检索结果-内蒙古大学图书馆

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pawar, Adesh Kelzarkar, Sanket G. Choudhary, Vishal Kumar Daburkar, Ashwini Kumar, Praveen Hore, Rutuja Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Blood Glucose Monitoring levels are essential to the treatment of diabetes and must be done continuously. Patients often don't comply with conventional glucose monitoring techniques since they cause discomfort and are invasive. An Arduino microcontroller and infrared (I.R.) sensor are used in this work to provide a robust, non-invasive glucose monitoring system. I.R. light is transmitted through the skin to measure the quantity of light reflected, and this relationship exists between the amount of light and the amount of glucose in the interstitial fluid. In real-time, the Arduino analyses blood glucose levels by analyzing IR Spectroscopy and using machine learning techniques. The device features wireless connectivity, enabling it to send glucose readings to a mobile application for continuous monitoring, historical data analysis, and customized alerts. Initial trials indicate that this non-invasive system provides accurate glucose measurements comparable to traditional methods, significantly improving user comfort and compliance. This innovative approach promises a pain-free, user-friendly, and cost-effective solution for diabetes management, paving the way for more widespread adoption of continuous glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

来源：评论

学校读者我要写书评

暂无评论

Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets

引用

SN computer science 2025年第3期6卷 1-30页

作者： Kumar, Vinod Kumar, Ravi Kant Singh, Sunil Kumar School of Computer Applications and Information Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering SRM University AP Andhra Pradesh Guntur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India karnataka Bengaluru India

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalanced Classifiers Machine learning Medical datasets Smote variants

来源：评论

学校读者我要写书评

暂无评论

Automated Driving License Testing system 4

Automated Driving License Testing System

引用

4th International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Rachel, Sheeba Sanjay Krishna, B. Saranya, A. Yashika, R. Sri Sairam Engineering College Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Computer Science Chennai India

ISBN: (纸本)9798331508845

The Automated Driving License Test system (ADLTS) is a cutting-edge project that aims to automate and enhance the process of evaluating candidates for driving licenses. It uses a combination of sensors and algorithms to objectively assess an applicant's driving skills and provide instant results on their performance. The ADLTS is a forward-looking approach to modernizing driving license testing in an era of evolving automotive *** applicants are fingerprinted with fingerprint sensor to ensure that no one else can impersonate them. This application and authentication process is handled by software application. The testing procedure is monitored by three sensors: a proximity sensor, an RPM sensor, and a human face identification system powered by OpenCV, a Machine Learning Library. Proximity sensor is used to monitor the distance between the vehicle and the external objects, while the RPM sensor is used to determine if the vehicle is turned on thereby monitoring the status of the engine. The Face Detection sensor monitors the presence of candidates in the vehicle. Violations of these testing procedures result in license rejection. The license is approved once the car is successfully parked without violating the *** sensors and the software system are integrated with a user-friendly UI to form a comprehensive model. This automated system improves safety, consistency, and cost efficiency in the licensing process, reducing the reliance on human evaluators and the risk of accidents during testing. © 2024 IEEE.

关键词： Proximity sensors

来源：评论

学校读者我要写书评

暂无评论

The Combination of the Leiden Algorithm and Graph Coloring for Influencers Detection in Large Scale Social Networks

The Combination of the Leiden Algorithm and Graph Coloring f...

引用

computer sciences, Engineering, and technology Innovation (ICoCSETI), International Conference on

作者： Handrizal Poltak Sihombing Erna Budhiarti Nababan Mohammad Andri Budiman Doctoral Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

The role of influencers, especially on social media platforms, has grown significantly. A commonly used feature among business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, underscoring the need for a more sophisticated approach to influencer *** study proposes a new method for influencer detection that integrates the Leiden Coloring Algorithm and Matrix Centrality. This approach leverages network analysis to identify patterns and relationships in large-scale datasets. First, the Leiden Coloring Algorithm partitions the network into various communities, which are considered potential influencer groups. Furthermore, Eigenvector and Degree Centrality augment this process by identifying nodes with high connectivity, representing potential *** proposed method is validated using crawled data from the Twitter (X) social media platform with the keyword "GarudaIndonesia." The results of the Leiden Coloring Algorithm recommend 10 accounts as influencers. Based on Eigenvector Centrality and Degree Centrality for a dataset of 1,000 rows, it is observed that the first and second ranks consistently identify the same influencers, namely IndonesiaGaruda and GarudaCares. However, the third to tenth ranks suggest different influencers. For a dataset of 5,000 rows, both methods again identify IndonesiaGaruda as the top-ranked influencer, while the second to tenth ranks differ between the two *** modularity value for the first test scenario is 0.9396, while for the second test scenario, it is 0.9381. The processing time for the first test scenario is 29.5493 seconds, compared to 434.1838 seconds for the second test scenario. Additionally, the number of communities identified by the Leiden Coloring Algorithm increases with dataset size, with 505 communities for the first test scenario and 1,969 communities for the second. This demonstrates that larger datasets res

关键词： Technological innovation Social networking (online) Blogs Network analyzers Feature extraction Partitioning algorithms Business

