Pub Date : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640818
B. S. Sai, N. K., T. V. Reddy, T. Suma, P. Ashok babu
A Tumor is known as the aberrant growth of cells over a particular region of human body. Brain tumor also being one among those and it is capable of causing serious mental disabilities and issues related to the central nervous system, excessive growth of these tissues could ultimately lead to further complications like muscle paralysis, may also lead to fatal death. Considering all these conflicts detection of the tumor in very early stages is essential or else it would end up in causing lethal effects to the nervous system. MRI (Magnetic resonance imaging) scans helps us in diagnosing these brain tumors, but the process involved in detecting these tumors is Human-driven and arduous, also neurologists do generally take reasonable time to detect these tumors, this method of detection can also lead to human errors, so to avoid all these conflicts it is highly required to choose cogentpaths and design an effective model for the detection of brain tumors. This research work has proposed a model that involves an autonomous tumor detection technique for detecting cancerous tumor named Gliomas using convolutional neural networks, robust networks like VGG16 and VGG-19 are used in the process of detection of tumor and further this research has used deconvolution process on the VGG-16 model for a better feature extraction followed by CRF-RNN in the final layer for classification purpose instead of FCN. All these different models used for detecting brain tumor have performed well and has yielded us a very high accuracy rate of 95% and 96% when trained on VGG-16 and VGG19 network respectively. Then, the model has applied deconvolutional process on VGG-16 followed by CRF-RNN is also be able to classify the brain tumor effectively and it have yielded us a good accuracy rate of 92.3%.
{"title":"Brain Tumour Detection Using Convolutional Neural Networks and Deconvolution","authors":"B. S. Sai, N. K., T. V. Reddy, T. Suma, P. Ashok babu","doi":"10.1109/I-SMAC52330.2021.9640818","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640818","url":null,"abstract":"A Tumor is known as the aberrant growth of cells over a particular region of human body. Brain tumor also being one among those and it is capable of causing serious mental disabilities and issues related to the central nervous system, excessive growth of these tissues could ultimately lead to further complications like muscle paralysis, may also lead to fatal death. Considering all these conflicts detection of the tumor in very early stages is essential or else it would end up in causing lethal effects to the nervous system. MRI (Magnetic resonance imaging) scans helps us in diagnosing these brain tumors, but the process involved in detecting these tumors is Human-driven and arduous, also neurologists do generally take reasonable time to detect these tumors, this method of detection can also lead to human errors, so to avoid all these conflicts it is highly required to choose cogentpaths and design an effective model for the detection of brain tumors. This research work has proposed a model that involves an autonomous tumor detection technique for detecting cancerous tumor named Gliomas using convolutional neural networks, robust networks like VGG16 and VGG-19 are used in the process of detection of tumor and further this research has used deconvolution process on the VGG-16 model for a better feature extraction followed by CRF-RNN in the final layer for classification purpose instead of FCN. All these different models used for detecting brain tumor have performed well and has yielded us a very high accuracy rate of 95% and 96% when trained on VGG-16 and VGG19 network respectively. Then, the model has applied deconvolutional process on VGG-16 followed by CRF-RNN is also be able to classify the brain tumor effectively and it have yielded us a good accuracy rate of 92.3%.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067954","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9641013
Lilin Zhou
Based on the systematic analysis of the current research status of highway widening and reconstruction projects at home and abroad, this paper expounds the purpose and significance of the research. The disease detection and the cause of the disease in the widened highway of the old road were studied, and corresponding treatment measures were put forward. Analyzed the existing problems of highway splicing and widening subgrade, proposed the measures to be taken to solve these problems, and expounded the application of subgrade widening and splicing technology in the widening and extension project of road widening. The improvement of short-distance sensing technology on highway pavement widening construction is studied, and the design theory of roadbed and pavement widening highway is discussed.
