2023 6th International Conference on Information and Computer Technologies (ICICT)最新文献

Named Entity Recognition for peer-review disambiguation in academic publishing 学术出版同行评审消歧的命名实体识别

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00025

Milos Cuculovic, Frédéric Fondement, M. Devanne, J. Weber, M. Hassenforder

{"title":"Named Entity Recognition for peer-review disambiguation in academic publishing","authors":"Milos Cuculovic, Frédéric Fondement, M. Devanne, J. Weber, M. Hassenforder","doi":"10.1109/ICICT58900.2023.00025","DOIUrl":"https://doi.org/10.1109/ICICT58900.2023.00025","url":null,"abstract":"In recent years, there has been a constant increase in the number of scientific peer-reviewed articles published. Each of these articles has to go through a laborious process, from peer review, through author revision rounds, to the final decision made by the editor-in-chief. Lacking time and being under pressure with diverse research tasks, senior scientists need new tools to automate parts of their activities. In this paper, we propose a new approach based on named entity recognition that is able to annotate review comments in order to extract meaningful information about changes requested by reviewers. This research focuses on deep learning models that are achieving state-of-the-art results in many natural language processing tasks. Exploring the performance of BERT-based and XLNet models on the review comments annotation task, a “review-annotation“ model based on SciBERT was trained, able to achieve an F1 score of 0.87. Its usage allows different players in the academic publishing process to better understand the review request. In addition, the correlation of the requested and the actual changes is made possible, allowing the final decision-maker to strengthen the article evaluation.","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124997309","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

Human Wellness in the Cape Fear River Basin Based on CAFO Data 基于CAFO数据的Cape Fear河流域人类健康

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00008

T. Hamilton, Elif Sahin, A. Ayers, Alexander Cossifos, Gülüstan Dogan, Eric Moore

{"title":"Human Wellness in the Cape Fear River Basin Based on CAFO Data","authors":"T. Hamilton, Elif Sahin, A. Ayers, Alexander Cossifos, Gülüstan Dogan, Eric Moore","doi":"10.1109/ICICT58900.2023.00008","DOIUrl":"https://doi.org/10.1109/ICICT58900.2023.00008","url":null,"abstract":"In the U.S, animal farms have moved to an industrial scale resulting in concentrated animal feeding operations (CAFOs) that manage to house thousands of live animals at high densities. Even though CAFOs have remarkably increased the production of animal agriculture, the outcomes related to their activity present possible health and wellness metrics risks to nearby communities. North Carolina has the highest density of swine CAFO activity in the U.S. and the entire world. In this work, we aimed to study the impacts on North Carolina communities and develop predictive models to predict the effects of potential future CAFOs.We analyzed how these variables relate to each other and CAFO abundance to apply classical machine learning models. We developed two groups of models. Group A models predict the areas of likely CAFO expansion and Group B models predict the effects on certain wellness metrics in those areas. Group A models can narrow down the areas of concern and allows us to apply group B models. Results of group B models predict changes in the wellness metrics if certain levels of CAFO development were to occur. The developed models prove effective in the objectives outlined. Additionally, the models could prove an effective tool when considering the expansion of CAFOs into currently unaffected areas.","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129802704","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

A Solution to Channel Aging in 5G Massive MIMO 5G大规模MIMO中信道老化的解决方案

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00034

Talha Younas, Muluneh Mekonnen, Ghulam Farid, H. Munir, Osama Younas

引用次数: 0

Lung Cancer Classification and Prediction of Disease Severity Score Using Deep Learning 基于深度学习的肺癌分类和疾病严重程度评分预测

