Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058276
Radhika Tayal, A. Shankar
Now these days, many tools have been developed by the researchers to analyze the impact of diabetes disease on common people within a definite period. However, all these tools have predicted the results based on the labeled dataset or smaller dataset. But in a recent environment, we have collected a large amount of data using both online and offline media. Consequently, data are generated from heterogeneous sources, are in unstructured form and voluminous, etc. As a result, it is not possible to use huge data by using traditional prediction algorithms because they work only on the structured dataset. In this paper, we have used the semi-supervised learning approach that works on a partially labeled dataset for predicting diabetes disease. The partial dataset is the combination of a labeled and unlabelled dataset. For prediction, we have considered 80% unlabelled datasets and 20% labeled datasets. We developed a user based interface for the user to build their prediction model using labeled and unlabeled datasets and analyze the data according to their requirements and interest. Our main objective is to develop a diabetes prediction system that can be used by the researcher and the common people using with minimal labelled datasets.
{"title":"Learning And Predicting Diabetes Data Sets Using Semi-Supervised Learning","authors":"Radhika Tayal, A. Shankar","doi":"10.1109/Confluence47617.2020.9058276","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058276","url":null,"abstract":"Now these days, many tools have been developed by the researchers to analyze the impact of diabetes disease on common people within a definite period. However, all these tools have predicted the results based on the labeled dataset or smaller dataset. But in a recent environment, we have collected a large amount of data using both online and offline media. Consequently, data are generated from heterogeneous sources, are in unstructured form and voluminous, etc. As a result, it is not possible to use huge data by using traditional prediction algorithms because they work only on the structured dataset. In this paper, we have used the semi-supervised learning approach that works on a partially labeled dataset for predicting diabetes disease. The partial dataset is the combination of a labeled and unlabelled dataset. For prediction, we have considered 80% unlabelled datasets and 20% labeled datasets. We developed a user based interface for the user to build their prediction model using labeled and unlabeled datasets and analyze the data according to their requirements and interest. Our main objective is to develop a diabetes prediction system that can be used by the researcher and the common people using with minimal labelled datasets.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"49 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":"130908586","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/Confluence47617.2020.9057811
B. Saxena, V. Saxena
Influence maximization (IM) in online social networks (OSNs) has been extensively studied in the past few years, owing to its potential of impacting online marketing. IM aims at solving the problem of selecting a small set of influential nodes, who can lead to maximum influence spread across a social network. An integral part of IM is the modelling of the underlying diffusion process, which has a substantial impact on the spread achieved by any seed set. In this paper, Hurst-based diffusion model for IM has been proposed, under which node’s activation depends upon the nature of self-similarity exhibited in its past activity pattern. Assessment of the self-similarity trend exhibited by a node’s activity pattern, has been done using Hurst exponent (H). On the basis of the results achieved, the proposed model has been found to perform significantly better than two widely popular diffusion models, Independent Cascade and Linear Threshold, which are often used for IM in OSNs.
{"title":"Influence Maximization in Social Networks using Hurst exponent based Diffusion Model","authors":"B. Saxena, V. Saxena","doi":"10.1109/Confluence47617.2020.9057811","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057811","url":null,"abstract":"Influence maximization (IM) in online social networks (OSNs) has been extensively studied in the past few years, owing to its potential of impacting online marketing. IM aims at solving the problem of selecting a small set of influential nodes, who can lead to maximum influence spread across a social network. An integral part of IM is the modelling of the underlying diffusion process, which has a substantial impact on the spread achieved by any seed set. In this paper, Hurst-based diffusion model for IM has been proposed, under which node’s activation depends upon the nature of self-similarity exhibited in its past activity pattern. Assessment of the self-similarity trend exhibited by a node’s activity pattern, has been done using Hurst exponent (H). On the basis of the results achieved, the proposed model has been found to perform significantly better than two widely popular diffusion models, Independent Cascade and Linear Threshold, which are often used for IM in OSNs.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"22 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":"132303686","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/Confluence47617.2020.9058060
Ramesh Chandra Sahoo, S. Pradhan, Poonam Tanwar
A deep neural network such as convolutional neural network is a popular and most commonly applied technique in image processing for classification for the last few years. The overhead of the feature extraction step will be avoided due to the implicit feature extraction nature of convolutional neural network (CNN) and these extracted features contain substantial information that could be sufficient for an image classification problem. Fully connected (FC) layers in CNN take the results of the last convolution and/or pooling layer and then use them to recognize or classifying images into labels. In this paper, we present an associative memory-based model named Hopfield network as a fully connected layer to store patterns for classification in CNN architecture like LeNet-5. The main purpose of using Hopfield network is to avoid backpropagation as it is a fully connected recurrent network as the state-of-art results which we have obtained are comparable with other models. To measure the performance of the new architecture, we used NIT, Rourkela, Odia characters dataset and compared it with other models for classification.
