2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)最新文献

{"title":"EICF – An Enhanced Intelligent Cloud-based Framework for Automated Product Quality Assurance","authors":"Aashish Arora, Rajeev Gupta","doi":"10.1109/ICIDCA56705.2023.10100073","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10100073","url":null,"abstract":"Quality is the most important issue for all producers in every manufacturing system, regardless of the products. Quality management is a set of tools and processes that can help a manufacturing unit's overall system performance. With the continuously changing company environment and fierce market competition, quality tools and continuous improvement (CI) has become critical for long-term success and embracing difficulties. A combination of increasing customer expectations and technical improvements has increased the complexity of industrial systems. Stronger quality control systems must be purchased, especially when a small manufacturing error could result in a big loss for the company. A non-automated quality control system necessitates a large human staff, which increases costs and lowers reliability. Consequently, this paper presents an outline of quality control for products that exhibit automated behavior. The paper also explores a methodology of using cloud computing to automate quality assurance. As cloud computing has risen to prominence in recent years. A growing number of manufacturers are considering moving their quality management to a cloud-based quality system. Therefore, in today's ever-changing and unpredictable manufacturing environment, the cloud's cost benefits, power, and agility have become critical to survival. Therefore, this paper has presented a service-based proposal system for visual quality assurance using machine learning algorithms and cloud computing. The model is evaluated in terms of response delay and accuracy of defect detection in manufacturing parts. The average response delay was evaluated to be approx. 8sec and the average accuracy of the model were approx. 93%.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430143","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}

{"title":"Improvement of Graph-Based Assets using Blockchain Quality Control","authors":"R. T. Selvi, T. C. S. Lakshmi, D. Karunkuzhali, R. A. A. Rosaline","doi":"10.1109/ICIDCA56705.2023.10099935","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10099935","url":null,"abstract":"Traceability through different sectors including logistics and shipping has had a positive effect from blockchain technologies. This innovative solution helps establish a decentralized and drum-resistant headline through the question of data silos between organisms and authority consolidation. Nevertheless, the use of fungible tokens should represent complicated traceability operations; a new model for the projection of digital and physical goods on a blockchain is discussed. In the present token architecture, however, there is no powerful model to restore history. Blockchain explorers, which are central trustworthy actors, are the most common means for extracting past information on tokens. Any headers of the block can only be naively verified, but it takes a lot of time. According to preliminary calculations, restoring the previous 1,030 tokens on the Ethereum main net would take about 57 minutes. Challenge of this system is to eliminate competition for new chain blocks and Transactions may be handled quicker with simultaneous node development. It can be proposed that a directed acyclic graph (DAG) token definition which allows the token history to be quickly discovered, without looking at the entire blockchain. The model connects any token state from time to time and covers concepts like fusion and breakup. The implementation and assessment suggest the major enhancements to the reliability of the transitional history analysis in the DAG-based token architecture. The outcome demonstrates that the discovery of 1,030 tokens can be done in seconds and that the method maintains long-term success even though the block chain expands.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129121571","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}

{"title":"An Improved Driver Drowsiness Detection using Haar Cascade Classifier","authors":"S. S. Saranya, Ravi Mytresh, Mylavarapu Manideep","doi":"10.1109/ICIDCA56705.2023.10100209","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10100209","url":null,"abstract":"Driver drowsiness detection is a system that uses various sensors and algorithms to detect when a driver is becoming fatigued or drowsy. When a driver is drowsy, they are not operating in full alert mode rather, they are only somewhat tired. The majority of traffic accidents throughout the world are caused by fatigued and sleepy drivers. This can be done through monitoring eye movements, facial expressions, and head position, as well as analyzing driving patterns such as sudden lane changes or prolonged periods of inactivity on the steering wheel. The system can then alert the driver to take a break or pull over to rest, potentially preventing accidents caused by drowsy driving. It can also be used in commercial vehicles for safety of drivers and other people on the road. Accidents in the present day are increasingly being caused by this consisting of ocular fatigue. These characteristics show that the driver's condition is not right. For the purpose of detecting sleepiness, the ratio of distances between the horizontal and vertical eye landmarks is calculated using the EAR (Eye Aspect Ratio). The proposed method calculates the landmark since the landmarks are identified precisely enough to do so. This study determines the eye aspect ratio (EAR), a single scalar quantity that characterizes the opening of the eyes in each frame, and extracts it. Finally, an SVM classifier developed by Haar Cascade recognizes eye blinks as a pattern of EAR values. However, both driver fatigue and distraction may result in slower response times, reduced driving efficiency, and a higher risk of being involved in an accident. The output is delivered to the detection system, and the alert will be activated, if the driver's degree of fatigue and the estimated amount of sleepiness are determined. The true number of accidents brought on by driver fatigue is difficult to ascertain because it is frequently under reported. The driver typically doesn't notice the small change from being tired to nodding off. This explains why it is critical to continue research in this area with the goal of reducing the frequency of driver drowsy accidents and motivating for a driver sleepiness detection system.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129213805","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}

