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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Feedback Design and Implementation for Integrated Posture Manipulation and Thrust Vectoring

arXiv

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arXiv 2024年

作者： Dhole, Aniket Shashikant The Department of Electrical and Computer Engineering Master of Science in Electrical and Computer Engineering Northeastern University BostonMA United States

This MS thesis outlines my contributions to the closed loop control and system integration of two robotic platforms: 1) Aerobat, a flapping wing robot stabilized by air jets, and 2) Harpy, a bipedal robot equipped with dual thrusters. Both systems share a common theme of the integration of posture manipulation and thrust vectoring to achieve stability and controlled movement. For Aerobat, I developed the software and control architecture that enabled its first untethered flights. The control system combines flapping wing dynamics with multiple air jet stabilization to maintain roll, pitch and yaw stability. These results were published in the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). For Harpy, I implemented a closed-loop control framework that incorporates active thruster assisted frontal dynamics stabilization . My work led to preliminary untethered dynamic walking. This approach demonstrates how thrust assisted stability can enhance locomotion in legged robots which has not been explored before. Copyright © 2024, The Authors. All rights reserved.

关键词： Closed loop control systems

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A New Service Time Framework for Expressway Toll Lane Capacity Model 21

A New Service Time Framework for Expressway Toll Lane Capaci...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Klaykul, Pattarapon Prayote, Akara Lee, Wilaiporn King Mongkut's University of Technology North Bangkok Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

In expressway systems, critical challenges in toll plaza management arise due to traffic congestion at toll plazas during rush hours. Existing studies often rely on prediction models or simulations, where their accuracy to real traffic conditions is compromised. This study proposes a new framework for the service time to improve the overall analysis of lane capacity calculation for toll plaza management. The investigation uses real transaction data from the three expressways with the highest traffic volume from the Expressway Authority of Thailand (EXAT). The preprocessing includes data cleaning and outlier removal, where the interquartile range method is used to eliminate data that are above the range of realistic service time. Afterward, the upper bound threshold and k-nearest neighbors algorithm are used to learn a traffic pattern model to categorize peak and off-peak traffic. Our new framework is investigated both with real transaction data and in simulations. As a result, the lognormal distribution is best suited for describing the service time. It can improve the accuracy of the service time by 80.59% on weekdays and by 72.64% on weekends for the calculation of the lane capacity. This marks a significant improvement in toll plaza management. © 2024 IEEE.

关键词： Traffic congestion

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Impact of Multimedia Sensor Placement to Car Licence Number Detection 2022

Impact of Multimedia Sensor Placement to Car Licence Number ...

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11th International Conference on Software and computer Applications, ICSCA 2022

作者： Amelia, Afritha Zarlis, Muhammad Suherman, Suherman Efendi, Syahril Graduate Program of Computer Science Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia Computer Science Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia Electrical Engineering Faculty of Engineering University of Sumatera Utara Indonesia

ISBN: (纸本)9781450385770

A vehicle has a license plate and can be identified by the license number attached to the vehicle plate. For detection and recognition, multimedia sensors can be used so that it requires a vehicle tracking application that has a vehicle monitoring function and has implications for various fields. One of them is a multimedia sensor that requires an optimal design and method in the form of the number of multimedia sensors to be used, the placement of the multimedia sensor positions and solutions in the event of a system failure. To achieve multimedia sensor performance, research on multimedia sensors is carried out using a camera type with Phase Detection Auto Focus (PDAF) technology which is similar to a pair of right and left eyes to see an object. The objects detected were 2 cars, where the car passed the camera sensor at a speed of 10-15 km/hour 20 times. Changes in the distance of the vehicle to the camera affect the disparity value and the object's accuracy value, namely and the object's Accuracy value of the minimum disparity value of 6.20×106 of 16.88×109. © 2022 ACM.

关键词： Disparity Vehicle plate Phase Detection Auto Focus (PDAF) Tracking application Object accuracy

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Cyber Attack Detection with Encrypted Network Connection Analysis 12th

Cyber Attack Detection with Encrypted Network Connection Ana...

