Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088311
K. Maniraj, Kiran Dasari, B. Ravi, Pallavi Madamanchi, Meghana Lanka, B. Kumar
Technology makes cleaning more intelligent and accessible. Future energy sources will become saturated and run out. Instead of using nonrenewable energies, consider solar power. Today, practically every field uses solar energy. Cleaning is one household task that never becomes obsolete and welcomes new technology. Floors are cleaned with broomsticks, vacuum cleaners, and advanced robot cleaners like Roomba. Middle-age vacuum cleaners and even advanced robotic cleaners are too expensive for low and middle-class consumers. Traditional vacuum cleaners reduce the amount of human energy needed to clean floors, but the user must remain behind the machine to direct the suction pipe to dusty areas. These vacuum cleaners are likewise plugins, meaning they can only be used while plugged in. Solar energy is used to charge the battery, which powers the driving circuit. This cleaner uses Arduino-Uno and Motor driver L293D. This designed solar floor cleaner is driven autonomously with sensor communication by recognizing obstructions and avoiding them. Another Bluetooth module lets the user steer the cleaner to any desired area. This module accepts commands and drives the model. This household and outdoor cleaner provide easy and rapid cleaning. It avoids regular vacuum cleaners ‘plugin and use’ method by self-moving and cleaning concurrently. Thus, automated solar floor cleaners have efficient cleaning benefits and uses.
{"title":"Design of Automated Solar Floor Cleaner using IOT","authors":"K. Maniraj, Kiran Dasari, B. Ravi, Pallavi Madamanchi, Meghana Lanka, B. Kumar","doi":"10.1109/ASSIC55218.2022.10088311","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088311","url":null,"abstract":"Technology makes cleaning more intelligent and accessible. Future energy sources will become saturated and run out. Instead of using nonrenewable energies, consider solar power. Today, practically every field uses solar energy. Cleaning is one household task that never becomes obsolete and welcomes new technology. Floors are cleaned with broomsticks, vacuum cleaners, and advanced robot cleaners like Roomba. Middle-age vacuum cleaners and even advanced robotic cleaners are too expensive for low and middle-class consumers. Traditional vacuum cleaners reduce the amount of human energy needed to clean floors, but the user must remain behind the machine to direct the suction pipe to dusty areas. These vacuum cleaners are likewise plugins, meaning they can only be used while plugged in. Solar energy is used to charge the battery, which powers the driving circuit. This cleaner uses Arduino-Uno and Motor driver L293D. This designed solar floor cleaner is driven autonomously with sensor communication by recognizing obstructions and avoiding them. Another Bluetooth module lets the user steer the cleaner to any desired area. This module accepts commands and drives the model. This household and outdoor cleaner provide easy and rapid cleaning. It avoids regular vacuum cleaners ‘plugin and use’ method by self-moving and cleaning concurrently. Thus, automated solar floor cleaners have efficient cleaning benefits and uses.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130261735","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088355
Esha Jain
Artificial Intelligence is getting expanding consideration in the corporates and humanity. In banking, the principal practices of AI were operative; nonetheless, AI is essentially applied in speculation backend and banking administrations without client interaction. Presenting AI in business banking might modify commercial cycles and collaborations with clients, which might set out research and open doors for conducting finance. The current study focuses on challenges in applying artificial intelligence in the banking sector by following a scientometric assessment and showed that innovations drastically change the idea of work. It was also found that web application weaknesses are security openings, which aggressors might endeavor to take advantage of, henceforth possibly making genuine harm to business, like taking touchy information and compromising business assets. It was concluded from the study that since web applications are currently broadly utilized, basic business conditions, for example, web banking, correspondence of touchy information, and internet shopping require powerful defensive measures against a wide scope of weaknesses.
