The inflammation is a disease to which the humans are facing from ancient times and their various methods of diagnosis, prophylaxis, and cure are mentioned in various traditional systems of medicines throughout the world. Many plants parts such as leaves were used in earlier times against inflammation which are proven by tests performed in laboratories. List of few plants is stated as Basella alba L. leaves ethanolic extract inhibit membrane lysis in HRBC Stabilization Method and leaves methanolic extract give significant activity against formaldehyde, egg albumin and turpentine oil induced paw edema in rat. Psidium guajava L. leaves methanolic extract give inhibitory action against carrageenan-induced paw edema in rats and the essential oil extracted from leaves inhibit 5-LOX causing anti-inflammatory activity. The Piper betle leaves methanolic extract give inhibitory action against carrageenan-induced paw edema in rats and ethanolic extract by inhibiting inflammatory modulators. Hibiscus rosa sinensis L. ethanolic extract inhibit paw oedema, carrageenan-induced in rat and inhibiting in HRBC hemolysis. Murraya koenigii L. leaf ethanolic extract inhibit heat induced denaturation of albumin and methanolic extract give inhibition of carrageenan-induced paw edema in rat. The leaves ethanolic extract and aqueous extract of Hibiscus rosa sinensis L. inhibit paw edema in rat induced by carrageenan. Three models of machine learning were used including Inception-v3 feature extractor using Logistic Regression, Inception-v3 Feature extractor used with Neural Network confusion matrix and Inception-v3 Feature extractor with Random Forest confusion matrix (Orange Classification Tree). A comparison matrix for every model utilized was generated and maximum accuracy of 99.5% was attained. For all used model the ROC curve was drawn for proper comparison and representation.
{"title":"Identification of Plants with Anti-Inflammatory Properties from their Leaves Using Machine Learning","authors":"Saurabh Pargaien, Amrita Verma Pargaien, Devendra Singh, Gauri Joshi, Jatin Pant, Himanshu Joshi","doi":"10.1109/ICECAA58104.2023.10212200","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212200","url":null,"abstract":"The inflammation is a disease to which the humans are facing from ancient times and their various methods of diagnosis, prophylaxis, and cure are mentioned in various traditional systems of medicines throughout the world. Many plants parts such as leaves were used in earlier times against inflammation which are proven by tests performed in laboratories. List of few plants is stated as Basella alba L. leaves ethanolic extract inhibit membrane lysis in HRBC Stabilization Method and leaves methanolic extract give significant activity against formaldehyde, egg albumin and turpentine oil induced paw edema in rat. Psidium guajava L. leaves methanolic extract give inhibitory action against carrageenan-induced paw edema in rats and the essential oil extracted from leaves inhibit 5-LOX causing anti-inflammatory activity. The Piper betle leaves methanolic extract give inhibitory action against carrageenan-induced paw edema in rats and ethanolic extract by inhibiting inflammatory modulators. Hibiscus rosa sinensis L. ethanolic extract inhibit paw oedema, carrageenan-induced in rat and inhibiting in HRBC hemolysis. Murraya koenigii L. leaf ethanolic extract inhibit heat induced denaturation of albumin and methanolic extract give inhibition of carrageenan-induced paw edema in rat. The leaves ethanolic extract and aqueous extract of Hibiscus rosa sinensis L. inhibit paw edema in rat induced by carrageenan. Three models of machine learning were used including Inception-v3 feature extractor using Logistic Regression, Inception-v3 Feature extractor used with Neural Network confusion matrix and Inception-v3 Feature extractor with Random Forest confusion matrix (Orange Classification Tree). A comparison matrix for every model utilized was generated and maximum accuracy of 99.5% was attained. For all used model the ROC curve was drawn for proper comparison and representation.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131334082","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212360
V. G, K. B. C., Shaik Nihal, K. Rakesh
In the fast developing world, EV plays a major role. Less resource utilization, cost efficient, less maintenance, long durability are some factors which make people to shift towards Electrical vehicles. Even though a huge number of people are now showing interest towards Electrical Vehicles, there are only limited power stations available and most of the time the coordinates of the power stations are unknown to EV users. Hence, a power management system is required wherein the EV users can access the power stations easily and increase the number of Electrical Vehicles stations. As in other side people are now shifting towards Internet of Things and Cloud technology to make their life better and to access things easily. Using EV's as their source of traveling, it reduces cost and enhances many other factors. Since both IoT and EV are becoming the fast adapting technologies, these two technologies can be merged in future. In the proposed research work, both IoT and EV's plays a main role. Here, a prototype to easily access charging station was developed and then integrated with Web Technology to show where and when the user can access the charging stations. The proposed idea would help in increase the number of charging stations, which indirectly increase the usage of EVs.
