Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243482
R. G, Ayman Gafoor, Hijas Ahammed, Aneesh Edavalath, Cijas Pk
The greatest infuriating task for a backpack traveller is to generate a well-organized and cost-effective trip plan. Even if the travel agency delivers some preplanned schedules, it will not be adequate for a particular customer. Many people likely to take rest and enjoy the period of holidays. However, the complex process of travel schedule arrangement is mostly unfavorable and results in the cancellation of travelling. Most people like travelling with fun. However, when it comes to planning it is always difficult to make it happen. It is the problem for every trip to be cancelled. The main aim of the proposed application is to collect information about the current traffic situations, food availability and accommodation. Travel assistant application, WeGo is all about planning a trip most effectively. It helps a user with travel routes, food, accommodation details, nearby gas stations etc., and helps to rent adventure-travelling gears. The proposed application organizes individual travel schedules, delivers particular data for banqueting, entertaining, and lodging. WeGo proposes a simple and rapid method, which diminishes the time that it takes travellers to plan their travel.
{"title":"WeGo: An Efficient Travel Assistant Application using Android","authors":"R. G, Ayman Gafoor, Hijas Ahammed, Aneesh Edavalath, Cijas Pk","doi":"10.1109/I-SMAC49090.2020.9243482","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243482","url":null,"abstract":"The greatest infuriating task for a backpack traveller is to generate a well-organized and cost-effective trip plan. Even if the travel agency delivers some preplanned schedules, it will not be adequate for a particular customer. Many people likely to take rest and enjoy the period of holidays. However, the complex process of travel schedule arrangement is mostly unfavorable and results in the cancellation of travelling. Most people like travelling with fun. However, when it comes to planning it is always difficult to make it happen. It is the problem for every trip to be cancelled. The main aim of the proposed application is to collect information about the current traffic situations, food availability and accommodation. Travel assistant application, WeGo is all about planning a trip most effectively. It helps a user with travel routes, food, accommodation details, nearby gas stations etc., and helps to rent adventure-travelling gears. The proposed application organizes individual travel schedules, delivers particular data for banqueting, entertaining, and lodging. WeGo proposes a simple and rapid method, which diminishes the time that it takes travellers to plan their travel.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"68 3 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":"125575421","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-SMAC49090.2020.9243382
S. Rane, Bhavik Sanghvi, Tirth Parekh, R. Shankarmani
Accidents tend to happen everywhere. The fast and apt response to an accident is very essential to ensure that the accident does not get severe. An appropriate help must be immediately contacted and arranged so that the accident can be handled properly before they get worse or it's too late. Accidents tend to happen more frequently in big industries and can harm people if not taken care of correctly. An appropriate emergency help needs to be arranged as soon as possible which is difficult sometimes because industries are in remote areas most of the time and they are spread over huge areas. Since industries are in remote areas and spread over lots of acres, reaching at the correct location in time could be delayed at times. The people working in an industry can get injured or require help due to an accident in the industry. The Emergency Service Responder App provides a platform to arrange appropriate help for the people of industry in case of any emergency by notifying people in an industry, ambulance, first aid center in an industry, family, concerned authorities, also provides navigation to the location of the accident This app helps significantly to provide the necessary help in case of accidents in big industries or premises which are situated in any place.
{"title":"Emergency Situation Responder: An efficient accident response app","authors":"S. Rane, Bhavik Sanghvi, Tirth Parekh, R. Shankarmani","doi":"10.1109/I-SMAC49090.2020.9243382","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243382","url":null,"abstract":"Accidents tend to happen everywhere. The fast and apt response to an accident is very essential to ensure that the accident does not get severe. An appropriate help must be immediately contacted and arranged so that the accident can be handled properly before they get worse or it's too late. Accidents tend to happen more frequently in big industries and can harm people if not taken care of correctly. An appropriate emergency help needs to be arranged as soon as possible which is difficult sometimes because industries are in remote areas most of the time and they are spread over huge areas. Since industries are in remote areas and spread over lots of acres, reaching at the correct location in time could be delayed at times. The people working in an industry can get injured or require help due to an accident in the industry. The Emergency Service Responder App provides a platform to arrange appropriate help for the people of industry in case of any emergency by notifying people in an industry, ambulance, first aid center in an industry, family, concerned authorities, also provides navigation to the location of the accident This app helps significantly to provide the necessary help in case of accidents in big industries or premises which are situated in any place.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"34 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":"125689592","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-SMAC49090.2020.9243422
G. Madhulatha, O. Ramadevi
Plant diseases can cause a reduction in the agricultural product quality and production. This is very vital to find out the plant diseases at an early stage for global health and wellbeing. Automatic plant disease detection is becoming a prominent research domain. It provides benefits in monitoring the large crop fields and helps in detecting the symptoms of the disease when they are found on the leaves. In this paper, the primarily focus on finding the plant diseases and which will reduce the crop loss and hence increases the production efficiency. Our proposed work detects the symptoms of plant diseases at the very initial stage and classifies plant disease based on the symptoms using a Deep Learning (DL) technique. The proposed approach recognizes the diseases using a deep CNN, with the best accuracy of 96.50%. This accuracy rate validates the model performance to early advisory or warming tool.
