Pub Date : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085666
K. M, U. S, Madhankumar C, Saibarathi Ravi, Sanjeevee S, Tamizh Kanal R
Global population expansion has raised the demand for food production. Farms are frequently threatened by intrusions from different animals, insects, and birds. Crop raiding has been one of the most prominent issues antagonising human-animal relationships as cultivated land has expanded into previous wildlife habitat. Crop-raiding animals can cause significant damage to agricultural crops, caused by animal assault. The farmlands close to the forest boundaries which has long been a source of conflict around the world. The current method used IOT to monitor farm fields using PIR and ultrasonic sensors, but it had the disadvantage of having a smaller detection range than LiDAR sensors. To detect animal trespass at the farm's perimeter, an intrusion detection system based in Lidar sensors placed outside the fence is deployed. A graphical representation of the LiDAR has also been created to indicate the state of the field conditions. An electric fence is used to keep out animals that can threaten the people inside the farm. In order to prevent farmers from electrocuting themselves and keep wild animals outside of the farm's boundaries, a safety device for electric fences by utilizing microwave sensor has been designed.
{"title":"An Integrated Security for Smart Farming and Monitoring System based on LiDAR Technology","authors":"K. M, U. S, Madhankumar C, Saibarathi Ravi, Sanjeevee S, Tamizh Kanal R","doi":"10.1109/ICEARS56392.2023.10085666","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085666","url":null,"abstract":"Global population expansion has raised the demand for food production. Farms are frequently threatened by intrusions from different animals, insects, and birds. Crop raiding has been one of the most prominent issues antagonising human-animal relationships as cultivated land has expanded into previous wildlife habitat. Crop-raiding animals can cause significant damage to agricultural crops, caused by animal assault. The farmlands close to the forest boundaries which has long been a source of conflict around the world. The current method used IOT to monitor farm fields using PIR and ultrasonic sensors, but it had the disadvantage of having a smaller detection range than LiDAR sensors. To detect animal trespass at the farm's perimeter, an intrusion detection system based in Lidar sensors placed outside the fence is deployed. A graphical representation of the LiDAR has also been created to indicate the state of the field conditions. An electric fence is used to keep out animals that can threaten the people inside the farm. In order to prevent farmers from electrocuting themselves and keep wild animals outside of the farm's boundaries, a safety device for electric fences by utilizing microwave sensor has been designed.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128152578","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085436
R. Lal Raja Singh, K. Arulselvan, A. Indhumathi, S. Iswarya, G. Namitha
Air pollution is becoming a severe problem in metropolitan areas, requiring special care due to high decibel levels and the presence of dangerous substances in the environment. Controlling pollution (including air pollution) is becoming increasingly necessary in order to maintain a healthy lifestyle and a better future. In this work, an effective Internet of Things implementation is employed to monitor ambient atmospheric conditions such as air pollution. This research presents a conceptual design for a versatile, cost-effective, and adaptive system for monitoring the air and acoustic quality of a specific venue. This system provides a monitoring system for air quality, allowing us to monitor and check in real time. This data is continually transmitted by the system, which employs Air sensors for detecting pollutants and carbon dioxide in the air. The monitoring devices like AM7000 which are currently used are felt difficult to implement because of its short range of monitoring, higher cost etc. Other devices which are used for monitoring faces a major disadvantage because of its higher volume, its huge weight etc., The major objective of the proposed system is to make a device which is lesser in volume and weight, cost efficient and also to provide a system which is much more effective in monitoring
{"title":"Design and Fabrication of Solar Powered Air Quality Monitoring System","authors":"R. Lal Raja Singh, K. Arulselvan, A. Indhumathi, S. Iswarya, G. Namitha","doi":"10.1109/ICEARS56392.2023.10085436","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085436","url":null,"abstract":"Air pollution is becoming a severe problem in metropolitan areas, requiring special care due to high decibel levels and the presence of dangerous substances in the environment. Controlling pollution (including air pollution) is becoming increasingly necessary in order to maintain a healthy lifestyle and a better future. In this work, an effective Internet of Things implementation is employed to monitor ambient atmospheric conditions such as air pollution. This research presents a conceptual design for a versatile, cost-effective, and adaptive system for monitoring the air and acoustic quality of a specific venue. This system provides a monitoring system for air quality, allowing us to monitor and check in real time. This data is continually transmitted by the system, which employs Air sensors for detecting pollutants and carbon dioxide in the air. The monitoring devices like AM7000 which are currently used are felt difficult to implement because of its short range of monitoring, higher cost etc. Other devices which are used for monitoring faces a major disadvantage because of its higher volume, its huge weight etc., The major objective of the proposed system is to make a device which is lesser in volume and weight, cost efficient and also to provide a system which is much more effective in monitoring","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125213415","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085456
K. Makanyadevi, P. S, S. R, S. S
Across the world, people are growing more dietary sensitive today. An unbalanced diet can result in a variety of issues, including weight gain, obesity, diabetes, etc. As a result, many techniques were created to analyse images of food and determine factors like calories as well as nutrition content. One of the most essential needs of every living thing on earth is food. Humans demand that the food they eat be of standard quality, freshness, and purity. Food quality is taken care of by the standards set and automation implemented in the food processing business. The idea of using food as medicine has gained traction in recent years, in part due to doctors' and practitioners' increased understanding of the importance of including food in the treatment of chronic illnesses alongside drugs. Food measurement is crucial for a good healthy diet. One of the difficult tasks in maintaining diet is calorie and nutritional content measurement in daily eating. In today's technology age, the smartphone exacerbates the problem with nutritional intake. The meal image recognition algorithm for calculating the nutritional and calorie values has been established in this survey analysis. The system classifies the meal once the user takes a picture of it to determine the type of food, the portion size, and the expected number of calories. This approach uses food area, size, and volume to accurately compute calories and nutrition. Due to the difficulty in achieving accuracy to classify food images, many images have been trained to attain high accuracy.
