Urban transport system is a time varying network. The variation in travel time and delay in travel faced by commuters is the adverse effect of traffic congestion. Traffic information in preceding time instances contributes in analyzing traffic in succeeding instances and spatial information of traffic is required for traffic flow assessment on highways. Sequencing spatial and temporal traffic information in preceding time instance helps in estimating traffic flow in sequence in successive time instances by formalizing Sequence Convolution based auto-encoder Long Short term Memory (SCAE-LSTM) network. The objective of this work is to estimate traffic flow on highways for different origin-destination (OD) pair based on spatial-temporal traffic sequences. Hence, Spatial-TemporAl Reconnect (STAR) algorithm is proposed. The performance of STAR is investigated by conducting extensive experimentation on real traffic network of Chennai Metropolitan City. The computational complexity of the algorithm is empirically analyzed. The proposed STAR algorithm is found to estimate traffic flow during peak hour traffic with reduced complexity in computation compared to other baseline methods in short term traffic flow predictions like LSTM, ConvLSTM and GRNN. Finally, conclusions on results are presented with directions for future research.
{"title":"Auto-Encoder LSTM for learning dependency of traffic flow by sequencing spatial-temporal traffic flow rate: A speed up technique for routing vehicles between origin and destination","authors":"Jayanthi Ganapathy, Thanushraam Sureshkumar, Medha Raghavendra Prasad, Cheekireddy Dhamini","doi":"10.1109/ICITIIT54346.2022.9744139","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744139","url":null,"abstract":"Urban transport system is a time varying network. The variation in travel time and delay in travel faced by commuters is the adverse effect of traffic congestion. Traffic information in preceding time instances contributes in analyzing traffic in succeeding instances and spatial information of traffic is required for traffic flow assessment on highways. Sequencing spatial and temporal traffic information in preceding time instance helps in estimating traffic flow in sequence in successive time instances by formalizing Sequence Convolution based auto-encoder Long Short term Memory (SCAE-LSTM) network. The objective of this work is to estimate traffic flow on highways for different origin-destination (OD) pair based on spatial-temporal traffic sequences. Hence, Spatial-TemporAl Reconnect (STAR) algorithm is proposed. The performance of STAR is investigated by conducting extensive experimentation on real traffic network of Chennai Metropolitan City. The computational complexity of the algorithm is empirically analyzed. The proposed STAR algorithm is found to estimate traffic flow during peak hour traffic with reduced complexity in computation compared to other baseline methods in short term traffic flow predictions like LSTM, ConvLSTM and GRNN. Finally, conclusions on results are presented with directions for future research.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496090","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744209
Anup Nair, Amit M. Joshi
Network processing has traditionally been a CPU-intensive operation where every device in the network has to do packet processing. With the upcoming needs for a digital world and rising technologies like 5G, the demand for faster processing has dramatically increased. In such cases, using only the CPU for network processing across core devices and edge devices can become a major bottleneck. This work aims to explore the use of GPUs for network processing and exploiting data-level parallelism in network-processing operations to speed up the overall network. The work throws light on how data transfer overheads can be minimized using CUDA Streams and achieves a 2x performance improvement with respect to synchronous data transfer. The subsequent part of this work deals with the implementation of packet switching on GPUs with the help of Bloom Filters. The exponentially increasing execution time on the CPU with respect to the number of packets is reduced to a constant execution time on GPU.
