Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574676
Alluri Harika, Satish Anamalamudi, Syeda Humayra, M. Enduri
The goal of steganography is to hide the data in another medium, meaning disguising the data, so that the existence of the messages can be concealed. Steganography can be applied to many formats of data, including audio, video, and images and can hide any kind of digital information through data hiding techniques. In this work, we propose an application of steganography imaging that would ensure the secure transfer of data along with integrity and confidentiality because steganography relies on hiding messages in unsuspected multimedia data. In this paper, we providing a steganography imaging application which is based on the Advanced Encryption Standard (AES) and random bit technique.
{"title":"Application of Steganography Imaging by AES and Random Bit","authors":"Alluri Harika, Satish Anamalamudi, Syeda Humayra, M. Enduri","doi":"10.1109/CICN51697.2021.9574676","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574676","url":null,"abstract":"The goal of steganography is to hide the data in another medium, meaning disguising the data, so that the existence of the messages can be concealed. Steganography can be applied to many formats of data, including audio, video, and images and can hide any kind of digital information through data hiding techniques. In this work, we propose an application of steganography imaging that would ensure the secure transfer of data along with integrity and confidentiality because steganography relies on hiding messages in unsuspected multimedia data. In this paper, we providing a steganography imaging application which is based on the Advanced Encryption Standard (AES) and random bit technique.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131567210","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574692
Fanqi Meng, Wenhui Wang, Jingdong Wang
With the development of artificial intelligence, the question answering system has penetrated into various industries and has become an important production factor. In the electricity field, problems such as the diversification of power equipment failures and the complicated terminology of the power industry are challenging the traditional power question answering system solutions. Therefore, it is of great significance to construct a question answering system based on the knowledge base in the electricity field. However, there are two problems to be solved in the question answering system in this field: (1) How to accurately segment the vocabulary (2) How to effectively match the sentence similarity. To solve the above problems, this paper proposes an algorithm model of cosine similarity combined with TF-IDF. First, add a custom electricity power dictionary in the word segmentation stage, secondly use the space vector model (VSM)-based TD-IDF algorithm for vectorization, and finally, use cosine similarity degree to perform similarity comparison. This method is verified on the electricity power question answering data set, and compared with the LDA model, TF -IDF algorithm and LSI model respectively. The experimental results show that the accuracy of the method proposed in this paper reaches 75.8%, which is significantly better than the other three. It proves that the research model can accurately match user questions, effectively reduce labor costs, and help electric power workers better solve the problems encountered in their work.
{"title":"Research on Short Text Similarity Calculation Method for Power Intelligent Question Answering","authors":"Fanqi Meng, Wenhui Wang, Jingdong Wang","doi":"10.1109/CICN51697.2021.9574692","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574692","url":null,"abstract":"With the development of artificial intelligence, the question answering system has penetrated into various industries and has become an important production factor. In the electricity field, problems such as the diversification of power equipment failures and the complicated terminology of the power industry are challenging the traditional power question answering system solutions. Therefore, it is of great significance to construct a question answering system based on the knowledge base in the electricity field. However, there are two problems to be solved in the question answering system in this field: (1) How to accurately segment the vocabulary (2) How to effectively match the sentence similarity. To solve the above problems, this paper proposes an algorithm model of cosine similarity combined with TF-IDF. First, add a custom electricity power dictionary in the word segmentation stage, secondly use the space vector model (VSM)-based TD-IDF algorithm for vectorization, and finally, use cosine similarity degree to perform similarity comparison. This method is verified on the electricity power question answering data set, and compared with the LDA model, TF -IDF algorithm and LSI model respectively. The experimental results show that the accuracy of the method proposed in this paper reaches 75.8%, which is significantly better than the other three. It proves that the research model can accurately match user questions, effectively reduce labor costs, and help electric power workers better solve the problems encountered in their work.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129195864","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574671
Giovani Manuel Pitra, Musti K. S. Sastry
Duck curve phenomena occurs when solar energy in higher quantities is integrated into the power grid. This results in excess generation that cannot be delivered during peak hours and a part of the load that cannot be supplied during off-peak hours. This paper proposes a novel, 2-step methodology to determine the effects of duck curve and also to flatten the same. This methodology uses two well-known opensource platforms - SAM (System Advisory Model) and IRENA FlexTool. Data for the energy capacity addition is obtained from SAM and optimization is done with FlexTool. A simple system is considered with a typical load profile and different energy sources. A few case scenarios are considered to demonstrate the effectiveness of the proposed approach and results are summarized.
