Pub Date : 2019-02-01DOI: 10.1109/iccids.2019.8862070
{"title":"ICCIDS 2019 Keynotes","authors":"","doi":"10.1109/iccids.2019.8862070","DOIUrl":"https://doi.org/10.1109/iccids.2019.8862070","url":null,"abstract":"","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128638372","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862163
S. Harish, K. Gayathri
Alzheimer’s disease is one of the most prevailing diseases in elderly society that leads to memory loss affecting their daily living. In this paper, an automated intelligent system is proposed to predict the multi-modal symptoms of Alzheimer’s disease in order to offer appropriate actions during critical situation. To model this system machine learning techniques and contextual approach is preferred. Smart home and an intelligent system are employed to predict the symptoms of Alzheimer’s disease with the help of sensors. In existing work, validation in terms of cognitive, mobility and depression states of the older adults were done using activity recognition. But the prediction of Mood plays a vital role among the multi-modal symptoms. Thus the proposed system in addition to cognitive also uses anxiety and depression states of the older adults’ together helps in predicting the multi-modal symptoms. The novelty of the proposed system deals with the contextual based analysis to predict the mood using ontology approach in addition to the statistical based analysis. Using these techniques, the system measures the health assessment scores and detects a reliable change based on the assessment points in a proficient way.
{"title":"Smart Home based Prediction of Symptoms of Alzheimer’s Disease using Machine Learning and Contextual Approach","authors":"S. Harish, K. Gayathri","doi":"10.1109/ICCIDS.2019.8862163","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862163","url":null,"abstract":"Alzheimer’s disease is one of the most prevailing diseases in elderly society that leads to memory loss affecting their daily living. In this paper, an automated intelligent system is proposed to predict the multi-modal symptoms of Alzheimer’s disease in order to offer appropriate actions during critical situation. To model this system machine learning techniques and contextual approach is preferred. Smart home and an intelligent system are employed to predict the symptoms of Alzheimer’s disease with the help of sensors. In existing work, validation in terms of cognitive, mobility and depression states of the older adults were done using activity recognition. But the prediction of Mood plays a vital role among the multi-modal symptoms. Thus the proposed system in addition to cognitive also uses anxiety and depression states of the older adults’ together helps in predicting the multi-modal symptoms. The novelty of the proposed system deals with the contextual based analysis to predict the mood using ontology approach in addition to the statistical based analysis. Using these techniques, the system measures the health assessment scores and detects a reliable change based on the assessment points in a proficient way.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110344","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862113
A. Augustus Devarajan, T. Sudalaimuthu
Cloud computing is an important technology on current demanding business requirements and it has been emerged as unavoidable technology. The usage of IaaS Service storage for Cloud Computing is being expanding exponential every year. The cloud storages are used by the cloud user due to its economy compared with other storage methods. The replications of files helps user for easy access with high availability which reduces the overall access time of the files, but at the same time it occupies more storage space and result in high storage cost. The cloud user holds multiple times of the storage than what he is actually needed. It is a dire need of system to find unwanted files in the cloud and also optimize the storage space by evaluating through file access frequency.This paper propose Cloud Storage Monitoring (CSM) system, which monitor the IaaS storage usage and analyze the file access patterns by various parameters to identify the frequency of access, size, future access prediction, replication of files in the cloud storage. This allocates a ranking for each file which also predicts future access pattern. This generates a recommendation dashboard to the user who can decide on the operations such as reorganize, delete or archive the files and eliminate duplicate files in the cloud storage which can increase the space for future use. This system is experimented in the CloudSim environment and validate through multiple simulations testing, by using comparison techniques related to file attributes, delta version-hashing, Data de-duplication. The ranking algorithm technique applied on frequency distribution shows that increase in the storage space upto 10.91% higher than the normal system. It also helps to forecast towards future files usability prediction and prevents the duplicate entries.
