检索结果-内蒙古大学图书馆

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Prasanna Kumar, R. Bharathi Mohan, G. Rithani, M. Pallavi, Gundala Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Existing methods for assessing an individual's personality are very cumbersome due to those tests being very monotonous and erroneous due to a self-report bias. Whereas data-driven, automatic personality deduction raises privacy concern due to them needing external personal information. In our model, a person's personality can be classified based on the 16 categories of Myers-Briggs personality types. With the availability of a huge amount of data on human behavior and personality traits, a machine learning model can be trained, and it can predict the personality trait of a person. A MBTI dataset is used to analyze and train the model. Upon analyzing the dataset, we found out that the classes are not balanced. To overcome this problem, we used binary classification. Next, we used the NLTK library to pre-process the data. We selected four machine learning models and trained our data in those models. Furthermore, we compared the results of four different machine learning models and chose the most accurate model. This research can be used to personalize advertisements, coaching for students and companies can use this research to attract specific types of audience. © 2024 IEEE.

关键词： Logistic regression Myers-Briggs personality types Naïve Bayes NLTK Personality prediction Random Forest SVM

来源：评论

学校读者我要写书评

暂无评论

Enhancing Credit Card Fraud Detection with Deep Learning and Graph Neural Networks 15

Enhancing Credit Card Fraud Detection with Deep Learning and...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sherwin Akshay, J.G. Vinusha, T. Sharon Bianca, R. Sarath Krishna, C.K. Radhika, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Financial fraud poses a major threat to financial service institutions and clients, facilitating anomaly detection capabilities. This paper dives into deep learning models which can be implemented to detect and distinguish fraudulent transactions from normal transactions and flag anomalies. Further, conversion of tabular data to graphs have been implemented for keen understanding and improved pattern analysis of fraudulent transactions, challenges include evolving attack strategies, class imbalance and model interpretability. The proposed methods utilize a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like Auto-Encoders and Recurrent Neural Networks. In the graphical representation, this study focuses on implementing Louvain Modularity, Girvan-Newman and Label Propagation Algorithm for effectiveness in community detection. The outcomes exhibit consistently reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security. © 2024 IEEE.

关键词： Auto-Encoders CatBoost GAN GNN GRU RNN Unsupervised learning XG-Boost

来源：评论

学校读者我要写书评

暂无评论

Prognostic Modeling of Diabetes Mellitus in Women: A comparitive analysis of Machine Learning algorithms integrating Clinical and Genetic Data

Prognostic Modeling of Diabetes Mellitus in Women: A compari...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Prashanth, M. Venkat Jose, Nikhil Tom Verma, Konda Hemesh Sreevidya, B. Rajesh, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350372502

Diabetes is considered as one of the most commonly observed medical condition among human beings. Millions of people worldwide are affected by a chronic condition that goes by the name of diabetes. There are 3 sorts of diabetes and they comprise of Type 1, Type 2 and Gestational diabetes. Early prediction of diabetes helps to avoid complications like diabetic retinopathy which leads to blindness. There is a 13% higher risk of death among people with diabetes, especially in women, cardiovascular disease caused by diabetes attributes to 50% higher risk of death, coronary heart disease caused by diabetes contributes to a 58% higher risk of death. The proposed research work aims in predicting diabetes from the symptoms a patient exhibit. The proposed system is a healthcare assisting system which is accessible to patients as well as medical practitioners. The system is developed as a web application using the features of Django python framework along with suitable database management system. The system is powered with gradient boosting machine learning algorithm for predictions. By using the web application along with a blood test to find out the insulin levels and glucose levels in blood, any user can predict whether he/she would be affected by diabetes in future and can avoid the complications caused by diabetes. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

PyroCyclone Eye: Satellite Detection System For Wildfire And Cyclone 15

PyroCyclone Eye: Satellite Detection System For Wildfire And...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vinayan, Anirudh C. Pankaj, P. Venugopal, Sreenadh Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Cyclones and wildfires are deadly;they do significant damage to human life and property. This requires an effective early warning system that would ensure a timely intervention since the normal methods are not efficient. Frequently fall short of the necessary pace and precision in successful detection as well as signaling: this results in response lag times and escalation of loss. In addition, most current systems often work on their own without any cooperation. It lacks integration and immediate communication means to the concerned authorities. However, in this study, we put forward a novel system that combines detection with early warning for cyclones and wild fires through satellite images using advanced computer vision techniques. We apply the YOLOv9 object detection algorithm on the satellite pictures which allows us to detect cyclones and wildfires with a high level of precision as well as speed. In addition, we design an alert system Using the Twilio WhatsApp API to report detected incidents to the relevant authorities without delay. Through this we aim to improve disaster management potential minimizing loss to life and property and ensure safety to the communities vulnerable to natural disasters. © 2024 IEEE.