{"title":"Parallelizing CPU-GPU Network Processing Flows","authors":"Anup Nair, Amit M. Joshi","doi":"10.1109/ICITIIT54346.2022.9744209","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744209","url":null,"abstract":"Network processing has traditionally been a CPU-intensive operation where every device in the network has to do packet processing. With the upcoming needs for a digital world and rising technologies like 5G, the demand for faster processing has dramatically increased. In such cases, using only the CPU for network processing across core devices and edge devices can become a major bottleneck. This work aims to explore the use of GPUs for network processing and exploiting data-level parallelism in network-processing operations to speed up the overall network. The work throws light on how data transfer overheads can be minimized using CUDA Streams and achieves a 2x performance improvement with respect to synchronous data transfer. The subsequent part of this work deals with the implementation of packet switching on GPUs with the help of Bloom Filters. The exponentially increasing execution time on the CPU with respect to the number of packets is reduced to a constant execution time on GPU.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128941821","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744223
K. Guravaiah, Gorinka Rithika, S. Raju
Nowadays, it is essential to secure your house from unauthenticated persons or thieves. To secure the home, different kind of approaches are considered by researchers. In this paper, securing the house with the help of Internet of Things and image processing techniques. The Proposed system implemented with the help of deep learning algorithms such as MTCNN (Multi-task cascaded convolutional neural network) for face detection and facenet for face recognition. These algorithms will check whenever any person is visiting a house, capture the images of visitors and process those images compared with database images and inform to owner of the house. Then owner can have a eye on those people as well as alert their family members about this.
{"title":"HomeID: Home Visitors Recognition using Internet of Things and Deep Learning Algorithms","authors":"K. Guravaiah, Gorinka Rithika, S. Raju","doi":"10.1109/ICITIIT54346.2022.9744223","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744223","url":null,"abstract":"Nowadays, it is essential to secure your house from unauthenticated persons or thieves. To secure the home, different kind of approaches are considered by researchers. In this paper, securing the house with the help of Internet of Things and image processing techniques. The Proposed system implemented with the help of deep learning algorithms such as MTCNN (Multi-task cascaded convolutional neural network) for face detection and facenet for face recognition. These algorithms will check whenever any person is visiting a house, capture the images of visitors and process those images compared with database images and inform to owner of the house. Then owner can have a eye on those people as well as alert their family members about this.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114701473","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744186
A. Bansal, Viraj Madaan, Rahul Gaur, Ritesh Shakya
Software change-proneness prediction aims to identity change prone parts of a software where focused attention is required by the managers and other stakeholders. This reduces development and maintenance costs by highlighting the classes which may change and work with the class in such a manner that prevents further changes from occurring often. Prediction requires training data which is generally obtained from historical data of the projects. However, this may not be the case for new projects which have limited or no historical data available. Cross-project change prediction helps solve this issue by using another project as training data to create a prediction model. With the vast number of candidate projects that can be used as a source to train the classifier, the problem of how to select an appropriate source project which can return a decent prediction accuracy with a model trained with it arises in cross-project change prediction.Through this paper, we propose an algorithm to select a source project which can be used to determine change prone classes in a target project with high accuracy. The source project is selected from a pool of 8 open-source projects. Three strategies are used to identity a suitable source project. The results of the three strategies are compared with one another and with a related change-proneness model proposed by Malhotra and Bansal known as the Random Cross-Project Prediction (RCP). Out of the three strategies in the proposed algorithm, the first two strategies performed better in comparison to the prediction performance of the random cross project prediction model with improvements in terms of AUC (1.04% and 1.27%), F-Measure (5.83% and 3.82%), and MCC (14.14% and 7.77%).
