检索结果-内蒙古大学图书馆

Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

Computer Systems Science & Engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

来源：评论

学校读者我要写书评

暂无评论

Extreme learning machine tuning by original sine cosine algorithm

Extreme learning machine tuning by original sine cosine algo...

引用

2022 IEEE World Conference on Applied Intelligence and computing, AIC 2022

作者： Salb, Mohamed Bacanin, Nebojsa Zivkovic, Miodrag Antonijevic, Milos Marjanovic, Marina Strumberger, Ivana Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Technical Faculty Belgrade Serbia

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Extreme learning machine (ELM) is a revolutionary approach for training single-hidden layer feedforward neural networks that combines both high performance and rapid learning speed. Because the input weights and hidden neurons biases are randomly initialized and stay fixed during the process of learning, and the output weights are analytically calculated. ELM produces high generalization capability with a huge number of hidden neurons. The sine cosine method was presented in this study for tuning the input weights and hidden biases. The suggested method is named SCA-ELM, and it selects the input weights and hidden biases using SCA while determining the output weights using the Moore-Penrose (MP) generalized inverse, The aim is to improve the original extreme learning machine *** suggested methodologies were evaluated on several benchmark classification data sets, and compared with other recent state-of-art algorithms. Simulations reveal that the suggested method outperforms the other alternatives in the comparative analysis. © 2022 IEEE.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

A Review of Machine Learning Botnet Detection Techniques based on Network Traffic Log

A Review of Machine Learning Botnet Detection Techniques bas...

引用

2022 IEEE International Conference on computing, ICOCO 2022

作者： Ibrahim, Zul-Azri Razali, Rina Azlin Ismail, Saiful Adli Azhar, Iman Hakimi Khairil Rahim, Fiza Abdul Azilan, Ahmad Muzafaraidil Ahmad Institute of Informatics and Computing in Energy College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Wilayah Persekutuan Kuala Lumpur Kuala Lumpur Malaysia Universiti Tenaga Nasional Department of Computing College of Computing and Informatics Selangor Kajang Malaysia

ISBN: (纸本)9781665489966

Cyber-attacks are a common issue in this modern era because of the introduction of high-speed networks and the use of new technologies like Internet of Things (IoT) devices, which fuel the rapid expansion of cyber-attack. One of the common cyber-attacks is botnet attacks. Hackers use botnet attacks to exploit newly discovered vulnerabilities in order to conduct intensive scraping, distributed denial of service (DDoS) attacks, and other large-scale cybercrime. With their adaptable and dynamic character, botnets work with a botmaster to plan their activities, modify their codes, and update the bots regularly to avoid detection. Researchers use numerous techniques to detect the botnet. However, botmasters nowadays have improved due to avoiding security in detection. As the communication can leave traces that allow researchers to detect the botnet's existence, this paper will review 15 related works on botnet detection that utilize machine learning to predict the botnet communication with the command-and-control (C&C or C2) center based on the network traffic log. This paper summarizes the related works based on the dataset, environment, botnet type, features employed, and machine learning techniques. © 2022 IEEE.

关键词： Botnet

来源：评论

学校读者我要写书评

暂无评论

Authentication Methods that use Haptic and Audio: A Review 8

Authentication Methods that use Haptic and Audio: A Review

引用

8th International Cryptology and Information Security Conference 2022, CRYPTOLOGY2022

作者： Kam, Yvonne Hwei-Syn Chin, Ji-Jian Faculty of Engineering Multimedia University Selangor Malaysia Faculty of Computing & Informatics Multimedia University Selangor Malaysia

Authentication is an important step for many applications eg. banking, communications etc. Identity theft can happen because the systems that we use are vulnerable to observation attacks both by a person (shoulder surfing) and also video recordings. Thus authentication methods that rely on the visual channel alone, which are displayed onscreen are more susceptible to shoulder surfing. In this paper, a review of haptic and audio authentication methods is presented where we describe and analyse various methods that utilise non-visual channels, namely haptic and audio, for authentication. We categorise the methods into the categories of recall, recognition and challenge-response. We focus on the security aspects of the methods, comparing the susceptibility of the methods to various attacks. The review indicates that most haptic and audio methods are less susceptible to observation attacks and intersection attacks. © 2022 Institute for Mathematical Research (INSPEM). All rights reserved.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

B2A, A Straight-Through Approach from Basics to Advanced Level: Case Study for Python Course 11

B2A, A Straight-Through Approach from Basics to Advanced Lev...

