Pub Date : 2020-08-29DOI: 10.18535/ijecs/v9i08.4522
C. Ray, A. Sasmal
The Coronavirus (COVID-19) infection has become a global threat in recent time. Many researchers have been dedicated to control COVID-19 pandemic. In this paper, an effective method is presented for detection and classification of COVID-19 infection based on genome sequences. First, the COVID-19 infection is detected based on the induction of changes in the DNA microarray gene expression pattern of the host during and after infection and comparing it with DNA sequences of Coronavirus (SARS-CoV-2). In order to analyse DNA microarray gene expression data, a bi-directional string matching algorithm is used and the analytical result is represented in terms of eight-directional chain code sequence. At the end of the work, an approach for categorization of Coronavirus infection is provided based on the distribution probabilities of eight-directional chain code sequences correspond to DNA microarray gene expression data of different Corona viruses by taking random samples from the GenBank. The categorization of Coronavirus infection will be helpful for forecasting rate of mortality, rate of infection, severity of the infection and other issues related to COVID-19.
{"title":"A Genome based Detection and Classification of Coronavirus Infection","authors":"C. Ray, A. Sasmal","doi":"10.18535/ijecs/v9i08.4522","DOIUrl":"https://doi.org/10.18535/ijecs/v9i08.4522","url":null,"abstract":"The Coronavirus (COVID-19) infection has become a global threat in recent time. Many researchers have been dedicated to control COVID-19 pandemic. In this paper, an effective method is presented for detection and classification of COVID-19 infection based on genome sequences. First, the COVID-19 infection is detected based on the induction of changes in the DNA microarray gene expression pattern of the host during and after infection and comparing it with DNA sequences of Coronavirus (SARS-CoV-2). In order to analyse DNA microarray gene expression data, a bi-directional string matching algorithm is used and the analytical result is represented in terms of eight-directional chain code sequence. At the end of the work, an approach for categorization of Coronavirus infection is provided based on the distribution probabilities of eight-directional chain code sequences correspond to DNA microarray gene expression data of different Corona viruses by taking random samples from the GenBank. The categorization of Coronavirus infection will be helpful for forecasting rate of mortality, rate of infection, severity of the infection and other issues related to COVID-19.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627639","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 : 2020-08-19DOI: 10.18535/ijecs/v9i08.4520
N SaiTanishq
Machine Learning (ML) is transforming the world with research breakthroughs that are leading to the progress of every field. We are living in an era of data explosion. This further improves the output as data that can be fed to the models is more than it has ever been. Therefore, prediction algorithms are now capable of solving many of the complex problems that we face by leveraging the power of data. The models are capable of correlating a dataset and its features with an accuracy that humans fail to achieve. Bearing this in mind, this research takes an in-depth look into the of problemsolving potential of ML in the area of Database Management Systems (DBMS). Although ML hallmarks significant scientific milestones, the field is still in its infancy. The limitations of ML models are also studied in this paper.
{"title":"Machine Learning for Database Management Systems","authors":"N SaiTanishq","doi":"10.18535/ijecs/v9i08.4520","DOIUrl":"https://doi.org/10.18535/ijecs/v9i08.4520","url":null,"abstract":"Machine Learning (ML) is transforming the world with research breakthroughs that are leading to the progress of every field. We are living in an era of data explosion. This further improves the output as data that can be fed to the models is more than it has ever been. Therefore, prediction algorithms are now capable of solving many of the complex problems that we face by leveraging the power of data. The models are capable of correlating a dataset and its features with an accuracy that humans fail to achieve. Bearing this in mind, this research takes an in-depth look into the of problemsolving potential of ML in the area of Database Management Systems (DBMS). Although ML hallmarks significant scientific milestones, the field is still in its infancy. The limitations of ML models are also studied in this paper.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127962195","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 : 2020-08-12DOI: 10.18535/ijecs/v9i08.4504
K. Sahu, K. Bhatt, Anika Saxena, Kaptan Singh
Clustering As a result of the rapid development in cloud computing, it & fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce Word Count utility. The main aim of this paper is to give implements of Hadoop map-reduce programming by giving a hands-on experience in developing Hadoop based Word-Count and Apriori application. Word count problem using Hadoop Map Reduce framework. The Apriori Algorithm has been used for finding frequent item set using Map Reduce framework.
