Pub Date : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085070
C. Ciufudean, C. Buzduga
The response time of the authorities in case of a plane crash is essential for lifesaving. The system presented in this paper with the acronym "AS Locating", it is an ultraportable system which has the role of locating in real time the victims of plane crashes. AS Locating system comes into operation automatically after certain values received from various sensors exceed certain threshold preset values, such as acceleration, velocity, air pressure, mechanical chocks, and pulse. Further development of AS Locating system is also presented.
{"title":"Portable Automatic System for Locating Victims of Plane Crashes","authors":"C. Ciufudean, C. Buzduga","doi":"10.1109/ICEARS56392.2023.10085070","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085070","url":null,"abstract":"The response time of the authorities in case of a plane crash is essential for lifesaving. The system presented in this paper with the acronym \"AS Locating\", it is an ultraportable system which has the role of locating in real time the victims of plane crashes. AS Locating system comes into operation automatically after certain values received from various sensors exceed certain threshold preset values, such as acceleration, velocity, air pressure, mechanical chocks, and pulse. Further development of AS Locating system is also presented.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":" 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":"113950600","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/ICEARS56392.2023.10085218
D. Diana, R. Hema, G. N. Kumar, R. Rohith Kumar
Support vector machine, a newly developed machine learning technology, is suggested as a tool for carrying out nonlinear equalization in communication networks. Support vector machine has the benefit of allowing the discovery of fewer model parameters while requiring less previous information and heuristic assumptions than some earlier systems. A support vector machine's optimization process also uses quadratic programming, a well-researched and well-understood mathematical programming paradigm.On nonlinear topics that have already been researched by other researchers utilizing neural networks, support vector machine simulations are run. This makes it possible to compare the suggested approach for nonlinear detection first to other methods in order to assess its viability. Results demonstrate that support vector machines outperform neural networks on the nonlinear issues studied.
{"title":"Support Vector based classification for Adaptive Channel Equalization","authors":"D. Diana, R. Hema, G. N. Kumar, R. Rohith Kumar","doi":"10.1109/ICEARS56392.2023.10085218","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085218","url":null,"abstract":"Support vector machine, a newly developed machine learning technology, is suggested as a tool for carrying out nonlinear equalization in communication networks. Support vector machine has the benefit of allowing the discovery of fewer model parameters while requiring less previous information and heuristic assumptions than some earlier systems. A support vector machine's optimization process also uses quadratic programming, a well-researched and well-understood mathematical programming paradigm.On nonlinear topics that have already been researched by other researchers utilizing neural networks, support vector machine simulations are run. This makes it possible to compare the suggested approach for nonlinear detection first to other methods in order to assess its viability. Results demonstrate that support vector machines outperform neural networks on the nonlinear issues studied.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"16 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":"122254608","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/ICEARS56392.2023.10085607
Shanmugam M, Kanagaraj Venusamy, S. S., Srivatsan S, Naresh Kumar O
The sense of touch and feel is an important asset to the human beings in all aspects across a variety of fields. For a long time, it has been almost impossible to mimic the sense of touch i.e. the natural feedback. Devices that had been developed have been based around piezo electric, Hall Effect sensors, and a combination of sensors and actuators to sense and replicate the feedback. To a certain extent, they have laid the foundation for the field of haptics. Haptics initially were limited to the vibrotactile responses, which were the ones commonly seen in the modern smartphone. Further research and development has led to it being closer than ever to mimic the sense of touch. Several devices were created by incorporating the field of haptics with existing design and devices. One such device is the Haptic glove. A haptic glove is a cutting-edge technology that allows human operators to physically feel the sensation of touch and force feedback when remotely controlling robots or other devices. These gloves are designed to mimic the sense of touch, allowing the operator to feel the shape, texture, and rigidity of the object they are manipulating through the robot. The use of haptic gloves in robotic teleoperation has been found to improve the precision and accuracy of remote tasks, as well as reducing the cognitive load on the operator. This technology can be adopted in field such as manufacturing, surgery, and space exploration. For example, in manufacturing, haptic gloves can be used to remotely operate machinery or perform inspections on hard-to-reach areas, while in surgery; haptic gloves can enable surgeons to perform remote surgeries with greater precision and control. In space exploration, haptic gloves can be used to remotely control robots for tasks such as sample collection and maintenance. Future developments in haptic technology could lead to even more advanced and realistic haptic feedback, further enhancing the capabilities of robotic teleoperation.
