Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151102
Monalisha Dash, Jyoti, K. Kaswan, Onkar Nath Mehra
Big data, particularly online reviews, has a major impact on customer experience. User-generated content (UGC) on social media sites significantly influences customer choice and enhances service providers' brand equity, revenue, and service innovations. The main objective of this study is to broaden the body of knowledge by analyzing research that have examined the impact of large data made accessible through UGC that helps to determine how the hospitality and tourism sectors should act in order to meet the expectations of their clients. All available research papers have been critically analyzed for future research. The finding of this paper is based on systematically reviewing 22 recent high-quality research papers using the keyword search method. The study begins by evaluating the importance of using big data and online reviews to meet consumer expectations through service, which serves to shape the hotel industry's decision-making process in relation to visitor happiness. Secondly, it also identifies the area for future research. The results indicate that the primary three components that influencing overall hospitality & tourism customer experience & satisfaction are service, room, and value evaluations. While negative emotions and the brand type have a detrimental impact on how satisfied guests are.
{"title":"Big Data for Customer Experience in Hospitality & Tourism Sector: A Review","authors":"Monalisha Dash, Jyoti, K. Kaswan, Onkar Nath Mehra","doi":"10.1109/ICDT57929.2023.10151102","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151102","url":null,"abstract":"Big data, particularly online reviews, has a major impact on customer experience. User-generated content (UGC) on social media sites significantly influences customer choice and enhances service providers' brand equity, revenue, and service innovations. The main objective of this study is to broaden the body of knowledge by analyzing research that have examined the impact of large data made accessible through UGC that helps to determine how the hospitality and tourism sectors should act in order to meet the expectations of their clients. All available research papers have been critically analyzed for future research. The finding of this paper is based on systematically reviewing 22 recent high-quality research papers using the keyword search method. The study begins by evaluating the importance of using big data and online reviews to meet consumer expectations through service, which serves to shape the hotel industry's decision-making process in relation to visitor happiness. Secondly, it also identifies the area for future research. The results indicate that the primary three components that influencing overall hospitality & tourism customer experience & satisfaction are service, room, and value evaluations. While negative emotions and the brand type have a detrimental impact on how satisfied guests are.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121986542","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150876
Om Pradyumana Gupta, Arun Prakash Agarwal, Om Pal
Since the inception of Facial Recognition (1960s) researchers began experimenting with computer-based facial recognition algorithms, but they were incompetent due to the limited processing power of computers. Then researchers developed feature-based recognition systems in the 1980s, which identified certain facial characteristics, such as the space between the eyes or the nose’s form, etc. to create a unique facial signature, however, they were still limited in their accuracy. 3D facial recognition systems were introduced in 1990s, which used depth perception to create more accurate facial models. These systems were primarily used in security and surveillance applications. Machine learning algorithms in 2000s could learn to recognize faces more accurately over time because it uses large datasets to train themselves to recognize patterns in facial features. Deep learning algorithms of 2010s could recognize faces with even greater accuracy as they use neural networks to analyze facial features at multiple levels of abstraction, allowing them to identify complex patterns. Real-time facial recognition systems were also developed during this period to recognize faces in real-time video streams and therefore found applicable in security and marketing. Covid-19 Pandemic incorporated Facial recognition technology with facemask requiring additional considerations and adjustments in order to be effective in accurately identifying individuals who are wearing masks. This paper presents a study of evolution of Facial recognition technology as viable biometrics since its inception and how it got molded over time due to technological, legal and global interventions. At the end, we conclude this paper with promising directions for future research on this field.
