Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150782
Rupali Aggarwal, P. Sarangi, A. Sahoo
In the era where most of our transactions whether it is for shopping, electricity bills, insurance payments, school and college fees are paid using plastic money through wireless and various online modes. Increase in both online transactions and ecommerce platforms has given rise to many online frauds these days and also security threats. To detect these fraudulent activities, we created a machine learning model. In this research we modeled a dataset using Machine Learning Algorithms. It is proposed to predict fraudulent transactions made by users. It is a real-life example of a binary Classification problem. This research emphasizes on analyzing and pre-processing the dataset and implementing various python libraries, and used concepts like Exploratory Data Analysis, Data Modeling, Feature Extraction etc. and implemented a fraud detection process using the four algorithms.
{"title":"Credit Card Fraud Detection: Analyzing the Performance of Four Machine Learning Models","authors":"Rupali Aggarwal, P. Sarangi, A. Sahoo","doi":"10.1109/ICDT57929.2023.10150782","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150782","url":null,"abstract":"In the era where most of our transactions whether it is for shopping, electricity bills, insurance payments, school and college fees are paid using plastic money through wireless and various online modes. Increase in both online transactions and ecommerce platforms has given rise to many online frauds these days and also security threats. To detect these fraudulent activities, we created a machine learning model. In this research we modeled a dataset using Machine Learning Algorithms. It is proposed to predict fraudulent transactions made by users. It is a real-life example of a binary Classification problem. This research emphasizes on analyzing and pre-processing the dataset and implementing various python libraries, and used concepts like Exploratory Data Analysis, Data Modeling, Feature Extraction etc. and implemented a fraud detection process using the four algorithms.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132568851","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/ICDT57929.2023.10151042
D. Praveenadevi, S. Rekha, B. Girimurugan, K. J. Narendra Kumar, B. Hemanjali, B. Lalitvamsi
There are only so many resources available; it is essential to put into action strategies that will lead to sustainable growth if one wishes to ensure their continued success over the long run. Despite this, a significant number of scholars have investigated the prospect that digital technologies may be able to increase sustainable performance in this age of digitalization and globalization. This research maintains collaboration and coordination in a digitally connected supply chain (SC) could contribute to sustainability is still in its early phases, and there is still a long way to go before it can be considered complete. Using SC, it is possible to cut down on the amount of energy that is consumed, cut down on the amount of time that is spent traveling, and make better use of the assets that are employed in logistics. Case studies conducted with a variety of manufacturers form the basis of this investigation and will serve as its primary focus. Researchers nevertheless give equal weight to the social and environmental sustainability components, even though the majority of studies in this subject concentrate on the financial aspect of the topic. The research concluded that incorporating SC into logistics and supply chain management led to a moderate improvement in terms of both environmental and social sustainability.
{"title":"Impact of Digitalization on Sustainable Supply Chain Management","authors":"D. Praveenadevi, S. Rekha, B. Girimurugan, K. J. Narendra Kumar, B. Hemanjali, B. Lalitvamsi","doi":"10.1109/ICDT57929.2023.10151042","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151042","url":null,"abstract":"There are only so many resources available; it is essential to put into action strategies that will lead to sustainable growth if one wishes to ensure their continued success over the long run. Despite this, a significant number of scholars have investigated the prospect that digital technologies may be able to increase sustainable performance in this age of digitalization and globalization. This research maintains collaboration and coordination in a digitally connected supply chain (SC) could contribute to sustainability is still in its early phases, and there is still a long way to go before it can be considered complete. Using SC, it is possible to cut down on the amount of energy that is consumed, cut down on the amount of time that is spent traveling, and make better use of the assets that are employed in logistics. Case studies conducted with a variety of manufacturers form the basis of this investigation and will serve as its primary focus. Researchers nevertheless give equal weight to the social and environmental sustainability components, even though the majority of studies in this subject concentrate on the financial aspect of the topic. The research concluded that incorporating SC into logistics and supply chain management led to a moderate improvement in terms of both environmental and social sustainability.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133348028","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/ICDT57929.2023.10150878
Rinku Garg, A. Sandhu, Bobbinpreet Kaur
Monitoring plant illnesses was just by vision, is insufficient for recognizing plant diseases. The leaf changes color, revealing blotches such as yellow dots, black spots, or chocolate brown patches, as a result of the symptoms. Diseases like Anthracnose, Powdery Mildew, and Sooty Mold can be found on some leaves. To diagnose the disease, manual observation and pathogen detection are used, which takes longer and costs more money and gives less precision results. Therefore, a superior option to fast and precise identification through image processing techniques can be used, which can be more dependable than some other old traditional ways. Fruit, leaves, stems, and lesions are examples of plant components that may exhibit symptoms. The goal is to accurately find and diagnose the disease based on the leaf photos. Image preprocessing, segmentation, feature extraction, and classification are all necessary phases in the process. This paper will go through how to recognize mango leaf disease. Leaf characteristics such as their axis, including main and minor axes, are acquired, and diagnosed using various classification methods for illness diagnosis.
