检索结果-内蒙古大学图书馆

5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Alothman, Basil Alenezi, Mariam Alhamdan, Danah Almutairat, Mariam Almousa, Zahraa Joumaa, Chibli Khan, Murad Kuwait College of Science and Technology Dept. of Computer Science and Engineering Kuwait-City Kuwait

ISBN: (纸本)9798350339239

The term 'electronic medical records' (EMRs) refers to information that is highly confidential and saved electronically. This information is associated with the diagnosis and treatment of a patient, and it must be routinely distributed among the concerned people such as doctors and nurses. The disclosure of private medical information or its alteration during an operation makes it extremely difficult for participants to exchange their medical files with one another. The most effective strategy for overcoming these obstacles is to implement an electronic medical records system that is based on blockchain technology. Within the scope of this study, we have addressed how the technology of blockchain might contribute to improved healthcare data management. Through the application of blockchain technology, the purpose of this research is to establish a connection between the health ministries and departments and both public and private hospitals in order to simplify the process of gaining access to one's medical records and transferring those records while maintaining patient confidentiality and improving safety. © 2023 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Multivariate Imputation by N Neighbour Mean and Chained Equation for Time Series Missing Data 2

Multivariate Imputation by N Neighbour Mean and Chained Equa...

引用

2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Pansari, Risham Kumar Gupta, Muktesh Wadhvani, Rajesh Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381979

Missing or incomplete data introduces numerous challenges during data analysis. If there exists any incomplete data then algorithms used for analysis can produce incorrect results. Data with missing or incomplete values may have a negative effect on computation. Thereby leading to an incorrect result. Certain algorithms are incapable of handling missing data properly, while other methods produce an efficient output to calculate the missing values. Because different machine learning algorithms become less effective when given incomplete data, it is extremely important to manage missing data properly. It could be possible that the data set might contain some missing values for various features due to the following reasons data were maintained in the file, data got corrupted, etc. This paper proposed a method that makes use of N neighbors of the data that is missing and makes use of the machine learning model to tune the computed value so as to impute a correct value for the missing data when the missing data is spread across different features. The proposed model makes use of the spatial characteristics of the time series data in order to predict missing data with the least error. Through experimentation, the proposed model demonstrated a high degree of accuracy in predicting missing values, even when the gaps spanned multiple feature columns. © 2023 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

AI-Enhanced Virtual Trial Room using Augmented Reality 6

AI-Enhanced Virtual Trial Room using Augmented Reality

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Harini, M. Janalyn maroula, L. Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

The Virtual Trial Room is an innovative augmented reality (AR) system that enables users to virtually try on apparel, enhancing the online shopping experience by simulating a physical fitting room. This technology leverages advanced computer vision, 3D modeling and machine learning to deliver an accurate and immersive experience. Data augmentation techniques are utilized to expand the training dataset, ensuring more precise clothing placement on different body types. The results demonstrate that our integrated approach, which combines AR and machine learning, significantly outperforms traditional virtual try-on systems, achieving enhanced user satisfaction, fit accuracy and personalized recommendations. The convolutional neural network (CNN) used in the system accurately detects body and facial landmarks, allowing for real-time garment fitting and recommendations. © 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

PREDICTING SHORT-TERM TRAFFIC WITH RANDOM FOREST: A GRANULAR DATA APPROACH

PREDICTING SHORT-TERM TRAFFIC WITH RANDOM FOREST: A GRANULAR...

引用

2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Mustafa, Ahmad Khattak, Khurram Shehzad Khan, Zawar Hussain Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar Pakistan College of Computer Science and Engineering University of Ha’il Ha’il Saudi Arabia

ISBN: (纸本)9781837243235

Accurate short-term traffic flow prediction is critical for improving the efficiency of Intelligent Transportation Systems (ITS), particularly in heterogeneous traffic environments. This paper presents a data-driven model based on the Random Forest (RF) algorithm to predict traffic flow using high-resolution data collected over a week. The dataset encompasses individual vehicle speed, vehicle classification (car, bicycle, motorbike, bus), timestamp, flow rate, peak hour factor, traffic density, time and distance headway, and density range, recorded from 9 AM to 5 PM over seven consecutive days. Six days of data are used for training the RF model, with Thursday’s traffic flow serving as the test case. The dataset features high-dimensional, heterogeneous traffic attributes, enabling the model to capture non-linear and complex interactions effectively. Our results demonstrate that the RF model effectively captures complex, non-linear relationships in the data, delivering highly accurate predictions with an R-squared value of 0.997. This study contributes to advancing machine learning applications in traffic management, emphasizing the importance of feature selection in enhancing model performance. Future research will investigate the inclusion of additional factors such as weather conditions and external events to further refine prediction accuracy. © The Institution of engineering & technology 2025.