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00024

Rajkumar Maharaju, R. Valupadasu

{"title":"Lung Cancer Classification and Prediction of Disease Severity Score Using Deep Learning","authors":"Rajkumar Maharaju, R. Valupadasu","doi":"10.1109/ICICT58900.2023.00024","DOIUrl":"https://doi.org/10.1109/ICICT58900.2023.00024","url":null,"abstract":"The World Health organization (WHO) recent statistics show that Cancer is a life-threatening disease that causes 10 million deaths every year around the globe. Lung Cancer is a leading cause of death worldwide, accounting for nearly 2.21 million deaths in 2020. Lung cancer is increasing day by day so early detection is much needed to initiate proper treatment to save the life of cancer patients. Lung cancer detection at an early stage has become very important and easy with image processing and deep learning techniques. The proposed work uses histopathological images (microscopic examination of a biopsy) to classify different cancer categories. This paper presents the use of Adaptive fine-tuned EfficientNetB7 architecture to classify three categories (2-cancer types Adenocarcinoma, Squamous cell carcinoma, and 1-normal i.e benign). The classification results enable the doctors to detect benign or malignant categories to initiate proper treatment. In this work measured performance ma such as Recall, Fl-Score, Precision, and classification accuracy. The proposed work enhanced the classification accuracy from 97.5% to 99.5% compared to the existing work. Later predicted the disease severity score in four levels based on the number of diseased cells present in the image.","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114462841","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

Research on Supermarket Marketing Data Analysis Based on Business Intelligence 基于商业智能的超市营销数据分析研究

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00011

Zhao Mei, Mingjie Li

{"title":"Research on Supermarket Marketing Data Analysis Based on Business Intelligence","authors":"Zhao Mei, Mingjie Li","doi":"10.1109/ICICT58900.2023.00011","DOIUrl":"https://doi.org/10.1109/ICICT58900.2023.00011","url":null,"abstract":"In recent years, with the rapid development of the new retail industry, consumers have more comparison and choice when purchasing goods, which leads to increasingly fierce competition in the supermarket industry and continuous compression of profit space. If you want to improve the competitiveness of supermarkets, you can conduct business intelligence analysis and sales forecast on a large number of data generated by supermarket operation and management, thus providing an important basis for supermarket operation and management strategy adjustment. This paper uses the marketing data of a global supermarket for four years as the data base, analyzes the current business situation from different angles, uses python to conduct data preprocessing, analysis and visualization, and explores the sales strategy to improve sales through sales analysis, commodity analysis and user analysis. It uses the data to find new growth points, and obtains methods to further improve the supermarket sales. Finally, the integrated learning algorithms XGBoost, lightGBM and RandomForest in machine learning are used to build a prediction model and extract four different types of feature set data. The average score values predicted by the three models for ‘Sales’ are different. Among the four types of feature set data, the Average Score value obtained from RandomForest is higher than XGBoost and lightGBM models, and the Average Score value obtained from the “sub_cate_all” feature set data is higher than the value obtained from the other three feature set data, which is 81.25%, indicating that RandomForest has the best prediction effect among the three models.","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324396","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

Adaptive Handover Decision Algorithm for Load Balancing in 5G Heterogeneous Networks 5G异构网络负载均衡的自适应切换决策算法

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/icict58900.2023.00037

Emre Gures, Ibraheem Shayea, S. A. Saad, Ayman A. El-Saleh

{"title":"Adaptive Handover Decision Algorithm for Load Balancing in 5G Heterogeneous Networks","authors":"Emre Gures, Ibraheem Shayea, S. A. Saad, Ayman A. El-Saleh","doi":"10.1109/icict58900.2023.00037","DOIUrl":"https://doi.org/10.1109/icict58900.2023.00037","url":null,"abstract":"Load balancing is one of the key challenges facing the practical implementation of future Heterogeneous networks (HetNets). The case becomes more critical with the implementation of millimeter wave (mmWave) for 5G and further more with 6G mobile networks. Conventional approaches for balancing load, such as maximum signal-to-noise plus noise ratio (max-SINR) and maximum received signal reference power (max-RSRP), may not be efficient and applicable to be utilized in future HetNets. This is due to the significant changes in networks deployment scenarios and network characterizations. In this paper, the proposed algorithm adaptively makes handover (HO) decisions (HODs) based on different decision algorithms selected according to resource availability of serving and target cells to balance traffic load and maximize throughput in fifth-generation (5G) HetNets. A two-step target cell selection strategy that takes into account resource availability of cells and SINR level is integrated into decision algorithms to select the most suitable target cell. Moreover, the proposed algorithm automatically adjusts the HO margin (HOM) level according to the resource availability of the serving cell in a self-optimized manner. The simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in traffic load balancing and throughput maximization.","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116046454","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

Copyright Page 版权页

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/icict58900.2023.00003

引用次数: 0

Discovering Coherent Topics from Urdu Text: A Comparative Study of Statistical Models, Clustering Techniques and Word Embedding 从乌尔都语文本中发现连贯主题:统计模型、聚类技术和词嵌入的比较研究

