Pub Date : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201716
S. S. Biswal, D. Swain, P. Rout, Adyashree Das, Ajay K. Mishra
This article comprehensively reviews the recently developed quantum mechanics-based optimization techniques. The quantum theory has been employed to speed up the evolutionary method and enhance the possibility of finding the optimal solution of conventional optimization techniques. Current developments in quantum computing have demonstrated that quantum theory can offer significant benefits over conventional theory for certain optimization techniques. The future prospective and benefits and drawbacks of this fresh category of quantum optimization techniques have been presented in this review. It also enables new researchers and algorithm developers to use these simple but extremely effective algorithms for problem-solving. This paper aims to give readers an overview of the fundamental elements and recent advances in optimization techniques so that they can develop and implement these for various applications. Finally, a few findings are reached, and future study on quantum-based optimization techniques is discussed.
{"title":"A Comprehensive Review of Metaheuristic Algorithms Inspired by Quantum Mechanics","authors":"S. S. Biswal, D. Swain, P. Rout, Adyashree Das, Ajay K. Mishra","doi":"10.1109/APSIT58554.2023.10201716","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201716","url":null,"abstract":"This article comprehensively reviews the recently developed quantum mechanics-based optimization techniques. The quantum theory has been employed to speed up the evolutionary method and enhance the possibility of finding the optimal solution of conventional optimization techniques. Current developments in quantum computing have demonstrated that quantum theory can offer significant benefits over conventional theory for certain optimization techniques. The future prospective and benefits and drawbacks of this fresh category of quantum optimization techniques have been presented in this review. It also enables new researchers and algorithm developers to use these simple but extremely effective algorithms for problem-solving. This paper aims to give readers an overview of the fundamental elements and recent advances in optimization techniques so that they can develop and implement these for various applications. Finally, a few findings are reached, and future study on quantum-based optimization techniques is discussed.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132692923","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 : 2023-06-09DOI: 10.1109/apsit58554.2023.10201740
S. Selvan, R. Scholar, T. Sivabalan, P. Student, Dr R Ramesh, H. Ragadeepa
An increase in industrial and commercial applications leads to rising the maximum demand for power to be supplied with the existing power capacity of the grids. The Energy management system is one of the critical roles of the grid to supply demand power. This can be achieved by managing the energy consumption at each end node (i.e., Households, industries, Agriculture, and others). In general, traditional energy meters are used to monitor the energy consumption of the user and they don't have any flexibility in remote sharing of the data. But a smart energy meter is an electronic device that monitors and records the energy consumption of a household or commercial establishment in real-time and has the capability of sharing data through any communication protocols. It provides accurate information on electricity usage, cost, and allows users to make informed decisions about their energy consumption. Smart energy meters also enable remote access to energy data, facilitating better energy management and control. For Energy Management, Smart Energy Meter is combined with Home Automation Technology. This system not only tracks energy consumption in real time but also enables the automation of various household appliances based on user preferences and energy. The remote access of the smart meter to the user can be achieved by using cloud-based data sharing via the internet at both ends. The developed system will help the user to provide the Energy consumption parameters and also helps to control the loads remotely through Radio Frequency Communication and internet connections. This provides an overview of the concept of smart energy meters with home automation, outlining their benefits such as energy savings and increased convenience.
{"title":"IoT Enabled Smart Energy Meter for Energy Management","authors":"S. Selvan, R. Scholar, T. Sivabalan, P. Student, Dr R Ramesh, H. Ragadeepa","doi":"10.1109/apsit58554.2023.10201740","DOIUrl":"https://doi.org/10.1109/apsit58554.2023.10201740","url":null,"abstract":"An increase in industrial and commercial applications leads to rising the maximum demand for power to be supplied with the existing power capacity of the grids. The Energy management system is one of the critical roles of the grid to supply demand power. This can be achieved by managing the energy consumption at each end node (i.e., Households, industries, Agriculture, and others). In general, traditional energy meters are used to monitor the energy consumption of the user and they don't have any flexibility in remote sharing of the data. But a smart energy meter is an electronic device that monitors and records the energy consumption of a household or commercial establishment in real-time and has the capability of sharing data through any communication protocols. It provides accurate information on electricity usage, cost, and allows users to make informed decisions about their energy consumption. Smart energy meters also enable remote access to energy data, facilitating better energy management and control. For Energy Management, Smart Energy Meter is combined with Home Automation Technology. This system not only tracks energy consumption in real time but also enables the automation of various household appliances based on user preferences and energy. The remote access of the smart meter to the user can be achieved by using cloud-based data sharing via the internet at both ends. The developed system will help the user to provide the Energy consumption parameters and also helps to control the loads remotely through Radio Frequency Communication and internet connections. This provides an overview of the concept of smart energy meters with home automation, outlining their benefits such as energy savings and increased convenience.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116339862","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201718
Bibekananda Jena, M. K. Naik, Rutuprana Panda
In this research, Kaniadakis entropy (KE) derived from the energy curve is adopted to construct an objective function for the thresholding of images at various levels. In addition to the histogram's property, the energy curve maintains the image's spatial contextual information. This additional data aids in the threshold selection process, resulting in a more accurate segmented image. To optimize the objective function, a new Black widow optimization with a gaussian mutation algorithm (BWOG) is also proposed in this paper with enhanced population diversity by incorporating an additional stage of powerful Gaussian mutation and random allocation of solutions using a levy flight mechanism in BWO. The proposed Kaniadakis entropy-based multilevel thresholding selection using energy curve and Black Widow optimization algorithm with Gaussian mutation (BWOG-KE) is performed on both grayscale and color images of different modalities and dimensions. Based on the quantitative measures: PSNR, the BWOG-KE is found superior to existing well-known methods. The results proposed method are further compared with minimum cross-entropy (MCE) based and Kapur's entropy-based thresholding and found a significant level of dominance over them.