{"title":"A Comprehensive Review of Haptic Gloves: Advances, Challenges, and Future Directions","authors":"Shanmugam M, Kanagaraj Venusamy, S. S., Srivatsan S, Naresh Kumar O","doi":"10.1109/ICEARS56392.2023.10085607","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085607","url":null,"abstract":"The sense of touch and feel is an important asset to the human beings in all aspects across a variety of fields. For a long time, it has been almost impossible to mimic the sense of touch i.e. the natural feedback. Devices that had been developed have been based around piezo electric, Hall Effect sensors, and a combination of sensors and actuators to sense and replicate the feedback. To a certain extent, they have laid the foundation for the field of haptics. Haptics initially were limited to the vibrotactile responses, which were the ones commonly seen in the modern smartphone. Further research and development has led to it being closer than ever to mimic the sense of touch. Several devices were created by incorporating the field of haptics with existing design and devices. One such device is the Haptic glove. A haptic glove is a cutting-edge technology that allows human operators to physically feel the sensation of touch and force feedback when remotely controlling robots or other devices. These gloves are designed to mimic the sense of touch, allowing the operator to feel the shape, texture, and rigidity of the object they are manipulating through the robot. The use of haptic gloves in robotic teleoperation has been found to improve the precision and accuracy of remote tasks, as well as reducing the cognitive load on the operator. This technology can be adopted in field such as manufacturing, surgery, and space exploration. For example, in manufacturing, haptic gloves can be used to remotely operate machinery or perform inspections on hard-to-reach areas, while in surgery; haptic gloves can enable surgeons to perform remote surgeries with greater precision and control. In space exploration, haptic gloves can be used to remotely control robots for tasks such as sample collection and maintenance. Future developments in haptic technology could lead to even more advanced and realistic haptic feedback, further enhancing the capabilities of robotic teleoperation.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"8 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":"123638325","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/ICEARS56392.2023.10084967
Juby Abraham, George Joseph Cherian, N. Jayapandian
In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now.
{"title":"Systematic Review on Humanizing Machine Intelligence and Artificial Intelligence","authors":"Juby Abraham, George Joseph Cherian, N. Jayapandian","doi":"10.1109/ICEARS56392.2023.10084967","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10084967","url":null,"abstract":"In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"52 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":"126913665","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/ICEARS56392.2023.10085128
T. N. Deepthi, S. Sharmila, M. Swarna, M. Gouthami, C. Akshaya
Blood vessels in brain serve a major function in supplying the brain with nutrients and oxygen. All body parts are meant to be worked out actively. One of the deadliest diseases in the world is a brain stroke. Most strokes fall within the ischemic embolic and haemorrhagic categories. A blood clot that originates away from the patient's brain, typically in the heart, travels through the patient's bloodstream to lodge in the brain's smaller arteries to cause an ischemic stroke. The second is haemorrhagic stroke occurs when a brain artery bursts or releases blood. When a blood vessel either bursts or becomes blocked by a clot, a stroke develops. This study has collected a variety of patients' datasets. It includes a number of medical factors. There are a variety of machine learning algorithms available for making predictions, here the K-Nearest Neighbour with Random Forest algorithms are considered.
{"title":"Prediction of Brain Stroke in Human Beings using Machine Learning","authors":"T. N. Deepthi, S. Sharmila, M. Swarna, M. Gouthami, C. Akshaya","doi":"10.1109/ICEARS56392.2023.10085128","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085128","url":null,"abstract":"Blood vessels in brain serve a major function in supplying the brain with nutrients and oxygen. All body parts are meant to be worked out actively. One of the deadliest diseases in the world is a brain stroke. Most strokes fall within the ischemic embolic and haemorrhagic categories. A blood clot that originates away from the patient's brain, typically in the heart, travels through the patient's bloodstream to lodge in the brain's smaller arteries to cause an ischemic stroke. The second is haemorrhagic stroke occurs when a brain artery bursts or releases blood. When a blood vessel either bursts or becomes blocked by a clot, a stroke develops. This study has collected a variety of patients' datasets. It includes a number of medical factors. There are a variety of machine learning algorithms available for making predictions, here the K-Nearest Neighbour with Random Forest algorithms are considered.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"83 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":"132951239","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/ICEARS56392.2023.10085157
Yuhes Raajha. M. R, K. A, Rajkumar. D, R. Reshma, Dr. R. Santhosh, N. Mekala
Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%.