{"title":"Survey on Customized Diet Assisted System based on Food Recognition","authors":"K. Makanyadevi, P. S, S. R, S. S","doi":"10.1109/ICEARS56392.2023.10085456","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085456","url":null,"abstract":"Across the world, people are growing more dietary sensitive today. An unbalanced diet can result in a variety of issues, including weight gain, obesity, diabetes, etc. As a result, many techniques were created to analyse images of food and determine factors like calories as well as nutrition content. One of the most essential needs of every living thing on earth is food. Humans demand that the food they eat be of standard quality, freshness, and purity. Food quality is taken care of by the standards set and automation implemented in the food processing business. The idea of using food as medicine has gained traction in recent years, in part due to doctors' and practitioners' increased understanding of the importance of including food in the treatment of chronic illnesses alongside drugs. Food measurement is crucial for a good healthy diet. One of the difficult tasks in maintaining diet is calorie and nutritional content measurement in daily eating. In today's technology age, the smartphone exacerbates the problem with nutritional intake. The meal image recognition algorithm for calculating the nutritional and calorie values has been established in this survey analysis. The system classifies the meal once the user takes a picture of it to determine the type of food, the portion size, and the expected number of calories. This approach uses food area, size, and volume to accurately compute calories and nutrition. Due to the difficulty in achieving accuracy to classify food images, many images have been trained to attain high accuracy.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166368","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085475
Lakshmi Devi N, Rajasekhar Reddy Bochu, Naveen Kumar Buddha
Salt segmentation is the process of identifying whether a subsurface target is salt or not. There are several places on Earth where there are significant amounts of salt as well as oil and gas. For businesses engaged in oil and gas development, finding the exact locations of significant salt deposits is crucial. Also, lands that have been impacted by salt are not useful for farming. The absorption capacity of the plant reduces due to the presence of salt in the soil solution. So, in order to identify the land that contains salt, salt segmentation is being done. The seismic image of a particular pixel is analysed to classify it either as salt or sediment. TGS Salt Identification Challenge dataset is used which consists of 4,000 seismic image patches of size (101x101x3) and corresponding segmentation masks of size (101x101x1) in training set. 18,000 seismic image patches are present in the test set which are used for evaluation of the model. The existing models have less detection rate. So, this study has proposed two models for identifying the salt region with high detection rate. The primary model used here is a combination of UNET with ResNet-18 and ResNet-34. The secondary model achieves segmentation results by ensembling UNET with ResNet-34, VGG16 and Inceptionv3. Using these two models, the salt region can be determined from the seismic data. IoU is used as performance metric in order to evaluate the model. The outcomes demonstrate that the ensemble model outperforms individual network models and achieves better segmentation results.
