Pub Date : 1900-01-01DOI: 10.58599/ijsmem.2023.1604
Pilli.Lalitha Kumari, S. Reddy, Sasikala Devireddy
The adaptability of software portability and the strength of hardware efficiency can both benefit a system with the ability to alter its configuration. This signifies a significant shift in the way embedded programmes are conceived about and developed. There has not been a comprehensive design methodology provided for run-time reconfigurable systems that enable real-time operating systems, despite the fact that many different reconfigurable technologies and tools based on those technologies have been developed. The RTOS, or real-time operating system, is an integral part of system and co-design. A new co-design technique to meet the requirements of real-time operating systems on reconfigurable embedded systems is proposed based on the findings of this study. Co-design techniques needed to create an adaptive signal filtering system on a commercially available reconfigurable platform are highlighted in this study’s presentation of a design scenario. The incident has been documented on paper. The findings indicate that, in comparison to a purely software
{"title":"Flexible Real-Time Operating System Co-Design Flow for Embedded Computing Systems","authors":"Pilli.Lalitha Kumari, S. Reddy, Sasikala Devireddy","doi":"10.58599/ijsmem.2023.1604","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1604","url":null,"abstract":"The adaptability of software portability and the strength of hardware efficiency can both benefit a system with the ability to alter its configuration. This signifies a significant shift in the way embedded programmes are conceived about and developed. There has not been a comprehensive design methodology provided for run-time reconfigurable systems that enable real-time operating systems, despite the fact that many different reconfigurable technologies and tools based on those technologies have been developed. The RTOS, or real-time operating system, is an integral part of system and co-design. A new co-design technique to meet the requirements of real-time operating systems on reconfigurable embedded systems is proposed based on the findings of this study. Co-design techniques needed to create an adaptive signal filtering system on a commercially available reconfigurable platform are highlighted in this study’s presentation of a design scenario. The incident has been documented on paper. The findings indicate that, in comparison to a purely software","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122837315","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1601
Shamili Srimani Pendyala
The quantity of food that must be provided is being affected by the progressively deteriorating state of India’s agricultural industry. Many Indian farmers have shifted their focus away from farming and into other industries. This research is useful because it evaluates alternative approaches to selecting crops, planting them, spotting weeds, and keeping tabs on the system. All of these factors add up to a productive output, but they are hampered by things like a lack of workers and unfavorable environmental conditions. This research looked at the system from multiple angles, primarily focusing on image processing, artificial intelligence,machine learning and the internet of things. The research includes a comparison of the current framework with its most up-to-date counterpart.
{"title":"Smart Farming based on Machine Learning and IOT","authors":"Shamili Srimani Pendyala","doi":"10.58599/ijsmem.2023.1601","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1601","url":null,"abstract":"The quantity of food that must be provided is being affected by the progressively deteriorating state of India’s agricultural industry. Many Indian farmers have shifted their focus away from farming and into other industries. This research is useful because it evaluates alternative approaches to selecting crops, planting them, spotting weeds, and keeping tabs on the system. All of these factors add up to a productive output, but they are hampered by things like a lack of workers and unfavorable environmental conditions. This research looked at the system from multiple angles, primarily focusing on image processing, artificial intelligence,machine learning and the internet of things. The research includes a comparison of the current framework with its most up-to-date counterpart.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124153516","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1605
J. Boga, T.Sunitha, K.Manjula
Improving existing animal husbandry practices is essential before introducing grazing animals to vineyards. In order to provide this type of assistance, it is necessary to monitor and condition the animals’ whereabouts and actions, especially their feeding posture. Using this strategy, sheep could graze in agricultural areas (such vineyards and orchards) without fear of harming them. Based on these findings, we have created an IoT-based platform for tracking animal habits. To facilitate unattended shepherding of ovine within vineyard areas, the system integrates a local Internet of Things network for data collection from the animals with a cloud platform with data dispensationalso storage competences. As a result, the system can tend to ovine flocks. Easy analysis and interpretation of Internet of Things (IoT) data is made possible by the machine learning capabilities built into the cloud platform. Therefore, we shall not only outline the platform but also supply some machine learning platform-specific results. To be more specific, testing looked at how well this platform could identify and characterize disorders related to animal posture. This page offers a comparison of the tested approaches because multiple algorithms were used.
