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Runtime-based metaheuristic prediction of the compressive strength of net-zero traditional concrete mixed with BFS, FA, SP considering multiple curing regimes

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Asian Journal of Civil engineering 2024年第2期25卷 1241-1253页

作者： Onyelowe, Kennedy C. Kontoni, Denise-Penelope N. Pilla, Sita Rama Murty Hanandeh, Shadi Ebid, Ahmed M. Razzaghian Ghadikolaee, Mehrdad Stephen, Liberty U. Department of Civil Engineering Michael Okpara University of Agriculture Umudike Umuahia 440109 Nigeria Department of Civil Engineering School of Engineering University of the Peloponnese Patras 26334 Greece School of Science and Technology Hellenic Open University Patras 26335 Greece Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Vishnupur Bhimavaram India Civil Engineering Department Faculty of Engineering Al-Balqa Applied University Salt Jordan Department of Civil Engineering Faculty of Engineering Future University in Egypt New Cairo Egypt School of Civil Engineering Iran University of Science and Technology Tehran Iran Department of Civil Engineering University of Agriculture and Environmental Sciences Umuagwo Nigeria

Toward carbon neutrality concrete technology (TCNCT) is the main topic in the net-zero construction industry, and designing a net-zero concrete optimized to meet the required near-zero carbon footprint specifications is one of the major goals of ecofriendly concrete technology. Several concrete mix-design methods were developed based on optimizing certain factor such as the unit weight of the concrete or the specific surface area of the aggregates. However, these available mix-design methods are limited to certain types of cement and aggregates while the construction industry today is looking for more advanced, sustainable, and ecofriendly (net-zero) materials such as blast furnace slag (BFS), fly ash (FA), and superplasticizer (SP). The aim of this study is to develop a predictive model for the compressive strength of certain net-zero concrete mixes at different ages considering the previously mentioned “towards carbon neutrality” industrial waste materials using the novel superspeed metaheuristic predictive techniques. A database of 1133 records of net-zero concrete mixes was collected from literature and studied to be used in training and testing the predictive models. The performance metrics used in this exercise are mean absolute error (MAE), mean squared error (MSE), and R ² score. The Automl tools were very handy as they presented the fastest models with the best performances also. The AutoSklearn models were presented for both 5 min and 10 min execution time. At the 5-min limit execution time, a performance of R ² equals 91.8%, with MAE of 3.395% and MSE of 23.224% was achieved in 262 s (4.37 min). At the 10-min set execution time, a performance of R ² equals 92.2%, with MAE of 3.197% and MSE of 22.224% was achieved in 597 s (9.95 min). The AutoGluon (TabularPredictor) was also deployed to model the CCS from the global database of nine features with no set time limits. This was executed in 785 s (13.08 min) and produced a mod

关键词： Blast furnace slag (BFS) Fly ash (FA) Metaheuristic modeling Multiple curing Net-zero concrete compressive strength Superplasticizer (SP) Sustainable concrete

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Interference Characterization and Mitigation for Multi-Beam ISAC Systems in Vehicular Networks

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IEEE Transactions on Wireless Communications 2024年第10期23卷 14729-14742页

作者： Wang, Yi Zhang, Qixun Zhang, J. Andrew Wei, Zhiqing Feng, Zhiyong Peng, Jinlin The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Quzhou324003 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW2007 Australia The Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing100071 China

— Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes. © 2002-2012 IEEE.

关键词： Resource allocation

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Brain Tumor Automated Detection System Based on Hybrid Deep Learning Networks Using MRI Images

Brain Tumor Automated Detection System Based on Hybrid Deep ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Salama, Gerges M. Ashraf, Shady Bayoumi, Esraa Salah Elwan, Mohammed Abd-Ellah, Mahmoud Khaled Minia University Faculty of Engineering Department of Electrical Engineering Minia6111 Egypt Egyptian Academy for Engineering and Advanced Technology Electrical Engineering Department Egypt College of Computing and Information Technology Arab Academy for Science Smart Village GizaB 2401 Egypt Egyptian Russian university Faculty of Artificial Intelligence Cairo11829 Egypt

ISBN: (纸本)9798350351408

technology undeniably has a significant impact on human life, particularly in advancing various aspects of healthcare. artificial intelligence (AI) has greatly contributed to the early and precise detection of tumors, especially in the realm of medical image processing, with a specific focus on brain tumor detection using magnetic resonance imaging (MRI). However, manually analyzing numerous MRI images is a challenging task in clinical settings. Hence, the utilization of a computer-aided diagnostic (CAD) system is imperative for the timely identification of normal and abnormal brain tissues. This study proposes a hybrid detection architecture to determine the presence of brain tumors. The system incorporates five different deep convolutional neural networks (CNNs) for feature extraction, each comprising five models that identify the optimal five layers for feature extraction before employing Support Vector Machine (SVM) for classification. The system undergoes training and evaluation to ascertain the most effective detection outcomes. The study utilizes two datasets to assess the system's performance and functionality. © 2024 IEEE.