{"title":"Highway Road Widening Construction Technology Based on Short-Distance Sensing Technology","authors":"Lilin Zhou","doi":"10.1109/I-SMAC52330.2021.9641013","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9641013","url":null,"abstract":"Based on the systematic analysis of the current research status of highway widening and reconstruction projects at home and abroad, this paper expounds the purpose and significance of the research. The disease detection and the cause of the disease in the widened highway of the old road were studied, and corresponding treatment measures were put forward. Analyzed the existing problems of highway splicing and widening subgrade, proposed the measures to be taken to solve these problems, and expounded the application of subgrade widening and splicing technology in the widening and extension project of road widening. The improvement of short-distance sensing technology on highway pavement widening construction is studied, and the design theory of roadbed and pavement widening highway is discussed.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129881102","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640986
Junli Wang
This paper uses an improved cyclic wavelet neural network algorithm to predict the safety of public buildings in BIM. First, by introducing the BIM safety warning model, the feasibility of the BIM model in the safety warning of public buildings is analyzed. Then, this paper proposes an improved cyclic wavelet neural network training algorithm, which composes the parameters of the wavelet neural network into a multi-dimensional vector, which is used as the particles in the algorithm to evolve. The BIM module extracts 4M1E basic factor information, combines the cyclic wavelet neural network algorithm to establish a safety prediction model, and adjusts the unsafe behavior and equipment in the BIM model through the prediction results. The prediction results show that the algorithm can effectively predict the safety problems of public buildings
{"title":"Public Building BIM Safety Early Warning Algorithm Based on Improved Cyclic Wavelet Neural Network","authors":"Junli Wang","doi":"10.1109/I-SMAC52330.2021.9640986","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640986","url":null,"abstract":"This paper uses an improved cyclic wavelet neural network algorithm to predict the safety of public buildings in BIM. First, by introducing the BIM safety warning model, the feasibility of the BIM model in the safety warning of public buildings is analyzed. Then, this paper proposes an improved cyclic wavelet neural network training algorithm, which composes the parameters of the wavelet neural network into a multi-dimensional vector, which is used as the particles in the algorithm to evolve. The BIM module extracts 4M1E basic factor information, combines the cyclic wavelet neural network algorithm to establish a safety prediction model, and adjusts the unsafe behavior and equipment in the BIM model through the prediction results. The prediction results show that the algorithm can effectively predict the safety problems of public buildings","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129635272","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640885
Gamini Joshi, Vidushi Sharma
The open-ended nature of the Internet of Things (IoT) had whipped them vulnerable to a variety of attacks, therefore the need of securing and stabilizing the network while keeping the integrity intact has become the most prominent requirement. Traditionally cryptographic methods were employed to secure networks but the demand of undesirable code size and processing time had given rise to trust-based schemes for addressing the misbehavior of attacks in the IoT networks. With reference to it, several trust-based schemes have been proposed by researchers. However, the prevailing schemes require high computational power and memory s pace; which weakens the network integrity and control. In this context, the paper presents a Light-weight Hidden Markov Model (L/W- HMT) for trust evaluation to alleviate the effect of compromised nodes and restricts the storage of unnecessary data to reduce overhead, memory, and energy consumption. This research work has presented a 2state HMM with Trusted state and compromised state together with essential and unessential output as observation state. Amount of packets forwarded, dropped, modified, and received are the parameters for state transition and emission matrices while the forward likelihood function evaluates the trust value of the node. Simulation performed on MATLAB indicates that the intended L/W-HMT scheme outperforms in connection with detection rate, packet delivery rate and energy consumption, on an average by 6% , 8% and 70% respectively when compared to the similar OADM trus t model.