{"title":"An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms","authors":"Yuhes Raajha. M. R, K. A, Rajkumar. D, R. Reshma, Dr. R. Santhosh, N. Mekala","doi":"10.1109/ICEARS56392.2023.10085157","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085157","url":null,"abstract":"Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"117 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":"133994543","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/ICEARS56392.2023.10085408
A. M, Thirumalai A
First, with regards to attribute-based encryption (ABE), it is an approach to access control that allows data to be encrypted and decrypted based on certain attributes, such as a user's role, location, or other characteristics. This approach provides granular control over who can access specific data, which is particularly useful for IoT applications where sensitive data is being generated by many devices. However, as I mentioned earlier, ABE can be computationally intensive, which may not be suitable for low-power IoT devices. One possible solution to this challenge is to use edge computing, where some of the computing tasks are performed at the edge of the network, closer to the devices generating the data. This can help reduce the amount of data that needs to be sent to the cloud and can improve overall system performance. Another challenge with ABE is that it does not provide protection against key sharing. If a user shares their decryption key with an unauthorized party, that party could potentially gain access to sensitive data. To address this challenge, it's important to have strong access controls in place to prevent unauthorized sharing of keys. In terms of data storage security, while outsourcing to cloud servers can certainly help with complex computing tasks, it's still important to implement sophisticated security measures. This might include encrypting the data at rest and in transit, implementing access controls, and monitoring the system for potential security breaches. Finally, it's important to follow regulations and best practices for key sharing to prevent unauthorized access to sensitive data. This might include policies around key management, user authentication, and data governance.
{"title":"Effective Management of IoT Devices that can Withstand Attacks on Cloud Data","authors":"A. M, Thirumalai A","doi":"10.1109/ICEARS56392.2023.10085408","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085408","url":null,"abstract":"First, with regards to attribute-based encryption (ABE), it is an approach to access control that allows data to be encrypted and decrypted based on certain attributes, such as a user's role, location, or other characteristics. This approach provides granular control over who can access specific data, which is particularly useful for IoT applications where sensitive data is being generated by many devices. However, as I mentioned earlier, ABE can be computationally intensive, which may not be suitable for low-power IoT devices. One possible solution to this challenge is to use edge computing, where some of the computing tasks are performed at the edge of the network, closer to the devices generating the data. This can help reduce the amount of data that needs to be sent to the cloud and can improve overall system performance. Another challenge with ABE is that it does not provide protection against key sharing. If a user shares their decryption key with an unauthorized party, that party could potentially gain access to sensitive data. To address this challenge, it's important to have strong access controls in place to prevent unauthorized sharing of keys. In terms of data storage security, while outsourcing to cloud servers can certainly help with complex computing tasks, it's still important to implement sophisticated security measures. This might include encrypting the data at rest and in transit, implementing access controls, and monitoring the system for potential security breaches. Finally, it's important to follow regulations and best practices for key sharing to prevent unauthorized access to sensitive data. This might include policies around key management, user authentication, and data governance.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"21 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":"122805623","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/ICEARS56392.2023.10085155
Kanagaraj Venusamy, Abdul Hafeel M, K. M, Muthukkaruppan S, Chandramohan P
The limitation in the procedure of India’s postal service is that it takes additional operations and human work, making it harder and impossible to reduce costs and time. Weighing, sorting, and updating information are the laborious processes which could be more productive and cheaper when automated. A barcode scanner-based courier sorting system conveyor belt design using IoT has been proposed in this paper. Barcode scanning, weight estimation, and product tracking utilizing an IoT-powered conveyor system are the key goals of this work. This allows postal service systems to combine contemporary technology for logistics monitoring, sorting by destination and weight, shipping cost estimates, and quick information access.