{"title":"Challenges In Applying Artificial Intelligence In Banking Sector: A Scientometric Review","authors":"Esha Jain","doi":"10.1109/ASSIC55218.2022.10088355","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088355","url":null,"abstract":"Artificial Intelligence is getting expanding consideration in the corporates and humanity. In banking, the principal practices of AI were operative; nonetheless, AI is essentially applied in speculation backend and banking administrations without client interaction. Presenting AI in business banking might modify commercial cycles and collaborations with clients, which might set out research and open doors for conducting finance. The current study focuses on challenges in applying artificial intelligence in the banking sector by following a scientometric assessment and showed that innovations drastically change the idea of work. It was also found that web application weaknesses are security openings, which aggressors might endeavor to take advantage of, henceforth possibly making genuine harm to business, like taking touchy information and compromising business assets. It was concluded from the study that since web applications are currently broadly utilized, basic business conditions, for example, web banking, correspondence of touchy information, and internet shopping require powerful defensive measures against a wide scope of weaknesses.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131542402","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088406
Ravi Shankar Jha, P. R. Sahoo, Shaktimaya Mohapatra
In the fast-moving era of the Industrial Revolution (Industry 4.0), digitally fueled devices and technologies are paramount for driving innovation and creating values across a myriad of industries. A case in point is - Healthcare Industry. Healthcare insurance companies, hospitals, and other providers around the world are belligerently leveraging digital tools and technologies such as Big Data analytics, Lake, Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing, smart sensors, and the Internet of Things (IoT), for improving the overall quality of care and overall process efficiency and effectiveness. The Healthcare industry has been a center of discussion for embracing Big Data practice across the value chain for the past couple of decades due to the prodigious potential that is concealed in it. With so much abundant information, there have been numerous provocations related to the apiece stage of maneuvering big data that can only be amplified by leveraging high-end computer science results for big data analytics, as mentioned above. Well-organized healthcare ecosystem, analysis, and magnification of big data can influence the course of the game by opening new paths in terms of offering unique yet innovative products and services for the modern age technology-propelled healthcare value chain. This paper emphasizes the impetus of Big Data across the healthcare value chain, which involves the amalgamation of technology, data, and business, yielding better decisions and improving the experience across all touch points.
{"title":"Healthcare Industry: Embracing Potential of Big Data across Value Chain","authors":"Ravi Shankar Jha, P. R. Sahoo, Shaktimaya Mohapatra","doi":"10.1109/ASSIC55218.2022.10088406","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088406","url":null,"abstract":"In the fast-moving era of the Industrial Revolution (Industry 4.0), digitally fueled devices and technologies are paramount for driving innovation and creating values across a myriad of industries. A case in point is - Healthcare Industry. Healthcare insurance companies, hospitals, and other providers around the world are belligerently leveraging digital tools and technologies such as Big Data analytics, Lake, Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing, smart sensors, and the Internet of Things (IoT), for improving the overall quality of care and overall process efficiency and effectiveness. The Healthcare industry has been a center of discussion for embracing Big Data practice across the value chain for the past couple of decades due to the prodigious potential that is concealed in it. With so much abundant information, there have been numerous provocations related to the apiece stage of maneuvering big data that can only be amplified by leveraging high-end computer science results for big data analytics, as mentioned above. Well-organized healthcare ecosystem, analysis, and magnification of big data can influence the course of the game by opening new paths in terms of offering unique yet innovative products and services for the modern age technology-propelled healthcare value chain. This paper emphasizes the impetus of Big Data across the healthcare value chain, which involves the amalgamation of technology, data, and business, yielding better decisions and improving the experience across all touch points.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133960298","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088392
Rupsa Rani Sahu, A. Raut, S. Samantaray
In the era of Artificial Intelligence the old paradigm of oral healthcare has got augmented with automation. Combining thinking abilities of human mind with the cutting edge technology of machine learning can aid the clinicians meet the growing needs and ensure cordial patient-doctor partnership. Advanced software and computing tools are being used to identify problem areas with lesser reporting time and appropriate clinical decision support system to track clinical outcomes. The perceptive abilities of machine learning is directly proportional to information obtained from patients, images, material applications and treatments done. The specialized algorithms are able to predict unexpected complications likely to be encountered and under-diagnosis of rare pathologies that otherwise might be missed due to limitations of clinicians expertise in that area. Today it is essential to embrace machine learning programmes to evolve age old working practices for greater performance and better outcomes by bridging the existing gap between diagnosis and treatment planning. The paper discusses and acknowledges the performance and futuristic applications of machine learning in various subareas of oral health and research.
