Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169737
Deepanshi Jalan, Anandita Tuli, Vanshika Chaudhary, N. Sharma, Manik Rakhra
Life expectancy (LE) models provide a lot of ways to improve healthcare and other social welfares related to society. Life expectancy models provide solutions to problems like how to decide on retirement age or manage financial issues related to public matters. These models are becoming prominent in many regions as they are being widely used by government bodies and private sector for their policy making and developing health integrated systems. Thus, this paper aims to analyze the Trends in Life Expectancy in about 72 countries of the world over a span of 16 years, i.e., from 2000-2015. The study gives plots of attributes such as life expectancy, GDP, infant deaths, adult mortality, etc. across year which would help the countries understand the life expectancy trends over the course of time and suggest areas which should be focused upon to efficiently increase the life expectancy of its population. The simulations are done in Google Collab by using various Python libraries like pandas, numpy, matplotlib (used for plotting graphs), seaborn (used for plotting 3-D graphs and advanced visualization features of python), sklearn (used for handling missing data), and plotly express (used for plotting choropleth).
{"title":"Machine Learning Models for Life Expectancy","authors":"Deepanshi Jalan, Anandita Tuli, Vanshika Chaudhary, N. Sharma, Manik Rakhra","doi":"10.1109/ICAIA57370.2023.10169737","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169737","url":null,"abstract":"Life expectancy (LE) models provide a lot of ways to improve healthcare and other social welfares related to society. Life expectancy models provide solutions to problems like how to decide on retirement age or manage financial issues related to public matters. These models are becoming prominent in many regions as they are being widely used by government bodies and private sector for their policy making and developing health integrated systems. Thus, this paper aims to analyze the Trends in Life Expectancy in about 72 countries of the world over a span of 16 years, i.e., from 2000-2015. The study gives plots of attributes such as life expectancy, GDP, infant deaths, adult mortality, etc. across year which would help the countries understand the life expectancy trends over the course of time and suggest areas which should be focused upon to efficiently increase the life expectancy of its population. The simulations are done in Google Collab by using various Python libraries like pandas, numpy, matplotlib (used for plotting graphs), seaborn (used for plotting 3-D graphs and advanced visualization features of python), sklearn (used for handling missing data), and plotly express (used for plotting choropleth).","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133891083","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169830
Shivdutt Dixit, Navneet Kaur
With the increasing scope of deep learning applications in various sectors, the detection of plant disease using the leaf sample using the same has also been one of the major areas to be studied by various researchers. This research proposes a new hybrid approach using AlexNet architecture of CNN and Random Forest that could be used to identify the disease easily with the less computation power and higher accuracy. In the research, the proposed model was employed to identify Tomato, Potato, and Bell Pepper diseases from the PlantVillage dataset, resulting in an accuracy rate of 99.68% and an fl-score of 0.9892. The dataset used had a total of 1,75,734 images divided across 38 categories of different plant species and their diseases out of which a total of 77221 images spread across 55894 images for training and 21327 images for validation and testing segregated across 15 categories have been used for the model proposed.
{"title":"An Improved Approach To Classify Plant Disease Using CNN And Random Forest","authors":"Shivdutt Dixit, Navneet Kaur","doi":"10.1109/ICAIA57370.2023.10169830","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169830","url":null,"abstract":"With the increasing scope of deep learning applications in various sectors, the detection of plant disease using the leaf sample using the same has also been one of the major areas to be studied by various researchers. This research proposes a new hybrid approach using AlexNet architecture of CNN and Random Forest that could be used to identify the disease easily with the less computation power and higher accuracy. In the research, the proposed model was employed to identify Tomato, Potato, and Bell Pepper diseases from the PlantVillage dataset, resulting in an accuracy rate of 99.68% and an fl-score of 0.9892. The dataset used had a total of 1,75,734 images divided across 38 categories of different plant species and their diseases out of which a total of 77221 images spread across 55894 images for training and 21327 images for validation and testing segregated across 15 categories have been used for the model proposed.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122386219","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169724
Madhvan Bajaj, Priyanshu Rawat, A. Bhatt., Satvik Vats, Vikrant Sharma
A great threat to global health continues to be posed by the extremely contagious illness of tuberculosis (TB). Controlling the spread of TB and enhancing patient outcomes depend on early and precise detection. By evaluating medical images and minimizing the time and effort needed for manual analysis, machine learning (ML) approaches have shown considerable promise in assisting in the diagnosis of tuberculosis (TB). In this study we cover the most recent ML-based TB detection techniques in and go over their benefits and drawbacks. Deep learning, conventional ML algorithms, and methods based on computer vision are among the techniques examined.