{"title":"HopNet based Associative Memory as FC layer in CNN for Odia Character Classification","authors":"Ramesh Chandra Sahoo, S. Pradhan, Poonam Tanwar","doi":"10.1109/Confluence47617.2020.9058060","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058060","url":null,"abstract":"A deep neural network such as convolutional neural network is a popular and most commonly applied technique in image processing for classification for the last few years. The overhead of the feature extraction step will be avoided due to the implicit feature extraction nature of convolutional neural network (CNN) and these extracted features contain substantial information that could be sufficient for an image classification problem. Fully connected (FC) layers in CNN take the results of the last convolution and/or pooling layer and then use them to recognize or classifying images into labels. In this paper, we present an associative memory-based model named Hopfield network as a fully connected layer to store patterns for classification in CNN architecture like LeNet-5. The main purpose of using Hopfield network is to avoid backpropagation as it is a fully connected recurrent network as the state-of-art results which we have obtained are comparable with other models. To measure the performance of the new architecture, we used NIT, Rourkela, Odia characters dataset and compared it with other models for classification.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"1 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":"129243594","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/Confluence47617.2020.9058042
Roger Singh Chugh, Vardaan Bhatia, K. Khanna, Vandana Bhatia
Image classification is a supervised learning method used to classify images. There is a challenge in today’s world that Image classification is complex and can be solved using machine learning algorithms. The paper focuses on these tasks using classical machine learning algorithms namely K-Nearest Neighbour (KNN), Multi-Layered Perceptron (MLP) and Random Forest classifier (RF). A comparative analysis is performed on the dataset on the parameters of accuracy, time complexity, F1 score, recall and precision. It is observed that MLP has the highest accuracy of 89.57% followed by random forests having accuracy of 89.2% and lastly a KNN model with an accuracy of 85.87%. Further, it is observed that RF has the lowest time complexity of 34.89 seconds followed by KNN having time complexity of 106.92 seconds and lastly MLP having time complexity of 521.78 second per 100 epochs. This paper can help to realize the potential of neural networks in classification-based tasks where non binary classifications are required which is a typical expectation when real world data is considered.
{"title":"A Comparative Analysis of Classifiers for Image Classification","authors":"Roger Singh Chugh, Vardaan Bhatia, K. Khanna, Vandana Bhatia","doi":"10.1109/Confluence47617.2020.9058042","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058042","url":null,"abstract":"Image classification is a supervised learning method used to classify images. There is a challenge in today’s world that Image classification is complex and can be solved using machine learning algorithms. The paper focuses on these tasks using classical machine learning algorithms namely K-Nearest Neighbour (KNN), Multi-Layered Perceptron (MLP) and Random Forest classifier (RF). A comparative analysis is performed on the dataset on the parameters of accuracy, time complexity, F1 score, recall and precision. It is observed that MLP has the highest accuracy of 89.57% followed by random forests having accuracy of 89.2% and lastly a KNN model with an accuracy of 85.87%. Further, it is observed that RF has the lowest time complexity of 34.89 seconds followed by KNN having time complexity of 106.92 seconds and lastly MLP having time complexity of 521.78 second per 100 epochs. This paper can help to realize the potential of neural networks in classification-based tasks where non binary classifications are required which is a typical expectation when real world data is considered.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"62 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":"114691873","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/Confluence47617.2020.9058197
R. Malhotra, K. Lata
Maintainability is an essential dimension of software quality. Software Maintainability Prediction (SMP) is gaining the attention of researchers to develop maintainable software systems. Early prediction of software maintainability aid the software practitioners to focus on those software modules or classes that requires high maintainability effort in the maintenance phase. However, the imbalanced distribution of training data is a challenging and serious problem that is encountered while developing prediction models for software maintainability. This paper apply oversampling methods namely: Adaptive Synthetic Oversampling technique (AdaS), BorderlineSynthetic Minority Oversampling technique (BSMOTE), Synthetic Minority Oversampling technique (SMOTE), and SafeLevel Synthetic Minority Oversampling technique (SSMOTE) to treat the imbalanced data before learning the models for software maintainability. We also investigate the effectiveness of hybridized techniques for learning the prediction models using three popular Apache datasets. The outcome of the study supports the use of investigated oversampling methods with hybridized techniques to develop effective prediction models for software maintainability.