{"title":"Cloud Storage Monitoring System analyzing through File Access Pattern","authors":"A. Augustus Devarajan, T. Sudalaimuthu","doi":"10.1109/ICCIDS.2019.8862113","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862113","url":null,"abstract":"Cloud computing is an important technology on current demanding business requirements and it has been emerged as unavoidable technology. The usage of IaaS Service storage for Cloud Computing is being expanding exponential every year. The cloud storages are used by the cloud user due to its economy compared with other storage methods. The replications of files helps user for easy access with high availability which reduces the overall access time of the files, but at the same time it occupies more storage space and result in high storage cost. The cloud user holds multiple times of the storage than what he is actually needed. It is a dire need of system to find unwanted files in the cloud and also optimize the storage space by evaluating through file access frequency.This paper propose Cloud Storage Monitoring (CSM) system, which monitor the IaaS storage usage and analyze the file access patterns by various parameters to identify the frequency of access, size, future access prediction, replication of files in the cloud storage. This allocates a ranking for each file which also predicts future access pattern. This generates a recommendation dashboard to the user who can decide on the operations such as reorganize, delete or archive the files and eliminate duplicate files in the cloud storage which can increase the space for future use. This system is experimented in the CloudSim environment and validate through multiple simulations testing, by using comparison techniques related to file attributes, delta version-hashing, Data de-duplication. The ranking algorithm technique applied on frequency distribution shows that increase in the storage space upto 10.91% higher than the normal system. It also helps to forecast towards future files usability prediction and prevents the duplicate entries.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123536187","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862158
R. Vaitheeshwari, V. SathieshKumar
In recent years, numerous people are affected by a common neurological disorder called Epilepsy or Epileptic seizure. It occurs abruptly without any symptoms and thus increases the mortality rate of the humans. In order to warn the patient prior to the onset of seizure, a reliable warning system is needed. Thus the proposed research work aim to create an artificial neural network model to detect and predict the seizure event before its onset. The proposed Artificial Neural Network model is simple and efficient architecture that predict and detect the seizure event at the sensitivity rate of 91.15%. Experimental testing of the data show that prediction accuracy is 91% with considerable amount of computation time (630 seconds).
{"title":"Performance Analysis of Epileptic Seizure Detection System Using Neural Network Approach","authors":"R. Vaitheeshwari, V. SathieshKumar","doi":"10.1109/ICCIDS.2019.8862158","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862158","url":null,"abstract":"In recent years, numerous people are affected by a common neurological disorder called Epilepsy or Epileptic seizure. It occurs abruptly without any symptoms and thus increases the mortality rate of the humans. In order to warn the patient prior to the onset of seizure, a reliable warning system is needed. Thus the proposed research work aim to create an artificial neural network model to detect and predict the seizure event before its onset. The proposed Artificial Neural Network model is simple and efficient architecture that predict and detect the seizure event at the sensitivity rate of 91.15%. Experimental testing of the data show that prediction accuracy is 91% with considerable amount of computation time (630 seconds).","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128047918","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862168
A. Subashini, G. Raghuraman, L. Sairamesh
Today, one in ten persons is affected by the cardiac diseases as worldwide. Earlier prediction of these kinds of diseases considered as an important assignment by medical experts. Moreover, many works are available for classifying the heart diseases through the ECG signal analysis. But, only few works are come out with Denoising process before the classification of ECG signals for reduce the unwanted artifact from the ECG signals. This work implements the Baye’s Shrink to remove the noise from the ECG signal images before classification process. The proposed image denoising process also uses the region of interest (ROI) techniques to reduce the computational time over the preprocessing which also improves the classification accuracy by clearly indicating the signal edges.
{"title":"Enhancing the Classification Accuracy of Cardiac Diseases using Image Denoising Technique from ECG signal","authors":"A. Subashini, G. Raghuraman, L. Sairamesh","doi":"10.1109/ICCIDS.2019.8862168","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862168","url":null,"abstract":"Today, one in ten persons is affected by the cardiac diseases as worldwide. Earlier prediction of these kinds of diseases considered as an important assignment by medical experts. Moreover, many works are available for classifying the heart diseases through the ECG signal analysis. But, only few works are come out with Denoising process before the classification of ECG signals for reduce the unwanted artifact from the ECG signals. This work implements the Baye’s Shrink to remove the noise from the ECG signal images before classification process. The proposed image denoising process also uses the region of interest (ROI) techniques to reduce the computational time over the preprocessing which also improves the classification accuracy by clearly indicating the signal edges.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115688784","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862141
J. Virdi, W. Peng, A. Sata
The service life of investment casting products is measured through its mechanical properties like ultimate tensile strength, yield strength, percentage elongation, hardness etc. These mechanical properties are procured through destructive testing which is time consuming and leads to material wastage. In the past, some machine learning models are utilized to predict the mechanical properties using the chemical composition and process parameters of the investment casting process. This industrial data contains a large number of input variables, which are complex to model and results in low prediction accuracy. In this proposed paper, two feature selection technique named least absolute shrinkage and selection operator (LASSO) and variable selection using random forests (VSURF) are implemented to select significant features from a total of 25 independent variables which are utilized for predicting the mechanical properties for the investment casting process. The efficacy of selected features is also evaluated by several machine learning models, including random forest (RF), K-nearest neighbor (KNN) algorithm and extreme gradient boosting (XGBOOST). The results show that the VSURF can extract a smaller subset of critical variables compared to LASSO, which helps to enhance the prediction accuracy and interpretation of the machine learning models; XGBOOST has the best capability to predict mechanical properties with the highest accuracy.