{"title":"A Systematic Analysis of Various Techniques for Mango Leaf Disease Detection","authors":"Rinku Garg, A. Sandhu, Bobbinpreet Kaur","doi":"10.1109/ICDT57929.2023.10150878","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150878","url":null,"abstract":"Monitoring plant illnesses was just by vision, is insufficient for recognizing plant diseases. The leaf changes color, revealing blotches such as yellow dots, black spots, or chocolate brown patches, as a result of the symptoms. Diseases like Anthracnose, Powdery Mildew, and Sooty Mold can be found on some leaves. To diagnose the disease, manual observation and pathogen detection are used, which takes longer and costs more money and gives less precision results. Therefore, a superior option to fast and precise identification through image processing techniques can be used, which can be more dependable than some other old traditional ways. Fruit, leaves, stems, and lesions are examples of plant components that may exhibit symptoms. The goal is to accurately find and diagnose the disease based on the leaf photos. Image preprocessing, segmentation, feature extraction, and classification are all necessary phases in the process. This paper will go through how to recognize mango leaf disease. Leaf characteristics such as their axis, including main and minor axes, are acquired, and diagnosed using various classification methods for illness diagnosis.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067744","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}
Cities and transportation have expanded together since the earliest significant human settlements. The same factors that tempt people to reside in densely populated areas also fuel the frequently atrocious levels of traffic congestion on city streets. Since the widespread use of vehicles, one of the primary issues modern cities confront is traffic congestion. A quick journey to the convenience store might take up to 30 minutes due to slowness or traffic congestion. Road rage, road bullies, and serious accidents are caused by traffic congestion. To overcome these challenges, we will be creating an app that will allow users to register their concerns so that assistance may be sent as quickly as possible in order to make the traffic management system and commuters' lives more convenient.
{"title":"Smart Tracking System for Traffic using Android based Application","authors":"Abhishek Goyal, Aakriti Singh, Aditi Dubey, Anurag Shukla","doi":"10.1109/ICDT57929.2023.10150852","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150852","url":null,"abstract":"Cities and transportation have expanded together since the earliest significant human settlements. The same factors that tempt people to reside in densely populated areas also fuel the frequently atrocious levels of traffic congestion on city streets. Since the widespread use of vehicles, one of the primary issues modern cities confront is traffic congestion. A quick journey to the convenience store might take up to 30 minutes due to slowness or traffic congestion. Road rage, road bullies, and serious accidents are caused by traffic congestion. To overcome these challenges, we will be creating an app that will allow users to register their concerns so that assistance may be sent as quickly as possible in order to make the traffic management system and commuters' lives more convenient.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124699331","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/ICDT57929.2023.10150897
Kesavan Nallaluthan, Jessnor Elmy Mat Jizat, S. Suhaimi, Normala S. Govindarajo, Dileep Kumar Mohanachandran, A. Ghouri
In the business programs of Universiti Pendidikan Sultan Idris (UPSI), the Three-Pronged teaching technique is implemented as a student-centered learning process. This approach combines elements of the game, problem, and challenge-based learning with the larger goal of preparing business students to handle complicated, unanticipated global or industrial problems. It promotes an interactive and dependable classroom that calls for students' innovative contributions, teamwork, and participation in the professional world. Micro credential platforms, artificial intelligence, and a new pedagogical strategy: that's the idea for UPSI's undergraduate business. Therefore, this kind of instruction is increasingly being used in business courses like Strategic Management. Undergraduate students benefit from this teaching method since they are exposed to industrial phenomena while developing 21st-century abilities (collaborative, creative, critical thinking, and communication).