来源：评论

学校读者我要写书评

暂无评论

Supplier Qualification Using FMEA in a Meat Company

引用

Periodica Polytechnica Transportation Engineering 2023年第4期51卷 323-328页

作者： Boldizsár, Adrienn Török, Erika Pásztor, Attila Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10. KecskemétH-6000 Hungary

The analysis of the processes between supplier and customer and the detection and handling of defects is based on objective, quantified criteria so that customer complaints can be handled as efficiently as possible, with the least possible inconvenience. One possible risk-management method is FMEA, or Failure Mode(s) and Effect(s) Analysis, which allows a team to anticipate and prevent failures from occurring anywhere. It is an analytical methodology to identify and check for potential problems when designing a product, service or process. It can be used to reduce the number of defective supplies and thus indirectly increase customer satisfaction (by more efficient purchasing, the price can be kept lower and the quality higher, and fewer customer complaints from end users can be expected). It was used in this study to assess the supplier quality of a Hungarian industrial company in the FMCG sector and to make tangible improvements in a short time to eliminate supplier defects. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Optimizing Artificial Neural Networks Trough Weight Adjustments 28th

Optimizing Artificial Neural Networks Trough Weight Adjustme...

引用

28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Akber, Syed Muhammad Abrar Szczesna, Agnieszka Kazmi, Sadia Nishat Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Longer training times pose a significant challenge in artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network. © 2024 The Authors.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparison of Machine Learning Methods for Air Quality Prediction Based on Particulate Matter (PM2.5)

Comparison of Machine Learning Methods for Air Quality Predi...

引用

2025 International Conference on Advancement in Data science, E-learning and information system, ICADEIS 2025

作者： Mar'i, Farhanna Marianingsih, Susi Abdillah, Rifqi Elfaiz, Ersha Aisyah Setyo Prayoga, Riza Akhsani Fahmi, Muhamad Universitas Negeri Surabaya Faculty of Engineering Department of Informatics Engineering Surabaya Indonesia Jayapura University of Science and Technology Department of Information System Jayapura Indonesia Universitas Negeri Surabaya Faculty of Engineering Department of Information Technology Education Surabaya Indonesia Universitas Airlangga Faculty of Science and Technology Department of Biomedical Engineering Surabaya Indonesia

ISBN: (纸本)9798331513320

Particulate PM2.5 has a major effect on human health compared to other types of pollutants such as NO2, 03, CO, and SO2. PM2.5 is the worst type of major pollutant for human health that can cause various types of serious diseases and even death. PM2.5 is defined as an ambient air particle, which is one of the particle elements found in the free air of the earth's surface in the troposphere layer, which is very small in size, reaching 2.5 pollutants. The method of predicting air quality index values can be done using machine learning techniques with time series data analysis. This study will compare machine learning methods with two approaches, namely statistical regression with RF and ARIMA and deep learning method, namely LSTM. Based on the research results, the best machine learning method approach is statistical regression, namely the RF method with accuracy evaluation using RMSE, MAE, and MSE, which are respectively with a minimum value of 0.162, 0.108, and 0.026, followed by the LSTM and ARIMA methods. © 2025 IEEE.

关键词： Polycyclic aromatic hydrocarbons

来源：评论

学校读者我要写书评

暂无评论

Wireless Resilience: A LoRa-Based Sensor Network and Mobile Application for Flood Victim Rescue system 9

Wireless Resilience: A LoRa-Based Sensor Network and Mobile ...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Imran, Mohammad Syameer Daniel, Muhammad Attarbashi, Zainab S. Handayani, Dini Oktarina Dwi Iftikhar, Saman Al-Madani, Dania International Islamic University Malaysia Faculty of Information and Communication Technology Department of Computer Science Selangor Malaysia Arab Open University Faculty of Computer Studies Saudi Arabia

ISBN: (纸本)9798350370126

This project aims to help flash floods' victims and rescuers facilitate rescue efforts. By integrating Internet of Things (loT) and Global Positioning system (GPS) capabilities, flood victims can use a device (emergency box) that will be activated to send a signal to rescue workers and volunteers to come to the location and rescue them. The box is long lasting, can withstand winds and floats and has a bright light which allows rescuers to locate the victims even in dark or areas with no electricity. Rescuers use a device attached to a signal receiver that receives distress signals from flood victim's emergency boxes. Then the navigation system will allow rescuers to locate the distress signals. This project has the potential to help rescuers and civilians alike, as this opens new ways to rescue people trapped in flooding by allowing different parties to join the rescue mission easily. The system was evaluated by the success of the rescuer's device and the mobile application to send and receive respectively the distress signal from the flood's victim device with accurate location coordinates in different weather conditions, indoor and outdoor, and in both open areas and between buildings. This will be done by taking measures of the power needed for both devices, the covering range between both devices, and the accuracy of the location coordinates. © 2023 IEEE.

关键词： Global positioning system

来源：评论

学校读者我要写书评

暂无评论

ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

引用

computer Modeling in Engineering & sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

来源：评论

学校读者我要写书评

暂无评论

Rice Leaves Disease Diagnose Empowered with Transfer Learning

引用

computer systems science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

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：