检索结果-内蒙古大学图书馆

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

computer systems science & engineering 2023年第3期44卷 1939-1950页

作者： Dhanalakshmi R Sovan Samanta Arun Kumar Sivaraman Jeong Gon Lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran Department of Computer Science and Engineering KCG College of TechnologyChennai600097Tamil NaduIndia Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Instrumentation Engineering PDEAs College of EngineeringManjari412307PuneIndia Engineering and Research Services HCL TechnologiesChennai600119India

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a *** can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently *** fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible ***,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation *** bipolar valued fuzzy set(HBVFS)is a generalization of *** paper aims to introduce a general framework of multi-attribute group decision-making using social *** propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making *** the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of *** Type-2 decision making processs,there are two major aspects we ***,consistency tests and checking of consensus models are given for detecting that the judgments are logically ***,the framework demands(partial)decision-makers to review their ***,the coherence and consensus of several HBVFSs are established forfinal ranking of *** proposed framework is clarified by an example of software packages selection of a university.

关键词： Group decision-making aggregation operators hesitant bipolar-valued fuzzy set

来源：评论

学校读者我要写书评

暂无评论

DAPP IT – Multipurpose ID Card System for Universities 1

引用

Proceedings of the 6th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2023

作者： Harshini, C. Ananth, J. Vishal Ahmad, B. Abdul Rahman Arshad Thejeswari, C.K. Sumathy, V. Department of Computer Science & Business Systems Rajalakshmi Engineering College Tamil Nadu Chennai India Department of CSE Rajalakshmi Engineering College Tamil Nadu Chennai India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

Universities massively depend on identity cards for the identification of students, faculties, and staff. For the longest time identity cards haven’t seen much of an upgrade apart from the ability to record attendance using them. We propose a cashless payment model through identity cards that will make transactions anywhere within a university campus very quick and simple. This will be implemented by using RFID tags with antennas in the identity card and RFID sensors that enable the user to make cashless payment by tapping the identity card onto the sensor to perform the necessary action. Additionally, actions like marking attendance at various spots in the campus and functioning like an e-library card can also be performed with this system. This system will also include additional software to perform functions of a library card, manage the monetary transactions and keep track of attendance of all the people on campus. This will make the identity card a multipurpose tag adding more value and functionality to it. © 2023, IFIP International Federation for Information Processing.

关键词： Radio frequency identification (RFID)

来源：评论

学校读者我要写书评

暂无评论

Safety Helmet Detection of Workers in Construction Site using YOLOv8 26

Safety Helmet Detection of Workers in Construction Site usin...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Mahmud, Syed Shakil Islam, Md. Ashraful Ritu, Khandaker Jannatul Hasan, Mahmudul Kobayashi, Yoshinori Mohibullah, Md Comilla University Department of Computer Science and Engineering Cumilla Bangladesh Saitama University Interactive Systems Lab. Saitama Japan

ISBN: (纸本)9798350359015

The safety of construction workers is a paramount concern in the modern construction industry. A significant proportion of injuries and fatalities on construction sites are attributed to a lack of adherence to safety regulations, often resulting from the non-utilization of Personal Protective Equipment (PPE) by workers. Additionally, effective monitoring of construction areas by site supervisors can be challenging due to the vastness and complexity of construction sites. Manual monitoring, while a crucial aspect of construction safety management, is often hindered by time constraints and associated costs. To address these challenges, the application of computer vision and Convolutional Neural Network(CNN) techniques has led to the development of automated helmet and jacket detection systems. This research employs the YOLOv8 object detection algorithm to explore the efficacy of safety helmet detection, aiming to enhance the accuracy of automated safety helmet detection systems. The YOLOv8 object detection algorithm is a robust tool for recognizing objects in images. Our dataset comprises a total of 4,200 images, including 4,000 images of hard hats and jackets obtained from Kaggle [25], supplemented by 200 images depicting foggy, rainy, and nighttime conditions from our custom collection sourced from the internet. The dataset was meticulously divided into training, testing, and validation sets, following a ratio of 60%, 20%, and 20%, respectively. The experimental results revealed that the YOLOv8m architecture achieved an accuracy of 92%, demonstrating its effectiveness in detecting safety helmets under various lighting conditions, including low-light environments. This performance highlights the potential of the proposed approach for real-world applications in construction site safety monitoring. This developed model primarily focuses on detecting helmets and jackets in real-time in adverse weather conditions such as rainy, foggy, and low-light environments. It trigg

关键词： and jacket computer vision helmet helmet detection image processing object detection openCV YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Predicted Blood Pressure using Machine Learning 3

Predicted Blood Pressure using Machine Learning

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Keswani, Arul Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Manipal University Jaipur Department of Iot & Intelligent Systems Jaipur India

ISBN: (纸本)9798350319125

High blood pressure or hypertension, is a prevalent health condition affecting a significant portion of the population. It is a major determinant factor for various cardiovascular diseases and can expedite to severe complications if left untreated. Accurate prediction of blood pressure levels plays a crucial role in early detection, prevention, and management of hypertension although it is hard for an individual to test a blood pressure on a regular basis and if though population take a blood test regularly it is hard to rely on one test this research gives a solid idea that a individual is suffering from a regular high blood pressure or hypertension. This research paper provides a comprehensive review of predictive models for blood pressure prediction. It covers various machine learning techniques like CNN, random forest, and covers future directions for further research purposes. This paper gives accuracy of 91.30% using random forest classifier. Our results also show that convolution neural network (CNN) and random forest classifier are suitable models for predicting the blood pressure. © 2023 IEEE.