在Pendidikan Sultan Idris大学(UPSI)的商业课程中，三管齐下的教学技术被实施为以学生为中心的学习过程。这种方法结合了游戏、问题和基于挑战的学习元素，其更大的目标是让商科学生准备好处理复杂的、意想不到的全球或行业问题。它促进了一个互动和可靠的课堂，呼吁学生的创新贡献，团队合作和参与专业领域。微证书平台、人工智能和新的教学策略:这就是UPSI本科业务的理念。因此，在战略管理等商业课程中越来越多地使用这种教学方式。本科学生受益于这种教学方法，因为他们在接触工业现象的同时发展21世纪的能力(协作、创造性、批判性思维和沟通)。
{"title":"AI in Student as Manager Model-Future Directions of Business Studies","authors":"Kesavan Nallaluthan, Jessnor Elmy Mat Jizat, S. Suhaimi, Normala S. Govindarajo, Dileep Kumar Mohanachandran, A. Ghouri","doi":"10.1109/ICDT57929.2023.10150897","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150897","url":null,"abstract":"In the business programs of Universiti Pendidikan Sultan Idris (UPSI), the Three-Pronged teaching technique is implemented as a student-centered learning process. This approach combines elements of the game, problem, and challenge-based learning with the larger goal of preparing business students to handle complicated, unanticipated global or industrial problems. It promotes an interactive and dependable classroom that calls for students' innovative contributions, teamwork, and participation in the professional world. Micro credential platforms, artificial intelligence, and a new pedagogical strategy: that's the idea for UPSI's undergraduate business. Therefore, this kind of instruction is increasingly being used in business courses like Strategic Management. Undergraduate students benefit from this teaching method since they are exposed to industrial phenomena while developing 21st-century abilities (collaborative, creative, critical thinking, and communication).","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123002825","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/ICDT57929.2023.10151338
D. Praveenadevi, S. Sreekala, B. Girimurugan, K. V. R. Krishna Teja, G. Naga Kamal, Asturi Chetan Chandra
One of the most significant issues that supply networks are currently facing is accurately estimating the level of demand for their products. Along with improving stock management and reducing overhead costs, some of the goals of the plan included growing sales, earnings, and customer base. The evaluation of historical data with the purpose of improving demand forecasting can be accomplished with the assistance of several different methods, some of which include methodologies based on machine learning, time series analysis, and deep learning models. This can be done to improve the accuracy of demand forecasting. The purpose of this investigation is to design an insightful strategy for forecasting future demand. In this paper, we develop an enhanced model to support the supply chain management and it uses a deep learning model to improve the process of supply chain management. The deep learning model is trained, tested and validated to improve the process of supplying the products via supply chain. The simulation is carried out in python for a set of objects that to be tracked and the results show that the model achieves higher accuracy of sending the products.
{"title":"An Enhanced Method on Using Deep Learning Techniques in Supply Chain Management","authors":"D. Praveenadevi, S. Sreekala, B. Girimurugan, K. V. R. Krishna Teja, G. Naga Kamal, Asturi Chetan Chandra","doi":"10.1109/ICDT57929.2023.10151338","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151338","url":null,"abstract":"One of the most significant issues that supply networks are currently facing is accurately estimating the level of demand for their products. Along with improving stock management and reducing overhead costs, some of the goals of the plan included growing sales, earnings, and customer base. The evaluation of historical data with the purpose of improving demand forecasting can be accomplished with the assistance of several different methods, some of which include methodologies based on machine learning, time series analysis, and deep learning models. This can be done to improve the accuracy of demand forecasting. The purpose of this investigation is to design an insightful strategy for forecasting future demand. In this paper, we develop an enhanced model to support the supply chain management and it uses a deep learning model to improve the process of supply chain management. The deep learning model is trained, tested and validated to improve the process of supplying the products via supply chain. The simulation is carried out in python for a set of objects that to be tracked and the results show that the model achieves higher accuracy of sending the products.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123631144","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}
