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2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)最新文献

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COVID 19 Severity of Pneumonia Analysis Using Chest X Rays 利用胸部X线分析COVID - 19肺炎严重程度
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342702
Narayana Darapaneni, Shweta Ranjane, Uday Shankar Pallavajula Satya, D.Krishna prashanth, M. Reddy, A. Paduri, Aravind Kumar Adhi, Vachaspathi Madabhushanam
{"title":"COVID 19 Severity of Pneumonia Analysis Using Chest X Rays","authors":"Narayana Darapaneni, Shweta Ranjane, Uday Shankar Pallavajula Satya, D.Krishna prashanth, M. Reddy, A. Paduri, Aravind Kumar Adhi, Vachaspathi Madabhushanam","doi":"10.1109/ICIIS51140.2020.9342702","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342702","url":null,"abstract":"Purpose: To identify pneumonia location and determine the severity of pneumonia using deep learning network on chest X-ray images Methods: Data from RSNA Pneumonia detection challenge [1] from Kaggle is used for train and test analysis. Identifying images and calculating severity percentage of lung opacity in pneumonia present images by drawing bounding box Results: With 4668 X-ray images trained and tested on 1500 X-ray images, initial model has shown a mean average precision (mAP) of 0.90 on train set and 0.89 on test set. Conclusion: The intention is to leverage on existing studies and develop a better performing and highly accurate deep learning model to calculate severity percentage in a pneumonia present chest x-ray image.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130371447","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}
引用次数: 7
Big Data in Precision Medicine and its Legal Implications 精准医疗中的大数据及其法律含义
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342723
S. Sethu, R. Nair, L. Sadath
{"title":"Big Data in Precision Medicine and its Legal Implications","authors":"S. Sethu, R. Nair, L. Sadath","doi":"10.1109/ICIIS51140.2020.9342723","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342723","url":null,"abstract":"Medical healthcare sector is undergoing a predominant transformation and thereby imperative to accept the technological advancements to support the initiation of precision medicine. The major components of precision medicine include genomic data, social factors, clinical trial data, several patient samples, drug responses etc. The compilation methodology of this personalized treatment is inevitably functioned with the aid of big data. In such a scenario, legal repercussions relating to data sharing and privacy factors need to be considered on priority. Moreover, data sharing should be a precondition to the precision medicine approach. In our research work, we are interlinking the legal aspects of big data in precision medicine. Hence, we have a proposed a framework LPDM (Legal Protection for Data in Medicine) to interconnect these relevant factors of legal protection and regulatory compliance in regard to the big data elements for the wellbeing and smooth functioning of this innovative approach in medicine.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130822413","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}
引用次数: 1
Impact of Population Count on the Presence of Nitrogen Dioxide in United Arab Emirates using Sentinel-5P Satellite Data 使用Sentinel-5P卫星数据的人口计数对阿拉伯联合酋长国二氧化氮存在的影响
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342661
R. Bhatkar, S. Syamala, J. Varghese
{"title":"Impact of Population Count on the Presence of Nitrogen Dioxide in United Arab Emirates using Sentinel-5P Satellite Data","authors":"R. Bhatkar, S. Syamala, J. Varghese","doi":"10.1109/ICIIS51140.2020.9342661","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342661","url":null,"abstract":"Year-by-year, we have witnessed the advancement in the population count due to sudden urban expansion that had occurred in many countries. Due to urbanization, a lot of people have migrated to these areas from rural areas and from other countries. While this is good for the economy of the country, this has also increased the air contamination in the vicinity, and NO2 is one of the primary hazardous pollutants. It is monitored using the available remote sensing technology. For this paper, the data of NO2 is obtained from the latest satellite from the Sentinel series; Sentinel – 5 Precursor (5P) Satellite launched by European Space Agency (ESA) to detect the presence of pollutants at a tropospheric level using an on-board sensor – Tropospheric Monitoring Instrument (TROPOMI).","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130892400","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}
引用次数: 0
EEG Traced Microstates Detection during Meditation- A State of Consciousness 冥想时的脑电图追踪微状态检测——一种意识状态
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342712
Laxmi Shaw, A. Routray