{"title":"Technological Empowerment: Applications of Machine Learning in Oral Healthcare","authors":"Rupsa Rani Sahu, A. Raut, S. Samantaray","doi":"10.1109/ASSIC55218.2022.10088392","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088392","url":null,"abstract":"In the era of Artificial Intelligence the old paradigm of oral healthcare has got augmented with automation. Combining thinking abilities of human mind with the cutting edge technology of machine learning can aid the clinicians meet the growing needs and ensure cordial patient-doctor partnership. Advanced software and computing tools are being used to identify problem areas with lesser reporting time and appropriate clinical decision support system to track clinical outcomes. The perceptive abilities of machine learning is directly proportional to information obtained from patients, images, material applications and treatments done. The specialized algorithms are able to predict unexpected complications likely to be encountered and under-diagnosis of rare pathologies that otherwise might be missed due to limitations of clinicians expertise in that area. Today it is essential to embrace machine learning programmes to evolve age old working practices for greater performance and better outcomes by bridging the existing gap between diagnosis and treatment planning. The paper discusses and acknowledges the performance and futuristic applications of machine learning in various subareas of oral health and research.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"149 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113999005","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088387
Nrushingh Charan Mahapatra, Prachet Bhuyan
The basic objective of the study is to establish the reinforcement learning technique in the decoding of imagined speech neural signals. The purpose of imagined speech neural computational studies is to give people who are unable to communicate due to physical or neurological limitations of speech generation alternative natural communication pathways. The advanced human-computer interface based on imagined speech decoding based on measurable neural activity could enable natural interactions and significantly improve quality of life, especially for people with few communication alternatives. Recent advances in signal processing and reinforcement learning based on deep learning algorithms have enabled high-quality imagined speech decoding from noninvasively recorded neural activity. Most of the prior research focused on the supervised classification of collected signals, with no naturalistic feedback-based training of imagined speech models for brain-computer interfaces. We employ deep reinforcement learning in this study to create an imagined speech decoder artificial agent based on the deep Q-network (DQN), so that the artificial agent could indeed learn effective policies directly from multidimensional neural electroencephalography (EEG) signal inputs adopting end-to-end reinforcement learning. We show that the artificial agent, supplied only with neural signals and rewards as inputs, was able to decode the imagined speech neural signals efficiently with 81.6947% overall accuracy.
{"title":"Decoding of Imagined Speech Neural EEG Signals Using Deep Reinforcement Learning Technique","authors":"Nrushingh Charan Mahapatra, Prachet Bhuyan","doi":"10.1109/ASSIC55218.2022.10088387","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088387","url":null,"abstract":"The basic objective of the study is to establish the reinforcement learning technique in the decoding of imagined speech neural signals. The purpose of imagined speech neural computational studies is to give people who are unable to communicate due to physical or neurological limitations of speech generation alternative natural communication pathways. The advanced human-computer interface based on imagined speech decoding based on measurable neural activity could enable natural interactions and significantly improve quality of life, especially for people with few communication alternatives. Recent advances in signal processing and reinforcement learning based on deep learning algorithms have enabled high-quality imagined speech decoding from noninvasively recorded neural activity. Most of the prior research focused on the supervised classification of collected signals, with no naturalistic feedback-based training of imagined speech models for brain-computer interfaces. We employ deep reinforcement learning in this study to create an imagined speech decoder artificial agent based on the deep Q-network (DQN), so that the artificial agent could indeed learn effective policies directly from multidimensional neural electroencephalography (EEG) signal inputs adopting end-to-end reinforcement learning. We show that the artificial agent, supplied only with neural signals and rewards as inputs, was able to decode the imagined speech neural signals efficiently with 81.6947% overall accuracy.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121824391","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088353
Kwabena Appiah Ampofo, E. Owusu, J. K. Appati
Electricity consumption is an important economic index, and it plays a significant role in drawing up an energy development policy for every country. Thus, having reliable information regarding the prediction of electricity consumption in a country is imperative to policy and decision-makers to plan and schedule the operation of power systems. Studies have shown that the Long Short-Term Memory (LSTM) neural network model is capable of learning long term temporary dependencies and nonlinear characteristic of a time series phenomenon and it can be a better alternative to the traditional neural networks and statistical methods for predicting electricity consumption. The LSTM neural network model has many hyperparameters, and one of the important hyperparameters is the optimization method. The optimization method plays a significant role in the performance of an LSTM neural network model, but selecting it is not a trivial task to end-users as there is no particular approach for selecting an appropriate method for a particular task. In this study, the LSTM neural network model was used to predict long term electricity consumption using socioeconomic data as predictors to analyze six popular optimization methods that have been implemented in the Keras machine learning library. The motivation is to determine which optimization method will be most suitable for electricity consumption prediction using LSTM neural network model. The results of the study show that the Stochastic Gradient Descent (SGD) optimizer is the most outstanding optimization method.
