Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243365
M. Hans, Mahesh A. Tamhane
In this paper, a hybrid energy solution is implemented for trivial scale green energy generation as an option for limited conventional energy source, standalone and manual system for the street illumination system and emergency e-vehicle charging. This hybrid energy system consists of two renewable energy sources as Solar PV panel and VAWT along with IoT based control method with the coordination of microcontroller provides effective controlling, monitoring, fault detection and preventive maintenance alert which makes the system intelligent and energy-efficient, resulting in less manpower requirement automation and saving in energy. The Solar PV Panel utilizes the photon energy from sunlight and VAWT utilizes the aerodynamic losses produced by moving vehicles for the generation of power. It provides the real-time monitoring of all connected street lights. Also, it can be an efficient, automated and attractive option for the development of smart cities.
{"title":"IoT based Hybrid Green Energy driven Street Lighting System","authors":"M. Hans, Mahesh A. Tamhane","doi":"10.1109/I-SMAC49090.2020.9243365","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243365","url":null,"abstract":"In this paper, a hybrid energy solution is implemented for trivial scale green energy generation as an option for limited conventional energy source, standalone and manual system for the street illumination system and emergency e-vehicle charging. This hybrid energy system consists of two renewable energy sources as Solar PV panel and VAWT along with IoT based control method with the coordination of microcontroller provides effective controlling, monitoring, fault detection and preventive maintenance alert which makes the system intelligent and energy-efficient, resulting in less manpower requirement automation and saving in energy. The Solar PV Panel utilizes the photon energy from sunlight and VAWT utilizes the aerodynamic losses produced by moving vehicles for the generation of power. It provides the real-time monitoring of all connected street lights. Also, it can be an efficient, automated and attractive option for the development of smart cities.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134020916","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243360
Rahul, R. Katarya
Every country's concern about its growth or development is education. This field creates a way to discover hidden examples from instructive information. The authors have researched by comparing the different classification techniques on the collected dataset which is present online on the UCI Machine Learning (ML) repository. The results of this learning identify an explanatory structure uniting multiple dimensions persuading the prediction. For this research, the authors conducted the experiments on the collected dataset using the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) and measure the performance using the metrics like Accuracy (Acc.), Precision (Pr.) and Recall (Rec.). This research will also help the schools, colleges and university teachers or faculty for identifying the weak students in the class and to help them separately by conducting remedial classes or any other suitable method.
{"title":"Impact of Supervised Classification Techniques for the Prediction of Student's Performance","authors":"Rahul, R. Katarya","doi":"10.1109/I-SMAC49090.2020.9243360","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243360","url":null,"abstract":"Every country's concern about its growth or development is education. This field creates a way to discover hidden examples from instructive information. The authors have researched by comparing the different classification techniques on the collected dataset which is present online on the UCI Machine Learning (ML) repository. The results of this learning identify an explanatory structure uniting multiple dimensions persuading the prediction. For this research, the authors conducted the experiments on the collected dataset using the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) and measure the performance using the metrics like Accuracy (Acc.), Precision (Pr.) and Recall (Rec.). This research will also help the schools, colleges and university teachers or faculty for identifying the weak students in the class and to help them separately by conducting remedial classes or any other suitable method.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681673","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243526
L. C, Namboori. P. K. Krıshnan
The major complication associated with cancer care is delayed cancer detection, which would also reduce the likelihood of survival. This situation could be resolved to some extend with an early diagnostic system. In the current study, designing an early detection system for TP53 mutation, which is a common primary mutation for most of the types of cancer, has been carried out using the ‘Pharmacogenomics’, ‘Gene expression profiling’ and ‘Deep imaging processing technique’. The input for the analysis is microscopic biopsy images collected from the ‘Expression atlas database’. The high level of expression of TP53 gene mutation has been observed in Breast and Ovarian cancers samples. The involvement of associated genes like BARD1, CHEK2, ATM, BRCA2, BRCA1, and RAD51 has also been analyzed. A deep neural network with a ‘Siamese Neural Network (SNN)’, architecture has been implemented using one-short learning process to comprehend the data and make valid predictions on TP53 mutation. This ‘algorithm and learning platform’ helps in making dependable predictions even from a low input data and the machine's measured predictive performance is 89%.
