2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)最新文献

A Study and Analysis on Various Types of Agricultural Drones and its Applications 各类农用无人机及其应用研究与分析

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296195

M. Dileep, A. Navaneeth, Savita Ullagaddi, A. Danti

{"title":"A Study and Analysis on Various Types of Agricultural Drones and its Applications","authors":"M. Dileep, A. Navaneeth, Savita Ullagaddi, A. Danti","doi":"10.1109/ICRCICN50933.2020.9296195","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296195","url":null,"abstract":"Drones are considered to be the greatest invention of mankind. Drones can be used in many areas widely. Drones can also be used in agriculture and it is called as unnamed aerial vehicles (UAV). In the traditional agriculture methods land vehicles are used to monitor various activities of the agriculture, this was consuming lot of human effort and time. Using drones in agriculture is more beneficial than using traditional methods for the activities. Usage of drones in agriculture provides a huge benefit in terms of economy and time due to their most astonishing features. In recent years many surveys have proved that drones can cover almost 10 to 15 times of the area which can be covered with traditional land based techniques. Drones can be controlled by computers according to their capacities, that is drones can be automated over some range of area, locating remote area, and even can be semi-automated. Drones can be efficiently used in agriculture for performing certain activities such as, studying weather conditions and variations, infection for the crops, land fertility and many more. Because of the efficiency of the drones they can be used in various activities of agriculture. In this paper, a detailed study has been made on various types of agricultural drones based on the feature, capacity, range as well as cost and the area of agriculture where they suit the most, and a statistical analysis about the usage of the drones in the field of agriculture.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115413537","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: 9

Entropy-Based Feature Selection for Data Clustering Using k-Means and k-Medoids Algorithms 基于熵的k-Means和k-Medoids聚类特征选择

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296186

M. Dhar, S. M. Nahid Hasan, Tahsin Rahaman Otushi, Musharrat Khan

引用次数: 2

Interpretable Underwater Image Enhancement based on Convolutional Neural Network 基于卷积神经网络的可解释水下图像增强

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296201

S. Dhar, Hiranmoy Roy, A. Mukhopadhyay, Antu Kundu, A. Ghosh, Soham Roy

引用次数: 1

A New Modified Red Deer Algorithm for Multi-level Image Thresholding 一种新的改进的Red Deer算法用于多级图像阈值分割

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296166

S. De, Sandip Dey, Soumyaratna Debnath, Abhirup Deb

{"title":"A New Modified Red Deer Algorithm for Multi-level Image Thresholding","authors":"S. De, Sandip Dey, Soumyaratna Debnath, Abhirup Deb","doi":"10.1109/ICRCICN50933.2020.9296166","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296166","url":null,"abstract":"This paper presents a modified evolution strategy based meta-heuristic, named Modified Red Deer Algorithm (MRDA), which can be effectively and methodically applied to solve single-objective optimization problems. Recently, the actions of red deers have been analysed during their breading time, that in turn inspired the researchers to develop a popular meta-heuristic, called Red Deer Algorithm (RDA). The RDA has been designed to deal with different combinatorial optimization problems in a variety of real-life applications. This paper introduces few adaptive approaches to modify the inherent operators and parameters of RDA to enhance its efficacy. As a comparative study, the performance of MRDA has been evaluated with RDA and Classical Genetic Algorithm (CGA) by utilizing some real-life gray-scale images. At the outset, the results of these competitive algorithms have been assessed with respect to optimum fitness, worst fitness, average fitness, standard deviation, convergence time at best case and average convergence time at three distinct level of thresholding for each test image. Finally, t-test and Friedman Test have been conducted among themselves to check out the superiority. This comparative analysis establishes that MRDA outperforms others in all facets and furnish exceedingly competitive results.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115987093","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

Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models 集成深度学习模型提高人像分割精度

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296196

Yong Woon Kim, J. Innila Rose, Addapalli V. N. Krishna

{"title":"Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models","authors":"Yong Woon Kim, J. Innila Rose, Addapalli V. N. Krishna","doi":"10.1109/ICRCICN50933.2020.9296196","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296196","url":null,"abstract":"Portrait segmentation is widely used as a preprocessing step in multiple applications. The accuracy of portrait segmentation models indicates its reliability. In recent times, portrait segmentation using deep learning models have achieved significant success in performance and accuracy. However, these portrait segmentation models are limited to a single model. In this paper, we propose ensemble approach using multiple portrait segmentation models to improve the segmentation accuracy. The result of experiment shows that the proposed ensemble approach produces better accuracy than individual models. Accuracy of single models and proposed ensemble approach were compared with Intersection over Union (IoU) metric and false prediction rate to evaluate the accuracy performance. The result shows reduced false negative rate and false discovery rate, this reduction in false prediction has enabled ensemble approach to produce segmented images with optimized error and improved result of segmentation in portrait area of human body than individual portrait segmentation models","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449531","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

Nondestructive Testing Image Segmentation based on Neutrosophic Set and Bat Algorithm 基于嗜中性集和Bat算法的无损检测图像分割

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9295965

S. Dhar, M. Kundu, Hiranmoy Roy

引用次数: 3

Patient Health Observation and Analysis With Machine Learning And IoT Based in Realtime Environment 基于实时环境的机器学习和物联网的患者健康观察和分析

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296193

Arnab Dey, P. Chanda, S. Sarkar

{"title":"Patient Health Observation and Analysis With Machine Learning And IoT Based in Realtime Environment","authors":"Arnab Dey, P. Chanda, S. Sarkar","doi":"10.1109/ICRCICN50933.2020.9296193","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296193","url":null,"abstract":"Today rapid growth of communication and internet technologies has resulted in a significant enhancement of IoT devices. Even the best hospitals and doctors need to develop more in terms of patient care. In case of long waiting periods, a long term gap between doctor visits, inadequate data collection, and other challenges may create problems to healthcare professionals from giving the best care possible. The patients who are suffering from chronic diseases for them ehealthcare are a daily concern. They require disease management tools not only during their doctor visits but every day. In global pandemic situations like today’s, this automated software with the features of Machine Learning will help patients and doctors to maintain physical distance; doctors can monitor patients and prescribe medication to the respective patient from anywhere. Whenever Doctor is unable to monitor patient then this IoT based Machine Learning model will help patients to provide proper medicine through medical staff available based on the symptoms and reports from the IoT sensors with the Machine Learning (ML) trained data set. Here the results obtained for prediction of diabetes and heart diseases, through various machine learning approaches are shown. The obtained results show that for the Gradient Boost, KNN, Random Forest Based classification approaches classify the diseases with higher accuracy rates than the existing models.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116600460","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: 4

Prediction of Covid-19 pandemic based on Regression 基于回归的Covid-19大流行预测

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296175

A. Mandayam, Rakshith A.C, S. Siddesha, S. Niranjan

引用次数: 14

Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification 网页分类中梯度增强与极值增强集成方法的比较

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296176

J. Dutta, Yong Woon Kim, Dalia Dominic

引用次数: 6

Functional Approach for Reliability Evaluation of WLAN Communication Networks 无线局域网通信网络可靠性评估的功能方法

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI: 10.1109/ICRCICN50933.2020.9296182

Ruchita Manohar, Ranjith Kumar Sreenilayam, V. Pandey

引用次数: 0