{"title":"Performance Evaluation of LSTM Optimizers for Long-Term Electricity Consumption Prediction","authors":"Kwabena Appiah Ampofo, E. Owusu, J. K. Appati","doi":"10.1109/ASSIC55218.2022.10088353","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088353","url":null,"abstract":"Electricity consumption is an important economic index, and it plays a significant role in drawing up an energy development policy for every country. Thus, having reliable information regarding the prediction of electricity consumption in a country is imperative to policy and decision-makers to plan and schedule the operation of power systems. Studies have shown that the Long Short-Term Memory (LSTM) neural network model is capable of learning long term temporary dependencies and nonlinear characteristic of a time series phenomenon and it can be a better alternative to the traditional neural networks and statistical methods for predicting electricity consumption. The LSTM neural network model has many hyperparameters, and one of the important hyperparameters is the optimization method. The optimization method plays a significant role in the performance of an LSTM neural network model, but selecting it is not a trivial task to end-users as there is no particular approach for selecting an appropriate method for a particular task. In this study, the LSTM neural network model was used to predict long term electricity consumption using socioeconomic data as predictors to analyze six popular optimization methods that have been implemented in the Keras machine learning library. The motivation is to determine which optimization method will be most suitable for electricity consumption prediction using LSTM neural network model. The results of the study show that the Stochastic Gradient Descent (SGD) optimizer is the most outstanding optimization method.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121921700","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088312
Kanuri Naveen, Kiran Dasari, G. Swapnasri, R. Swetha, S. Nishitha, B. Anusha
The high speed 5G network requires the more gain, the micro strip patch antenna array is the better solution for the high speed data network system. The novel proposed antenna array has the 2x4 structure with the dimension of (28.3 mm x 30 mm) at 5 GHz simulated and the results observed as the gain of 17.6dB S11 reported that as - 24.7,radiation efficiency of 67%.directivity of 17.4801. This novel proposed design has the application of vehicle to vehicle communication and vehicle to other communication and internet of things and modern communication systems. This novel proposed 2x4 antenna array design overcome the above mentioned literature and the gain enhancement is achieved as 17.6dB
高速5G网络对增益的要求更高，微带贴片天线阵列是高速数据网络系统较好的解决方案。该天线阵列具有2x4结构，尺寸为(28.3 mm x 30 mm)，在5 GHz时进行仿真，结果显示增益为17.6dB, S11报道为- 24.7，辐射效率为67%。指向性为17.4801。本设计具有车对车通信、车对其他通信以及物联网和现代通信系统的应用。本文提出的2x4天线阵列设计克服了上述文献的缺陷，实现了17.6dB的增益增强
{"title":"Microstrip Patch Antenna Array With Gain Enhancement for WLAN Applications","authors":"Kanuri Naveen, Kiran Dasari, G. Swapnasri, R. Swetha, S. Nishitha, B. Anusha","doi":"10.1109/ASSIC55218.2022.10088312","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088312","url":null,"abstract":"The high speed 5G network requires the more gain, the micro strip patch antenna array is the better solution for the high speed data network system. The novel proposed antenna array has the 2x4 structure with the dimension of (28.3 mm x 30 mm) at 5 GHz simulated and the results observed as the gain of 17.6dB S11 reported that as - 24.7,radiation efficiency of 67%.directivity of 17.4801. This novel proposed design has the application of vehicle to vehicle communication and vehicle to other communication and internet of things and modern communication systems. This novel proposed 2x4 antenna array design overcome the above mentioned literature and the gain enhancement is achieved as 17.6dB","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127371519","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088373
Debasis Prasad Sahoo, M. Rout, P. Mallick, Sasmita Rani Samanta
Deep learning is becoming more popular in practically every industry, but especially in medical imaging for better diagnostics of various deadly diseases. Deep learning is used to explain difficulties based on medical image processing as part of machine learning artificial intelligence. Most commonly used machine learning algorithm named Convolutional Neural Network (CNN) grasps a resilient position for image recognition tasks. In this paper, we compared the performance of basic CNN and three state of the art transfer-learning models namely, VGG-16, ResNet50 and GoogleNet (Inception-v3) by extracting features from pre-trained CNN architecture. Small datasets of three fatal diseases, which are brain tumor, breast cancer and skin cancer are used. The determination of this study is to discover the finest trade-off between accuracy.
