Pub Date : 2023-05-26DOI: 10.1109/INCET57972.2023.10170531
Atul Kumar, Ishu Sharma
The healthcare industry has been revolutionized by the Internet of Things (IoT), which has made it possible to develop various applications to monitor patients' health conditions and provide customized care. One of the ways in which IoT is being used in healthcare is through remote patient monitoring. This involves collecting real-time data from IoT-enabled devices such as blood pressure monitors, thermometers, and heart rate monitors, which can help healthcare professionals detect and respond to changes in a patient's health condition before they become critical. Despite the numerous benefits of IoT healthcare applications, there are critical security concerns that need to be addressed. One such concern is data privacy, as IoT devices collect a significant amount of sensitive patient information that needs to be protected from unauthorized access, hacking, and breaches. Another issue is the vulnerability of IoT devices to malware and hacking attacks due to inadequate security protections and outdated software. IoT devices can be utilized by cyber attackers to remotely get the patent’s data by causing keylogger attacks. The harm caused by keylogger attacks is significant, as they compromise private information such as patients’ private details, leading to identity theft and other crimes. These attacks can also cause operational problems such as degraded response time of IoT healthcare, system crashes, and corrupted files. Keyloggers can be difficult to detect as they run covertly in the background. In this paper, a methodology is proposed for early detection of keylogger attacks in IoT healthcare to preserve the patient’s identity from cyber attackers using the machine learning-based approach. The proposed framework is experimented on IoT healthcare dataset for comparing the performance of LightGBM, CNN, and ANN machine learning models.
{"title":"Enhancing Data Privacy of IoT Healthcare with Keylogger Attack Mitigation","authors":"Atul Kumar, Ishu Sharma","doi":"10.1109/INCET57972.2023.10170531","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170531","url":null,"abstract":"The healthcare industry has been revolutionized by the Internet of Things (IoT), which has made it possible to develop various applications to monitor patients' health conditions and provide customized care. One of the ways in which IoT is being used in healthcare is through remote patient monitoring. This involves collecting real-time data from IoT-enabled devices such as blood pressure monitors, thermometers, and heart rate monitors, which can help healthcare professionals detect and respond to changes in a patient's health condition before they become critical. Despite the numerous benefits of IoT healthcare applications, there are critical security concerns that need to be addressed. One such concern is data privacy, as IoT devices collect a significant amount of sensitive patient information that needs to be protected from unauthorized access, hacking, and breaches. Another issue is the vulnerability of IoT devices to malware and hacking attacks due to inadequate security protections and outdated software. IoT devices can be utilized by cyber attackers to remotely get the patent’s data by causing keylogger attacks. The harm caused by keylogger attacks is significant, as they compromise private information such as patients’ private details, leading to identity theft and other crimes. These attacks can also cause operational problems such as degraded response time of IoT healthcare, system crashes, and corrupted files. Keyloggers can be difficult to detect as they run covertly in the background. In this paper, a methodology is proposed for early detection of keylogger attacks in IoT healthcare to preserve the patient’s identity from cyber attackers using the machine learning-based approach. The proposed framework is experimented on IoT healthcare dataset for comparing the performance of LightGBM, CNN, and ANN machine learning models.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131671996","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170510
Bo Shan, Xiaoyang Wang, Xuekai Zhang, P. Huang, Qian Li
In recent years, the safety situation is not optimistic because of the frequent construction site safety accidents and the rising death toll. Therefore, the Ministry of Housing and Urban Rural Development issued the document "Several Opinions on Promoting the Development and Reform of the Construction Industry", requesting to comprehensively promote the construction of "smart construction sites". Compared with the traditional construction site management, "smart construction site" refers to the use of information technology, combining construction site management theory with big data analysis and artificial intelligence technology, and unifying the management of scattered information on the construction site for data analysis to ensure the safety of personnel and equipment on the construction site, improve the communication efficiency between the government and construction enterprises, and provide a basis for the orderly progress of the project. The research on intelligent recognition of illegal behaviors in construction sites based on artificial intelligence image recognition technology is a research work on identifying illegal behaviors in construction sites. The main purpose of this study is to detect violations and their locations from images taken by cameras installed at different locations around the construction site. This work will help engineers, architects and others who deal with construction sites. The project aims to develop a system that can identify violations on construction sites, which is very useful for site workers. The system will be able to determine whether there are any violations by analyzing images taken by cameras installed in different areas of the construction site.