引用

11th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Shazmeen, Abbasi Dakić, Pavle Zoltan, Alwahab Dhulfiqar Department of Programming Languages and Compilers Faculty of Informatics Eötvös Loránd University 1/C Pázmány Péter st. BudapestH-1117 Hungary Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia

In this study, we present a straightforward teaching strategy for teaching the Python programming language. Our method emphasizes a clear progression from fundamental to sophisticated concepts. Beginning with a thorough explanation of core Python ideas, we show a beginner-level script that dissects each line of code and displays the results. One distinguishing feature of our system is that students use ChatGPT to optimize code, which encourages them to improve the core program with fewer lines. After getting the updated solutions, students carefully compare the original and optimized programs to discover improvements and potential flaws. This study underlines the effectiveness of the Straight-Through Approach, which focuses on improving Python understanding while also developing critical thinking and code optimization skills. Our findings show that this strategy is a simple and effective way for teachers to assist students in growing from basic to advanced Python abilities, resulting in a dynamic and successful learning environment. The proposed technique will teach students how to use AI-driven modules correctly, such as ChatGPT. © 2024 Copyright for this paper by its authors.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Towards Optimizing the Logistic Transportation Selection in Hajj 1

Towards Optimizing the Logistic Transportation Selection in ...

引用

1st International Conference on Logistics, ICL 2024

作者： Mohamed, Marwa F. Elsherif, Samiha M. Eltoukhy, Mohamed Meselhy Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia Egypt College of Computing and Information Technology at Khulais University of Jeddah Department of Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331518844

The Hajj is an international Islamic event that takes place in Mecca, Saudi Arabia, annually and attracts millions of pilgrims. One of the most important services during the Hajj period is pilgrims' transportation services, they transport pilgrims between the holy sites (e.g., walking facilities, traditional buses, shuttle buses, and the holy sites train). The shuttle bus, among these services, is the busiest for Hajj. It operates during the Hajj and transports most of the pilgrims. For example, during the first Muzdalifah holiday, when on the ninth-day pilgrims move from Arafat to Muzdalifah, the transportation serves more than one million pilgrims. Therefore, there are many transportation companies, and choosing an appropriate company is one of the most important challenges facing Hajj organizers. There are many criteria for selecting transportation companies, such as cost and service quality;therefore, this paper presents a framework that uses the NSGA-III optimization algorithm to choose the best companies based on two objectives (e.g., costs and quality of service). The results of NSGA-III have been compared with heuristic algorithms (e.g., best fit and first fit), and NSGA-III outperformed these algorithms. © 2024 IEEE.

关键词： Bus transportation

来源：评论

学校读者我要写书评

暂无评论

Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open Data

来源：评论

学校读者我要写书评

暂无评论

An Ensemble-Based Extra Feature Selection Approach for Predicting Heart Disease 6th

An Ensemble-Based Extra Feature Selection Approach for Predi...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Abubaker, Howida Singh, Jyotsna Muchtar, Farkhana Fattah, Salmah Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia School of Technology Management and Engineering Narsee Monjee Institute of Management Studies Chandigarh Chandigarh India Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789819728381

