Pub Date : 2020-10-07DOI: 10.1109/i-smac49090.2020.9243470
{"title":"Index","authors":"","doi":"10.1109/i-smac49090.2020.9243470","DOIUrl":"https://doi.org/10.1109/i-smac49090.2020.9243470","url":null,"abstract":"","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132978512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243414
Mohammad Faiz, A. K. Daniel
Cloud computing is one of the emerging domains of information technology as most of the applications are moving towards Cloud because of its features like availability, performance, security, cost, and maintenance. In recent advancement due to the rapid growth of Cloud computing technology, a vast number of Cloud service providers are available to fulfill the needs of Cloud customers. So, it is quite difficult for a customer to choose a Cloud service provider that will satisfy his needs. This paper has reviewed popular Cloud ranking models to prioritize the ranking of Cloud services based on different parameters of Cloud and proposed a fuzzy trust model for the ranking of different cloud service providers using three basic parameters capacity, cost and performance.
{"title":"Fuzzy Cloud Ranking Model based on QoS and Trust","authors":"Mohammad Faiz, A. K. Daniel","doi":"10.1109/I-SMAC49090.2020.9243414","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243414","url":null,"abstract":"Cloud computing is one of the emerging domains of information technology as most of the applications are moving towards Cloud because of its features like availability, performance, security, cost, and maintenance. In recent advancement due to the rapid growth of Cloud computing technology, a vast number of Cloud service providers are available to fulfill the needs of Cloud customers. So, it is quite difficult for a customer to choose a Cloud service provider that will satisfy his needs. This paper has reviewed popular Cloud ranking models to prioritize the ranking of Cloud services based on different parameters of Cloud and proposed a fuzzy trust model for the ranking of different cloud service providers using three basic parameters capacity, cost and performance.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134516867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243532
Keerti Narezal, Vijay H. Kalmani
Cloud computing has made possible that resources are available when needed and on-demand. Cloud computing is used to access and store data on remote servers using internet. Storing data in remote cloud servers leads to privacy and security issues. Attribute based encryption is used extensively for access control in cloud. There has been an upsurge in the IoT devices and IoT devices have been using cloud for data storage. When IoT and cloud converge there is need for newer, lightweight and efficient access control techniques for cloud based IoT, as the IoT devices are resource constrained, and may not be able to support the access control techniques currently used. It is observed that, there is need for lightweight Attribute based encryption (ABE) technique for cloud based IoT.
{"title":"Study of lightweight ABE for cloud based IoT","authors":"Keerti Narezal, Vijay H. Kalmani","doi":"10.1109/I-SMAC49090.2020.9243532","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243532","url":null,"abstract":"Cloud computing has made possible that resources are available when needed and on-demand. Cloud computing is used to access and store data on remote servers using internet. Storing data in remote cloud servers leads to privacy and security issues. Attribute based encryption is used extensively for access control in cloud. There has been an upsurge in the IoT devices and IoT devices have been using cloud for data storage. When IoT and cloud converge there is need for newer, lightweight and efficient access control techniques for cloud based IoT, as the IoT devices are resource constrained, and may not be able to support the access control techniques currently used. It is observed that, there is need for lightweight Attribute based encryption (ABE) technique for cloud based IoT.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243335
J. A. Rasheedh, S. Saradha
The Service Oriented Architecture (SOA) is developed as a pattern to distributed computing, enterprise integration and process of e-business in the early decade of 2000. The sudden increase of SOA and web services are subjected to the hype and virtual in which each organization has tried for adopting them with no matter in their indeed appropriateness. There are several SOA adopted by the user which may lead to massive fail on various attempts that tried for modifying the issues to solutions fit. At present, the microservices act as a recent technique for accomplishing a similar goal established to SOA a decade ago. However, the microservice has described a specific design concept in software application as an independent set, modularity, obtaining dynamism, and heterogeneous system integration and distribution development. Therefore, the microservices have provided applications with agility and scalability. This study of literature has discovered such challenges by an evolutionary concept from the SOA early years to microservices. This paper has also discussed various models for a run time of dynamic official, web association, a slight extension of association plan and AI technique are considered as a view at issues.
