2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)最新文献_第2页

Application of Steganography Imaging by AES and Random Bit AES和随机比特在隐写成像中的应用

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574676

Alluri Harika, Satish Anamalamudi, Syeda Humayra, M. Enduri

引用次数: 0

Research on Short Text Similarity Calculation Method for Power Intelligent Question Answering 面向电力智能问答的短文本相似度计算方法研究

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574692

Fanqi Meng, Wenhui Wang, Jingdong Wang

{"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}

引用次数: 3

Duck Curve with Renewable Energies and Storage Technologies 鸭子曲线与可再生能源和存储技术

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574671

Giovani Manuel Pitra, Musti K. S. Sastry

引用次数: 6

IoT and ANN Based Automatic Water Level Monitoring For Dams 基于物联网和人工神经网络的大坝水位自动监测

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574685

V. Aditya, C. Tanishq, V. Sai, Sateeshkrishna Dhuli

{"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}

引用次数: 3

Predictive Neural Networks Model for Detection of Water Quality for Human Consumption 人类用水水质检测的预测神经网络模型

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574673

Renzo Chafloque, Ciro Rodríguez, Yuri Pomachagua, Manuel Hilario

{"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}

引用次数: 3

Leveraging SDN for Load Balancing on Campus Network (CN) 利用SDN实现校园网(CN)负载均衡

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574640

Suruchi Karnani, H. K. Shakya

{"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}

引用次数: 1

A comprehensive review on Adverse Drug Reactions (ADRs) Detection and Prediction Models 药物不良反应(adr)检测与预测模型综述

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574639

A. Pandit, S. Dubey

{"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}

引用次数: 1

Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks 基于卷积神经网络的视频监控摄像机图像中的枪支检测

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574690

Maverick Poma Rosales, Ciro Rodríguez, Yuri Pomachagua, Carlos Navarro

引用次数: 0

Visual Analysis of the Research Status of Intelligent Question Answering System 智能问答系统研究现状的可视化分析

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574674

Fanqi Meng, Wenhui Wang, Jingdong Wang

引用次数: 0

A Review of Image-Based Deep Learning Algorithms for Cervical Cancer Screening 基于图像的宫颈癌筛查深度学习算法综述

2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2021-09-22 DOI: 10.1109/CICN51697.2021.9574680

Franco Tasso Parraga, Ciro Rodríguez, Yuri Pomachagua, Diego Rodriguez

{"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}

引用次数: 3