Pub Date : 2023-05-26DOI: 10.1109/INCET57972.2023.10170102
V. Sudha, R. P. Devi, K. Kavitha, A. Prakash, G. Ramachandran
In the direction of independent on-device AI .By deploying AI to edge devices, on-device AI may power a variety of functions in our daily lives, such as search and rescue with unmanned aerial vehicles, health care in robots, and augmented reality (AR)/mixed reality (XR) glasses (UAVs).However, it can be difficult to implement DL on edge devices and use it in practical applications. Real applications of on-device AI are not possible because the computational and energy costs of model inference are excessively high for edge devices with constrained computing power and battery capacity. Additionally, pre-trained models may not be accurate for new input instances because they cannot dynamically adapt to the real world after being deployed to edge devices. Two projects are carried out in order to achieve effective and adaptive on-device AI. A machine-learning-based analogue circuit regression model offers an alternate propose methodology for dealing with swiftly increasing invent complexity. The more modern technology structures are proposed, such as SOI or FinFET, the more robust calculation engine is needed to meet various design specifications while assuring operative resilience.
{"title":"Artificial Intelligence Energy Efficiency in Low Power Applications","authors":"V. Sudha, R. P. Devi, K. Kavitha, A. Prakash, G. Ramachandran","doi":"10.1109/INCET57972.2023.10170102","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170102","url":null,"abstract":"In the direction of independent on-device AI .By deploying AI to edge devices, on-device AI may power a variety of functions in our daily lives, such as search and rescue with unmanned aerial vehicles, health care in robots, and augmented reality (AR)/mixed reality (XR) glasses (UAVs).However, it can be difficult to implement DL on edge devices and use it in practical applications. Real applications of on-device AI are not possible because the computational and energy costs of model inference are excessively high for edge devices with constrained computing power and battery capacity. Additionally, pre-trained models may not be accurate for new input instances because they cannot dynamically adapt to the real world after being deployed to edge devices. Two projects are carried out in order to achieve effective and adaptive on-device AI. A machine-learning-based analogue circuit regression model offers an alternate propose methodology for dealing with swiftly increasing invent complexity. The more modern technology structures are proposed, such as SOI or FinFET, the more robust calculation engine is needed to meet various design specifications while assuring operative resilience.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"75 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":"117116307","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/INCET57972.2023.10170058
Apurva Jadhav, Piyush Atre, Akshay Andhare, Ujjwal Chaturvedi, Priyanka P. Boraste, D. Medhane
Daily wage labourers and workers are an integral part of the economy of any country. Especially in a country like India where the estimated population of daily wage workers is nearly around 187 million, they are the backbone of our economy. The problem here is that a lot of workers are financially exploited by contractors who are able to take advantage of the helplessness of the labour who need to find work every single day. In order to tackle this issue, the proposed system aims to establish a meeting point between the labour, contractors, and customers. Therefore, we are designing and implementing an Android application for the purpose of enabling daily wage labour and workers to connect with their customers and contractors and facilitate a fully-fledged platform for end users to obtain their services. Upon registration on the application, the labour can directly come in contact with customers by making themselves available and thus provide their services. The users can also obtain services through the contractor in case multiple labours are required by them. Multi-lingual support, GPS, standard prices, a payment interface, and a rating system are some of the key features of the application.
