Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914076
Yousif Mudhafar, Djamila Talbi, Zoltán Gál
Extension of real data by synthetic data becomes more important aspect of the virtualization technics today. In this paper we demonstrate how synthetic data generated from real data can be used in the supervised classification process of three different recurrent neural networks: Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU). Other aspect is presented concerning the influence of the noise to the classification of real and synthetic data series. The paper demonstrates that LSTM network has better classification performance than GRU, even the last one has higher accuracy during the training. Synthetic data can eternalize just part of the features of the original real data and extraction efficiency of these characteristics depend on the applied neural network.
{"title":"Neural Network Based Comparison of Real and Synthetic Data Series in TeraHertz Domain","authors":"Yousif Mudhafar, Djamila Talbi, Zoltán Gál","doi":"10.1109/CITDS54976.2022.9914076","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914076","url":null,"abstract":"Extension of real data by synthetic data becomes more important aspect of the virtualization technics today. In this paper we demonstrate how synthetic data generated from real data can be used in the supervised classification process of three different recurrent neural networks: Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU). Other aspect is presented concerning the influence of the noise to the classification of real and synthetic data series. The paper demonstrates that LSTM network has better classification performance than GRU, even the last one has higher accuracy during the training. Synthetic data can eternalize just part of the features of the original real data and extraction efficiency of these characteristics depend on the applied neural network.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164741","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914161
G. Wersényi, Á. Csapó
The concept of Internet of Digital Reality (IoD) was introduced as the next level organization of cognitive entities following the concept of the Internet of Things (IoT) and Internet of Everything (IoE). As virtual-immersive environments are a fundamental component of IoD which allow human and non-human entities to interact in real time, the ability a wide range of communication modalities is crucial. This paper briefly presents the concept of IoD together with an overview of various I/O solutions for human users, with a focus on research directions and (re)emerging technologies in the near future.
{"title":"Auditory and Haptic Solutions for Access and Feedback in Internet of Digital Reality Applications","authors":"G. Wersényi, Á. Csapó","doi":"10.1109/CITDS54976.2022.9914161","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914161","url":null,"abstract":"The concept of Internet of Digital Reality (IoD) was introduced as the next level organization of cognitive entities following the concept of the Internet of Things (IoT) and Internet of Everything (IoE). As virtual-immersive environments are a fundamental component of IoD which allow human and non-human entities to interact in real time, the ability a wide range of communication modalities is crucial. This paper briefly presents the concept of IoD together with an overview of various I/O solutions for human users, with a focus on research directions and (re)emerging technologies in the near future.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129140684","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914099
G. Gombos, Zsolt Zoltán Sajti, J. Szalai-Gindl
The similarity between songs is an important part of the MIR (Music Information Retrieval), but the definition of the similarity is very subjective. Similarity can be used, for example, in recommendation systems. These systems recommend similar songs based on the user history from a database. To find similar songs in a fast way we have to index the data. Most database indexes are created for exact item searches. GiST (Generalized Search Tree) gives us the possibility to create an index with a distance function between items. These distances can be used for similarity measures. In this paper, we show how can use music similarity for distance in M-tree, which is a distance-based index. Two similarity metrics are used to create an index of music data: song tags and cosine contour.
