检索结果-内蒙古大学图书馆

Emerging technology (INCET), International Conference for

作者： Naga Pranava Shashank V Vimal S Ramanan Altrin Lloyd Hudson D A Sheik Abdullah School of Computer Science & Engineering Vellore Institute of Technology Chennai Tamil Nadu India

In this Research, we have built Machine Learning Models to predict the next day price for the Nifty Index, Reliance and TCS Stocks. We will use R Project Library, quantmod, TTR for data acquisition and technical analysis. We have used the R Project Machine Learning Library caret along with the daily data for the last five years (June 2017 - June 2022) for the NIFTY Index and the Reliance and TCS Stocks. Our data will consist of Open, High, Low, Last (Close) and Volume. Our objective is to predict the price change for the next day using 6 technical indicators as features which include RSI, Williams%R, etc. For building Machine Learning Models, we have used the R Library caret. The primary methods which are used are KNN, Classification Regression Trees, Naive Bayes, SVM with Radial Basis Function and DNN. For k-nearest neighbors, we are able to achieve an accuracy of 72.78%, for DNN 72.533%, for CART 72.36%, for NB 70.08%, and for SVM 72.69% for the prediction accuracy of the next day.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine learning assisted investigation on properties of TiO2 reinforced aluminium metal matrix composites

引用

Materials Today: Proceedings 2023年 74卷 80-84页

作者： Nithya, M. Pritima, D. Vijayalakshmi, S. Beulah David, D. Muthukumar, K. Veerappan, G. Jayasathya Kawin, S. Department of Computer Science and Engineering Sri Sairam Engineering College Chennai India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Institute of Information Technology Saveetha School of Engineering Thandalam India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India Department of Mechanical Engineering K.Ramakrishnan College of Engineering Tamil Nadu Tiruchirapalli India

The aim of this study is to investigate deeply about the properties of AA7075 aluminum alloy with varying stirring speed, temperature and different weight percentage of Titanium oxide reinforced with aluminum alloy using machine learning. Machine learning give consistent result with quantitative *** method was effectively employed to find the optimal values of the input variables for the maximized values of hardness, impact strength and tensile strength. Stirring speed of 600 rpm, temperature of 600 °C and 9 wt% of TiO2 was finally found as the optimal parameters from this investigation. The model is perfectly fit as R square value is 99.5 %. The maximum brinell hardness is attained for the set of parameter (i) temperature 660 °C (ii) stirring speed 600 rpm and (iii) percentage of reinforcement is 6 wt%. © 2022

关键词： Aluminum alloys

来源：评论

学校读者我要写书评

暂无评论

RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

引用

Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

来源：评论

学校读者我要写书评

暂无评论

Neural Jump-Diffusion Temporal Point Processes 41

Neural Jump-Diffusion Temporal Point Processes

引用

41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Shuai Zhou, Chuan Liu, Yang Zhang, Peng Lin, Xixun Ma, Zhi-Ming Academy of Mathematics and Systems Science Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Cyberspace Institute of Advanced Technology Guangzhou University China Institute of Information Engineering Chinese Academy of Sciences China

We present a novel perspective on temporal point processes (TPPs) by reformulating their intensity processes as solutions to stochastic differential equations (SDEs).In particular, we first prove the equivalent SDE formulations of several classical TPPs, including Poisson processes, Hawkes processes, and self-correcting *** on these proofs, we introduce a unified TPP framework called Neural Jump-Diffusion Temporal Point Process (NJDTPP), whose intensity process is governed by a neural jump-diffusion SDE (NJDSDE) where the drift, diffusion, and jump coefficient functions are parameterized by neural *** to previous works, NJDTPP exhibits model flexibility in capturing intensity dynamics without relying on any specific functional form, and provides theoretical guarantees regarding the existence and uniqueness of the solution to the proposed *** on both synthetic and real-world datasets demonstrate that NJDTPP is capable of capturing the dynamics of intensity processes in different scenarios and significantly outperforms the state-of-the-art TPP models in prediction tasks. Copyright 2024 by the author(s)

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

引用

computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approach for Recognition and Classification of Tomato Fruit Diseases

Deep Learning Approach for Recognition and Classification of...

引用

Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： A. Seetharam Nagesh G.N. Balaji Department of Information Technology CVR College of Engineering Ibrahimpattnam School of Computer Science and Engineering Vellore Institute of Technology Vellore

ISBN: (纸本)9798350333855

Diseases devastate food crops, causing significant losses for farmers and jeopardising food security, resulting in a low production rate. Health monitoring and disease detection are critical for sustainable agriculture. Monitoring the fruit diseases manually is extremely difficult. It necessitates a significant amount of work, knowledge of fruit diseases, and an extended processing time. Hence, deep learning is used to detect fruit diseases with high accuracy. This paper proposes the method to detect the tomato fruit diseases in color images. In the proposed method, convolutional neural network is used for extracting the features and classification. Further, the augmentation strategy is applied on existing samples to create the dataset with large number of samples. The convolutional neural network method based on VGG16 will be used to train augmented data for recognition and classification of tomato diseases. The results of the CNN after testing affirms that the tomato disease can be predicted efficiently, and it can be utilised for early detection purpose.