{"title":"ServiceArc: A Systematic Approach towards Daily Wage Labour Management through Automation System","authors":"Apurva Jadhav, Piyush Atre, Akshay Andhare, Ujjwal Chaturvedi, Priyanka P. Boraste, D. Medhane","doi":"10.1109/INCET57972.2023.10170058","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170058","url":null,"abstract":"Daily wage labourers and workers are an integral part of the economy of any country. Especially in a country like India where the estimated population of daily wage workers is nearly around 187 million, they are the backbone of our economy. The problem here is that a lot of workers are financially exploited by contractors who are able to take advantage of the helplessness of the labour who need to find work every single day. In order to tackle this issue, the proposed system aims to establish a meeting point between the labour, contractors, and customers. Therefore, we are designing and implementing an Android application for the purpose of enabling daily wage labour and workers to connect with their customers and contractors and facilitate a fully-fledged platform for end users to obtain their services. Upon registration on the application, the labour can directly come in contact with customers by making themselves available and thus provide their services. The users can also obtain services through the contractor in case multiple labours are required by them. Multi-lingual support, GPS, standard prices, a payment interface, and a rating system are some of the key features of the application.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"129 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":"123065251","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/INCET57972.2023.10169972
Nayana Shenvi, Hassanali Virani
The ionosphere is a highly dynamic region of the Earth's atmosphere that plays a crucial role in global navigation and communication systems. Accurate forecasting of ionospheric activity is essential for mitigating its impact on these systems. In recent years, machine learning techniques have shown promise in predicting ionospheric activity, but there is limited research on their comparative performance. This paper presents a comparative analysis of various machine learning techniques for forecasting ionospheric total electron content (TEC) data. Specifically, we compare the performance of five popular machine learning techniques- linear regression, multi-layer perceptron neural networks, K-nearest neighbors, support vector regression and random forest regressor. We use TEC data along with exogenous parameters namely By, Bz, Vp, Np, F10.7, Kp, Dst and Ap. We evaluate the performance of the models at different latitudes and during solar quiet and active years. Our results show that the Random Forest Regressor (RFR) outperformed the other techniques with the lowest root mean square error (RMSE) and mean absolute error (MAE). The R2 value suggests that the RFR model provides the best fit to the TEC data compared to other models evaluated and can be used for ionospheric TEC forecasting.
{"title":"Comparative Analysis of Machine learning techniques for Forecasting Ionospheric Total Electron Content Data","authors":"Nayana Shenvi, Hassanali Virani","doi":"10.1109/INCET57972.2023.10169972","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10169972","url":null,"abstract":"The ionosphere is a highly dynamic region of the Earth's atmosphere that plays a crucial role in global navigation and communication systems. Accurate forecasting of ionospheric activity is essential for mitigating its impact on these systems. In recent years, machine learning techniques have shown promise in predicting ionospheric activity, but there is limited research on their comparative performance. This paper presents a comparative analysis of various machine learning techniques for forecasting ionospheric total electron content (TEC) data. Specifically, we compare the performance of five popular machine learning techniques- linear regression, multi-layer perceptron neural networks, K-nearest neighbors, support vector regression and random forest regressor. We use TEC data along with exogenous parameters namely By, Bz, Vp, Np, F10.7, Kp, Dst and Ap. We evaluate the performance of the models at different latitudes and during solar quiet and active years. Our results show that the Random Forest Regressor (RFR) outperformed the other techniques with the lowest root mean square error (RMSE) and mean absolute error (MAE). The R2 value suggests that the RFR model provides the best fit to the TEC data compared to other models evaluated and can be used for ionospheric TEC forecasting.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"81 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":"124805545","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/INCET57972.2023.10170643
Kiran Kesarapu, Nelluru Sai Kiran, Erothi Manju Dhara, R. Rupa, Gurpreet Singh Chhabra
India’s economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil’s alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. The system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue.
{"title":"A Comprehensive Data Driven Approach on Crop Yield and Fertilizer Efficiency","authors":"Kiran Kesarapu, Nelluru Sai Kiran, Erothi Manju Dhara, R. Rupa, Gurpreet Singh Chhabra","doi":"10.1109/INCET57972.2023.10170643","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170643","url":null,"abstract":"India’s economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil’s alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. The system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"178 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":"124891498","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/INCET57972.2023.10170149
Swati Laxmi Sahu, Renta Chintala Bhargavi
Potato, among the most vegetables is commercially significant and well-known vegetable which is known for its high nutritional content and delicious flavor. India is one of the world’s leading producers of potato. Unfortunately, plant diseases in potato have been one of the causes of decreased production. So, it is necessary to detect them. Collecting images of plants diseases is a big challenge as it is a very time-consuming process. Often, we do not have sufficient data to train our deep learning models, so data augmentation techniques are used for increasing the dataset which lead to poor generalization. This study focuses on detecting whether the plant is healthy or diseased. In this proposed method, limited dataset is used for potato plant disease classification without using any data augmentation techniques. Popular pre-trained models — VGG16, InceptionResNetV2, ResNet50V2 are used for feature extraction and traditional machine learning algorithms — XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest are used as classifiers. From the study, it is observed that the combination of VGG16 model as a feature extractor and SVM as a classifier achieved the highest accuracy of 93% compared to rest of the combination of models and algorithms. The method proposed in this study can be used for potato plant disease detection with limited dataset.
