Pub Date : 2022-12-16DOI: 10.1109/SMART55829.2022.10047651
Anandan Chinnalagu, A. Durairaj
The state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) and Deep Learning (DL) models are used for Natural Language Processing (NLP) applications. Social media marketing and customers positive sentiments play major role for many online businesses.It is a crucial task for companies to predict customers sentiment based on context from online reviews. Predicting accurate sentiment is a time-consuming and challenging task due to high volume of unstructured customers review dataset. There are many previous experimental results reveals the performance and inaccuracy issues on large scale customer reviews datasets. This paper presents the comparative analysis of experimental research work on BERT, Hybrid fastText-BILSTM, and fastText Trigram models overcome more accurate sentiment prediction challenges. We propose fine-tuned BERT and Hybrid fastText-BILSTM models for large customer review datasets. This comparative analysis results show that the proposed fine-tuned BERT model performs better compare to other DL models in terms of accuracy and other performance measures.
{"title":"Comparative Analysis of BERT-base Transformers and Deep Learning Sentiment Prediction Models","authors":"Anandan Chinnalagu, A. Durairaj","doi":"10.1109/SMART55829.2022.10047651","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047651","url":null,"abstract":"The state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) and Deep Learning (DL) models are used for Natural Language Processing (NLP) applications. Social media marketing and customers positive sentiments play major role for many online businesses.It is a crucial task for companies to predict customers sentiment based on context from online reviews. Predicting accurate sentiment is a time-consuming and challenging task due to high volume of unstructured customers review dataset. There are many previous experimental results reveals the performance and inaccuracy issues on large scale customer reviews datasets. This paper presents the comparative analysis of experimental research work on BERT, Hybrid fastText-BILSTM, and fastText Trigram models overcome more accurate sentiment prediction challenges. We propose fine-tuned BERT and Hybrid fastText-BILSTM models for large customer review datasets. This comparative analysis results show that the proposed fine-tuned BERT model performs better compare to other DL models in terms of accuracy and other performance measures.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115379507","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}
Blockchain is a decentralized log which is used to conduct business and anonymously trade virtual money. To verify a business request, every connected network member receives access to the most updated version of the protected ledger. The network ledger is a database of all previously completed Cryptocurrency payments. In essence, it's a type of database that keeps track of ever-expanding vandal standard data chunks that include batch of actual accounts. A sequential and temporal sequence is maintained when the finished blocks are introduced. Each block has a stamp and also an info link that refers to the block before it. Every user may join to the mentoring, permit Bitcoin system and transmit fresh transactions to validate and add new blocks. In a 2008 research article that was uploaded to a cryptographic newsgroup, Nakamoto provided architectural features for the Bit virtual currency. Nakamoto's idea provided cryptologists with a long-needed solution and created the groundwork for digital currency. This essay describes Bitcoin's operation as well as its idea, traits, and need. It aims to emphasize the influence of cryptocurrency on the growth of the Internet of Everything (I), financial firms, and banks in the past).