{"title":"Intelligent Identification of Violation of Rules in Construction Site Based on Artificial Intelligence Image Recognition Technology","authors":"Bo Shan, Xiaoyang Wang, Xuekai Zhang, P. Huang, Qian Li","doi":"10.1109/INCET57972.2023.10170510","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170510","url":null,"abstract":"In recent years, the safety situation is not optimistic because of the frequent construction site safety accidents and the rising death toll. Therefore, the Ministry of Housing and Urban Rural Development issued the document \"Several Opinions on Promoting the Development and Reform of the Construction Industry\", requesting to comprehensively promote the construction of \"smart construction sites\". Compared with the traditional construction site management, \"smart construction site\" refers to the use of information technology, combining construction site management theory with big data analysis and artificial intelligence technology, and unifying the management of scattered information on the construction site for data analysis to ensure the safety of personnel and equipment on the construction site, improve the communication efficiency between the government and construction enterprises, and provide a basis for the orderly progress of the project. The research on intelligent recognition of illegal behaviors in construction sites based on artificial intelligence image recognition technology is a research work on identifying illegal behaviors in construction sites. The main purpose of this study is to detect violations and their locations from images taken by cameras installed at different locations around the construction site. This work will help engineers, architects and others who deal with construction sites. The project aims to develop a system that can identify violations on construction sites, which is very useful for site workers. The system will be able to determine whether there are any violations by analyzing images taken by cameras installed in different areas of the construction site.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"524 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133733446","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 cost control system of power grid project based on digital direction is studied. The main purpose of this study is to understand the impact of digitalization on reducing total cost and improving service quality. Another objective is to determine the impact of digitization on various parameters, such as operating expenses, investment, maintenance, operation and maintenance costs,etc. This will help to make a decision on BHEL’s future investment strategy. The study was completed by using various tools (such as simulation, analysis and modeling)to find the best way to reduce the cost of the grid. It also includes an assessment of factors that will help reduce costs, such as distribution networks, generation capacity, transmission lines and other related factors.
{"title":"Cost Control System of Power Grid Project Based on Digital Orientation","authors":"Yue Li, Yicheng Han, Hao Zhang, Kaikiang Zhang, Xia Wenlong","doi":"10.1109/INCET57972.2023.10170674","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170674","url":null,"abstract":"The cost control system of power grid project based on digital direction is studied. The main purpose of this study is to understand the impact of digitalization on reducing total cost and improving service quality. Another objective is to determine the impact of digitization on various parameters, such as operating expenses, investment, maintenance, operation and maintenance costs,etc. This will help to make a decision on BHEL’s future investment strategy. The study was completed by using various tools (such as simulation, analysis and modeling)to find the best way to reduce the cost of the grid. It also includes an assessment of factors that will help reduce costs, such as distribution networks, generation capacity, transmission lines and other related factors.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114481255","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}
This paper presents an AI-based plant disease identification system that utilizes deep learning algorithms such as ResNet50, MobileNet, and Inception V3. The proposed system is divided into two phases: the training phase and the testing phase. In the training phase, the collected dataset undergoes preprocessing, data cleaning, feature extraction where data augmentation is also applied to prevent the neural network from learning irrelevant patterns, thereby boosting overall performance. Once the dataset is optimized, it is fed to the deep learning algorithm to create a model that can predict the disease of an infected plant. Finally, during the testing phase the model shall be given an input image where distinct unique patterns will be extracted and the prediction would be displayed
{"title":"Plant Disease Identification Using Deep Learning","authors":"Shivam Prajapati, Sarim Qureshi, Yashas Rao, Swati Nadkarni, Minakshi Retharekar, Anil Avhad","doi":"10.1109/INCET57972.2023.10170463","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170463","url":null,"abstract":"This paper presents an AI-based plant disease identification system that utilizes deep learning algorithms such as ResNet50, MobileNet, and Inception V3. The proposed system is divided into two phases: the training phase and the testing phase. In the training phase, the collected dataset undergoes preprocessing, data cleaning, feature extraction where data augmentation is also applied to prevent the neural network from learning irrelevant patterns, thereby boosting overall performance. Once the dataset is optimized, it is fed to the deep learning algorithm to create a model that can predict the disease of an infected plant. Finally, during the testing phase the model shall be given an input image where distinct unique patterns will be extracted and the prediction would be displayed","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116868220","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170563
Pratheek R Kaushik, P. M, Rahul S Srinivas, Sakshi Puri, A. M
This paper presents a novel solution to address the challenge of recipe selection based on available ingredients in a household, particularly for new cooks or even experienced chefs. Leveraging the power of technology, specifically machine learning, this study introduces a "recipes as a service" concept that utilizes object recognition through image processing. By taking a single photograph of the ingredients on a kitchen counter or refrigerator in real-time, the system generates a list of all possible recipes that can be made from the identified ingredients, enabling users to maximize their kitchen innovation. The study evaluates several image classification and correlation models, including Efficient Net-Lite, faster-RCNN, YOLOv4, and YOLOv5, to identify the best model for the image recognition tasks. The comparison is based on various metrics, including accuracy and efficiency, and the results show that YOLOv5 is the optimal model for the purpose. The proposed solution provides an automated recipe generation system that can help users overcome the challenge of selecting recipes and planning meals daily. The system can be operated in real-time, making it a valuable tool for households. The results of the study can potentially contribute to the development of smart kitchens and future innovations in the field of culinary technology.