{"title":"Light-Weight Hidden Markov Trust Evaluation Model for IoT network","authors":"Gamini Joshi, Vidushi Sharma","doi":"10.1109/I-SMAC52330.2021.9640885","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640885","url":null,"abstract":"The open-ended nature of the Internet of Things (IoT) had whipped them vulnerable to a variety of attacks, therefore the need of securing and stabilizing the network while keeping the integrity intact has become the most prominent requirement. Traditionally cryptographic methods were employed to secure networks but the demand of undesirable code size and processing time had given rise to trust-based schemes for addressing the misbehavior of attacks in the IoT networks. With reference to it, several trust-based schemes have been proposed by researchers. However, the prevailing schemes require high computational power and memory s pace; which weakens the network integrity and control. In this context, the paper presents a Light-weight Hidden Markov Model (L/W- HMT) for trust evaluation to alleviate the effect of compromised nodes and restricts the storage of unnecessary data to reduce overhead, memory, and energy consumption. This research work has presented a 2state HMM with Trusted state and compromised state together with essential and unessential output as observation state. Amount of packets forwarded, dropped, modified, and received are the parameters for state transition and emission matrices while the forward likelihood function evaluates the trust value of the node. Simulation performed on MATLAB indicates that the intended L/W-HMT scheme outperforms in connection with detection rate, packet delivery rate and energy consumption, on an average by 6% , 8% and 70% respectively when compared to the similar OADM trus t model.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129960101","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640935
T. Rao, B. J. Vazram, S. Devi, B. Rao
Nowadays, the demand for automatic driving assistance system is growing rapidly because it is reducing risk on drivers and helping to reduce the road accidents. In existing systems, many technological solutions are there but they are failing to produce promising accuracy. This paper has implemented a deep learning model for recognizing traffic signs that are present in the real world. The dataset that is utilized here is GTSRB which consists of 50,000 images of variable sizes. Due to modern technology and improvement in the automobile industry, numerous problems are encountering due to the huge number of vehicles. As a result, there is an increase in the number of accidents that happen due to the misreading of data by humans. To overcome the problem of misinterpretation, Traffic Sign Recognition is developed. Traffic Sign Recognition system capable of extracting traffic signs in real-time and can recognize the sign associated with the image. This model is being developed by using CNN. Our model producing 99.99% accuracy on training as well as validation data set. Traffic Sign Recognition is also a great contribution to the driver-less car technology that is being developed by Tesla. For a car to be driven without the help of a human, it should be able to detect traffic signs and act accordingly. The proposed model works effectively in different illuminating conditions and directions, where existing systems fail to produce promising results. This model helps to provide high accurate driver assisting system which can help to reduce accidents due traffic signal identification.
{"title":"A Novel Approach for Detecting Traffic Signs using Deep Learning","authors":"T. Rao, B. J. Vazram, S. Devi, B. Rao","doi":"10.1109/I-SMAC52330.2021.9640935","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640935","url":null,"abstract":"Nowadays, the demand for automatic driving assistance system is growing rapidly because it is reducing risk on drivers and helping to reduce the road accidents. In existing systems, many technological solutions are there but they are failing to produce promising accuracy. This paper has implemented a deep learning model for recognizing traffic signs that are present in the real world. The dataset that is utilized here is GTSRB which consists of 50,000 images of variable sizes. Due to modern technology and improvement in the automobile industry, numerous problems are encountering due to the huge number of vehicles. As a result, there is an increase in the number of accidents that happen due to the misreading of data by humans. To overcome the problem of misinterpretation, Traffic Sign Recognition is developed. Traffic Sign Recognition system capable of extracting traffic signs in real-time and can recognize the sign associated with the image. This model is being developed by using CNN. Our model producing 99.99% accuracy on training as well as validation data set. Traffic Sign Recognition is also a great contribution to the driver-less car technology that is being developed by Tesla. For a car to be driven without the help of a human, it should be able to detect traffic signs and act accordingly. The proposed model works effectively in different illuminating conditions and directions, where existing systems fail to produce promising results. This model helps to provide high accurate driver assisting system which can help to reduce accidents due traffic signal identification.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"215 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122382002","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640954
Gaurav Sharma
Artificial Intelligence simplifies people’s job by eliminating repeated tasks and providing unbiased and valuable insights. People have a perception that artificial intelligence will replace human efforts and can be the reason for mass termination of human resources. According to one research, 71% of companies see HR analytics as a high priority in their organizations. Also 8% of organizations report that they have usable data.The present paper studies the concept of Artificial Intelligence (AI) and its application on various human resource dimensions. For that, a conceptual framework is also given depicting how Artificial Intelligence (AI) benefits HR and its various dimensions. Based on an extensive literature review, this paper will discuss the use of best practices of Artificial Intelligence (AI) in Human resource functions. HR analytics is also considered a main component of Artificial Intelligence (AI) in HR practices.