{"title":"Recognition of Plant Diseases using Convolutional Neural Network","authors":"G. Madhulatha, O. Ramadevi","doi":"10.1109/I-SMAC49090.2020.9243422","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243422","url":null,"abstract":"Plant diseases can cause a reduction in the agricultural product quality and production. This is very vital to find out the plant diseases at an early stage for global health and wellbeing. Automatic plant disease detection is becoming a prominent research domain. It provides benefits in monitoring the large crop fields and helps in detecting the symptoms of the disease when they are found on the leaves. In this paper, the primarily focus on finding the plant diseases and which will reduce the crop loss and hence increases the production efficiency. Our proposed work detects the symptoms of plant diseases at the very initial stage and classifies plant disease based on the symptoms using a Deep Learning (DL) technique. The proposed approach recognizes the diseases using a deep CNN, with the best accuracy of 96.50%. This accuracy rate validates the model performance to early advisory or warming tool.","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":"126612535","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-SMAC49090.2020.9243468
Shilpa P. Khedkar, R. Aroulcanessane
In today's world, it becomes very important to improve network security as well as the quality of service (QoS). Internets of Things (IoT) with machine learning techniques are used to provide services to users with a classification of the network traffic. So it is very important to separate malicious traffic from normal traffic. After detecting malicious traffic it has to be blocked and forwarded the normal traffic to the specified nodes for serving the users requirements. Here, presents machine learning algorithms for classifying the network traffic, for controlling the congestion in the network.
{"title":"Machine Learning Model for classification of IoT Network Traffic","authors":"Shilpa P. Khedkar, R. Aroulcanessane","doi":"10.1109/I-SMAC49090.2020.9243468","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243468","url":null,"abstract":"In today's world, it becomes very important to improve network security as well as the quality of service (QoS). Internets of Things (IoT) with machine learning techniques are used to provide services to users with a classification of the network traffic. So it is very important to separate malicious traffic from normal traffic. After detecting malicious traffic it has to be blocked and forwarded the normal traffic to the specified nodes for serving the users requirements. Here, presents machine learning algorithms for classifying the network traffic, for controlling the congestion in the network.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"4 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":"123648676","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-SMAC49090.2020.9243445
P. Upender, P. A. Harsha Vardhini
The design of Rectangular Microstrip Antenna (RMSA) and Circular Microstrip Antenna (CMSA) with coaxial feed at S-band frequency is investigated in this paper. RMSA is equipped for 2.4 GHz resonant frequency operations and also CMSA for 2.4 GHz resonant frequency operations. How RMSA and CMSA operate at the same frequency are discussed. This choice of frequency has created the antenna an ideal alternative to be used within the wireless Local Area Network [WLAN] and WiMAX, wifi, and Zigbee applications. The dielectric material used for both MSAs is epoxy material FR-4 having a permittivity of 4.4. Both antennas are designed using HFSS and fabricated. In comparison to the CMSA VSWR 1.31, the RMSA has an improved value of 1.0 for the VSWR. CSMA has a Return Loss (RL) of -26.7dB and RSMA has a -31.4 dB return loss. The RMSA has shown a return loss of approximately 6.0 dB greater than the CMSA return loss.
{"title":"Design Analysis of Rectangular and Circular Microstrip Patch Antenna with coaxial feed at S-Band for wireless applications","authors":"P. Upender, P. A. Harsha Vardhini","doi":"10.1109/I-SMAC49090.2020.9243445","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243445","url":null,"abstract":"The design of Rectangular Microstrip Antenna (RMSA) and Circular Microstrip Antenna (CMSA) with coaxial feed at S-band frequency is investigated in this paper. RMSA is equipped for 2.4 GHz resonant frequency operations and also CMSA for 2.4 GHz resonant frequency operations. How RMSA and CMSA operate at the same frequency are discussed. This choice of frequency has created the antenna an ideal alternative to be used within the wireless Local Area Network [WLAN] and WiMAX, wifi, and Zigbee applications. The dielectric material used for both MSAs is epoxy material FR-4 having a permittivity of 4.4. Both antennas are designed using HFSS and fabricated. In comparison to the CMSA VSWR 1.31, the RMSA has an improved value of 1.0 for the VSWR. CSMA has a Return Loss (RL) of -26.7dB and RSMA has a -31.4 dB return loss. The RMSA has shown a return loss of approximately 6.0 dB greater than the CMSA return loss.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"103 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":"122579544","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-SMAC49090.2020.9243427
Ying Wang, Saisai Guo, Jingwen Wu, H. Wang
Construction of the audit internal control system based on the online big data mining and decentralized model is done in this paper. How to integrate the novel technologies to internal control is the attracting task. IT audit is built on the information system and is independent of the information system itself. Application of the IT audit in enterprises can provide a guarantee for the security of the information system that can give an objective evaluation of the investment. This paper integrates the online big data mining and decentralized model to construct an efficient system. Association discovery is also called a data link. It uses similarity functions, such as the Euclidean distance, edit distance, cosine distance, Jeckard function, etc., to establish association relationships between data entities. These parameters are considered for comprehensive analysis.