熔模铸造产品的使用寿命是通过其极限抗拉强度、屈服强度、伸长率、硬度等力学性能来衡量的。这些机械性能是通过破坏性测试获得的，这种测试既耗时又会导致材料浪费。在过去，一些机器学习模型是利用熔模铸造过程的化学成分和工艺参数来预测力学性能的。该工业数据包含大量的输入变量，建模复杂，导致预测精度低。本文采用最小绝对收缩和选择算子(LASSO)和随机森林变量选择(VSURF)两种特征选择技术，从25个自变量中选择重要特征，用于预测熔模铸造过程的力学性能。所选特征的有效性也通过几种机器学习模型进行评估，包括随机森林(RF)， k -最近邻(KNN)算法和极端梯度增强(XGBOOST)。结果表明，与LASSO相比，VSURF可以提取更小的关键变量子集，这有助于提高机器学习模型的预测精度和解释;XGBOOST具有预测机械性能的最佳能力，精度最高。
{"title":"Feature selection with LASSO and VSURF to model mechanical properties for investment casting","authors":"J. Virdi, W. Peng, A. Sata","doi":"10.1109/ICCIDS.2019.8862141","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862141","url":null,"abstract":"The service life of investment casting products is measured through its mechanical properties like ultimate tensile strength, yield strength, percentage elongation, hardness etc. These mechanical properties are procured through destructive testing which is time consuming and leads to material wastage. In the past, some machine learning models are utilized to predict the mechanical properties using the chemical composition and process parameters of the investment casting process. This industrial data contains a large number of input variables, which are complex to model and results in low prediction accuracy. In this proposed paper, two feature selection technique named least absolute shrinkage and selection operator (LASSO) and variable selection using random forests (VSURF) are implemented to select significant features from a total of 25 independent variables which are utilized for predicting the mechanical properties for the investment casting process. The efficacy of selected features is also evaluated by several machine learning models, including random forest (RF), K-nearest neighbor (KNN) algorithm and extreme gradient boosting (XGBOOST). The results show that the VSURF can extract a smaller subset of critical variables compared to LASSO, which helps to enhance the prediction accuracy and interpretation of the machine learning models; XGBOOST has the best capability to predict mechanical properties with the highest accuracy.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995678","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862160
N. Anusha, B. Bharathi
Satellite imagery based change detection plays an important role in analyzing the after effects of natural disasters, detecting the changes in city limits due to rapid urbanization, updating the map database, monitoring the factors impacting agriculture, etc., The remote sensors mounted on satellites or aircrafts absorb the light reflected by the earth’s surface. The output of these sensors will be a digital image which represents the scene being perceived. In order to extract the useful information from these images, various image processing techniques need to be employed. In this paper, a detailed outline of the steps and various techniques used for detecting the changes in multi temporal remote sensing images is discussed and a case study is done by taking multi-temporal Landsat-8 images covering Hyderabad city. Image differencing method is applied in order to find the changes in the Hyderabad city limits over 2013December and 2017 December time periods.
{"title":"An overview on Change Detection and a Case Study Using Multi-temporal Satellite Imagery","authors":"N. Anusha, B. Bharathi","doi":"10.1109/ICCIDS.2019.8862160","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862160","url":null,"abstract":"Satellite imagery based change detection plays an important role in analyzing the after effects of natural disasters, detecting the changes in city limits due to rapid urbanization, updating the map database, monitoring the factors impacting agriculture, etc., The remote sensors mounted on satellites or aircrafts absorb the light reflected by the earth’s surface. The output of these sensors will be a digital image which represents the scene being perceived. In order to extract the useful information from these images, various image processing techniques need to be employed. In this paper, a detailed outline of the steps and various techniques used for detecting the changes in multi temporal remote sensing images is discussed and a case study is done by taking multi-temporal Landsat-8 images covering Hyderabad city. Image differencing method is applied in order to find the changes in the Hyderabad city limits over 2013December and 2017 December time periods.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128029388","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862030
Prabhudas Janjanam, CH Pradeep Reddy
The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance. To quickly have an overview of abundant information, Text Summarization (TS) comes into play. TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge. Over the decades Text Summarization techniques have been transformed by the usage of linguistics to advanced machine learning models, this study explores summarization approaches along with their recent state-of-art models in single and multi-document summarization. This survey is intended to make an extensive study from features representation to sentence selection and summary generation using machine learning, recent graph and evolutionary based methods. The overall investigation will help the researchers to effectively handle large quantities of data in building effective Natural Language Processing applications. Eventually, this study draws popular abstractive mechanisms and observations that would be helpful for the intended research.
