2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)最新文献_第3页

Algorithmic Study on Facial Emotion Recognition model with Optimal Feature Selection via Firefly Plus Jaya Algorithm 基于Firefly + Jaya算法的最优特征选择面部情绪识别模型算法研究

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142942

B. Devi, M. S. J. Jain Preetha

{"title":"Algorithmic Study on Facial Emotion Recognition model with Optimal Feature Selection via Firefly Plus Jaya Algorithm","authors":"B. Devi, M. S. J. Jain Preetha","doi":"10.1109/ICOEI48184.2020.9142942","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9142942","url":null,"abstract":"Facial emotions are significant constraints that assist us to identify the intents of others. Generally, inhabitants understand the emotional condition of other people, like anger, sadness, and joy, using vocal tone and facial expressions. Here, a novel Facial Emotion Recognition system (FER) is developed that includes four major processes: (a) Face detection (b) Feature extraction (c) Optimal feature selection and (d) Classification. The input facial images are provided as input to a face detection model referred to as the viola-jones method. Then, from the detected facial images, the Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) features are extracted. The length of the features is large, so there is a requirement to choose the optimal features from the image. After selecting the optimal features, it is subjected to the classification process via Neural Network (NN). As a novelty, the optimal feature selection and the weight optimization of NN are carried out via a new hybrid algorithm called Mean Fitness Oriented JA+FF position update (MF-JFF). Later, an algorithmic analysis is performed for validating the performance of the presented model. From the analysis, the accuracy obtained for the values γ attained at 0.6 was 2.2% better than the values attained when γ = 0.2, 0.4, 0.8, and 1 respectively.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115069856","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: 2

A Review on the Gradiation Towards Pelamis Wave Energy Converter Pelamis波能转换器的梯度研究进展

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142874

K. Reddy, K. Prajwal, T.S.Saradhi Satwik, A. R

引用次数: 5

Malaria Detection using Deep Learning 利用深度学习进行疟疾检测

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143023

Gautham Shekar, S. Revathy, Ediga Karthick Goud

{"title":"Malaria Detection using Deep Learning","authors":"Gautham Shekar, S. Revathy, Ediga Karthick Goud","doi":"10.1109/ICOEI48184.2020.9143023","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9143023","url":null,"abstract":"Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. However, practical performance has not been sufficient so far. This paper proposes a new and highly robust machine learning model based on a convolutional neural network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. A ten-fold cross-validation layer of the convolutional neural network on 27,558 single-cell images is used to understand the parameter of the cell. Three types of CNN models are compared based on their accuracy and select the precise accurate - Basic CNN, VGG-19 Frozen CNN, and VGG-19 Fine Tuned CNN. Then by comparing the accuracy of the three models, the model with a higher rate of accuracy is acquired.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"77 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123639618","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: 25

Design of CMOS Low Noise Amplifier for 5G Applications Using 45nm Technology 基于45nm技术的5G CMOS低噪声放大器设计

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142915

Vishniu. Ks, V. V

引用次数: 1

A Critical Approach Towards a Smarter Battery Management System for Electric Vehicle 智能电动汽车电池管理系统的关键方法

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142869

Jeevak S. Lokhande, P. M. Daigavhane, M. Sarkar

{"title":"A Critical Approach Towards a Smarter Battery Management System for Electric Vehicle","authors":"Jeevak S. Lokhande, P. M. Daigavhane, M. Sarkar","doi":"10.1109/ICOEI48184.2020.9142869","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9142869","url":null,"abstract":"The most basic component in the Electric Vehicle is the Battery, that acts as a main source of energy and gives it mobility which is sustainable. In electric vehicles, the technology which is highly acknowledged and used for energy storage is based on Lithium chemistry. However, there is scope for research is still open. This includes the selection of the materials for cell manufacturing. The development of algorithms and designing of the electronic circuits for a better and effective utilization of the battery is also one of the area of research. For optimal performance of the batteries, it is important to keep check on the vital operational parameters of the battery during the charging and discharging. A battery management system (BMS) is one such mechanism for monitoring of the battery internal and ambient temperature, current, voltage and controlling the charging and discharging operation. In this paper, some of the interesting approaches and system for battery management is discussed. The requirement for the state of art system for optimal battery performance and its general architecture is also discussed.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"11 suppl_1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124463596","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: 14