区块链是一种分散的日志，用于开展业务和匿名交易虚拟货币。为了验证业务请求，每个连接的网络成员都可以访问受保护分类账的最新版本。网络分类账是所有先前完成的加密货币支付的数据库。从本质上讲，它是一种数据库，可以跟踪不断扩展的破坏标准数据块，其中包括一批实际帐户。当完成的块被引入时，保持一个顺序和时间序列。每个块都有一个戳记和一个指向它之前的块的信息链接。每个用户都可以加入辅导，允许比特币系统并传输新的交易来验证和添加新的区块。在2008年上传到加密新闻组的一篇研究文章中，中本聪提供了比特虚拟货币的架构特征。中本聪的想法为密码学家提供了一个长期需要的解决方案，并为数字货币奠定了基础。这篇文章描述了比特币的运作，以及它的理念、特征和需求。它旨在强调加密货币对万物互联(Internet of Everything)、金融公司和过去的银行增长的影响。
{"title":"Blockchain Implementation in Financial Sector and Cyber Security System","authors":"Jeidy Panduro-Ramirez, Ashvine Kumar Sharma, Gurpreet Singh, Kumari H. Pavana, Claudia Poma-Garcia, Surendra Kumar Shukla","doi":"10.1109/SMART55829.2022.10047779","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047779","url":null,"abstract":"Blockchain is a decentralized log which is used to conduct business and anonymously trade virtual money. To verify a business request, every connected network member receives access to the most updated version of the protected ledger. The network ledger is a database of all previously completed Cryptocurrency payments. In essence, it's a type of database that keeps track of ever-expanding vandal standard data chunks that include batch of actual accounts. A sequential and temporal sequence is maintained when the finished blocks are introduced. Each block has a stamp and also an info link that refers to the block before it. Every user may join to the mentoring, permit Bitcoin system and transmit fresh transactions to validate and add new blocks. In a 2008 research article that was uploaded to a cryptographic newsgroup, Nakamoto provided architectural features for the Bit virtual currency. Nakamoto's idea provided cryptologists with a long-needed solution and created the groundwork for digital currency. This essay describes Bitcoin's operation as well as its idea, traits, and need. It aims to emphasize the influence of cryptocurrency on the growth of the Internet of Everything (I), financial firms, and banks in the past).","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115650962","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047674
Hem Lata Sharma, Meenakshi Sharma
Investigation on sentimental analysis via face image identification is ongoing in the domain of human-computer interaction (HCI). With their body language and facial expressions individuals are able to communicate a wide range of feelings and experiences. In this assignment, we will use a method that enables the machine to identify individual the facial identification of human feelings with the aid of Convolution Neural Network (CNN) and OpenCV in order to recognise the real feelings from the person's face gesture. Emotion Recognition is ultimately a synthesis of data gathered from many patterns. The barrier among humans and technology will be closed if computers are able to comprehend more human emotions. In this study article, we'll show how to read a person's frontal facial expression to accurately identify emotions including neutrality, pleased, unhappy, surprised, furious, frightened, and contempt.
{"title":"Using CNN and Open CV, Mood Identification with Face Feature Learning","authors":"Hem Lata Sharma, Meenakshi Sharma","doi":"10.1109/SMART55829.2022.10047674","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047674","url":null,"abstract":"Investigation on sentimental analysis via face image identification is ongoing in the domain of human-computer interaction (HCI). With their body language and facial expressions individuals are able to communicate a wide range of feelings and experiences. In this assignment, we will use a method that enables the machine to identify individual the facial identification of human feelings with the aid of Convolution Neural Network (CNN) and OpenCV in order to recognise the real feelings from the person's face gesture. Emotion Recognition is ultimately a synthesis of data gathered from many patterns. The barrier among humans and technology will be closed if computers are able to comprehend more human emotions. In this study article, we'll show how to read a person's frontal facial expression to accurately identify emotions including neutrality, pleased, unhappy, surprised, furious, frightened, and contempt.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121830529","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10046835
Shalom Akhai, Vincent Balu
The majority of businesses now want to conduct their operations online, and web applications are one of the most popular targets for web application assaults, which are quickly emerging as the biggest security risk facing modern businesses. Denial of services (DOS), malware, and brute force assaults, for instance, are the most frequent cyberattacks on web applications nowadays. Basically, Exploit is a flaw on the web that enables attackers to manipulate the queries that a website relies on. An attacker may actually read, remove, add, and retrieve the stored data with the use of these queries.
{"title":"Code Injection Assault & Mitigation Model to Prevent Attacks","authors":"Shalom Akhai, Vincent Balu","doi":"10.1109/SMART55829.2022.10046835","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046835","url":null,"abstract":"The majority of businesses now want to conduct their operations online, and web applications are one of the most popular targets for web application assaults, which are quickly emerging as the biggest security risk facing modern businesses. Denial of services (DOS), malware, and brute force assaults, for instance, are the most frequent cyberattacks on web applications nowadays. Basically, Exploit is a flaw on the web that enables attackers to manipulate the queries that a website relies on. An attacker may actually read, remove, add, and retrieve the stored data with the use of these queries.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127241513","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047385
Dipesh Uike, Sandeep Agarwalla, V. Bansal, M. Chakravarthi, Rajesh Singh, Prabhdeep Singh
The information is accessible to everyone using a block chain-capable app. Each single set of information that comes has its own block. When a blocks is loaded with information and linked to the block before it, a historic path for data are generated. The openness and virtual moral purity of IoT technologies shield smart devices from cyber-attacks. The brick chain's ability to store data in events and validate these activities with node may be utilized to provide secure connection amongst Connected systems. Automation has advanced due to the Internet of Things (IOT). By using block chain, we can increase the safety and confidentiality. This paper's focus is on the architecture and functionality of blockchain-based IoT technologies for industrial automation. Along with the Blockchain's numerous characteristics, its benefits are being investigated. The use cases and blockchain appropriateness studies for secure industrial automation have also been performed. In the last section, We examine the security components of the blockchain for a comparative analysis in both Man-in-the-middle and denial-of-service attack scenarios.