{"title":"Salt Segment Identification in Seismic Images of Earth Surface using Deep Learning Techniques","authors":"Lakshmi Devi N, Rajasekhar Reddy Bochu, Naveen Kumar Buddha","doi":"10.1109/ICEARS56392.2023.10085475","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085475","url":null,"abstract":"Salt segmentation is the process of identifying whether a subsurface target is salt or not. There are several places on Earth where there are significant amounts of salt as well as oil and gas. For businesses engaged in oil and gas development, finding the exact locations of significant salt deposits is crucial. Also, lands that have been impacted by salt are not useful for farming. The absorption capacity of the plant reduces due to the presence of salt in the soil solution. So, in order to identify the land that contains salt, salt segmentation is being done. The seismic image of a particular pixel is analysed to classify it either as salt or sediment. TGS Salt Identification Challenge dataset is used which consists of 4,000 seismic image patches of size (101x101x3) and corresponding segmentation masks of size (101x101x1) in training set. 18,000 seismic image patches are present in the test set which are used for evaluation of the model. The existing models have less detection rate. So, this study has proposed two models for identifying the salt region with high detection rate. The primary model used here is a combination of UNET with ResNet-18 and ResNet-34. The secondary model achieves segmentation results by ensembling UNET with ResNet-34, VGG16 and Inceptionv3. Using these two models, the salt region can be determined from the seismic data. IoU is used as performance metric in order to evaluate the model. The outcomes demonstrate that the ensemble model outperforms individual network models and achieves better segmentation results.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134085173","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085204
A. Pugazhenthi, R. Thiyagarajan, P. Srividhya, R. Udhayasankar, S. R
A silicon carbide strengthened aluminium 6463 composite was formed by stir casting. To assess crucial process parameters, the composite was machined. At three different levels, three variables—current, pulse ON time, and feed of the wire—were incorporated in Taguchi's experimental setup. The components that impact the process were found using a statistical analysis. The ON time of the pulse of 160 s, the current of 18 A, and the feed of the wire of 2 mm/min had the highest removal rate. The pulse on-time of 100 s, the current of 12 A, and the feed of the wire rate of 2 mm/min remained the most effective factors for obtaining a good surface quality. Feed of the wire had minimal impact on output characteristics, but pulse duty cycle and current were important elements in achieving high material removal rates with acceptable surface quality. The experimental Taguchi design improved machinability characteristics while milling the synthesized composites by maintain the higher value of the ON time of the pulse and current. The artificial neural network model is developed to predict the experimental outcome and the model predicts the result with an accuracy of 100%.
{"title":"Artificial Neural Network and Process Optimization of Electrical Discharge Machining of Al 6463","authors":"A. Pugazhenthi, R. Thiyagarajan, P. Srividhya, R. Udhayasankar, S. R","doi":"10.1109/ICEARS56392.2023.10085204","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085204","url":null,"abstract":"A silicon carbide strengthened aluminium 6463 composite was formed by stir casting. To assess crucial process parameters, the composite was machined. At three different levels, three variables—current, pulse ON time, and feed of the wire—were incorporated in Taguchi's experimental setup. The components that impact the process were found using a statistical analysis. The ON time of the pulse of 160 s, the current of 18 A, and the feed of the wire of 2 mm/min had the highest removal rate. The pulse on-time of 100 s, the current of 12 A, and the feed of the wire rate of 2 mm/min remained the most effective factors for obtaining a good surface quality. Feed of the wire had minimal impact on output characteristics, but pulse duty cycle and current were important elements in achieving high material removal rates with acceptable surface quality. The experimental Taguchi design improved machinability characteristics while milling the synthesized composites by maintain the higher value of the ON time of the pulse and current. The artificial neural network model is developed to predict the experimental outcome and the model predicts the result with an accuracy of 100%.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134456323","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10084974
Saikrishna, Chapala Venkataramana, T. Sandeep, Venkata Varma, Rd. Suryakanth, M. Nageshwar Rao
A multi-variant, multi-constrain problem called load unbalancing reduces the effectiveness and performance of computer resources. Overloading and under loading are two undesired aspects of load unbalancing that are addressed by load balancing solutions. To the extent possible, various load balancing approaches currently in use are not organised in a comprehensive, thorough, systematic, or hierarchical manner.Furthermore, the literature neither studies nor considers the causes of load unbalancing. This study provides a comprehensive analysis various load-balancing strategy. Build efficient load balancing algorithms in the future, the benefits and drawbacks of current systems are emphasised, and significant problems are addressed.