{"title":"A literature review on application of Artificial Intelligence in Human Resource Management and its practices in current organizational scenario","authors":"Gaurav Sharma","doi":"10.1109/I-SMAC52330.2021.9640954","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640954","url":null,"abstract":"Artificial Intelligence simplifies people’s job by eliminating repeated tasks and providing unbiased and valuable insights. People have a perception that artificial intelligence will replace human efforts and can be the reason for mass termination of human resources. According to one research, 71% of companies see HR analytics as a high priority in their organizations. Also 8% of organizations report that they have usable data.The present paper studies the concept of Artificial Intelligence (AI) and its application on various human resource dimensions. For that, a conceptual framework is also given depicting how Artificial Intelligence (AI) benefits HR and its various dimensions. Based on an extensive literature review, this paper will discuss the use of best practices of Artificial Intelligence (AI) in Human resource functions. HR analytics is also considered a main component of Artificial Intelligence (AI) in HR practices.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130740606","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9641021
Hitha K C, Kiran V K
Topic identification and similarity detection are two related essential task in data mining, information retrieval, and bibliometric data analysis, which aims to identify significant topics and to find similarity between text collections.It is an essential activity to identify research papers according to their research topics to enhance their retrievability, help create smart analytics, and promote a range of approaches to evaluating the research environment and making sense of it.The proposed frame work deals with three main steps: text extraction, topic identification, and similarity detection.The PyPDF2 module is used to extract text from pdf file. CSO classifier is used for topic identification and similarity between documents is calculated using different models, such as Tf-Idf, Bert, Glove, Word2vec, and Doc2vec.and compared these models with respect to cosine similarity and Eucleadian distance obtained from these models.
{"title":"Topic Recognition and Correlation Analysis of Articles in Computer Science","authors":"Hitha K C, Kiran V K","doi":"10.1109/I-SMAC52330.2021.9641021","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9641021","url":null,"abstract":"Topic identification and similarity detection are two related essential task in data mining, information retrieval, and bibliometric data analysis, which aims to identify significant topics and to find similarity between text collections.It is an essential activity to identify research papers according to their research topics to enhance their retrievability, help create smart analytics, and promote a range of approaches to evaluating the research environment and making sense of it.The proposed frame work deals with three main steps: text extraction, topic identification, and similarity detection.The PyPDF2 module is used to extract text from pdf file. CSO classifier is used for topic identification and similarity between documents is calculated using different models, such as Tf-Idf, Bert, Glove, Word2vec, and Doc2vec.and compared these models with respect to cosine similarity and Eucleadian distance obtained from these models.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130886725","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640669
Mohammed Abdul Rahman, Qasim Ali Farooqui, K. Sridevi
The accumulation of varied features which were earlier provided only by high-performance computers being scaled down to a portable device has drawn the entire world to use them. S mart phones have been integral in replacing the need to store information on pieces of paper and carry hefty materials thereby providing an interactive digital repository to record the same. Educational institutions often restrict the usage of smartphones with regard to their misuse by students. However, efficient use can bring revolutionary enhancements to the education centers. Keeping in view the recent advancements brought about by the integration of IoT devices and smartphones, this research work proposes a methodology to automate institutions by providing an efficient, scalable and low-cost solution to aid the process of teaching and learning for teachers and students respectively. The aim of the application developed is to provide students the facility to create personalized digital records of notes, record audio lectures and generate portable documents for the same, mark attendance which operates using computer vision. In addition to this, excessive power consumption in institutions can be minimized drastically by using cameras to detect human presence and operate appliances accordingly. The integration of the IoT module via Bluetooth enables users to remotely control the appliances using the proposed application. Moreover, a smart door mechanism designed especially with regard to the current pandemic and also to support physically challenged people.