{"title":"M-tree index for music search based on similarity of cosine contours and tags","authors":"G. Gombos, Zsolt Zoltán Sajti, J. Szalai-Gindl","doi":"10.1109/CITDS54976.2022.9914099","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914099","url":null,"abstract":"The similarity between songs is an important part of the MIR (Music Information Retrieval), but the definition of the similarity is very subjective. Similarity can be used, for example, in recommendation systems. These systems recommend similar songs based on the user history from a database. To find similar songs in a fast way we have to index the data. Most database indexes are created for exact item searches. GiST (Generalized Search Tree) gives us the possibility to create an index with a distance function between items. These distances can be used for similarity measures. In this paper, we show how can use music similarity for distance in M-tree, which is a distance-based index. Two similarity metrics are used to create an index of music data: song tags and cosine contour.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123299191","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914341
Máté Michelisz, D. Varga, J. Szalai-Gindl
Object recognition in 3D point clouds is an important and widely researched topic. We propose a novel method based on local point descriptors. We detect edge points on the scene and object clouds, and construct a weighted edge graph on the object clouds. We find point chains on the objects based on the constructed graph, and seek similar point chains on the scene cloud using local descriptor matching and geometric constraints. We estimate transformations using corresponding point chains, and validate the transformations with a voxel-based method. Our method is capable of multi-instance object recognition. In this paper we present our method and compare it with a similar solution. Based on our evaluation, the proposed method is able to find various objects on scene clouds and robust to noise.
{"title":"CatMat: 3D Object Recognition Using Catenarian Matching","authors":"Máté Michelisz, D. Varga, J. Szalai-Gindl","doi":"10.1109/CITDS54976.2022.9914341","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914341","url":null,"abstract":"Object recognition in 3D point clouds is an important and widely researched topic. We propose a novel method based on local point descriptors. We detect edge points on the scene and object clouds, and construct a weighted edge graph on the object clouds. We find point chains on the objects based on the constructed graph, and seek similar point chains on the scene cloud using local descriptor matching and geometric constraints. We estimate transformations using corresponding point chains, and validate the transformations with a voxel-based method. Our method is capable of multi-instance object recognition. In this paper we present our method and compare it with a similar solution. Based on our evaluation, the proposed method is able to find various objects on scene clouds and robust to noise.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117276931","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914136
Tsegaye Misikir Tashu, Sara Fattouh, Peter Kiss, Tomáš Horváth
In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems.
{"title":"Multimodal E-Commerce Product Classification Using Hierarchical Fusion","authors":"Tsegaye Misikir Tashu, Sara Fattouh, Peter Kiss, Tomáš Horváth","doi":"10.1109/CITDS54976.2022.9914136","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914136","url":null,"abstract":"In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129558957","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914332
P. Jeszenszky, Renátó Besenczi, M. Szabó, M. Ispány
Traffic flows gain more and more attention in transportation engineering. One possible means of understanding the traffic flow in a city is to gather sequences of traffic position data, called link flows, measured by vehicle-mounted sensors, which are increasingly available by various providers for municipalities. Link flows can be used for planning of operation and maintenance, and for forecasting of future traffic events. In this paper, we investigate how the microscopic Markov traffic model can be used to predict traffic congestion on the roads between different nodes or regions of a city. The proposed model is evaluated in a numerical study by using real traffic data recorded in the city of Porto. The results show that the model developed for simulation is of limited use for predicting the traffic between different areas of a city.
{"title":"Estimating road traffic flows in macroscopic Markov model","authors":"P. Jeszenszky, Renátó Besenczi, M. Szabó, M. Ispány","doi":"10.1109/CITDS54976.2022.9914332","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914332","url":null,"abstract":"Traffic flows gain more and more attention in transportation engineering. One possible means of understanding the traffic flow in a city is to gather sequences of traffic position data, called link flows, measured by vehicle-mounted sensors, which are increasingly available by various providers for municipalities. Link flows can be used for planning of operation and maintenance, and for forecasting of future traffic events. In this paper, we investigate how the microscopic Markov traffic model can be used to predict traffic congestion on the roads between different nodes or regions of a city. The proposed model is evaluated in a numerical study by using real traffic data recorded in the city of Porto. The results show that the model developed for simulation is of limited use for predicting the traffic between different areas of a city.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129205320","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914061
Christopher G. Harris, I. Andika, Y. Trisyono
Brown planthoppers (BPH) are insect pests that cause significant damage to rice crop yields throughout the Asia-Pacific region. Early identification of BPH forms has ramifications for forecasting potential outbreaks. To address this, we use Adaboost and Haar features to discover areas of interest in images of rice plants. We apply two separate techniques to identify the BPH in images: we compare a technique that utilizes HOG descriptors and another that utilizes SIFT feature descriptors. To each of these techniques, we apply a Support Vector Machine (SVM) to allow us to classify areas of interest in the images. Our approach achieves a weighted average classification rate of 95.38% for HOG and 96.38% for SIFT, improving upon state-of-the-art BPH detection methods and our findings lay the groundwork for other insect pest identification and detection efforts.