{"title":"A Hierarchical Taxonomy of Load Balancing in Cloud Computing","authors":"Saikrishna, Chapala Venkataramana, T. Sandeep, Venkata Varma, Rd. Suryakanth, M. Nageshwar Rao","doi":"10.1109/ICEARS56392.2023.10084974","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10084974","url":null,"abstract":"A multi-variant, multi-constrain problem called load unbalancing reduces the effectiveness and performance of computer resources. Overloading and under loading are two undesired aspects of load unbalancing that are addressed by load balancing solutions. To the extent possible, various load balancing approaches currently in use are not organised in a comprehensive, thorough, systematic, or hierarchical manner.Furthermore, the literature neither studies nor considers the causes of load unbalancing. This study provides a comprehensive analysis various load-balancing strategy. Build efficient load balancing algorithms in the future, the benefits and drawbacks of current systems are emphasised, and significant problems are addressed.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115876472","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085334
E. K, S. K
The proposed RCNN-based classification system for vitamin deficiency in skin surface microscopy images involves several important steps. The first step is to extract relevant features from the images, which in this case will be border/edge information obtained through the use of Blur Trace (BT) techniques. The BT analysis is a powerful tool for extracting meaningful information from images, and it has been shown to be effective in pattern recognition tasks similar to the one being proposed here. The next step in the process is to perform preprocessing on the images to remove unwanted elements such as hair and noise. This is achieved through the use of nonlinear filtering, specifically median filtering, which has been chosen for its superior performance compared to linear filtering methods. The filtered images are then analyzed to extract energy characteristics that are used to accurately categorize the patterns of vitamin deficiency present in the images. The final stage of the system is the classification of the dermoscopy image into one of the predefined categories, such as Normal, Benign, or Malignant. This is accomplished through the use of the RCNN, which has been trained on the features extracted from the images. The RCNN is a highly advanced machine learning algorithm that has been shown to perform well in a wide range of pattern recognition tasks, making it an ideal choice for this application. The ultimate goal of this research is to contribute to the field of dermatology by improving the accuracy of diagnosing vitamin deficiency and enhancing therapy efficacy through the use of cutting-edge imaging technology. By combining the power of the RCNN with the capabilities of the BT analysis, it is expected that a highly accurate and effective classification system will be developed, which will benefit patients and healthcare practitioners alike.
{"title":"Diagnosis of Vitamin Deficiency in Human Beings using DNN Algorithm","authors":"E. K, S. K","doi":"10.1109/ICEARS56392.2023.10085334","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085334","url":null,"abstract":"The proposed RCNN-based classification system for vitamin deficiency in skin surface microscopy images involves several important steps. The first step is to extract relevant features from the images, which in this case will be border/edge information obtained through the use of Blur Trace (BT) techniques. The BT analysis is a powerful tool for extracting meaningful information from images, and it has been shown to be effective in pattern recognition tasks similar to the one being proposed here. The next step in the process is to perform preprocessing on the images to remove unwanted elements such as hair and noise. This is achieved through the use of nonlinear filtering, specifically median filtering, which has been chosen for its superior performance compared to linear filtering methods. The filtered images are then analyzed to extract energy characteristics that are used to accurately categorize the patterns of vitamin deficiency present in the images. The final stage of the system is the classification of the dermoscopy image into one of the predefined categories, such as Normal, Benign, or Malignant. This is accomplished through the use of the RCNN, which has been trained on the features extracted from the images. The RCNN is a highly advanced machine learning algorithm that has been shown to perform well in a wide range of pattern recognition tasks, making it an ideal choice for this application. The ultimate goal of this research is to contribute to the field of dermatology by improving the accuracy of diagnosing vitamin deficiency and enhancing therapy efficacy through the use of cutting-edge imaging technology. By combining the power of the RCNN with the capabilities of the BT analysis, it is expected that a highly accurate and effective classification system will be developed, which will benefit patients and healthcare practitioners alike.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719743","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085636
S. Sujanthi, A. S, H. K, S. S
The fourth most frequent illness-related cause of death in women is cervical malignant growth. Cervical cancer is associated with the presence of the human papillomavirus (HPV). Early detection has made cervical cancer preventable, which has decreased overall impact of the disease. Due to the high expense of routine exams, a lack of awareness, and limited access to medical facilities, women do not participate in enough screening programs in underdeveloped countries. This way, each patient is expected to be at extremely high risk. There are numerous threat factors that can lead to the growth of cervical cancer. As a result, the datasets will be checked for cervical cancer by using a variety of data analytics tools, including machine learning and deep learning algorithms. The classification of normal and abnormal cervical data is done by performing a quick overview of how cervical cancer works and is detected.
