检索结果-内蒙古大学图书馆

44th International Conference on Information systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Kurnia, Sherah Li, Mingye Liu, Libo Cui, Tingru Li, Wen Tortorella, Guilherme Luz Palmer, Martin Ali, Mazen School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Mechanical Engineering University of Melbourne Melbourne Australia Department of Chemical Engineering University of Melbourne Melbourne Australia College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9781713893622

Digital food supply chain traceability (DFSCT) has the potential to address global sustainability concerns. However, limited studies investigate the use of technologies to support food supply chain traceability. Furthermore, empirical studies that show the impacts of DFSCT on sustainability are lacking. This research in progress aims to investigate the impact of DFSCT on the economic, environmental, and social benefits, and to explore how these three benefits affect each other. Through a survey of 310 organisations in the Australian food industry, our study findings show the importance of DFSCT in enhancing organisations’ sustainability performance, with empirical evidence demonstrating the positive impact of social and environmental performance on economic performance. This paper offers modest contributions to research and practice related to the role of technology in supporting traceability and sustainable supply chain management. Copyright © 2023 Kurnia, Li, Liu, Cui, Li, Tortorella, Palmer and Ali.

关键词： Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Sea Cense - A Commercially Valued Sea Cucumber Analyzer 5

Sea Cense - A Commercially Valued Sea Cucumber Analyzer

引用

5th International Conference on Advancements in computing, ICAC 2023

作者： Hettiarachchi, Thavishi Herath, Gagani Jayasekara, Kasuni Thushamini, Malmi Attanayaka, Buddhima Gamage, Narmada Nishanthan, G. Dissanayake, D.C.T. SLIIT Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka SLIIT Faculty of Computing Department of Information Technology Malabe Sri Lanka South Easetern University Faculty of Technology Department of BST Oluvil Sri Lanka University of Sri Jayawardanapura Department of Zoology Nugegoda Sri Lanka

ISBN: (纸本)9798350358131

In Sri Lanka, sea cucumbers are a highly sought-after marine product that is traded abroad. It has gained significant attention in recent years due to its high demand in the seafood market and its medicinal properties. Yet, the Sri Lankan Sea cucumber industry has not been able to fully realize its potential due to barriers like inaccurate methods for identifying and predicting the quality of sea cucumber species and the inability to provide a proper market value. This study offers an innovative solution that automates the evaluation and classification of sea cucumbers utilizing advanced computer vision and machine learning approaches. The proposed analyzer incorporates a multi-step approach, including image preprocessing, feature extraction, and classification. A set of preprocessed images are used to extract textural and distinguished color-based characteristics. These characteristics are then used to accurately classify sea cucumber species and grade them according to quality criteria using a machine learning algorithm. This project introduces a novel approach to analyzing sea cucumber length to calculate the price using a mobile application, making the task more efficient and accessible. The proposed solution 'Sea Cense' comprises 4 models that are developed using the CNN algorithm. These models' experimental findings demonstrated accuracy levels exceeding 85%. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Toward a Cognitive Data Model: Exploring a Mind-Inspired Approach to Database Design

arXiv

引用

arXiv 2025年

作者： Pieris, Dhammika Department of Information Systems Engineering and Informatics Faculty of Computing University of Sri Jayawardenepura Sri Lanka

The Cognitive Data Model (CDM) is proposed. A novel approach to database design, inspired by the belief that the human brain operates with a logical data model independent of its anatomical structure. The study aims to identify and replicate this CDM to enhance database design, resolving limitations of the existing models in handling complex relationships, scalability, and adaptability. The methodology involves empirical observation, cognitive experiments, iterative modelling and critical thinking. Findings suggest that the information processing in the brain occurs sequentially with forming associations on atomic static data in a pairwise, time-dependent manner. This insight led to the development of a meta-framework for the brain’s information processing to design CDM based on it. The CDM offers improved data modelling and database design, efficient querying, and potential applications in AI, machine learning, and big data analytics. Future research will focus on formalizing the model, implementing it in 3D environments, and developing a query language. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Time Series Forecasting Using State Space Models 24th

Machine Learning for Time Series Forecasting Using State Sp...

