Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073695
D. Shende, Nikhil P. Wyawahare, L. Thakare, Rahul Agrawal
In modern days Agriculture industry requires automation on the industry standard 4.0 to increase the need for food demand to make satisfy the 103.5 million Metric Tons of grains per year, like rice crops where there is a need for precautionary health monitoring and plant cultivation must be in future looking at growth in recent years of the human population. Here the suggestive approach is to design and implementation of a robotics mechanism that holds records of plants-health, and environmental conditions, and the effective use of pesticides as per need should be monitored in real-time. Since the existing geographical conditions for agriculture plants and their proper growth depend on either sufficient rainfall or a good water supply with better knowledge of the fertilization process suited as per fertility of the land. In India during cultivation very few farmers are going for the fertility test of their lands, hence without knowing the actual indexed value it is exceedingly difficult to the prediction of fertilizer mixing & use of different types. Therefore, many such farmers do work on belief in the knowledge of ancient ways of traditional cultivation. This is the root cause of failure in agriculture where the crops are not getting proper nutrition, which results in a minimal amount of production & loss of crops. According to one of the case studies of rice cultivation types, such as transplantation, drilling, Japanese, and broadcast methods. Pesticides have been used mainly for the protection of plants as well as humans from malaria, dengue, and fever. In the Indian pesticide industry investment in pesticides in 2019 is over 73 billion Indian rupees. Different types of pesticides have been used for farming such as herbicides, insecticides, fungicides, bactericides, etc. the effect of pesticides on the fertility of crops and land is due to the hard treatment of soil with pesticides causing the population of soil microorganisms to decrease. As per the Government of India, 30 percent of land gets degraded. This review paper is focused on such cases where the complex situation can be minimized and how IoT is helpful in the Adaptive Spraying of Pesticides Depending on Mutual Plant Health Detection and Monitoring is suggested.
{"title":"Design Process for Adaptive Spraying of Pesticides Based on Mutual Plant Health Detection and Monitoring: A Review","authors":"D. Shende, Nikhil P. Wyawahare, L. Thakare, Rahul Agrawal","doi":"10.1109/ICAIS56108.2023.10073695","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073695","url":null,"abstract":"In modern days Agriculture industry requires automation on the industry standard 4.0 to increase the need for food demand to make satisfy the 103.5 million Metric Tons of grains per year, like rice crops where there is a need for precautionary health monitoring and plant cultivation must be in future looking at growth in recent years of the human population. Here the suggestive approach is to design and implementation of a robotics mechanism that holds records of plants-health, and environmental conditions, and the effective use of pesticides as per need should be monitored in real-time. Since the existing geographical conditions for agriculture plants and their proper growth depend on either sufficient rainfall or a good water supply with better knowledge of the fertilization process suited as per fertility of the land. In India during cultivation very few farmers are going for the fertility test of their lands, hence without knowing the actual indexed value it is exceedingly difficult to the prediction of fertilizer mixing & use of different types. Therefore, many such farmers do work on belief in the knowledge of ancient ways of traditional cultivation. This is the root cause of failure in agriculture where the crops are not getting proper nutrition, which results in a minimal amount of production & loss of crops. According to one of the case studies of rice cultivation types, such as transplantation, drilling, Japanese, and broadcast methods. Pesticides have been used mainly for the protection of plants as well as humans from malaria, dengue, and fever. In the Indian pesticide industry investment in pesticides in 2019 is over 73 billion Indian rupees. Different types of pesticides have been used for farming such as herbicides, insecticides, fungicides, bactericides, etc. the effect of pesticides on the fertility of crops and land is due to the hard treatment of soil with pesticides causing the population of soil microorganisms to decrease. As per the Government of India, 30 percent of land gets degraded. This review paper is focused on such cases where the complex situation can be minimized and how IoT is helpful in the Adaptive Spraying of Pesticides Depending on Mutual Plant Health Detection and Monitoring is suggested.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128575457","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073799
L. Anand, Padmalal S, J. Seetha, R. Juliana, PS Naveen Kumar, Gayatri Parasa
Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.
