检索结果-内蒙古大学图书馆

Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

来源：评论

学校读者我要写书评

暂无评论

Fast Exam Video Summarization Using Targeted Evaluation of Scene Changes Based on User Behavior 9

Fast Exam Video Summarization Using Targeted Evaluation of S...

引用

9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Mohajer, Mahdi Marvi Hassanpour, Hamid Shahrood University of Technology Faculty of Computer Engineering and Information Technology Iran

ISBN: (纸本)9798350369922

As the demand for distance learning has increased, so has the need for electronic exams (e-exams). In e-exams, where video monitoring is used, one of the main tasks of the proctors is to detect cheating in exam videos. Monitoring a large number of videos is challenging for proctors, and summarizing videos to focus on cheating scenes can significantly reduce the difficulty of the monitoring task. Several important points should be taken into account in e-exam video summarization, such as various background, the clothes of each examinee, and the variety of video surveillance devices. One of the most effective methods for video summarization is the use of deep neural networks. However, the existing methods require a large dataset for training which is time consuming, besides, they are unable to accurately summarize the video containing a new background scene. In this paper, a deep learning-based method is proposed to recognize the objects in the scene and monitor any change in the examinee's body parts with different priorities. In this approach, the abnormal behaviors of the examinee can be automatically detected without the need for a pre-training with multiple background, then the video is automatically summarized. The experimental results demonstrate that the proposed method, while having a high processing speed, can summarize exam videos up to 70%. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Detection of Agricultural Crops Using CNN 14

A Novel Approach for Detection of Agricultural Crops Using C...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mahesh, T.R. Singh Chandel, Aman Gupta, Harshitha Jain, Rakshit Garg, Sadhvi Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Crop classification is an important aspect of farming because it improves crop management and increases crop yield. This study proposes a CNN-based crop classification technique using high-resolution images. To use this method, we created a database of 30 crops, including jute, maize, almonds, rice, sugarcane, banana, wheat, and others. The Convolutional Neural Network (CNN) is an Artificial Intelligence-based algorithm that learns similar features from images and is capable of performing a variety of tasks such as image classification, object detection, and segmentation. In this system, CNN or ConvNet is used to learn the features provided in the input images by sequentially entering several layers such as Convolution, Activation function (RELU), Pooling, and Flattening. Finally, with only a few explanations in the training set, this providing outstanding classifies new images. The proposed method has two major components: preprocessing and categorization. The experimental results show that the proposed tactic worked extremely well, with a ground truth of 0.9264. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

引用

SN computer Science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Methods for Materials Inverse Design: A Review

引用

computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

来源：评论

学校读者我要写书评

暂无评论

Statistical Testing & TOPSIS Analysis of Acute Sinusitis Interventions

Statistical Testing & TOPSIS Analysis of Acute Sinusitis Int...

引用

2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Pisal, Tejas Kumar, Hemant Kumar, Praveen Gote, Pradanyawant Faculty of Engineering & Technology Department of Computer Science & Design Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Basic Sciences and Humanities Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Computer Science & Medical Engineering Wardha Maharashtra Sawangi442001 India

ISBN: (纸本)9798350361537

Acute sinusitis, often occurring as a consequence of recovering from a cold, presents a significant health concern due to its impact on the interconnected hollow spaces located behind the cheekbones, forehead, and nose. These sinus passages play a crucial role in the respiratory process by facilitating the smooth flow of air towards the lungs. Given the discomfort and potential complications associated with acute sinusitis, there is a pressing need for effective pharmaceutical interventions. This paper embarks on a comprehensive analysis of such interventions, leveraging real-world data subjected to rigorous examination through the Kruskal-Wallis statistical testing and the TOPSIS decision-making method. Our study is dedicated to discerning the most effective drug for acute sinusitis, considering a spectrum of factors including efficacy, ease of use, user satisfaction, and cost-effectiveness. By synthesizing empirical evidence and statistical analyses, we aim to provide valuable insights that can inform clinical decision-making and optimize patient care in the management of acute sinusitis. This research holds promise in guiding healthcare providers towards evidence-based prescribing practices, enhancing treatment outcomes, and ultimately improving the quality of life for individuals suffering from acute sinusitis. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

引用

IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

引用

Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

来源：评论

学校读者我要写书评

暂无评论

Analysis of Deep Learning and Machine Learning Methods for Breast Cancer Detection 1

Analysis of Deep Learning and Machine Learning Methods for B...

引用

1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Latha, D.U. Mahesh, T.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350341737

Breast cancer is the disease with the greatest incidence rate and the fastest global spread. Compared to men, women are diagnosed with breast cancer far more frequently. If detected early enough, breast cancer can be successfully treated. One of the leading causes of death internationally, breast cancer accounted for 22.6% of all cancer-related deaths worldwide in 2013. This indicates that more than one crore cancer-related deaths were brought on by breast cancer. 14.7% of all cancer cases in India are caused by breast cancer. Early breast cancer recognition has been the subject of extensive investigation since it can help with treatment start-up and reduce overall mortality rates. Many instances can be managed and the overall death rate can be lowered with an early diagnosis. Breast cancer has been the subject of numerous research studies. Machine learning is the method being applied in research. Older machine learning-based research that excelled in their respective fields include those that used decision trees, KNN, SVM, and naive bays techniques. Better algorithms have been created as a result of these investigations. On the other hand, deep learning, a recently created technique, is currently being utilised to categorize breast cancer. Data science commonly makes use of convolutional neural networks, recurrent neural networks, and other deep learning-based techniques. Deep learning algorithms outperform machine learning algorithms in terms of performance. The most captivating portions of the photographs have been removed. Many academics utilize CNN to categorize the images. In a nutshell, CNN is the technique that is most frequently used to categorize images. This piece of work compares and contrasts the employment of machine learning and deep learning techniques in numerous journals. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Design of Orthogonal Switchable Feed Networks Integrated Microstrip Antenna for Six Polarization States 3

A Design of Orthogonal Switchable Feed Networks Integrated M...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Rahman, Muhammad Asad Rahman, Md. Naimur Hossain, Md. Azad Chittagong University of Engineering and Technology Faculty of Electrical and Computer Engineering Bangladesh

ISBN: (纸本)9798350345360

In this paper, an aperture-coupled microstrip antenna with polarization reconfigurability in the sub-6 GHz range is proposed. The proposed antenna can excite horizontal and vertical linear polarization (LP), +45°and -45°LP, right-hand and left-hand circular polarization (CP). To attain polarization reconfigurability, a square shaped patch antenna is excited by either single or dual-feeding mode with two orthogonal aperture-coupled switchable feed networks where each network has either a quadrature length shifter or a 180° phase shifter. Single port is used to excite horizontal and vertical LP. On the other side, switchable dual port excitation can produce +45°LP, -45°LP, RHCP, and LHCP. Basically, 180° phase shifter helps to achieve ±45° LP and quadrature length shifter is used to excite dual-CP sense. For wireless applications, the suggested antenna is more appealing due to its low cross-polarization level and good isolation in each polarization state. The proposed antenna has 10-dB impedance bandwidth (BW) of 1.17% for LP, 0.72% for LHCP, and 0.60% for RHCP. Moreover, 3-dB axial ratio (AR) BW is more than 2.50% for both CP senses. Better isolation characteristic ( © 2023 IEEE.

关键词： Polarization

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：