapidly and has a poor prediction. In this paper we have made an attempt to discuss various artificial intelligence methods to detect pancreatic cancer and proposing new AI method to spot subtle patterns and provide accurate information to pathologist.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133653576","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058077
F. Siddiqui, Shubham Gupta, Shashwat Dubey, Shariq Murtuza, Arti Jain
In the past decades, researchers have demonstrated abilities to automate the process of detection and analysis of different kinds of cancers using Whole Slide Images (WSI) datasets. The breast cancer detection in histopathology images (one of the WSI dataset) using deep learning is one of the key research areas among the Computer AiDed (CAD) diagnostic systems. When it is done manually, it is a very tedious and challenging task for a pathologist as it involves thorough scanning of tissues to detect malignancy. This paper presents Convolutional Neural Network (CNN) classifier for breast cancer detection on the Breast Histopathology Images (BHI) dataset. A confusion matrix is computed for the BHI samples to analyze the prediction results of the CNN classifier. The CNN detects carcinoma tissues while labeling 55,505 image test samples as positive or negative; and achieves accuracy of 84.93%, recall of 84.70% and F-measure as 76.07% respectively.
{"title":"Classification and Diagnosis of Invasive Ductal Carcinoma Using Deep Learning","authors":"F. Siddiqui, Shubham Gupta, Shashwat Dubey, Shariq Murtuza, Arti Jain","doi":"10.1109/Confluence47617.2020.9058077","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058077","url":null,"abstract":"In the past decades, researchers have demonstrated abilities to automate the process of detection and analysis of different kinds of cancers using Whole Slide Images (WSI) datasets. The breast cancer detection in histopathology images (one of the WSI dataset) using deep learning is one of the key research areas among the Computer AiDed (CAD) diagnostic systems. When it is done manually, it is a very tedious and challenging task for a pathologist as it involves thorough scanning of tissues to detect malignancy. This paper presents Convolutional Neural Network (CNN) classifier for breast cancer detection on the Breast Histopathology Images (BHI) dataset. A confusion matrix is computed for the BHI samples to analyze the prediction results of the CNN classifier. The CNN detects carcinoma tissues while labeling 55,505 image test samples as positive or negative; and achieves accuracy of 84.93%, recall of 84.70% and F-measure as 76.07% respectively.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134522498","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057842
Sarthak Gupta, Virain Malhotra, Vasudha Vashisht
India is one of the largest producers of agricultural products. Main source of India’s GDP is its vast agricultural produce that accounts to 16% of the total. About 58 percent of the India’s workforce is involved in agriculture. But due to variable climatic condition of the country farmers are unprepared for these harsh and inevitable conditions. The farmers don’t have any effective way to deal with natural disasters such as drought and flooding which results in damaging of the crop and steep loss to the farmers. This research paper proposes a system through which we can reduce the problems of the farmers by automated smart irrigation system in drought conditions and smart suction pump which will suck out the excess water during flooding conditions. A database will be maintained for thorough analysis of amount of water irrigated in the fields, measurement of amount of rainfall, amount of water sucked during flooding and humidity level of soil in timeline manner. This database will be used for prediction of such climatic conditions and informing the farmers to take appropriate measures so that they can reduce or nullify the losses under such conditions.
{"title":"Water Irrigation and Flood Prevention using IOT","authors":"Sarthak Gupta, Virain Malhotra, Vasudha Vashisht","doi":"10.1109/Confluence47617.2020.9057842","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057842","url":null,"abstract":"India is one of the largest producers of agricultural products. Main source of India’s GDP is its vast agricultural produce that accounts to 16% of the total. About 58 percent of the India’s workforce is involved in agriculture. But due to variable climatic condition of the country farmers are unprepared for these harsh and inevitable conditions. The farmers don’t have any effective way to deal with natural disasters such as drought and flooding which results in damaging of the crop and steep loss to the farmers. This research paper proposes a system through which we can reduce the problems of the farmers by automated smart irrigation system in drought conditions and smart suction pump which will suck out the excess water during flooding conditions. A database will be maintained for thorough analysis of amount of water irrigated in the fields, measurement of amount of rainfall, amount of water sucked during flooding and humidity level of soil in timeline manner. This database will be used for prediction of such climatic conditions and informing the farmers to take appropriate measures so that they can reduce or nullify the losses under such conditions.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115799856","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057810
Shaurya Uppal, Arti Jain, Anuja Arora
Text Mining refers to an extraction of certain nontrivial, hidden and interesting knowledge from an unstructured textual data. In this paper, efforts are directed to interpret text mining queries in the healthcare domain. To do so, the dataset is taken from the 1mg-company that has emerged during 2015 to provide transparent, authentic and accessible healthcare information for the millions of people while guiding customers with the quality care that too at affordable prices. The different text mining algorithms are compared to generate knowledge extraction of keyterms while linking the personalized search concepts with respect to the healthcare domain, and for the better search recommendations. The algorithms are: basic TF-IDF, SGRank with IDF, TextRank, and modified TF-IDF. The best results are obtained with the modified TF-IDF with the Shingle analyzer where post-release overall is reduced.