{"title":"An IoT -based Power Management System for EV Chargers","authors":"V. G, K. B. C., Shaik Nihal, K. Rakesh","doi":"10.1109/ICECAA58104.2023.10212360","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212360","url":null,"abstract":"In the fast developing world, EV plays a major role. Less resource utilization, cost efficient, less maintenance, long durability are some factors which make people to shift towards Electrical vehicles. Even though a huge number of people are now showing interest towards Electrical Vehicles, there are only limited power stations available and most of the time the coordinates of the power stations are unknown to EV users. Hence, a power management system is required wherein the EV users can access the power stations easily and increase the number of Electrical Vehicles stations. As in other side people are now shifting towards Internet of Things and Cloud technology to make their life better and to access things easily. Using EV's as their source of traveling, it reduces cost and enhances many other factors. Since both IoT and EV are becoming the fast adapting technologies, these two technologies can be merged in future. In the proposed research work, both IoT and EV's plays a main role. Here, a prototype to easily access charging station was developed and then integrated with Web Technology to show where and when the user can access the charging stations. The proposed idea would help in increase the number of charging stations, which indirectly increase the usage of EVs.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"22 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126770256","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212299
P. Priya, T. Vaishnavi, N. Selvakumar, G. R. Kalyan, A. Reethika
Animal species classification is a fundamental task in wildlife conservation, animal behavior studies, and biodiversity research. Convolutional Neural Networks (CNNs) have become a potent technique for automatic classification tasks in recent times. This abstract presents an overview of the use of CNNs for animal species classification. The proposed approach involves pre-processing of the input images, followed by feature extraction and classification using CNN architecture. The pre-processing step involves image resizing, normalization, and augmentation to enhance the resilience of the model. The feature extraction is performed by convolutional layers, followed by max-pooling layers, and fully connected layers for classification. Transfer learning is also utilized to leverage the pre-trained CNN models and fine-tune them for specific animal species classification tasks. The proposed approach achieves high accuracy of 98% and can be extended to various animal species classification tasks. Overall, CNNs provide an effective means for automated animal species classification, enabling more efficient and accurate animal behavior studies, and wildlife conservation efforts.