{"title":"IoT based Comprehensive Approach Towards Shaping Smart Classrooms","authors":"Mohammed Abdul Rahman, Qasim Ali Farooqui, K. Sridevi","doi":"10.1109/I-SMAC52330.2021.9640669","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640669","url":null,"abstract":"The accumulation of varied features which were earlier provided only by high-performance computers being scaled down to a portable device has drawn the entire world to use them. S mart phones have been integral in replacing the need to store information on pieces of paper and carry hefty materials thereby providing an interactive digital repository to record the same. Educational institutions often restrict the usage of smartphones with regard to their misuse by students. However, efficient use can bring revolutionary enhancements to the education centers. Keeping in view the recent advancements brought about by the integration of IoT devices and smartphones, this research work proposes a methodology to automate institutions by providing an efficient, scalable and low-cost solution to aid the process of teaching and learning for teachers and students respectively. The aim of the application developed is to provide students the facility to create personalized digital records of notes, record audio lectures and generate portable documents for the same, mark attendance which operates using computer vision. In addition to this, excessive power consumption in institutions can be minimized drastically by using cameras to detect human presence and operate appliances accordingly. The integration of the IoT module via Bluetooth enables users to remotely control the appliances using the proposed application. Moreover, a smart door mechanism designed especially with regard to the current pandemic and also to support physically challenged people.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961261","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640929
Wenyan Peng
With the continuous progress of the web2.0 era and the advent of the web3.0 era, the traditional marketing methods of e-commerce have gradually shown their drawbacks. The marketing methods that rely solely on price wars and smashing advertising fees have not brought about the conversion rate of orders. Further improve. In order to improve the above limitations, decentralized SNS (DSN) is constantly being valued. Based on the SNS party’s e-commerce marketing model, this article studies the decentralized storage design of e-commerce, and directly transfers independent social marketing methods to the e-commerce platform, so as to shorten the marketing process, master market information, stimulate product sales, and satisfy netizens to make friends. demand.
{"title":"Decentralized storage design of SNS open ecommerce marketing model","authors":"Wenyan Peng","doi":"10.1109/I-SMAC52330.2021.9640929","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640929","url":null,"abstract":"With the continuous progress of the web2.0 era and the advent of the web3.0 era, the traditional marketing methods of e-commerce have gradually shown their drawbacks. The marketing methods that rely solely on price wars and smashing advertising fees have not brought about the conversion rate of orders. Further improve. In order to improve the above limitations, decentralized SNS (DSN) is constantly being valued. Based on the SNS party’s e-commerce marketing model, this article studies the decentralized storage design of e-commerce, and directly transfers independent social marketing methods to the e-commerce platform, so as to shorten the marketing process, master market information, stimulate product sales, and satisfy netizens to make friends. demand.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126576969","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640681
M. Babu, M. Sreedevi
The most well-recognized fields in data mining is association rule mining. It’s been used within various applications including industry baskets, computer networks, recommendation systems and healthcare. Exploratory data analysis and data mining (DM) applications rely heavily on clustering. Cluster analysis seeks to categorize a group of patterns into groups based on their similarity. This paper aims to enhance the clustering technique of association rules over transactional datasets. At the outset the concepts behind association rules are explained followed by an overview of some of the recent research in this field. The benefits and drawbacks are addressed and a conclusion is drawn.
{"title":"A Comprehensive Study on Enhanced Clustering Technique of Association Rules over Transactional Datasets","authors":"M. Babu, M. Sreedevi","doi":"10.1109/I-SMAC52330.2021.9640681","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640681","url":null,"abstract":"The most well-recognized fields in data mining is association rule mining. It’s been used within various applications including industry baskets, computer networks, recommendation systems and healthcare. Exploratory data analysis and data mining (DM) applications rely heavily on clustering. Cluster analysis seeks to categorize a group of patterns into groups based on their similarity. This paper aims to enhance the clustering technique of association rules over transactional datasets. At the outset the concepts behind association rules are explained followed by an overview of some of the recent research in this field. The benefits and drawbacks are addressed and a conclusion is drawn.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125728635","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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