检索结果-内蒙古大学图书馆

3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350322842

The type and condition of the road the vehicle travels;how long the battery is used for are just a few of the variables that affect the battery life of electric cars (EVs). These elements might affect the battery's lifespan by causing it to drain more quickly than usual. By understanding the various factors that impact battery life, work can be done to mitigate these issues and extend the useful life of the battery. To find the best circumstances for increasing the battery's longevity, this research paper is concentrated on examining the impacts of road conditions on battery life. The previous article classified routes with an accuracy of 83.56% based on battery health while arriving at the destination [1]. This article examines EV routed and categorises them on the basis of the number of brakes applied upon arrival at the destination. By doing so, it aims to develop recommendations for drivers on how to optimize their driving habits to extend the battery's lifespan. Observations indicate that bad routes generally involve a lot more brakes as compared to good routes. Vehicle speed has a negative correlation with heater power in a bad route and a positive correlation in a good route. Battery voltage and Battery state- of-charge have opposing correlations in both categories. In this research, a prediction of the route category was attempted by using Deep Hybrid Learning to integrate three pre-trained models., VGGI6, Resnet50 and VGG19 with two boosting algorithms:- XGBoost and AdaBoost. The accuracy of the six deep hybrid learning models in predicting the category of these routes are as follows:-0.87625, 0.84875, 0.80125, 0.7925, 0.8425 and 0.855. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Music Recommendation based on Facial Expressions using data Augmentation 3

Music Recommendation based on Facial Expressions using Data ...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kapoor, Tanisha Ganguly, Arnaja Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users' emotion based on their facial expressions. © 2023 IEEE.

关键词： CNN Emotion recognition Facial expression analysis FER-2013 Music Recommendation

来源：评论

学校读者我要写书评

暂无评论

Predicting the Impact of Road Conditions on Battery Health Via Transfer Learning 3

Predicting the Impact of Road Conditions on Battery Health V...

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665462167

Battery life of EV (electric vehicles) depends on various factors. These factors include temperature of the battery, duration of use or the type and condition of the road traversed by the vehicle. This may cause the battery to discharge faster than it used to. Understanding what may cause the battery life to shorten can help us alleviate the given problem and can preserve the battery for longer time. This research focuses on the effects road conditions have on battery life to be able to recommend the favourable conditions to extend it. It proposes to find the routes taken by the vehicles and classifying them based on their battery health upon reaching the destination. In this paper, an attempt has been made to predict the category of the routes by employing Transfer learning via a pre-trained model VGG-16. Observations indicate that paths which have more turns and curves are generally worse for battery life as compared to straight and narrow paths. Another model which is an artificial neural network has been developed to predict the based on the average vehicle speed and the distance covered by the vehicle. The VGG-16 model predicts the data with which it is validated with an accuracy of 84.29% and the testing data with an accuracy of 83.56%. The artificial neural network model predicts the category of the data with an accuracy of 88.02%. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning 3

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

引用

3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Singh Rathore, Bhanu Pratap Dave, Rutul Dhanasekaran, K. Priyadarsini, K. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350322842

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Singe Image Dehazing using Deep Neural Networks 2

Singe Image Dehazing using Deep Neural Networks

引用

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

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： Computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Conditional selection with CNN augmented transformer for multimodal affective analysis

引用

CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

来源：评论

学校读者我要写书评

暂无评论

Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

引用

2024 International Conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

The implicit bias of adam on separable data 24

The implicit bias of adam on separable data

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chenyang Zhang Difan Zou Yuan Cao Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Department of Computer Science School of Computing and Data Science & Institute of Data Science The University of Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science & Department of Mathematics The University of Hong Kong

ISBN: (纸本)9798331314385

Adam has become one of the most favored optimizers in deep learning problems. Despite its success in practice, numerous mysteries persist regarding its theoretical understanding. In this paper, we study the implicit bias of Adam in linear logistic regression. Specifically, we show that when the training data are linearly separable, the iterates of Adam converge towards a linear classifier that achieves the maximum ℓ∞ -margin in direction. Notably, for a general class of diminishing learning rates, this convergence occurs within polynomial time. Our result shed light on the difference between Adam and (stochastic) gradient descent from a theoretical perspective.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

引用

2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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：