{"title":"A study on Evolution of Facial Recognition Technology","authors":"Om Pradyumana Gupta, Arun Prakash Agarwal, Om Pal","doi":"10.1109/ICDT57929.2023.10150876","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150876","url":null,"abstract":"Since the inception of Facial Recognition (1960s) researchers began experimenting with computer-based facial recognition algorithms, but they were incompetent due to the limited processing power of computers. Then researchers developed feature-based recognition systems in the 1980s, which identified certain facial characteristics, such as the space between the eyes or the nose’s form, etc. to create a unique facial signature, however, they were still limited in their accuracy. 3D facial recognition systems were introduced in 1990s, which used depth perception to create more accurate facial models. These systems were primarily used in security and surveillance applications. Machine learning algorithms in 2000s could learn to recognize faces more accurately over time because it uses large datasets to train themselves to recognize patterns in facial features. Deep learning algorithms of 2010s could recognize faces with even greater accuracy as they use neural networks to analyze facial features at multiple levels of abstraction, allowing them to identify complex patterns. Real-time facial recognition systems were also developed during this period to recognize faces in real-time video streams and therefore found applicable in security and marketing. Covid-19 Pandemic incorporated Facial recognition technology with facemask requiring additional considerations and adjustments in order to be effective in accurately identifying individuals who are wearing masks. This paper presents a study of evolution of Facial recognition technology as viable biometrics since its inception and how it got molded over time due to technological, legal and global interventions. At the end, we conclude this paper with promising directions for future research on this field.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122810116","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}
A requirement for any corporation to successfully embrace & execute the Fourth Industrial Revolution is the use of information technology (IT) in the area of human resource management (HRM) platforms (Industry 4.0). These techniques are required to provide an environment that is impartial, effective, transparent & secure. Blockchain a decentralized distributed ledger-based technology can make it easier to implement these requirements successfully. This study aims to determine the current state of application usage of blockchain technology in the area of human resource management. It also identifies potential opportunities related to the application utilization of blockchain technology in the realm of HRM along with awaited adoption challenges that may limit its application. Considering the suggested system to the current recruitment methods, there are clear advantages. So, the administration of human resources has also seen an extensive application of blockchain technology. The appliance potential of blockchain technology within HRM will be examined and analyzed in greater detail in this article.
{"title":"Automated Intervention of Blockchain in Human Resource Management","authors":"Bipin Kandpal, Deepti Sharma, Shweta Pandey, A. Gehlot, Sudhanshu Sudhanshu, Angel Swastik Duggal","doi":"10.1109/ICDT57929.2023.10150995","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150995","url":null,"abstract":"A requirement for any corporation to successfully embrace & execute the Fourth Industrial Revolution is the use of information technology (IT) in the area of human resource management (HRM) platforms (Industry 4.0). These techniques are required to provide an environment that is impartial, effective, transparent & secure. Blockchain a decentralized distributed ledger-based technology can make it easier to implement these requirements successfully. This study aims to determine the current state of application usage of blockchain technology in the area of human resource management. It also identifies potential opportunities related to the application utilization of blockchain technology in the realm of HRM along with awaited adoption challenges that may limit its application. Considering the suggested system to the current recruitment methods, there are clear advantages. So, the administration of human resources has also seen an extensive application of blockchain technology. The appliance potential of blockchain technology within HRM will be examined and analyzed in greater detail in this article.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126291090","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}
Forest fire is one of the concern issues for society in terms of a loss in natural resources & loss of wildlife. In recent years this problem has become more prominent because of global warming. In this paper, a comprehensive framework is proposed for the early detection and prediction of forest fire in real time environment by the use of edge computing (FOG) and Internet of things (IOT). Entire wireless sensor network (WSN) is designed by using sensor nodes which are designed by taking several environment parameters like humidity, temperature, infrared (IR) radiation and combustible gas. A sensor node is designed by taking ARDUINO 2560 as a processing element, LM35- temperature sensor, RHT-03 – Relative Humidity & Temperature sensor, MQ-02 smoke sensor & infrared sensor. A weather index is generated. Moreover this paper provides a conceptual depth to the technology that can be use for the early prediction of forest fire.
