检索结果-内蒙古大学图书馆

2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Singhal, Prateek Yadav, Rakesh Kumar Maharishi University of Information Technology Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350302820

A pattern or manner of walking called 'human gait analysis' is now being used in medical diagnosis. The examination of human gait points to a number of orthopaedic and neurological system problems. This article provides a state-of-the-art examination of gait analysis techniques, strategies. The literature is gathered from a number of digital libraries linked to reputable SCI/(E) indexing journals. This study provides a detailed explanation of the theoretical underpinnings of gait parameters, including the many types, methodology, applications and tools that have been utilised in the past. Additionally, attention is placed on the numerous methods for gait analysis that are based on kinematics, kinetics, and computational intelligence in the literature. Numerous readers in the community who are working on gait pattern analysis for diagnosis and related rehabilitation programmes will also benefit from the study that has been provided. © 2023 IEEE.

关键词： Gait analysis

来源：评论

学校读者我要写书评

暂无评论

Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

引用

Journal of Bionic Engineering 2023年第5期20卷 2240-2275页

作者： Jiaochen Chen Zhennao Cai Huiling Chen Xiaowei Chen José Escorcia-Gutierrez Romany F.Mansour Mahmoud Ragab College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Department of Rheumatology and Immunology The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China Department of Computational Science and Electronics Universidad de la CostaCUC080002BarranquillaColombia Department of Mathematics Faculty of ScienceNew Valley University72511El-KhargaEgypt Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing *** assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN *** method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS *** DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 *** addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological *** results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal *** to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation *** research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

关键词： Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm Swarm intelligence algorithms Bionic algorithm

来源：评论

学校读者我要写书评

暂无评论

Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

引用

International Journal of computers and Applications 2024年第9期46卷 702-714页

作者： Al-Rjoob, Ayat M. Ababnah, Ahmad A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. Electrical and Communication Engineering Department German-Jordanian University and Technology Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Computer Engineering Department School of Engineering The University of Jordan Amman Jordan

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ((Formula presented.)) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary

关键词： Cognitive radio

来源：评论

学校读者我要写书评

暂无评论

A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness Detection systems

引用

Informatica (Slovenia) 2024年第3期48卷 359-378页

作者： Latreche, Imene Slatnia, Sihem Kazar, Okba Barka, Ezedin Harous, Saad Department of Computer Science University of Mohamed Khider Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates Department of Information Systems and Security UAE University Al Ain United Arab Emirates Computer Science Department College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Driver Drowsiness is considered one of the significant causes of road accidents and fatal injuries. Due to this, creating systems that can monitor drivers and detect early drowsiness has become an important field of research and a challenging task in recent years. Several research attempts were proposed to solve this problem based on several approaches and techniques. The Electroencephalogram (EEG) is one of the most efficient and reliable method, among the physiological signals-based monitoring approaches. In this area, many Machine Learning (ML) techniques have been used to detect EEG-based driver drowsiness. However, due to the limitations of ML techniques, many researchers have shifted their focus to the use of deep learning (DL) techniques, which have demonstrated superior performance in many fields including the physiological signals classification tasks. This paper reviews and discusses numerous new research papers that have proposed and implemented driver drowsiness detection systems based on EEG and deep learning techniques. In addition, we have outlined the limitations and difficulties of the existing works and highlighted and proposed some propositions that will help future field researchers enhance and generalize the results. Based on our thorough analysis, we have determined that the latest advancements in detecting driver drowsiness have employed the convolutional neural network (CNN) technique, which has demonstrated effective performance in classifying signals. Furthermore, the primary issue encountered in all works is developing a more precise and accurate method. Nevertheless, we seek a precise system capable of swiftly identifying a state of drowsiness while using minimal spatial memory and processing resources. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Cervical Cancer Prediction Empowered with Federated Machine Learning

引用

computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

来源：评论

学校读者我要写书评

暂无评论

A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

引用

computers, Materials & Continua 2022年第9期72卷 5889-5907页

作者： Esraa Hassan Mahmoud Y.Shams Noha A.Hikal Samir Elmougy Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Infectious diseases are an imminent danger that faces human beings around the *** is considered a highly contagious *** diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human ***,there is a need to investigate an efficient and fast automatic diagnosis *** deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image *** study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected *** focuses on detecting infected cells,which aids in the computation of parasitemia,or infection *** proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the *** comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

关键词： Deep learning malaria dataset diagnostic medical approach

来源：评论

学校读者我要写书评

暂无评论

Implementing a land registration system using non-fungible tokens to represent land in the system and side-chain for data storage 5

Implementing a land registration system using non-fungible t...