关键词： Deep learning Measurement Production Feature extraction Market research Agriculture Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Examining the Impact of Data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

来源：评论

学校读者我要写书评

暂无评论

Fused Feature Vector based DenseNet201 with Deep Convolutional Generative Adversarial Network for Forest Fire Prediction 1

Fused Feature Vector based DenseNet201 with Deep Convolution...

引用

1st International Conference on Software, systems and information technology, SSITCON 2024

作者： Fallah, Mohammed Hussein Spandana, Somireddy Parameswari, M. Saritha, G. Das, Abhijeet The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Maisammaguda School of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Information Technology Salem India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Department of Civil Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350352931

The rise in temperature on the earth is one of the main sources for the cause of fire in the forest area. The dry vegetation of the forest leads to sudden fire in the forest. Various technologies have been introduced to capture the aerial views of the forest to monitor the ongoing actions. The different existing methods failed to predict the occurrence of the fire in the forest area due to lack of feature dimensionalities handling and noises generated during the training process that reduced the performance of appropriate forest fire prediction. To overcome the existing problems the Fused Feature Vector Based DenseNet201 (FFV-DenseNet201) is proposed with the Deep Convolutional Generative Adversarial Network (DCGAN). The min-max normalization was used in the pre-processing stage to rescale the image pixel ranges. The FFV-DenseNet201 is used to extract the significant features from the images. The DCGAN algorithm was used to predict the fire and non-fire forest images. The developed FFV-DenseNet 201 and DCGAN has obtained better results of 9 8. 3 4 %accuracy, 9 9. 0 1 %of precision, 9 8. 2 9 %recall and 9 8. 6 4 %f1-score respectively for the FLAME dataset compared to existing YOLOv5 model. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Harris Hawk Algorithm (HHA) to Optimize Deep Autoencoder (DAE) Method of Deep Learning for Cancer Diagnosis

A Novel Harris Hawk Algorithm (HHA) to Optimize Deep Autoenc...

引用

2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Kumar, N. Jagadish Yuvaraj, G. Hameed, A. Thariq Noor, A. Mohamed Sheik Johindhan, E. Vasanth, P. Rudresh Sri Sai Ram Engineering College Department of Computer Science and Business Systems West Tambaram Chennai India West Tambaram Chennai India Velammal Institute of Technology Department of Information Technology Pancheti Chennai India

ISBN: (纸本)9798350394580

Over the past decade, the significance of data analytics in health informatics has surged due to the overwhelming influx of multi-modality data. Artificial neural networks have emerged as one of the most important and powerful influences in machine learning, rooted in deep learning. The exact prognostic of cancer remains a challenging problem for healthcare practitioners, even though cancer research has been a steady emphasis throughout the last decades. To increase a patient's chances of survival, early diagnosis is crucial, effectively differentiating between benign and malignant tumors is a considerable challenge. Deep learning has become widely accepted tool among medical researchers as an effective method in this area. A deep autoencoder architecture which was primarily designed for feature extraction is improved by using the Harris Hawk algorithm. Surprisingly, this approach comprises a pre-training step rather than requiring labelled data for training. During this pre-training stage, the proposed Harris Hawk algorithm makes use of exploration, exploitation, and attacking techniques, increasing feature extraction accuracy significantly to an outstanding 93%. This in turn greatly improves the performance of deep auto encoder applications, including those for cell grouping, 3D- brain reconstruction, cancer detection, and the prediction of demographic data. The deep autoencoder optimized using the Harris Hawk method stood out as the obvious choice in terms of accuracy when compared to other autoencoder models including sparse autoencoders (SAE), denoising autoencoders (DAE), contractive autoencoders (CAE)and convolutional autoencoders (CNAE). © 2023 IEEE.

关键词： Cancer diagnosis Deep Autoencoder Deep learning Feature extraction Optimization

来源：评论

学校读者我要写书评

暂无评论

Investigating the Benefits of Machine Learning Algorithms for Text Classification 3

Investigating the Benefits of Machine Learning Algorithms fo...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gowrishankar, J. Vinithra Banu, T. Parikshit, N. Singh, Vandana Kalra, Hitesh Sahoo, Girija Shankar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper affords a research into using gadget studying algorithms for textual content category duties, with a focal point on understanding the importance of these algorithms and their ability to enhance the accuracy of textual content classification. it's miles located that, while conventional techniques, which include rule based models and hand-engineered features, can attain first-class outcomes, there are compelling motives to explore the capability of device getting to know algorithms to further improve the accuracy and performance of textual content class. The paper discusses the 3 maximum famous gadget gaining knowledge of algorithms for this project: the NaÏve Bayes classifier, help Vector Machines, and synthetic Neural Networks, and presents a top level view of the latest research findings that demonstrate the prevalence of these algorithms over traditional methods. The paper additionally presents an outline of the distinctive approaches for introducing era into text type, and discusses modern-day strategies for implementing and deploying those algorithms. Finally, the paper outlines ability areas for destiny research associated with information the results of system studying algorithms for this mission, and suggests viable instructions for exploration. © 2023 IEEE.

关键词： Classification (of information)

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：