{"title":"Evaluation of Wireless Sensor Networks Module using IoT Approach","authors":"L. Anand, Padmalal S, J. Seetha, R. Juliana, PS Naveen Kumar, Gayatri Parasa","doi":"10.1109/ICAIS56108.2023.10073799","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073799","url":null,"abstract":"Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115998997","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073694
Iswarya Devi R, Mohamed Shibili, Sreeshma C V, V. V. Prasad, Nikhilbinoy C, Neethu K
The detection of lies in the internet age continues to be a difficult problem for law enforcement and researchers. This paper proposes an Arduino-based technique for lie detection that relies on the psychosomatic interaction between lying and small physiological modifications. The three physiological variables used in this model are heart rate, skin resistance, and temperature. These variables differ depending on whether a person answers the questionnaire honestly or dishonestly.
{"title":"Design and Development of Automatic Lie Detector using Arduino","authors":"Iswarya Devi R, Mohamed Shibili, Sreeshma C V, V. V. Prasad, Nikhilbinoy C, Neethu K","doi":"10.1109/ICAIS56108.2023.10073694","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073694","url":null,"abstract":"The detection of lies in the internet age continues to be a difficult problem for law enforcement and researchers. This paper proposes an Arduino-based technique for lie detection that relies on the psychosomatic interaction between lying and small physiological modifications. The three physiological variables used in this model are heart rate, skin resistance, and temperature. These variables differ depending on whether a person answers the questionnaire honestly or dishonestly.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116728236","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073907
R. S. Priya, K. Narayanan, B. V. Nirmala, R. Krishnan
In this effort, a deep learning technique for segmenting and detecting hemorrhagic lesions on brain CT images is proposed. This study intends to develop a framework for deep learning convolutional neural networks for processing CT brain images with hemorrhagic strokes and picture recognition. An adaptive median filter is used as a pre-processing step to remove noise from the input image. Following preprocessing, the picture with the noise removed is supplied into the segmentation block to be divided into numerous segments for subsequent processing. In addition, the K-means clustering technique is used in the suggested network to increase segmentation accuracy. The contrast between the hemorrhagic area and healthy brain tissue is enhanced. The findings that were acquired by employing CNN Classifier were precise. To prevail the incidence of computation is indeed slow and signals only move in one direction in feed forward setups.
{"title":"A Hybrid Deep Learning based Classification of Brain Lesion Classification in CT Image using Convolutional Neural Networks","authors":"R. S. Priya, K. Narayanan, B. V. Nirmala, R. Krishnan","doi":"10.1109/ICAIS56108.2023.10073907","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073907","url":null,"abstract":"In this effort, a deep learning technique for segmenting and detecting hemorrhagic lesions on brain CT images is proposed. This study intends to develop a framework for deep learning convolutional neural networks for processing CT brain images with hemorrhagic strokes and picture recognition. An adaptive median filter is used as a pre-processing step to remove noise from the input image. Following preprocessing, the picture with the noise removed is supplied into the segmentation block to be divided into numerous segments for subsequent processing. In addition, the K-means clustering technique is used in the suggested network to increase segmentation accuracy. The contrast between the hemorrhagic area and healthy brain tissue is enhanced. The findings that were acquired by employing CNN Classifier were precise. To prevail the incidence of computation is indeed slow and signals only move in one direction in feed forward setups.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115581328","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073859
K. Radha, R. Priya, K. Jeevitha
In a power system, the energy fed to the grid control and management is accomplished using various architectures system. The variation in the control and manage systems is based on the performance and features and the overall cost. Energy saving is one of the most critical issues to cope with the scarcity of fossil oil and climate change. For several reasons, estimating energy consumption can be helpful for experts in machine learning. This article summarizes the recent research works on machine learning. In recent years, this machine learning technology has become quite popular for neuro imaging analysis. Support Vector Machines (SVMs) deliver balanced projected performance even in studies with limited sample sets because of their relative simplicity and adaptability in tackling a number of classification challenges. The Home Energy Management System (HEMS) is a potential solution for monitoring and regulating home consumers' electricity use. In this paper, an SVM system for the classification of appliances is suggested. Due to its simplicity, ease of operation and performance, SVM is a commonly used classification algorithm. The results of the SVM-based load scheduling are predicted, as is the energy consumption. The gathered data show the dispersion of power usage based on that hour and one day power consumption of such Actual approach against SVM. Because of the variance in load utilizations as horizon planning, the ultimate consumer's discontent and expense are decreased. The device classification findings demonstrate that SVM classification device can be an appropriate solution to the HEMS device classification characteristic.
