Pub Date : 2016-10-01DOI: 10.1109/INCITE.2016.7857584
Shruti Gupta, Abha Thakral, Shilpi Sharma
Clustering is the unsupervised classification of spatterns in a dataset. Clustering is widely used to discover distributed patterns and classify them as clusters. Clustering algorithms uses a similarity measure based on distance. In order to cluster data points, k-means uses Euclidean distance measure and central point choice. In the K-means clustering, data points will be stacked and a central point is chosen. From the central point chosen, Euclidean distance will be computed and on that basis clusters will be assigned to the data points. One of the drawbacks of K-means is that numbers of clusters has to be provided due to which some data points remains un-clustered. In this paper, we propose a clustering calculation through which number of clusters can be characterised naturally. The proposed technique will improve accuracy and decrease clustering time moreover cluster quality will also be improved through multiple iterations.
{"title":"Novel technique for prediction analysis using normalization for an improvement in K-means clustering","authors":"Shruti Gupta, Abha Thakral, Shilpi Sharma","doi":"10.1109/INCITE.2016.7857584","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857584","url":null,"abstract":"Clustering is the unsupervised classification of spatterns in a dataset. Clustering is widely used to discover distributed patterns and classify them as clusters. Clustering algorithms uses a similarity measure based on distance. In order to cluster data points, k-means uses Euclidean distance measure and central point choice. In the K-means clustering, data points will be stacked and a central point is chosen. From the central point chosen, Euclidean distance will be computed and on that basis clusters will be assigned to the data points. One of the drawbacks of K-means is that numbers of clusters has to be provided due to which some data points remains un-clustered. In this paper, we propose a clustering calculation through which number of clusters can be characterised naturally. The proposed technique will improve accuracy and decrease clustering time moreover cluster quality will also be improved through multiple iterations.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73345377","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857610
Ishan Tripathi
Traditional environmental parameter monitoring systems are either wired or wireless. Wireless systems generally consume less power for short ranges but may incur high power consumption for long ranges due to error corrections involved in the wireless media. This paper investigates and presents a long range, low power, and cheap wireless parameter monitoring system with the functionality to send alert to the operator on the crossover of any parameter beyond its predefined limit.
{"title":"Wireless environmental parameters monitoring and SMS alert system","authors":"Ishan Tripathi","doi":"10.1109/INCITE.2016.7857610","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857610","url":null,"abstract":"Traditional environmental parameter monitoring systems are either wired or wireless. Wireless systems generally consume less power for short ranges but may incur high power consumption for long ranges due to error corrections involved in the wireless media. This paper investigates and presents a long range, low power, and cheap wireless parameter monitoring system with the functionality to send alert to the operator on the crossover of any parameter beyond its predefined limit.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77942109","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857578
Vasundhara Bhatia, Abhishek Singhal
Mutation testing is a software testing technique which works on the principle of applying simple changes to a program which is known as a mutant. A mutant helps to map the effects of real faults and generate test suite which helps to detect these faults. If the faults are detected using a given test input then the mutant is said to be “killed”. If the faults are not detected thereupon the mutant is “live”. Equivalent mutants are live mutants, which will not exhibit a different output from the original program's output, no matter what test input is given. It is important to find out if a mutant is equivalent. In this paper, we have proposed a Fuzzy model for weak and strong mutation testing to find out whether a mutant is equivalent or not.
