检索结果-内蒙古大学图书馆

5th IEEE International Conference on Electrical, Computer and Communication technologies, ICECCT 2023

作者： Shyamala devi, M. Shanthana, S. Hemasri, B. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9781665493604

Biochemical or environmental factors will impact strawberry plants, posing a serious warning to the crop's output and efficiency that increases the likelihood of strawberry diseases. Traditional identification techniques, however, perform poorly in real-time and have a high percentage of judgement errors. It is clear that in the present day of increasing strawberries production and quality requirements, the traditional techniques for strawberry disease recognition, which primarily rely on individual perspective and untrained eye monitoring, cannot meet the expectations of people for strawberry disease identification. A broader framework needs to be established in order to precisely recognize strawberry diseases and provide suitable treatment alternatives. Accordingly, utilizing the Strawberry disease dataset from KAGGLE, this research suggests employing the Inception Adaptive Gradient L2 regularized Learning (IAGLr) CNN model to recognize strawberry calcium shortage leaf disease. The proposed IAGLr model was developed using convolutional layers of 3 × 3-dimension, maximum pooling layer, dropout layer of 1 × 1 dimension, and dense layer for each 3 × 3 convolution. Then, it was trained using the adaptive gradient optimizer and L2 regularized gradient descent learning rates. An Inception network, an Adaptive Gradient optimizer with 0.5 dropout, and weight decay of Gradient descent L2 regularisation were used to build the IAGLr. In order to evaluate the performance of the proposed IAGLr model, the dataset for Strawberry illness was divided into training and testing data. The training data was mapped to both the IAGLr model and other CNN models. A Gtx Geforce Tesla V100 Nvidia graphics card server was used to programme Python, with a batch size of 64 and 40 training epochs. When compared to current CNN models, implementation outcomes reveal that the suggested IAGLr model exhibits FScore of 97.77%, precision of 97.62%, accuracy of 97.77% and recall of 97.25%. © 2023 IEEE.

关键词： diseases

来源：评论

学校读者我要写书评

暂无评论

Inception Nesterov Momentum Adam L2 regularized Learning rate CNN for Sugarcane disease Classification 3

Inception Nesterov Momentum Adam L2 Regularized Learning Rat...

引用

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable technologies, ICAECT 2023

作者： Aruna, r. devi, M. Shyamala Anand, Ashish dutta, Utkarsh Shruti Sagar, Chilakapati Narasimha Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9781665494007

The primary source of sugar extractor is sugarcane, which is regarded as a revenue source crop globally. Sugarcane is a cash crop that greatly benefits the economy. Therefore, it is crucial for farmers to identify and cure diseases in the sugarcane production at the appropriate time. To address this issue, deep learning techniques are employed. The disease in the sugarcane crop could affect the overall yield of the crop. With this view, this paper recommends Inception Nadam L2 regularized Gradient descent (NLrGd) CNN model to classify the sugar cane disease like red rot, smut, mosaic, yellow leaf, red-orange rust using the Sugarcane disease dataset from KAGGLE. The proposed NLrGd model uses Sugarcane disease dataset from KAGGLE with 3000 sugarcane plant leaflets having 1000 Bacterial Blight, 1000 red dot and 1000 healthy sugarcane plant images. The CXAGd model was designed using 3 X 3 convolutional layers and maximum pooling layer followed by 1 X 1 dropout and dense layer for each 3 X 3 convolution and trained with learning rate of L2 regularized Gradient descent and NAdAM optimizer. The NLrGd built with Inception network with L2 regularized Gradient descent and NAdAM optimizer with 0.5 dropout, weight decay of Gradient descent L2 regularization. The dataset for sugarcane disease was partitioned into training and testing data, and the training data were fitted to both the NLrGd model and other CNN models in order to compare the effectiveness of the proposed NLrGd model. Python was coded on a Gtx Geforce Tesla V100 Nvidia graphics card server with a batch size of 64 and 40 training epochs. results of execution indicate that the suggested NLrGd model showcase the accuracy of 96.75%, Precision of 96.62%, recall of 96.25% and FScore of 96.76% when compared with existing CNN models. © 2023 IEEE.