{"title":"Technology based on the Internet of Things to Monitor Animals","authors":"J. Boga, T.Sunitha, K.Manjula","doi":"10.58599/ijsmem.2023.1605","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1605","url":null,"abstract":"Improving existing animal husbandry practices is essential before introducing grazing animals to vineyards. In order to provide this type of assistance, it is necessary to monitor and condition the animals’ whereabouts and actions, especially their feeding posture. Using this strategy, sheep could graze in agricultural areas (such vineyards and orchards) without fear of harming them. Based on these findings, we have created an IoT-based platform for tracking animal habits. To facilitate unattended shepherding of ovine within vineyard areas, the system integrates a local Internet of Things network for data collection from the animals with a cloud platform with data dispensationalso storage competences. As a result, the system can tend to ovine flocks. Easy analysis and interpretation of Internet of Things (IoT) data is made possible by the machine learning capabilities built into the cloud platform. Therefore, we shall not only outline the platform but also supply some machine learning platform-specific results. To be more specific, testing looked at how well this platform could identify and characterize disorders related to animal posture. This page offers a comparison of the tested approaches because multiple algorithms were used.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582094","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1204
S. Imran, Muthukumaran M, V.Tharakeswari
In today’s technology-driven and Internet-obsessed society, it can be challenging to go through huge amounts of information and find relevant knowledge for various educational contexts. Simple, fast, and adaptable machine learning algorithms make such tasks easier to complete. K-means is the most effective unsupervised learning technique for classifying data into meaningful groups. K-means groups data by shared characteristics. K-means clusters are determined by k. Unfortunately, standard k-means requires a lot of math. Scholars have suggested strategies to improve k-means grouping. This work recommends computing initial centroids and establishing a distance between data points that are unlikely to change their cluster in subsequent iterations and those that are extremely likely to do so to lessen the load of k-means clustering for very large data sets. This piece will find information digits whose cluster is statistically likely to alter in the following few cycles. After processing several datasets, it is compared to other K-Means methods
{"title":"Clustering of massive datasets using an Adaptive and efficient K-Means approach","authors":"S. Imran, Muthukumaran M, V.Tharakeswari","doi":"10.58599/ijsmem.2023.1204","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1204","url":null,"abstract":"In today’s technology-driven and Internet-obsessed society, it can be challenging to go through huge amounts of information and find relevant knowledge for various educational contexts. Simple, fast, and adaptable machine learning algorithms make such tasks easier to complete. K-means is the most effective unsupervised learning technique for classifying data into meaningful groups. K-means groups data by shared characteristics. K-means clusters are determined by k. Unfortunately, standard k-means requires a lot of math. Scholars have suggested strategies to improve k-means grouping. This work recommends computing initial centroids and establishing a distance between data points that are unlikely to change their cluster in subsequent iterations and those that are extremely likely to do so to lessen the load of k-means clustering for very large data sets. This piece will find information digits whose cluster is statistically likely to alter in the following few cycles. After processing several datasets, it is compared to other K-Means methods","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122994675","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1502
Gajendra Singh Rajawat, Kamal Singh Rao, M. Sisodia
To reduce the number of car crashes that result in deaths or severe property damage was the primary goal of accident prevention programmes. Recognising Danger and Taking Precautions This strategy reduces the amount of time it takes for emergency services to reach a dangerous situation, saving lives. This apparatus can detect fog and clear it from the screen, allowing for safer navigation. If the car detects that you have been drinking, it will not start for you. A buzzer will ring if a blink sensor detects the driver is closing their eyes while driving. If the motorist ignores the warning, the car’s ignition will be turned off. The windscreen wipers will activate automatically if the rain sensor detects rain. When an accident happens, a limit switch is triggered, which shuts off the system and notifies the parents by the Twilio account they provided before the accident.