{"title":"Duck Curve with Renewable Energies and Storage Technologies","authors":"Giovani Manuel Pitra, Musti K. S. Sastry","doi":"10.1109/CICN51697.2021.9574671","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574671","url":null,"abstract":"Duck curve phenomena occurs when solar energy in higher quantities is integrated into the power grid. This results in excess generation that cannot be delivered during peak hours and a part of the load that cannot be supplied during off-peak hours. This paper proposes a novel, 2-step methodology to determine the effects of duck curve and also to flatten the same. This methodology uses two well-known opensource platforms - SAM (System Advisory Model) and IRENA FlexTool. Data for the energy capacity addition is obtained from SAM and optimization is done with FlexTool. A simple system is considered with a typical load profile and different energy sources. A few case scenarios are considered to demonstrate the effectiveness of the proposed approach and results are summarized.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128153837","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574685
V. Aditya, C. Tanishq, V. Sai, Sateeshkrishna Dhuli
Dams play a momentous role to store and capitalize on the water. This paper aims to give an idea to manage the flood water collected in the reservoir of a dam and route it automatically into the canal using the Internet of Things and ANN-based monitoring system. An IoT system is developed for Routing the river water by utilizing the over-abundance of water that is present near the dam. The ratio of the distribution of river water from dams to canals will be decided based on several aspects such as command area, water requirement, etc. During calamities and disasters, the presence of a human is limited. The immediate actions need to be performed by the IoT devices during these situations. A weather prediction system has been developed to predict the weather before ahead before disaster using artificial neural networks. This paper will provide an idea about the efficient and automated operation of dams and routing systems to canals. Our work will be extremely useful in effectively managing the water resources during floods and other calamities that emerged in a locality and avoid submergence in low-lying areas within the catchment area.
{"title":"IoT and ANN Based Automatic Water Level Monitoring For Dams","authors":"V. Aditya, C. Tanishq, V. Sai, Sateeshkrishna Dhuli","doi":"10.1109/CICN51697.2021.9574685","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574685","url":null,"abstract":"Dams play a momentous role to store and capitalize on the water. This paper aims to give an idea to manage the flood water collected in the reservoir of a dam and route it automatically into the canal using the Internet of Things and ANN-based monitoring system. An IoT system is developed for Routing the river water by utilizing the over-abundance of water that is present near the dam. The ratio of the distribution of river water from dams to canals will be decided based on several aspects such as command area, water requirement, etc. During calamities and disasters, the presence of a human is limited. The immediate actions need to be performed by the IoT devices during these situations. A weather prediction system has been developed to predict the weather before ahead before disaster using artificial neural networks. This paper will provide an idea about the efficient and automated operation of dams and routing systems to canals. Our work will be extremely useful in effectively managing the water resources during floods and other calamities that emerged in a locality and avoid submergence in low-lying areas within the catchment area.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167323","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574673
Renzo Chafloque, Ciro Rodríguez, Yuri Pomachagua, Manuel Hilario
Water is an important element that is related to the human being because drinking water is a necessary element for health, also drinking water is considered as an element that also participates in the economy of a society, since it has a defined and industrialized process. Due to the presence of drinking water in different aspects of society, it is important to carry out research that contributes to this topic. The present research work is focused on a predictive analysis using a neural network model, which will allow us to predict and detect whether a given body of water is suitable for human consumption. The proposed model is based on an architecture that uses neural networks that was developed in the Python language, and a dataset obtained from the Kaggle web page was also used. This data set was used for training and validation. Within the preprocessing, the MinMax scaling method obtained from the Sklearn library was used. For the development of the model, the Keras library was used, which provided the necessary methods for the implementation of the seven dense layers that make up the neural network. At the end of the development, a model with an accuracy of approximately 70% was obtained. Finally, we invite for future research, to consider new architectures based on neural networks or other models based on other machine learning classification algorithms.