{"title":"Design and Development of A Diagnostic System for Early Prediction of P53 Mutation Causing Cancer from Microscopic Biopsy Images","authors":"L. C, Namboori. P. K. Krıshnan","doi":"10.1109/I-SMAC49090.2020.9243526","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243526","url":null,"abstract":"The major complication associated with cancer care is delayed cancer detection, which would also reduce the likelihood of survival. This situation could be resolved to some extend with an early diagnostic system. In the current study, designing an early detection system for TP53 mutation, which is a common primary mutation for most of the types of cancer, has been carried out using the ‘Pharmacogenomics’, ‘Gene expression profiling’ and ‘Deep imaging processing technique’. The input for the analysis is microscopic biopsy images collected from the ‘Expression atlas database’. The high level of expression of TP53 gene mutation has been observed in Breast and Ovarian cancers samples. The involvement of associated genes like BARD1, CHEK2, ATM, BRCA2, BRCA1, and RAD51 has also been analyzed. A deep neural network with a ‘Siamese Neural Network (SNN)’, architecture has been implemented using one-short learning process to comprehend the data and make valid predictions on TP53 mutation. This ‘algorithm and learning platform’ helps in making dependable predictions even from a low input data and the machine's measured predictive performance is 89%.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782107","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243505
M. Gupta, Lava Bhargava, I. Sreedevi
A learning-based manager that controls the power budget through dynamic voltage frequency scaling (DVFS) in a multi-core processor has been proposed in this paper. The core statistics are collected and employed to predict the next interval power consumption and are thereby used to determine the best suited voltage-frequency setting for each core. The aim is to maximize perforformance while containing the power consumption per-core. The presented solution is realized in Snipersim and the fine-grained DVFS algorithm is included through Python scripting. Simulation results demonstrate that the proposed approach is able to achieve 6.6 % energy-reduction and average power-savings of 27.4% against the existing state-of-the-art algorithm (Steepest Drop) for various allocation schemes.
{"title":"Dynamic Voltage Frequency Scaling in Multi-core Systems using Adaptive Regression Model","authors":"M. Gupta, Lava Bhargava, I. Sreedevi","doi":"10.1109/I-SMAC49090.2020.9243505","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243505","url":null,"abstract":"A learning-based manager that controls the power budget through dynamic voltage frequency scaling (DVFS) in a multi-core processor has been proposed in this paper. The core statistics are collected and employed to predict the next interval power consumption and are thereby used to determine the best suited voltage-frequency setting for each core. The aim is to maximize perforformance while containing the power consumption per-core. The presented solution is realized in Snipersim and the fine-grained DVFS algorithm is included through Python scripting. Simulation results demonstrate that the proposed approach is able to achieve 6.6 % energy-reduction and average power-savings of 27.4% against the existing state-of-the-art algorithm (Steepest Drop) for various allocation schemes.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133110929","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243520
Kaitai Xiao
Application of modern evolutionary algorithm in the intelligent analysis of mining instruments is studied in this paper. Modern digital technology, the automatic control technology, communication technology, information technology, big data technology and the other advanced technologies are increasingly used in the general construction of intelligent mines to realize the coordination of coal mining, sorting processing, transportation, sales and other links. Hence, 2 novelties are proposed. First, intelligent mine platform mainly relies on the Internet of Things coding principle, which standardizes various basic information coding and recognition systems of the mines, and combines mine automation, the IoT framework is used to construct the system. Second, the automated instruments have been then widely used in many industrial production fields such as electric power, chemical industry and petroleum. The intelligent model is used to achieve an efficient analysis of the mentioned question. The basic performance of the model is evaluated through the experiment.