{"title":"An IoT-based Novel Framework for Early Prediction of Forest Fire","authors":"Rahul Chauhan, Himadri Vaidya, Megh Singhal, Alok Barddhan, Shashank Awasthi, Mahaveer Singh Naruka, S. Chauhan","doi":"10.1109/ICDT57929.2023.10151047","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151047","url":null,"abstract":"Forest fire is one of the concern issues for society in terms of a loss in natural resources & loss of wildlife. In recent years this problem has become more prominent because of global warming. In this paper, a comprehensive framework is proposed for the early detection and prediction of forest fire in real time environment by the use of edge computing (FOG) and Internet of things (IOT). Entire wireless sensor network (WSN) is designed by using sensor nodes which are designed by taking several environment parameters like humidity, temperature, infrared (IR) radiation and combustible gas. A sensor node is designed by taking ARDUINO 2560 as a processing element, LM35- temperature sensor, RHT-03 – Relative Humidity & Temperature sensor, MQ-02 smoke sensor & infrared sensor. A weather index is generated. Moreover this paper provides a conceptual depth to the technology that can be use for the early prediction of forest fire.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131824557","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151349
G. Kumar, S. Agrawal
This paper reviews, analyzes and proposes a new method for high frequency applications of carbon nanotube field effect Transistors (CNTFETs) with validation based of the simulation results for high frequency applications, which are promising technology for High-performance electronic devices. CNTFETs face several challenges due to their nanometer dimensions, including issues with leakage current, power consumption regulation, switching speed, and short channel effects like drain-induced barrier lowering (DIBL) and sub-threshold swing (SS). In this respect Low power CNTFET devices are of particular interest and will result in an enhanced and efficient battery life in modern portable smartphones and devices. With the increasing demand for high-speed and high-performance electronic devices, CNTFET technology is emerging as a promising field of research, especially in the context of 5G technology. Here, various methods, approaches, and strategies have been discussed to address the issues related to the performance of the device. By analyzing and discussing these new approaches published in the literature, researchers can identify the most effective strategies for improving the performance of electronic devices, including CNTFETs. Some of the new approaches that have been discussed in the literature including the use of novel materials for CNTFET Fabrication, such as graphene and other 2D materials, as well as the development of new device architectures that can reduce power consumption and improve switching speed. Other strategies include optimizing the gain, doping concentration and channel length of CNTFETs with respect to the operating frequency along with a dedicated emphasis on exploring the nascent techniques to reduce leakage current and minimize short-channel effects. A general conclusion is also presented that is based on comparison of contemporary technologies. Researchers in the field of electronic solid state devices, especially those working on CNTFET circuit design and fabrication, could benefit from these findings. Index Terms— Carbon Nanotubes, field-effect transistor.
{"title":"Carbon Nanotube Field Effect Transistors: An Aspect of Low Power and High Frequency Applications of CNTFETs","authors":"G. Kumar, S. Agrawal","doi":"10.1109/ICDT57929.2023.10151349","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151349","url":null,"abstract":"This paper reviews, analyzes and proposes a new method for high frequency applications of carbon nanotube field effect Transistors (CNTFETs) with validation based of the simulation results for high frequency applications, which are promising technology for High-performance electronic devices. CNTFETs face several challenges due to their nanometer dimensions, including issues with leakage current, power consumption regulation, switching speed, and short channel effects like drain-induced barrier lowering (DIBL) and sub-threshold swing (SS). In this respect Low power CNTFET devices are of particular interest and will result in an enhanced and efficient battery life in modern portable smartphones and devices. With the increasing demand for high-speed and high-performance electronic devices, CNTFET technology is emerging as a promising field of research, especially in the context of 5G technology. Here, various methods, approaches, and strategies have been discussed to address the issues related to the performance of the device. By analyzing and discussing these new approaches published in the literature, researchers can identify the most effective strategies for improving the performance of electronic devices, including CNTFETs. Some of the new approaches that have been discussed in the literature including the use of novel materials for CNTFET Fabrication, such as graphene and other 2D materials, as well as the development of new device architectures that can reduce power consumption and improve switching speed. Other strategies include optimizing the gain, doping concentration and channel length of CNTFETs with respect to the operating frequency along with a dedicated emphasis on exploring the nascent techniques to reduce leakage current and minimize short-channel effects. A general conclusion is also presented that is based on comparison of contemporary technologies. Researchers in the field of electronic solid state devices, especially those working on CNTFET circuit design and fabrication, could benefit from these findings. Index Terms— Carbon Nanotubes, field-effect transistor.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130326813","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151300
Anup Kumar Srivastava, Hoor Fatima, M. Dharwal, V. Sarin
The insurance sector is an immense data-driven enterprise with no produced product to develop and market. The data created in such an industry would be financial, risk, customer, producer, and actuarial data. Data acquired by such sectors from prior decades was structured data complemented by information on the goods and the policyholders. However, a vast volume of unstructured/semi-structured data is now available, which is still not investigated. Further to this, the insurer will still be ignorant to utilize the data fruitfully. Healthcare delivery and funding have been obscured throughout the last century by life insurance issues, although there are major similarities between the two. Research finds the optimum places for organizations that require unstructured and structured data for their success. Applied analytics will enhance the usage of insurance sector data. Additionally, insurance-industry big data analytics are examined with adoption methods of big data such as educating, Exploring, Engaging, and Executing. This article addresses the data transformation techniques used in the Insurance Industry and highlights all the models of the data adoption and transformation mechanisms that assist the Insurance Industry to develop better and enhanced data analysis and prediction. Using "Big Data Analytics" necessitates a fundamental rethinking of the current structure of health care services. Aside from examining how this new era of sophisticated and enhanced data management is benefiting the insurance industry, we'll also analyze the different consequences, characteristics, and use cases that lead to new technologies and ultimately contribute to economic success, which we'll cover in this study.