{"title":"An Enhanced Animal Species Classification and Prediction Engine using CNN","authors":"P. Priya, T. Vaishnavi, N. Selvakumar, G. R. Kalyan, A. Reethika","doi":"10.1109/ICECAA58104.2023.10212299","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212299","url":null,"abstract":"Animal species classification is a fundamental task in wildlife conservation, animal behavior studies, and biodiversity research. Convolutional Neural Networks (CNNs) have become a potent technique for automatic classification tasks in recent times. This abstract presents an overview of the use of CNNs for animal species classification. The proposed approach involves pre-processing of the input images, followed by feature extraction and classification using CNN architecture. The pre-processing step involves image resizing, normalization, and augmentation to enhance the resilience of the model. The feature extraction is performed by convolutional layers, followed by max-pooling layers, and fully connected layers for classification. Transfer learning is also utilized to leverage the pre-trained CNN models and fine-tune them for specific animal species classification tasks. The proposed approach achieves high accuracy of 98% and can be extended to various animal species classification tasks. Overall, CNNs provide an effective means for automated animal species classification, enabling more efficient and accurate animal behavior studies, and wildlife conservation efforts.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123415922","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212202
Femy N S, Sasi Gopalan
This paper uses a hybrid model combining Self-Organizing Maps (SOM), Johnson-Lindenstrauss Lemma(JLL), and Fuzzy Logic for time-series data prediction. It is named as SJLF model. SOM is used to group data having similar characteristics. By JLL, high-dimensional data is projected to low-dimensional space by approximately preserving the distance between the input vectors. The fuzziness in data is also carried on to the projected values. These projected values are input into the fuzzy logic system to obtain the predicted value as output. For experimental analysis, the available data on COVID-19 is taken from humanitarian data exchange. The proposed SJLF model is applied to five coronavirus-affected countries Belgium, Brazil, Colombia, India, and Iran. The SJLF model's prediction shows promising results as the average MAPE for five countries is 1.199, and the prediction accuracy on an average is 98.8%. The proposed model is compared with the ANFIS model and is found that the proposed model shows better forecasting results.
{"title":"Time-Series Data Prediction Using Johnson-Lindenstrauss Lemma, Fuzzy Logic, And Self Organizing Maps","authors":"Femy N S, Sasi Gopalan","doi":"10.1109/ICECAA58104.2023.10212202","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212202","url":null,"abstract":"This paper uses a hybrid model combining Self-Organizing Maps (SOM), Johnson-Lindenstrauss Lemma(JLL), and Fuzzy Logic for time-series data prediction. It is named as SJLF model. SOM is used to group data having similar characteristics. By JLL, high-dimensional data is projected to low-dimensional space by approximately preserving the distance between the input vectors. The fuzziness in data is also carried on to the projected values. These projected values are input into the fuzzy logic system to obtain the predicted value as output. For experimental analysis, the available data on COVID-19 is taken from humanitarian data exchange. The proposed SJLF model is applied to five coronavirus-affected countries Belgium, Brazil, Colombia, India, and Iran. The SJLF model's prediction shows promising results as the average MAPE for five countries is 1.199, and the prediction accuracy on an average is 98.8%. The proposed model is compared with the ANFIS model and is found that the proposed model shows better forecasting results.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121405803","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212100
Lalitha. B, Siddardha. S, Ibrahim. M, Rao G Ramakoteswara, P. Srinivas
Due to the variety of spamming techniques used, detecting SMS spam is a difficult task. This research study suggests a novel approach to improving SMS spam detection accuracy by leveraging the power of hybrid voting techniques. This research aims to combine the outputs of various machine learning models. Experiment results on a publicly available dataset show that the proposed hybrid voting technique outperforms individual models, detecting SMS spam with a high accuracy of over 98%. This approach has a lot of potential for improving SMS spam detection and can be applied to other types of spam detection tasks in different domains.