{"title":"Review of micro-services architectures and runtime dynamic binding","authors":"J. A. Rasheedh, S. Saradha","doi":"10.1109/I-SMAC49090.2020.9243335","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243335","url":null,"abstract":"The Service Oriented Architecture (SOA) is developed as a pattern to distributed computing, enterprise integration and process of e-business in the early decade of 2000. The sudden increase of SOA and web services are subjected to the hype and virtual in which each organization has tried for adopting them with no matter in their indeed appropriateness. There are several SOA adopted by the user which may lead to massive fail on various attempts that tried for modifying the issues to solutions fit. At present, the microservices act as a recent technique for accomplishing a similar goal established to SOA a decade ago. However, the microservice has described a specific design concept in software application as an independent set, modularity, obtaining dynamism, and heterogeneous system integration and distribution development. Therefore, the microservices have provided applications with agility and scalability. This study of literature has discovered such challenges by an evolutionary concept from the SOA early years to microservices. This paper has also discussed various models for a run time of dynamic official, web association, a slight extension of association plan and AI technique are considered as a view at issues.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132145552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243572
Chandrika Prasad, Jagdish S. Kallimani, Divakar Harekal, N. Sharma
Increasing acquisition of digitization over the information storing and processing in our daily lives has increased the demand of digitization in multiple facets including in investigation processes as well. In fact, for crimes involving computer systems requires the adoption of best practices for the process of evidence extraction from acquired devices from the crime scenes. Over the past years, summarization has become a topic of research. Various techniques of Natural Language Processing (NLP) enabling researchers to generate efficient results for a wide spectrum of documents. In the proposed work Seq2Seq Architecture with RNN is used to perform summarization tasks for documents. The nature of the summary is abstractive and allows the generation of internal meaning by the model itself. With refinement and continual work, this model becomes a strong foundation to perform summarization on longer and legal documents. The results are efficient summary generation and ROUGE scores in the range of 0.6 - 0.7.
{"title":"Automatic Text Summarization Model using Seq2Seq Technique","authors":"Chandrika Prasad, Jagdish S. Kallimani, Divakar Harekal, N. Sharma","doi":"10.1109/I-SMAC49090.2020.9243572","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243572","url":null,"abstract":"Increasing acquisition of digitization over the information storing and processing in our daily lives has increased the demand of digitization in multiple facets including in investigation processes as well. In fact, for crimes involving computer systems requires the adoption of best practices for the process of evidence extraction from acquired devices from the crime scenes. Over the past years, summarization has become a topic of research. Various techniques of Natural Language Processing (NLP) enabling researchers to generate efficient results for a wide spectrum of documents. In the proposed work Seq2Seq Architecture with RNN is used to perform summarization tasks for documents. The nature of the summary is abstractive and allows the generation of internal meaning by the model itself. With refinement and continual work, this model becomes a strong foundation to perform summarization on longer and legal documents. The results are efficient summary generation and ROUGE scores in the range of 0.6 - 0.7.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132264288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243457
S. Routray, A. Javali, Anindita Sahoo, Wogderes Semunigus, M. Pappa
In the recent years, Internet of things (IoT) has become an integral part of the modern digital ecosystem. It has the ability to handle the tasks smartly for many different situations. Therefore, it is one of the main technologies for autonomous systems. These IoTs deal with a lot of information. As the resources of the IoT are limited, data compression is an essential need. Some of the information transmitted over the IoTs cannot be compromised at all. Any loss of such sensitive data may cause serious consequences. Therefore, lossless data compression techniques are preferred for such data so that the integrity can be maintained. The low bandwidth IoTs are very popular in the recent times. They provide services over large coverage area with limited resources. These networks are known as low power wide area networks (LPWANs). In the 3GPP framework, there are some popular LPWANs such as narrowband IoT (NBIoT), and LTE machine-type communication (LTE-M). This article focuses on the lossless compression techniques employed in these popular LPWANs. This research work shows the reasons why lossless compression techniques are needed in NBIoT and LTE-M. It also goes through the challenges posed by the low bandwidth IoTs. Further, the recently used compression techniques for these low bandwidth IoTs are also discussed.
{"title":"Lossless Compression Techniques for Low Bandwidth Io Ts","authors":"S. Routray, A. Javali, Anindita Sahoo, Wogderes Semunigus, M. Pappa","doi":"10.1109/I-SMAC49090.2020.9243457","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243457","url":null,"abstract":"In the recent years, Internet of things (IoT) has become an integral part of the modern digital ecosystem. It has the ability to handle the tasks smartly for many different situations. Therefore, it is one of the main technologies for autonomous systems. These IoTs deal with a lot of information. As the resources of the IoT are limited, data compression is an essential need. Some of the information transmitted over the IoTs cannot be compromised at all. Any loss of such sensitive data may cause serious consequences. Therefore, lossless data compression techniques are preferred for such data so that the integrity can be maintained. The low bandwidth IoTs are very popular in the recent times. They provide services over large coverage area with limited resources. These networks are known as low power wide area networks (LPWANs). In the 3GPP framework, there are some popular LPWANs such as narrowband IoT (NBIoT), and LTE machine-type communication (LTE-M). This article focuses on the lossless compression techniques employed in these popular LPWANs. This research work shows the reasons why lossless compression techniques are needed in NBIoT and LTE-M. It also goes through the challenges posed by the low bandwidth IoTs. Further, the recently used compression techniques for these low bandwidth IoTs are also discussed.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130149297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243469
Nalini M.K, Radhika K.R
Deep learning has had remarkable success in several applications such as classification, clustering, regression etc. Several assumptions are made during the learning process which may not be apt for all real-world applications due to change in the feature space. For the classification task, deep learning models are most appropriate if a large amount of data is used for training. Therefore, enhancement is made from deep learning to transfer learning by knowledge transfer from feature space. In this paper, the accuracy obtained, number of iterations, and time taken for classification of various pre-trained networks is compared through transfer learning. The results reveal that the accuracy is higher when the training data is large compared to that with a small dataset.