{"title":"Application of Evolutionary Algorithm in Intelligent Analysis of Mining Instruments","authors":"Kaitai Xiao","doi":"10.1109/I-SMAC49090.2020.9243520","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243520","url":null,"abstract":"Application of modern evolutionary algorithm in the intelligent analysis of mining instruments is studied in this paper. Modern digital technology, the automatic control technology, communication technology, information technology, big data technology and the other advanced technologies are increasingly used in the general construction of intelligent mines to realize the coordination of coal mining, sorting processing, transportation, sales and other links. Hence, 2 novelties are proposed. First, intelligent mine platform mainly relies on the Internet of Things coding principle, which standardizes various basic information coding and recognition systems of the mines, and combines mine automation, the IoT framework is used to construct the system. Second, the automated instruments have been then widely used in many industrial production fields such as electric power, chemical industry and petroleum. The intelligent model is used to achieve an efficient analysis of the mentioned question. The basic performance of the model is evaluated through the experiment.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122140108","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}
Computation is the primary task performed for the evaluation of the solution for a specific problem, and in realtime, having better challenges to implementing the solution path with the better computational mechanisms. The concept of quantum computation mechanism using the neural networks is having the highest amount of the success rate in prediction models design and implementation. The idea of a dynamic routing mechanism using quantum computing and neural networks are the main essence. A better prediction model is performed for this specific kind of problem, which needs a particular focus on the latest problem-solving mechanisms. The problem-solving tools like neural networks will dynamically perform with real-time data, but a new add-on is needed to add like big data to implement the live data. The live data can help implement and understand the importance of solving the problem like dynamic routing mechanism. There is a chance of random growth in such a field of computer science. This computational mechanism using quantum computing and the neural network will track the live operations and form the dynamic route changes in the real-time scenario. This real-time scenario worked with a 95% accuracy rate. The accuracy will differ based on the number of connecting nodes are being considered to evaluate the hidden layers of the problem-solving mechanism.
{"title":"Quantum Neural Networks for Dynamic Route Identification to avoid traffic","authors":"Sumati Boyapati, Srinivasa Rao Swarna, Abhishek Kumar","doi":"10.1109/I-SMAC49090.2020.9243322","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243322","url":null,"abstract":"Computation is the primary task performed for the evaluation of the solution for a specific problem, and in realtime, having better challenges to implementing the solution path with the better computational mechanisms. The concept of quantum computation mechanism using the neural networks is having the highest amount of the success rate in prediction models design and implementation. The idea of a dynamic routing mechanism using quantum computing and neural networks are the main essence. A better prediction model is performed for this specific kind of problem, which needs a particular focus on the latest problem-solving mechanisms. The problem-solving tools like neural networks will dynamically perform with real-time data, but a new add-on is needed to add like big data to implement the live data. The live data can help implement and understand the importance of solving the problem like dynamic routing mechanism. There is a chance of random growth in such a field of computer science. This computational mechanism using quantum computing and the neural network will track the live operations and form the dynamic route changes in the real-time scenario. This real-time scenario worked with a 95% accuracy rate. The accuracy will differ based on the number of connecting nodes are being considered to evaluate the hidden layers of the problem-solving mechanism.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125426377","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243510
A. M. John, K. Khanna, R. R. Prasad, Lakshmi G Pillai
Fourier Transform (FT) has been widely used as an image processing tool for analysis, filtering, reconstruction, and compression of images. The relevance of FT is considered in the image reconstruction process. Reconstruction algorithms supported by FT are identified and implemented. Analysis of the performance is made with the image quality assurance metrics like MSE, PSNR, SNR, SSIM, and NIQE. Various filters like an inverse filter, pseudoinverse filter, and Wiener filter are implemented and performance analysis is conducted. Out of these filters, the Wiener filter performs the best, then the other two methods when considering all the image assurance metrics.