{"title":"The emerging trend of big data in the insurance industry and its Impacts","authors":"Anup Kumar Srivastava, Hoor Fatima, M. Dharwal, V. Sarin","doi":"10.1109/ICDT57929.2023.10151300","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151300","url":null,"abstract":"The insurance sector is an immense data-driven enterprise with no produced product to develop and market. The data created in such an industry would be financial, risk, customer, producer, and actuarial data. Data acquired by such sectors from prior decades was structured data complemented by information on the goods and the policyholders. However, a vast volume of unstructured/semi-structured data is now available, which is still not investigated. Further to this, the insurer will still be ignorant to utilize the data fruitfully. Healthcare delivery and funding have been obscured throughout the last century by life insurance issues, although there are major similarities between the two. Research finds the optimum places for organizations that require unstructured and structured data for their success. Applied analytics will enhance the usage of insurance sector data. Additionally, insurance-industry big data analytics are examined with adoption methods of big data such as educating, Exploring, Engaging, and Executing. This article addresses the data transformation techniques used in the Insurance Industry and highlights all the models of the data adoption and transformation mechanisms that assist the Insurance Industry to develop better and enhanced data analysis and prediction. Using \"Big Data Analytics\" necessitates a fundamental rethinking of the current structure of health care services. Aside from examining how this new era of sophisticated and enhanced data management is benefiting the insurance industry, we'll also analyze the different consequences, characteristics, and use cases that lead to new technologies and ultimately contribute to economic success, which we'll cover in this study.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128864329","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150667
A. Agarwal
In order to pick multi-talented employees from a large number of resumes, the human resource department (HR) is required to apply more accurate talent evaluation programs. However, rather of focusing on risk issues, the majority of talent evaluation tools evaluate talent. This article suggests a technique for selecting qualified competent staff resumes without taking risks into account using the technology for data mining called mining by association rules (ARM). The system's automatic intelligence agents (AIAS), which was created making decisions using a knowledge-based system based using logic principles and data gathered employing the ARM methodology, information from subject matter experts and prior learning experiences, directs the activities of the HR Department. The relevant experimental findings from AIAS allow HR departments to quickly decide who to hire for talent employees without wasting time for both candidates and employers during interviews. The useful experimental findings from AIAS allow HR departments to make quick selections for accurately hiring talented employees without squandering both employer and candidate time during interviews.
{"title":"Automating Decision-Making for Hiring Brilliant People While Taking Risk Factors Into Account: A Data Mining Approach","authors":"A. Agarwal","doi":"10.1109/ICDT57929.2023.10150667","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150667","url":null,"abstract":"In order to pick multi-talented employees from a large number of resumes, the human resource department (HR) is required to apply more accurate talent evaluation programs. However, rather of focusing on risk issues, the majority of talent evaluation tools evaluate talent. This article suggests a technique for selecting qualified competent staff resumes without taking risks into account using the technology for data mining called mining by association rules (ARM). The system's automatic intelligence agents (AIAS), which was created making decisions using a knowledge-based system based using logic principles and data gathered employing the ARM methodology, information from subject matter experts and prior learning experiences, directs the activities of the HR Department. The relevant experimental findings from AIAS allow HR departments to quickly decide who to hire for talent employees without wasting time for both candidates and employers during interviews. The useful experimental findings from AIAS allow HR departments to make quick selections for accurately hiring talented employees without squandering both employer and candidate time during interviews.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639202","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150488
Nitin Garg, A. Rizvi, Astuti Chandra, Anjali Singh
This paper is based on a comparative analysis of four types of MOSFETs on the basis of various electrical parameters of a MOSFET. A number of research papers have been reviewed to understand the trend that is followed by the various MOSFETs like Single Gate Junction-less MOSFET, Double Gate Junction-less MOSFET, Gate All Around MOSFET and FINFET with respect to electrical parameters. Based on this study we have analyzed the electrical parameters like drain current, gate voltage, transconductance, Ion /Ioff ratio, output conductance and channel length. Each parameter is observed and graphs for each kind of MOSFET for these parameters have been mapped in this paper. We have then compared the four MOSFETs on the basis of these parameters to analyze the most efficient MOSFET. We have simulated these parameters on SILVACO. As a result of which we were able to find that Gate All Around Junction-less MOSFET provides better overall frequency analysis as compared to other MOSFETs.