{"title":"A novel Kaniadakis entropy-based multilevel thresholding using energy curve and Black Widow optimization algorithm with Gaussian mutation","authors":"Bibekananda Jena, M. K. Naik, Rutuprana Panda","doi":"10.1109/APSIT58554.2023.10201718","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201718","url":null,"abstract":"In this research, Kaniadakis entropy (KE) derived from the energy curve is adopted to construct an objective function for the thresholding of images at various levels. In addition to the histogram's property, the energy curve maintains the image's spatial contextual information. This additional data aids in the threshold selection process, resulting in a more accurate segmented image. To optimize the objective function, a new Black widow optimization with a gaussian mutation algorithm (BWOG) is also proposed in this paper with enhanced population diversity by incorporating an additional stage of powerful Gaussian mutation and random allocation of solutions using a levy flight mechanism in BWO. The proposed Kaniadakis entropy-based multilevel thresholding selection using energy curve and Black Widow optimization algorithm with Gaussian mutation (BWOG-KE) is performed on both grayscale and color images of different modalities and dimensions. Based on the quantitative measures: PSNR, the BWOG-KE is found superior to existing well-known methods. The results proposed method are further compared with minimum cross-entropy (MCE) based and Kapur's entropy-based thresholding and found a significant level of dominance over them.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116077279","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201660
A. Patra, Girija Sankar Panigrahi, Vijaya Laxmi Patra, A. Mishra, Narayan Nahak, B. Rout
In order to control blood glucose levels in TIDM patients, this paper explains the creation of a Teaching Learning Based Optimization-PID (TLBO-PID) controller that delivers appropriate insulin doses through an artificial pancreas (AP). Using the Teaching Learning Based Optimization (TLBO), that adjusts the controller gains to improve the BG control of the proposed patient model. This classic controller with TLBO is intended to increase the performance and toughness of patient's problems with glycemic management which are resulting from nonlinearities in the patient model. The nonlinearity of patient models can be effectively handled by using an AP-based TLBO, which also helps to keep blood sugar levels in the glycemic range (70–120 mg/dL). The accuracy, robustness, stability, noise reduction, and enhanced capacity to handle uncertainties are examined while using the proposed patient model with TLBO-PID. A comparison of data from different control strategies indicates the reasons for the suggested approach's superior control performance.