{"title":"A Review on Application of Fourier Transform in Image Restoration","authors":"A. M. John, K. Khanna, R. R. Prasad, Lakshmi G Pillai","doi":"10.1109/I-SMAC49090.2020.9243510","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243510","url":null,"abstract":"Fourier Transform (FT) has been widely used as an image processing tool for analysis, filtering, reconstruction, and compression of images. The relevance of FT is considered in the image reconstruction process. Reconstruction algorithms supported by FT are identified and implemented. Analysis of the performance is made with the image quality assurance metrics like MSE, PSNR, SNR, SSIM, and NIQE. Various filters like an inverse filter, pseudoinverse filter, and Wiener filter are implemented and performance analysis is conducted. Out of these filters, the Wiener filter performs the best, then the other two methods when considering all the image assurance metrics.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124157952","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243524
M. Naresh, D. V. Reddy, K. Reddy
In the latest trends, a plethora of wireless technology becomes effectively utilizing Hetnets (Heterogeneous Networks) infrastructure when the User Equipment (UE) is moving in different networks. The major challenge in Hetnets is to attain everywhere, any time and best service. To achieve this Vertical Handover (VHO) is the one the most important handover network selecting strategy and selecting the simplest network for selected application to the user supported quality of service (QoS) parameter. In this paper, the various VHO algorithms are compared and simulations results show GRA, TOPSIS and SCS provides similar performances. FHAP and EMGRA will improve the quality of service parameters.
{"title":"A Comprehensive study on Vertical Handover for IEEE 802.21 Wireless Networks","authors":"M. Naresh, D. V. Reddy, K. Reddy","doi":"10.1109/I-SMAC49090.2020.9243524","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243524","url":null,"abstract":"In the latest trends, a plethora of wireless technology becomes effectively utilizing Hetnets (Heterogeneous Networks) infrastructure when the User Equipment (UE) is moving in different networks. The major challenge in Hetnets is to attain everywhere, any time and best service. To achieve this Vertical Handover (VHO) is the one the most important handover network selecting strategy and selecting the simplest network for selected application to the user supported quality of service (QoS) parameter. In this paper, the various VHO algorithms are compared and simulations results show GRA, TOPSIS and SCS provides similar performances. FHAP and EMGRA will improve the quality of service parameters.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130241229","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243312
Tianxiao Jiang
Clostridium difficile (C. Diff) Infection (CDI) is one of the most severe hospital-acquired diseases, it is caused by the disturbance of intestinal commensal bacteria such as the antimicrobial treatment. It can result in several symptoms including diarrhea, pseudomembranous colitis and even death. CDI can hardly be treated with antibiotic agents due to its high resistance to antibiotics. The most commonly used treatment for C. diff infection is a faecal transplant, which aims to recover the normal population of the commensal bacteria. To improve the effectiveness of prevention and treatment, a simulation of the population dynamics between commensal bacteria and C. diff would be helpful. This project mainly focused on the establishment of such a model with the application of evolutionary game theory. The simulation was able to give the critical value of the population of commensal bacteria that shifts the population dynamic from healthy to disease state. It suggested that the CDI is not caused by the gradual decrease of commensal bacteria but by the population of commensal bacteria decreased to a certain level. Antibiotics were also involved in the simulation. The result showed the antibiotics could kill a large proportion of commensal bacteria thus resulting in the CDI. Increase in the antibiotic resistance of C. diff will increase the incidence of CDI. The high flexibility of this model also allowed other types of population dynamics to be simulated. However, this model is still of concept, there is a long way to go before its practical application.