{"title":"Energy Management based on K-Nearest Neighbour Approach in Residential Application","authors":"K. Radha, R. Priya, K. Jeevitha","doi":"10.1109/ICAIS56108.2023.10073859","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073859","url":null,"abstract":"In a power system, the energy fed to the grid control and management is accomplished using various architectures system. The variation in the control and manage systems is based on the performance and features and the overall cost. Energy saving is one of the most critical issues to cope with the scarcity of fossil oil and climate change. For several reasons, estimating energy consumption can be helpful for experts in machine learning. This article summarizes the recent research works on machine learning. In recent years, this machine learning technology has become quite popular for neuro imaging analysis. Support Vector Machines (SVMs) deliver balanced projected performance even in studies with limited sample sets because of their relative simplicity and adaptability in tackling a number of classification challenges. The Home Energy Management System (HEMS) is a potential solution for monitoring and regulating home consumers' electricity use. In this paper, an SVM system for the classification of appliances is suggested. Due to its simplicity, ease of operation and performance, SVM is a commonly used classification algorithm. The results of the SVM-based load scheduling are predicted, as is the energy consumption. The gathered data show the dispersion of power usage based on that hour and one day power consumption of such Actual approach against SVM. Because of the variance in load utilizations as horizon planning, the ultimate consumer's discontent and expense are decreased. The device classification findings demonstrate that SVM classification device can be an appropriate solution to the HEMS device classification characteristic.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115657043","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073795
Anubhava Srivastava, S. Biswas
For various decision support systems, the detection of land use and land cover (LULC) change based on remote sensing data is a crucial source of information. Land conservation, sustainable development, and the management of water resources all benefit from the information gathered through the detection of changes in land use and land cover. Therefore, determining the change in land use and land cover detection of Lucknow is a primary issue of this work. Landsat 30 m resolution pictures, remote sensing data, satellite photos, and image processing techniques were used to determine changes in land cover across three dates the years 2005, 2015, and 2021. Built-up, high vegetation, water, and Low Vegetation were the four land cover classes used in the classification. Pre-processing and classification of the images were extensively analyzed, and the accuracy of the results was tested individually using the confusion matrix and kappa coefficient. According to the findings, the overall accuracy was 88.21%, 90.32%, and 92.40% for the years 2005, 2015, and 2021 respectively, with kappa coefficients of 84.02%, 88.32%, and 90.66%. According to this study, the amount of residential and agricultural land in the study area has dramatically expanded over the past 16 years, and high vegetation areas like forest ad dense green fields are decreased.
