检索结果-内蒙古大学图书馆

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

引用

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 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

Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

引用

computers, Materials & Continua 2024年第11期81卷 3395-3412页

作者： Arar Al Tawil Laiali Almazaydeh Doaa Qawasmeh Baraah Qawasmeh Mohammad Alshinwan Khaled Elleithy Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan College of Engineering Abu Dhabi UniversityAbu DhabiAl AinP.O.Box 1790United Arab Emirates Faculty of Artificial Intelligence Al-Balqa Applied UniversitySalt19117Jordan Department of Civil and Construction Engineering Western Michigan UniversityKalamazooMI 49008USA MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science and Engineering University of BridgeportBridgeportCT 06604USA

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as *** study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing *** study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron *** best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching *** study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

关键词： Attacks email phishing machine learning security representations from transformers(BERT) text classifeir natural language processing(NLP)

来源：评论

学校读者我要写书评

暂无评论

Applying Text Mining to Analyze Human Question Asking in Creativity Research 12th

Applying Text Mining to Analyze Human Question Asking in C...

引用

12th International Conference on Big Data Analytics, BDA 2024

作者： Wròblewska, Anna Korbin, Marceli Kenett, Yoed N. Dan, Daniel Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel School of Applied Data Science Modul University Vienna Vienna Austria Systems Research Institute Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9783031861925

Creativity relates to the ability to generate novel, and effective, ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation, and is gaining increased empirical attention, is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving [15]. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful, or helpful, in the study. Furthermore, a novel approach is proposed, implemented, and applied to five datasets. Obtained experimental results are comprehensively analyzed, suggesting that natural language processing has a role to play in the creativity research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Creativity Measuring questions Natural language processing Question asking

来源：评论

学校读者我要写书评

暂无评论

K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

引用

computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

来源：评论

学校读者我要写书评

暂无评论

Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

引用

IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

引用

computers, Materials & Continua 2022年第6期71卷 5979-5994页

作者： Anwer Mustafa Hilal Fahd NAl-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad Almustafa Nadhem Nemri Mesfer Al Duhayyim Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Computer and Information SystemsPrince Sultan UniversitySaudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision *** the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare *** this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare *** initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried *** addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature ***,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

关键词： Cognitive internet of things machine learning parameter tuning alzheimer’s disease healthcare decision making

来源：评论

学校读者我要写书评

暂无评论

Medical Diagnoses: Heart Disease Prediction 2

Medical Diagnoses: Heart Disease Prediction

引用

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Mahfuri, Mahmoud Ghazal, Taher M. Mudassar, Muhammad Siddiqui, Shahan Yamin Farooq, Sajid Kanwal, Nayab Ahmad, Munir International University for Science and Technolgy Computer and Information Technolgy Department Faculty of Engineering Syria Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College School of Information Technology University City Sharjah 1797 United Arab Emirates The University of Lahore Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan School of Computer Science National College of Business Administration & Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The successful and continuously increasing applications of machine learning (ML) and data mining (DM) in the wide range of fields such as e-businesses, public or private sectors, marketing and retails has led the ML and DM applications in other industries and health sectors. In the health sector the data is information wise too rich but not knowledge rich. It can be due to the lack of effective tools and research study in this era. In this research work, an efficient model is proposed based on the machine learning and data mining tools to predict a particular medical disease that is the heart disease. The solution for the problem which is addressed that data is not knowledge-rich, is presented in this paper. Waikato Environment and Python is used for to analyze the data, so that an automation system can be formed. Several experiments are to be implemented to compare the performance through Supervised Learning and Unsupervised Learning. Bayesian learning (Naïve Bayes), Tree's algorithm (Decision Tree), and Neural Networks (MLPNN) are applied on the same dataset. Comparatively, Decision Tree gives best accuracy when compared with Naïve Bayes, ANN performs more effective when compared with others two Bayesian algorithms (Naïve Bayes) and Tree's algorithm (Decision tree). © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

引用

IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： Mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

来源：评论

学校读者我要写书评

暂无评论

Efficient Video Object Segmentation Using JIT K-Means Clustering

Efficient Video Object Segmentation Using JIT K-Means Cluste...

引用

International Conference on Knowledge and Smart Technology (KST)

作者： Wanida Khamprapai Fuangfar Pensiri Porawat Visutsak Department of Computer Science and Information Faculty of Science at Sriracha Kasetsart University Sriracha Campus Chonburi Thailand Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

Video segmentation dissects video sequences into distinct regions based on visual cues like object borders, movement patterns, color variations, and textures. This partitioning aims to isolate individual objects and temporal events from the background, ultimately revealing a more structured and interpretable representation of the video content. In this work, we propose K-means clustering and MATLAB Just-In-Time (JIT) accelerator within a video segmentation framework to boost both accuracy and efficiency, especially in resource-limited environments. This work aims to evaluate K-means for video object segmentation, assess JIT's impact on its speed and resource usage, and optimize their integration considering feature representation and clustering parameters. Our experimental results show that the MATLAB JIT is able to achieve a significant speedup compared to the video segmentation in a normal mode with the average acceleration = 89.88%.

关键词：

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：