{"title":"Comparative Analysis of Medical Images using Transfer Learning Based Deep Learning Models","authors":"Debasis Prasad Sahoo, M. Rout, P. Mallick, Sasmita Rani Samanta","doi":"10.1109/ASSIC55218.2022.10088373","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088373","url":null,"abstract":"Deep learning is becoming more popular in practically every industry, but especially in medical imaging for better diagnostics of various deadly diseases. Deep learning is used to explain difficulties based on medical image processing as part of machine learning artificial intelligence. Most commonly used machine learning algorithm named Convolutional Neural Network (CNN) grasps a resilient position for image recognition tasks. In this paper, we compared the performance of basic CNN and three state of the art transfer-learning models namely, VGG-16, ResNet50 and GoogleNet (Inception-v3) by extracting features from pre-trained CNN architecture. Small datasets of three fatal diseases, which are brain tumor, breast cancer and skin cancer are used. The determination of this study is to discover the finest trade-off between accuracy.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127442487","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088404
M. Kandpal, Chandramouli Das, C. Misra, Abhaya Kumar Sahoo, Jagannath Singh, R. K. Barik
Blockchain offers decentralized and immutable data storage. In recent years, logistics and supply chain management are slowly realizing Blockchain's impact. Leading-edge companies are trying to fight supply chain network complexity with block chain. Blockchain helps in enabling steady and cost-efficient delivery of products and improving traceability of products, coordination between the consumer's, partners, and financial aid. By considering this, the main objective of the proposed work is to merge decentralized behavior of blockchain with supply chain management to make it more protective, secure and transparent. For the implementation of the proposed framework, it uses Ganache, Metamask, MySQL, PHP, NodeJS, Solidity and JavaScript. Adding blockchain also helps in minimizing the interference of middle man attack in the processes. This technology helps in discarding forged products flowing in the marketplace. Hence, it overall maintains the integrity and authentication among all, the stages in between producer and consumer.
{"title":"Blockchain assisted Supply Chain Management System for Secure Data Management","authors":"M. Kandpal, Chandramouli Das, C. Misra, Abhaya Kumar Sahoo, Jagannath Singh, R. K. Barik","doi":"10.1109/ASSIC55218.2022.10088404","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088404","url":null,"abstract":"Blockchain offers decentralized and immutable data storage. In recent years, logistics and supply chain management are slowly realizing Blockchain's impact. Leading-edge companies are trying to fight supply chain network complexity with block chain. Blockchain helps in enabling steady and cost-efficient delivery of products and improving traceability of products, coordination between the consumer's, partners, and financial aid. By considering this, the main objective of the proposed work is to merge decentralized behavior of blockchain with supply chain management to make it more protective, secure and transparent. For the implementation of the proposed framework, it uses Ganache, Metamask, MySQL, PHP, NodeJS, Solidity and JavaScript. Adding blockchain also helps in minimizing the interference of middle man attack in the processes. This technology helps in discarding forged products flowing in the marketplace. Hence, it overall maintains the integrity and authentication among all, the stages in between producer and consumer.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129322758","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088410
Tridiv Swain, Suravi Sinha, Awantika Singh, Khushali Verma, S. Das
Human Pose Estimation is a method of capturing a collection of coordinates for each joint (arm, head, torso, etc.) that may be used to characterize a person's pose. The initial goal is to create a skeleton-like depiction of a human body, which will then be processed for task-specific applications. The ability to identify and estimate the position of a human body is valuable in a wide range of applications and conditions like action recognition, animation, gaming, and so on. It is a crucial first step toward understanding people through images and media. In this study, graph neural networks were utilised to predict human poses by modelling the human skeleton as an unordered list, greatly enhancing 3D human pose estimation. This paper describes the approach as an efficient way to determine the 3D posture of many persons in a picture. Our model gives a validation accuracy of 92%.
{"title":"Human Pose Estimation Using GNN","authors":"Tridiv Swain, Suravi Sinha, Awantika Singh, Khushali Verma, S. Das","doi":"10.1109/ASSIC55218.2022.10088410","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088410","url":null,"abstract":"Human Pose Estimation is a method of capturing a collection of coordinates for each joint (arm, head, torso, etc.) that may be used to characterize a person's pose. The initial goal is to create a skeleton-like depiction of a human body, which will then be processed for task-specific applications. The ability to identify and estimate the position of a human body is valuable in a wide range of applications and conditions like action recognition, animation, gaming, and so on. It is a crucial first step toward understanding people through images and media. In this study, graph neural networks were utilised to predict human poses by modelling the human skeleton as an unordered list, greatly enhancing 3D human pose estimation. This paper describes the approach as an efficient way to determine the 3D posture of many persons in a picture. Our model gives a validation accuracy of 92%.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125369295","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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