{"title":"Automated Recipe Generation using Ingredient Classification based on an Image from a Real-Time Photo Station","authors":"Pratheek R Kaushik, P. M, Rahul S Srinivas, Sakshi Puri, A. M","doi":"10.1109/INCET57972.2023.10170563","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170563","url":null,"abstract":"This paper presents a novel solution to address the challenge of recipe selection based on available ingredients in a household, particularly for new cooks or even experienced chefs. Leveraging the power of technology, specifically machine learning, this study introduces a \"recipes as a service\" concept that utilizes object recognition through image processing. By taking a single photograph of the ingredients on a kitchen counter or refrigerator in real-time, the system generates a list of all possible recipes that can be made from the identified ingredients, enabling users to maximize their kitchen innovation. The study evaluates several image classification and correlation models, including Efficient Net-Lite, faster-RCNN, YOLOv4, and YOLOv5, to identify the best model for the image recognition tasks. The comparison is based on various metrics, including accuracy and efficiency, and the results show that YOLOv5 is the optimal model for the purpose. The proposed solution provides an automated recipe generation system that can help users overcome the challenge of selecting recipes and planning meals daily. The system can be operated in real-time, making it a valuable tool for households. The results of the study can potentially contribute to the development of smart kitchens and future innovations in the field of culinary technology.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107500","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170666
Archana Saini, Kalpna Guleria, Shagun Sharma
White blood cells, or leukocytes, are indispensable for the optimal functioning of the immune system. They play a critical role in protecting the body against infections, diseases, and other foreign invaders by identifying and fighting harmful bacteria and pathogens that can cause illness. Additionally, they contribute to the elimination of dead and damaged cells from the body and facilitate tissue healing and repair processes. The absence of white blood cells would render the body defenceless against infections and diseases, exposing it to a variety of harmful pathogens. This could result in significant health issues and potentially even lead to death in severe instances. White blood cell classification is an important task in medical diagnosis and treatment because healthcare professionals diagnose and treat a variety of immune system-related diseases and conditions, including autoimmune disorders, infections, and cancers by identifying the structure, characteristics and functions of white blood cells. In this work, a convolutional neural network (CNN) model has been trained to classify white blood cells. The proposed model has achieved an accuracy of 88.78%, which has been identified as the highest among all the models implemented by various authors in the literature review. This implies that the proposed model has correctly classified white blood cells in almost 9 out of 10 cases. Moreover, the error rate of the model is only 0.108967 which indicates that the model is very reliable and consistent in its predictions. Additionally, this work shows the promising result for white blood cell classification using deep learning techniques. Furthermore, with improvements and refinements in the future, it can be possible to achieve higher levels of accuracy and precision, which could have a significant impact on medical diagnosis and treatment.