{"title":"Cross-Project Change-Proneness Prediction with Selected Source Project","authors":"A. Bansal, Viraj Madaan, Rahul Gaur, Ritesh Shakya","doi":"10.1109/ICITIIT54346.2022.9744186","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744186","url":null,"abstract":"Software change-proneness prediction aims to identity change prone parts of a software where focused attention is required by the managers and other stakeholders. This reduces development and maintenance costs by highlighting the classes which may change and work with the class in such a manner that prevents further changes from occurring often. Prediction requires training data which is generally obtained from historical data of the projects. However, this may not be the case for new projects which have limited or no historical data available. Cross-project change prediction helps solve this issue by using another project as training data to create a prediction model. With the vast number of candidate projects that can be used as a source to train the classifier, the problem of how to select an appropriate source project which can return a decent prediction accuracy with a model trained with it arises in cross-project change prediction.Through this paper, we propose an algorithm to select a source project which can be used to determine change prone classes in a target project with high accuracy. The source project is selected from a pool of 8 open-source projects. Three strategies are used to identity a suitable source project. The results of the three strategies are compared with one another and with a related change-proneness model proposed by Malhotra and Bansal known as the Random Cross-Project Prediction (RCP). Out of the three strategies in the proposed algorithm, the first two strategies performed better in comparison to the prediction performance of the random cross project prediction model with improvements in terms of AUC (1.04% and 1.27%), F-Measure (5.83% and 3.82%), and MCC (14.14% and 7.77%).","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129853867","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744239
Simran Jakhodia, Babita Jajodia
Researchers are currently working on computational solutions based on quantum systems to accelerate the speed of complex mathematical models. This work presented how to formulate complex computational problems as a quantum system of linear equations and find solutions using Quantum Linear System Algorithm (QLSA), also called Quantum Harrow-Hassidim-Lloyd (HHL) algorithm. This paper showed experimental evaluation of multiple problem statements (curve-fitting functions, interpolating polynomials) as a quantum system of linear equations that involve computation of Vandermonde matrices as co-efficient matrices on IBM Quantum Information Software Kit for Quantum Computation (QISKit) platform. Along with a few examples demonstrating its evaluation on diagonal, Hermitian, and Non-Hermitian matrices as co-efficient matrices. The fidelity is used as a measure of performance for comparing the accuracy of quantum results with respect to existing classical solutions on IBM QISKit and drawing conclusions from the experimental results. Experimental evaluation shows that the fidelity depends on the sparsity of the input matrices and therefore the results vary depending on those matrices.
{"title":"Numerical Methods for Solving High-Order Mathematical Problems using Quantum Linear System Algorithm on IBM QISKit Platform","authors":"Simran Jakhodia, Babita Jajodia","doi":"10.1109/ICITIIT54346.2022.9744239","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744239","url":null,"abstract":"Researchers are currently working on computational solutions based on quantum systems to accelerate the speed of complex mathematical models. This work presented how to formulate complex computational problems as a quantum system of linear equations and find solutions using Quantum Linear System Algorithm (QLSA), also called Quantum Harrow-Hassidim-Lloyd (HHL) algorithm. This paper showed experimental evaluation of multiple problem statements (curve-fitting functions, interpolating polynomials) as a quantum system of linear equations that involve computation of Vandermonde matrices as co-efficient matrices on IBM Quantum Information Software Kit for Quantum Computation (QISKit) platform. Along with a few examples demonstrating its evaluation on diagonal, Hermitian, and Non-Hermitian matrices as co-efficient matrices. The fidelity is used as a measure of performance for comparing the accuracy of quantum results with respect to existing classical solutions on IBM QISKit and drawing conclusions from the experimental results. Experimental evaluation shows that the fidelity depends on the sparsity of the input matrices and therefore the results vary depending on those matrices.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130089913","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744146
H. Prasad, M. Samson, J. Jebanazer
Polar codes are known for capacity-attaining capability, low encoding, and decoding intricacy. The two well-known approaches for decoding polar codes are Successive Cancellation Decoding (SCD) and Belief Propagation Decoding (BPD). SCD is having latency problems due to serial in type. For soft latency applications, BPD is further desirable due to parallel type. The energy-dissipation and latency enhance in proportion with a number of repetitions. In this paper, we used parallel self-timed adder (PASTA) in approximate belief propagation decoder and implemented using Xilinx tool selecting device XC3S250E of Spartan3E family. In comparison with other types, this decoder achieves a reduction in delay. Simulation outcomes reveal that the proposed belief propagation decoder for polar codes achieved 13.74% improvement in delay.