关键词： Support vector machines

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Early Detection of Coconut Palm Leaflet Diseases using Convolutional Neural Networks based Deep Learning Approach

Early Detection of Coconut Palm Leaflet Diseases using Convo...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Santhiya S Lalitha Krishnasamy Sharmila C Jayadharshini P Dharshan P P Haripreetha S Department of Artificial Intelligence Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of CSE SRM Institute of Science and Technology Tiruchirappalli India Artificial Intelligence and Data Science Kongu Engineering College Erode India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

In tropical regions, coconut palms are an essential part of the economy and daily life, providing food, oil, and many other products. However, their health and productivity can be affected by various diseases. Early and precise disease identification is essential for the management and prevention of many diseases. Leaflet diseases in coconut plants are those that impact individual leaflets, or leaf segments, on the fronds of coconut palms. Drying leaflets in coconut tree leaves can result from a variety of factors, including biotic (living organisms) and abiotic (environmental or non-living) impacts. In coconut trees, chlorosis, or the yellowing of the leaves, is a prevalent issue that can be caused by some factors. The CNN model is trained on a sizable set of high-resolution images of both healthy and diseased coconut leaves. Each disease's unique characteristics and patterns are automatically acquired with the aid of the deep learning architecture, enabling accurate categorization. Using an additional set of annotated photos of coconut tree leaves, the CNN model is assessed with remarkable accuracy in identifying illnesses. Comparing the system to traditional manual diagnosis procedures demonstrates the technology's efficacy and potential for early disease identification. Using this strategy raises the algorithm's accuracy to 95%

关键词： Deep learning Productivity Accuracy Prevention and mitigation Oils Vegetation Prediction algorithms Organisms Convolutional neural networks Diseases

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Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2309-2321页

作者： Raghupathy, Bala Krishnan Vairavasundaram, Subramaniyaswamy Ganesan, Manikandan Namachivayam, Rajesh Kumar Kotecha, Ketan Herencsar, Norbert SASTRA Deemed University Department of CSE India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India SASTRA Deemed University School of Computing India Symbiosis Centre for Applied Artificial Intelligence Pune India Brno University of Technology Faculty of Electrical Engineering and Communication Department of Telecommunications Technicka 3082/12 Brno61600 Czech Republic

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain. © 2024 IEEE.

关键词： Convolutional neural networks

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Serverless Blockchain-based AI-Powered Financial Transaction Management System on Cloud 3

Serverless Blockchain-based AI-Powered Financial Transaction...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Sri Sathya, K.B. Nithyasri, A. Amirtha, E. Fowjiya, S. KPR Institute of Engineering and Technology Computer Science and Engineering Coimbatore India M. Kumarasamy College of Engineering Artificial Intelligence and Data Science Karur India Computer Science and Technology Tiruchengode India

ISBN: (纸本)9781665456647

The Finance Manager Application is used to automate the existing finance tracking system and to offer the user to have a more engaging and aesthetic user experience with the bill tracking feature. The existing systems are used to record several financial transactions and events. They are not giving an overview of the amount spent and in which category the user has transacted more amount and hence it does not support expense management. Mainly the existing finance tracking systems are not using AI-based authentication which is more convenient for the user. Our web application is proposed to give transaction charts and graphs that give more clarity of the income, expenses, and transfers. In addition, we can analyse the transactions in category-wise, and we can track the history. Along with form-data authentication, we are providing OAuth and Zero-effort authentication that is more user friendly. Our SPA provides the facility to add multiple transactions at a time and it is to show the balance in the wallet on the future according to the planned transactions. Also, it can automate the data entry for recurrent transactions. © 2022 IEEE.

关键词： Finance

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Federated Learning for SMS Spam Detection: A Privacy-Focused Approach 15

Federated Learning for SMS Spam Detection: A Privacy-Focused...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vats, Satvik Shastri, Suryakant Mehta, Shiva Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Iimt College of Engineering Artificial intelligence and Data Science Department Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

Herein proposes a new classification technique of SMS spam within a federated learning system, based on which the process occurs on the user's device. The objective is to realize the perfect balance of the two;on the one hand, it should be proactive with spam detection, and on the other hand, it should have the highest respect for privacy protection. Our research demonstrates that this method has significantly boosted classification accuracy without privacy issues by equating an appropriate evaluation technique for extensive testing and assessment. The statistical analysis shows that our federated learning algorithm significantly reduces loss;according to the statistical analysis results, this value increases from 0.267 to 0.389 from the first to the tenth epoch. Also, the system reveals that this mechanics remains excellent and keeps showing values of more than 90%, and the third epoch shows nothing but a high accuracy value of 98.3%. In effect, we analyze the cornerstone of our knowledge based on the method of the critical priorities potentially tightened privacy protection for federated learning. The federated learning solution offers a transparent approach that enables users to access essential services directly on their devices, and then the trained machine learning models are merged securely. This study underscores the competency to differentiate spam from anything useful in electronic communication and considers it a significant issue as regards privacy protection. The outcome gives a sense of hope in the idea of federated learning to have specific SMS spam. It creates a good gap between effectiveness and privacy protection, which is especially important considering that mobile communication covers a vast territory today. © 2024 IEEE.

关键词： Federated Learning Machine Learning Mobile Communication On-Device Learning Privacy Preservation SMS Spam Detection

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AUDIRE: A Casting Website

AUDIRE: A Casting Website

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： V Ganapriya Nisarga Hospet Rakshita G Hiremath N Tejaswini G K Ravikumar M Sushma Department of Artificial Intelligence & Data Science BGS College of Engineering and Technology Bengaluru India BGS College of Engineering and Technology Bengaluru India

ISBN: (数字)9798331537012

ISBN: (纸本)9798331537029

This study investigates the challenges faced by aspiring actors and theatre graduates in building sustainable careers. Highlighting the limitations of academic training in preparing students for professional realities, it examines audition systems, career development, and marketing strategies. Using qualitative methods, the research offers practical solutions, including creating effective audition packages, excelling in performance etiquette, and leveraging marketing tools like resumes and demo reels. The findings provide actionable insights to empower actors to navigate the competitive performing arts industry.

关键词： Training Knowledge engineering Industries Career development Casting engineering profession Navigation Communication systems Buildings Interviews

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