关键词： Bounding box regresssion Intersection over Union(IoU) Twilio's Whatsapp API Yolov9 object detection algorithm

来源：评论

学校读者我要写书评

暂无评论

DeepPave: Detection of Potholes Using Deep Learning Techniques 2

DeepPave: Detection of Potholes Using Deep Learning Techniqu...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Varshith, Tummala Koneri, Thanush S Dhanush, Uppalapati Rahul, Sriramoju Jyotsna, C. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

In a developing country like India, the major difficulty lies in identifying and addressing problems. Improving road quality is crucial for economic growth, as well-maintained roads contribute significantly. The proposed model DeepPave, a combination of Visual Geometry Group 16 (VGG 16), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP), along with regularization techniques such as dropout and L2 regularization and optimization techniques, has been developed for the detection of potholes. The aim is to enhance road infrastructure and contribute positively to the nation's economy. The proposed pothole detection system holds great potential for enhancing road safety, optimizing maintenance operations, and reducing infrastructure maintenance expenses. By promptly identifying potholes, transportation authorities can efficiently allocate resources, improve road quality, and ensure safer driving conditions for motorists. © 2024 IEEE.

关键词： Convolutional Neural Network DeepPave Multilayer Perceptron Optimization Pothole Detection Regularization Support Vector Machines

来源：评论

学校读者我要写书评

暂无评论

Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Glycemic Index Based Food Recommendation System Using Deep Learning 1

Glycemic Index Based Food Recommendation System Using Deep L...

引用

1st IEEE International Conference on Communications and computer science, InCCCS 2024

作者： Gopinath, Suraj Udith, S. Jathin, Sri Naga P. Prashanth, V.J. Palaniswamy, Suja Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350358858

Diabetes has become a common occurrence in the world at this moment. Though many food recommendation systems consider food based on nutrient contents like protein, carbohydrates etc often does not include Gylcemic Index (GI) as a factor. GI is a tool used for blood sugar management which is a rating system for foods that contains carbohydrates. It shows how each food affects one’s blood sugar level when consumed. This research aims to recognize the category of food from the images uploaded and predict its nutrient values like GI, proteins, carbohydrates, and fats. It also recommends the necessary calories needed for a person based on their age, sex, weight and physical conditions and disorders. Furthermore, this research also recommends similar food options for diabetic patients that have lesser GI values. The food images were augmented using histogram equalization and noise reduction. This research uses a custom Inception V3 model for food image recognition and resulted in an accuracy of 75%. © 2024 IEEE.

关键词： Carbohydrates

来源：评论

学校读者我要写书评

暂无评论

An Investigation into YOLO-v8 Model Optimization for Small Object Detection in UAVs using Attention Mechanism 15

An Investigation into YOLO-v8 Model Optimization for Small O...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rupak, N.S. Rayvanth, N. Kushank, Pulipati Singh, Rimjhim Padam Amrita School of Computing Bengaluru Department of Computer Science & Engineering Amrita Vishwa Vidyapeeetham India

ISBN: (纸本)9798350370249

The growth of UAV technology has led to advancements in aerial surveillance, resulting in the development of specialized algorithms for object detection in complex environments like university campuses, beaches etc. This study proposes to leverage the recent YOLOv8 model with various sophisticated attention mechanism like Spatial attention, channel attention, squeeze and excitation block attention, Efficient channel attention network, etc. to efficiently detect the tiny person objects in images captured using Unmanned aerial vehicles (UAVs) like drones. For the purpose of this analysis, the work utilizes a recent dataset, Manipal-UAV dataset, that focuses on detecting tiny objects in the form of person. The work also provides a comprehensive comparison of recent YOLO models for model selection and provides a detailed analysis of state-of-art attention mechanism when incorporated into the best baseline YOLOv8x model. The findings from this study will suggest that tailored YOLO models are effective for UAV-based person detection tasks, potentially driving advancements in aerial surveillance technology, particularly in scenarios requiring accurate identification of small objects like individuals. The research over-all aims to highlight the importance of dataset-specific optimizations to enhance the performance of object detection models in UAV imagery with mAP50 of 50.2%, thereby, promoting more reliable surveillance applications in various real-world settings. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Machine Learning Classifiers for Audio-Based Disease Classification 15

A Comparative Analysis of Machine Learning Classifiers for A...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sai Varun, B.P. Jonnalagadda, Ishita Vishnu Vardhan, Ch. Srinivas, Marimganti Ullas, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In order to address the increasingly significant role played by the monitoring of health issues during the times of growing healthcare challenges, this research paper will discuss more about the use of audio signals for diagnosing respiratory diseases by the means of analyzing of cough sounds, breathing patterns, and other audio cues. However, the vision of this work comes from the desire to make a non-invasive and efficient diagnostic process to overcome the difficulty of the traditional invasive ways of imageries. Combining the machine learning techniques using Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction we aimed to construct a powerful tool that can do health state labels using the audio signals with accuracy. In the end, our multi-classifier approach in conjunction with accuracy measurements reveal the promising applications of machine learning algorithms to accurately differentiate between various respiratory diseases, thereby opening the avenue for more robust health monitoring systems. © 2024 IEEE.

关键词： MFCC Random Forest SVM XG Boost

来源：评论

学校读者我要写书评

暂无评论

Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

引用

IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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：