{"title":"Comparative analysis of deep network models through transfer learning","authors":"Nalini M.K, Radhika K.R","doi":"10.1109/I-SMAC49090.2020.9243469","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243469","url":null,"abstract":"Deep learning has had remarkable success in several applications such as classification, clustering, regression etc. Several assumptions are made during the learning process which may not be apt for all real-world applications due to change in the feature space. For the classification task, deep learning models are most appropriate if a large amount of data is used for training. Therefore, enhancement is made from deep learning to transfer learning by knowledge transfer from feature space. In this paper, the accuracy obtained, number of iterations, and time taken for classification of various pre-trained networks is compared through transfer learning. The results reveal that the accuracy is higher when the training data is large compared to that with a small dataset.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/i-smac49090.2020.9243559
{"title":"4th International Conference on loT in Social, Mobile, Analytics and Cloud (ISMAC 2020)","authors":"","doi":"10.1109/i-smac49090.2020.9243559","DOIUrl":"https://doi.org/10.1109/i-smac49090.2020.9243559","url":null,"abstract":"","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134218764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243596
Anuradha Pandey, Pooja Patre, Jasmine Minj
Glaucoma disease becomes a more common eye disease that occurs due to pressure on eye cells. Many image processing based methods have been applied earlier for the detection of glaucoma disease but their accuracy of classification was not up to the mark. The pressure on eye cells increases with the use of mobile phones, video games in the daily life of human beings. In this paper, the three different methods ares shared for the detection of glaucoma disease using image processing techniques, machine learning techniques, and using a convolutional neural network model of deep learning on the Bin Rushed database. The image processing techniques are used for the extraction of features like CDR and RDR, then classification performed using a neural network, support vector machine, decision tree, and K nearest machine learning model. The highest accuracy of 98% got for K nearest neighbor method and the VVG-16 deep learning model accuracy was 99.6%.
{"title":"Detection of Glaucoma Disease using Image Processing, Soft Computing and Deep Learning Approaches","authors":"Anuradha Pandey, Pooja Patre, Jasmine Minj","doi":"10.1109/I-SMAC49090.2020.9243596","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243596","url":null,"abstract":"Glaucoma disease becomes a more common eye disease that occurs due to pressure on eye cells. Many image processing based methods have been applied earlier for the detection of glaucoma disease but their accuracy of classification was not up to the mark. The pressure on eye cells increases with the use of mobile phones, video games in the daily life of human beings. In this paper, the three different methods ares shared for the detection of glaucoma disease using image processing techniques, machine learning techniques, and using a convolutional neural network model of deep learning on the Bin Rushed database. The image processing techniques are used for the extraction of features like CDR and RDR, then classification performed using a neural network, support vector machine, decision tree, and K nearest machine learning model. The highest accuracy of 98% got for K nearest neighbor method and the VVG-16 deep learning model accuracy was 99.6%.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134464496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243338
M. N, D. B. Srinivas
Industries are the key contributors to the development of an economy. The technological progression and transition have changed the performance of these industries significantly. The fourth industrial revolution in the area of Cyber Physical Systems (CPS), the Internet of Things (IoT), Machine learning (ML), autonomous agents and things (Robotics), Cloud computing, Cognitive computing and Artificial Intelligence (AI), has impacted significantly over the organizational functioning. The influence of industrial 4.0 has been observed in almost all the functional areas of businesses including the human resource management function. In the era of 4.0, human resource department has seen the digital transformation on various Human Resource (HR) functions like recruitment, onboarding, learning and development, Performance management, social sharing and compensation and benefits. Technological transcends in the area of HR has made a tremendous transformation in managing the organization workforce. The study is a conceptual effort which sheds light on the impact of 4th industrial revolution over the various functions of the Human Resource Management.
{"title":"Technological Transcends: Impact of Industrial 4.0 on Human Resource Functions","authors":"M. N, D. B. Srinivas","doi":"10.1109/I-SMAC49090.2020.9243338","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243338","url":null,"abstract":"Industries are the key contributors to the development of an economy. The technological progression and transition have changed the performance of these industries significantly. The fourth industrial revolution in the area of Cyber Physical Systems (CPS), the Internet of Things (IoT), Machine learning (ML), autonomous agents and things (Robotics), Cloud computing, Cognitive computing and Artificial Intelligence (AI), has impacted significantly over the organizational functioning. The influence of industrial 4.0 has been observed in almost all the functional areas of businesses including the human resource management function. In the era of 4.0, human resource department has seen the digital transformation on various Human Resource (HR) functions like recruitment, onboarding, learning and development, Performance management, social sharing and compensation and benefits. Technological transcends in the area of HR has made a tremendous transformation in managing the organization workforce. The study is a conceptual effort which sheds light on the impact of 4th industrial revolution over the various functions of the Human Resource Management.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134016906","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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