{"title":"Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation","authors":"Dipesh Uike, Sandeep Agarwalla, V. Bansal, M. Chakravarthi, Rajesh Singh, Prabhdeep Singh","doi":"10.1109/SMART55829.2022.10047385","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047385","url":null,"abstract":"The information is accessible to everyone using a block chain-capable app. Each single set of information that comes has its own block. When a blocks is loaded with information and linked to the block before it, a historic path for data are generated. The openness and virtual moral purity of IoT technologies shield smart devices from cyber-attacks. The brick chain's ability to store data in events and validate these activities with node may be utilized to provide secure connection amongst Connected systems. Automation has advanced due to the Internet of Things (IOT). By using block chain, we can increase the safety and confidentiality. This paper's focus is on the architecture and functionality of blockchain-based IoT technologies for industrial automation. Along with the Blockchain's numerous characteristics, its benefits are being investigated. The use cases and blockchain appropriateness studies for secure industrial automation have also been performed. In the last section, We examine the security components of the blockchain for a comparative analysis in both Man-in-the-middle and denial-of-service attack scenarios.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127480803","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047090
Anand Shukla, Vijender Kumar Solanki
Any technique that uses Extracts inscriptions and historical writing to identify characters translates into the modern Tamil text encoding. One of most difficult parts is identifying the oldest Tamil symbols. It is more hard to identify the symbols if the lettering are now on the surfaces. In texts written in English or other tongues, text detection has almost perfected itself. Owing to their existence from the third millennium B.c.e. to the fourth century CE, ancient sanskrit symbols in medieval manuscripts are exceedingly hard to ascertain. First, the vowels resemble phonetic vowel symbols; next, the publisher doesn't quite accurately transcribe the Extracts no.19; and third, the font are transcribed using various genres and stroked. As a result, the average accuracy in Written in sanskrit letters is not high. If this continues, the next population won't be able to identify the vital information our ancestors provided. Only one small number of individuals are recognized to have historic characteristics. In these seismic surveys, we evaluate several 'll look using tables.
{"title":"A Review Mostly on Identification of Early Tamil Extracts Actors Through Historical Writing","authors":"Anand Shukla, Vijender Kumar Solanki","doi":"10.1109/SMART55829.2022.10047090","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047090","url":null,"abstract":"Any technique that uses Extracts inscriptions and historical writing to identify characters translates into the modern Tamil text encoding. One of most difficult parts is identifying the oldest Tamil symbols. It is more hard to identify the symbols if the lettering are now on the surfaces. In texts written in English or other tongues, text detection has almost perfected itself. Owing to their existence from the third millennium B.c.e. to the fourth century CE, ancient sanskrit symbols in medieval manuscripts are exceedingly hard to ascertain. First, the vowels resemble phonetic vowel symbols; next, the publisher doesn't quite accurately transcribe the Extracts no.19; and third, the font are transcribed using various genres and stroked. As a result, the average accuracy in Written in sanskrit letters is not high. If this continues, the next population won't be able to identify the vital information our ancestors provided. Only one small number of individuals are recognized to have historic characteristics. In these seismic surveys, we evaluate several 'll look using tables.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921707","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047023
Sajithunisa Hussain, Remya P George, Nazia Ahmad, R. Jahan
The automation in the industrial sector plays a revolutionary change in the advancement in industries with improved communication network. The manufacturing and control sectors are enabled with complete automation to adopt higher productivity. Thus the demand of the consumers are highly accomplished through the automation in the industrial sectors. This automation is established by using joystick and robotic technology with sensors. The industrial joystick is used as a control devices for operating the machines at varied size with appropriate directions. The application of robotics in the industry is versatile in nature. The robotic arm is functioned through controlling the joystick. The overall movement of the robot is controlled and functioned with the joystick. It helps to reduce several minute errors with improved accuracy. This automation in the industry helps to enhance newer innovations with real time implementation.