{"title":"Construction of Audit Internal Control System Based on Online Big Data Mining and Decentralized Model","authors":"Ying Wang, Saisai Guo, Jingwen Wu, H. Wang","doi":"10.1109/I-SMAC49090.2020.9243427","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243427","url":null,"abstract":"Construction of the audit internal control system based on the online big data mining and decentralized model is done in this paper. How to integrate the novel technologies to internal control is the attracting task. IT audit is built on the information system and is independent of the information system itself. Application of the IT audit in enterprises can provide a guarantee for the security of the information system that can give an objective evaluation of the investment. This paper integrates the online big data mining and decentralized model to construct an efficient system. Association discovery is also called a data link. It uses similarity functions, such as the Euclidean distance, edit distance, cosine distance, Jeckard function, etc., to establish association relationships between data entities. These parameters are considered for comprehensive analysis.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"19 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":"125633200","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-SMAC49090.2020.9243544
Ankit Kumar Shaw, Amit Chakraborty, Debaniranjan Mohapatra, S. Dutta
The role of IoT and related internet-based applications in otherwise mechanical devices to monitor, manage and enhance the performance of the same is quite widespread now. Almost all public cloud service providers provide scalable, fully managed and elastic IoT related services. The data flows from these services are essentially streaming and can be consumed for further use in various predictive, descriptive and visualization modules. The cloud platforms enable ingestion, transformation and usage of the data by providing streaming, machine learning and sharable visualization services. This ecosystem greatly reduces the time to create IoT based minimum viable product creation which in turn enhances the business value realization cycle. The effect of cycle time reduction to design, architect and develop IoT solutions leads to a rapid improvement of business lead time and makes it easier for businesses to gain from the data insights and plan the next course of action. In this paper, one such enterprise graded use case is explored, in which the Azure IoT platform in terms of the offerings and associated ecosystem of Azure Stream Analytics and Azure Machine learning services are explained. This paper covers design, architecture, development and deployment of the solution prepared and how the same is monitored once in production. Security is a very important aspect of the same and here the security architecture is being explored. A conclusion is presented with the scope of future enhancements using auto ML services in serverless platforms to enable real-time automated decision making augmented with human expertise and intelligence.
{"title":"Scalable IoT Solution using Cloud Services – An Automobile Industry Use Case","authors":"Ankit Kumar Shaw, Amit Chakraborty, Debaniranjan Mohapatra, S. Dutta","doi":"10.1109/I-SMAC49090.2020.9243544","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243544","url":null,"abstract":"The role of IoT and related internet-based applications in otherwise mechanical devices to monitor, manage and enhance the performance of the same is quite widespread now. Almost all public cloud service providers provide scalable, fully managed and elastic IoT related services. The data flows from these services are essentially streaming and can be consumed for further use in various predictive, descriptive and visualization modules. The cloud platforms enable ingestion, transformation and usage of the data by providing streaming, machine learning and sharable visualization services. This ecosystem greatly reduces the time to create IoT based minimum viable product creation which in turn enhances the business value realization cycle. The effect of cycle time reduction to design, architect and develop IoT solutions leads to a rapid improvement of business lead time and makes it easier for businesses to gain from the data insights and plan the next course of action. In this paper, one such enterprise graded use case is explored, in which the Azure IoT platform in terms of the offerings and associated ecosystem of Azure Stream Analytics and Azure Machine learning services are explained. This paper covers design, architecture, development and deployment of the solution prepared and how the same is monitored once in production. Security is a very important aspect of the same and here the security architecture is being explored. A conclusion is presented with the scope of future enhancements using auto ML services in serverless platforms to enable real-time automated decision making augmented with human expertise and intelligence.","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":"124717435","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}
Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.
