2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)最新文献_第4页

Richter's Predictor: Modelling Earthquake Damage Using Multi-class Classification Models 里氏预测器:用多等级分类模型模拟地震损害

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339484

Aishwarya Kumaraswamy, B. N. Reddy, Rithvik Kolla

{"title":"Richter's Predictor: Modelling Earthquake Damage Using Multi-class Classification Models","authors":"Aishwarya Kumaraswamy, B. N. Reddy, Rithvik Kolla","doi":"10.1109/ICAECC50550.2020.9339484","DOIUrl":"https://doi.org/10.1109/ICAECC50550.2020.9339484","url":null,"abstract":"Natural calamities like earthquakes cause damage to life and property. Estimation of damage grade to buildings is essential for post-calamity response and recovery, elimination of the tedious process of manual validation and authentication of property damage before granting relief funds to people. By considering basic aspects like building location, age of the building, construction details and it's secondary uses, taken from the Gorkha earthquake dataset, this paper explores various multi-class classification machine learning models and techniques for predicting the damage grade of structures. The proposed architecture of the model involves three major steps, Feature Selection, XGBoost Classifier, and Parameter Tuning. The paper presents the results of the experiments with feature engineering, training variations and ensemble learning. The paper delves into the analysis of each model, to understand the reason behind their performance. This paper also infers the agents that play a major role in deciding the seismic vulnerabilities of the buildings. The proposed classifier in the paper provides significant input to understanding earthquake damage and also provides a paradigm to model other natural disaster damage.","PeriodicalId":196343,"journal":{"name":"2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125173580","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

Machine Learning at Resource Constraint Edge Device Using Bonsai Algorithm 基于盆景算法的资源约束边缘设备机器学习

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339514

Soumyalatha Naveen, Manjunath R. Kounte

引用次数: 5

Image Encryption and Decryption Using Key Sequence of Triple Logistic Map for Medical Applications 基于三重逻辑映射密钥序列的图像加解密医学应用

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339529

S. Rohith, L. Jahnavi, S. Bhuvaneshwari, S. Supreeth, B. Sujatha

{"title":"Image Encryption and Decryption Using Key Sequence of Triple Logistic Map for Medical Applications","authors":"S. Rohith, L. Jahnavi, S. Bhuvaneshwari, S. Supreeth, B. Sujatha","doi":"10.1109/ICAECC50550.2020.9339529","DOIUrl":"https://doi.org/10.1109/ICAECC50550.2020.9339529","url":null,"abstract":"In the present world scenario, security of medical images from unauthorized access is one of the key challenges. This paper provides one of the solutions to solve the security of medical images. The proposed scheme uses a combination of key sequences of Triple Logistic Map (TLM) for the encryption process. To generate the key sequence Logistic function is used with initial value X<inf>0</inf>and r. The three key sequences{X <sup>11</sup>}, {X<inf>2i</inf>and {X<inf>3i</inf>} with different initial value are used (X<inf>10</inf>:/ X<inf>20</inf> :/ X<inf>30</inf>) with r=3.99. The key sequences {X<inf>li</inf>}‘ {X<inf>2i</inf>} and {X<inf>3i</inf>} are converted into discrete key sequence {K<inf>li</inf>}, {K<inf>2i</inf>} and {K<inf>3i</inf>} in the range of 0 to 255. The combined key sequence {K<inf>i</inf>} is obtained using bit by bit logical XOR operation between {K<inf>li</inf>}, {K<inf>2i</inf>}, and {K<inf>3i</inf>}. The resultant Key sequence {Ki} is used forthe encryption process of the medical image. To evaluate the performance of the proposed scheme different grayscale images are chosen. The proposed TLM based image encryption scheme is compared with the encryption scheme using (i) Logistic Map (LM) alone ii) Double Logistic Map (DLM). To evaluate the proposed scheme parameters such as (i) Visual analysis (ii) Histogram plot (iii) Mean Square Error (MSE) iv) Correlation (v) Entropyis used. The simulation result shows that the proposed TLM based scheme provides better performance and immune to statistical attacks.","PeriodicalId":196343,"journal":{"name":"2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132455968","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

Energy Harvesting Techniques for Monitoring Devices in Smart Grid Application 智能电网中监测设备的能量收集技术

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339526

H. Pavana, Rohini Deshpande

引用次数: 1

Speaker Recognition in Emotional Environment using Excitation Features 情绪环境下基于激励特征的说话人识别

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339501

T. Thomas, S. V, N. Sobhana, S. Koolagudi

{"title":"Speaker Recognition in Emotional Environment using Excitation Features","authors":"T. Thomas, S. V, N. Sobhana, S. Koolagudi","doi":"10.1109/ICAECC50550.2020.9339501","DOIUrl":"https://doi.org/10.1109/ICAECC50550.2020.9339501","url":null,"abstract":"Speaker Recognition is known as the task of recognizing the person speaking from his/her speech. Speaker recognition has many applications including transaction authentication, access control, voice dialing, web services, etc. Emotive speaker recognition is important because in real life, human beings extensively express emotions during conversations, and emotions alter the human voice. A text-independent speaker recognition system is proposed in the work. The system designed is for emotional environment. The proposed system in this work is trained using the speech samples recorded in neutral environment and the system evaluation is performed in an emotional environment. Here, excitation source features are used to represent speaker-specific details contained in speech signal. The excitation source signal is obtained after separating the segmental level features from the voice samples. The excitation source signal is almost considered as a noise so identifying a speaker in an emotive environment is a challenging task. Excitation features include Linear Prediction (LP) residual, Glottal Closure Instance (GCI), LP residual phase, residual cepstrum, Residual Mel-Frequency Cepstral Coefficient (R-MFCC), etc. A decrease in performance is observed when the system is trained with neutral speech samples and tested with emotional speech samples. Different emotions considered for emotional speaker identification are happy, sad, anger, fear, neutral, surprise, disgust, and sarcastic For the classification of speakers the algorithms used are Gaussian Mixture Model (GMM), Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Random Forest and Naive Bayes.","PeriodicalId":196343,"journal":{"name":"2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129748376","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

