检索结果-内蒙古大学图书馆

2023 IEEE International Conference on research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, rMKMATE 2023

作者： rajesh, r. Vishnupriyan, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

Contingency theory is an organizational approach that holds there is not a perfect way to build a firm, lead an enterprise, or make policies. The model employed to ascertain and obtain an accurate result is compared to existing accuracy outcomes. Contingent leaders are dynamic in their choice and adaptation of concise tactics to suite environmental shifts at a certain moment in the company's activities. Plans for emergencies are a crucial component of your organization's overall business continuity strategy and business logic since they assist make sure your company is prepared for anything. The contingency model uses feature selection to create sustainable enterprises. The Naïve Bayes and feature selection algorithm uses a search method for finding new feature subsets with a ranking metric that rates different subsets of features. Feature selection helps every firm develop a backup strategy that reduces the risk of its primary strategy failing. Given the necessary input from the user, this results in more efficiency and accuracy in the results, producing the most accurate prediction value and result. The quality of an evaluation metric has a significant impact on the algorithm, and it is these evaluation metrics that separate between wrappers, filters, and embedding techniques, the three main categories of feature selection algorithms. They will be categorized in order to improve calculation accuracy and data handling efficiency. © 2023 IEEE.

关键词： Contingency Model Evaluation Metric Feature Selection Feature Subsets Naïve Bayes

来源：评论

学校读者我要写书评

暂无评论

regression Method-Based Prediction of Wind Power Generation

Regression Method-Based Prediction of Wind Power Generation

引用

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

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

ISBN: (纸本)9798350315127

Wind power prediction is essential in tackling the challenges of balancing supply and demand in any electrical system, given the uncertainty surrounding the output of wind farms. To cut emissions and encourage sustainable growth, certain countries have recently shifted to renewable energy sources, particularly wind and solar energy. Given that renewable energy sources are incorporated into already-existing grids and blended with conventional sources, determining the amount of electricity that will be produced is crucial to minimising the wind farm's operational costs and guaranteeing the power grid's safety. Accurate wind power projections help to reduce the need for extra reserve and balancing power to incorporate wind power. With the help of machine learning (ML) techniques and historical wind power generation data, this system provides a prediction model to address a real-world problem in the field of renewable energy. It accurately predicts the quantity of wind energy production each hour during the following 24 hours. Since meteorological conditions are unpredictable, wind power generated by wind turbines cannot be planned. So, in order to conduct activities such as energy trading, load tracking, pre-load sharing, turbine control, and others, wind power forecasts are required. data mining is a commonly used approach for analysing enormous amounts of data in all fields. researchers employ a variety of data mining and machine learning techniques to analyse massive, complex environmental data and help experts forecast the generation of wind power. The proposed method entails developing a machine learning model that can forecast power generation. When evaluating alternative regression algorithms, the decision tree is the most effective model for outcome prediction. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Analysis on recent Trends in Augmented and Virtual reality with Haptic Interferences 11

Analysis on Recent Trends in Augmented and Virtual Reality w...

引用

11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Potluri, Swathi Kumar, N.K. Senthil Department of Computer Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360059

Tele-haptics, telemedicine, including training, education, and amusement are a few examples of applications for the sense of touch (haptics). A wide range of multi- and cross-disciplinary sectors are now using and researching haptics. The inclusion of new media kinds, such as music, film, and even aroma, expands the usage of haptics. In order to provide users a more realistic experience, haptics, for instance, are widely employed in virtual reality situations. While haptic interface science has improved throughout the years, that are still a number of barriers that stand in the way of further development. In this overview, the tactile interfaces for virtual reality—which comprise wearables, handhelds, encountered-type devices, props, and mid-air methods—are discussed. This study intends to investigate the current state and potential uses of hands-on material-based haptic technology, with a focus on Vr/Ar. ©2023 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Experimental investigation on hybrid fibre reinforced polymer epoxy LY556 composites by vacuum bag molding

引用

Materials Today: Proceedings 2022年 62卷 714-720页

作者： Karthik, K. ramesh, V. Kolappan, S. Arunkumar, K. Udayaprakash, J. rameshkumar, r. Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai-600062 India

Hybrid fiber reinforced polymer composite materials had significant properties more strength light weigh. In current research is to recognize the different stacking sequences were used. Two different fibers were namely carbon and kenaf with epox Ly556 matrix. The originality of this work is combined the natural and synthetic fibre to vaccum bag molding methods, to fabricated sandwich is used 5-layers with four sample stacking sequence are prepared. The developed Samples are cued and it has been performed various mechanical properties test. The C/C/K/C/C sacking sequences are produced 170 MPa tensile strength when compared other samples. The neat carbon fibres are have better properties, but when compared cost and environmental effect normally synthetic fibre are produced some effect. © 2022