引用

5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Kunjumon, Cyril Mutharia, Aditya Shareef, Salemula Suchithra, M.S. Indian Institute of Information Technology Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350334128

Blockchain technology has been increasing in popularity in recent times due to its decentralized nature, immutability, transparency, high security and transparency. Several private and public organisations have been using it to develop new and innovative projects like cryptocurrency, NFTs, Metaverse, etc., and also to upgrade and improve old technologies like supply chains in agriculture and government voting systems. Land Registration is one such sector that can be upgraded using blockchain because in the current system there are a lot of middlemen involved and data is centralized and not secure, these things make the current system vulnerable to frauds and corruption. In this paper we have proposed a new implementation for the existing land registration system in the country that uses Non-Fungible tokens in the blockchain to represent a property and a sidechain like IPFS for storing its data in a decentralized manner. With our implementation we can overcome the flaws in the old system and make land registration secure, transparent and trustless. Here by "trustless"we mean that users do not have to trust any centralized authority or intermediary to verify transactions or maintain the integrity of the system. Instead, trust is placed in the system's consensus mechanism and cryptographic protocols. © 2023 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Hybrid deep Ensemble for Fine-Grained Race Estimation

引用

Multimedia Tools and Applications 2024年 1-24页

作者： Ahmed, Mazida A. Choudhury, Ridip Dev Sarma, Shikhar Kr. Borbora, Khurshid A. Bhuyan, Manash P. Barman, Utpal Department of Information Technology Gauhati University Assam Guwahati India Krishna Kanta Handiqui State Open University Assam Guwahati India Gauhati University Institute of Distance and Open Learning Gauhati University Assam Guwahati India Department of Computer Science Dibru College Assam Dibrugarh India Faculty of Computer Technology Assam down town University Assam Guwahati India

Race identification has made advances over the past few years, and finds application in numerous areas including surveillance, law enforcement, and even in administrative policy. It is, yet, limited to the groups with major diversifications like Asian, African, Caucasian, and Indian and is still under-explored for sub-racial categorization probably due to the unavailability of sufficient data. This article is aimed at estimating the recognized sub-groups of the ‘Indian’ class. An exclusive dataset (North East India Dataset) has been created for this purpose. Also, a hybrid ensemble blending the concepts of Bagging and Stacking (Super Learner) with minute customizations has been put forward that trains on specialized augmented patterns of data along with intermediate features. Extensive experiments have been conducted and the findings reveal the potential effectiveness of the method especially in lowering the loss. The model has also been validated against another benchmark dataset, FairFace. On the North East India and FairFace set, the proposed model achieves an accuracy of 91% and 80% respectively, outperforming the base models by at least 4%. The proposed ensemble performs better than the standard classical methods such as Bagging and Boosting (AdaBoost) on both datasets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

A privacy-preserving approach for detecting smishing attacks using federated deep learning

引用

International Journal of information technology (Singapore) 2025年第1期17卷 547-553页

作者： Remmide, Mohamed Abdelkarim Boumahdi, Fatima Ilhem, Bousmaha Boustia, Narhimene LRDSI Laboratory Department of Computer Science Faculty of Sciences University of Blida 1 Blida Algeria Electronics Department Faculty of Technology University of Blida 1 Blida Algeria

Smishing is a type of social engineering attack that involves sending fraudulent SMS messages to trick recipients into revealing sensitive information. In recent years, it has become a significant threat to mobile communications. In this study, we introduce a novel smishing detection method based on federated learning, which is a decentralized approach ensuring data privacy. We develop a robust detection model within a federated learning framework based on deep learning methods such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Our experiments show that the federated learning method using Bi-LSTM achieves an accuracy of 88.78%, highlighting its effectiveness in tackling smishing detection while preserving user privacy. This approach not only offers a promising solution to smishing attacks but also lays the groundwork for future research in mobile security and privacy-preserving machine learning. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Bi-LSTM Data privacy Federated learning Smishin detection Social engineering

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

引用

computer systems science & Engineering 2023年第8期46卷 1863-1877页

作者： Abdullah S AL-Malaise AL-Ghamdi Sultanah MAlshammari Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing *** a face mask in public areas protects people from spreading *** intelligence(AI)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several *** study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 *** DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass *** accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast *** face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 *** addition,the face detection performance can be improved by the seeker optimization algorithm(SOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the *** last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)*** stimulation study of the DLFMD-RMG algorithm is examined on a benchmark *** results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

关键词： Religious mass gathering Hajj and Umrah covid-19 pandemic face mask computer vision deep learning

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：