关键词： Highway traffic control

来源：评论

学校读者我要写书评

暂无评论

Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

引用

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

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Zhang, Wei Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China City University of Hong Kong Dept. of Computer Science 999077 Hong Kong College of Computer Beijing University of Technology Beijing100124 China University of Connecticut Dept. of Computer Science and Engineering StorrsCT06269 United States Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In recent years, distributed computing has wit-nessed 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 inter-dependencies 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 SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition 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 SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively. © 2024 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Analysis on Quantum Cryptographic Approaches for Image Encryption 3

Analysis on Quantum Cryptographic Approaches for Image Encry...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Satre, Shilpa M. Joshi, Bharti Deemed to Be University Bharati Vidyapeeth College of Engineering Ramrao Adik Institute of Technology University of Mumbai Dept. of Computer Engineering Navi-Mumbai India Ramrao Adik Institute of Technology Deemed to Be University Dept. of Computer Engineering Navi-Mumbai India

ISBN: (纸本)9798350338607

To secure and protect the photos from unfair tactics, the encryption process is used. the advent of the digital era has made it exceedingly easy to hack any data kept on the cloud. Encryption is critical to ensuring communication security, which is a crucial issue. There are numerous photo encryption methods that mostly rely on chaotic logistic maps;however, they have downsides such a small key space and low security. Current analysis introduced experimental results of quantum chaotic cryptosystem,3D-Chaotic map and quantum chaotic logistic map. This paper we measure image encryption parameters are Entropy, structural similarity index measure, combined average changing intensity and net pixel change rate which defines utilization of suitable image encryption approaches. For the cypher and the original image, we quantify the correlation coefficient value in both the horizontal and vertical positions with comparison of all three existing Approach-es performance. The scientific and practical economic results reveal that the quantum chaotic cryptosystem has outstanding performance and in future with utilization of advanced Approaches we can resist various cryptographic attacks. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Underwater Image Enhancement by Using a Colour-Correction and Dehazing Approach

引用

Procedia computer Science 2025年 259卷 818-825页

作者： Shubhankar Nalawade Mohit Dua Shubhankar Nalawade Dept. of Computer Engineering National Institute of Technology Kurukshetra India Mohit Dua Dept. of Computer Engineering National Institute of Technology Kurukshetra India

Capturing clear images underwater is difficult because light gets absorbed and scattered, resulting in degraded image quality with reduced visibility and colour inaccuracy. This research paper proposes a comprehensive approach that combines colour correction and dehazing techniques to enhance such deteriorated underwater images. In the colour correction step, principles of colour constancy are utilized to address colour distortions caused by underwater conditions. This correction significantly improves colour accuracy and enhances the visual fidelity of the image by first converting the RGB image into CIELab colour space and then performing white balancing in the CIELab colour space. This step successfully eliminates colour casts and restores the natural colours of underwater scenes, leading to visually pleasing and accurate representations. In the dehazing step, our method utilizes a dark channel prior-based approach to remove the haze by estimating the overall atmospheric light and the transmission map. The dehazing step significantly reduces haze, enhancing the visibility of submerged objects and fine details. Our technique has been evaluated subjectively and objectively, and the results have demonstrated its efficacy in reducing haze, correcting colour cast, and preserving the natural appearance of the image. The enhanced results consistently achieve great results for RMSE, PNSR, SSIM, and UCIQE, surpassing several existing underwater enhancement methods.

关键词： Underwater Image Enhancement Colour Correction Dark Channel Prior

来源：评论

学校读者我要写书评

暂无评论

Enhancing UAV capabilities for Object Picking: A flying Manipulator with Stiffness Control 8

Enhancing UAV capabilities for Object Picking: A flying Mani...

