2023 International Conference on Disruptive Technologies (ICDT)最新文献

{"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}

{"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}

{"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}

{"title":"ICDT 2023 Cover Page","authors":"","doi":"10.1109/icdt57929.2023.10150808","DOIUrl":"https://doi.org/10.1109/icdt57929.2023.10150808","url":null,"abstract":"","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"20 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":"114475274","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":"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}

{"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}

{"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}

{"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}

{"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}

{"title":"An Enhanced Ensemble Machine Learning Methods in Financial Marketing","authors":"Venkateswararao. Podile, Anuradha Averineni, Dhanush Kethineni, Darapaneni Brahma Naidu, Bezawada Venkata Naga Sai Vignesh, M. R. Krishna Reddy","doi":"10.1109/ICDT57929.2023.10150967","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150967","url":null,"abstract":"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.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"4 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":"131291432","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}