2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)最新文献_第2页

Broadband Planar Waveguide Power Splitter Based on Symmetrical S-bends 基于对称s弯的宽带平面波导功率分配器

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767169

Shayna Kumari, S. Prince

引用次数: 0

Time-series analysis and Flood Prediction using a Deep Learning Approach 使用深度学习方法的时间序列分析和洪水预测

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767102

S. G., C. P, Umamaheswari Rajasekaran

{"title":"Time-series analysis and Flood Prediction using a Deep Learning Approach","authors":"S. G., C. P, Umamaheswari Rajasekaran","doi":"10.1109/wispnet54241.2022.9767102","DOIUrl":"https://doi.org/10.1109/wispnet54241.2022.9767102","url":null,"abstract":"Deep neural networks have been used successfully to solve time series prediction problems. Given their ability to automatically understand the temporal connections found in time series, they have shown to be an effective solution. In this proposed research, a Deep Learning (DL) based flood prediction model is explored and utilized for interpretation and prediction using meteorological data to reduce computational and time complexity with high accuracy. Gated Recurrent Networks (GRU) a variant of recurrent neural network model which can effectively use past data information for prediction and is faster in terms of training speed is the deep learning architecture deployed. Correlation analysis was performed on the weather parameters and the appropriate parameters were chosen. The dataset compromises 52 years (19022 records) of weather data in which 80% is used for training 20% for testing. The predictive modeling of rainfall associated with the South-west monsoon can guide the prediction of flood occurrence. The model deployed was evaluated with the performance metrics such as RMSE, MAE against LSTM model. The deployed RNN-GRU model had relatively low RMSE and MAE values when compared with LSTM architecture with improved prediction accuracy.","PeriodicalId":432794,"journal":{"name":"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130478684","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

Development of Multilingual Speech Database for Speaker Recognition in Indian Languages 面向印度语说话人识别的多语言语音数据库的开发

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767127

B. P, R. M.

引用次数: 0

Deep Learning Techniques for Cooperative Spectrum Sensing Under Generalized Fading Channels 广义衰落信道下协同频谱感知的深度学习技术

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767160

Pradeep Balaji Muthukumar, Samudhyatha B., Sanjeev Gurugopinath

引用次数: 2

Hexagon Shaped UWB Monopole MIMO Antenna for WBAN Applications 用于WBAN应用的六角形超宽带单极MIMO天线

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767155

Thennarasi Govindan, S. Palaniswamy, M. Kanagasabai, Sachin Kumar, R. T., Lekha Kannappan

引用次数: 1

Medical Color Image Encryption Using Chaotic Framework and AES Through Poisson Regression Model 基于泊松回归模型的混沌框架和AES医学彩色图像加密

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767183

A. S., G. K, Premaladha J., N. V

{"title":"Medical Color Image Encryption Using Chaotic Framework and AES Through Poisson Regression Model","authors":"A. S., G. K, Premaladha J., N. V","doi":"10.1109/wispnet54241.2022.9767183","DOIUrl":"https://doi.org/10.1109/wispnet54241.2022.9767183","url":null,"abstract":"This paper suggests a novel diversion in color medical image encryption using a chaotic framework and Advanced Encryption Standard AES with Poisson regression model. Nowa-days, the remote healthcare monitoring application is getting prominent by providing better assistance to people's life. We proposed a secure color image encryption algorithm for the medical images using the 2D Arnold cat map, AES-128 and Poisson regression. The workflow explained sequentially in this way. First, the plain medical image is decoupled into the corresponding RGB channels. Next, the chaotic map is applied to the plain image for converting it into a scrambled one. This scrambled image is transmitted to the AES-128 encryption block which converts the scrambled image into the encoded text form and encrypted using the hashed symmetric Key. Then the Encrypted image is formed through the Poisson regression model to predict the pixels based on the text encrypted. Finally, the resultant image is transmitted to the receiver with the NPCR score of 99.0174 and average UACI score of 33.0690. The results for the experimental work and its formulated security analyses reveal that this image encryption technique is applicable for medical image encryption and transmission.","PeriodicalId":432794,"journal":{"name":"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129655942","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

Voice Activity Detection Through Adversarial Learning 通过对抗性学习进行语音活动检测

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767144

Supritha M. Shetty, Heena M Shirahatti, Ujwala Patil, Deepak K. T.