{"title":"Adaptive Control with Disturbance Modelling for BG Regulation in TIDM Patient","authors":"A. Patra, Girija Sankar Panigrahi, Vijaya Laxmi Patra, A. Mishra, Narayan Nahak, B. Rout","doi":"10.1109/APSIT58554.2023.10201660","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201660","url":null,"abstract":"In order to control blood glucose levels in TIDM patients, this paper explains the creation of a Teaching Learning Based Optimization-PID (TLBO-PID) controller that delivers appropriate insulin doses through an artificial pancreas (AP). Using the Teaching Learning Based Optimization (TLBO), that adjusts the controller gains to improve the BG control of the proposed patient model. This classic controller with TLBO is intended to increase the performance and toughness of patient's problems with glycemic management which are resulting from nonlinearities in the patient model. The nonlinearity of patient models can be effectively handled by using an AP-based TLBO, which also helps to keep blood sugar levels in the glycemic range (70–120 mg/dL). The accuracy, robustness, stability, noise reduction, and enhanced capacity to handle uncertainties are examined while using the proposed patient model with TLBO-PID. A comparison of data from different control strategies indicates the reasons for the suggested approach's superior control performance.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123192091","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201784
Neil Archein I. Gomez, Gernel S. Lumacad, Isabela Loren R. Saludes, Princess Aravela A. Castino, Ozzy Tyrone B. Ligtao
MIR4, is a play to earn game that uses Non-Fungible Tokens (NFT) and cryptocurrency- or in MIR4, Draco Tokens- as a reward. Draco is obtained through mining an in-game resource called Darksteel and is then traded to Wemix Wallet, where real-world money is obtained. Cryptocurrencies are volatile, which gives MIR4 players and traders a decision dilemma of when is the preferable time to buy, sell, or trade Draco Tokens. In this study we present deep learning models, specifically the Long-Short Term Memory (LSTM) neural network, and Neural Prophet (NP) time series machine learning models to forecast future Draco-token exchange value. Historical data of Draco-token value from Yahoo Finance is utilized as a univariate parameter for the analysis, model development, and the forecasting of the future Draco-token exchange values. Performance of formulated models are assessed and compared based on the following regression metrics: RMSE, MSE, MAE and MAPE. Experimental results indicated that the LSTM Neural Network yielded better forecast estimates with lower error than the Neural Prophet. Findings of the study showed that LSTM can be utilized as a tool for forecasting future Draco token exchange values. future research direction suggests improving prediction accuracy by incorporating other parameters such as MIR-4 players sentiments, newly added players, and google search interest over time.
{"title":"Deep Learning Models for MIR-4 Draco Token Exchange Value Forecasting","authors":"Neil Archein I. Gomez, Gernel S. Lumacad, Isabela Loren R. Saludes, Princess Aravela A. Castino, Ozzy Tyrone B. Ligtao","doi":"10.1109/APSIT58554.2023.10201784","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201784","url":null,"abstract":"MIR4, is a play to earn game that uses Non-Fungible Tokens (NFT) and cryptocurrency- or in MIR4, Draco Tokens- as a reward. Draco is obtained through mining an in-game resource called Darksteel and is then traded to Wemix Wallet, where real-world money is obtained. Cryptocurrencies are volatile, which gives MIR4 players and traders a decision dilemma of when is the preferable time to buy, sell, or trade Draco Tokens. In this study we present deep learning models, specifically the Long-Short Term Memory (LSTM) neural network, and Neural Prophet (NP) time series machine learning models to forecast future Draco-token exchange value. Historical data of Draco-token value from Yahoo Finance is utilized as a univariate parameter for the analysis, model development, and the forecasting of the future Draco-token exchange values. Performance of formulated models are assessed and compared based on the following regression metrics: RMSE, MSE, MAE and MAPE. Experimental results indicated that the LSTM Neural Network yielded better forecast estimates with lower error than the Neural Prophet. Findings of the study showed that LSTM can be utilized as a tool for forecasting future Draco token exchange values. future research direction suggests improving prediction accuracy by incorporating other parameters such as MIR-4 players sentiments, newly added players, and google search interest over time.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126084203","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}
Breast cancer is one major contributor to global mortality and the second-leading reason of cancer deaths in women worldwide. Early prediction of breast cancer plays a vital part in improving patient's survival outcome by examining tumors whether malignant or benign. In this paper, the researchers formulated a machine learning (ML) classifier based on an ensemble learning called extreme gradient boosting (XGBoost) algorithm in predicting a benign or malignant (cancerous) tumor. The researchers integrated the synthetic minority oversampling technique (SMOTE) to resolve the class imbalance problem found in the dataset. Data-set utilized in this study are clinical cases of patients from the University of Wisconsin Hospitals. Experimental results showed that the proposed approach yielded better performance as compared to methods used in previous literature's, with an accuracy of 98.87%, a kappa statistic of 0.9774, and an f - score of 0.9890. Further, feature importance analysis showed that, among all input features, ‘Bare Nuclei’ variable contributed the greatest predictive power in classifying a malignant or benign tumor. This result is consistent with previous literature's, which emphasizes that Bare nuclei are typically seen in benign tumors as compared to malignant tumors.