{"title":"A Comparison of HOG-SVM and SIFT-SVM Techniques for Identifying Brown Planthoppers in Rice Fields","authors":"Christopher G. Harris, I. Andika, Y. Trisyono","doi":"10.1109/CITDS54976.2022.9914061","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914061","url":null,"abstract":"Brown planthoppers (BPH) are insect pests that cause significant damage to rice crop yields throughout the Asia-Pacific region. Early identification of BPH forms has ramifications for forecasting potential outbreaks. To address this, we use Adaboost and Haar features to discover areas of interest in images of rice plants. We apply two separate techniques to identify the BPH in images: we compare a technique that utilizes HOG descriptors and another that utilizes SIFT feature descriptors. To each of these techniques, we apply a Support Vector Machine (SVM) to allow us to classify areas of interest in the images. Our approach achieves a weighted average classification rate of 95.38% for HOG and 96.38% for SIFT, improving upon state-of-the-art BPH detection methods and our findings lay the groundwork for other insect pest identification and detection efforts.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127751184","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914240
Nusrat Islam, Rokeya Siddiqua, S. Momen
The methodical approach to crime detection, crime pattern classification and crime tendency guessing is called crime analysis and prediction. Crime is naturally unpredictable and socially disruptive. With the increase in the population of Bangladesh, the tendency of crime is also increasing, which is destroying our society in various ways. Therefore, crime data analysis has become essential in order to predict future crime types. In our research paper, six types of Machine learning algorithms were used in order to classify the crime news. Crime news were fetched from online Bangla newspapers and TV channels using Web Scraper. In order to extract the features (important words), two types of feature extractors have been used including CountVectorizer and TfidfVectorizer where CountVectorizer was from a well-known python pre-trained package named BnVec. Accuracies of 87.69% and 86.09% were found from the Logistic Regression and SVM models respectively. Besides, Logistic regression provided less false negative with 86.65% recall and 86.58% F1-score. This research has a potential to be used to prevent crime and to apprehend, investigate and prosecute the criminals.
{"title":"Machine Learning Techniques Applied To Bangla Crime News Classification","authors":"Nusrat Islam, Rokeya Siddiqua, S. Momen","doi":"10.1109/CITDS54976.2022.9914240","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914240","url":null,"abstract":"The methodical approach to crime detection, crime pattern classification and crime tendency guessing is called crime analysis and prediction. Crime is naturally unpredictable and socially disruptive. With the increase in the population of Bangladesh, the tendency of crime is also increasing, which is destroying our society in various ways. Therefore, crime data analysis has become essential in order to predict future crime types. In our research paper, six types of Machine learning algorithms were used in order to classify the crime news. Crime news were fetched from online Bangla newspapers and TV channels using Web Scraper. In order to extract the features (important words), two types of feature extractors have been used including CountVectorizer and TfidfVectorizer where CountVectorizer was from a well-known python pre-trained package named BnVec. Accuracies of 87.69% and 86.09% were found from the Logistic Regression and SVM models respectively. Besides, Logistic regression provided less false negative with 86.65% recall and 86.58% F1-score. This research has a potential to be used to prevent crime and to apprehend, investigate and prosecute the criminals.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128638649","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914293
Gleb Glukhov, Klavdiya Olegovna Bochenina
We propose a new clustering-based method for customer feature vector extraction based on the history of their financial transactions. Customer vector representations can be used to solve downstream tasks, such as customer segmentation or next purchase category prediction. The main advantage of the proposed method is that the obtained feature vectors may be interpreted in terms of temporal activity while preserving sufficient quality for solving downstream tasks. Using this method, we were able to extract well-interpreted customer segments (using the debit card transaction data from a large Russian bank) which are useful for various business cases (e.g., planning of marketing campaigns or customized recommendations of financial products). This interpretation would help meet the tasks of analyzing the typical customer behavior and its reasons. In addition, we demonstrate that our method of constructing embeddings provides comparable quality for several downstream tasks (customer purchase category forecasting, missing category prediction, and campaign targeting) with non-interpretable algorithms such as word2vec and autoencoders approaches.
