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2021 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021

作者： Yang, Liqun Liu, Yan Zeng, Wei Yang, Yijun School of Information Science and Computing Florida International University Miami United States College of Civil Engineering and Architecture Qingdao Agricultural University Qingdao China School of Mathematics and Statistics Xi'An Jiaotong University Xi'an China School of Computer Science and Technology Xi'An Jiaotong University Xi'an China

Like the textbook for students' learning, the training data plays a significant role in the network's training. In most cases, people intend to use big-data to train the network, which leads to two problems. Firstly, the knowledge learned by the network is out of control. Secondly, the space occupation of big-data is huge. In this paper, we use the concepts-based knowledge visualization [33] to visualize the knowledge learned by the model. Based on the observation results and information theory, we make three conjectures about the key information provided by the dataset. Finally, we use experiments to prove that the artificial abstracted data can be used in networks' training, which can solve the problem mentioned above. The experiment is designed based on Mask-RCNN, which is used to detect and classify three typical human poses on the construction site. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Role of Natural Language Processing in Improving Lives of Disabled 10

Role of Natural Language Processing in Improving Lives of Di...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Nadeem, Warda Shukla, Vinod Kumar Preetha, V.K. Kaur, Gagandeep Bhardwaj, Aradhana Balodi Amity University Dubai Department of Information Technology United Arab Emirates Amity University Dubai Department of Engineering and Architecture United Arab Emirates University of Stirling Rak Department of Computing United Arab Emirates School of Law University of Petroleum and Energy Studies Dehradun India Amity University Department of Psychology Dubai United Arab Emirates

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Natural Language Processing is a branch of AI that aims to assist the machine in deciphering natural language. We can implement this Artificial Intelligence to assist Real Intelligence where people of determination need it. A vast amount of potential can be reached in NLP to help people with impairments. NLP has both a negative and positive role in the lives of the disabled, but correct implementation can advance recent work to improve the future. This paper talks about the role of natural language processing in the lives of people of determination. It suggests a model that could most likely be the tool that hearing, and visually impaired people need, to keep up with abled people. It also mentions the negative side of NLP in the lives of people of determination and social biases. Possible faults can arise during implementation that has been addressed along with other work from authors. A system must understand natural language to proceed and implement it, which is a difficult task due to the many complexities of natural language. However, once implemented, NLP can be used as a tool in many areas of life, from aiding the disabled to allowing diverse people to communicate easily. © 2022 IEEE.

关键词： Artificial intelligence

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Probabilistic Grading and Classification System for End-of-Life Building Components Toward Circular Economy Loop

arXiv

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arXiv 2025年

作者： Meng, Yiping Cavalaro, Sergio Osmani, Mohamed School of Computing Engineering and Digital Technologies Teesside University United Kingdom School of Architecture Building and Civil Engineering Loughborough University United Kingdom

The longevity and viability of construction components in a circular economy demand a robust, data-informed framework for reuse decision-making. This paper introduces a Multi-Level Grading and Classification System (MGCS) that combines Bayesian probabilistic modeling with scenario-based performance thresholds to assess the reusability of end-of-life (EoL) modular components. By grading components across a five-tier scale (A–E), the system supports strategic decisions for reuse, up-use, or down-use, ensuring alignment with engineering standards and sustainability objectives. The model’s development is grounded in empirical data from precast concrete wall panels, and its explainability is enhanced through decision tree logic and Sankey visualizations that trace the influence of contextual scenarios on classification outcomes. MGCS addresses the environmental, economic, and operational challenges of EoL management—reducing material waste, optimizing value recovery, and improving workflow efficiency. Through dynamic feature weighting and transparent reasoning, the system offers a practical yet rigorous pathway to embed circular thinking into construction industry practices. © 2025, CC BY.

关键词： Reusability

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Cuddapah Anitha Dalia Al-Tarazi GD. Vignesh A. Vinotha Vasuki Jayant Giri M. Dinesh Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of Architecture Engineering Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.

关键词： Vehicular ad hoc networks Routing Throughput Synchronization Resource management Dedicated short range communication Particle swarm optimization Artificial intelligence Vehicle dynamics Testing

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Morphological analysis of historical lanscapes based on cultural DNA approach

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Digital Applications in Archaeology and Cultural Heritage 2023年 30卷

作者： Goodarzi, Parichehr Ansari, Mojtaba Mahdavinejad, Mohammadjavad Russo, Alessio Haghighatbin, Mahdi Pour Rahimian, Farzad Department of Architecture Faculty of Art & Architecture Tarbiat Modares University Tehran Iran School of Arts University of Gloucestershire Francis Close Hall Campus Swindon Road Cheltenham GL50 4AZ United Kingdom School of Computing Engineering & Digital Technologies Teesside University Middlesbrough United Kingdom

Recent trends in landscape architecture investigate new approaches, methods, and technologies to understand, monitor, manage, continuity, and sustainably develop heritage landscapes. Cultural DNA (CD) represents designs' transferable geometric, behavioural and functional properties. The morphological structure of Persian historical gardens possesses discoverable hidden patterns, computable through the CD's mathematical and computational models. This study aims to investigate and discover effective parameters in the spatial structure of historical Persian gardens using Space Syntax. Six Persian Gardens were surveyed, and spatial structures were analysed using DepthMap10 software. The results show that these spaces follow meaningful mathematical patterns and rules that can be formulated in the context of generative processes and generalized for the evolutionary continuity of gardens. However, further research is proposed to develop more advanced computational methods, such as artificial intelligence (AI) algorithms and AI-based decision support tools, to help to generate design scenarios and to transmit culture through the design process. © 2023

关键词： Cultural DNA (CD) Historic Persian garden Pattern language Space syntax Unmanned aerial vehicles (UAVs) Visual computing

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Visible Light Positioning Based on a Single Luminaire: A Novel Visual Odometry Assisted Algorithm

Visible Light Positioning Based on a Single Luminaire: A Nov...