{"title":"EEG Traced Microstates Detection during Meditation- A State of Consciousness","authors":"Laxmi Shaw, A. Routray","doi":"10.1109/ICIIS51140.2020.9342712","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342712","url":null,"abstract":"This paper presents a case study to detect the state of consciousness in meditation research. Specifically in this study, 23 long-term Kriya Yoga (KY) practitioners have participated. The microstate is one of the ways to detect Spatiotemporal activity of brain dynamics in specific cognitive traits using Global Field Potential (GFP). This investigation shows the recognition of short periods during which the electroencephalograph (EEG) scalp topography stays quasi stable, recognized as ‘microstates’.Considering the hypothesis that the human brain is active even when there is no explicit input or output stimulus. Surprisingly, EEG topographical patterns remain fixed in one particular global functional state at a precise moment in microseconds. Our findings indicated that the microstate in the episodic period of 150-220ms was associated with the higher label of consciousness in the meditator group. The present study has provided a functional interpretation of brain dynamics in the meditator group which has been verified in all subjects.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134060962","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}
引用次数: 0
Automatic Face Detection and Recognition for Attendance Maintenance 考勤维护中的自动人脸检测与识别
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342670
Narayana Darapaneni, Aruna Kumari Evoori, Vijaya Babu Vemuri, Thangaselvi Arichandrapandian, G. Karthikeyan, A. Paduri, D. Babu, J. Madhavan
{"title":"Automatic Face Detection and Recognition for Attendance Maintenance","authors":"Narayana Darapaneni, Aruna Kumari Evoori, Vijaya Babu Vemuri, Thangaselvi Arichandrapandian, G. Karthikeyan, A. Paduri, D. Babu, J. Madhavan","doi":"10.1109/ICIIS51140.2020.9342670","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342670","url":null,"abstract":"This paper focuses on building a deep learning based efficient attendance capturing system. Contemporary world is heading towards AI where every second creates a new vision with an enormous change. In Artificial Intelligence (AI), face recognition is one of the fastest growing domains. Instead of using traditional methods for marking attendance, we propose to automate it by identifying human faces with their unique face features known as Face Recognition. Face detection is a prerequisite process for face recognition which aims to identify and locate all faces irrespective of their position, scale, orientation, lighting conditions, expression etc. We created a system architectural solution using YOLO, MTCNN, FaceNet embeddings by applying multiple augmentations, picture quality check and de-noise methods to get a better attendance system with less maintenance, low cost hardware (Google Colab - Free Version), better performance and accuracy.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132578425","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}
引用次数: 10
Handling of Class Imbalance for Plant Disease Classification with Variants of GANs GANs变异对植物病害分类类不平衡的处理
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342728
Barshneya Talukdar
{"title":"Handling of Class Imbalance for Plant Disease Classification with Variants of GANs","authors":"Barshneya Talukdar","doi":"10.1109/ICIIS51140.2020.9342728","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342728","url":null,"abstract":"Plant leaf diseases are one of the major threats to the agriculture sector that significantly contribute to yield losses. Swift and accurate detection of plant leaf diseases is essential to reduce the intensity of the disease thereby minimising economic losses. Here, a deep learning-based Inception-v3 methodology has been proposed to identify and classify various plant leaf diseases using plant leaf image datasets. The approach also employs Generative Adversarial Networks (GANs) to augment the limited datasets. Different classes of GANs are adopted for experimental analysis to evaluate the performance of the proposed model. From the experiment’s results, it is observed that the DCGAN model achieves the highest accuracy and performs better than CGAN as a data augmentation technique in terms of Class Accuracy, Precision, Recall, F1 Score and Accuracy. The DCGAN model also outperforms in terms of evaluation parameters when compared with other techniques in literature.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114430701","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}
引用次数: 3
NearBy-Offload: An Android based Application for Computation Offloading NearBy-Offload:一个基于Android的计算卸载应用
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342724
Ashutosh Kumar, Ravi Yadav, G. Baranwal
{"title":"NearBy-Offload: An Android based Application for Computation Offloading","authors":"Ashutosh Kumar, Ravi Yadav, G. Baranwal","doi":"10.1109/ICIIS51140.2020.9342724","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342724","url":null,"abstract":"Computation offloading in fog computing not only addresses the computation-intensive and delay-sensitive needs of IoT applications but also improves the network lifetime of the IoT-Fog paradigm. As fog node suits as a place for availing offloading services, it introduces another challenge, i.e., selection of most suitable fog node among available heterogeneous fog nodes. Since fog node selection involves multiple criteria such as CPU power, energy, etc., the selection of fog node can be modeled as a multi-criteria decision making (MCDM) problem. In this work, we have designed an android based application, named Nearby-Offload, to provide a realtime implementation of computation offloading. Nearby-Offload also integrates two well-known MCDM methods, i.e., MAAU (Multi-Attribute Additive Utility) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for selecting the most suitable fog node based on the preference given by the user for criteria. This application is designed in such a way that new MCDM methods can be integrated by the user if required.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134285300","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}