引用

24th International Conference on Intelligent Data engineering and Automated Learning, IDEAL 2023

作者： Sanchez-Bornot, Jose M. Sotero, Roberto C. School of Computing Engineering and Intelligent Systems Ulster University Derry/Londonderry United Kingdom Department of Radiology and Hotchkiss Brain Institute University of Calgary CalgaryAB Canada

ISBN: (纸本)9783031482311

State-space models (SSMs) are becoming mainstream for time series analysis because their flexibility and increased explainability, as they model observations separately from unobserved dynamics. Critically, using SSMs based on multivariate autoregressive equations enhances understanding of system evolution and its dynamical interactions. However, some challenges remain unsolved, such as estimation in large-scale scenarios, with very noisy data, and model selection. Here, we explore a state-space alternating least squares (SSALS) algorithm for time series forecasting, demonstrating its application with simulated and real data, and how to solve model selection in noisy scenarios with a novel cross-validation technique. Altogether, testing this methodology with time series forecasting is ideal to demonstrate its strengths and weaknesses, and appreciate its advantages compared to current methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Abnormal Image Classification Using Vision Transformer for Smart Agriculture 8

Abnormal Image Classification Using Vision Transformer for S...

引用

8th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Hafeez, Talha Shahid, Muhammad Farrukh Khanzada, Tariq J.S. Fast School of Computing FAST-NUCES Karachi Pakistan King Abdulaziz University Faculty of Computing and Information Technology Department of Information System Jeddah21589 Saudi Arabia Mehran Uet Computer Systems Engineering Departments Jamshoro Pakistan

ISBN: (纸本)9798350327090

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Prioritizing Network-On-Chip Routers for Countermeasure Techniques against Flooding Denial-of-Service Attacks:A Fuzzy Multi-Criteria Decision-Making Approach

引用

Computer Modeling in engineering & Sciences 2025年第3期142卷 2661-2689页

作者： Ahmed Abbas Jasim Al-Hchaimi Yousif Raad Muhsen Wisam Hazim Gwad Entisar Soliman Alkayal Riyadh Rahef Nuiaa Al Ogaili Zaid Abdi Alkareem Alyasseri Alhamzah Alnoor Department of Electromechanical Systems Engineering Thi-Qar Technical CollegeSouthern Technical UniversityBasra61001Iraq Department of Civil Engineering Wasit UniversityAl Kut52001WasitIraq Technical Engineering College Al-Ayen UniversityThi-Qar64001Iraq Department of Artificial Intelligence Engineering College of EngineeringAlnoor UniversityNinavah41012Iraq Information Technology Department Faculty of Computing and Information Technology at Rabigh King Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyWasit UniversityAl kut52001WasitIraq Information Technology Research and Development Center University of KufaNajaf54001Iraq College of Engineering University of Warith Al-AnbiyaaKarbala56001Iraq Management Department College of Business Administration(COBA)A’Sharqiyah University(ASU)Ibra400Oman

The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an *** this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA ***,an MCDM approach is essential to address these emerging *** the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the *** results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of *** contrast,the Hop count is identified as the least significant criterion,with a weighted score of *** remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring *** terms of ranking,SvN-FDOSM has two approaches:individual and *** the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under *** sensitivity analysis provides a high stability in ranking among all 10 *** approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.

关键词： NoC-based MPSoC security flooding DoS attack MCDM FDOSM FWZIC fuzzy set

来源：评论

学校读者我要写书评

暂无评论

Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

引用

2024 IEEE International Conference on Electronic systems and Intelligent computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Monitoring Lightning Activity on a Wind Turbine in Croatia: Observations During Spring and Summer Season

Monitoring Lightning Activity on a Wind Turbine in Croatia: ...