关键词： Economics

来源：评论

学校读者我要写书评

暂无评论

Optimizing Flight routes for Better Fuel Efficiency and Optimal Path using Grover's Algorithm & QAOA Algorithm - Quantum Computing

Optimizing Flight Routes for Better Fuel Efficiency and Opti...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Khan, M. Imran Govindharaj, I. Sathishkumar, r. Jenifer, d. Maeghasree, T. Yohini, K. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

Optimization of flight routes is one of the hard problems in the aviation sector. This paper focuses on the prospects of quantum computing developed using Grover's Algorithm and the Quantum Approximate Optimization Algorithm (QAOA). Grover's Algorithm provides a quadratic speedup for searching an unsorted database and is therefore quite useful for identifying optimum flight routes amongst millions of choices. Preliminary benchmarking suggests that it can efficiently search a space of more than one million routes in far less time than the conventional approach, which is important for timely decisions over aviation. Simultaneously, QAOA refines candidate routes with the aid of real-time inputs such as weather and air traffic, and each time delivers near-optimal solutions even during sudden changes. The quantum framework in development would make it possible to improve the accuracy of routes recommended and respond fast to changing circumstances. This includes weather conditions whereby, based on research, estimated savings in fuel by optimizing routes run between 5% to 10%, which translates to costs lower with fewer carbon emissions playing a role in environmental sustainability. Flight scheduling also improves through quantum algorithms because of increased operational efficiency, which would enable airlines to cope better with complicated constraints and not allow delays when flights are involved. In summary, the Grover's Algorithm and QAOA hybrid shows a more holistic approach of giving an advantage regarding speed over the traditional approach as well as flexibility. © 2024 IEEE.

关键词： Air transportation

来源：评论

学校读者我要写书评

暂无评论

Feature Over Exemplification-Based Classification for revelation of Hypothyroid 11th

Feature Over Exemplification-Based Classification for Revela...

引用

11th International Conference on recent Trends in Computing, ICrTC 2022

作者： Shyamala devi, M. ramesh, P.S. Vinoth Kumar, S. Bhuvana Shanmuka Sai Sivani, r. Muskaan Sultan, S. Srinivas, Thaninki Adithya Siva Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789811988240

Thyroid hypothyroidism is an ailment during which the endocrine system generates insufficient thyroid hormone. Heart rate, body temperature, and many other components of metabolic activity can become problematic when there is an inadequacy of hormone insulin in a hypothyroid patient. Conventional thyroid stimulating hormone levels-based diagnosis and controlling does not always contribute in symptomatic improvement in hypothyroid symptoms, much to the despair of both patients and physicians. The machine learning technology could help the healthcare industry for the prediction of hypothyroid. With this review, this project aims to predict the presence of hypothyroid by using hypothyroid data source obtained from the KAGGLE machine learning library. The hypothyroid dataset is pre-processed with encoding and missing values and contains 24 components and 3164 patient details. To examine the performance measures, the original information is deployed to all classifiers with and without feature scaling. The exploratory data analysis is done to analyze the distribution of target variable. The target data distribution is found to have 91.7% of non-hypothyroid and 8.3% of hypothyroid, which clearly specifies the imbalanced target distribution. The dataset is applied with oversampling methods like borderline, smote, SVM smote, and AdASYN methods to balance the target feature distribution. The above approaches then apply multiple classifiers to the oversampled dataset to predict hypothyroidism both before and after feature scaling. The performance metrics are precision, recall, F-score, and accuracy. The accuracy of the random forest classifier before applying oversampling of the target hypothyroid feature is 89 percent, according to experimental observations. After using oversampling techniques like Borderline1, Borderline2, Smote, and SVMSmote, the same random forest classifier displays accuracy of 99 percent. However, the random forest classifier exhibits the accuracy of 100

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Utilizing Machine Learning techniques for Forecasting Agricultural Commodity Prices 2

Utilizing Machine Learning Techniques for Forecasting Agricu...

引用

2nd International Conference on Intelligent data Communication technologies and Internet of Things, IdCIoT 2024

作者： Benjamin, Minu Inba Shanthini Watson Gowtham, d. reddy, S. Anand Kumar Krishna, d. durga radha Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linear regression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informed decisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting. © 2024 IEEE.

