2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)最新文献_第2页

Network Traffic Classification Using Supervised Learning Algorithms 使用监督学习算法的网络流量分类

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10084931

Mira Rani Choudhury, M. N, P. Acharjee, Aleena Terese George

引用次数: 0

Investigation on Efficient Machine Learning Algorithm for DDoS Attack Detection 用于DDoS攻击检测的高效机器学习算法研究

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085248

R. Devi, R. Bharathi, P. K. Kumar

引用次数: 1

Real-Time Emotional Analysis 实时情绪分析

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10084955

A. Savva, Vasso Stylianou

引用次数: 0

Optimal Design of (α + β)-Order Butterworth Filter and Its Realization Using RLβCα Circuit (α + β)阶巴特沃斯滤波器的优化设计及rl - β c - α电路实现

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085113

S. Mahata, R. R. De (Maity)

引用次数: 0

A Fast-Converging Radial Basis Function Neural Network-Based MPPT Controller for Static and Dynamic Variations in Solar Irradiation 基于径向基函数神经网络的太阳辐照静态和动态变化快速收敛MPPT控制器

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085281

Chepuri Venkateswararao, K. A. Naik

引用次数: 0

A Novel Low-Complexity Power-Efficient Design of Standard Ternary Logic Gates using CNTFET 一种基于CNTFET的低复杂度、高能效标准三元逻辑门设计

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085528

Anisha Paul, B. Pradhan

引用次数: 1

TiO2 Thick film Gas sensor for Detection H2S Gas Using ANN and Machine Learning Technique 利用人工神经网络和机器学习技术检测H2S气体的TiO2厚膜气体传感器

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085220

Amit Gupta, S. K. Dargar, Abha Dargar

{"title":"TiO2 Thick film Gas sensor for Detection H2S Gas Using ANN and Machine Learning Technique","authors":"Amit Gupta, S. K. Dargar, Abha Dargar","doi":"10.1109/ICCECE51049.2023.10085220","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085220","url":null,"abstract":"Undoped CuO doped thick film gas sensor have been developed for H2S toxic gas detection to review the sensitivity and sensor response using ANN technique at 150°C . TiO2 based thick film sensor was untrue on a 1\" x 1\" alumina substrate. It incorporate of a gas sensitive layer TiO2 based thick film sensor with doped of undoped CuO, a couple of electrodes in radical to gas sensing layer serving as a channel pad for sensor. The sensitivity of sensor has been investigated at undoped CuO-doped concentration at constant temperature of 150°C upon liability of H2S toxic gas .An advanced approach is made to measure the sensitivity of undoped CuO-doped TiO2 based thick film sensor by using ANN algorithm.The training algorithm of feed –forward algorithm namely with learning heuristic was used. The performance of ANN models with specific algorithm is evaluated on reasonable sensitivity of sensor with different network transfer function. Empirically, we found that ANN model with training algorithm is more advisable for simulation of sensor and predict the sensitivity. Simulation results demonstrated in the paper shown ANN as an effective tool in the area of TiO2 based thick film sensor design.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116261898","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

Building a Classification Model based on Feature Engineering for the Prediction of Wine Quality by Employing Supervised Machine Learning and Ensemble Learning Techniques 利用监督机器学习和集成学习技术建立基于特征工程的葡萄酒质量预测分类模型

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085272

Mauparna Nandan, Harsh Raj Gupta, Moutusi Mondal

{"title":"Building a Classification Model based on Feature Engineering for the Prediction of Wine Quality by Employing Supervised Machine Learning and Ensemble Learning Techniques","authors":"Mauparna Nandan, Harsh Raj Gupta, Moutusi Mondal","doi":"10.1109/ICCECE51049.2023.10085272","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085272","url":null,"abstract":"In today’s world, consumers are more concerned regarding the quality of any product. Different approaches are being deployed by various industries to guarantee the excellent quality of their products. Thus, quality certification serve as a vital authentication mechanism for majority of the industries for promoting their numerous products in the market. In the past, only human specialists were employed to evaluate and measure quality. But, nowadays, the majority of validation jobs are automated by software, which curtails the workload of human experts by assisting them to predict the quality of the product and thereby leading to a considerable amount of time saving. Over the past few decades, there has been a sharp rise in wine consumption due to its intrinsic health benefits, particularly for the human heart, as well as for recreational reasons. The main focus of this study is two-fold: the first objective is to predict the quality of wine based upon the correlation between the various physicochemical factors in order to determine the most prominent factors which play a significant role for determining the quality of wine by implementing several supervised machine learning and ensemble learning techniques and the final results being confirmed by employing a variety of quantitative indicators and the second objective is the classification of wine into 3 categories, namely, Best, Good and Poor in order to rank the quality of wine. However, during testing the models with the test dataset, it has been observed that the Random Forest classifier outperformed the other machine learning classifiers with an accuracy of 98%.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114086190","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

Effectiveness of Feature Collaboration in Speaker Identification for Voice Biometrics 特征协同在语音生物识别中的有效性

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085318

Arunima Das, L. P. Roy, Santos Kumar Das

{"title":"Effectiveness of Feature Collaboration in Speaker Identification for Voice Biometrics","authors":"Arunima Das, L. P. Roy, Santos Kumar Das","doi":"10.1109/ICCECE51049.2023.10085318","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085318","url":null,"abstract":"Voice biometrics is a promising solution to online banking that doesn’t need one’s physical presence, unlike fingerprint and retina scanners. Systems for identifying speakers are a crucial component of biometric technologies. Over the past few years, numerous speaker identification systems have been developed and used; these systems rely on various feature extraction methodologies. Due to its capacity to capture’ the repeated nature and effectiveness of signals, short-time characteristics like perceptual linear predictive (PLP) and Mel frequency cepstral coefficients (MFCC) have been used in the majority of studies on speaker identification. The efficiency of MFCC characteristics in accurately identifying speakers has been demonstrated in various research. However, the’ performance of these features degrades in noisy environments. To address this feature, a novel feature fusion of some spectral and time-domain features has been suggested in this paper. Moreover, this study evaluates the effectiveness of feature collaboration for speaker identification. The experimental results show that the suggested feature vector and classifying model can be widely applied to different types of voice biometric systems.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604922","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

A Microcontroller Based FIR Filter With Dynamic Stabilization of Sampling Frequency 基于单片机的动态稳定采样频率的FIR滤波器

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085266

Sayantan Banerjee, Biswajit Bhattacharyya, S. Munshi

引用次数: 0