{"title":"A Review on Comparative Analysis of Various Mosfets on The Basis of Electrical Parameters","authors":"Nitin Garg, A. Rizvi, Astuti Chandra, Anjali Singh","doi":"10.1109/ICDT57929.2023.10150488","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150488","url":null,"abstract":"This paper is based on a comparative analysis of four types of MOSFETs on the basis of various electrical parameters of a MOSFET. A number of research papers have been reviewed to understand the trend that is followed by the various MOSFETs like Single Gate Junction-less MOSFET, Double Gate Junction-less MOSFET, Gate All Around MOSFET and FINFET with respect to electrical parameters. Based on this study we have analyzed the electrical parameters like drain current, gate voltage, transconductance, Ion /Ioff ratio, output conductance and channel length. Each parameter is observed and graphs for each kind of MOSFET for these parameters have been mapped in this paper. We have then compared the four MOSFETs on the basis of these parameters to analyze the most efficient MOSFET. We have simulated these parameters on SILVACO. As a result of which we were able to find that Gate All Around Junction-less MOSFET provides better overall frequency analysis as compared to other MOSFETs.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133208775","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}
We contend that robotic manipulation is not necessary for human-machine collaboration. We outline a cutting-edge system architecture that uses computer vision to guide intelligent cooking interventions. This human-centered strategy encourages fluid, organic collaboration without the use of actuators. We demonstrate how automation that supports user-led operations can provide the picture databases required for future completely autonomous robotic systems and provide useful cooking assistance. We invite the research community to expand on our work by offering an open-source implementation of it. The emergence of intelligent appliances with multimedia capabilities has become a part of daily life as a result of the development of computer technology and the widespread use of the Internet. An intelligent smart kitchen appliance can help people live joyful lives because modern lifestyles are making people spend less time in the kitchen. The project's primary goal is to reduce human labor in the kitchen by automating as much as is practical. The goal of the smart kitchen project is to provide dependable performance at an affordable price for the users. The goal of this project is to create a smart kitchen that will make it easier for regular consumers to shop for groceries and other kitchen necessities. The smart kitchen also alerts the user to the items they'll need to buy and helps ensure their safety. This paper offers a novel solution to the challenge of group cooking. We recommend that, in order for the system to be viable in a dynamic environment, it should take a more passive role, supporting user-led actions as opposed to performing the chef's duties. With present technology, manipulation problems can be avoided by removing the robot arm. Instead, we make use of computer vision methods to create contextual instructions that the user can follow. We provide a system that promotes organic and dynamic interactions by balancing manual control with automatic temperature regulation.