{"title":"A Study on Tuberculosis With Deep Learning and Machine Learning Approaches","authors":"Madhvan Bajaj, Priyanshu Rawat, A. Bhatt., Satvik Vats, Vikrant Sharma","doi":"10.1109/ICAIA57370.2023.10169724","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169724","url":null,"abstract":"A great threat to global health continues to be posed by the extremely contagious illness of tuberculosis (TB). Controlling the spread of TB and enhancing patient outcomes depend on early and precise detection. By evaluating medical images and minimizing the time and effort needed for manual analysis, machine learning (ML) approaches have shown considerable promise in assisting in the diagnosis of tuberculosis (TB). In this study we cover the most recent ML-based TB detection techniques in and go over their benefits and drawbacks. Deep learning, conventional ML algorithms, and methods based on computer vision are among the techniques examined.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124718932","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169413
Kadiyam Sreelekha, C. S. Sudeep, S. Sreekar, B. R. Behera
In this present paper, two compact and efficient printed monopole antennas of different shapes such as circular shaped & rectangular shaped are investigated based on the EM optimization techniques. The proposed antennas are capable of covering out GSM 1800 (1.8 GHz), UMTS (2.1 GHz), LTE (2.3-2.4 GHz), Wi-Fi(2.4 GHz), ISM (2.4-2.5 GHz), LTE-Advanced (2.5 GHz), WiMAX (3.6 GHz), WLAN (5.2/5.S GHz), and IEEE 802.11 b/g/n (2.4/5.2 GHz) frequency bands. They are designed using the EM solver CST-microwave studio 2022, in which FR-4 substrate is used, a low cost commercially available substrate. The sizes of antennas are $80times 60times 1.6mathrm{mm}^{3}$. To study the use of optimization in the printed monopole antenna, both of designed antennas are interpreted with the EM optimization techniques, available in the CST-MWS’22 platform. The optimized printed circular shaped monopole antenna (PCSMA) exhibits a −10dB impedance bandwidth of 1.3-7.4 GHz (6.1 GHz) with average realized gain of 3.98 dB & antenna efficiency of 90%, whereas printed rectangular shaped monopole antenna (PRSMA) offers −10dB impedance bandwidth of 1.3-7.45 GHz (6.15 GHz) with average realized gain of 4.21 dB & antenna efficiency of 93%. With outlined outcomes, the proposed antennas can be utilized in the case of RF energy harvesting, RFID Tags, & even can be extended to onboard telemetry applications.