{"title":"RDPC: Secure Cloud Storage with Deduplication Technique","authors":"R. Patil Rashmi, Yatin Gandhi, Vinaya Sarmalkar, Prajakta Pund, Vinit Khetani","doi":"10.1109/I-SMAC49090.2020.9243442","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243442","url":null,"abstract":"Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.","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":"130683825","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-SMAC49090.2020.9243583
S. Karthik, A. Sharmila, Gokul Anand K R, Dhivya Priya E L
Healthy nation is the prosperous nation. Preeminent sanitation provisions are mandatory to guide the healthy living. Sanitation is one of the real-world problem. The proposed system deals with the floods, blockage of water, and other lavish materials. It may result in an overflow of drainage water and set off a source for abounding disorders. In succession to manipulate this complication and to diagnose the obstacle, smart sanitation will be the finest resolution. The suggested system embrace Arduino microcontroller, Water level sensor, Gas sensor, GSM (Global System for Mobile communications) module, Ultrasonic sensor, DC motor and LCD (Liquid Crystal Display) display. The sensors are employed in the walls of drainage. At any moment a blockage comes about at one end, the water level in the drainage will get increased automatically. It will be sensed by the water level sensor and ultrasonic sensor connected to the microcontroller. The controller in conjunction assists DC motor to lift the solid waste out of blockage. It also sends an alert message to the municipal office through GSM and displays the messages by using LCD. The controller conveys the alert signal to the GSM module as it senses the overflow of drainage. The proposed system will be compassionate in maintaining a hygienic environment. GSM module is associated in the blocked drainage and is placed in the municipal corporation to receive the alert message accompanied by LCD.
{"title":"A Novel Smart Sanitation Module for Green Environment","authors":"S. Karthik, A. Sharmila, Gokul Anand K R, Dhivya Priya E L","doi":"10.1109/I-SMAC49090.2020.9243583","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243583","url":null,"abstract":"Healthy nation is the prosperous nation. Preeminent sanitation provisions are mandatory to guide the healthy living. Sanitation is one of the real-world problem. The proposed system deals with the floods, blockage of water, and other lavish materials. It may result in an overflow of drainage water and set off a source for abounding disorders. In succession to manipulate this complication and to diagnose the obstacle, smart sanitation will be the finest resolution. The suggested system embrace Arduino microcontroller, Water level sensor, Gas sensor, GSM (Global System for Mobile communications) module, Ultrasonic sensor, DC motor and LCD (Liquid Crystal Display) display. The sensors are employed in the walls of drainage. At any moment a blockage comes about at one end, the water level in the drainage will get increased automatically. It will be sensed by the water level sensor and ultrasonic sensor connected to the microcontroller. The controller in conjunction assists DC motor to lift the solid waste out of blockage. It also sends an alert message to the municipal office through GSM and displays the messages by using LCD. The controller conveys the alert signal to the GSM module as it senses the overflow of drainage. The proposed system will be compassionate in maintaining a hygienic environment. GSM module is associated in the blocked drainage and is placed in the municipal corporation to receive the alert message accompanied by LCD.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"37 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":"134421377","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}
The work which has been presented here mainly concentrates on the prediction of breast cancer. For this purpose, convolution neural network is used. In this work, previous records of breast cancer were taken in to account. Convolutional neural network has been used in the identification of breast cancer. In this method first of all pictures are organized. After that this organized picture is separated on the basis of its qualities. In the next step these pictures are developed in a new form and in the end prediction work is done. For the reduction of comparison time, space edge based pictures are taken. Because of that performance improves. In the introduction part of this work, fundamental ideology related to breast cancer prediction system is explained. In the next part of this work those researches are highlighted in which a lot of work is already done in the determination of breast cancer. Inspiration in addition to problem related research has been highlighted afterward. In the end, results of computerized calculation related to this research are shown. It has been clearly comes out of results that when edge based pictures are treated in convolution neural network, time and space reduced Which makes the performance of work better.
{"title":"Machine Learning model for Breast Cancer Prediction","authors":"Ankur Gupta, Dushyant Kaushik, Muskan Garg, Apurv Verma","doi":"10.1109/I-SMAC49090.2020.9243323","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243323","url":null,"abstract":"The work which has been presented here mainly concentrates on the prediction of breast cancer. For this purpose, convolution neural network is used. In this work, previous records of breast cancer were taken in to account. Convolutional neural network has been used in the identification of breast cancer. In this method first of all pictures are organized. After that this organized picture is separated on the basis of its qualities. In the next step these pictures are developed in a new form and in the end prediction work is done. For the reduction of comparison time, space edge based pictures are taken. Because of that performance improves. In the introduction part of this work, fundamental ideology related to breast cancer prediction system is explained. In the next part of this work those researches are highlighted in which a lot of work is already done in the determination of breast cancer. Inspiration in addition to problem related research has been highlighted afterward. In the end, results of computerized calculation related to this research are shown. It has been clearly comes out of results that when edge based pictures are treated in convolution neural network, time and space reduced Which makes the performance of work better.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"69 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":"134435522","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：