Heart disease is a severe condition that has a significant impact on human life and is the leading cause of death in many countries. Clinicians can access clinical datasets to assist in diagnosing cardiovascular conditions and reduce the spread of this disease. Understanding these datasets and their patterns is essential in predicting the illness accurately. However, the proliferation of medical datasets in the era of big data has posed challenges for medical professionals in identifying the most relevant attributes for predicting heart disease. Therefore, this study aims to identify the most distinguishing characteristics within a high-dimensional dataset that will facilitate the accurate classification of patients with heart disease and non-patients with minimal complexity. The study conducted an experimental assessment of model performance generated through different classification algorithms, using both relevant features selected and the complete features set. An ensemble-based extra tree feature selection approach was employed for this purpose. The experiment utilized four datasets from reputable sources, employing various machine learning classification models with both complete and reduced feature subsets as inputs for the analysis. The evaluation of the classification models was based on accuracy, precision, and recall. The proposed feature selection technique achieved the highest classification accuracy, precision, and recall, reaching 97% when utilizing the extra tree classifier algorithm. These results underscore the potential of the feature selection method in identifying the most significant, related, and well-represented features for the target class, thereby enhancing classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Small Target Object Detection with Transformer (STO-DETR) Algorithm Based on Swin Transformer 4th

Small Target Object Detection with Transformer (STO-DETR) Al...

引用

4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Xu, Fengchang Alfred, Rayner Chew, Jackel Vui Lung Du, Shifeng Lv, Ge Pailus, Rayner Henry Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu88400 Malaysia Department of Information Engineering Shandong Light Industry Vocational College 30 Misshan Road Zhoucun District Shandong Province Zibo City China Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan87000 Malaysia

ISBN: (纸本)9789819729760

With the development of deep learning, the method based on Convolutional Neural Network (CNN) has made a series of major breakthroughs in the detection field. However, due to the shortcomings of low image resolution, small object size, inconspicuous feature information, and lack of small target data, the detection of small objects is still a challenging task in the field of computer vision and a hot research topic worldwide. To improve the accuracy and speed of small object detection, a new algorithm model of Detection with Transformer (DETR) small target object detection based on Swin Transformer is proposed in this paper. This paper proposes a new algorithm for small target object detection based on Swin Transformer, called Swin Transformer’s DETR, which is different from DETR which utilizes the CNN as the backbone network to extract the feature information. It extracts the features of the input image by the Swin Transformer backbone and reduces its size, and then reshapes the output feature map as C × H W, and a 1 × 1 convolution reduces the dimension, and then input to Transformer Encoder. Self-Attention is used to make global analysis on the feature graph, and Transformer Decoder is used to realize one-to-one correspondence between the target prediction frame and token. The output results of each layer of the Decoder are also predicted by the last Feed Forward Networks (FFN) shared by parameters, and the loss is calculated, thus realizing deep supervision. Finally, several potential directions of this research field in the future are provided, and the experimental results of this detection algorithm are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Bornean Orangutan Nests Classification using Multiclass SVM

Bornean Orangutan Nests Classification using Multiclass SVM

引用

2023 IEEE Symposium on Computers and informatics, ISCI 2023

作者： Amran, Amanda Aiza On, Chin Kim Hung, Lai Po Rossdy, Munirah Simon, Donna See, Chai Soo Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Research Centre Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Science Sabah Kota Kinabalu Malaysia WWF-Malaysia Orangutan Conservation Sabah Kota Kinabalu Malaysia Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Malaysia

ISBN: (纸本)9798350302363

Bornean orangutans are critically endangered, and experts are working to manually spot and classify their nests. This is a challenging task, as it requires a deep understanding of orangutan behavior and their natural habitats. To address this challenge, researchers are developing advanced image analysis techniques that can minimize human intervention. In this paper, we propose a method for classifying orangutan nests using Support Vector Machines (SVMs). SVMs are a powerful machine learning algorithm that has been shown to be effective for image classification. Our method includes a comprehensive image pre-processing and enhancement pipeline, as well as a rigorous training and testing procedure. The results of our study show that SVMs can achieve high accuracy in classifying orangutan nests. We achieved an accuracy of 79.90%, a F1-score of 79.87%, a precision of 79.91%, and a recall of 79.82%. These results demonstrate the effectiveness of our method and the potential of SVMs for this task. © 2023 IEEE.

关键词： Support vector machines

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：