{"title":"A Deep Learning-based Convolutional Neural Networks Model for White Blood Cell Classification","authors":"Archana Saini, Kalpna Guleria, Shagun Sharma","doi":"10.1109/INCET57972.2023.10170666","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170666","url":null,"abstract":"White blood cells, or leukocytes, are indispensable for the optimal functioning of the immune system. They play a critical role in protecting the body against infections, diseases, and other foreign invaders by identifying and fighting harmful bacteria and pathogens that can cause illness. Additionally, they contribute to the elimination of dead and damaged cells from the body and facilitate tissue healing and repair processes. The absence of white blood cells would render the body defenceless against infections and diseases, exposing it to a variety of harmful pathogens. This could result in significant health issues and potentially even lead to death in severe instances. White blood cell classification is an important task in medical diagnosis and treatment because healthcare professionals diagnose and treat a variety of immune system-related diseases and conditions, including autoimmune disorders, infections, and cancers by identifying the structure, characteristics and functions of white blood cells. In this work, a convolutional neural network (CNN) model has been trained to classify white blood cells. The proposed model has achieved an accuracy of 88.78%, which has been identified as the highest among all the models implemented by various authors in the literature review. This implies that the proposed model has correctly classified white blood cells in almost 9 out of 10 cases. Moreover, the error rate of the model is only 0.108967 which indicates that the model is very reliable and consistent in its predictions. Additionally, this work shows the promising result for white blood cell classification using deep learning techniques. Furthermore, with improvements and refinements in the future, it can be possible to achieve higher levels of accuracy and precision, which could have a significant impact on medical diagnosis and treatment.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116258707","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170374
Minaxi, S. Saini
In this paper, using MATLAB/Simulink software environment, connectivity of multiple power plant component models is established for simulating the operation of a typical small hydropower plant with open channels, regulators, Semi Kaplan turbines, synchronous generators, and exciters. To reduce the errors of various types during the transient phase, the simulated model is augmented with a PI controller. Firefly and cuckoo Search optimization are used for tuning the PI controller parameters. It is observed that Firefly optimization provides the best tuning of PI controller parameters for this problem resulting in better error reduction as compared to Cuckoo search optimization.
{"title":"Hydro Power Plant Performance Optimization Using Metaheuristics","authors":"Minaxi, S. Saini","doi":"10.1109/INCET57972.2023.10170374","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170374","url":null,"abstract":"In this paper, using MATLAB/Simulink software environment, connectivity of multiple power plant component models is established for simulating the operation of a typical small hydropower plant with open channels, regulators, Semi Kaplan turbines, synchronous generators, and exciters. To reduce the errors of various types during the transient phase, the simulated model is augmented with a PI controller. Firefly and cuckoo Search optimization are used for tuning the PI controller parameters. It is observed that Firefly optimization provides the best tuning of PI controller parameters for this problem resulting in better error reduction as compared to Cuckoo search optimization.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419477","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170090
V. Sawant, Swarup D. Vishwas, Rakshanda A. Giri, Akshay D. Shingote, Pushkar S. Joglekar
With the growing importance of cybersecurity, secure encryption techniques have become essential in protecting sensitive information. In this research, we propose a novel method for enhancing the security of password-based encryption systems by combining facial recognition technology with the Advanced Encryption Standard (AES) algorithm. The system uses facial recognition technology as an additional layer of authentication to verify the user's identity before allowing access to encrypted data. We implemented the system using a combination of software and hardware components and evaluated its effectiveness through experiments. The results demonstrate that the proposed system offers a high level of security and precision in the processes for authentication, encryption and decryption, making it suitable for various applications, including banking, healthcare, and e-commerce. The proposed system contributes to the development of secure and efficient encryption techniques for protecting sensitive data.
{"title":"Face Recognition Based Password Encryption and Decryption System","authors":"V. Sawant, Swarup D. Vishwas, Rakshanda A. Giri, Akshay D. Shingote, Pushkar S. Joglekar","doi":"10.1109/INCET57972.2023.10170090","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170090","url":null,"abstract":"With the growing importance of cybersecurity, secure encryption techniques have become essential in protecting sensitive information. In this research, we propose a novel method for enhancing the security of password-based encryption systems by combining facial recognition technology with the Advanced Encryption Standard (AES) algorithm. The system uses facial recognition technology as an additional layer of authentication to verify the user's identity before allowing access to encrypted data. We implemented the system using a combination of software and hardware components and evaluated its effectiveness through experiments. The results demonstrate that the proposed system offers a high level of security and precision in the processes for authentication, encryption and decryption, making it suitable for various applications, including banking, healthcare, and e-commerce. The proposed system contributes to the development of secure and efficient encryption techniques for protecting sensitive data.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125831302","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170406
Rishabh Sharma, V. Kukreja, Satvik Vats
Tomato-spotted wilt virus (TSWV) is a severe plant disease that causes significant economic losses in tomato production worldwide. Early detection and intensity classification of TSWV-infected tomato plants is critical for effective disease management. This study proposes a novel TSWV detection and intensity classification approach based on a convolutional neural network (CNN) and a long short-term memory (LSTM) network ensemble model. A dataset comprising 30,000 images of tomato plants infected with TSWV was gathered and annotated with six intensity levels, ranging from 0 (indicating no symptoms) to 5 (indicating severe symptoms). A framework approach was developed, with aiming to enhancing the model’s performance r proposed approach achieved an overall accuracy of 97.37% on the test set, outperforming several state-of-the-art approaches. We also performed a statistical analysis of the inter-intensity level variability of the classification accuracy and found that the accuracy increased with the intensity level. Our results suggest that the proposed approach has the potential to be used in the early detection and intensity classification of TSWV-infected tomato plants, which could aid in the timely application of preventive measures and reduce the economic losses caused by TSWV.