{"title":"Compact & Efficient Monopole Antenna Designs Based on AI-Driven EM Optimization Techniques","authors":"Kadiyam Sreelekha, C. S. Sudeep, S. Sreekar, B. R. Behera","doi":"10.1109/ICAIA57370.2023.10169413","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169413","url":null,"abstract":"In this present paper, two compact and efficient printed monopole antennas of different shapes such as circular shaped & rectangular shaped are investigated based on the EM optimization techniques. The proposed antennas are capable of covering out GSM 1800 (1.8 GHz), UMTS (2.1 GHz), LTE (2.3-2.4 GHz), Wi-Fi(2.4 GHz), ISM (2.4-2.5 GHz), LTE-Advanced (2.5 GHz), WiMAX (3.6 GHz), WLAN (5.2/5.S GHz), and IEEE 802.11 b/g/n (2.4/5.2 GHz) frequency bands. They are designed using the EM solver CST-microwave studio 2022, in which FR-4 substrate is used, a low cost commercially available substrate. The sizes of antennas are $80times 60times 1.6mathrm{mm}^{3}$. To study the use of optimization in the printed monopole antenna, both of designed antennas are interpreted with the EM optimization techniques, available in the CST-MWS’22 platform. The optimized printed circular shaped monopole antenna (PCSMA) exhibits a −10dB impedance bandwidth of 1.3-7.4 GHz (6.1 GHz) with average realized gain of 3.98 dB & antenna efficiency of 90%, whereas printed rectangular shaped monopole antenna (PRSMA) offers −10dB impedance bandwidth of 1.3-7.45 GHz (6.15 GHz) with average realized gain of 4.21 dB & antenna efficiency of 93%. With outlined outcomes, the proposed antennas can be utilized in the case of RF energy harvesting, RFID Tags, & even can be extended to onboard telemetry applications.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323049","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169188
S. Vamsi, K. M. Nagabushanam, K. V. Kumar, Somesh Vinayak Tewari, Tarkeshwar Mahto
Lithium ion batteries are a promising energy source for electric vehicles due to their high specific energy and power output. Overall system reliability and stability can be improved by effectively planning battery replacement intervals and monitoring their condition. To guarantee the battery system operates safely, steadily, and effectively, it is necessary to accurately assess the state of health (SOH) of the lithium-ion battery. Capacity might be used to anticipate it directly. To improve the accuracy of the SOH estimate, hyperparameter-optimized Gaussian process regression (GPR) is used. Gaussian process models have the advantage of being flexible, stochastic, nonparametric models with uncertainty forecasts, and may have variance around the mean forecast to account for the associated uncertainties in evaluation and forecasting. The lithium-ion battery data set made available by NASA is examined in this article. The outcomes demonstrate its efficacy and demonstrate that the algorithm may be successfully used for battery monitoring and prognostics. Additionally, the prediction for battery health has been improved through the comparison of predictions with various quantities of training data.
{"title":"State of Health of Lithium-ion Batteries by Data-Driven Technique with Optimized Gaussian Process Regression","authors":"S. Vamsi, K. M. Nagabushanam, K. V. Kumar, Somesh Vinayak Tewari, Tarkeshwar Mahto","doi":"10.1109/ICAIA57370.2023.10169188","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169188","url":null,"abstract":"Lithium ion batteries are a promising energy source for electric vehicles due to their high specific energy and power output. Overall system reliability and stability can be improved by effectively planning battery replacement intervals and monitoring their condition. To guarantee the battery system operates safely, steadily, and effectively, it is necessary to accurately assess the state of health (SOH) of the lithium-ion battery. Capacity might be used to anticipate it directly. To improve the accuracy of the SOH estimate, hyperparameter-optimized Gaussian process regression (GPR) is used. Gaussian process models have the advantage of being flexible, stochastic, nonparametric models with uncertainty forecasts, and may have variance around the mean forecast to account for the associated uncertainties in evaluation and forecasting. The lithium-ion battery data set made available by NASA is examined in this article. The outcomes demonstrate its efficacy and demonstrate that the algorithm may be successfully used for battery monitoring and prognostics. Additionally, the prediction for battery health has been improved through the comparison of predictions with various quantities of training data.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121254258","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169252
Smriti Kantroo, Vikram Singh, Ritika Mattoo, Neeraj Tripathi, A. Bhardwaj
This paper presents a four staged voltage-controlled ring oscillator (VCO) operating in wide range of frequency that consumes low power, improves the performance and provides stability to the circuit. Due to the limitation of MOSFET that they cannot be scaled down after certain range due to some of its limitations such as high power and leakage current. To overcome the drawback, we used CNTFET technology that uses Carbon Nano Tubes in place of silicon. The VCO that is made in this paper using CNTFET operates in Terahertz frequency range varying from 0.331 THz to 0.091 THz and similarly the power dissipated ranges from 0.9565 to 0.2506 mW for control Voltage of 0 to 1 volts. The proposed VCO shows 97.7% improvement in power dissipation 99.4% increase in the frequency range in comparison to the VCOs based on CMOS technology. VCO produces a sinusoidal waveform and we checked the correctness of our design by verifying the waveform produced and simulated results.