随着云计算的快速发展，研究Hadoop MapReduce的性能和实现云集群中导致性能提高或降低的性能瓶颈是非常重要的。通常首先要研究云集群服务器中的底层硬件，以便对程序和硬件进行优化，以实现最高的性能。Hadoop建立在MapReduce的基础上，MapReduce是并行计算环境中用于海量知识分析的最流行的编程项目之一。在本文中，我们奖励了Hadoop MapReduce Word Count实用程序的特定效率分析，表征和评估。本文的主要目的是通过提供开发基于Word-Count和Apriori的Hadoop应用程序的实践经验来实现Hadoop map-reduce编程。使用Hadoop Map Reduce框架的字数统计问题。Apriori算法在Map Reduce框架下用于频繁项集的查找。
{"title":"Implementation of Big-Data Applications Using Map Reduce Framework","authors":"K. Sahu, K. Bhatt, Anika Saxena, Kaptan Singh","doi":"10.18535/ijecs/v9i08.4504","DOIUrl":"https://doi.org/10.18535/ijecs/v9i08.4504","url":null,"abstract":"Clustering As a result of the rapid development in cloud computing, it & fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce Word Count utility. The main aim of this paper is to give implements of Hadoop map-reduce programming by giving a hands-on experience in developing Hadoop based Word-Count and Apriori application. Word count problem using Hadoop Map Reduce framework. The Apriori Algorithm has been used for finding frequent item set using Map Reduce framework.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115807413","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 : 2020-07-14DOI: 10.18535/ijecs/v9i07.4507
L. O. Olatunbosun, A. A. Adam, K. Gbolagade
In this paper we present an RNS bases algorithm with architecture implementation for gene sequence applications. Based on the existing RNS arithmetic algorithm, investigations were made on RNS application and its inherent arithmetic advantages; data conversion algorithm from Decimal/Binary to RNS; the forward conversion; Conversion from RNS to Binary/Decimal; the reverse conversion using the Chinese remainder theorem CRT, conversion from RNS to mixed radix form with capability for effective computation performance, analysis of Smith Waterman Algorithm based on DNA sequence computing. Its limitations and open issues for future research were highlighted.
{"title":"RNS Bases in Computer Architecture for DNA Sequence Application","authors":"L. O. Olatunbosun, A. A. Adam, K. Gbolagade","doi":"10.18535/ijecs/v9i07.4507","DOIUrl":"https://doi.org/10.18535/ijecs/v9i07.4507","url":null,"abstract":"In this paper we present an RNS bases algorithm with architecture implementation for gene sequence applications. Based on the existing RNS arithmetic algorithm, investigations were made on RNS application and its inherent arithmetic advantages; data conversion algorithm from Decimal/Binary to RNS; the forward conversion; Conversion from RNS to Binary/Decimal; the reverse conversion using the Chinese remainder theorem CRT, conversion from RNS to mixed radix form with capability for effective computation performance, analysis of Smith Waterman Algorithm based on DNA sequence computing. Its limitations and open issues for future research were highlighted.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132607976","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 : 2020-07-04DOI: 10.18535/ijecs/v9i07.4499
Minakshi Roy, Prakar Pradhan, Jesson George, N. Pradhan
Since we are now currently present in an era of Computing Technology, it is essential for everyone and everything to be connected to the internet. IOT is a technology that brings us more and more close to this goal. Our project comprises of smart water monitoring system which is a small prototype for flood detection and avoidance system. This paper explains the working and the workflow of all the components present inside our project. The sensors sense the environment and sends real-time data to the cloud (firebase cloud) and users can view and access this data via their mobile platform. The model gives a warning after the water level rises to a particular height. Since it is a small scaled prototype for flood detection and avoidance system, the working of this model is good. The data are uploaded and changed in the cloud in precision to the sensor and real-time changes in the mobile application is achieved. This model can be used to greatly reduce the casualties in a devastating event of flood. Introduction: We are witnessing various drastic advancements in the fields of science and technology over the past few decades. The current industrial age has revolutionized our lives and provides us with plenty of comforts and conveniences. However, this industrial progress has come at a hefty cost of global warming and other environmental disasters such as flood, earthquake, etc. Furthermore, the loss caused by such disasters to life and property is
{"title":"Flood Detection and Water Monitoring System Using IOT","authors":"Minakshi Roy, Prakar Pradhan, Jesson George, N. Pradhan","doi":"10.18535/ijecs/v9i07.4499","DOIUrl":"https://doi.org/10.18535/ijecs/v9i07.4499","url":null,"abstract":"Since we are now currently present in an era of Computing Technology, it is essential for everyone and everything to be connected to the internet. IOT is a technology that brings us more and more close to this goal. Our project comprises of smart water monitoring system which is a small prototype for flood detection and avoidance system. This paper explains the working and the workflow of all the components present inside our project. The sensors sense the environment and sends real-time data to the cloud (firebase cloud) and users can view and access this data via their mobile platform. The model gives a warning after the water level rises to a particular height. Since it is a small scaled prototype for flood detection and avoidance system, the working of this model is good. The data are uploaded and changed in the cloud in precision to the sensor and real-time changes in the mobile application is achieved. This model can be used to greatly reduce the casualties in a devastating event of flood. Introduction: We are witnessing various drastic advancements in the fields of science and technology over the past few decades. The current industrial age has revolutionized our lives and provides us with plenty of comforts and conveniences. However, this industrial progress has come at a hefty cost of global warming and other environmental disasters such as flood, earthquake, etc. Furthermore, the loss caused by such disasters to life and property is","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131984759","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 : 2020-06-30DOI: 10.18535/ijecs/v9i07.4502
A. Adedoyin, Olamide Timothy Tawose, O. S. Adetolaju
Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.