Hybrid Model for Optimal Container Resource Allocation in Cloud 云环境下容器资源最优分配的混合模型

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143038

K. Vhatkar, G. Bhole

引用次数: 0

Real Time Haze Removal Using Filter Function 实时雾霾去除使用过滤器功能

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142952

R. Aishwarya, Madineni Prem Sai, M. S. Prasad

{"title":"Real Time Haze Removal Using Filter Function","authors":"R. Aishwarya, Madineni Prem Sai, M. S. Prasad","doi":"10.1109/ICOEI48184.2020.9142952","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9142952","url":null,"abstract":"Nowadays the cameras are arriving with high pixels and high software techniques. But this thing is not suitable for all the situations due to cost-effectiveness. For Example, CCTV attached to signal lights, organizations, and so on. To overcome this situation haze removal technique is used to adjust the saturation and contrast to bring clear images. But the main problem in this technique, haze removal is away from its value at the idle situation. In the area of PC pictorial superiority & perceivability levels of a picture is influenced via air-light & weakening marvels. Inflight subdivisions, which were present in climate influence the perceivability glassy of some artifact, termed commotion or undesirable sign among spectator & item. For improvising the proposed methodology perceivability level of a pic & decreasing haze & clamor different upgrade strategies are utilized. After improvement is again reestablished, upgraded image by rebuilding techniques. For enlightening the perceivability level 4 significant advances are utilized. The initial phase is securing the formula of unclear imageries. 2nd is the approximation method (gauge dissipating wonders, perceivability level). 3rd is upgrade progression (advance perceivability, decrease Smog or clamor). Preceding advance is rebuilding practice (reestablish improved pic). The principle point of the proposed methodology is to audit condition workmanship picture improvement & rebuilding strategies for refining the eminence & perceivability of an image which give clear focus in awful climate circumstance. Also, it requires only 0.52 seconds to process an image of 1-MP.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131681535","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

Real-time Gender Identification from Face Images using you only look once (yolo) 基于人脸图像的实时性别识别，你只看一眼(yolo)

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142989

V. K, C. Ramachandran

引用次数: 8

Effective Image Segmentation using Modified K-Means Technique 基于改进k均值技术的有效图像分割

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142910

B. Bharathi, K. Swamy

引用次数: 5

Convolutional LSTM: A Deep learning approach for Dynamic MRI Reconstruction 卷积LSTM:一种动态MRI重构的深度学习方法

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142982

Shashidhar V. Yakkundi, S. P

{"title":"Convolutional LSTM: A Deep learning approach for Dynamic MRI Reconstruction","authors":"Shashidhar V. Yakkundi, S. P","doi":"10.1109/ICOEI48184.2020.9142982","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9142982","url":null,"abstract":"Dynamic Magnetic Resonance Imaging (MRI) has been a choice of modality in capturing time-varying anatomical structures of different organs within the body in a sequential format. However, its applications are limited by slower acquisition time because of both physical and physiological constraints. Dynamic MRI is proved to have spatio-temporal redundancy in its frequency domain(k-space). The acquisition period can be minimized significantly by reducing the number of k-space samples but at the cost of introduction of artifacts in the corresponding image domain. The proposed work develops a cascaded Convolutional Long Short Term Memory (ConvLSTM) architecture for reconstructing T2-weighted dynamic MRI sequences from highly undersampled k-space data to accelerate the overall acquisition process. In particular, fully sampled data acquired from the ADNI database will be undersampled using a Cartesian undersampling mask. ConvLSTM architecture proposed is then used to remove the aliasing artifacts introduced by undersampling. In addition, ConvLSTM model learns both spatial and temporal dependencies of the imagery to reconstruct it efficiently while outperforming the Convolutional Neural Network (CNN) based reconstruction in terms of reconstruction accuracy.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126846100","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