引用次数: 0
Fe2O3 based Nanocomposites for Enhanced Thermal Energy Storage 基于Fe2O3的纳米复合材料增强热能储存
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342699
Malvika Satish, Sharon Santhosh, Sujith Kalluri, A. Yadav, A. Madhavan
{"title":"Fe2O3 based Nanocomposites for Enhanced Thermal Energy Storage","authors":"Malvika Satish, Sharon Santhosh, Sujith Kalluri, A. Yadav, A. Madhavan","doi":"10.1109/ICIIS51140.2020.9342699","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342699","url":null,"abstract":"Phase change materials (PCM) are commonly utilized materials in the latent heat energy storage systems. However, they have the limitation of low thermal conductivities which leads to poor charging and discharging rates. Lauric acid dispersed with the iron oxide nanoparticle was tested for its thermal performance to characterize its phase change properties. Different compositions of lauric acid were formulated with varying concentrations of iron oxide ranging from 1 wt. % to 4 wt. % were investigated. From the thermal charging and discharging studies, it was observed that lauric acid with 4% had shown the maximum heat transfer rate. Also, the FTIR spectrum confirmed the chemical stability and uniform dispersion of nanoparticle in lauric acid even after several thermal cycles. Such, lauric acid nanocomposites embedded with the iron oxide could be potential candidates in PCM applications.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133433339","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}
引用次数: 2
An Adaptive Multi-level Block-wise Encryption based Reversible Data Hiding Scheme 一种基于自适应多级块加密的可逆数据隐藏方案
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342695
Ravi Srihitha, Yadlapalli Sai Harshini, V. Manikandan
{"title":"An Adaptive Multi-level Block-wise Encryption based Reversible Data Hiding Scheme","authors":"Ravi Srihitha, Yadlapalli Sai Harshini, V. Manikandan","doi":"10.1109/ICIIS51140.2020.9342695","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342695","url":null,"abstract":"The design and development of reversible data hiding algorithms got a lot of consideration in recent years because of its wide applicability in medical image transmission and cloud computing. In this paper, we propose a new reversible data hiding scheme though a block-wise multi-level image encryption process to hide lengthy secret messages in an image. In the proposed technique, the data hider will use two additional encryption keys K0 and K1 for reversible data hiding purpose other than the image encryption key K. During the process of data hiding, an encrypted image will be taken as the input and produces a final modified encrypted image with hidden data. For the extraction of data and recovery of image, the receiver needs the image decryption key K and the additional decryption keys K0 and K1. The naturalness property of the image blocks is analyzed to recover the image blocks through a multi-level decryption process. The standard images from the USC-SIPI dataset controlled by the University of Southern California are used to conduct the experimental study.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106732","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}
引用次数: 0
Semantic Segmentation of Solar PV Panels and Wind Turbines in Satellite Images Using U-Net 基于U-Net的卫星图像中太阳能光伏板和风力涡轮机语义分割
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342701
Narayana Darapaneni, A. Jagannathan, V. Natarajan, Guruprasadh Swaminathan, S. Subramanian, A. Paduri
{"title":"Semantic Segmentation of Solar PV Panels and Wind Turbines in Satellite Images Using U-Net","authors":"Narayana Darapaneni, A. Jagannathan, V. Natarajan, Guruprasadh Swaminathan, S. Subramanian, A. Paduri","doi":"10.1109/ICIIS51140.2020.9342701","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342701","url":null,"abstract":"Global mission is to reduce the carbon footprint by using “Renewable Energy resources”. It is important to speed up the development of Renewable Energy Resources like Solar, Wind, Hydro electric et al. Implementation of Renewable Energy helps to tackle the climate change issue, as most of the energy resources are currently fossil fuel based. Information on installed capacity of Solar PV Panels and Wind Turbines along with forecasted load can enable grid operators to ensure optimal and reliable operation of system. Deep learning framework is used here to detect the Wind Turbines and Solar PV Panels in Satellite images. The current work aims to remove the manual effort which is currently involved in surveying the renewable energy resources. Building-level or neighborhood-level information on Solar PV panels and Wind Turbines enable analysis of Solar PV panels and Wind turbines deployment. Carbon footprint and Payback period can be calculated using the Deep Learning model outcome approximately for the installed locations and proposed locations. Dataset was acquired from Google Maps (Satellite view) for this work.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127082523","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}
引用次数: 5
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