{"title":"Design of a Fuzzy model to detect equivalent mutants for weak and strong mutation testing","authors":"Vasundhara Bhatia, Abhishek Singhal","doi":"10.1109/INCITE.2016.7857578","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857578","url":null,"abstract":"Mutation testing is a software testing technique which works on the principle of applying simple changes to a program which is known as a mutant. A mutant helps to map the effects of real faults and generate test suite which helps to detect these faults. If the faults are detected using a given test input then the mutant is said to be “killed”. If the faults are not detected thereupon the mutant is “live”. Equivalent mutants are live mutants, which will not exhibit a different output from the original program's output, no matter what test input is given. It is important to find out if a mutant is equivalent. In this paper, we have proposed a Fuzzy model for weak and strong mutation testing to find out whether a mutant is equivalent or not.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76173893","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857593
Swati Chauhan, Prachi Chauhan
For over a decade now, due to the introduction of UTF-8 encoding, the digitization of Hindi content has increased rapidly because of which Hindi-music has accomplished popularity on the web. The focus is to identify the emotion, a person is experiencing while listening to a song track. The aim of this research work is to analyze the lyrics of Hindi-language based songs, in order to detect the mood of the listener. We used unigram and term-frequency as the main features. The songs were reduced to a level where only relevant words will be used for mood-detection. We employ unsupervised machine learning namely topic-modeling (Latent Dirichlet Allocation model) for mining the mood out of every song in the corpus. We created our own dataset of 1900 songs consisting of Bollywood tracks, bhajans (spiritual prayers) and ghazals. A mood taxonomy is used to distinguish songs into Happy or Sad. Data is applied to LDA model to discover the hidden emotions within each song. At the end of experimentation, we compare the results with manually pre-annotated dataset for validation purpose and observe good results.
{"title":"Music mood classification based on lyrical analysis of Hindi songs using Latent Dirichlet Allocation","authors":"Swati Chauhan, Prachi Chauhan","doi":"10.1109/INCITE.2016.7857593","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857593","url":null,"abstract":"For over a decade now, due to the introduction of UTF-8 encoding, the digitization of Hindi content has increased rapidly because of which Hindi-music has accomplished popularity on the web. The focus is to identify the emotion, a person is experiencing while listening to a song track. The aim of this research work is to analyze the lyrics of Hindi-language based songs, in order to detect the mood of the listener. We used unigram and term-frequency as the main features. The songs were reduced to a level where only relevant words will be used for mood-detection. We employ unsupervised machine learning namely topic-modeling (Latent Dirichlet Allocation model) for mining the mood out of every song in the corpus. We created our own dataset of 1900 songs consisting of Bollywood tracks, bhajans (spiritual prayers) and ghazals. A mood taxonomy is used to distinguish songs into Happy or Sad. Data is applied to LDA model to discover the hidden emotions within each song. At the end of experimentation, we compare the results with manually pre-annotated dataset for validation purpose and observe good results.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79060188","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857580
Shivani Arora, Adheesh Budree
There is a constant need to study E-Commerce space in any country, especially in one such as India where its growing at a very fast pace. Both online and offline businesses are co-existing as opposed to the belief that online will drive the offline stores out of business, with the possible exception of bookstores, which are seen to be impacting on the closure of traditional bookstores. Online sales have become a key buzzword but the detailed analyses of how they fare, the highlights and the learnings would help in establishing a blueprint. This article analyses these concepts and conclusively concludes based on the findings that India is a hot destination for online companies and the fight for consumer attention through different strategies including delivery options and sales are intensifying amongst the major players in the market.
{"title":"Basket loyalty tussle amongst Indian online retailers","authors":"Shivani Arora, Adheesh Budree","doi":"10.1109/INCITE.2016.7857580","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857580","url":null,"abstract":"There is a constant need to study E-Commerce space in any country, especially in one such as India where its growing at a very fast pace. Both online and offline businesses are co-existing as opposed to the belief that online will drive the offline stores out of business, with the possible exception of bookstores, which are seen to be impacting on the closure of traditional bookstores. Online sales have become a key buzzword but the detailed analyses of how they fare, the highlights and the learnings would help in establishing a blueprint. This article analyses these concepts and conclusively concludes based on the findings that India is a hot destination for online companies and the fight for consumer attention through different strategies including delivery options and sales are intensifying amongst the major players in the market.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79823404","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857639
Rohit Ahlawat, Sushil Sahay, S. Sabitha, Abhay Bansal
The growth of the economy of a country is affected by several factors like economic system, natural resources, social organisation, literacy rate, skilled manpower, etc. Higher education also plays an important role in the economic growth of a country. The Indian education sector has a lot of data that can produce valuable information. In recent times data mining techniques have been widely used for educational data for discovering useful trends or patterns. It provides interesting patterns which can be used to improve the overall performance of the education sector. The main objective of this research work is to analyse enrollment patterns in Indian universities and the factors affecting these patterns with the help of k-means clustering technique. The obtained clusters are analysed for various case studies to provide a trend of enrollments.