{"title":"A Multimodal AI Framework for Hyper Automation in Industry 5.0","authors":"Sudharson D, Bhuvaneshwaran A, Kalaiarasan Tr, Satheesh kumar D, Sushmita V, Jyothi Lakshmi N","doi":"10.1109/ICIDCA56705.2023.10099581","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10099581","url":null,"abstract":"Organizations utilize the logics of hyperautomation which is a business-oriented technique, to quickly perceive, validate, and automate many business and IT activities. The coordinated employment of numerous software tools and technologies such as event-driven software architecture, Artificial Intelligence (AI) and Machine Learning (ML) is known as hyperautomation. The idea is found on the comprehension that Robotic Process Automation (RPA), a new method for automating computerized operations, is difficult to scale at the communal level and has a restricted scope for automation. A multimodal framework for the tactical utilization of multiple automation software technologies, separately or together with AI and ML is provided by hyperautomation.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123536918","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}

{"title":"Survey on Job Recommendation Systems using Machine Learning","authors":"Raj Thali, Suyog Mayekar, Shubham More, Sanjana Barhate, Sangeetha Selvan","doi":"10.1109/ICIDCA56705.2023.10100122","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10100122","url":null,"abstract":"With the development of internet technology online job hunting has been boosted as it helps to save the time and efforts, [theory providing ease of search]. It's hard for job seekers to rely solely on keyword acquisition to find a job that befit their needs. To overcome this problem, the system will be made using article-based collaborative filtering and content-based filtering job recommended algorithm. The proposed system will be notified where the information about various jobs could be scrapped from vivid websites to form a huge database comprising majority of information regarding real time job opportunities. The users' information would be fetched from the CV submitted with our data from the dataset and recommendations would be provided on the same. This would prove to be of greater help as one doesn't have to hunt across varied websites. Job recommender systems' goal is to offer suggestions based on data about the users' preferences that has been recorded. The major goal is to provide skill recommendations to users so they can learn them, discover appropriate work, and streamline the application process for both novice and seasoned job seekers. The difficulties lie in identifying the best individuals based on their skill sets.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121688555","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}

{"title":"A Review on Sentiment Analysis using Machine Learning","authors":"Sumit Sindhu, Sanjeev Kumar, Amandeep Noliya","doi":"10.1109/ICIDCA56705.2023.10099665","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10099665","url":null,"abstract":"Sentiment Analysis [SA] is a method that is used to detect the state of some information or text. It can be applied to tasks such as text classification and summarization, and various methods can be used to extract sentiment from text, including machine learning algorithms and rule-based systems. Sentiment analysis is an important tool for businesses to understand customer feedback and measure customer satisfaction and loyalty. It classifies the given text into three categories: positive, negative, or neutral. The statement “the picture quality of TV is good” expresses a positive opinion about a particular TV, while “the sound is not adequate” expresses a negative opinion. Recent challenges with sentiment analysis indude capturing sarcasm, emotion in text and dealing with long texts. The paper presents a literature survey of many machine learning techniques as well as deep learning models such as CNN, LS TM, etc.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114748669","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}

{"title":"Detailed Investigation on Blockchain Smart Contracts Application and its Usefulness","authors":"Mahalakshmi Arumugam, Jeidy Panduro-Ramirez, Jesús Padilla-Caballero, Pallavi Gildhiyal, Manjusha Goel, Dilip Kumar Jang Bahadur Saini","doi":"10.1109/ICIDCA56705.2023.10099575","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10099575","url":null,"abstract":"The blockchain and digital industry has emerged in the last decade as a result of the swift advancement of blockchain technology and virtual currencies, which has had an impact on the banking sector. However, owing to the advent of smart contracts, which seem to be the computer systems created to naturally enable, monitor, & execute the negotiations and agreements between several unreliable individuals, successful decentralised apps without engaging a reliable third party have arisen. Despite the good aspects of smart contracts, proliferation is still hindered by a number of obstacles, including risks, weaknesses, and statutory provisions. This article provides a thorough intellectual and applied examination of block chain-enabled smart contracts. Moreover, the study gives a classification of current blockchain -enabled smart contract solutions, and discusses the emergence of smart contract-based research by reviewing the existing research works. Based on the study's findings, a variety of challenges & open issues that must be investigated are highlighted. Notably, security and privacy challenges along with rising costs, scalability and lack of adoption are identified as the key challenges of blockchain smart contract application. Ultimately, the potential patterns are emphasized.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281583","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}

