检索结果-内蒙古大学图书馆

Priority Detector and Classifier Techniques Based on ML for the IoMT

computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： Rayan A.Alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

来源：评论

学校读者我要写书评

暂无评论

Shared Parking Concept in the Smart City Environment 8th

Shared Parking Concept in the Smart City Environment

引用

8th International Congress on information and Communication technology, ICICT 2023

作者： Špitálová, Zuzana Mandová, Lucia Opatovský, Martin Faculty of Informatics and Information Technology Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9789819930906

With the modern city infrastructure and increasing number of subjects in the traffic environment, there is a need for increased number of parking places. As the parking place is usually not used for a whole day, it could be shared with the others in the certain community. As it eliminates the useless driving due to the finding of free parking place, the traffic becomes more effective and produces less emissions. We designed the application for smart city environment, which helps to achieve the described situation. It consists of two parts, the frontend and backend. The data processed by the backend part can be further used for the city infrastructure improvements. The front-end part, designed in React Native, represents the graphical interface. The back-end part, designed in PostgreSQL, represents the database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

引用

IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

M2auth: A multimodal behavioral biometric authentication using feature-level fusion

引用

Neural Computing and Applications 2024年第34期36卷 21781-21799页

作者： Mahfouz, Ahmed Mostafa, Hebatollah Mahmoud, Tarek M. Sharaf Eldin, Ahmed Faculty of Computer Studies Arab Open University Muscat Oman Computer Science Department Minia University Minya Egypt Faculty of Computers and Information Minia University Minya Egypt Faculty of Computers and Artificial Intelligence Sadat City University Sadat City Egypt Faculty of Information Technology and Computer Science Sinai University Arish Egypt Information Systems Department Helwan University Helwan Egypt

Conventional authentication methods, such as passwords and PINs, are vulnerable to multiple threats, from sophisticated hacking attempts to the inherent weaknesses of human memory. This highlights a critical need for a more secure, convenient, and user-friendly approach to authentication. This paper introduces M2auth, a novel multimodal behavioral biometric authentication framework for smartphones. M2auth leverages a combination of multiple authentication modalities, including touch gestures, keystrokes, and accelerometer data, with a focus on capturing high-quality, intervention-free data. To validate the efficacy of M2auth, we conducted a large-scale field study involving 52 participants over two months, collecting data from touch gestures, keystrokes, and smartphone sensors. The resulting dataset, comprising over 5.5 million action points, serves as a valuable resource for behavioral biometric research. Our evaluation involved two fusion scenarios, feature-level fusion and decision-level fusion, that play a pivotal role in elevating authentication performance. These fusion approaches effectively mitigate challenges associated with noise and variability in behavioral data, enhancing the robustness of the system. We found that the decision-level fusion outperforms the feature level, reaching a 99.98% authentication success rate and an EER reduced to 0.84%, highlighting the robustness of M2auth in real-world scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor MRI Images Classification Using Fine-Tuned Deep Learning Models 6

Brain Tumor MRI Images Classification Using Fine-Tuned Deep ...

引用

6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Tolba, Mahmoud A. Atia, Bahaa S. William, Michal A. Noureldin, Arwa K. Ragab, Sama G. Mansy, Silvana N. Ramadan, Mennatallah A. Mabrouk, Mai S. Biomedical Informatics Information Technology and Computer Science Nile University El-Sheikh Zayed Egypt

ISBN: (纸本)9798350378511

Accurate identification of brain tumors plays a crucial role in diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) is one of the used methods in the detection of brain tumors, as it offers safety and provides good images. In this study, a fine-tuned deep-learning model for brain tumor classification was applied. In this respect, a publicly available dataset that consists of brain MRI images, which were labeled Glioma, Meningioma, Pituitary, and no tumor has been used. The methodology involved resizing the images, labeling the classes, shuffling the data, and splitting it into training and testing datasets. After that, two CNN models, ResNet101 and Xception, were fine-tuned on this dataset. The ResNet101 model evaluated an accuracy of 96%, while the Xception model evaluated an accuracy of 97%. These findings show how effectively one can identify these types of brain tumors using fine-tuned deep-learning models. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Empowered Secure Data Communication for UAV

引用

SN computer science 2024年第8期5卷 1-16页

作者： Kumar, Ajay Bhatt, Dheeraj Singh, Yashwant Kumar, Neerendra Department of Computer Science and Informatics Central University Himachal Pradesh Dharamshala India Department of Computer Science and Information Technology Central University of Jammu Jammu India