{"title":"Predictive Neural Networks Model for Detection of Water Quality for Human Consumption","authors":"Renzo Chafloque, Ciro Rodríguez, Yuri Pomachagua, Manuel Hilario","doi":"10.1109/CICN51697.2021.9574673","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574673","url":null,"abstract":"Water is an important element that is related to the human being because drinking water is a necessary element for health, also drinking water is considered as an element that also participates in the economy of a society, since it has a defined and industrialized process. Due to the presence of drinking water in different aspects of society, it is important to carry out research that contributes to this topic. The present research work is focused on a predictive analysis using a neural network model, which will allow us to predict and detect whether a given body of water is suitable for human consumption. The proposed model is based on an architecture that uses neural networks that was developed in the Python language, and a dataset obtained from the Kaggle web page was also used. This data set was used for training and validation. Within the preprocessing, the MinMax scaling method obtained from the Sklearn library was used. For the development of the model, the Keras library was used, which provided the necessary methods for the implementation of the seven dense layers that make up the neural network. At the end of the development, a model with an accuracy of approximately 70% was obtained. Finally, we invite for future research, to consider new architectures based on neural networks or other models based on other machine learning classification algorithms.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131842100","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574640
Suruchi Karnani, H. K. Shakya
The next-generation campus network (CN) is turning into a complex network with a growing number of users, applications, wired and wireless devices. Therefore, to support all the connectivity modes and user demands CN needs to shift from a static, inflexible to a dynamic, flexible, and automated behavior. Software-Defined Networking (SDN) provides flexible network management through its programmable feature and layered architecture. Deployment of multi-controller enhances availability, scalability and brought in a new idea of load sharing between switches and controllers. The CN has thousands of internal, external, and remote users communicating with the network infrastructure. To address the above issues, this article presents SDN based load balancing strategy for campus Networks. Firstly, this article presents an overview of the SDN- based CN framework. Then discuss the conceptual design of a bi-fold load balancing module to shape traffic spikes in SDN-based CN framework. The bi-fold load balancing module consists of dynamic round-robin scheduling for switch load balancing and fractional flow request migration for controller load balancing. This paper proposes a novel load balancing module integrated into SDN-based CN framework.
{"title":"Leveraging SDN for Load Balancing on Campus Network (CN)","authors":"Suruchi Karnani, H. K. Shakya","doi":"10.1109/CICN51697.2021.9574640","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574640","url":null,"abstract":"The next-generation campus network (CN) is turning into a complex network with a growing number of users, applications, wired and wireless devices. Therefore, to support all the connectivity modes and user demands CN needs to shift from a static, inflexible to a dynamic, flexible, and automated behavior. Software-Defined Networking (SDN) provides flexible network management through its programmable feature and layered architecture. Deployment of multi-controller enhances availability, scalability and brought in a new idea of load sharing between switches and controllers. The CN has thousands of internal, external, and remote users communicating with the network infrastructure. To address the above issues, this article presents SDN based load balancing strategy for campus Networks. Firstly, this article presents an overview of the SDN- based CN framework. Then discuss the conceptual design of a bi-fold load balancing module to shape traffic spikes in SDN-based CN framework. The bi-fold load balancing module consists of dynamic round-robin scheduling for switch load balancing and fractional flow request migration for controller load balancing. This paper proposes a novel load balancing module integrated into SDN-based CN framework.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131797419","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574639
A. Pandit, S. Dubey
In medical domain ADRs are defined as unintended harmful reactions of drugs. Several incidences of ADR reports related to a medicinal product can lead to an intervention by higher medical authorities. It can result in label change or complete ban from consumer market. The main aim of this review paper is to elaborate different techniques and methodologies implemented for several ADR datasources using research works related to ADR detection and prediction domain. The relevant research works are collected from known sites like Pubmed & ResearchGate. The papers are selected on the basis of some research questions that are ‘Identify the different datasets used for ADR detection & prediction?’ ‘Why early detection of ADRs are important for better patient safety and healthcare? ’ and ‘How recent trends in artificial intelligence and machine learning domain are useful in accurate prediction of ADRs? On the basis of the research questions a total 172 research papers are collected. After analyzing thoroughly the authors had identified 87 research studies of actual interest that can be categorized into 51 research papers related to ADR detection theme and 36 research works are related to ADR prediction theme. Furthermore the authors present a gap analysis and based on it a novel deep learning framework have been designed. Through this review study the authors have successfully highlighted the fact that early detection and prediction of ADR is crucial for better patient safety and healthcare.