关键词： Bayesian regression decision tree Ensemble methods exploratory data analysis hybrid models K-Nearest Neighbors linear regression Machine learning random Forest XGBoost

来源：评论

学校读者我要写书评

暂无评论

Investigation of impact strength at different infill rates biodegradable PLA constituent through fused deposition modeling

引用

Materials Today: Proceedings 2022年 62卷 551-558页

作者： dharmalingam, G. Arun Prasad, M. Salunkhe, Sachin Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology Chennai 600062 India

Polylactic Acid (PLA) is a natural source of corn, cassava, and sugarcane which is an economically produced renewable, biodegradable, compostable, and bio plastic resource of this world. It plays an important role in automobile, aerospace, and consumer applications. Moreover, the PLA was in research and development with high expectations for the industrial consumer, moving towards the development of a sustainable vehicle and the goal is greatly contributed to environmental friendly applications such as in two-wheeler and four-wheeler vehicles. The present investigation consists of different infill rates (50%, 75% & 100%). PLA was performed for two-wheeler mudguard material for improving the property through additive manufacturing (AM) followed by fused deposition modeling (FdM). Based on the literature survey, it was found that FdM printed PLA's impact strength was lower than that fabricated through the Injection molding (IM) process due to improper selection of printing process parameters. In order to overcome this drawback were taken into account and to make a good environmentally friendly and biodegradable material was chosen as PLA. Finally, it was observed that the FdM-based PLA deposition has higher impact strength than IM because of introducing the printed sample of On-edge orientation and 100% infill rate. The finite element analysis (FEA) was performed to assess the impact strength of PLA at different infill rates by using the ANSYS software 17.2. The simulation results were compared with the experimental results, it was found that almost close to each other. © 2022

关键词： Impact strength

来源：评论

学校读者我要写书评

暂无评论

Unipolar and Bipolar Mathematical Inference of Weight Adjustment Mode of Single Layer Perceptron on and Logic Gate 3

Unipolar and Bipolar Mathematical Inference of Weight Adjust...

引用

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable technologies, ICAECT 2023

作者： devi, M. Shyamala Goni, S. Tasneem, P. Vintha, Karthikeyani Kumar, dubba Sai Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9781665494007

In order to ensure that electrical terminals only 'switch on' after the proper logic process has been applied, logic gates are employed to make judgments. Each logic gate has a name that explains how various inputs will affect the potential outcomes. To perform logical functions on one or more binary sequence of inputs and produce a single digital output, logic gates are utilized. Embedded systems, microcontrollers, electrical and electronics circuit boards, and programmable logic applications generally make use of logic gates. When the sensor receives no information, it generates low Output impedance for logic 0. It has been determined that using logic gates to create an edge avoider robotics that lowers project budget and speeds up the robot. With the growth of technology, the neural network could also be used for implementing the logic gate using Single Layer Perceptron neural network often termed as Linear Threshold Gate. This paper attempt to analyze the mathematical inference on the number of execution steps for the weight adjustment in the activation function of ANd Logic gate. This inference carried out by varying the input of Single layer Perceptron Neural Network in the form of unipolar and bipolar input. The activation function weight adjustment had carried out using batch and sequential mode of execution. Mathematical implementation of ANd logic gate exhibits that Bipolar sequential and Bipolar batch mode weight adjustment had less number of execution steps reaching 100% accuracy by matching the desired and predicted output when compared to the unipolar. The detailed execution steps are produced herewith for the validation of outcome. © 2023 IEEE.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Feature reduced Anova Element Oversampling Elucidation Based Categorisation for Hepatitis C Virus Prognostication 11th

Feature Reduced Anova Element Oversampling Elucidation Based...