{"title":"Synergistic Detection of SMS Spam: Harnessing the Power of Hybrid Voting Technique","authors":"Lalitha. B, Siddardha. S, Ibrahim. M, Rao G Ramakoteswara, P. Srinivas","doi":"10.1109/ICECAA58104.2023.10212100","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212100","url":null,"abstract":"Due to the variety of spamming techniques used, detecting SMS spam is a difficult task. This research study suggests a novel approach to improving SMS spam detection accuracy by leveraging the power of hybrid voting techniques. This research aims to combine the outputs of various machine learning models. Experiment results on a publicly available dataset show that the proposed hybrid voting technique outperforms individual models, detecting SMS spam with a high accuracy of over 98%. This approach has a lot of potential for improving SMS spam detection and can be applied to other types of spam detection tasks in different domains.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121465852","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212300
M. Nagarajapandian, J. Aishwariya, S.K. Gayathri, P. Sivaranjani, M. Yazhini, T. Rajesh
In day-to-day life, petrol fraudulent activities are faced by people and petrol scam awareness isn't familiar to people. Currently, bikes only have fuel pointer displays that show the level of fuel in the tank rather than a detailed count of how much fuel is present. The proposed model solutes the above problem by calculating the fuel consumption in a fuel tank using water flow sensors and displaying it in digital values using digital meters. The amount of fuel in the tank is displayed in liters using numerical digits (ex:1.2 L). This solution builds the numeric fuel pointer display which indicates the correct measure of fuel regarding liters (L). The main aim of this paper is to minimize the burden of the riders continuously checking the fuel level in the tank helping them know the amount of fuel in tank with maximum accuracy without any physical check. This indicator will be powered by a rechargeable battery and the fuel quantity will be displayed using LCD (Liquid Crystal Display). The fuel amount will be measured using a magnetic type flow sensor working under the principle of the Hall Effect. The Digital Petrol quantity calculator system is easily fixed to the petrol tank of the vehicles externally, such that the proposed system is aware of the safety purpose.
{"title":"Digital Quantity Calculation in Petrol Tanks through Numeric Fuel Indicator for Motor Cycles","authors":"M. Nagarajapandian, J. Aishwariya, S.K. Gayathri, P. Sivaranjani, M. Yazhini, T. Rajesh","doi":"10.1109/ICECAA58104.2023.10212300","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212300","url":null,"abstract":"In day-to-day life, petrol fraudulent activities are faced by people and petrol scam awareness isn't familiar to people. Currently, bikes only have fuel pointer displays that show the level of fuel in the tank rather than a detailed count of how much fuel is present. The proposed model solutes the above problem by calculating the fuel consumption in a fuel tank using water flow sensors and displaying it in digital values using digital meters. The amount of fuel in the tank is displayed in liters using numerical digits (ex:1.2 L). This solution builds the numeric fuel pointer display which indicates the correct measure of fuel regarding liters (L). The main aim of this paper is to minimize the burden of the riders continuously checking the fuel level in the tank helping them know the amount of fuel in tank with maximum accuracy without any physical check. This indicator will be powered by a rechargeable battery and the fuel quantity will be displayed using LCD (Liquid Crystal Display). The fuel amount will be measured using a magnetic type flow sensor working under the principle of the Hall Effect. The Digital Petrol quantity calculator system is easily fixed to the petrol tank of the vehicles externally, such that the proposed system is aware of the safety purpose.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116619444","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212211
W. Auccahuasi, Oscar Linares, Luis Vivanco-Aldon, Martin Campos-Martinez, Humberto Quispe-Peña, Julia Sobrino-Mesias
In the studies of medical images, being able to classify the objects present in the images is of vital importance; these objects can be some structure of the human body, some malformation, and tumors, among others. One of the fundamental tasks is to be able to find the characteristics that help to classify the desired object; these characteristics can be found manually using mainly shape and color descriptors. In the present work we describe a methodology of how to use the RADIOMICS tool, to carry out the search for the characteristics automatically, we indicate the necessary steps and the procedures to be carried out. To demonstrate the methodology, we use the mammography modality in the detection and classification of micro calcifications, where the problem is related to being able to find them in a high-density image, taking as a starting point that their representation in the image is very small. We start the methodology with the analysis of the original image in DICOM format, then we carry out the location and marking of the images and finally as a result we present the description of the characteristics found as well as the recommendation to be used with the different classification algorithms. The methodology presented is scalable and can be used in different imaging modalities.