Unmanned Aerial Vehicles (UAVs), originally used for agriculture, military and typical videography applications, now expanded into many exciting areas but also carries potential security risks. Blockchain, widely renowned for its decentralized and secure properties will materialize the cure for these security threats faced by UAV networks. In this research, we introduced a blockchain based UAV communication algorithm in which both are jointly used to ensure a secure data exchange between drones. This algorithm makes sure that only validated data blocks are added to the Blockchain and ensures integrity & security during communication. By conducting an in-depth literature review, we have addressed some of the issues and challenges propagated by UAV systems. To summarize, the findings lay out that a combination of blockchain and UAV communication can enhance data integrity mechanisms as well as security layers;hence proving to be an optimal design for shaping even trustable solutions in wider domains dealing with professional aerial systems operation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Algorithm Blockchain Decentralized Drone Security UAV

来源：评论

学校读者我要写书评

暂无评论

Defending Adversarial Examples by a Clipped Residual U-Net Model

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2237-2256页

作者： Kazim Ali Adnan N.Qureshi Muhammad Shahid Bhatti Abid Sohail Mohammad Hijji Department of Computer Science Faculty of Information TechnologyUniversity of Central Punjab Lahore54000Pakistan Department of Computer Science COMSAT University IslamabadLahore CampusLahore54000Pakistan Faculty of Computers and Information Technology Computer Science DepartmentUniversity of TabukTabuk47711Saudi Arabia

Deep learning-based systems have succeeded in many computer vision ***,it is found that the latest study indicates that these systems are in danger in the presence of adversarial *** attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classiﬁcation to object *** adversarial examples are carefully designed by injecting a slight perturbation into the clean *** proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network *** have experimentally proved that our approach is better than previous defensive *** proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial *** proposed defensive approach is based on residual and U-Net *** experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionﬁ*** have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model.

关键词： Adversarial examples adversarial attacks defense method residual learning u-net cgan cru-et model

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals 46

Deep Learning-based Open-set Person Identification using Rad...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Xing, Zelin Bouazizi, Mondher Ohtsuki, Tomoaki Keio University Faculty of Science and Technology Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798350371499

Person identification based on radar-extracted vital signs has become increasingly popular due to its non-contact measurement capabilities. This paper introduces a novel deep learning-based person identification algorithm leveraging radar- extracted vital signs. While current studies mainly focus on closeset conditions with consistent training and testing categories, real-world scenarios often involve open-set circumstances, in which there are more data categories in the testing data. The algorithm involves extracting heart pulse signals from Doppler radar echoes, training two Convolutional Neural Network (CNN)-based models using transfer learning, and utilizing a distribution model for calibration. By combining the models' outputs through a strategic decision-making process, we achieve superior person identification results. Experimental results on a public radar vital signs dataset demonstrate an identification accuracy of 99.61% in close-set conditions and 94.35% in openset conditions, surpassing existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

The Large Language Models on Biomedical Data Analysis: A Survey

引用

IEEE Journal of Biomedical and Health informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

来源：评论

学校读者我要写书评

暂无评论

Channel Estimation Model and Their Role in Optimizing Customer Lifetime Value with Robust Optimization 24

Channel Estimation Model and Their Role in Optimizing Custom...

引用

7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Elveny, Marischa Nasution, Mahyuddin K.M. Purnamasari, Fanindia Wook, Tengku Siti Meriam Tengku Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9798400717833

In a dynamic and competitive business world, optimizing customer lifetime value (CLV) is an important goal for companies to maximize the effectiveness of their marketing strategies. Utilizing effective channel estimation techniques and adapting robust statistical methodologies is critical to accurately understanding and improving CLV. The study aims to investigate the part that channel estimation plays in CLV optimization by using the robust M-estimation and iteratively reweighted least squares (IRLS) approaches. This study specifically integrates RFM (recency, frequency, and monetary) analysis to measure and improve customer value predictions. We integrate the RFM variable as the main predictor in the regression model to estimate CLV based on different channel interactions. The research results show an R-square value of 0.94, which means 94% of data variability shows very good agreement between models. An RMSE value of 0.20 means that the average model prediction deviates from the actual value by around 0.20 units. A low RMSE indicates high prediction accuracy. Channel estimation techniques, combined with RFM and robust methods, provide significant insight into the factors influencing CLV. Increased email interactions and social media engagement are associated with higher CLV values, indicating that businesses should invest more in digital marketing strategies. We have proven that the frequency of current customer interactions and engagement through optimal channels has the most significant positive impact on increasing CLV. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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：