马铃薯是我国最具商业价值的蔬菜之一，以其营养价值高、风味鲜美而闻名于世。印度是世界主要的马铃薯生产国之一。不幸的是，马铃薯的植物病害已成为产量下降的原因之一。因此，有必要对它们进行检测。植物病害图像的采集是一项巨大的挑战，因为这是一个非常耗时的过程。通常，我们没有足够的数据来训练我们的深度学习模型，因此使用数据增强技术来增加数据集，从而导致较差的泛化。这项研究的重点是检测植物是健康还是患病。该方法采用有限数据集进行马铃薯病害分类，不使用任何数据增强技术。流行的预训练模型- VGG16, InceptionResNetV2, ResNet50V2用于特征提取，传统的机器学习算法- XGBoost，支持向量机(SVM)， k -最近邻(KNN)，随机森林被用作分类器。从研究中可以看出，VGG16模型作为特征提取器，SVM作为分类器的组合，与其他模型与算法的组合相比，准确率最高，达到93%。该方法可用于有限数据集的马铃薯病害检测。
{"title":"Prediction of Diseases in Potato Plant using Pre-trained and Traditional Machine Learning Models","authors":"Swati Laxmi Sahu, Renta Chintala Bhargavi","doi":"10.1109/INCET57972.2023.10170149","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170149","url":null,"abstract":"Potato, among the most vegetables is commercially significant and well-known vegetable which is known for its high nutritional content and delicious flavor. India is one of the world’s leading producers of potato. Unfortunately, plant diseases in potato have been one of the causes of decreased production. So, it is necessary to detect them. Collecting images of plants diseases is a big challenge as it is a very time-consuming process. Often, we do not have sufficient data to train our deep learning models, so data augmentation techniques are used for increasing the dataset which lead to poor generalization. This study focuses on detecting whether the plant is healthy or diseased. In this proposed method, limited dataset is used for potato plant disease classification without using any data augmentation techniques. Popular pre-trained models — VGG16, InceptionResNetV2, ResNet50V2 are used for feature extraction and traditional machine learning algorithms — XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest are used as classifiers. From the study, it is observed that the combination of VGG16 model as a feature extractor and SVM as a classifier achieved the highest accuracy of 93% compared to rest of the combination of models and algorithms. The method proposed in this study can be used for potato plant disease detection with limited dataset.","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":"123757433","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/INCET57972.2023.10170399
Kaustav Sen, Bindu Verma
Heart disease is associated with a high mortality rate because it affects a significant number of people around the world. There is a pressing need for improved diagnostic methods that are both effective and accurate. Techniques from the field of machine learning have been put to extensive use on tabular data from the healthcare sector, where they have proven to be effective in prediction and analysis. To address the issue of the traditional machine learning model’s low accuracy, precision, and recall value, we propose a soft voting meta classifier composed of Catboost, Light-Gradient Boosting Machine, Gaussian Naive Bayes , Random Forest, and XGBoost. The proposed soft voting ensemble outperformed the other models used in this experiment, which was conducted on a fused UCI heart disease and Statlog dataset. The proposed soft voting ensemble model achieved 91.85% accuracy and a 0.9344 Area Under The Curve Score.