引用

11th International Conference on recent Trends in Computing, ICrTC 2022

作者： devi, M. Shyamala Kumar, S. Vinoth ramesh, P.S. Kavitha, Ankam Jayasree, Konkala rajesh, Venna Sri Sai Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789811988240

Hepatitis C virus is a virus-borne infection that attacks the liver and causes inflammation. disease infection is caused due to exposure to infectious blood, such as from sharing needles or using unsterile tattoo equipment. Identification of this disease is a challenging task as it has no predefined symptoms. The machine learning technology could help with the analysis of clinical parameters for the classification of Hepatitis C virus. With this review, this project aims to predict the presence of Hepatitis C virus by using Hepatitis C virus dataset retrieved from the KAGGLE machine learning repository. The Hepatitis C virus sample has been preprocessed with encoding and incomplete data. It has 12 attributes and 615 individual person records. To examine the outcome measures, the original database is fed to all classifier models. To look at how the target feature variable is distributed, exploratory data analysis is done. The target data distribution is found to have 86.7% of blood donor, 4% of suspect blood donor, 3.9% of Hepatitis, 3.4% of fibrosis, 1.1% of cirrhosis patients, which evidently stipulates the unprovoked target dissemination. The dataset is applied to Anova test to identify the essential features for classifying the target. The features like sex, alkaline phosphatase (ALP) and protein exhibit the value of Pr(>F) to be more than 0.05, and it is detached from the database to form Anova compressed datasample. The Anova compressed datasample is applied with oversampling techniques like borderline, smote, SVM smote and AdASYN methods to stabilise the distribution of Hepatitis C virus target distribution. The Anova oversampled datasample by the above methods is then deployed with various classifier models to forecast the hypothyroid to examine the performance metrics. Implementation results show that the decision Tree classifier model shows accuracy of 84% before applying Anova test and oversampling of target Hepatitis C virus feature. The same decision Tre

关键词： decision trees

来源：评论

学校读者我要写书评

暂无评论

Learning rate regularized EfficientNet based rice Grain Classification

Learning Rate Regularized EfficientNet based Rice Grain Clas...

引用

2023 IEEE Global Conference on Information technologies and Communications, GCITC 2023

作者： devi, M. Shyamala Sridevi, S. Umanandhini, d. reddy, Yarramreddy rohith Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350308167

One of the most popular grain items cultivated across the globe is rice. rice seeds may be recognized by their appearance, form, and ethnicity. rice types may be distinguished owing to their various traits. This rice category assessment relates to the seed efficiency, which includes various hereditary pairings. Along with population development, there is a rise in the demand for rice, thus farmers require high-quality rice grains. due to the variety of rice grain instances currently available on the market, rice grain classification becomes crucial. A significant amount of time and effort might be consumed on manual classification of grains. The technological innovations could be utilized to automatically identify rice grain images and provide grain categorization based on grain features. This paper suggests Learning rate regularized Efficient Net (Lrr-EN) to classify the rice grain more efficiently. The 30,000 rice images of rice Image dataset from Kaggle is used in the suggested model. The examination of image distribution is the first step in the Lrr-EN model. The best CNN model is selected by fitting the rice images with existing CNN models after image augmentation. When compared to other CNN models, the Efficient Net demonstrated an accuracy of 92.41%. The learning rate parameter was now fine-tuned to increase accuracy using the EfficientNet CNN. The augmented rice images are fitted to Lrr-EN using several learning rates, such as 0.0001, 0.001, 0.01, and 0.1. The experimental findings reveal that, when compared to different learning rates, the suggested Lrr-EN with a 0.001 learning rate demonstrates the greatest Accuracy of 99.01%. © 2023 IEEE.

关键词： accuracy augmentation CNN learning rate regularization

来源：评论

学校读者我要写书评

暂无评论

Storage Network Attached Using raspberry PI

Storage Network Attached Using Raspberry PI

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Chithambaramani, r. Sankar, M. Sivaprakash, P. Marichamy, d. Yazhinian, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315127

Our dependence on the Internet is primarily to blame for the rising market demand for more storage space. A dedicated file server is provided by the network for file management. It is a standalone storage device that is properly connected to the network. It is easily accessible to any number of diverse clients due to its network availability. The Network Attached Storage devices that are currently available on the market are highly overpriced and do not offer much room for improvement. Additionally, it is important to keep in mind that these devices typically consume a fair amount of power. A low- cost, simple-to-configure NAS sys-tem is the goal of this proposed strategy. It also has Web Server capabilities and additional security features. It allows you to access data on and off the network and provides you with a convenient, energy-efficient, and always-on storage device. © 2023 IEEE.

关键词： Virtual storage

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：