{"title":"Analysis of factors affecting enrollment pattern in Indian universities using k-means clustering","authors":"Rohit Ahlawat, Sushil Sahay, S. Sabitha, Abhay Bansal","doi":"10.1109/INCITE.2016.7857639","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857639","url":null,"abstract":"The growth of the economy of a country is affected by several factors like economic system, natural resources, social organisation, literacy rate, skilled manpower, etc. Higher education also plays an important role in the economic growth of a country. The Indian education sector has a lot of data that can produce valuable information. In recent times data mining techniques have been widely used for educational data for discovering useful trends or patterns. It provides interesting patterns which can be used to improve the overall performance of the education sector. The main objective of this research work is to analyse enrollment patterns in Indian universities and the factors affecting these patterns with the help of k-means clustering technique. The obtained clusters are analysed for various case studies to provide a trend of enrollments.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84164411","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857598
Akash Sharma, Smriti Sehgal
Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. Clustering is used to segment the image. Clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.
{"title":"Image segmentation using firefly algorithm","authors":"Akash Sharma, Smriti Sehgal","doi":"10.1109/INCITE.2016.7857598","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857598","url":null,"abstract":"Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. Clustering is used to segment the image. Clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88083372","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857603
A. Giri, S. Dutta, S. Neogy
With the proliferation of smart devices, Internet can be extended into the physical realm of Internet-of-Things (IoT) by deploying them into a communicating-actuating network. In Ion, sensors and actuators blend seamlessly with the environment; collaborate globally with each other through internet to accomplish a specific task. Wireless Sensor Network (WSN) can be integrated into Ion to meet the challenges of seamless communication between any things (e.g., humans or objects). The potentialities of IoT can be brought to the benefit of society by developing novel applications in transportation and logistics, healthcare, agriculture, smart environment (home, office or plant). This research gives a framework of optimizing resources (water, fertilizers, insecticides and manual labour) in agriculture through the use of IoT. The issues involved in the implementation of applications are also investigated in the paper. This frame work is named as AgriTech.
{"title":"Enabling agricultural automation to optimize utilization of water, fertilizer and insecticides by implementing Internet of Things (IoT)","authors":"A. Giri, S. Dutta, S. Neogy","doi":"10.1109/INCITE.2016.7857603","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857603","url":null,"abstract":"With the proliferation of smart devices, Internet can be extended into the physical realm of Internet-of-Things (IoT) by deploying them into a communicating-actuating network. In Ion, sensors and actuators blend seamlessly with the environment; collaborate globally with each other through internet to accomplish a specific task. Wireless Sensor Network (WSN) can be integrated into Ion to meet the challenges of seamless communication between any things (e.g., humans or objects). The potentialities of IoT can be brought to the benefit of society by developing novel applications in transportation and logistics, healthcare, agriculture, smart environment (home, office or plant). This research gives a framework of optimizing resources (water, fertilizers, insecticides and manual labour) in agriculture through the use of IoT. The issues involved in the implementation of applications are also investigated in the paper. This frame work is named as AgriTech.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86478199","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857607
P. Mishra, Ranjana Rajnish, Pankaj Kumar
Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of Twitter data and lot needs to be done. In our work we are trying to perform sentiment analysis of the Twitter data set that expresses opinion about Modi ji's Digital India Campaign. In my work, I have collected these sentiments and classified polarity of sentiments in these opinions w.r.t. Positive, Negative or Neutral. Twitter data is collected for analysis using Twitter API. Out of the two widely used approaches used for sentiment analysis, Machine Learning & Dictionary Based approach, we are using Dictionary Based approach to analyze data posted by different users. Then polarity classification of this data is done. In this paper we discuss sentiment analysis of Twitter data, existing tools available for sentiment analysis, related work, framework used, case study to demonstrate the work followed by the results section. Results clearly demonstrate that the 50% of the collected opinions are positive, 20% are Negative and rests 30% are neutral.