{"title":"Rapid Method for Feature Extraction Using RADIOMICS Applied to Medical Imaging","authors":"W. Auccahuasi, Oscar Linares, Luis Vivanco-Aldon, Martin Campos-Martinez, Humberto Quispe-Peña, Julia Sobrino-Mesias","doi":"10.1109/ICECAA58104.2023.10212211","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212211","url":null,"abstract":"In the studies of medical images, being able to classify the objects present in the images is of vital importance; these objects can be some structure of the human body, some malformation, and tumors, among others. One of the fundamental tasks is to be able to find the characteristics that help to classify the desired object; these characteristics can be found manually using mainly shape and color descriptors. In the present work we describe a methodology of how to use the RADIOMICS tool, to carry out the search for the characteristics automatically, we indicate the necessary steps and the procedures to be carried out. To demonstrate the methodology, we use the mammography modality in the detection and classification of micro calcifications, where the problem is related to being able to find them in a high-density image, taking as a starting point that their representation in the image is very small. We start the methodology with the analysis of the original image in DICOM format, then we carry out the location and marking of the images and finally as a result we present the description of the characteristics found as well as the recommendation to be used with the different classification algorithms. The methodology presented is scalable and can be used in different imaging modalities.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121743878","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212152
P. Chandre, Viresh Vanarote, Moushmee Kuri, A. Uttarkar, Abhishek Dhore, Shafiq Y. Pathan
The objective of this study is to develop an AI model that can correctly identify which patients are most likely to require hospital readmission within a predetermined window of time after being discharged. Given that readmissions are linked to higher healthcare costs and poorer patient outcomes; this is a crucial problem in healthcare. The model must, nonetheless, also be explicable, which means that healthcare professionals must be able to comprehend the rationale behind why it made certain predictions. This is essential for establishing the model's credibility and making sure it is being used properly. To do this, the study may employ a range of machine learning methods renowned for their interpretability, like decision trees or random forests. Additionally, the study could investigate how to generate feature importance plots or partial dependence plots to visualize the model's decision-making process. Overall, by enhancing patient outcomes and fostering openness and confidence in the use of AI, this research subject has the potential to have a significant impact on healthcare.
{"title":"Developing an Explainable AI Model for Predicting Patient Readmissions in Hospitals","authors":"P. Chandre, Viresh Vanarote, Moushmee Kuri, A. Uttarkar, Abhishek Dhore, Shafiq Y. Pathan","doi":"10.1109/ICECAA58104.2023.10212152","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212152","url":null,"abstract":"The objective of this study is to develop an AI model that can correctly identify which patients are most likely to require hospital readmission within a predetermined window of time after being discharged. Given that readmissions are linked to higher healthcare costs and poorer patient outcomes; this is a crucial problem in healthcare. The model must, nonetheless, also be explicable, which means that healthcare professionals must be able to comprehend the rationale behind why it made certain predictions. This is essential for establishing the model's credibility and making sure it is being used properly. To do this, the study may employ a range of machine learning methods renowned for their interpretability, like decision trees or random forests. Additionally, the study could investigate how to generate feature importance plots or partial dependence plots to visualize the model's decision-making process. Overall, by enhancing patient outcomes and fostering openness and confidence in the use of AI, this research subject has the potential to have a significant impact on healthcare.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"738 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123861951","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212379
E. Malarvizhi, G. E. Visuvanathan, M. Tholkapiyan, S. Parthasarathy
A solar PhotoVoltaic (PV) water pumping system is a possible replacement for traditional power and diesel-based pumping systems, particularly in agriculture and water distribution in communities. This paper describes the PV-fed BrushLess Direct Current (BLDC) motor for water pump application and the motor parameters are monitored using Internet of Things (IoT) technology. At the intermediate phase, an effective direct current (DC)/DC converter is necessary to get the most power out of the solar array. The PV array is run at its highest power using a cuk converter with the Maximum Power Point Tracking (MPPT) using Incremental conductance (IC) method. The converter is connected to a voltage source inverter (VSI) which feeds the BLDC motor by controlling its switches. Using the back electromotive force (EMF) observer, the motor's current and speed is inferred from the terminal currents and voltages. The IoT monitoring scheme is integrated into the system and a sensor is used to collect data about motor status, which may then be viewed remotely on a monitoring panel that includes a web application. The overall performance of the developed model is examined using Matlab results.