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00028

Mubashar Mustafa, Feng Zeng, Usama Manzoor, Lin Meng

{"title":"Discovering Coherent Topics from Urdu Text: A Comparative Study of Statistical Models, Clustering Techniques and Word Embedding","authors":"Mubashar Mustafa, Feng Zeng, Usama Manzoor, Lin Meng","doi":"10.1109/ICICT58900.2023.00028","DOIUrl":"https://doi.org/10.1109/ICICT58900.2023.00028","url":null,"abstract":"The volume of data on the internet is continuously expanding due to the abundance of news sources, journals, blogs, contents, and other online publications. The use of Urdu online has grown significantly, much like other languages. Information retrieval (IR) is getting more challenging as data amount rises. The natural language processing (NLP) technique of topic modelling (TM) is crucial for extracting themes or aspects from text. Although there is a long tradition of TM in both English and other western languages, Urdu falls behind in terms of sophisticated NLP tools and resources for TM. The rich morphology of the Urdu language makes TM a challenging task. In this study, we developed a framework of TM and analysed word embedding, statistical models, and clustering techniques for Urdu documents. The aim of this work is to evaluate and compare three distinct approaches based on the coherence measure of extracted topics. The findings of a thorough experiment and evaluation demonstrate that word embedding fails to extract coherent topics in Urdu language, and that the average coherence measure of topics retrieved by clustering approaches outperforms that discovered through statistical models.","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123978984","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

Intracranial Brain Hemorrhage Diagnosis and Classification: A Hybrid Approach 颅内脑出血的诊断和分类:一种混合方法

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00023

Md. Imdadul Haque Emon, Khondoker Nazia Iqbal, Istinub Azad, Amena Akter Aporna, Nibraj Safwan Amlan, M. S. Islam, Rafeed Rahman

{"title":"Intracranial Brain Hemorrhage Diagnosis and Classification: A Hybrid Approach","authors":"Md. Imdadul Haque Emon, Khondoker Nazia Iqbal, Istinub Azad, Amena Akter Aporna, Nibraj Safwan Amlan, M. S. Islam, Rafeed Rahman","doi":"10.1109/ICICT58900.2023.00023","DOIUrl":"https://doi.org/10.1109/ICICT58900.2023.00023","url":null,"abstract":"Intracranial brain hemorrhage is a very common problem with a high mortality rate and often can be life-threatening if necessary steps cannot be taken on time. Patients with hemorrhagic cases need to undergo a CT scan of the brain and for taking further steps, the scans should be examined immediately. For this purpose, we proposed a CAD system using a hybrid machine-learning approach which will help radiologists to diagnose intracranial hemorrhage in a more robust way. We used VGG16 and VGG19 models for feature extraction and then trained random forest (RF) and multilayer perceptron (MLP) models with these features. For our research, we have collected a CT brain image dataset that contains 2,501 images with five hemorrhage classes: intraventricular, intraparenchymal, subarachnoid, epidural, and subdural. After training our models it resulted in an overall accuracy of 97.24% using the VGG16-MLP model and 97.02% accuracy using the VGG19-MLP model for classifying brain hemorrhage from CT scans images. A comparative result of our best approach vs. the previous best approach (from our reviewed papers) for each hemorrhage class is as follows; epidural: VGG19-MLP (0.97) vs. YOLOv4 (0.98), intraparenchymal: VGG16-MLP (0.95) vs. YOLOv4 (0.95), intraventricular: VGG19-MLP (0.90) vs. DB-RF (0.97), subarachnoid: VGG19-MLP (0.94) vs. DB-RF (0.90), and subdural: VGG16-MLP (1.00) vs. YOLOv4 (0.95).","PeriodicalId":425057,"journal":{"name":"2023 6th International Conference on Information and Computer Technologies (ICICT)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129012983","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

SE-RCN: An Economical Capsule Network SE-RCN:经济型胶囊网络

2023 6th International Conference on Information and Computer Technologies (ICICT) Pub Date : 2023-03-01 DOI: 10.1109/ICICT58900.2023.00017

Sami Naqvi, M. El-Sharkawy

引用次数: 0