{"title":"Using Hybridized techniques for Prediction of Software Maintainability using Imbalanced data","authors":"R. Malhotra, K. Lata","doi":"10.1109/Confluence47617.2020.9058197","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058197","url":null,"abstract":"Maintainability is an essential dimension of software quality. Software Maintainability Prediction (SMP) is gaining the attention of researchers to develop maintainable software systems. Early prediction of software maintainability aid the software practitioners to focus on those software modules or classes that requires high maintainability effort in the maintenance phase. However, the imbalanced distribution of training data is a challenging and serious problem that is encountered while developing prediction models for software maintainability. This paper apply oversampling methods namely: Adaptive Synthetic Oversampling technique (AdaS), BorderlineSynthetic Minority Oversampling technique (BSMOTE), Synthetic Minority Oversampling technique (SMOTE), and SafeLevel Synthetic Minority Oversampling technique (SSMOTE) to treat the imbalanced data before learning the models for software maintainability. We also investigate the effectiveness of hybridized techniques for learning the prediction models using three popular Apache datasets. The outcome of the study supports the use of investigated oversampling methods with hybridized techniques to develop effective prediction models for software maintainability.","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":"124520140","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/Confluence47617.2020.9058048
Aabhas Dhaka, Prabhishek Singh
The Rapid spread of a disease is known as an epidemic. The catastrophe brought by an epidemic not only effects the people of an area, but also brings about a lot of distress in every sector of social strata. An epidemic alerting system has a potential to carve the path how medical surveillance could become more efficient. The epidemic causing diseases are usually vector borne. The diseases are spread by pathogens present in these vectors. An epidemic alerting system could predict how the weather conditions and several other factors effect the growth and propagation of these vectors. The weather conditions could be predicted using the high-end instruments and satellites currently available. Using this prediction, we could forecast the next targets of the epidemic. To implement this epidemic alert system, four algorithms are used namely Random Forest Regression, Decision Tree Regression, Support Vector Regression and Multiple Linear Regression. For dengue, the state wise cases data of the year 2013 to 2017 has been used in the system while for chikungunya the data used is of the year 2013 to 2016. This dataset has been downloaded from a government website, i.e., https://www.data.gov.in/. For the case of dengue, the model has been trained on the data of the year 2013 to 2016 and predictions of the year 2017 have been done. On the other hand, the model has been trained on the data of the year 2013 to 2015 and predictions for the year 2017 have been made regarding Chikungunya. At last, a contrastive analysis has been made on the four algorithms used for both the diseases.
{"title":"Comparative Analysis of Epidemic Alert System using Machine Learning for Dengue and Chikungunya","authors":"Aabhas Dhaka, Prabhishek Singh","doi":"10.1109/Confluence47617.2020.9058048","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058048","url":null,"abstract":"The Rapid spread of a disease is known as an epidemic. The catastrophe brought by an epidemic not only effects the people of an area, but also brings about a lot of distress in every sector of social strata. An epidemic alerting system has a potential to carve the path how medical surveillance could become more efficient. The epidemic causing diseases are usually vector borne. The diseases are spread by pathogens present in these vectors. An epidemic alerting system could predict how the weather conditions and several other factors effect the growth and propagation of these vectors. The weather conditions could be predicted using the high-end instruments and satellites currently available. Using this prediction, we could forecast the next targets of the epidemic. To implement this epidemic alert system, four algorithms are used namely Random Forest Regression, Decision Tree Regression, Support Vector Regression and Multiple Linear Regression. For dengue, the state wise cases data of the year 2013 to 2017 has been used in the system while for chikungunya the data used is of the year 2013 to 2016. This dataset has been downloaded from a government website, i.e., https://www.data.gov.in/. For the case of dengue, the model has been trained on the data of the year 2013 to 2016 and predictions of the year 2017 have been done. On the other hand, the model has been trained on the data of the year 2013 to 2015 and predictions for the year 2017 have been made regarding Chikungunya. At last, a contrastive analysis has been made on the four algorithms used for both the diseases.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"48 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":"123687843","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/Confluence47617.2020.9058135
Varun Malik, Taruna Sharma, Manish Sharma
In this research article, a square monopole multiband antenna is designed for applications including Wireless Wide Area Network which includes Digital Cellular System (1.71GHz-1.88GHz) and Personal Communication System (1.85GHz-1.99GHz), Bluetooth (2.402GHz-2.480GHz) and World Wide Interoperability for Microwave Access (3.30GHz-3.80GHz). These above said operating wireless technologies are obtained by using 2 L-Shaped stubs embedded with patch and etched L-shaped slot on radiating patch. Lengths of the stubs are optimized by using simulators and algorithm used by artificial intelligence (Radial Basis Model) Antenna results are simulated on two different EM simulators to validate and offers gain of 3.86, 4.42 and 4.18dBi respectively in operating bands.