{"title":"Voice Activity Detection Through Adversarial Learning","authors":"Supritha M. Shetty, Heena M Shirahatti, Ujwala Patil, Deepak K. T.","doi":"10.1109/wispnet54241.2022.9767144","DOIUrl":"https://doi.org/10.1109/wispnet54241.2022.9767144","url":null,"abstract":"Voice activity detection (VAD) plays an important role as a pre-processing block in many speech processing applications like speech coding, speech enhancement, speech recognition systems, etc. The main objective of VAD algorithm is to identify speech and non-speech regions in a given audio signal. However the challenging task for the VAD systems would be classifying speech/non-speech frames in an input audio signal that are corrupted by noise i.e environmental noise. With a view to address such a problem, we propose a new approach to VAD using a deep generative model. These models have the ability to learn the underlying distribution of target data through adversarial learning process. In this work, we explore Speech Enhancement GAN (SEGAN) which is a variant of GAN, to analyze the VAD application. The proposed work is evaluated on a subset of Apollo speech corpus as the dataset contain speech files with multiple challenges such as multiple speakers with different noise types, different Signal-to-Noise Ratio (SNR) levels, channel distortion and non-speech for a long duration. The performance of the system is evaluated using detection cost function(DCF) metric. The proposed work gives a better result when compared to other state-of-the-art methods.","PeriodicalId":432794,"journal":{"name":"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130197503","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

Analyzing Emotional Stability on Social Media Post using Machine Learning Approach 使用机器学习方法分析社交媒体帖子的情绪稳定性

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767174

U. M, P. A, V. V, Swarnalatha M.

{"title":"Analyzing Emotional Stability on Social Media Post using Machine Learning Approach","authors":"U. M, P. A, V. V, Swarnalatha M.","doi":"10.1109/wispnet54241.2022.9767174","DOIUrl":"https://doi.org/10.1109/wispnet54241.2022.9767174","url":null,"abstract":"Depression or emotion is the most important concern of health organizations today. We consider the capability of influencing social media postings as a new type of mirror in understanding depression and also the type of personality in populations. Social network analysis is the study of a group of people and the relationships that exist between them. It has become so important in our lives that if I want to know anything about a stranger, I can find out with the help of social media websites. The arrival of various social media networking sites has helped everyone to easily express and share their opinions and feelings about anything with millions of people around the world. Social media is a valuable resource for identifying an individual's personality traits based on their posts, comments, or activities on social media. The proposed methodology, we have developed the application of a web extension to connect with social media networks to extract the post by the individual person. Extracted post has been used to identify the emotional stability of a person. NLP and Machine Learning algorithms are used to classify individual emotional stability as stable, depressed, or tending towards depression. According to our study, significant feature selections and their combinations were considered. Hence it improves the performance and accuracy of classification.","PeriodicalId":432794,"journal":{"name":"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126911981","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

Deep Convolutional Neural Networks for Multiclass Cervical Cell Classification 基于深度卷积神经网络的多类宫颈细胞分类

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767129

M.C.P. Archana, J. V. Panicker

引用次数: 5

CoViMask: A Novel Face Mask Type Detector Using Convolutional Neural Networks CoViMask:一种基于卷积神经网络的面罩型检测器

2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2022-03-24 DOI: 10.1109/wispnet54241.2022.9767107

Sahana Rangasrinivasan, Sri Lohitha Bhagam, Nair K. Athira, Kondapi Niharika, Anjuna D. Raj, T. Anjali

引用次数: 4