Developing a higher-level security system for iden- tification or authentication has long been an active research subject in many fields. Traditional security systems utilize a key or a password to secure a process or a product, whereas bio metric security systems use a person’s physical or behavioral attributes. Iris patterns play a significant role in a number of potential recognition or authentication applications because of their uniqueness, universality, dependability, and stability. The use of iris recognition techniques in bio metric identification and authentication systems has increased significantly. In this work, a novel approach for classification of iris is presented, making it simple for anybody to apply this technology. This model allows for the usage of any eye image and only selects photos that pass the model’s internal filters. Besides, this study provides iris identification model that starts with eye detection and ends with iris image recognition. In addition, a method for iris classification is presented in this study that combines Transfer learning and Convolutional Neural Networks (CNNs) algorithms using Ensemble learning. The automatic segmentation technique for iris detection uses Hough Transform and is capable of localizing the pupil and iris region, as well as obstructing eyelids, eyelashes, and reflections. To overcome the image irregularities, the iris region is extracted and then extracted iris is converted into rectangular block using Normalization. In this paper, a weighted ensemble technique is proposed that demonstrates iris classification which is made by combining the weighted average sum of the accuracy attained by various classifiers. This model is trained and tested on well- known iris datasets: Ubiris Version 2 (part1) and Ubiris Version 2 (part2), Casia Iris Interval. The paper resulted in the accuracy of the proposed system of Ensemble Learning on various epochs stating that the accuracy is directly dependent on the number of epochs as on Casia Iris Interval Dataset, the accuracy of the Ensemble model at epoch 10 (77.86%), epoch 30 (83.79%), epoch 50 (86.00%) and epoch 100 (87.24%) which is increasing as number of epochs increases. The paper also proves that the performance of the new system is better than the other base models. According to one of the dataset, Casia Iris Interval dataset, the proposed Ensemble Learning model’s accuracy on 100 epoch was 87.24%, which is significantly higher than the accuracy of the other base models, including DenseNet121 (70.88%), MobileNet (86.51%), InceptionV3 (63.61%), InceptionResNetV2 (34.09%), Xception (68.45%), and CNN (4.07%) respectively.
长期以来，开发更高层次的身份识别或认证安全系统一直是许多领域的活跃研究课题。传统的安全系统使用密钥或密码来保护过程或产品，而生物识别安全系统使用人的物理或行为属性。由于其唯一性、通用性、可靠性和稳定性，虹膜模式在许多潜在的识别或身份验证应用程序中发挥着重要作用。虹膜识别技术在生物识别和认证系统中的应用已经显著增加。本文提出了一种新颖的虹膜分类方法，使其易于应用。这个模型允许使用任何眼睛图像，并且只选择通过模型内部过滤器的照片。此外，本研究还提供了从眼部检测开始到虹膜图像识别结束的虹膜识别模型。此外，本研究提出了一种结合迁移学习和卷积神经网络(cnn)算法的虹膜分类方法。虹膜检测的自动分割技术采用霍夫变换，能够对瞳孔和虹膜区域进行定位，也能够遮挡眼睑、睫毛和反射。为了克服图像的不规则性，首先提取虹膜区域，然后用归一化方法将提取的虹膜转换为矩形块。本文提出了一种加权集成技术，该技术通过将各种分类器的分类精度加权平均相加来进行虹膜分类。该模型在著名的鸢尾数据集Ubiris Version 2 (part1)和Ubiris Version 2 (part2)， Casia iris Interval上进行了训练和测试。结果表明，在Casia Iris区间数据集上，集成学习系统在不同时期的准确率直接依赖于时期数，随着时期数的增加，集成模型在时期10(77.86%)、时期30(83.79%)、时期50(86.00%)和时期100(87.24%)的准确率呈上升趋势。本文还证明了新系统的性能优于其他基本模型。根据其中一个数据集Casia Iris Interval数据集，所提出的集成学习模型在100 epoch上的准确率为87.24%，显著高于其他基础模型，包括DenseNet121(70.88%)、MobileNet(86.51%)、InceptionV3(63.61%)、InceptionResNetV2(34.09%)、Xception(68.45%)和CNN(4.07%)。
{"title":"Smart Iris Classification Using Weighted Average Ensemble Learning","authors":"Aditi Arora, Aanchal Gupta, Bhavya Jindal, Gaurish Gupta","doi":"10.1109/ICDT57929.2023.10151036","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151036","url":null,"abstract":"Developing a higher-level security system for iden- tification or authentication has long been an active research subject in many fields. Traditional security systems utilize a key or a password to secure a process or a product, whereas bio metric security systems use a person’s physical or behavioral attributes. Iris patterns play a significant role in a number of potential recognition or authentication applications because of their uniqueness, universality, dependability, and stability. The use of iris recognition techniques in bio metric identification and authentication systems has increased significantly. In this work, a novel approach for classification of iris is presented, making it simple for anybody to apply this technology. This model allows for the usage of any eye image and only selects photos that pass the model’s internal filters. Besides, this study provides iris identification model that starts with eye detection and ends with iris image recognition. In addition, a method for iris classification is presented in this study that combines Transfer learning and Convolutional Neural Networks (CNNs) algorithms using Ensemble learning. The automatic segmentation technique for iris detection uses Hough Transform and is capable of localizing the pupil and iris region, as well as obstructing eyelids, eyelashes, and reflections. To overcome the image irregularities, the iris region is extracted and then extracted iris is converted into rectangular block using Normalization. In this paper, a weighted ensemble technique