{"title":"Sentiment analysis of Twitter data: Case study on digital India","authors":"P. Mishra, Ranjana Rajnish, Pankaj Kumar","doi":"10.1109/INCITE.2016.7857607","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857607","url":null,"abstract":"Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of Twitter data and lot needs to be done. In our work we are trying to perform sentiment analysis of the Twitter data set that expresses opinion about Modi ji's Digital India Campaign. In my work, I have collected these sentiments and classified polarity of sentiments in these opinions w.r.t. Positive, Negative or Neutral. Twitter data is collected for analysis using Twitter API. Out of the two widely used approaches used for sentiment analysis, Machine Learning & Dictionary Based approach, we are using Dictionary Based approach to analyze data posted by different users. Then polarity classification of this data is done. In this paper we discuss sentiment analysis of Twitter data, existing tools available for sentiment analysis, related work, framework used, case study to demonstrate the work followed by the results section. Results clearly demonstrate that the 50% of the collected opinions are positive, 20% are Negative and rests 30% are neutral.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75820700","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 : 2016-10-01DOI: 10.1109/INCITE.2016.7857629
Rittwik Sood, Shubham Sharma, V. Yadav
The ever increasing cases of ailments because of noise pollution, both physical and mental presents the direst need for a sustainable and an economically viable solution. The aggressive honking of the horn from the vehicles treading on a road is a major source of noise pollution and is highly undesirable and irritating. The residential areas, schools, hospitals and other workplaces nearby are adversely affected. Our work aims at developing the disincentive measure for unwanted honking by developing a real time (smart) honking system which enables the vehicles on the road to communicate amongst them without releasing horn in surroundings. Such seamless transport system involves the integration of vehicular technology and communication networks. Priority to the emergency vehicles (like ambulance, fire brigade)is incorporated as a prominent feature. This system also includes features to lessen road accidents caused due to partial hearing of driver and inability of the driver to listen to horn due to loud music being played inside the vehicle. With the advent of such type of smart system, authors look forward to efficient and sustainable transport system in the future.
{"title":"Real time smart honking system","authors":"Rittwik Sood, Shubham Sharma, V. Yadav","doi":"10.1109/INCITE.2016.7857629","DOIUrl":"https://doi.org/10.1109/INCITE.2016.7857629","url":null,"abstract":"The ever increasing cases of ailments because of noise pollution, both physical and mental presents the direst need for a sustainable and an economically viable solution. The aggressive honking of the horn from the vehicles treading on a road is a major source of noise pollution and is highly undesirable and irritating. The residential areas, schools, hospitals and other workplaces nearby are adversely affected. Our work aims at developing the disincentive measure for unwanted honking by developing a real time (smart) honking system which enables the vehicles on the road to communicate amongst them without releasing horn in surroundings. Such seamless transport system involves the integration of vehicular technology and communication networks. Priority to the emergency vehicles (like ambulance, fire brigade)is incorporated as a prominent feature. This system also includes features to lessen road accidents caused due to partial hearing of driver and inability of the driver to listen to horn due to loud music being played inside the vehicle. With the advent of such type of smart system, authors look forward to efficient and sustainable transport system in the future.","PeriodicalId":59618,"journal":{"name":"下一代","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78656538","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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