关键词： CNN Decision Tree Model Machine Learning model ensemble model Random Forest Model xgboost classifier

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Severity Detection in Alzheimer's Disease: A Comprehensive Study on Cognitive Impairment

Deep Learning-Based Severity Detection in Alzheimer's Diseas...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kayalvizhi, M. Senthil Kumar, G. Tushal, V. Yashvanth, M. Santhosh, G. Chennai Institute of Technology Department of Biomedical Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350348910

Alzheimer's disease is a neurodegenerative disorder affecting millions worldwide, causing cognitive decline. Early detection is crucial for effective treatment, but manual diagnosis is error-prone and time-consuming. This study proposes a deep learning-based model for diagnosing and classifying Alzheimer's disease using VGG-16 CNN architectures. The model uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes MRI scans from individuals with Alzheimer's and healthy controls. The model uses a pre-trained VGG-16 convolutional neural network as the base, fine-tuning on the dataset, and data augmentation techniques. The trained model achieves a 96.75% accuracy on the testing dataset, distinguishing MRI scans between Alzheimer's patients and healthy controls. The VGG-16 architecture demonstrated superior performance during training and testing. © 2023 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Yolo Based License Plate Detection of Triple Riders and Violators

Yolo Based License Plate Detection of Triple Riders and Viol...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Nandhakumar, R.G. Devi, K. Nirmala Krishnamoorthy, N. Shanthi, S. Pranesh, V.R. Nikhalyaa, S. Kongu Enginering College Perundurai Department of Computer Science and Engineering Tamilnadu Erode India Vellore Institutte of Technology School of Information and Engineering Department of Software Systems and Engineering Vellore India

ISBN: (纸本)9798350348217

Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can't continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m∗m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model's detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Method of Hardware Implementation of Membrane Computing Architectures for Mobile Robot Control 16

A Method of Hardware Implementation of Membrane Computing Ar...

引用

16th International Conference on Development and Application systems, DAS 2022

作者： Ababii, Victor Sudacevschi, Viorica Carbune, Viorel Munteanu, Silvia Alexei, Victoria Lasco, Victor Technical University of Moldova Computer Science and Systems Engineering Department Chisinau Moldova

ISBN: (纸本)9781665481625

This paper proposes a method of hardware implementation of the membrane computing architecture for the control of a mobile robot. The basic idea is to use in the development of control systems the models of functional description of living cells, which should ensure a process of design, modeling and implementation based on cognitive models. The implementation algorithm presents a sequence of operations involving: functional development and modeling of Computing Cells, development and modeling of the topology of the membrane computing system (P-System), their implementation in hardware description languages (AHDL), and configuration of the FPGA circuit for realization of the control system. For validation and analysis of the performance of the Computing Cells, Petri Net models are used, which ensure the identification of the concurrent processes while maintaining a maximum parallelism. Functional testing of membrane computing models was performed based on the Quartus II development environment, the AHDL hardware description language, and the Altera DE0 Board. © 2022 IEEE.

关键词： computer hardware description languages

来源：评论

学校读者我要写书评

暂无评论

Image Caption And Speech Generation Using LSTM And GTTS API 2

Image Caption And Speech Generation Using LSTM And GTTS API

引用

2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Ram, K. Bhargav Venkatesh, B. Sree, Sala Pooja Sai Anilkumar, Chunduru Reddy, V. Surya Narayana Kodumuri, Bhavya Bvrit Hyderabad College of Engineering for Women Department of Computer Science and Engineering Hyderabad India Systems Engineer Infosys Hyderabad India Gmr Institute of Technology Dept of Information Technology Andhra Pradesh Rajam532127 India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Vidya Jyothi Institute of Technology Department of Computer Science and Engineering HYderabad India

ISBN: (纸本)9798350325799

Image Caption generation is one of the challenging tasks in the field of artificial intelligence. It is used to generate a textual description for a given picture. But due to, the recent advancement in deep learning techniques requires only one single end-to-end model to create a caption for the given image. This paper is intended to provide an image caption and speech generator used to generate a single-line description for a given image and audio/speech for the report generated. The researchers used a sophisticated pipeline of specifically designed models in the previous models. To achieve the proposed model, VGG16 and LSTM models are used to obtain descriptions for the image and GTTS API is used for the audio/speech generation. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming 24

Enhanced cyber-physical system with semantic technologies an...

引用

24th International Conference on Control systems and computer science, CSCS 2023

作者： Ciolofan, Sorin N. Dragoicea, Monica Popeanga, Daniel-Catalin Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

An important aspect related to the effects of agricultural activities on the environment is represented by the nutrient loss in water and air (specifically nitrogen). The interactions between catchments hydrological processes, management of farm activities, climate changes and nitrogen losses constitute a complex phenomenon yet not well understood, being an important concern from the sustainable agriculture perspective. Nitrogen can be lost with water as leaching or runoff, or as gas as ammonia volatilization. Nitrous oxide (N2O) is particularly problematic because it is also a powerful greenhouse gas. The goal of the current article is to present innovative digital techniques to advance in understanding of this phenomena through an Information System that integrates Artificial Intelligence techniques such as Semantic Technologies and Machine Learning (ML) into Cyber-Physical systems (CPS) to support smart farming and sustainable agriculture. © 2023 IEEE.

关键词： Semantics

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：