{"title":"Image Segmentation Techniques in Bone Structure Psychiatry","authors":"A. Adedoyin, Olamide Timothy Tawose, O. S. Adetolaju","doi":"10.18535/ijecs/v9i07.4502","DOIUrl":"https://doi.org/10.18535/ijecs/v9i07.4502","url":null,"abstract":"Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130125366","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 : 2020-06-26DOI: 10.18535/ijecs/v9i06.4463
Moayad Al Falahi, J. Ganesh Sivakumar
1 Student, Bachelor of Software Engineering, Department of Computing Muscat College 2 Assistant Professor Department of Computing Muscat College Abstract: The main objective of this project is to develop an application to find the best compression technique to store Muscat College students' photographs in less storage. MATLAB software will be used to develop a Graphical User Interface GUI application and implement two image compression techniques which are lossless compression using the DCT algorithm and lossy compression using the LBG algorithm. The application shall allow the user to select and test a sample image by applying both these techniques for any student image heshe selects in order to compare the results by display the image after compression and the histogram to find which the most suitable compression technique is. Also, the application shall show the size of images before and after applying the compression process and show the compression ratio and relative data redundancy of compressed image/images. The main functionality is that the application shall allow the user to do bulk processing to apply image enhancement and image compression technique to enhance and compress all the photographs of students and store them in less space.
{"title":"Comparitive Analysis and Findings on Dct & Lbg Compression Techniques","authors":"Moayad Al Falahi, J. Ganesh Sivakumar","doi":"10.18535/ijecs/v9i06.4463","DOIUrl":"https://doi.org/10.18535/ijecs/v9i06.4463","url":null,"abstract":"1 Student, Bachelor of Software Engineering, Department of Computing Muscat College 2 Assistant Professor Department of Computing Muscat College Abstract: The main objective of this project is to develop an application to find the best compression technique to store Muscat College students' photographs in less storage. MATLAB software will be used to develop a Graphical User Interface GUI application and implement two image compression techniques which are lossless compression using the DCT algorithm and lossy compression using the LBG algorithm. The application shall allow the user to select and test a sample image by applying both these techniques for any student image heshe selects in order to compare the results by display the image after compression and the histogram to find which the most suitable compression technique is. Also, the application shall show the size of images before and after applying the compression process and show the compression ratio and relative data redundancy of compressed image/images. The main functionality is that the application shall allow the user to do bulk processing to apply image enhancement and image compression technique to enhance and compress all the photographs of students and store them in less space.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116860572","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 : 2020-06-24DOI: 10.18535/ijecs/v9i06.4500
Chandrakala G Raju, Rahul S Hangal, R ShashidharaA, D SrinathaT
Facial recognition algorithm should be able to work even when the similar looking people are found i.e. also in the extreme case of identical looking twins. An experimental data set which contains 40 images of 20 pairs of twins collected randomly from the internet. The training is done with the selected images of the twins using different training algorithms and inbuilt functions available. The extracted features are stored over the Amazon public cloud. As a part of testing phase random images from the dataset trained are selected and upon running it over the system we get the features of those images which then will be compared by extracting the features already stored in Amazon cloud. The stored values and the current image features are compared and result will be displayed on the GUI. Identical twin’s facial recognition system uses the machine learning, image processing algorithms and deep learning algorithms. Regardless of the conditions of the images acquired, distinguishing identical twins is significantly harder than distinguishing faces that are not identical twins for all the algorithms.