{"title":"Design of a Low Power Complementary Current Controlled Skewed Delay Voltage Controlled Oscillator using CNTFET","authors":"Smriti Kantroo, Vikram Singh, Ritika Mattoo, Neeraj Tripathi, A. Bhardwaj","doi":"10.1109/ICAIA57370.2023.10169252","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169252","url":null,"abstract":"This paper presents a four staged voltage-controlled ring oscillator (VCO) operating in wide range of frequency that consumes low power, improves the performance and provides stability to the circuit. Due to the limitation of MOSFET that they cannot be scaled down after certain range due to some of its limitations such as high power and leakage current. To overcome the drawback, we used CNTFET technology that uses Carbon Nano Tubes in place of silicon. The VCO that is made in this paper using CNTFET operates in Terahertz frequency range varying from 0.331 THz to 0.091 THz and similarly the power dissipated ranges from 0.9565 to 0.2506 mW for control Voltage of 0 to 1 volts. The proposed VCO shows 97.7% improvement in power dissipation 99.4% increase in the frequency range in comparison to the VCOs based on CMOS technology. VCO produces a sinusoidal waveform and we checked the correctness of our design by verifying the waveform produced and simulated results.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121397963","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169729
Chitra Saini, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar
According to the World Health organization’s (WHO) 2020 report, 2.3 million new cases of breast cancer were recorded, and 685,000 women died due to breast cancer. To treat breast cancer early, a lot of research has been proposed using different types of techniques in the past few years. In recent years, machine learning algorithms (MLAs) have become popular for detection due to their improved accuracy and performance. This paper used 13 supervised machine learning (SML) techniques, namely: Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Adaptive Boosting (AB), Categorical Boosting (CB), Light Gradient Boosting Machine (LGBM), Multi-Layer Perceptron (MLP), and Extra trees (ET) to predict the outcomes of the Wisconsin Breast Cancer Original (WBCO) dataset from the UCI repository. When all thirteen algorithms were evaluated and compared, MLP outperforms them all with the highest accuracy (98.76%). This accuracy value is 0.56% greater than the recently reported accuracy value of 98.2% for the MLP classifier for the same dataset.
{"title":"Breast Cancer Prediction Using Different Machine Learning Algorithms: A Comparative Study","authors":"Chitra Saini, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar","doi":"10.1109/ICAIA57370.2023.10169729","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169729","url":null,"abstract":"According to the World Health organization’s (WHO) 2020 report, 2.3 million new cases of breast cancer were recorded, and 685,000 women died due to breast cancer. To treat breast cancer early, a lot of research has been proposed using different types of techniques in the past few years. In recent years, machine learning algorithms (MLAs) have become popular for detection due to their improved accuracy and performance. This paper used 13 supervised machine learning (SML) techniques, namely: Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Adaptive Boosting (AB), Categorical Boosting (CB), Light Gradient Boosting Machine (LGBM), Multi-Layer Perceptron (MLP), and Extra trees (ET) to predict the outcomes of the Wisconsin Breast Cancer Original (WBCO) dataset from the UCI repository. When all thirteen algorithms were evaluated and compared, MLP outperforms them all with the highest accuracy (98.76%). This accuracy value is 0.56% greater than the recently reported accuracy value of 98.2% for the MLP classifier for the same dataset.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132305534","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169852
Supriya, Ashutosh Shukla, Mahesh Manchanda
Precision agriculture is a cutting technology in the field for agriculture, which deals with the challenges of the traditional methodology. This research work is a review of the recent studies published and discussed for detection of plant disease using ML & DL models on various plants dataset. This literature analysis is performed for publications from 2017 to 2022. More than 30 publications were selected and studied. In this present work, some of the existing ML & DL algorithms that are used to process the images for detecting crop diseases are discussed. The study highlights the results of the investigation of several existing ML and DL models, datasets used and gaps in work. Finally, this identified gaps that may decide the future direction of the research in this area. The purpose of this study is to provide knowledge for future research in building an accurate and effective classification plant diseases.