{"title":"Heart Disease Prediction Using a Soft Voting Ensemble of Gradient Boosting Models, RandomForest, and Gaussian Naive Bayes","authors":"Kaustav Sen, Bindu Verma","doi":"10.1109/INCET57972.2023.10170399","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170399","url":null,"abstract":"Heart disease is associated with a high mortality rate because it affects a significant number of people around the world. There is a pressing need for improved diagnostic methods that are both effective and accurate. Techniques from the field of machine learning have been put to extensive use on tabular data from the healthcare sector, where they have proven to be effective in prediction and analysis. To address the issue of the traditional machine learning model’s low accuracy, precision, and recall value, we propose a soft voting meta classifier composed of Catboost, Light-Gradient Boosting Machine, Gaussian Naive Bayes , Random Forest, and XGBoost. The proposed soft voting ensemble outperformed the other models used in this experiment, which was conducted on a fused UCI heart disease and Statlog dataset. The proposed soft voting ensemble model achieved 91.85% accuracy and a 0.9344 Area Under The Curve Score.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"6 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":"123876955","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/INCET57972.2023.10170132
P. J. Subrahmanya Hande, Rakeshgowda D S, Naveen Kumar, Nandana K A, P. Kanwal
Human activity surveillance video systems are gaining popularity in the field of computer vision due to user demands for security as well as their growing importance in many applications such as elder care, home nursing, and unusual event alarming. Automatic activity recognition is the key to video surveillance. This paper presents a method for human activity recognition in office surveillance videos using machine learning models including convLSTM, GRCNN and LRCN with three main steps: pre-processing, feature extraction and activity classification. The main targeted activities are walking, sleeping on desk, handshaking, typing, opening or closing door. Experimental results demonstrate the effectiveness of the proposed LRCN approach in accurately recognizing human activities in office surveillance videos with acceptable training and testing accuracy.
{"title":"Human Activity Recognition for Office Surveillance","authors":"P. J. Subrahmanya Hande, Rakeshgowda D S, Naveen Kumar, Nandana K A, P. Kanwal","doi":"10.1109/INCET57972.2023.10170132","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170132","url":null,"abstract":"Human activity surveillance video systems are gaining popularity in the field of computer vision due to user demands for security as well as their growing importance in many applications such as elder care, home nursing, and unusual event alarming. Automatic activity recognition is the key to video surveillance. This paper presents a method for human activity recognition in office surveillance videos using machine learning models including convLSTM, GRCNN and LRCN with three main steps: pre-processing, feature extraction and activity classification. The main targeted activities are walking, sleeping on desk, handshaking, typing, opening or closing door. Experimental results demonstrate the effectiveness of the proposed LRCN approach in accurately recognizing human activities in office surveillance videos with acceptable training and testing accuracy.","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":"126114401","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/INCET57972.2023.10170223
Narendra Kumar Mourya, Bharti Koul
This paper proposes a standalone microgrid in integration with photovoltaic array and battery storage system based on fuzzy logic MPPT technique. Solar PV systems are affected by variables like temperature and irradiance and have nonlinear I-V characteristics. Fuzzy logiv MPPT can handle nonlinearities better than any other conventional MPPT technique. in addition with the battery storage system the microgrid can be operate in both islanded mode and grid connected mode. To simulate this system with Solar PV and battery storage system MATLAB Simulink is used. This model can be used more efficiently for remote locations or villages where electricity supplies are not present or it gets affected by too much load shedding. Microgrids in integration with the renewable sources is the future for electrical power generation.
{"title":"Fuzzy Logic Based PV-Battery system for a Standalone Microgrid","authors":"Narendra Kumar Mourya, Bharti Koul","doi":"10.1109/INCET57972.2023.10170223","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170223","url":null,"abstract":"This paper proposes a standalone microgrid in integration with photovoltaic array and battery storage system based on fuzzy logic MPPT technique. Solar PV systems are affected by variables like temperature and irradiance and have nonlinear I-V characteristics. Fuzzy logiv MPPT can handle nonlinearities better than any other conventional MPPT technique. in addition with the battery storage system the microgrid can be operate in both islanded mode and grid connected mode. To simulate this system with Solar PV and battery storage system MATLAB Simulink is used. This model can be used more efficiently for remote locations or villages where electricity supplies are not present or it gets affected by too much load shedding. Microgrids in integration with the renewable sources is the future for electrical power generation.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"28 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":"126475397","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/INCET57972.2023.10170274
Rajesh Kumar C, Aroul Canessane R
Cloud computing has emerged as a technology behemoth with applications in a wide range of fields. When data is being migrated from offline data centres and stored in multiple cloud environments part of the control is always with the Cloud Service Providers(CSPs) leads to security concerns. The data stored in the cloud may sometimes be compromised even though the CSPs may take precautions to avoid such situations. In this paper, we discuss securely storing the data using the data dispersion technique by breaking the data into multiple segments and combining it with encryption along with replication. The division of data and storing it in the cloud helps in protecting the complete data even if an attacker tries to access the data it will not be easy for him to make sense of the retrieved data because the data is already being encrypted and combined with dispersion and replication adds to the complexity of retrieval. Security is achieved as the dispersed data is spread across multiple locations which makes it difficult for an attacker to get all the segments. In most scenarios be able it depends on traditional encryption techniques alone to protect the data. Here, We propose focusing more on how data is stored in the cloud to relieve the system of costly computational methodologies. In this strategy, the trade-off between security and the data retrieval time must also be considered.