{"title":"Clustering-based customer representation learning from dynamic transactional data","authors":"Gleb Glukhov, Klavdiya Olegovna Bochenina","doi":"10.1109/CITDS54976.2022.9914293","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914293","url":null,"abstract":"We propose a new clustering-based method for customer feature vector extraction based on the history of their financial transactions. Customer vector representations can be used to solve downstream tasks, such as customer segmentation or next purchase category prediction. The main advantage of the proposed method is that the obtained feature vectors may be interpreted in terms of temporal activity while preserving sufficient quality for solving downstream tasks. Using this method, we were able to extract well-interpreted customer segments (using the debit card transaction data from a large Russian bank) which are useful for various business cases (e.g., planning of marketing campaigns or customized recommendations of financial products). This interpretation would help meet the tasks of analyzing the typical customer behavior and its reasons. In addition, we demonstrate that our method of constructing embeddings provides comparable quality for several downstream tasks (customer purchase category forecasting, missing category prediction, and campaign targeting) with non-interpretable algorithms such as word2vec and autoencoders approaches.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114345479","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 : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914276
Rasha S. Gargees
Recently, the emergence of smart cities (SC), where data streams come from various geographically distributed places, has posed new challenges. Cloud Computing provides excellent services for smart cities, such as powerful computation and storage. However, processing the geographically distributed data using cloud computing only is not an ideal solution in some cases. Additionally, moving all the big raw data to the remote cloud is another challenge for cloud computing since there will be shortcomings in terms of delay and high bandwidth consumption. A solution that allows fog-to-cloud or fog-to-fog communication can address these limitations as fogs are typically located locally near the data sources. However, the questions related to the efficient frameworks design, workload distribution, cost, and various key technologies and communication challenges remain. To this end, this research investigates the impact of fog, employing our proposed architecture, on the efficient utilization and management of resources in highly distributed systems through experiments. The comparison showed that fog computing reduces the cost in terms of time and resource utilization. Additionally, the collaboration of autonomous agents locally (within one fog) or globally (across multiple fogs and cloud) supports scalability and automation. It also facilitates large-scale data processing across various real-world distributed locations.
{"title":"Leveraging Fog Computing for Geographically Distributed Smart Cities","authors":"Rasha S. Gargees","doi":"10.1109/CITDS54976.2022.9914276","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914276","url":null,"abstract":"Recently, the emergence of smart cities (SC), where data streams come from various geographically distributed places, has posed new challenges. Cloud Computing provides excellent services for smart cities, such as powerful computation and storage. However, processing the geographically distributed data using cloud computing only is not an ideal solution in some cases. Additionally, moving all the big raw data to the remote cloud is another challenge for cloud computing since there will be shortcomings in terms of delay and high bandwidth consumption. A solution that allows fog-to-cloud or fog-to-fog communication can address these limitations as fogs are typically located locally near the data sources. However, the questions related to the efficient frameworks design, workload distribution, cost, and various key technologies and communication challenges remain. To this end, this research investigates the impact of fog, employing our proposed architecture, on the efficient utilization and management of resources in highly distributed systems through experiments. The comparison showed that fog computing reduces the cost in terms of time and resource utilization. Additionally, the collaboration of autonomous agents locally (within one fog) or globally (across multiple fogs and cloud) supports scalability and automation. It also facilitates large-scale data processing across various real-world distributed locations.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131027655","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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