{"title":"Extreme Gradient Boosting with Synthetic Minority Over Sampling Technique for an Improved Breast Cancer Prediction","authors":"Alexa Xyrel Rey, Aljhen Wahiman, Ferriel Atasan, Gernel S. Lumacad, Shaina Claire Bustamante, Ravien Glanida","doi":"10.1109/APSIT58554.2023.10201666","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201666","url":null,"abstract":"Breast cancer is one major contributor to global mortality and the second-leading reason of cancer deaths in women worldwide. Early prediction of breast cancer plays a vital part in improving patient's survival outcome by examining tumors whether malignant or benign. In this paper, the researchers formulated a machine learning (ML) classifier based on an ensemble learning called extreme gradient boosting (XGBoost) algorithm in predicting a benign or malignant (cancerous) tumor. The researchers integrated the synthetic minority oversampling technique (SMOTE) to resolve the class imbalance problem found in the dataset. Data-set utilized in this study are clinical cases of patients from the University of Wisconsin Hospitals. Experimental results showed that the proposed approach yielded better performance as compared to methods used in previous literature's, with an accuracy of 98.87%, a kappa statistic of 0.9774, and an f - score of 0.9890. Further, feature importance analysis showed that, among all input features, ‘Bare Nuclei’ variable contributed the greatest predictive power in classifying a malignant or benign tumor. This result is consistent with previous literature's, which emphasizes that Bare nuclei are typically seen in benign tumors as compared to malignant tumors.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127233122","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201782
John Daves S. Baguio, Billy A. Lu, Christine F. Peña
The climate change discourse on social media happens rapidly with microblogging sites such as Twitter. On these types of sites, there is a divide of stances. Some people believe that climate change is man-made, and some people deny its existence. This study aimed to classify climate change tweets in the given labeled dataset with the created text classification model that used Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation. Additionally, domain-specific preprocessing methods for climate change tweets and adding features by appending the majority topic of a given tweet between each word are applied. This study has shown that the created text classification model improved the F1 score of the two undersampled classes by 1 % and 6 % respectively while still maintaining a good F1 score for the majority class. The text classification model overall increased both macro and weighted averages by 3 % and 1 % respectively.
{"title":"Text Classification of Climate Change Tweets using Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation","authors":"John Daves S. Baguio, Billy A. Lu, Christine F. Peña","doi":"10.1109/APSIT58554.2023.10201782","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201782","url":null,"abstract":"The climate change discourse on social media happens rapidly with microblogging sites such as Twitter. On these types of sites, there is a divide of stances. Some people believe that climate change is man-made, and some people deny its existence. This study aimed to classify climate change tweets in the given labeled dataset with the created text classification model that used Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation. Additionally, domain-specific preprocessing methods for climate change tweets and adding features by appending the majority topic of a given tweet between each word are applied. This study has shown that the created text classification model improved the F1 score of the two undersampled classes by 1 % and 6 % respectively while still maintaining a good F1 score for the majority class. The text classification model overall increased both macro and weighted averages by 3 % and 1 % respectively.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114142767","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201707
Cheepurupalli Raghuram, Dr. M. Thenmozhi
Due to more advancements in deep learning approaches, medical image analysis has become more popular in research. Image segmentation plays an indispensable role in image processing. Digital images are classified as segments, and segmentation approaches are used to analyze the important features and data presented in the input digital images. Segmentation is mainly performed to recover the essential features easily from the region of interest. The segmentation process generates a meaningful digital image, and they are easy to analyze. Recently, segmentation approaches have become more popular in a medical environment, and also they secured more numbers of successful applications in neutrosopy. Therefore, it is decided to make the comparative analysis of the medical image segmentation techniques based on the deep learning concept. This survey encloses various existing contrastive learning-based segmentation techniques for performing algorithmic classification in the medical domain. These surveys also compare different performance measures, datasets utilized, and tools used for the implementation. Then, upcoming research and current research gaps in medical image segmentation are analyzed. This review on state-of-the-art medical image segmentation tools has shown their potential in clinical practices for effectively diagnosing diseases with better segmentation approaches using contrastive learning.