{"title":"Prediction of Cervical Cancer using Multilayer Perceptron Algorithm","authors":"S. Sujanthi, A. S, H. K, S. S","doi":"10.1109/ICEARS56392.2023.10085636","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085636","url":null,"abstract":"The fourth most frequent illness-related cause of death in women is cervical malignant growth. Cervical cancer is associated with the presence of the human papillomavirus (HPV). Early detection has made cervical cancer preventable, which has decreased overall impact of the disease. Due to the high expense of routine exams, a lack of awareness, and limited access to medical facilities, women do not participate in enough screening programs in underdeveloped countries. This way, each patient is expected to be at extremely high risk. There are numerous threat factors that can lead to the growth of cervical cancer. As a result, the datasets will be checked for cervical cancer by using a variety of data analytics tools, including machine learning and deep learning algorithms. The classification of normal and abnormal cervical data is done by performing a quick overview of how cervical cancer works and is detected.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133421715","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10084944
P. Ilampiray, A. Thilagavathy, Challa Sai Hari Uma Sahith, Penumathsa Girish Sai Varma, Bhuvanendra Chowdary V, M. Dhanush
Home Automation has become an important part nowadays. Home automation and the Internet of Things are becoming popular because automatic systems are preferred by most people. Life has become easier with automation. The emerging technologies like Machine Learning (ML) and Deep Learning (DL) play a major role in home automation. Nowadays there is a tremendous growth of mobile devices. But with machine learning systems, there is an increasing demand for smartphone applications. This paper focuses on an interactive mobile application that can control household electronic devices such as fans, AC, light, Television, etc., with the help of on-device machine learning and the Internet of Things. The proposed machine-learning algorithm automatically detects the type of device in just a photo-scan and performs the basic operations.
{"title":"An Ingenious Deep Learning Approach for Home Automation using Tensorflow Computational Framework","authors":"P. Ilampiray, A. Thilagavathy, Challa Sai Hari Uma Sahith, Penumathsa Girish Sai Varma, Bhuvanendra Chowdary V, M. Dhanush","doi":"10.1109/ICEARS56392.2023.10084944","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10084944","url":null,"abstract":"Home Automation has become an important part nowadays. Home automation and the Internet of Things are becoming popular because automatic systems are preferred by most people. Life has become easier with automation. The emerging technologies like Machine Learning (ML) and Deep Learning (DL) play a major role in home automation. Nowadays there is a tremendous growth of mobile devices. But with machine learning systems, there is an increasing demand for smartphone applications. This paper focuses on an interactive mobile application that can control household electronic devices such as fans, AC, light, Television, etc., with the help of on-device machine learning and the Internet of Things. The proposed machine-learning algorithm automatically detects the type of device in just a photo-scan and performs the basic operations.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133419810","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085056
Haritha J, B. T, B. S, Darshan S, A. A., Mooses Pradeep A
The predominant genotype in Indian native breeds of cows and buffaloes is A2A2, meaning they produce A2 milk. Cow milk has calcium, phosphorus, rich fats, potassium which helps to maintain blood pressure. This work focuses on the capacitive sensor method to identify the native breed cow milk from different varieties of milk. The milk variety taken for study are Holstein Friesian(HF), Girr, Jersey and Native breed. This study is carried out using parallel plate capacitor and cylindrical type capacitor. The parameters taken for study are capacitance, magnitude and phase value for both the type of capacitors. The results show that parallel plate capacitor characteristics provides differentiation of milk samples in capacitance at 1kHz frequency and in magnitude values of impedance at 100Hz and 120Hz frequencies. The characteristics of the cylindrical capacitor shows clear identification of native breed in magnitude of impedance at all the frequency from 100Hz to 100kHz but in capacitance value the difference is very low at all the frequency range. Hence, by considering capacitance as a parameter, parallel plate capacitor characteristics provide better results at 1kHz frequency compared to the cylindrical capacitor.
{"title":"Characteristics Study of Capacitive Sensor to Identify Native Breed Cow Milk","authors":"Haritha J, B. T, B. S, Darshan S, A. A., Mooses Pradeep A","doi":"10.1109/ICEARS56392.2023.10085056","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085056","url":null,"abstract":"The predominant genotype in Indian native breeds of cows and buffaloes is A2A2, meaning they produce A2 milk. Cow milk has calcium, phosphorus, rich fats, potassium which helps to maintain blood pressure. This work focuses on the capacitive sensor method to identify the native breed cow milk from different varieties of milk. The milk variety taken for study are Holstein Friesian(HF), Girr, Jersey and Native breed. This study is carried out using parallel plate capacitor and cylindrical type capacitor. The parameters taken for study are capacitance, magnitude and phase value for both the type of capacitors. The results show that parallel plate capacitor characteristics provides differentiation of milk samples in capacitance at 1kHz frequency and in magnitude values of impedance at 100Hz and 120Hz frequencies. The characteristics of the cylindrical capacitor shows clear identification of native breed in magnitude of impedance at all the frequency from 100Hz to 100kHz but in capacitance value the difference is very low at all the frequency range. Hence, by considering capacitance as a parameter, parallel plate capacitor characteristics provide better results at 1kHz frequency compared to the cylindrical capacitor.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117341310","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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