{"title":"Comparative Analysis for KeyTerms Extraction Methods for Personalized Search Engines","authors":"Shaurya Uppal, Arti Jain, Anuja Arora","doi":"10.1109/Confluence47617.2020.9057810","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057810","url":null,"abstract":"Text Mining refers to an extraction of certain nontrivial, hidden and interesting knowledge from an unstructured textual data. In this paper, efforts are directed to interpret text mining queries in the healthcare domain. To do so, the dataset is taken from the 1mg-company that has emerged during 2015 to provide transparent, authentic and accessible healthcare information for the millions of people while guiding customers with the quality care that too at affordable prices. The different text mining algorithms are compared to generate knowledge extraction of keyterms while linking the personalized search concepts with respect to the healthcare domain, and for the better search recommendations. The algorithms are: basic TF-IDF, SGRank with IDF, TextRank, and modified TF-IDF. The best results are obtained with the modified TF-IDF with the Shingle analyzer where post-release overall is reduced.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132471593","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057853
Ishika Dhall, Shubham Vashisth, Garima Aggarwal
The tremendous growth in the domain of deep learning has helped in achieving breakthroughs in computer vision applications especially after convolutional neural networks coming into the picture. The unique architecture of CNNs allows it to extract relevant information from the input images without any hand-tuning. Today, with such powerful models we have quite a flexibility build technology that may ameliorate human life. One such technique can be used for detecting and understanding various human gestures as it would make the human-machine communication effective. This could make the conventional input devices like touchscreens, mouse pad, and keyboards redundant. Also, it is considered as a highly secure tech compared to other devices. In this paper, hand gesture technology along with Convolutional Neural Networks has been discovered followed by the construction of a deep convolutional neural network to build a hand gesture recognition application.
{"title":"Automated Hand Gesture Recognition using a Deep Convolutional Neural Network model","authors":"Ishika Dhall, Shubham Vashisth, Garima Aggarwal","doi":"10.1109/Confluence47617.2020.9057853","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057853","url":null,"abstract":"The tremendous growth in the domain of deep learning has helped in achieving breakthroughs in computer vision applications especially after convolutional neural networks coming into the picture. The unique architecture of CNNs allows it to extract relevant information from the input images without any hand-tuning. Today, with such powerful models we have quite a flexibility build technology that may ameliorate human life. One such technique can be used for detecting and understanding various human gestures as it would make the human-machine communication effective. This could make the conventional input devices like touchscreens, mouse pad, and keyboards redundant. Also, it is considered as a highly secure tech compared to other devices. In this paper, hand gesture technology along with Convolutional Neural Networks has been discovered followed by the construction of a deep convolutional neural network to build a hand gesture recognition application.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134435979","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057921
S. Namazi, L. Brankovic, B. Moghtaderi, J. Zanganeh
Global warming is a long-term environmental hazard demonstrated by a gradual increase in the temperature of the Earth. It is caused by the accumulation of greenhouse gases in the atmosphere, including carbon dioxide and methane. Although, in terms of the volume, methane is considered secondary to carbon dioxide, it is about 21 times more damaging when compared over a 100-year period. Fugitive methane emissions from underground coal mines significantly contribute to global warming. Amongst all the known methods to reduce the fugitive methane, application of thermal oxidation (or, simply, burning) is deemed the most effective and practical. This process produces water vapour and carbon dioxide, which has significantly lower adverse impact on the atmosphere than methane. The thermal oxidisers operate at high temperatures, which may introduce a risk of fire and explosion to the mine. In order to mitigate such risk, a thorough understanding of the methane explosion characteristics is essential. Methane fire and explosion experiments under conditions pertinent to underground coal mines are expensive, risky and necessitate significant effort, and thus require enormous preparation and safety procedures. It is cheaper and safer to analyse existing data to discover patterns and predict explosions than to conduct new extensive experiments. In this paper, we present a comparative study of data mining and machine learning techniques used for these purposes.
{"title":"Comparative Study of Data Mining Techniques for Predicting Explosions in Coal Mines","authors":"S. Namazi, L. Brankovic, B. Moghtaderi, J. Zanganeh","doi":"10.1109/Confluence47617.2020.9057921","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057921","url":null,"abstract":"Global warming is a long-term environmental hazard demonstrated by a gradual increase in the temperature of the Earth. It is caused by the accumulation of greenhouse gases in the atmosphere, including carbon dioxide and methane. Although, in terms of the volume, methane is considered secondary to carbon dioxide, it is about 21 times more damaging when compared over a 100-year period. Fugitive methane emissions from underground coal mines significantly contribute to global warming. Amongst all the known methods to reduce the fugitive methane, application of thermal oxidation (or, simply, burning) is deemed the most effective and practical. This process produces water vapour and carbon dioxide, which has significantly lower adverse impact on the atmosphere than methane. The thermal oxidisers operate at high temperatures, which may introduce a risk of fire and explosion to the mine. In order to mitigate such risk, a thorough understanding of the methane explosion characteristics is essential. Methane fire and explosion experiments under conditions pertinent to underground coal mines are expensive, risky and necessitate significant effort, and thus require enormous preparation and safety procedures. It is cheaper and safer to analyse existing data to discover patterns and predict explosions than to conduct new extensive experiments. In this paper, we present a comparative study of data mining and machine learning techniques used for these purposes.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116616476","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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