{"title":"Estimated Decoder for Polar Codes Based on Belief Propagation","authors":"H. Prasad, M. Samson, J. Jebanazer","doi":"10.1109/ICITIIT54346.2022.9744146","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744146","url":null,"abstract":"Polar codes are known for capacity-attaining capability, low encoding, and decoding intricacy. The two well-known approaches for decoding polar codes are Successive Cancellation Decoding (SCD) and Belief Propagation Decoding (BPD). SCD is having latency problems due to serial in type. For soft latency applications, BPD is further desirable due to parallel type. The energy-dissipation and latency enhance in proportion with a number of repetitions. In this paper, we used parallel self-timed adder (PASTA) in approximate belief propagation decoder and implemented using Xilinx tool selecting device XC3S250E of Spartan3E family. In comparison with other types, this decoder achieves a reduction in delay. Simulation outcomes reveal that the proposed belief propagation decoder for polar codes achieved 13.74% improvement in delay.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131079047","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744141
Aindrila Saha, Vartika Mishra, S. K. Rath
One of the biggest health challenges that the world has faced in recent times is the pandemic due to coronavirus disease known as SARS-CoV-2, or Covid-19 as officially named by the World Health Organization (WHO). To plan medical facilities in a certain location in order to combat the disease in near future, public health policy makers expect reliable prediction of the number of Covid-19 positive cases in that location. The requirement of reliable prediction gives rise to the need for studying growth in the number of Covid-19 positive cases in the past and predicting the growth in the number in near future. In this study, the growth in the number of Covid-19 positive cases have been modelled using several machine learning based regression techniques viz., Multiple Linear Regression, Decision Tree Regression and Support Vector Regression. Further, different feature selection techniques based on Filter and Wrapper methods have been applied to select the suitable features based on which prediction is to be done. This study proposes the best observed method for modelling the pattern of growth in number of Covid-19 cases in the near future for a locality and also the best selection method that can be employed for obtaining the optimal feature set. It has been observed that unregularized Multiple Linear regression model yields promising results on the test data set, compared to the other regression models, for predicting the future number of Covid-19 cases and Backward Elimination feature selection method performs better than other feature selection methods.
{"title":"Prediction of growth in COVID-19 Cases in India based on Machine Learning Techniques","authors":"Aindrila Saha, Vartika Mishra, S. K. Rath","doi":"10.1109/ICITIIT54346.2022.9744141","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744141","url":null,"abstract":"One of the biggest health challenges that the world has faced in recent times is the pandemic due to coronavirus disease known as SARS-CoV-2, or Covid-19 as officially named by the World Health Organization (WHO). To plan medical facilities in a certain location in order to combat the disease in near future, public health policy makers expect reliable prediction of the number of Covid-19 positive cases in that location. The requirement of reliable prediction gives rise to the need for studying growth in the number of Covid-19 positive cases in the past and predicting the growth in the number in near future. In this study, the growth in the number of Covid-19 positive cases have been modelled using several machine learning based regression techniques viz., Multiple Linear Regression, Decision Tree Regression and Support Vector Regression. Further, different feature selection techniques based on Filter and Wrapper methods have been applied to select the suitable features based on which prediction is to be done. This study proposes the best observed method for modelling the pattern of growth in number of Covid-19 cases in the near future for a locality and also the best selection method that can be employed for obtaining the optimal feature set. It has been observed that unregularized Multiple Linear regression model yields promising results on the test data set, compared to the other regression models, for predicting the future number of Covid-19 cases and Backward Elimination feature selection method performs better than other feature selection methods.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134096986","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744132
D. K, U. K
The voting mechanism is extremely important in a democratic country like India. And we all know that any flaw in the voting mechanism will raise serious concerns about the entire electoral process. Creating a crowd in the existing scenario of Covid-19 also adds a lot of complications. As a result, in such a situation, the online voting system will be a huge success in the election. However, the online system’s security and transparency raise certain concerns. So incorporating blockchain into online E-voting will eliminate all of these flaws. The method allows voters to register and vote for any candidate. The vote will be saved in a secure block chain, but all other information, such as the voter’s name, city, and whether they voted or not, will be accessible to anybody via the website. This system will provide security by denying duplication of votes.