{"title":"Internet of Things-Based Car Wiper and Accident Location Notification","authors":"Gajendra Singh Rajawat, Kamal Singh Rao, M. Sisodia","doi":"10.58599/ijsmem.2023.1502","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1502","url":null,"abstract":"To reduce the number of car crashes that result in deaths or severe property damage was the primary goal of accident prevention programmes. Recognising Danger and Taking Precautions This strategy reduces the amount of time it takes for emergency services to reach a dangerous situation, saving lives. This apparatus can detect fog and clear it from the screen, allowing for safer navigation. If the car detects that you have been drinking, it will not start for you. A buzzer will ring if a blink sensor detects the driver is closing their eyes while driving. If the motorist ignores the warning, the car’s ignition will be turned off. The windscreen wipers will activate automatically if the rain sensor detects rain. When an accident happens, a limit switch is triggered, which shuts off the system and notifies the parents by the Twilio account they provided before the accident.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124594696","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1602
P. Saleem, B. Reddy, K. Venkatesan
The widespread availability of the Internet has pushed the state of the art in research into Internet of Things-based applications. Today’s state-of-the-art software typically makes use of web and androidbased technologies to improve the user experience. An energy-efficient and smart home automation system is presented here. Incorporating this technology into your home grants you the opportunity to control and manage your home’s systems from anywhere in the world. The brains of the home system include a module for connecting to the web that may be accessed remotely. This lesson is available on the web. The use of a static IP address is what makes a wireless connection possible. One example of a multimodal application that forms the basis of home automation is the Google Assistant. Voice-recognition based interfaces are just one type of application that may be used to control smart home devices. Since this is the case, our primary motivation for conducting this study is to find ways to make our current home automation system safer and smarter.
{"title":"Energy-Saving Smart-Home Automation System Based on the Internet of Things","authors":"P. Saleem, B. Reddy, K. Venkatesan","doi":"10.58599/ijsmem.2023.1602","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1602","url":null,"abstract":"The widespread availability of the Internet has pushed the state of the art in research into Internet of Things-based applications. Today’s state-of-the-art software typically makes use of web and androidbased technologies to improve the user experience. An energy-efficient and smart home automation system is presented here. Incorporating this technology into your home grants you the opportunity to control and manage your home’s systems from anywhere in the world. The brains of the home system include a module for connecting to the web that may be accessed remotely. This lesson is available on the web. The use of a static IP address is what makes a wireless connection possible. One example of a multimodal application that forms the basis of home automation is the Google Assistant. Voice-recognition based interfaces are just one type of application that may be used to control smart home devices. Since this is the case, our primary motivation for conducting this study is to find ways to make our current home automation system safer and smarter.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114393993","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1104
Kiran Kumar Gopathoti, Naguri Divya Sruthi
Recently, digital circuitry has demanded a decrease in space and power by decreasing time while simultaneously improving performance in speed. This has resulted in a need for more efficient use of the available space. Adders are fundamental components that are used in the construction of digital circuits. As a consequence of this, the performance of adders has to be improved in order to enhance the performance of integrated circuits that are used in the real world. The creation of a novel parallel prefix adder (PPA) architecture known as Hybrid PPA is the primary topic of this article. Hybrid PPA makes use of full carrier generation (FCG), full sum generation (FSG), half carry generation (HCG), and half sum generation (HSG) blocks. In addition to this, the N-bit Hybrid-PPA is constructed with features that may be reconfigured, and these features utilise square root additions through modified sum carry selection (MSCS). In addition, the implementation of multiplexer switching logic, which selects the whole sum bits and carry bits in a high-speed manner, reduces the amount of propagation time necessary for the generation of the sum and carry output. The results of the simulation show that using the proposed Hybrid PPA results in a reduction in area, latency, and power consumption when compared to using basic adders or approaches that are considered to be state of the art.