is proposed that demonstrates iris classification which is made by combining the weighted average sum of the accuracy attained by various classifiers. This model is trained and tested on well- known iris datasets: Ubiris Version 2 (part1) and Ubiris Version 2 (part2), Casia Iris Interval. The paper resulted in the accuracy of the proposed system of Ensemble Learning on various epochs stating that the accuracy is directly dependent on the number of epochs as on Casia Iris Interval Dataset, the accuracy of the Ensemble model at epoch 10 (77.86%), epoch 30 (83.79%), epoch 50 (86.00%) and epoch 100 (87.24%) which is increasing as number of epochs increases. The paper also proves that the performance of the new system is better than the other base models. According to one of the dataset, Casia Iris Interval dataset, the proposed Ensemble Learning model’s accuracy on 100 epoch was 87.24%, which is significantly higher than the accuracy of the other base models, including DenseNet121 (70.88%), MobileNet (86.51%), InceptionV3 (63.61%), InceptionResNetV2 (34.09%), Xception (68.45%), and CNN (4.07%) respectively.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123640054","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}
Conversely, cutting-edge innovations, such as the Internet of Things (IoT), virtual reality (VR), artificial intelligence (AI), and 5G wireless connectivity techniques, are indeed being created to address these difficulties in order to increase the patient outcomes and quality healthcare efficiency while lowering total medical costs. It’s not an impossible ideal, since new technologies are already influencing and reconstructing healthcare in insidious ways. Even though the capabilities described above are linked, this study will focus on situations involving the use of 5G wireless connectivity in healthcare settings to transmute a healthiness insurance arrangement that is fading to deal with the weight of modern illnesses and the problem of scale - up towards cumulative inhabitants. We further outline possible roadblocks to the deployment of 5G technology.
{"title":"5G Intrusion for Monitoring Healthcare Services","authors":"Diwan Singh Rawat, Deepti Sharma, Samta Kathuria, Angel Swastik Duggal, Rajesh Singh, Manish Gupta","doi":"10.1109/ICDT57929.2023.10151053","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151053","url":null,"abstract":"Conversely, cutting-edge innovations, such as the Internet of Things (IoT), virtual reality (VR), artificial intelligence (AI), and 5G wireless connectivity techniques, are indeed being created to address these difficulties in order to increase the patient outcomes and quality healthcare efficiency while lowering total medical costs. It’s not an impossible ideal, since new technologies are already influencing and reconstructing healthcare in insidious ways. Even though the capabilities described above are linked, this study will focus on situations involving the use of 5G wireless connectivity in healthcare settings to transmute a healthiness insurance arrangement that is fading to deal with the weight of modern illnesses and the problem of scale - up towards cumulative inhabitants. We further outline possible roadblocks to the deployment of 5G technology.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125219639","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/ICDT57929.2023.10150967
Venkateswararao. Podile, Anuradha Averineni, Dhanush Kethineni, Darapaneni Brahma Naidu, Bezawada Venkata Naga Sai Vignesh, M. R. Krishna Reddy
In recent years, financial institutions (FIs) have been hesitant when it comes to using supply chain finance (SCF), which is short for supply chain financing. This is because SCF stands for supply chain financing, which is used to address the financing needs of small and medium-sized businesses. One of the most difficult and time-consuming tasks in the industry of financial planning is currently the assessment of the credit risk that is posed by small and medium-sized enterprises (SME). On the other hand, the requirements of such forecasting are not something that can be provided by employing conventional models of credit risk. This article uses a stacking model, which takes into account both technical aspects and macroeconomic data, in order to make predictions regarding the movement of the stock price index in reference to the price that was in effect not too long ago. A recursive application of the cross-validation procedure is carried out in order to produce the input for the second-level classifier. This is done to mitigate the risk of the model being overly constrained by the data. Logistic regression and its regularized version are used as meta-classifiers in the second layer to the fundamental classifier to class learning. The outcome of our research is an exhaustive stacking architecture that has the potential to be applied in the banking sector.
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