{"title":"A New Dawn for Tomato-spotted wilt virus Detection and Intensity Classification: A CNN and LSTM Ensemble Model","authors":"Rishabh Sharma, V. Kukreja, Satvik Vats","doi":"10.1109/INCET57972.2023.10170406","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170406","url":null,"abstract":"Tomato-spotted wilt virus (TSWV) is a severe plant disease that causes significant economic losses in tomato production worldwide. Early detection and intensity classification of TSWV-infected tomato plants is critical for effective disease management. This study proposes a novel TSWV detection and intensity classification approach based on a convolutional neural network (CNN) and a long short-term memory (LSTM) network ensemble model. A dataset comprising 30,000 images of tomato plants infected with TSWV was gathered and annotated with six intensity levels, ranging from 0 (indicating no symptoms) to 5 (indicating severe symptoms). A framework approach was developed, with aiming to enhancing the model’s performance r proposed approach achieved an overall accuracy of 97.37% on the test set, outperforming several state-of-the-art approaches. We also performed a statistical analysis of the inter-intensity level variability of the classification accuracy and found that the accuracy increased with the intensity level. Our results suggest that the proposed approach has the potential to be used in the early detection and intensity classification of TSWV-infected tomato plants, which could aid in the timely application of preventive measures and reduce the economic losses caused by TSWV.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124812107","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170122
Sonali Mahajan, S. B. A. Aaditya, Aakansh Avasthi, P. Karandikar
Electrical Energy can be easily converted to other forms of energy like heat, light, sound, etc. Electrical energy storage devices are necessary because electrical energy created after conversion of primary energy source is not consumed immediately. There are several electrical energy storage devices like batteries, fuel cells, and supercapacitors. Asymmetric supercapacitors are the newest innovation in the field of electrical energy storage devices. Asymmetric supercapacitors are pulse current devices that have high power densities and long-life cycles, making them a candidate that have the potential to replace conventional energy storage devices. The intent of this research work arises because most electrical energy storage devices have rectangle shaped electrodes but since there is no binder material that is electrically conductive, it hinders the performance of the device. Research has been previously conducted on performance of asymmetric supercapacitors with binder free rectangle shaped electrodes with respect to electrode configuration. In this paper, fork shaped electrode structure of asymmetric supercapacitors are compared alongside generic rectangle shaped electrodes of asymmetric supercapacitors in terms of specific capacitance (mF per sq. cm) and its variation over time.
{"title":"Investigation of Fork Shaped Electrodes for Asymmetric Supercapacitors","authors":"Sonali Mahajan, S. B. A. Aaditya, Aakansh Avasthi, P. Karandikar","doi":"10.1109/INCET57972.2023.10170122","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170122","url":null,"abstract":"Electrical Energy can be easily converted to other forms of energy like heat, light, sound, etc. Electrical energy storage devices are necessary because electrical energy created after conversion of primary energy source is not consumed immediately. There are several electrical energy storage devices like batteries, fuel cells, and supercapacitors. Asymmetric supercapacitors are the newest innovation in the field of electrical energy storage devices. Asymmetric supercapacitors are pulse current devices that have high power densities and long-life cycles, making them a candidate that have the potential to replace conventional energy storage devices. The intent of this research work arises because most electrical energy storage devices have rectangle shaped electrodes but since there is no binder material that is electrically conductive, it hinders the performance of the device. Research has been previously conducted on performance of asymmetric supercapacitors with binder free rectangle shaped electrodes with respect to electrode configuration. In this paper, fork shaped electrode structure of asymmetric supercapacitors are compared alongside generic rectangle shaped electrodes of asymmetric supercapacitors in terms of specific capacitance (mF per sq. cm) and its variation over time.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128739464","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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