{"title":"Implementation of low power N-bit hybrid parallel prefix adder using Xilinx-ISE","authors":"Kiran Kumar Gopathoti, Naguri Divya Sruthi","doi":"10.58599/ijsmem.2023.1104","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1104","url":null,"abstract":"Recently, digital circuitry has demanded a decrease in space and power by decreasing time while simultaneously improving performance in speed. This has resulted in a need for more efficient use of the available space. Adders are fundamental components that are used in the construction of digital circuits. As a consequence of this, the performance of adders has to be improved in order to enhance the performance of integrated circuits that are used in the real world. The creation of a novel parallel prefix adder (PPA) architecture known as Hybrid PPA is the primary topic of this article. Hybrid PPA makes use of full carrier generation (FCG), full sum generation (FSG), half carry generation (HCG), and half sum generation (HSG) blocks. In addition to this, the N-bit Hybrid-PPA is constructed with features that may be reconfigured, and these features utilise square root additions through modified sum carry selection (MSCS). In addition, the implementation of multiplexer switching logic, which selects the whole sum bits and carry bits in a high-speed manner, reduces the amount of propagation time necessary for the generation of the sum and carry output. The results of the simulation show that using the proposed Hybrid PPA results in a reduction in area, latency, and power consumption when compared to using basic adders or approaches that are considered to be state of the art.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124299712","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1107
Naguri Sobha Rani, Naguri Divya Sruthi
For a long time, machine learning is an application spanning from a wide variety of subjects – from vehicles to data extraction. When you take an image of your cell phone, the picture is a little tangy. It’s simple. It often happens that people take random pictures using phones, which may end up in a corner of the frame. This work blends computer study with tools for photo editing. It will explore the options of how to automatically create photos with aesthetic pleasure through machine learning and how to create a portrait cutting tool. It also explores how to use machine learning to incorporate a streamlined function. Finally, the tools will be compared to other automated machine cropping tools.
{"title":"Automatic Portrait Image Cropping using Machine Learning Models","authors":"Naguri Sobha Rani, Naguri Divya Sruthi","doi":"10.58599/ijsmem.2023.1107","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1107","url":null,"abstract":"For a long time, machine learning is an application spanning from a wide variety of subjects – from vehicles to data extraction. When you take an image of your cell phone, the picture is a little tangy. It’s simple. It often happens that people take random pictures using phones, which may end up in a corner of the frame. This work blends computer study with tools for photo editing. It will explore the options of how to automatically create photos with aesthetic pleasure through machine learning and how to create a portrait cutting tool. It also explores how to use machine learning to incorporate a streamlined function. Finally, the tools will be compared to other automated machine cropping tools.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116958795","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}
Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.
{"title":"An exposition on the prediction of load on a Smart Grid","authors":"Vijendra Pratap Singh, Praveen Kumar Reddy K, Nagarjuna Reddy Gujjula","doi":"10.58599/ijsmem.2023.1202","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1202","url":null,"abstract":"Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124075294","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 : 1900-01-01DOI: 10.58599/ijsmem.2023.1405
L.Ravi Kumar, Ram Kumar Yadav, S.Yuvaraj
Forgeries created with deep face techniques have become increasingly common in several fields in recent years, including politics, education, and the democratic process, and as a result, many scholars are working on strategies to detect and prevent such forgeries. Since these programmes typically employ machine learning or fuzzy logic, accurate data classification is not something they can promise. However, it is common knowledge that forgery detection methods necessitate a shared, massive dataset, and that facial recognition systems benefit most from deep learning’s precision. Our suggested approaches make use of images and videos from the VGG-19 shared dataset, with genetic algorithm-based feature extraction and an improved convolutional neural network handling classifications for the trained datasets, respectively. A gaussian filter is used as preliminary processing on the VGG-19 common dataset.
{"title":"Detecting Fake Faces with AI: A Deep Neural Network Improvement Project","authors":"L.Ravi Kumar, Ram Kumar Yadav, S.Yuvaraj","doi":"10.58599/ijsmem.2023.1405","DOIUrl":"https://doi.org/10.58599/ijsmem.2023.1405","url":null,"abstract":"Forgeries created with deep face techniques have become increasingly common in several fields in recent years, including politics, education, and the democratic process, and as a result, many scholars are working on strategies to detect and prevent such forgeries. Since these programmes typically employ machine learning or fuzzy logic, accurate data classification is not something they can promise. However, it is common knowledge that forgery detection methods necessitate a shared, massive dataset, and that facial recognition systems benefit most from deep learning’s precision. Our suggested approaches make use of images and videos from the VGG-19 shared dataset, with genetic algorithm-based feature extraction and an improved convolutional neural network handling classifications for the trained datasets, respectively. A gaussian filter is used as preliminary processing on the VGG-19 common dataset.","PeriodicalId":103282,"journal":{"name":"International Journal of Scientific Methods in Engineering and Management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126099924","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：