{"title":"A Multiband (WWAN/Bluetooth/WiMAX) Square Monopole Antenna with Simple Structure for Wireless Communication System Applications And Optimization by using Artificial Intelligence","authors":"Varun Malik, Taruna Sharma, Manish Sharma","doi":"10.1109/Confluence47617.2020.9058135","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058135","url":null,"abstract":"In this research article, a square monopole multiband antenna is designed for applications including Wireless Wide Area Network which includes Digital Cellular System (1.71GHz-1.88GHz) and Personal Communication System (1.85GHz-1.99GHz), Bluetooth (2.402GHz-2.480GHz) and World Wide Interoperability for Microwave Access (3.30GHz-3.80GHz). These above said operating wireless technologies are obtained by using 2 L-Shaped stubs embedded with patch and etched L-shaped slot on radiating patch. Lengths of the stubs are optimized by using simulators and algorithm used by artificial intelligence (Radial Basis Model) Antenna results are simulated on two different EM simulators to validate and offers gain of 3.86, 4.42 and 4.18dBi respectively in operating bands.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"8 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":"115070904","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/Confluence47617.2020.9057821
Gordon Morrison, Jean-Paul Van Belle
Fully Autonomous Vehicles (AVs), or self-driving vehicles, are expected to enter the automobile market in the coming years. This technology is expected to provide society with a range of benefits, from increased mobility for the elderly and adolescents, to decreasing carbon emissions and improving traffic flow. These benefits, however, will not be achieved unless consumers are willing to accept the technology into their lives and daily routine. In acknowledging this potential barrier to AV proliferation, this study developed a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model with constructs Trust in Safety and Hedonic Motivation added. Data was collected by an online questionnaire. Effort expectancy, performance expectancy, facilitating conditions, and social influence were found to have a statistically significant positive influence on behavioural intention, with performance expectancy having the greatest impact. Trust in safety was found to consist of two separate dimensions: fears versus assurances and trust. The findings of this study can be used by government and private sectors to better understand consumers’ current perception of the technology and to introduce supporting legislation accordingly.
{"title":"Customer Intentions Towards Autonomous Vehicles in South Africa: An Extended UTAUT Model","authors":"Gordon Morrison, Jean-Paul Van Belle","doi":"10.1109/Confluence47617.2020.9057821","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057821","url":null,"abstract":"Fully Autonomous Vehicles (AVs), or self-driving vehicles, are expected to enter the automobile market in the coming years. This technology is expected to provide society with a range of benefits, from increased mobility for the elderly and adolescents, to decreasing carbon emissions and improving traffic flow. These benefits, however, will not be achieved unless consumers are willing to accept the technology into their lives and daily routine. In acknowledging this potential barrier to AV proliferation, this study developed a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model with constructs Trust in Safety and Hedonic Motivation added. Data was collected by an online questionnaire. Effort expectancy, performance expectancy, facilitating conditions, and social influence were found to have a statistically significant positive influence on behavioural intention, with performance expectancy having the greatest impact. Trust in safety was found to consist of two separate dimensions: fears versus assurances and trust. The findings of this study can be used by government and private sectors to better understand consumers’ current perception of the technology and to introduce supporting legislation accordingly.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"49 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":"116114975","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/Confluence47617.2020.9057972
P. S. Das, Harsh Chhabra, S. Dubey
Eradicating poverty is the numero uno objective of the United Nations for sustainable development of the world by 2030. But, in order to develop a feasible, targeted solution to this problem, an exact poverty map is required. In India, especially in rural areas, there is a dearth of reliable and frequent data related to indicators of poverty line as the national statistics division of the country releases data only once in five years. In this paper, we look at an alternative to the slow, ineffective collection of data on ground: mapping poverty from outer space using medium and high-resolution satellite imagery. Using both satellite imagery and survey data for the rural areas of India, we review how machine learning tools like convolutional neural networks have been harnessed to efficiently identify image features that help us effectively predict socio-economic indicators of poverty. We also explore how these methods offer promising means for policy makers to tackle poverty at the grassroot level and a potential for application across several domains of science.