{"title":"Text Summarization: An Essential Study","authors":"Prabhudas Janjanam, CH Pradeep Reddy","doi":"10.1109/ICCIDS.2019.8862030","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862030","url":null,"abstract":"The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance. To quickly have an overview of abundant information, Text Summarization (TS) comes into play. TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge. Over the decades Text Summarization techniques have been transformed by the usage of linguistics to advanced machine learning models, this study explores summarization approaches along with their recent state-of-art models in single and multi-document summarization. This survey is intended to make an extensive study from features representation to sentence selection and summary generation using machine learning, recent graph and evolutionary based methods. The overall investigation will help the researchers to effectively handle large quantities of data in building effective Natural Language Processing applications. Eventually, this study draws popular abstractive mechanisms and observations that would be helpful for the intended research.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132252722","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862093
Hemang M Shah, Aadhithya Dinesh, T. Sharmila
The number of applications which use human face analysis are going up by the day and face orientation or pose detection is an important and upcoming research in this area. This paper uses a mathematical technique which compares real world coordinates of facial feature points with that of 2D points obtained from an image or live video using a projection matrix and Levenberg-Marquardt optimization to determine the Euler angles of the face. Further, this technique is used to find the best set of facial landmarks which give the maximum range of detection. The preliminary steps of the face orientation technique are face detection and facial landmark detection. For face detection, the Haar Cascade and Deep Neural Network techniques are experimented. Based on the analysis it is concluded that DNN is more robust, accurate and optimal. Facial landmarks are extracted by passing an image or video frame through a cascade of pre-trained regression trees. After analyzing various sets of facial features for their use in face orientation detection techniques and testing the results of each, a set of six facial points nose tip, chin, corner points of the eyes and corner points of the mouth are found to be enough for the algorithm to be able to detect the orientation of the face in a wide range of view with lesser computations.
{"title":"Analysis of Facial Landmark Features to determine the best subset for finding Face Orientation","authors":"Hemang M Shah, Aadhithya Dinesh, T. Sharmila","doi":"10.1109/ICCIDS.2019.8862093","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862093","url":null,"abstract":"The number of applications which use human face analysis are going up by the day and face orientation or pose detection is an important and upcoming research in this area. This paper uses a mathematical technique which compares real world coordinates of facial feature points with that of 2D points obtained from an image or live video using a projection matrix and Levenberg-Marquardt optimization to determine the Euler angles of the face. Further, this technique is used to find the best set of facial landmarks which give the maximum range of detection. The preliminary steps of the face orientation technique are face detection and facial landmark detection. For face detection, the Haar Cascade and Deep Neural Network techniques are experimented. Based on the analysis it is concluded that DNN is more robust, accurate and optimal. Facial landmarks are extracted by passing an image or video frame through a cascade of pre-trained regression trees. After analyzing various sets of facial features for their use in face orientation detection techniques and testing the results of each, a set of six facial points nose tip, chin, corner points of the eyes and corner points of the mouth are found to be enough for the algorithm to be able to detect the orientation of the face in a wide range of view with lesser computations.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115264501","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862153
M. Pruthvi, S. Karthika, N. Bhalaji
The coexistence of the Internet of Things (IoT)technologies with social networking concepts has led us to the invention of a new concept called the Social Internet of Things (SIoT), which involves large number of smart objects that have the capability to copy the behavioral characteristics of humans and act like them. They also have the capability to build their relationships based on the regulations or demands put forth by their owner in order to improve the network scalability in information/service discovery. In this paper, the information received from the environment is dealt by the IoT, and the social network deals with human-to-device interactions. The SIoT paradigm has been only an area for simulations and research, until now. The objective of this paper is to present a proposed system for college environment which will lead to the establishment of an SIoT platform. The Authors also launch the chief operations of the proposed SIoT system: methods to add a new and unique social object to the college platform, the way of creating and identifying new relationships among objects and manages them in the system, and the of managing the formation of fresh groups of members with close characteristics among the devices and find trusted “things” that can deliver required services when they meet each other opportunistically.
{"title":"A Novel Framework for SIoT College","authors":"M. Pruthvi, S. Karthika, N. Bhalaji","doi":"10.1109/ICCIDS.2019.8862153","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862153","url":null,"abstract":"The coexistence of the Internet of Things (IoT)technologies with social networking concepts has led us to the invention of a new concept called the Social Internet of Things (SIoT), which involves large number of smart objects that have the capability to copy the behavioral characteristics of humans and act like them. They also have the capability to build their relationships based on the regulations or demands put forth by their owner in order to improve the network scalability in information/service discovery. In this paper, the information received from the environment is dealt by the IoT, and the social network deals with human-to-device interactions. The SIoT paradigm has been only an area for simulations and research, until now. The objective of this paper is to present a proposed system for college environment which will lead to the establishment of an SIoT platform. The Authors also launch the chief operations of the proposed SIoT system: methods to add a new and unique social object to the college platform, the way of creating and identifying new relationships among objects and manages them in the system, and the of managing the formation of fresh groups of members with close characteristics among the devices and find trusted “things” that can deliver required services when they meet each other opportunistically.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124438963","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：