艰难梭菌(Clostridium difficile, C. Diff)感染(CDI)是最严重的医院获得性疾病之一，它是由肠道共生菌的紊乱引起的，如抗菌治疗。它会导致包括腹泻、假膜性结肠炎甚至死亡在内的几种症状。由于CDI对抗生素的高耐药性，很难用抗生素治疗。最常用的治疗艰难梭菌感染的方法是粪便移植，目的是恢复正常的共生菌群。为了提高预防和治疗的有效性，模拟共生菌与C. diff之间的种群动态将有所帮助。本项目主要是应用进化博弈论建立这样一个模型。该模拟能够给出使种群动态从健康状态转变为疾病状态的共生细菌种群的临界值。说明CDI不是由共生菌逐渐减少引起的，而是共生菌数量减少到一定程度所致。抗生素也参与了模拟。结果表明，抗生素能杀死大量的共生菌，从而导致CDI的发生。C. diff抗生素耐药性的增加会增加CDI的发病率。该模型的高度灵活性也允许模拟其他类型的种群动态。然而，这种模式还处于概念阶段，离实际应用还有很长的路要走。
{"title":"The dynamics of Clostridium Difficile and commensal bacteria through the lens of evolutionary game theory from perspectives of artificial intelligence","authors":"Tianxiao Jiang","doi":"10.1109/I-SMAC49090.2020.9243312","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243312","url":null,"abstract":"Clostridium difficile (C. Diff) Infection (CDI) is one of the most severe hospital-acquired diseases, it is caused by the disturbance of intestinal commensal bacteria such as the antimicrobial treatment. It can result in several symptoms including diarrhea, pseudomembranous colitis and even death. CDI can hardly be treated with antibiotic agents due to its high resistance to antibiotics. The most commonly used treatment for C. diff infection is a faecal transplant, which aims to recover the normal population of the commensal bacteria. To improve the effectiveness of prevention and treatment, a simulation of the population dynamics between commensal bacteria and C. diff would be helpful. This project mainly focused on the establishment of such a model with the application of evolutionary game theory. The simulation was able to give the critical value of the population of commensal bacteria that shifts the population dynamic from healthy to disease state. It suggested that the CDI is not caused by the gradual decrease of commensal bacteria but by the population of commensal bacteria decreased to a certain level. Antibiotics were also involved in the simulation. The result showed the antibiotics could kill a large proportion of commensal bacteria thus resulting in the CDI. Increase in the antibiotic resistance of C. diff will increase the incidence of CDI. The high flexibility of this model also allowed other types of population dynamics to be simulated. However, this model is still of concept, there is a long way to go before its practical application.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128818194","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243339
N. Kalyani, J. G, Bindu Sri Sai U, Samanvitha M, M. J., B. Kiranmayee
Prior prediction of flight arrival delays is necessary for both travelers and airlines because delays in flights not only trigger huge economic loss but also airlines end up losing their reputation that was built for several years and passengers lose their valuable time. Our paper aims at predicting the arrival delay of a scheduledindividual flight at the destination airport by utilizing available data. The predictive model presented in this work is to foresee airline arrival delays by employing supervised machine learning algorithms. US domestic flight data along with the weather data from July 2019 to December 2019 were acquired and are used while training the predictive model. XGBoost and linear regression algorithms were applied to develop the predictive model that aims at predicting flight delays. The performance of each algorithm was analyzed. Flight data along with the weather data was given to the model. Using this data, binary classification was carried out by the XGBoost trained model to predict whether there would be any arrival delay or not, and then linear regression model predicts the delay time of the flight.
{"title":"Machine Learning Model - based Prediction of Flight Delay","authors":"N. Kalyani, J. G, Bindu Sri Sai U, Samanvitha M, M. J., B. Kiranmayee","doi":"10.1109/I-SMAC49090.2020.9243339","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243339","url":null,"abstract":"Prior prediction of flight arrival delays is necessary for both travelers and airlines because delays in flights not only trigger huge economic loss but also airlines end up losing their reputation that was built for several years and passengers lose their valuable time. Our paper aims at predicting the arrival delay of a scheduledindividual flight at the destination airport by utilizing available data. The predictive model presented in this work is to foresee airline arrival delays by employing supervised machine learning algorithms. US domestic flight data along with the weather data from July 2019 to December 2019 were acquired and are used while training the predictive model. XGBoost and linear regression algorithms were applied to develop the predictive model that aims at predicting flight delays. The performance of each algorithm was analyzed. Flight data along with the weather data was given to the model. Using this data, binary classification was carried out by the XGBoost trained model to predict whether there would be any arrival delay or not, and then linear regression model predicts the delay time of the flight.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129394609","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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