{"title":"Identical Twins Facial Recognition System Using Cloud","authors":"Chandrakala G Raju, Rahul S Hangal, R ShashidharaA, D SrinathaT","doi":"10.18535/ijecs/v9i06.4500","DOIUrl":"https://doi.org/10.18535/ijecs/v9i06.4500","url":null,"abstract":"Facial recognition algorithm should be able to work even when the similar looking people are found i.e. also in the extreme case of identical looking twins. An experimental data set which contains 40 images of 20 pairs of twins collected randomly from the internet. The training is done with the selected images of the twins using different training algorithms and inbuilt functions available. The extracted features are stored over the Amazon public cloud. As a part of testing phase random images from the dataset trained are selected and upon running it over the system we get the features of those images which then will be compared by extracting the features already stored in Amazon cloud. The stored values and the current image features are compared and result will be displayed on the GUI. Identical twin’s facial recognition system uses the machine learning, image processing algorithms and deep learning algorithms. Regardless of the conditions of the images acquired, distinguishing identical twins is significantly harder than distinguishing faces that are not identical twins for all the algorithms.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966514","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 : 2020-06-02DOI: 10.18535/ijecs/v9i06.4492
G.Tejaswi, D. Likhitha, D. S. Sree, B. Vishnuvardhan
To predict the crop rotation for varied sorts of soil and might be done through soil fertility, water level, chemical level, climatic condition and etc. Here, mistreatment totally different data processing techniques over agriculture land soil testing is that the methodology is mistreatment wide wherever that crop is often farmed when that crop. As an example, rice is going to be cultivated at intervals seven months to eight months. The remaining time the farm won't be left empty. Another crop could cultivate for the opposite four months.
{"title":"The Intercropping Using NRC Algorithm","authors":"G.Tejaswi, D. Likhitha, D. S. Sree, B. Vishnuvardhan","doi":"10.18535/ijecs/v9i06.4492","DOIUrl":"https://doi.org/10.18535/ijecs/v9i06.4492","url":null,"abstract":"To predict the crop rotation for varied sorts of soil and might be done through soil fertility, water level, chemical level, climatic condition and etc. Here, mistreatment totally different data processing techniques over agriculture land soil testing is that the methodology is mistreatment wide wherever that crop is often farmed when that crop. As an example, rice is going to be cultivated at intervals seven months to eight months. The remaining time the farm won't be left empty. Another crop could cultivate for the opposite four months.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115151626","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 : 2020-05-20DOI: 10.18535/ijecs/v9i05.4483
A. Kadam, Anupama Mhatre, Sayali Redasani, Amit K. Nerurkar
Current lighting technologies extend the options for changing the appearance of rooms and closed spaces, as such creating ambiences with an affective meaning. Using intelligence, these ambiences may instantly be adapted to the needs of the room‟s occupant(s), possibly improving their well-being. In this paper, we set actuate lighting in our surrounding using mood detection. We analyze the mood of the person by Facial Emotion Recognition using deep learning model such as Convolutional Neural Network (CNN). On recognizing this emotion, we will actuate lighting in our surrounding in accordance with the mood. Based on implementation results, the system needs to be developed further by adding more specific data class and training data.
{"title":"Light Actuation Based On Facial Mood Recognition","authors":"A. Kadam, Anupama Mhatre, Sayali Redasani, Amit K. Nerurkar","doi":"10.18535/ijecs/v9i05.4483","DOIUrl":"https://doi.org/10.18535/ijecs/v9i05.4483","url":null,"abstract":"Current lighting technologies extend the options for changing the appearance of rooms and closed spaces, as such creating ambiences with an affective meaning. Using intelligence, these ambiences may instantly be adapted to the needs of the room‟s occupant(s), possibly improving their well-being. In this paper, we set actuate lighting in our surrounding using mood detection. We analyze the mood of the person by Facial Emotion Recognition using deep learning model such as Convolutional Neural Network (CNN). On recognizing this emotion, we will actuate lighting in our surrounding in accordance with the mood. Based on implementation results, the system needs to be developed further by adding more specific data class and training data.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130422694","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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