关键词： Carbon fibers

来源：评论

学校读者我要写书评

暂无评论

deep Learning-Based Lung Cancer Classification: recent developments and Future Prospects 2

Deep Learning-Based Lung Cancer Classification: Recent Devel...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Begum, Almas Alex david, S. Hemalatha, d. Kollipara, Lavanya Sita Sai Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

This article discusses about potential of deep learning in lung cancer diagnosis using medical imaging, specifically focusing on the performance of various deep learning models in accurately identifying lung nodules from CT scans. Based on the findings, it was revealed that various deep learning algorithms, mainly deals with convolutional neural networks, demonstrate a remarkable degree of accuracy and an appropriate trade-off between sensitivity and specificity in correctly identifying true positives and true negatives. However, there is a need for more detailed analysis and comparison of these methods to fully understand their performance characteristics. The article also identifies several potential future scopes of deep learning in medical imaging and lung cancer diagnosis, including integrating deep learning models into clinical practice, large-scale multi-center studies to validate the effectiveness of deep learning-based approaches, improving the interpretability of deep learning models, integrating information from multiple imaging modalities, and implementing deep learning-based approaches in screening programs to improve early detection and patient outcomes. Overall, the results suggest that deep learning has significant potential to improve lung cancer diagnosis and treatment, but further research is needed to fully realize this potential. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

resNet repeat Vector Optimized Output Layer based Brain Stroke Prediction 1

ResNet Repeat Vector Optimized Output Layer based Brain Stro...

引用

1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： devi, M. Shyamala Ajith Kumar, dongari Laharee, Chamarthi Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350342796

A cerebral infarction is an illness in the nervous system that occurs when the arterial blood circulation to the central nervous system is interrupted, resulting in harm to the cerebral cortex. There might be negative effects if the blood and other resources to the brain are restricted. Immediate recognition of numerous stroke indicators can help to lessen the risk of mortality. This research presents a model repeat Vector Optimized resNet (rVO-resNet) to predict the brain stroke with optimal accuracy. The brain stroke dataset from Kaggle have been utilized for this research that has 2500 images. The brain stroke dataset consists of 2500 images, 1550 of which are healthy brain images and 950 of them are brain stroke images. The proposed rVO-resNet model initiates with labelling the brain images as '0' for healthy and '1' for brain stroke images. The labelled images are fitted with several CNN models to select the best model that has high efficiency and the analysis reveals that the resNet model is showcasing high accuracy with 84% comparing to all other existing models. The resNet is choosen to improvize the accuracy by finetuning the optimizers in the output layer. The proposed rVO-resNet model is designed with single convolution coupled with max pooling layer followed by three residual network blocks ending with global average pooling optimized with repeat Vector optimizer for the output layer. The labelled images are fitted various optimizers of resNet and proposed rVO-resNet model. The evaluation results demonstrate that the recommended rVO-resNet with repeat Vector optimizer exhibits highest Accuracy of 97.51 when analyzed against other optimizers. © 2023 IEEE.

关键词： CNN GPU optimization PSNr super resolution

来源：评论

学校读者我要写书评

暂无评论

Investigation of performance and emissions of dI diesel engine with cottonseed biodiesel along with diethyl ether

引用

International Journal of Ambient Energy 2022年第1期43卷 28-31页

作者： Sugash, G. rubalingam, S. Praveen Kumar, U. Vinod Kumar, Gurram Venkata Akashragam, L.r. Department of Automobile Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

In this paper, we found a way to solve the demand for compression ignition engine fuels and their good fuel efficiency but they produce severe exhaust emissions. This will result in the further examination of ways which the composition of the fuel used in a diesel engine could be changed to decrease emissions. To get oxidation in diesel fuel by adding oxygen enriched fuel is a method for complete combustion. In addition, many oxygenates have higher cetane values and their addition with diesel fuel results in the increase of overall cetane number which in turns leads to higher combustion rate and enhanced combustion with lower emissions. diesel oxygenates blends are the recent research areas. In this paper, biodiesel was blended at different proportions of cottonseed oil and diesel with diethyl ether as diesel oxygenates. investigation of performance and emission characteristics was done on varying loads. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： diesel fuels

来源：评论

学校读者我要写书评

暂无评论

Investigation on mechanical and Tribological properties of Al7075 reinforced with silicon carbide and graphite particulates

引用

Materials Today: Proceedings 2022年 54卷 858-865页

作者： Anand, P. rajesh, d. Balaji, V. Surrya Prakash, d. Karthick, M. Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai600062 India