{"title":"A comprehensive review on Adverse Drug Reactions (ADRs) Detection and Prediction Models","authors":"A. Pandit, S. Dubey","doi":"10.1109/CICN51697.2021.9574639","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574639","url":null,"abstract":"In medical domain ADRs are defined as unintended harmful reactions of drugs. Several incidences of ADR reports related to a medicinal product can lead to an intervention by higher medical authorities. It can result in label change or complete ban from consumer market. The main aim of this review paper is to elaborate different techniques and methodologies implemented for several ADR datasources using research works related to ADR detection and prediction domain. The relevant research works are collected from known sites like Pubmed & ResearchGate. The papers are selected on the basis of some research questions that are ‘Identify the different datasets used for ADR detection & prediction?’ ‘Why early detection of ADRs are important for better patient safety and healthcare? ’ and ‘How recent trends in artificial intelligence and machine learning domain are useful in accurate prediction of ADRs? On the basis of the research questions a total 172 research papers are collected. After analyzing thoroughly the authors had identified 87 research studies of actual interest that can be categorized into 51 research papers related to ADR detection theme and 36 research works are related to ADR prediction theme. Furthermore the authors present a gap analysis and based on it a novel deep learning framework have been designed. Through this review study the authors have successfully highlighted the fact that early detection and prediction of ADR is crucial for better patient safety and healthcare.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132958249","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574690
Maverick Poma Rosales, Ciro Rodríguez, Yuri Pomachagua, Carlos Navarro
The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.
{"title":"Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks","authors":"Maverick Poma Rosales, Ciro Rodríguez, Yuri Pomachagua, Carlos Navarro","doi":"10.1109/CICN51697.2021.9574690","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574690","url":null,"abstract":"The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133589081","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574674
Fanqi Meng, Wenhui Wang, Jingdong Wang
As a new type of question retrieval method, intelligent question answering can provide users with answers to the questions they need in a short time. The rapid development of the Internet makes the emergence of intelligent question answering systems inevitable and lays the foundation for their extensive application in various fields. This article uses Citespace to visually analyze more than 500 academic papers in the field of intelligent question and answer from 2010 to 2020 included in Web of Science and IEEE access, including the distribution of countries, institutions, and authors, as well as keyword and research topic clustering, etc., in order to obtain the Field research hotspots and future development trends. On this basis, it focuses on the summary of intelligent question answering based on knowledge graph and intelligent question answering with sentiment analysis, providing a reference for the close integration of intelligent question answering with knowledge graph and sentiment analysis.