{"title":"IoT Monitoring Scheme in Solar-based Motor Drive for Water Pump Applications","authors":"E. Malarvizhi, G. E. Visuvanathan, M. Tholkapiyan, S. Parthasarathy","doi":"10.1109/ICECAA58104.2023.10212379","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212379","url":null,"abstract":"A solar PhotoVoltaic (PV) water pumping system is a possible replacement for traditional power and diesel-based pumping systems, particularly in agriculture and water distribution in communities. This paper describes the PV-fed BrushLess Direct Current (BLDC) motor for water pump application and the motor parameters are monitored using Internet of Things (IoT) technology. At the intermediate phase, an effective direct current (DC)/DC converter is necessary to get the most power out of the solar array. The PV array is run at its highest power using a cuk converter with the Maximum Power Point Tracking (MPPT) using Incremental conductance (IC) method. The converter is connected to a voltage source inverter (VSI) which feeds the BLDC motor by controlling its switches. Using the back electromotive force (EMF) observer, the motor's current and speed is inferred from the terminal currents and voltages. The IoT monitoring scheme is integrated into the system and a sensor is used to collect data about motor status, which may then be viewed remotely on a monitoring panel that includes a web application. The overall performance of the developed model is examined using Matlab results.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121364917","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212349
G. Gopika, M. Sreekrishna, Katika Karthik, C. Reddy
One of the major worldwide crimes, phishing entail the burglary of the user's secretive information. Phishing websites frequently target the websites of business, institutions, government, and cloud storage space providers. While using the internet, the best parts of individuals are not aware of phishing assaults. Several phishing techniques now in use don't inefficiently address the troubles caused by email attacks. To combat software attacks, hardware-based phishing techniques are now deployed. The proposed effort concentrated on a three-stage spoofing series attempt for precisely identifying the difficulties in a material manner because of the increase in these types of problems. Uniform resource locators, circulation, and internet content based on phishing attack and non-phishing website strategy aspects were the three input variables. A dataset from previous phishing campaigns is gathered to apply the suggested phishing attack technique. Realistic phishing cases were found to provide a higher level of accuracy in phishing detection mechanisms and zero- day phishing attack. The categorization accuracy for phishing recognition using three dissimilar classifiers was indomitable to be 95.18 percent, 85.45 percent, and 78.89 % for NN, SVM, and RF, correspondingly. The findings indicate that a method based on machine learning works the best for phishing detection.
{"title":"Privacy Preserving Secure and Efficient Detection of Phishing Websites Using Machine Learning Approach","authors":"G. Gopika, M. Sreekrishna, Katika Karthik, C. Reddy","doi":"10.1109/ICECAA58104.2023.10212349","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212349","url":null,"abstract":"One of the major worldwide crimes, phishing entail the burglary of the user's secretive information. Phishing websites frequently target the websites of business, institutions, government, and cloud storage space providers. While using the internet, the best parts of individuals are not aware of phishing assaults. Several phishing techniques now in use don't inefficiently address the troubles caused by email attacks. To combat software attacks, hardware-based phishing techniques are now deployed. The proposed effort concentrated on a three-stage spoofing series attempt for precisely identifying the difficulties in a material manner because of the increase in these types of problems. Uniform resource locators, circulation, and internet content based on phishing attack and non-phishing website strategy aspects were the three input variables. A dataset from previous phishing campaigns is gathered to apply the suggested phishing attack technique. Realistic phishing cases were found to provide a higher level of accuracy in phishing detection mechanisms and zero- day phishing attack. The categorization accuracy for phishing recognition using three dissimilar classifiers was indomitable to be 95.18 percent, 85.45 percent, and 78.89 % for NN, SVM, and RF, correspondingly. The findings indicate that a method based on machine learning works the best for phishing detection.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121388829","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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