{"title":"Short Review on Contrastive Learning-based Segmentation Techniques for Medical Image Processing","authors":"Cheepurupalli Raghuram, Dr. M. Thenmozhi","doi":"10.1109/APSIT58554.2023.10201707","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201707","url":null,"abstract":"Due to more advancements in deep learning approaches, medical image analysis has become more popular in research. Image segmentation plays an indispensable role in image processing. Digital images are classified as segments, and segmentation approaches are used to analyze the important features and data presented in the input digital images. Segmentation is mainly performed to recover the essential features easily from the region of interest. The segmentation process generates a meaningful digital image, and they are easy to analyze. Recently, segmentation approaches have become more popular in a medical environment, and also they secured more numbers of successful applications in neutrosopy. Therefore, it is decided to make the comparative analysis of the medical image segmentation techniques based on the deep learning concept. This survey encloses various existing contrastive learning-based segmentation techniques for performing algorithmic classification in the medical domain. These surveys also compare different performance measures, datasets utilized, and tools used for the implementation. Then, upcoming research and current research gaps in medical image segmentation are analyzed. This review on state-of-the-art medical image segmentation tools has shown their potential in clinical practices for effectively diagnosing diseases with better segmentation approaches using contrastive learning.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114308625","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}
Food is a basic need for every living being. With the increasing population, it has become necessary to yield enough amount of crops to satiate their hunger. In the mean while we face a lot of problems while considering the needs of such a huge population. Lots of crops gets destroyed which affects the overall yield of the crops, hence leading to shortage of food. We know that it is very common for plants to get affected by diseases. Some of the factors are fertilizers and pesticides, cultural practices, environmental and surrounding conditions etc. These diseases affect overall yield as well as the economy based on it. Any approach to overcome this problem would help the farmers to cultivate crops efficiently. Hence, detection of disease in crops plays a vital role in agriculture. The proposed manuscript aims for the detection of crop diseases by using classification algorithm in deep learning concept. An automatic technique to detect symptoms of plant diseases would be highly beneficial to the agricultural society as it would eliminate the work of constant monitoring. In this paper, we will propose different disease classification algorithms which can be used for the detection of disease in plant leaves.
{"title":"Crop Disease Prediction by Using Machine Learning","authors":"Anuja Nanda, Sangam Nayak, A. Patra, Abhipsha Nanda, Saswata Pani, Bhabani Shankar Nanda","doi":"10.1109/APSIT58554.2023.10201690","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201690","url":null,"abstract":"Food is a basic need for every living being. With the increasing population, it has become necessary to yield enough amount of crops to satiate their hunger. In the mean while we face a lot of problems while considering the needs of such a huge population. Lots of crops gets destroyed which affects the overall yield of the crops, hence leading to shortage of food. We know that it is very common for plants to get affected by diseases. Some of the factors are fertilizers and pesticides, cultural practices, environmental and surrounding conditions etc. These diseases affect overall yield as well as the economy based on it. Any approach to overcome this problem would help the farmers to cultivate crops efficiently. Hence, detection of disease in crops plays a vital role in agriculture. The proposed manuscript aims for the detection of crop diseases by using classification algorithm in deep learning concept. An automatic technique to detect symptoms of plant diseases would be highly beneficial to the agricultural society as it would eliminate the work of constant monitoring. In this paper, we will propose different disease classification algorithms which can be used for the detection of disease in plant leaves.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124081797","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201783
S. Routray, Srikanta Mohapatra, P. Sahu, B. K. Sahu, M. Debnath
Microgrid is meant to a distribution grid and capable to supply electrical power as per the consumer's demand effectively. This grid is normally located at the point of common demand and provides improved power quality in concern to the demand side behavior. Since the grid level in KW based or hardly in few MW based hence the grid is so named. In this proposed study a microgrid is modelled by integrating different distributed generation (DG) based power generating equipments. Some limitations are also found in the microgrid system which could be solved through different fundamental approached. The inertial less microgrid can not able to support the instant load deviation and consequently produces frequency and voltage fluctuations in the system especially in islanding situations. In grid connected operation, the power grid injects high inertia the time of disturbances so as to maintain steady performance after few little instabilities. This article specially focuses the importance of utility grid for a microgrid and same time highlights the effectiveness of a novel water cycle algorithm based three degree of freedom controller (3dof) in regard to optimization & control action of this study.
{"title":"Microgrid performance study: Grid connected and Islanding under common disturbance & approach","authors":"S. Routray, Srikanta Mohapatra, P. Sahu, B. K. Sahu, M. Debnath","doi":"10.1109/APSIT58554.2023.10201783","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201783","url":null,"abstract":"Microgrid is meant to a distribution grid and capable to supply electrical power as per the consumer's demand effectively. This grid is normally located at the point of common demand and provides improved power quality in concern to the demand side behavior. Since the grid level in KW based or hardly in few MW based hence the grid is so named. In this proposed study a microgrid is modelled by integrating different distributed generation (DG) based power generating equipments. Some limitations are also found in the microgrid system which could be solved through different fundamental approached. The inertial less microgrid can not able to support the instant load deviation and consequently produces frequency and voltage fluctuations in the system especially in islanding situations. In grid connected operation, the power grid injects high inertia the time of disturbances so as to maintain steady performance after few little instabilities. This article specially focuses the importance of utility grid for a microgrid and same time highlights the effectiveness of a novel water cycle algorithm based three degree of freedom controller (3dof) in regard to optimization & control action of this study.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122307219","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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