{"title":"Blockvoting:An Online Voting System Using Block Chain","authors":"D. K, U. K","doi":"10.1109/ICITIIT54346.2022.9744132","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744132","url":null,"abstract":"The voting mechanism is extremely important in a democratic country like India. And we all know that any flaw in the voting mechanism will raise serious concerns about the entire electoral process. Creating a crowd in the existing scenario of Covid-19 also adds a lot of complications. As a result, in such a situation, the online voting system will be a huge success in the election. However, the online system’s security and transparency raise certain concerns. So incorporating blockchain into online E-voting will eliminate all of these flaws. The method allows voters to register and vote for any candidate. The vote will be saved in a secure block chain, but all other information, such as the voter’s name, city, and whether they voted or not, will be accessible to anybody via the website. This system will provide security by denying duplication of votes.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235220","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744230
Lova Raju K, V. V
Wireless communication technologies are now being applied in new sectors because of technological advancements in the Internet of Things (IoT). Agricultural monitoring is an example of how the Internet of Things helps in improving productivity, efficiency, and yield. However, because all these devices are frequently used in locations where energy is not easily available, the powering device is a problem. For agricultural monitoring, this study examines IoT devices with energy harvesting capabilities employing three wireless technologies like Wi-Fi, HC-12, and the Long-Range Wireless Communication Network (LoRa). The objective of this investigation was to see how each technology performed in different types of environments. According to the observations, LoRa is the best wireless communication technology the use of an agricultural monitoring system where network lifetime and power consumption are essential.
{"title":"Wireless Communication Technologies with IoT-Based Cloud-Enabled Service for Smart Agriculture Monitoring System","authors":"Lova Raju K, V. V","doi":"10.1109/ICITIIT54346.2022.9744230","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744230","url":null,"abstract":"Wireless communication technologies are now being applied in new sectors because of technological advancements in the Internet of Things (IoT). Agricultural monitoring is an example of how the Internet of Things helps in improving productivity, efficiency, and yield. However, because all these devices are frequently used in locations where energy is not easily available, the powering device is a problem. For agricultural monitoring, this study examines IoT devices with energy harvesting capabilities employing three wireless technologies like Wi-Fi, HC-12, and the Long-Range Wireless Communication Network (LoRa). The objective of this investigation was to see how each technology performed in different types of environments. According to the observations, LoRa is the best wireless communication technology the use of an agricultural monitoring system where network lifetime and power consumption are essential.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"43 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120906654","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744229
Shubham Mishra, Vinod A, Kala S
Diabetes is one of the common diseases that affect our health, which results in high glucose level in blood. Diabetes can affect the functioning of various parts of our body including heart, kidney, eyes and nerves. Diagnosis of diabetes is performed by checking the blood sugar level and if detected earlier, controlling will be much easier. Prediction in healthcare field is a challenging task, since timely precautions and decisions are to be taken based on the predicted result, for treatment of the patient. Here, performance and accuracy of the predictive algorithms play a vital role. Machine learning is a popular research area, which finds immense application in medical field and remote healthcare. In this paper we analyze six machine learning algorithms for predicting type-2 diabetes mellitus and perform experiments to choose the algorithm, which gives best accuracy compared to others. We also develop a prediction software (prediction application) which facilitates prediction of type-2 diabetes mellitus, at a very early stage.
{"title":"Machine Learning Approaches for Type-2 Diabetes Software Predictor","authors":"Shubham Mishra, Vinod A, Kala S","doi":"10.1109/ICITIIT54346.2022.9744229","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744229","url":null,"abstract":"Diabetes is one of the common diseases that affect our health, which results in high glucose level in blood. Diabetes can affect the functioning of various parts of our body including heart, kidney, eyes and nerves. Diagnosis of diabetes is performed by checking the blood sugar level and if detected earlier, controlling will be much easier. Prediction in healthcare field is a challenging task, since timely precautions and decisions are to be taken based on the predicted result, for treatment of the patient. Here, performance and accuracy of the predictive algorithms play a vital role. Machine learning is a popular research area, which finds immense application in medical field and remote healthcare. In this paper we analyze six machine learning algorithms for predicting type-2 diabetes mellitus and perform experiments to choose the algorithm, which gives best accuracy compared to others. We also develop a prediction software (prediction application) which facilitates prediction of type-2 diabetes mellitus, at a very early stage.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123851951","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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