{"title":"A Survey Paper on Precision Agriculture based Intelligent system for Plant Leaf Disease Identification","authors":"Supriya, Ashutosh Shukla, Mahesh Manchanda","doi":"10.1109/ICAIA57370.2023.10169852","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169852","url":null,"abstract":"Precision agriculture is a cutting technology in the field for agriculture, which deals with the challenges of the traditional methodology. This research work is a review of the recent studies published and discussed for detection of plant disease using ML & DL models on various plants dataset. This literature analysis is performed for publications from 2017 to 2022. More than 30 publications were selected and studied. In this present work, some of the existing ML & DL algorithms that are used to process the images for detecting crop diseases are discussed. The study highlights the results of the investigation of several existing ML and DL models, datasets used and gaps in work. Finally, this identified gaps that may decide the future direction of the research in this area. The purpose of this study is to provide knowledge for future research in building an accurate and effective classification plant diseases.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114169279","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169472
Sowmiya A, Karunamoorthy B
A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, Relu, Pooling, Fully Connected layer. We tested several well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect. Finally, we demonstrate the result by classification and how accurately the defect identified.
{"title":"Fabric fault and Extra thread Detection Using Convolutional Neural Network","authors":"Sowmiya A, Karunamoorthy B","doi":"10.1109/ICAIA57370.2023.10169472","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169472","url":null,"abstract":"A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, Relu, Pooling, Fully Connected layer. We tested several well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect. Finally, we demonstrate the result by classification and how accurately the defect identified.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124815469","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}
Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169372
Prerak Moolchandani, Shreya Hegde, Muskan Hassanandani, Garv Jhangiani, G. Bhatia, A. Tewari, S. Dugad
With the Coronavirus pandemic taking its toll all over the world, and social distancing measures being adopted, there is an urgent need to digitise all the processes for the smooth functioning of organisations. Thus, Pehchaan presents a nocontact system for recording the attendance of entities by verifying face and voice. It makes use of low-cost ESP microcontrollers with a camera and microphone module to extract face measurements using a Deep Convolutional Neural Network and apply Mel Frequency Cepstrum techniques to the audio files. We verify the entities’ claim by comparing the similarity with the encodings stored in the database. We are using wifi networks to connect ESP with the backend server. Face and voice recognition together act as two-factor verification and an admin will be able to access the records of a particular day and time and thus would be able to capture the attendance without any manual effort.
{"title":"Pehchaan: A Touchless Attendance System","authors":"Prerak Moolchandani, Shreya Hegde, Muskan Hassanandani, Garv Jhangiani, G. Bhatia, A. Tewari, S. Dugad","doi":"10.1109/ICAIA57370.2023.10169372","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169372","url":null,"abstract":"With the Coronavirus pandemic taking its toll all over the world, and social distancing measures being adopted, there is an urgent need to digitise all the processes for the smooth functioning of organisations. Thus, Pehchaan presents a nocontact system for recording the attendance of entities by verifying face and voice. It makes use of low-cost ESP microcontrollers with a camera and microphone module to extract face measurements using a Deep Convolutional Neural Network and apply Mel Frequency Cepstrum techniques to the audio files. We verify the entities’ claim by comparing the similarity with the encodings stored in the database. We are using wifi networks to connect ESP with the backend server. Face and voice recognition together act as two-factor verification and an admin will be able to access the records of a particular day and time and thus would be able to capture the attendance without any manual effort.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019228","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}
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