引用

8th International Conference on Control, Robotics and Cybernetics, CRC 2023

作者： Sewalt, Marinus Johannes Tim Woo, Kam The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350330588

Unmanned aerial vehicles (UAVs) have gained widespread popularity in various applications. However, their physical interaction capabilities, especially object picking, remain a challenge. This paper presents the system overview of a novel flying manipulator, combining a quadrotor platform with a direct-drive gripper. The study examines the viability of high-speed scooping based on stiffness control with a flying manipulator. By leveraging stiffness control, the gripper offers a significant advantage over rigid grippers by avoiding large contact forces that may destabilize the flying platform. This enables efficient and reliable retrieval of flat objects from the ground without the need for overly cautious maneuvers. Successful results are demonstrated, showcasing the practicality and effectiveness of the gripper-drone integration for physical interactions. This development opens avenues for further advancements in UAV capabilities and expands their potential for real-world applications. © 2024 IEEE.

关键词： Manipulators

来源：评论

学校读者我要写书评

暂无评论

Modified Deep Neural Network Approach to Identify Heart Disease using IoMT: Artificial Neural Networks or Convolutional Neural Networks!

Modified Deep Neural Network Approach to Identify Heart Dise...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Nayak, Debasish Swapnesh Kumar Priyadarshini, Arpita Mahanta, Pabani Panigrahi, Soumyarashmi Meher, Sushanta Swain, Satyananda Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India Utkal University Department of Statistics Bhubaneswar India University Dept. of Computer Science & Engineering Bhubaneswar752050 India

ISBN: (纸本)9798350369175

Unconventional methods of predicting and treating heart disease (HD) are required because HD is still the top mortality on a global scale. This study delves into the potential of combining deep learning (DL) methods with IoMT devices to improve HD prediction and early diagnosis. Despite their usefulness, traditional risk assessment approaches can misrepresent the intricate relationship between cardiovascular risk factors. On the other hand, DL and ML algorithms can sift through mountains of data related to lifestyle, genetics, demographics, and clinical conditions to find complex relationships. To increase generalization performance across varied patient demographics and datasets, our study is focused on designing a novel DL architecture that is specialized for HD prediction. We optimize its design and hyperparameters. With feature engineering, when selecting features, we use linear discriminant analysis (LDA). In our work, two different DL techniques neural network and convolutional neural network (CNN) for HD identification are studied. We then evaluate the studied model's efficacy with measures including recall, accuracy, precision, F1-score, and Matthews Correlation Coefficient (MCC). By attaining 96% accuracy, 97% precision, 95% specificity, 97% F1-score, and 93% MCC on chosen features, the experimental findings show that a one-dimensional (1-D) CNN performs better than other models. Our suggested method, which combines DL and IoMT, offers a powerful answer to the problems of real-time health monitoring and HD risk assessment, which in turn improves early intervention and tailors treatment plans to each patient. The findings of this study highlight the promise of cutting-edge AI-driven approaches to revolutionizing cardiovascular healthcare. © 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

Identification of Influential Genes for Lung Cancer Using Machine Learning Approaches 4

Identification of Influential Genes for Lung Cancer Using Ma...

引用

4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Imam Hasan, Md. Nahar, Sabikun Roy, Bithy Kabir Rana, Humayan Kumar Podder, Nitun Babu Sen Omit, Shudeb Green University of Bangladesh Dept of Computer Science and Engineering Bangladesh Pabna University of Science and Technology Dept of Computer Science and Engineering Bangladesh Noakhali Science and Technology University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665490450

Lung cancer (LC) is a kind of cancer that develops in lung tissues, most often in the cells that line the airways. It is the second most severe disease in the world, and making predictions about it is the hardest thing to do right now. As most of the cancer cells overlap with each other, it is difficult to detect the cells. However, it is very important to find cancer cells in their early stages. Understanding the molecular pathways behind how LC starts and spreads can help with early diagnosis, treatment, and figuring out how likely it is to get better. However, it is still insufficient, and more research is needed in this regard. In the case of treatment for LC, the identification of influential genes (IFGs) is very important. Thus, to further contributions, we have taken the Cancer Genome Atlas (TCGA) data-set to detect the IFGs of LC using Kruskal-Wallis H test and Bonferroni correction methods. Then we have successfully identified 14 IFGs from 18784 genes and using the fold change values and heat-map plot, we separated the up and down regulated genes. Apart from this, the accuracy of our suggested method was predicted using a classifier algorithm known as the Support Vector Machine (SVM), and we discovered an accuracy of 84.54% which is satisfactory. Furthermore, the identified 14 significant genes may be employed in additional lab-based analysis and to create therapeutic treatment options, perhaps leading to a new understanding of the progression of LC. © 2022 IEEE.

关键词： Genes

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：