{"title":"Analyzing Land Cover Changes over Landsat-7 Data using Google Earth Engine","authors":"Anubhava Srivastava, S. Biswas","doi":"10.1109/ICAIS56108.2023.10073795","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073795","url":null,"abstract":"For various decision support systems, the detection of land use and land cover (LULC) change based on remote sensing data is a crucial source of information. Land conservation, sustainable development, and the management of water resources all benefit from the information gathered through the detection of changes in land use and land cover. Therefore, determining the change in land use and land cover detection of Lucknow is a primary issue of this work. Landsat 30 m resolution pictures, remote sensing data, satellite photos, and image processing techniques were used to determine changes in land cover across three dates the years 2005, 2015, and 2021. Built-up, high vegetation, water, and Low Vegetation were the four land cover classes used in the classification. Pre-processing and classification of the images were extensively analyzed, and the accuracy of the results was tested individually using the confusion matrix and kappa coefficient. According to the findings, the overall accuracy was 88.21%, 90.32%, and 92.40% for the years 2005, 2015, and 2021 respectively, with kappa coefficients of 84.02%, 88.32%, and 90.66%. According to this study, the amount of residential and agricultural land in the study area has dramatically expanded over the past 16 years, and high vegetation areas like forest ad dense green fields are decreased.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145969","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073783
D. Hethesia, S. Mercy Gnana Gandhi
This research is an experimental study which was done to test whether using Read Theory, a gamification tool in academic activities, might have an effect and inspire learners in enhancing the level of reading comprehension. Since the technical education have been provided with the requisite knowledge, students are being benefited from large-scale, integrated, self-centred learning by a mix of personalized, practical, and flexible external innovative technical concepts. Besides advanced training in the digital world, the education sector has a collection of skills and dimensions through various activity plans to make the process of reading efficient in both short and medium term. The perspective therefore focuses on language teachers of engineering colleges in implementing this specific tool examining data by analysing and recording students’ responses to the given content. In this study, fifteen students from ECE and fifteen from EEE were taken as control group during the first semester. After the examination based on conventional method, Digital platform, read-theory was introduced in the second semester where the same group played as an experimented one. Reading test scores were collected from both groups during the course of study were analysed statistically. Then the survey was collected in which questionnaire was framed based on like scale provides the good outcome of meta-cognitive understanding procedures in reading comprehension. Finally, the research findings provided good outcome for both the language teachers and the student in enhancing reading comprehension skills through read-theory using various gamification elements such as credentials, knowledge points (KP), levels and feedback.
{"title":"Acquiring Metacognitive Reading Technique through Web 2.0 Application – An Empirical study with ESL Learners","authors":"D. Hethesia, S. Mercy Gnana Gandhi","doi":"10.1109/ICAIS56108.2023.10073783","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073783","url":null,"abstract":"This research is an experimental study which was done to test whether using Read Theory, a gamification tool in academic activities, might have an effect and inspire learners in enhancing the level of reading comprehension. Since the technical education have been provided with the requisite knowledge, students are being benefited from large-scale, integrated, self-centred learning by a mix of personalized, practical, and flexible external innovative technical concepts. Besides advanced training in the digital world, the education sector has a collection of skills and dimensions through various activity plans to make the process of reading efficient in both short and medium term. The perspective therefore focuses on language teachers of engineering colleges in implementing this specific tool examining data by analysing and recording students’ responses to the given content. In this study, fifteen students from ECE and fifteen from EEE were taken as control group during the first semester. After the examination based on conventional method, Digital platform, read-theory was introduced in the second semester where the same group played as an experimented one. Reading test scores were collected from both groups during the course of study were analysed statistically. Then the survey was collected in which questionnaire was framed based on like scale provides the good outcome of meta-cognitive understanding procedures in reading comprehension. Finally, the research findings provided good outcome for both the language teachers and the student in enhancing reading comprehension skills through read-theory using various gamification elements such as credentials, knowledge points (KP), levels and feedback.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114281215","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073820
P. V, K. Gowrishankar, E. Sivanantham, K. S. Rao, N. Kiran, A. Vimal
The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system.
{"title":"Detection of Early Fault in Power Electronic Converters through Machine Learning and Data Mining Techniques","authors":"P. V, K. Gowrishankar, E. Sivanantham, K. S. Rao, N. Kiran, A. Vimal","doi":"10.1109/ICAIS56108.2023.10073820","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073820","url":null,"abstract":"The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114344729","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073834
S. Menaka, Jonnalagadda Harshika, Sarah Philip, Rashi John, N. Bharathiraja, S. Murugesan
Phishing attacks are now one of the prevalent dangers that firms, service providers and internet users must deal with. Rather than targeting software vulnerabilities, it targets human vulnerabilities. It is the act of enticing users to attain their personal data using fake emails and websites. Like how e-commerce sectors are growing, phishing attacks are also developing. Preventing phishing attempts is a critical aspect of protecting online transactions. Since hacktivists, spy agencies and cybercriminals now have a rich field in which they can operate sophisticated phishing attacks, prompt detection of phishing attempts is more critical than ever. To properly respond to various phishing attacks, it is required to gain a thorough understanding of these attacks, and suitable response techniques must be used. The challenges faced in this research is finding the appropriate datasets and Feature extraction prompted the study of several modules, in addition to understanding every module and attaining the desired outcome from it. Machine learning techniques are used to accurately identify phishing attacks before cause harm to a user. Being able to handle the changing nature of phishing attempts and offering an accurate method of classification, it is one of the most practical ways to approach the situation.