引用

International Colloquium on Smart Grid Metrology (SmaGriMet)

作者： Franjo Vukovic Bozidar Filipovic-Grcic Nina Stipetic Bojan Franc Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350357028

ISBN: (纸本)9798350357035

This paper provides an overview of lightning currents measured on a wind turbine in the southern part of Croatia during the summer and spring months in 2023. The wind turbine has been equipped with two Rogowski coils installed at the base of the tower since July 2022. A high-speed camera has been installed since April 2023. In addition, Croatian territory has been covered by a ground-based lightning location system since 2009, and therefore lightning flash data, including amplitude, timing, and polarity, is also available onsite. This paper presents and briefly reviews lightning events by examining current waveforms, high-speed camera frames of the lightning channels and associated luminosity profiles, and lightning location system data. In the considered period, there were 34 lightning flashes. There were 33 upward flashes initiated from the WT blades and only 1 downward flash initiated from the cloud. In terms of polarity, 18 flashes transferred negative charge to the ground, 12 transferred positive charge, and 4 transferred both positive and negative charges (bipolar flashes).

关键词： Reviews Poles and towers Lightning Metrology Cameras Wind turbines Timing Smart grids Springs Monitoring

来源：评论

学校读者我要写书评

暂无评论

Exploring Potential Drivers Influencing BIM Adoption in the AEC Industry: A Systematic Approach

Exploring Potential Drivers Influencing BIM Adoption in the ...

引用

2023 IEEE International Conference on computing, ICOCO 2023

作者： Biswas, Humayun Kabir Sim, Tze Ying Lau, Sian Lun Sunway University Department of Computing and Information Systems Subang Jaya Malaysia School of American Education Sunway University Subang Jaya Malaysia Sunway University Department of Engineering Subang Jaya Malaysia

ISBN: (纸本)9798350302684

In the recent year Architecture, engineering and Construction (AEC) industry achieved tremendous attention among the stakeholders to optimise the facility management and operations of construction workflows. The application of numerous disruptive technologies is developed and practiced to digitalise the functionalities in the AEC industry. Though incredible development has been accomplished through Building Information Modelling (BIM) to optimise construction facility management and operations, BIM adoption and implementation are still under the satisfactory level in developing countries. It is noticed that BIM is practised by professionals for new and ongoing construction facilities. Existing research has failed to address the research gap associated with the driver identification methodology that has significant impact and influences on enhancing the BIM adoption in the AEC industry. This research proposes a systematic approach to investigate the potential BIM adoption drivers facilitating BIM adoption among stakeholders in the AEC industry. The content of Ninety-eight existing BIM adoption drivers was evaluated and grouped concerning the proposed new label of drivers. Nine BIM adoption drivers were shortlisted and are considered as potential drivers impacting BIM adoption in the AEC industry. The developed BIM adoption training model automatically extracts the three sets of BIM adoption drivers using the free text of 24 source references to assess the potential BIM adoption drivers. The proposed approach is evaluated considering the content analysis and Machine learning-based methods. Though the preliminary results of this study were not validated due to time constraints, however providing a precise guideline for the researchers and professionals in the AEC industry to optimise the functionalities and facility management of ongoing and existing construction facilities. Future research should consider the more robust and authentic BIM adoption driver validation pr

关键词： Developing countries

来源：评论

学校读者我要写书评

暂无评论

Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress

引用

Computers in Biology and Medicine 2025年 185卷 109534-109534页

作者： Hameed, Ibtehaaj Khan, Danish M. Ahmed, Syed Muneeb Aftab, Syed Sabeeh Fazal, Hammad Department of Telecommunications Engineering NED University of Engineering and Technology Karachi Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Selangor47500 Malaysia

This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces. © 2024 Elsevier Ltd

关键词： Electroencephalography

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：