{"title":"A Smart IOT and AI Based Cooking System for Kitchen","authors":"Garima Shukla, Aradhna Saini, Shashank Rawat, Aditya Upadhyay, Garima Gupta, Manav Pal","doi":"10.1109/ICDT57929.2023.10150658","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150658","url":null,"abstract":"We contend that robotic manipulation is not necessary for human-machine collaboration. We outline a cutting-edge system architecture that uses computer vision to guide intelligent cooking interventions. This human-centered strategy encourages fluid, organic collaboration without the use of actuators. We demonstrate how automation that supports user-led operations can provide the picture databases required for future completely autonomous robotic systems and provide useful cooking assistance. We invite the research community to expand on our work by offering an open-source implementation of it. The emergence of intelligent appliances with multimedia capabilities has become a part of daily life as a result of the development of computer technology and the widespread use of the Internet. An intelligent smart kitchen appliance can help people live joyful lives because modern lifestyles are making people spend less time in the kitchen. The project's primary goal is to reduce human labor in the kitchen by automating as much as is practical. The goal of the smart kitchen project is to provide dependable performance at an affordable price for the users. The goal of this project is to create a smart kitchen that will make it easier for regular consumers to shop for groceries and other kitchen necessities. The smart kitchen also alerts the user to the items they'll need to buy and helps ensure their safety. This paper offers a novel solution to the challenge of group cooking. We recommend that, in order for the system to be viable in a dynamic environment, it should take a more passive role, supporting user-led actions as opposed to performing the chef's duties. With present technology, manipulation problems can be avoided by removing the robot arm. Instead, we make use of computer vision methods to create contextual instructions that the user can follow. We provide a system that promotes organic and dynamic interactions by balancing manual control with automatic temperature regulation.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133221244","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150916
Sayan Nath, D. Pal, Sajal Saha
A network-related environment called a “honeyed framework” served to defend official network resources against harm. This framework creates a scenario that motivates the intrusive person to engage in resource-stealing activity. To recognise an unauthorised assault, this framework applied the Attack-detection-procedure. Here, we attempt to identify DoS attacks using the suggested Honeyed framework system. In order to safeguard your network from assaults, NIDS (Network Intrusion Detection System) is one of the first security solutions to make it easier to identify intrusions. In this work, we offer a system that reveals an assault while validating the defense against it. The new cyber security benchmark IoT dataset is used in this white paper to assess the most recent machine learning techniques. This work’s major goal is to develop an architecture that can foresee and stop DDOS attacks, malware, and botnet attacks using these Honeyed designs. Deep Convolution Reinforcement Neural Networks are used for network surveillance and to categories network users from potential threats (DCRNN). A two-step technique of network understanding is used to enhance the functionality of the suggested solution. DSAE (Deep Sparse Auto Encoder) is used for feature engineering challenges at the initial step of the processing process, data pre-processing. The Deep Convolution Reinforcement Neural Network learning strategy is used in the second step to facilitate categorization. The honeyed firewall and web server are then implemented, following the deployment of the honeyed framework. The DCRNN deployment is finished, and users can now be monitored and analyzed as well as data on network users collected. In this study, data from a loT environment was used to test the effectiveness of the published technique. This data included the heterogeneous datasets "IoT-23," "NetML-2020," and "LITNET-2020." With contemporary methods for network discovery, the statistical relevance of this strategy is evaluated.
一个与网络相关的环境被称为“蜜糖框架”，用来保护官方网络资源不受损害。这个框架创造了一个场景，激励侵入者从事资源窃取活动。为了识别未经授权的攻击，该框架应用了攻击检测过程。在这里，我们尝试使用建议的蜜糖框架系统来识别DoS攻击。为了保护您的网络免受攻击，NIDS(网络入侵检测系统)是最早的安全解决方案之一，可以更容易地识别入侵。在这项工作中，我们提供了一个系统，可以在验证防御的同时揭示攻击。本白皮书使用新的网络安全基准物联网数据集来评估最新的机器学习技术。这项工作的主要目标是开发一个架构，可以预见和阻止DDOS攻击，恶意软件和僵尸网络攻击使用这些蜜糖设计。深度卷积强化神经网络用于网络监控和对网络用户进行潜在威胁分类(DCRNN)。网络理解的两步技术被用来增强建议的解决方案的功能。DSAE (Deep Sparse Auto Encoder，深度稀疏自动编码器)用于特征工程挑战处理过程的初始步骤，即数据预处理。在第二步中使用深度卷积强化神经网络学习策略来促进分类。然后，随着蜜化框架的部署，实现蜜化防火墙和web服务器。DCRNN部署完成，现在可以对用户进行监控和分析，并收集网络用户的数据。在本研究中，使用loT环境中的数据来测试已发表技术的有效性。这些数据包括异构数据集“IoT-23”、“NetML-2020”和“LITNET-2020”。使用现代网络发现方法，评估该策略的统计相关性。
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