{"title":"Socio Economic Analysis of India with High Resolution Satellite Imagery to Predict Poverty","authors":"P. S. Das, Harsh Chhabra, S. Dubey","doi":"10.1109/Confluence47617.2020.9057972","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057972","url":null,"abstract":"Eradicating poverty is the numero uno objective of the United Nations for sustainable development of the world by 2030. But, in order to develop a feasible, targeted solution to this problem, an exact poverty map is required. In India, especially in rural areas, there is a dearth of reliable and frequent data related to indicators of poverty line as the national statistics division of the country releases data only once in five years. In this paper, we look at an alternative to the slow, ineffective collection of data on ground: mapping poverty from outer space using medium and high-resolution satellite imagery. Using both satellite imagery and survey data for the rural areas of India, we review how machine learning tools like convolutional neural networks have been harnessed to efficiently identify image features that help us effectively predict socio-economic indicators of poverty. We also explore how these methods offer promising means for policy makers to tackle poverty at the grassroot level and a potential for application across several domains of science.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"17 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891450","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/Confluence47617.2020.9057998
S. Dubal, Anjali A. Chaudhari
In today’s evolutionary world of wireless technology, reconfigurable antenna plays a very important role. Wireless technologies such as mobile communication, military, cognitive radio, radar, satellite communication are needed to be dynamic in their functions to improve the performance in changing scenario. This can be achieved using a single reconfigurable antenna where various performance parameters like resonant frequency, polarization and radiation pattern are altered as per user end requirement. Hence, a single reconfigurable antenna replaces multiple conventional antennas resulting in a compact, low cost system. In this paper, electrical and mechanical switching mechanism for reconfigurable antennas has been reviewed. In electrical switching mechanism, reconfigurability is obtained using the p-i-n diode, Radio Frequency Micro Electro Mechanical switch (RF-MEM’s) and varactor diode, where ON and OFF state of diode, activates or deactivates the part of antenna structure offering modified characteristics of antenna; while in mechanical mechanism actuators and motors mechanically modify the antenna structure. Apart from the reconfigurability mechanisms, the reviewed structures are analyzed with respect to their design theory and applications. Equivalent circuit of the diodes have been studied and presented in this paper, it being the key component in reconfigurable antennas.
{"title":"Mechanisms of Reconfigurable Antenna: A Review","authors":"S. Dubal, Anjali A. Chaudhari","doi":"10.1109/Confluence47617.2020.9057998","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057998","url":null,"abstract":"In today’s evolutionary world of wireless technology, reconfigurable antenna plays a very important role. Wireless technologies such as mobile communication, military, cognitive radio, radar, satellite communication are needed to be dynamic in their functions to improve the performance in changing scenario. This can be achieved using a single reconfigurable antenna where various performance parameters like resonant frequency, polarization and radiation pattern are altered as per user end requirement. Hence, a single reconfigurable antenna replaces multiple conventional antennas resulting in a compact, low cost system. In this paper, electrical and mechanical switching mechanism for reconfigurable antennas has been reviewed. In electrical switching mechanism, reconfigurability is obtained using the p-i-n diode, Radio Frequency Micro Electro Mechanical switch (RF-MEM’s) and varactor diode, where ON and OFF state of diode, activates or deactivates the part of antenna structure offering modified characteristics of antenna; while in mechanical mechanism actuators and motors mechanically modify the antenna structure. Apart from the reconfigurability mechanisms, the reviewed structures are analyzed with respect to their design theory and applications. Equivalent circuit of the diodes have been studied and presented in this paper, it being the key component in reconfigurable antennas.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"106 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":"116681850","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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