{"title":"Machine Learning Methods of Industrial Automation System in Manufacturing and Control Sector using Joystick with and Robotic Technology","authors":"Sajithunisa Hussain, Remya P George, Nazia Ahmad, R. Jahan","doi":"10.1109/SMART55829.2022.10047023","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047023","url":null,"abstract":"The automation in the industrial sector plays a revolutionary change in the advancement in industries with improved communication network. The manufacturing and control sectors are enabled with complete automation to adopt higher productivity. Thus the demand of the consumers are highly accomplished through the automation in the industrial sectors. This automation is established by using joystick and robotic technology with sensors. The industrial joystick is used as a control devices for operating the machines at varied size with appropriate directions. The application of robotics in the industry is versatile in nature. The robotic arm is functioned through controlling the joystick. The overall movement of the robot is controlled and functioned with the joystick. It helps to reduce several minute errors with improved accuracy. This automation in the industry helps to enhance newer innovations with real time implementation.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122690692","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047114
V. Gunturu, P. Kumari, S. Chithra, Bhargabjyoti Saikia, Rajesh Singh, D. P. Singh
The present article discusses the use of stream processing to gather data from large-scale WIFI networks. Along with the foundational techniques for deliberate sampling, data collecting, likewise network monitoring in wireless networks, we also examine how understanding extraction may be viewed as an ML problem for applications for large-scale data streaming. We highlight the major This article discusses advancements in large data stream processing methods. We also look more closely at the database collection, edge detection, and methods for machine learning that may be used in the context of WIFI analytics. We discuss challenges, academic research, and the results of wireless network monitoring and stream analysis. Further research is anticipated into other dataflow improvements, such as pattern recognition and optimization algorithms.
{"title":"The Emerging Role of the Knowledge Driven Applications of Wireless Networks for Next Generation Online Stream Processing","authors":"V. Gunturu, P. Kumari, S. Chithra, Bhargabjyoti Saikia, Rajesh Singh, D. P. Singh","doi":"10.1109/SMART55829.2022.10047114","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047114","url":null,"abstract":"The present article discusses the use of stream processing to gather data from large-scale WIFI networks. Along with the foundational techniques for deliberate sampling, data collecting, likewise network monitoring in wireless networks, we also examine how understanding extraction may be viewed as an ML problem for applications for large-scale data streaming. We highlight the major This article discusses advancements in large data stream processing methods. We also look more closely at the database collection, edge detection, and methods for machine learning that may be used in the context of WIFI analytics. We discuss challenges, academic research, and the results of wireless network monitoring and stream analysis. Further research is anticipated into other dataflow improvements, such as pattern recognition and optimization algorithms.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"5 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114034905","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10046682
B. E. Narasimhayya, S. B. Vinay Kumar
In today's agribusiness, using robotics, machine learning, and also the web of things has become standard practise. The agricultural business is being forced to embrace such new methods in order to address issues like massive unemployment, rising food consumption with declining quality farmland, efficiency per square land, and deteriorating food quality due to global warming. As a result of the aforementioned difficulties and the world's increasing population, research and innovation in agricultural technology have become more crucial nowadays. In this document, we suggest an AI-powered wire automaton with six levels of autonomy and an omega automaton with degrees of opportunity that can help with agricultural tasks like sorting, pest management, picking strawberries and flowers, fertiliser application, and tracking of plant development, NPK tiers, and soil moisture. The robotic is called Farmbot. The Based criteria may communicate crop information to distant servers where it can be processed and retrieved to get further insight into soil quality. It can also be managed and watched remotely by a home computer or via a mobile internet software. The Farmbot may be programmed to do routine tasks including continuous monitoring, collecting fruit, cleaning, applying fertiliser, and gathering information about each plant. The Farmbot has direct exposure to just about any area of the field thanks to its placement over a moveable four wheels bot. This Farmbot's goal is mainly on providing individualised plant maintenance and aiding producers in raising output. As a design illustration of a functioning version for the imagined Farmbot, a 3D depiction is shown. The simulation's outcomes are shown.