Estimation of Evapotranspiration for Seasonal Water Usage Analysis 季节水分利用分析中的蒸散发估算

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339527

B. N. Aryalekshmi, R. C. Biradar, J. Mohammed Ahamed, K. Chandrasekar

{"title":"Estimation of Evapotranspiration for Seasonal Water Usage Analysis","authors":"B. N. Aryalekshmi, R. C. Biradar, J. Mohammed Ahamed, K. Chandrasekar","doi":"10.1109/ICAECC50550.2020.9339527","DOIUrl":"https://doi.org/10.1109/ICAECC50550.2020.9339527","url":null,"abstract":"This paper focuses on estimating Evapotranspiration over the Cauveri river basin in Mandya District of Karnataka, India (12° 31'N, 76°53'E), using LANDSAT-8 satellite data. ET will indicate the amount of water loss from that particular land surface, and in turn, this can be used for water budget analysis. Accurate estimation of evapotranspiration (ET) is essential for the proper water usage analysis. Evapotranspiration estimation using hydrological methods, micro-meteorological methods can only be considered as point measurements for smaller areas. Extrapolation of ET rates over a larger area using point data has limited accuracy. Airborne or satellite images using remote sensing techniques are practical method for developing ET spatial variation at a regional scale. In this paper, we provide an analysis of ET estimation using Landsat-8 data during Jan & Feb 2018 for the identified study area. The reason for choosing this area is that a significant part of the land cover of Mandya district is under agricultural use. We propose Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) - a satellite-based image-processing model for calculating ET as a residual of the surface energy balance. The study indicated that the Normalized Vegetation Index (NDVI) and Land Surface Temperature (LST) are inversely proportional to each other, indicates that as temperature increases, vegetation index decreases.","PeriodicalId":196343,"journal":{"name":"2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335151","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

Toxic Comment Detection using LSTM 使用LSTM检测有毒注释

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339521

K. Dubey, Rahul Nair, Mohd. Usman Khan, Prof. Sanober Shaikh

引用次数: 8

Investigations and Compression of Genomic Data 基因组数据的调查与压缩

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/icaecc50550.2020.9339492

Raveendra Gudodagi, R. Venkata Siva Reddy, M. Riyaz Ahmed

引用次数: 2

Performance Analysis of Fixed Point FIR Filter Architectures 定点FIR滤波器结构的性能分析

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339519

P. Sreesh, L. Kumar

引用次数: 0

Spectral Features for Emotional Speaker Recognition 情感说话人识别的频谱特征

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2020-12-11 DOI: 10.1109/ICAECC50550.2020.9339502

P. Sandhya, V. Spoorthy, S. Koolagudi, N. Sobhana

{"title":"Spectral Features for Emotional Speaker Recognition","authors":"P. Sandhya, V. Spoorthy, S. Koolagudi, N. Sobhana","doi":"10.1109/ICAECC50550.2020.9339502","DOIUrl":"https://doi.org/10.1109/ICAECC50550.2020.9339502","url":null,"abstract":"Speaker recognition in an emotive environment is a bit challenging task because of influence of emotions in a speech. Identifying the speaker from the speech can be done by analyzing the features of the speech signal. In normal conditions, identifying a speaker is not a tedious task. Whereas, identifying the speaker in an emotional environment such as happy, sad, anger, surprise, sarcastic, fear etc. is really challenging, since speech becomes altered under emotions and noise. The spectral features of speech signal include Mel Frequency Cepstral Co-efficients(MFCC), Shifted Delta Cepstral Coefficients (SDCC), spectral centroid, spectral roll off, spectral flatness, spectral contrast, spectral bandwidth, chroma-stft, zero crossing rate, root mean square energy, Linear Prediction Cepstral Coefficients (LPCC), spectral subband centroid, Teager energy based MFCC, line spectral frequencies, single frequency cepstral coefficients, formant frequencies, Power Normalized Cepstral Coefficients (PNCC), etc. The features that are extracted from the speech signal are classified using classifiers. Support Vector Machine(SVM), Gaussian Mixture Model, Gaussian Naive Bayes, K-Nearest Neighbour, Random Forest and a simple Neural Network using Keras is used for classification. The important application include security systems in which a person can be identified by biometrics that is voice of the person. The work aims to identify the speaker in an emotional environment using spectral features and classify using any of the classification techniques and to achieve a high speaker recognition rate. Feature combinations can also be used to improve accuracy. The proposed model performed better than most of the state-of-the-art methods.","PeriodicalId":196343,"journal":{"name":"2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519673","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: 11