Aluminium and its alloys specifically Aluminium 7075 is widely used for the engineering application predominantly in structural applications like in aerospace, aircraft and various other fields. In spite of their usage in alloy form still they do not meet the requirement they are intended to be used. Hence their properties are enhanced by formation of composite material. The present study is: Formation of Aluminium 7075 alloy based composite reinforced with silicon and graphite particulates by powder metallurgy process. The graphite content is changed from 2%, 6%, 8% with average grain size 20 µm homogeneously distributed throughout the aluminium matrix resulting in the formation of three different specimens. Followed by the determination of Microstructure, Mechanical properties, Tribological, characteristics and Machinability (Mrr). Finally comparison between the three specimens. © 2021

关键词： Silicon carbide

来源：评论

学校读者我要写书评

暂无评论

Conv2d Xception Adadelta Gradient descent Learning rate deep learning Optimizer for Plant Species Classification 3

Conv2D Xception Adadelta Gradient Descent Learning Rate Deep...

引用

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

作者： devi, M. Shyamala Aruna, r. rajeswari, d. raja Manogna, r. Sai R&D Institute of Science and Technology Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala Tamilnadu Chennai India

ISBN: (纸本)9781665494007

Almost every element of living beings is undergoing extraordinary transformation as a result of technological advances. While many areas of agriculture are leveraging cutting-edge technological innovations, farming remains far behind in adopting these advancements. Ayurvedic medicine and other plant-based medical systems employ the classification of plants by their leaflets for a variety of purposes, including ecosystem, farming, disease diagnosis, rare plant maintenance, and Herbal remedies. due to the utilization of biological nomenclature, standard methods of plant recognition and classification are difficult, time-consuming, and aggravating for non-experts. For learners who are seeking to learn about species, this poses a real challenge. Based on this consequence, this paper recommends Conv2d Xception Adadelta Gradient descent (CXAGd) deep Learning to classify the plant species based on the leaflet feature structure. The proposed CXAGd model uses Leaf classification dataset from KAGGLE with 4500 plant leaflets of various species. One input, a dense average pooling layer, and an output layer were used in the development of the proposed Conv2d Adadelta Gradient deep CNN model. The CXAGd model was constructed using 36 depth wise separable convolutional layers and a maximum pooling layer for each convolution and trained with learning rate of Adadelta optimizer. The CXAGd built with Xception network with the Adadelta optimizer with the dropout of 0.5, weight decay of L2 regularization. The leaf dataset subjected to the training and testing data division and the training data applied to CXAGd model and also applied to other CNN models for comparing the efficiency of the proposed CXAGd model. Python was used for programming, with a batch size of 64 and 30 training epochs, on a Geforce Tesla V100 Graphics card server. Implementation outcomes projects that proposed CXAGd model exhibits accuracy of 97.85%, Precision of 97.42%, recall of 97.75% and FScore of 97.76% when co

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Batch Normalization based Convolutional Block YOLOv3 real Time Object detection of Moving Images with Backdrop Adjustment 9

Batch Normalization based Convolutional Block YOLOv3 Real Ti...

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

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

ISBN: (纸本)9798350314090

Moving object detection and counting the number of objects is a prominent challenge for researchers in various application fields. The most significant things that play a vital part in the creation and advancement of computer vision systems are anomaly detection and object recognition. The most important aspect of object recognition utilizing an image, video, or webcam is the prediction of objects with high efficiency and accuracy. This study seeks to solve the performance issue by combining a deep Learning-based neural network method with the model You Only Look Once (YOLO), and OpenCV. Unlike previous Algorithms, YOLOv3 simply takes a whole image once and forecasts the class probabilities for the bounding boxes. To detect the items in the frame, final weights acquired by training the dataset on the darknet are necessary. The performance of the YOLOv3 model mainly depends on the convolutional block layer creation. The main contribution of this research work is that the convolution block creation is iteratively examined with several layers to suggest the optimal model that portray higher accuracy. When using moving cameras, the visual background frequently seems to rotate, translate, and scale. As a result, modelling the backdrop or detecting moving objects based on optical flow is challenging. With this overview, Batch Normalization based Convolutional Block YOLOv3 (BNCBY) is proposed for detecting the objects and the number of objects from the real time image. We first estimate the background movements using a perspective transformation model, and then use a deep learning classifier built on hand labelled datasets to recognize moving product contenders there in foreground subtracked image. We discover spatial-temporal traits with each floating entity candidate through optical flow matching and then prune them based on their motion patterns relative to the backdrop. The proposed BNCBY model offer a techniquefor detecting moving objects that combines backdrop adjust

关键词： Convolution

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：