{"title":"Study on Conveyor Belt System enabled with IoT in Postal and Courier Services","authors":"Kanagaraj Venusamy, Abdul Hafeel M, K. M, Muthukkaruppan S, Chandramohan P","doi":"10.1109/ICEARS56392.2023.10085155","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085155","url":null,"abstract":"The limitation in the procedure of India’s postal service is that it takes additional operations and human work, making it harder and impossible to reduce costs and time. Weighing, sorting, and updating information are the laborious processes which could be more productive and cheaper when automated. A barcode scanner-based courier sorting system conveyor belt design using IoT has been proposed in this paper. Barcode scanning, weight estimation, and product tracking utilizing an IoT-powered conveyor system are the key goals of this work. This allows postal service systems to combine contemporary technology for logistics monitoring, sorting by destination and weight, shipping cost estimates, and quick information access.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"9 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":"121302823","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/ICEARS56392.2023.10085551
S. Sharmila, R. Bhargavi, R. Anusha, K. Anusha, B. Divya
Deep learning is a subset of artificial intelligence. It's a form of artificial intelligence and machine learning that attempts to simulate the way humans pick up specific types of information. The goal of this project is to create a deep learning model based on convolutional neural networks that can distinguish between healthy and diseased leaves. Due to its useful features in learner autonomy and extraction of features, it has drawn a great deal of attention in past years from researchers and industry professionals alike. Images of healthy and rotting leaves are included in the dataset. It is widely used in fields such as computational linguistics, voice processing, image processing, and video processing. It has also become a center for studies on agricultural plant protection, such as the detection of plant diseases and the assessment of pest ranges. This study has also discussed about some of the problems and issues that are currently being faced and need to be addressed. Library packages such as KERAS, MATPLOTLIB, NUMPY, and OPENCV have been utilized here.
{"title":"Cotton Leaf Disease Detection using Convolutional Neural Networks (CNN)","authors":"S. Sharmila, R. Bhargavi, R. Anusha, K. Anusha, B. Divya","doi":"10.1109/ICEARS56392.2023.10085551","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085551","url":null,"abstract":"Deep learning is a subset of artificial intelligence. It's a form of artificial intelligence and machine learning that attempts to simulate the way humans pick up specific types of information. The goal of this project is to create a deep learning model based on convolutional neural networks that can distinguish between healthy and diseased leaves. Due to its useful features in learner autonomy and extraction of features, it has drawn a great deal of attention in past years from researchers and industry professionals alike. Images of healthy and rotting leaves are included in the dataset. It is widely used in fields such as computational linguistics, voice processing, image processing, and video processing. It has also become a center for studies on agricultural plant protection, such as the detection of plant diseases and the assessment of pest ranges. This study has also discussed about some of the problems and issues that are currently being faced and need to be addressed. Library packages such as KERAS, MATPLOTLIB, NUMPY, and OPENCV have been utilized here.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"6 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":"121347911","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/ICEARS56392.2023.10085491
N. Kumar, N. Sathyanarayana
Patients with diabetes are more likely to be readmitted to the hospital than those who are nondiabetic. The earlier patients with a strong probability of readmission are monitored and cared for, the better. The goal of this research is to develop a decision - making framework that can identify diabetes patients who are at risk of early readmission. Many data analysis approaches have been employed to perform this. Computer vision is used to create a novel model in this study. Individuals at high risk of complications to be readmitted are prioritized in the early stages, which in turn reduces healthcare costs and improves the reputation of the hospital, thus enhancing the health service and saving money. Predictions made using machine learning are more accurate than those made using traditional methods. In this study, patients' hospital readmissions may be predicted by utilizing a standard scaler, a decision tree, and random forests for classification, CATboost for categorical features, and XGBoost classifiers. When applied to real-world data, a machine learning method that incorporates deep learning technique has outperformed the other methods. As a response to a number of modules, including extracting features, the analysis has been enhanced and a more useful framework has been created.
{"title":"Prediction of Diabetic Patients with High Risk of Readmission using Smart Decision Support Framework","authors":"N. Kumar, N. Sathyanarayana","doi":"10.1109/ICEARS56392.2023.10085491","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085491","url":null,"abstract":"Patients with diabetes are more likely to be readmitted to the hospital than those who are nondiabetic. The earlier patients with a strong probability of readmission are monitored and cared for, the better. The goal of this research is to develop a decision - making framework that can identify diabetes patients who are at risk of early readmission. Many data analysis approaches have been employed to perform this. Computer vision is used to create a novel model in this study. Individuals at high risk of complications to be readmitted are prioritized in the early stages, which in turn reduces healthcare costs and improves the reputation of the hospital, thus enhancing the health service and saving money. Predictions made using machine learning are more accurate than those made using traditional methods. In this study, patients' hospital readmissions may be predicted by utilizing a standard scaler, a decision tree, and random forests for classification, CATboost for categorical features, and XGBoost classifiers. When applied to real-world data, a machine learning method that incorporates deep learning technique has outperformed the other methods. As a response to a number of modules, including extracting features, the analysis has been enhanced and a more useful framework has been created.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"22 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":"121428801","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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