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IEEE International Conference on Communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

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Strength of polypropylene fiber reinforced cement mortar composites with variable water/cement ratio

Strength of polypropylene fiber reinforced cement mortar com...

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International Symposium on Advanced Materials and Application, ISAMA 2021

作者： Nikoloutsopoulos, N.D. Nikolopoulou, S.P. Pandermarakis, Z.G. School of Architecture Computing and Engineering University of East London UK – AKMI Metropolitan Campus Akadimias 42 Athens106 72 Greece Region of Attica Directorate of Environment & Climate Change Polytechniou 4 Athens104 33 Greece

ISBN: (纸本)9783035738360

For cement mortars the reinforcement techniques by randomly distributed short fibers, contribute highly to their micro-cracking stabilization and to improvement of their flexibility and tensile strength. Among the wide different types of short fiber reinforcement that have been used in the past for mortars, those made of steel, polymers and natural fibers dominate the area. It is a common sense that the reinforcement of mortars by polypropylene fibers (PP) is considered a very efficient method for reducing their curing shrinkage and enhanced toughness and strength of un-reinforced cemented material. In this study, PP fiber reinforced mortars were prepared with specific composition but with variable water to cement (W/C) ratio and appropriate superplasticizer amounts. For all mixtures their workability, air content and flexural and compressive strengths were measured. In conclusion it can be stated that even though W/C ratio of mortars is varied considerably, and also do their mechanical response, it is possible by appropriate mixture designing, for all studied compositions to produce suitable mortars that can be used successfully in a wide range of demands and applications, achieving high mortar strengths and ideal workability behavior. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Mortar

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Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

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Predictive Classification of Covid-19: Assessing the Impact of Digital Technologies

Predictive Classification of Covid-19: Assessing the Impact ...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Bonda Kiran Kumar Anurima Majumdar Saidatul Akmar Ismail Rohit R Dixit Hairani Wahab Md Hafizi Ahsan KL School of Architecture KL Deemed to be University Koneru Lakshmaiah Educational Foundation vaddeswaram Andhra Pradesh Department of Electronics and Communication Engineering Guru Nanak Institute of Technology School of Information Science College of Computing Informatics & Mathematics Universiti Teknologi MARA Shah Alam Malaysia Siemens Healthineers School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Labuan Faculty of International Finance Universiti Malaysia Sabah

The recent severe outbreak of the disease has been linked to the COVID-19 virus. It is essential for healthcare professionals and individuals with the capacity to provide intensive care to offer clear explanations of this situation. To address this issue, the World Health Organization (WHO) has implemented several measures. The National Emergency Telecritical Care Network (NETCCN) has the capability to assess these actions using digital technologies. This section provides a comprehensive explanation of these developments, and NETCCN has put forth a recommendation. Version 2.0 of this digital health data will be made available; it will significantly enhance potential crises in the future. Artificial Intelligence (AI) is used in the suggested digital prediction approach to gather past data on the infection. Telemetry, sophisticated maintenance robotics, and monitoring intelligence may all be found with this technology. These may be created to incorporate data relevant to various infections. Health experts will coordinate all of these. To maintain sustained preparedness for emergency catastrophe protection, frequent testing should be conducted.

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Analyzing Children’s Behaviors Based on AI Recognition Approach to Promote Child-Friendly school-to-Home Street Design 18th

Analyzing Children’s Behaviors Based on AI Recognition Appr...

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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Zhang, Qianxi Wang, Gang Loo, Yat Ming Wang, Xinkai Xia, Xiumin Mu, Xingyu School of Design NingboTech University Ningbo315100 China Department of Architecture and Engineering University of Nottingham Ningbo China Ningbo315100 China The Bartlett Development Planning Unit University College London LondonWC1H 9EZ United Kingdom School of Computing and Data Engineering NingboTech University Ningbo315100 China Zhejiang Fashion Institute of Technology Ningbo315211 China

ISBN: (纸本)9789819707904

school-to-home streets are an important part of the public space network of child-friendly cities. The improvement of their child-friendliness needs to be based on the investigation and evaluation of the current usage status. The traditional site investigation methods are limited by manpower and time, lack of efficiency and effectiveness. This paper proposes a new analysis framework for children’s street behaviors based on AI recognition methods, including action recognition algorithm, human flow statistic algorithm, and target tracking algorithm. It takes one school-to-home street in the Mingdong community, Ningbo city as a case study. Based on the AI recognition of this street’s surveillance videos, the result data verifies the effectiveness of street usage analysis. This is valuable to promote the child-friendly school-to-home street design in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Target tracking

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