{"title":"Super Six - Axis Cord Influenced Linear Laser for Personal Flower Management in Cultivation with Live Control Systems is Connected to The Robot Manipulator","authors":"B. E. Narasimhayya, S. B. Vinay Kumar","doi":"10.1109/SMART55829.2022.10046682","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046682","url":null,"abstract":"In today's agribusiness, using robotics, machine learning, and also the web of things has become standard practise. The agricultural business is being forced to embrace such new methods in order to address issues like massive unemployment, rising food consumption with declining quality farmland, efficiency per square land, and deteriorating food quality due to global warming. As a result of the aforementioned difficulties and the world's increasing population, research and innovation in agricultural technology have become more crucial nowadays. In this document, we suggest an AI-powered wire automaton with six levels of autonomy and an omega automaton with degrees of opportunity that can help with agricultural tasks like sorting, pest management, picking strawberries and flowers, fertiliser application, and tracking of plant development, NPK tiers, and soil moisture. The robotic is called Farmbot. The Based criteria may communicate crop information to distant servers where it can be processed and retrieved to get further insight into soil quality. It can also be managed and watched remotely by a home computer or via a mobile internet software. The Farmbot may be programmed to do routine tasks including continuous monitoring, collecting fruit, cleaning, applying fertiliser, and gathering information about each plant. The Farmbot has direct exposure to just about any area of the field thanks to its placement over a moveable four wheels bot. This Farmbot's goal is mainly on providing individualised plant maintenance and aiding producers in raising output. As a design illustration of a functioning version for the imagined Farmbot, a 3D depiction is shown. The simulation's outcomes are shown.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114391287","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047661
R. Shukla, A. Sengar, Anurag Gupta, Nupa Ram Chauhar
In this paper, we are obtaining and solving the problem of face identification and verification including with mask and without mask face images. In this algorithm they model allows users to use the webcam, digital cameras and multimedia cameras for identify and detect several face related features in the faces. In this paper, we are conducting detailed and systematic result to verify the effectiveness of these classic feature learning systems on linear and nonlinear class imbalanced outcomes. We also demonstrate more discriminatory deep representation features can be learned through the implementation of a deep network model. This model is maintaining the margin of the both classes including clusters. With using Convolutional Neural Network (CNN), they are providing efficient result in with mask and without mask face image. They are providing good result in both offline and real time performance with predictable value of accuracy. They are done research in evaluations of being made for publicly available datasets like DEEPFace and with mask and without mask dataset. The proposed model is working best result in different-different face related datasets to identify with face mask and without face mask images.
{"title":"Deep Learning Model to Identify Hide Images using CNN Algorithm","authors":"R. Shukla, A. Sengar, Anurag Gupta, Nupa Ram Chauhar","doi":"10.1109/SMART55829.2022.10047661","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047661","url":null,"abstract":"In this paper, we are obtaining and solving the problem of face identification and verification including with mask and without mask face images. In this algorithm they model allows users to use the webcam, digital cameras and multimedia cameras for identify and detect several face related features in the faces. In this paper, we are conducting detailed and systematic result to verify the effectiveness of these classic feature learning systems on linear and nonlinear class imbalanced outcomes. We also demonstrate more discriminatory deep representation features can be learned through the implementation of a deep network model. This model is maintaining the margin of the both classes including clusters. With using Convolutional Neural Network (CNN), they are providing efficient result in with mask and without mask face image. They are providing good result in both offline and real time performance with predictable value of accuracy. They are done research in evaluations of being made for publicly available datasets like DEEPFace and with mask and without mask dataset. The proposed model is working best result in different-different face related datasets to identify with face mask and without face mask images.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114448660","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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