智能问答作为一种新型的问题检索方法，可以在短时间内为用户提供所需问题的答案。互联网的快速发展使得智能问答系统的出现成为必然，也为其在各个领域的广泛应用奠定了基础。本文利用Citespace对Web of Science和IEEE access收录的2010 - 2020年智能问答领域的500多篇学术论文进行可视化分析，包括国家、机构、作者分布，以及关键词和研究主题聚类等，以获取该领域的研究热点和未来发展趋势。在此基础上重点总结了基于知识图谱的智能问答和基于情感分析的智能问答，为基于知识图谱和情感分析的智能问答的紧密结合提供参考。
{"title":"Visual Analysis of the Research Status of Intelligent Question Answering System","authors":"Fanqi Meng, Wenhui Wang, Jingdong Wang","doi":"10.1109/CICN51697.2021.9574674","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574674","url":null,"abstract":"As a new type of question retrieval method, intelligent question answering can provide users with answers to the questions they need in a short time. The rapid development of the Internet makes the emergence of intelligent question answering systems inevitable and lays the foundation for their extensive application in various fields. This article uses Citespace to visually analyze more than 500 academic papers in the field of intelligent question and answer from 2010 to 2020 included in Web of Science and IEEE access, including the distribution of countries, institutions, and authors, as well as keyword and research topic clustering, etc., in order to obtain the Field research hotspots and future development trends. On this basis, it focuses on the summary of intelligent question answering based on knowledge graph and intelligent question answering with sentiment analysis, providing a reference for the close integration of intelligent question answering with knowledge graph and sentiment analysis.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128029451","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574680
Franco Tasso Parraga, Ciro Rodríguez, Yuri Pomachagua, Diego Rodriguez
The significant advance in artificial intelligence has posed many challenges, with disease detection being one of the most important. Early detection can be very important in preventing progressive disease progression and can help provide accurate treatment options. Cervical cancer is the fourth type of cancer most common in women. In 2018, 570 000 cases were estimated in women around the world. This article aims to present a review of different image-based algorithms for cervical cancer screening. For the research process, three important sources of information were considered: Scopus, Web of Science, and PubMed, considering a total of 12 articles taking into account the last five years. The articles were analyzed considering the databases used, the preprocessing of the images, the segmentation of the images, the classification of images, and the proposals' results. The results show great advances in the techniques used for cervical cancer screening, with convolutional neural networks being the most widely used technique. In addition, including the segmentation stage in the construction of the models can significantly increase precision. Finally, it is shown that the k-fold cross validation technique is one of the most used and efficient techniques to validate the models.
人工智能的重大进步带来了许多挑战，疾病检测是最重要的挑战之一。早期发现对于预防疾病进展非常重要，并有助于提供准确的治疗方案。子宫颈癌是女性中最常见的第四种癌症。2018年，世界各地估计有57万例女性病例。本文旨在介绍不同的基于图像的子宫颈癌筛查算法的综述。在研究过程中，考虑了三个重要的信息来源:Scopus, Web of Science和PubMed，总共考虑了过去五年的12篇文章。从数据库的使用、图像的预处理、图像的分割、图像的分类以及建议的结果等方面对文章进行分析。结果显示，用于宫颈癌筛查的技术取得了巨大进步，卷积神经网络是最广泛使用的技术。此外，在模型构建中加入分割阶段可以显著提高精度。最后，证明了k-fold交叉验证技术是验证模型最常用和最有效的技术之一。
{"title":"A Review of Image-Based Deep Learning Algorithms for Cervical Cancer Screening","authors":"Franco Tasso Parraga, Ciro Rodríguez, Yuri Pomachagua, Diego Rodriguez","doi":"10.1109/CICN51697.2021.9574680","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574680","url":null,"abstract":"The significant advance in artificial intelligence has posed many challenges, with disease detection being one of the most important. Early detection can be very important in preventing progressive disease progression and can help provide accurate treatment options. Cervical cancer is the fourth type of cancer most common in women. In 2018, 570 000 cases were estimated in women around the world. This article aims to present a review of different image-based algorithms for cervical cancer screening. For the research process, three important sources of information were considered: Scopus, Web of Science, and PubMed, considering a total of 12 articles taking into account the last five years. The articles were analyzed considering the databases used, the preprocessing of the images, the segmentation of the images, the classification of images, and the proposals' results. The results show great advances in the techniques used for cervical cancer screening, with convolutional neural networks being the most widely used technique. In addition, including the segmentation stage in the construction of the models can significantly increase precision. Finally, it is shown that the k-fold cross validation technique is one of the most used and efficient techniques to validate the models.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117264761","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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