{"title":"Analysing the Accuracy of Detecting Phishing Websites using Ensemble Methods in Machine Learning","authors":"S. Menaka, Jonnalagadda Harshika, Sarah Philip, Rashi John, N. Bharathiraja, S. Murugesan","doi":"10.1109/ICAIS56108.2023.10073834","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073834","url":null,"abstract":"Phishing attacks are now one of the prevalent dangers that firms, service providers and internet users must deal with. Rather than targeting software vulnerabilities, it targets human vulnerabilities. It is the act of enticing users to attain their personal data using fake emails and websites. Like how e-commerce sectors are growing, phishing attacks are also developing. Preventing phishing attempts is a critical aspect of protecting online transactions. Since hacktivists, spy agencies and cybercriminals now have a rich field in which they can operate sophisticated phishing attacks, prompt detection of phishing attempts is more critical than ever. To properly respond to various phishing attacks, it is required to gain a thorough understanding of these attacks, and suitable response techniques must be used. The challenges faced in this research is finding the appropriate datasets and Feature extraction prompted the study of several modules, in addition to understanding every module and attaining the desired outcome from it. Machine learning techniques are used to accurately identify phishing attacks before cause harm to a user. Being able to handle the changing nature of phishing attempts and offering an accurate method of classification, it is one of the most practical ways to approach the situation.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126818446","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}
The major objective of this project is to develop and construct an Android-operable autonomous robotic lawnmower. Here, a Bluetooth module connects arduino uno to the phone. Grass-cutting machinery formerly required costly fuel to operate. In this instance, a solar panel is employed to replenish the battery, obviating the requirement for an additional power source. Solar energy is easier to adopt and more cost-effective than other energy sources. Utilizing solar panels allows us to capture the sun's energy for the production of free power. The solar panel charges the battery that provides power to the lawnmower. Everything the machine performs is controlled via an Android application. Arduino uno is the controller of the system. Through a variety of connections, arduino uno can communicate with a Bluetooth module and DC motors. The solar grass cutter's DC motor is controlled by a Arduino uno that gets data from an Android app through a Bluetooth module. In addition, an ultrasonic obstacle detector is plugged into the input; once an impediment is detected, the machine is stopped and the sensor's data is transferred to the cloud.
{"title":"Solar Powered Bluetooth Controlled Grass Cutting Robot","authors":"Saranya Athipatla, Bonendra Kandati, Subhash Kanipaku, Deepak Kalluri","doi":"10.1109/ICAIS56108.2023.10073789","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073789","url":null,"abstract":"The major objective of this project is to develop and construct an Android-operable autonomous robotic lawnmower. Here, a Bluetooth module connects arduino uno to the phone. Grass-cutting machinery formerly required costly fuel to operate. In this instance, a solar panel is employed to replenish the battery, obviating the requirement for an additional power source. Solar energy is easier to adopt and more cost-effective than other energy sources. Utilizing solar panels allows us to capture the sun's energy for the production of free power. The solar panel charges the battery that provides power to the lawnmower. Everything the machine performs is controlled via an Android application. Arduino uno is the controller of the system. Through a variety of connections, arduino uno can communicate with a Bluetooth module and DC motors. The solar grass cutter's DC motor is controlled by a Arduino uno that gets data from an Android app through a Bluetooth module. In addition, an ultrasonic obstacle detector is plugged into the input; once an impediment is detected, the machine is stopped and the sensor's data is transferred to the cloud.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126149244","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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