2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)最新文献_第4页

Neural Network Based Comparison of Real and Synthetic Data Series in TeraHertz Domain 基于神经网络的太赫兹域真实与合成数据序列比较

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914076

Yousif Mudhafar, Djamila Talbi, Zoltán Gál

引用次数: 0

Auditory and Haptic Solutions for Access and Feedback in Internet of Digital Reality Applications 数字现实互联网应用中听觉和触觉的访问和反馈解决方案

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914161

G. Wersényi, Á. Csapó

引用次数: 0

M-tree index for music search based on similarity of cosine contours and tags 基于余弦轮廓和标签相似性的音乐搜索m树索引

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914099

G. Gombos, Zsolt Zoltán Sajti, J. Szalai-Gindl

引用次数: 0

CatMat: 3D Object Recognition Using Catenarian Matching CatMat:使用Catenarian匹配的3D物体识别

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914341

Máté Michelisz, D. Varga, J. Szalai-Gindl

引用次数: 0

Multimodal E-Commerce Product Classification Using Hierarchical Fusion 基于层次融合的多模式电子商务产品分类

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914136

Tsegaye Misikir Tashu, Sara Fattouh, Peter Kiss, Tomáš Horváth

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Estimating road traffic flows in macroscopic Markov model 基于宏观马尔可夫模型的道路交通流估计

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914332

P. Jeszenszky, Renátó Besenczi, M. Szabó, M. Ispány

引用次数: 0

A Comparison of HOG-SVM and SIFT-SVM Techniques for Identifying Brown Planthoppers in Rice Fields HOG-SVM与SIFT-SVM技术在稻田褐飞虱识别中的比较

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914061

Christopher G. Harris, I. Andika, Y. Trisyono

引用次数: 0

Machine Learning Techniques Applied To Bangla Crime News Classification 机器学习技术在孟加拉犯罪新闻分类中的应用

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914240

Nusrat Islam, Rokeya Siddiqua, S. Momen

{"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}

引用次数: 0

Clustering-based customer representation learning from dynamic transactional data 从动态事务数据中学习基于聚类的客户表示

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914293

Gleb Glukhov, Klavdiya Olegovna Bochenina

{"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}

引用次数: 0

Leveraging Fog Computing for Geographically Distributed Smart Cities 利用雾计算实现地理分布的智慧城市

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914276

Rasha S. Gargees

{"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}

引用次数: 0