{"title":"Securing Data storage in Cloud after Migration using Immutable Data Dispersion","authors":"Rajesh Kumar C, Aroul Canessane R","doi":"10.1109/INCET57972.2023.10170274","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170274","url":null,"abstract":"Cloud computing has emerged as a technology behemoth with applications in a wide range of fields. When data is being migrated from offline data centres and stored in multiple cloud environments part of the control is always with the Cloud Service Providers(CSPs) leads to security concerns. The data stored in the cloud may sometimes be compromised even though the CSPs may take precautions to avoid such situations. In this paper, we discuss securely storing the data using the data dispersion technique by breaking the data into multiple segments and combining it with encryption along with replication. The division of data and storing it in the cloud helps in protecting the complete data even if an attacker tries to access the data it will not be easy for him to make sense of the retrieved data because the data is already being encrypted and combined with dispersion and replication adds to the complexity of retrieval. Security is achieved as the dispersed data is spread across multiple locations which makes it difficult for an attacker to get all the segments. In most scenarios be able it depends on traditional encryption techniques alone to protect the data. Here, We propose focusing more on how data is stored in the cloud to relieve the system of costly computational methodologies. In this strategy, the trade-off between security and the data retrieval time must also be considered.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"1 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":"129262617","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/INCET57972.2023.10170173
T. Muthamizhan, A. Sivakumar
Permanent magnet brushless dc (PMBLDC) machines can indeed be powered by photovoltaic systems (PV) and seem to be cheap, compact, and reliable for use in electric vehicle applications. The PMBLDC motors have increasingly gained a significant amount of attention to the incredibly quiet operation and low maintenance cost. Additionally, these motors are the most recent alternative option for researchers and industrial needs. Furthermore, the control of the PMBLDC motor is by electronic commutation, requires rotor-position sensing by regulating the current to the excitation of phase windings. Using reboost Luo dc-dc converters to limit current to the inverter is the most desirable method for regulating a PMBLDC motor. The maximum power point tracking (MPPT) of photovoltaic system employing a reboost Luo converter is handled by proportional Integral (PI) controller-based MPPT, and the controller’s parameters are optimised with the help of a grey-wolf algorithm. The reboost Luo converter increases the voltage from a photovoltaic (PV) source by boosting the PV source’s voltage by a factor of eight. MATLAB/SIMULINK is used to carry out the simulation, which is subsequently cross-checked against the experimental data by means of an FPGA controller.
{"title":"A Solar PV Array Fed High-Gain Reboost Luo Converter by Grey Wolf Optimizer Algorithm based MPPT in BLDC Motor Drive for Electric Vehicles","authors":"T. Muthamizhan, A. Sivakumar","doi":"10.1109/INCET57972.2023.10170173","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170173","url":null,"abstract":"Permanent magnet brushless dc (PMBLDC) machines can indeed be powered by photovoltaic systems (PV) and seem to be cheap, compact, and reliable for use in electric vehicle applications. The PMBLDC motors have increasingly gained a significant amount of attention to the incredibly quiet operation and low maintenance cost. Additionally, these motors are the most recent alternative option for researchers and industrial needs. Furthermore, the control of the PMBLDC motor is by electronic commutation, requires rotor-position sensing by regulating the current to the excitation of phase windings. Using reboost Luo dc-dc converters to limit current to the inverter is the most desirable method for regulating a PMBLDC motor. The maximum power point tracking (MPPT) of photovoltaic system employing a reboost Luo converter is handled by proportional Integral (PI) controller-based MPPT, and the controller’s parameters are optimised with the help of a grey-wolf algorithm. The reboost Luo converter increases the voltage from a photovoltaic (PV) source by boosting the PV source’s voltage by a factor of eight. MATLAB/SIMULINK is used to carry out the simulation, which is subsequently cross-checked against the experimental data by means of an FPGA controller.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"50 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":"129487383","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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