{"title":"The Impact of Blockchain Technology to Protect Image and Video Integrity from Identity Theft using Deepfake Analyzer","authors":"Jefferson A. Costales, Shikhar Shiromani, M. Devaraj","doi":"10.1109/ICIDCA56705.2023.10099668","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10099668","url":null,"abstract":"Deepfake is a critical problem that poses a significant threat to the authenticity and integrity of visual media. Although computer vision and pattern recognition have been employed in previous studies, there exist limitations on the quantity of layers that are permissible for use. This article proposes a strategy based on decentralized blockchain to create a secure and reliable system that can determine the authenticity of content in order to tackle this problem. tracing the original video, and safeguarding media integrity from identity theft. The proposed approach employs a combination of technologies including Blockchain, the encryption, media filtering, The proposed approach utilizes the The Convolutional Neural Network, Consensus Algorithm, and SHA-256 Hashing Algorithm are employed to ensure the protection and integrity of the media. Additionally, the implementation of a Deepfake Analyzer is recommended to augment media protection. The proposed method's efficacy is assessed by utilizing an available dataset from GitHub, and the outcomes are extensively examined. This research offers valuable perspectives on how blockchain technology can be leveraged to safeguard the integrity of images, videos, and reduce the spread of misinformation in the media. The proposed Deepfake Analyzer adds an additional layer of protection, ensuring video and image integrity against identity theft.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134505651","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}

{"title":"Face Manipulation Detection in Images using Inception Layers and Graph Convolutional Networks","authors":"Aman Mahendroo, Aaradhya Beri, Anukriti Kaushal","doi":"10.1109/ICIDCA56705.2023.10099546","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10099546","url":null,"abstract":"Recently, there has been an upsurge in the availability and advancement of technologies that can morph images. With the advancement in machine learning and deep learning methods, it is now increasingly difficult to detect which image is real or manipulated generated by the latest technologies. When done nonconsensually, images of manipulated faces can harm people's public image and warrant the development of a method that can contrast authentic images from morphed ones. Therefore, image manipulation detection is paramount for regulating online video frame/image data and protecting their authenticity. This study has generated the latest tampered image dataset and subsequently pioneered an efficient model of a two-fold contribution based on Inception Layers with a Graph Convolutional Network (GCN). The Inception layers consist of several convolutional layers of incrementally increasing kernel sizes that help capture morphological information, which GCN processes to capture the relationship between features, followed by a dense layer module. Further, this study conduct experiments on the proposed dataset and compare the results of the proposed method with existing techniques. The result shows that the proposed approach yields better accuracy than the existing state-of-the-art methods.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124289388","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}

{"title":"ENSEMBLED CROPIFY – Crop & Fertilizer Recommender System with Leaf Disease Prediction","authors":"K. Devi Priya, A. S. Samyogitha, A. K. Krishna Reddy, B. Divya Sri","doi":"10.1109/ICIDCA56705.2023.10100117","DOIUrl":"https://doi.org/10.1109/ICIDCA56705.2023.10100117","url":null,"abstract":"In India, farming is the most popular occupation. It has a significant impact on the Indian economy and offers plenty of employment prospects to Indians. Nowadays, farmers do not choose the best crop for each soil type. The yields from agriculture are directly impacted. Farmers in the area are suffering severe financial losses as a result. There are now a lot of considerations to consider while cultivating a specific sort of crop on a specific type of soil. Numerous soil variables are considered to choose the best crop to grow. This innovative opinion mining approach will take factors like soil moisture content, pressure, and temperature. To estimate the ideal crop, this prediction model would in fact use machine learning algorithms. The optimal fertilizer will also be suggested based on the soil moisture levels, pH value, temperature, and crop-related environmental circumstances. The best fertilizer will be suggested using this innovative system for fertilizer recommendations in order to produce the best harvests. This application uses deep neural networks to forecast leaf disease. To determine whether leaf image in the system contains diseases, the test set is being examined. If leaf does not have any disease it is said to be normal. Otherwise, the leaf is infected, and crop disease treatment is promptly advised.","PeriodicalId":108272,"journal":{"name":"2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114534955","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}