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12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023

作者： Gonen, Serkan Karacayilmaz, Gokce Artuner, Harun Bariskan, Mehmet Ali Yilmaz, Ercan Nurcan Software Engineering Department Istanbul Gelisim University Istanbul Turkey Hacettepe University Institute of Science Ankara Turkey Computer Engineering Department Hacattepe University Ankara Turkey Computer Engineering Department Istanbul Gelisim University Istanbul Turkey Electrical – Electronic Engineering Department Gazi University Ankara Turkey

ISBN: (纸本)9789819960613

The evolution of science and technology has led to increasingly complex cyber security threats, with advanced evasion techniques and encrypted communication channels making attacks harder to detect. While encryption has improved privacy and confidentiality for users, it has also provided a new avenue for attackers to exploit. Traditional intrusion detection systems, which transitioned from signature-based to behavior-based approaches, have struggled to keep up with these challenges. To address this issue, researchers have turned to continuous system monitoring and network traffic packet analysis. However, this method can be resource-intensive and time-consuming, particularly when analyzing encrypted packets. In this study, the JA3 fingerprint infrastructure was examined as a potential solution for quickly detecting attacks conducted over encrypted sessions while minimizing system downtime and damage. The results demonstrated that the JA3 infrastructure effectively detected attacks carried out via encrypted channels. Although Windows 10 and Kali 2020.4 operating systems were used as the victim and attacker systems respectively, the methodology can be applied to other operating systems and network hardware by following the outlined steps. This research is expected to make a significant contribution to the field of encryption-based attack prevention. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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High Data Throughput Wireless Networks Based on a Quantum Dash / Dot Multi-Wavelength Laser and Millimeter-Wave-Over-Fiber Transmission Links

High Data Throughput Wireless Networks Based on a Quantum Da...

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2023 Photonics North, PN 2023

作者： Lu, Z.G. Liu, G.C. Zeb, K. Xie, X.R. Liu, J.R. Poole, P.J. Mao, Y.X. Barrios, P. Rahim, M. Vachon, M. Song, C.Y. Poitras, D. Weber, J. Sabourin, N. Askariann, A. Zhang, X.P. Yao, J.P. Wu, K. Advanced Electronics and Photonics Research Centre National Research Council OttawaON Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada Concordia University IPhotonics Labs Depart. Electrical and Computer Engineering MontrealQC Canada

ISBN: (纸本)9798350326734

We have designed, epitaxy-grown, nano-fabricated and investigated several different-type InAs/InP quantum dash / dot (QD) multi-wavelength lasers (MWLs) around 1550 nm with very low relative intensity noise and ultra-narrow optical linewidth. By using those QD MWLs, we have experimentally demonstrated optical-heterodyne millimeter-wave-over-fiber fronthaul systems with high data throughput wireless links through 25- and 50-km single mode fiber featuring a free-space data capacity of up to 36 Gb/s, which has clearly indicated that the developed QD MWLs are critical building blocks of achieving low-noise millimeter-wave (mmW) signal generation and transmission for 5G & beyond wireless networks. © 2023 IEEE.

关键词： Microwave photonics

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Lung Cancer Classification Using Improvised CNN

Lung Cancer Classification Using Improvised CNN

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International Conference on Business Intelligence and Information Technology, BIIT 2022

作者： Ganesan, Subramaniam Joshua, Eali Stephen Neal Satyanarayana, K.V. Nagu, V. Department of Electrical and Computer Engineering Oakland University Engineering Centre Room 301 115 Library Drive RochesterMI48309-4479 United States Department of Computer Science Engineering GITAM School of Technology GITAM Deemed-to-be-University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9789819934157

State-of-the-art approaches have been enabled by neural networks to attain accurate results on tasks such as detection of objects which are related to computer vision, but the success of these approaches relies on computational resources that are costly and hinders people who prefer cheap devices to advanced technology. A network named Cross Stage Partial Network (CSPNet) is proposed in this paper to diminish the problem that requires computations based on heavy inference in the view of network architecture. This problem is caused due to the duplicate gradient information that is present within the network optimization. The maintenance of the variability of gradients by the proposed networks is done by combining feature maps both at the beginning and the end of a network stage, and the computations is reduced by 20% with equal or even greater accuracy on the image dataset of Chest CT scan. The implementation of CSPNet is quite easy and also standard in nature to deal with architectures that are built on DenseNet and ResNet. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Designing an IT2 Fuzzy Rule-Based System for Emotion Recognition Using Biological Data

Designing an IT2 Fuzzy Rule-Based System for Emotion Recogni...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Mahsa Keshtkar Hooman Tahayori Department of Computer Science and Engineering & IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these impacts can be beneficial in various fields, ranging from developing healthcare surveillance systems for the elderly to identifying adverse emotions in those engaged in high-stakes occupations, such as pilots, sailors, and long-haul truckers. This paper presents a solution based on the application of interval type two fuzzy sets and systems to detect individuals' emotions using their physiological signals. The physiological signals on the same emotion varies from person to person and even for the same person in different situations. This variability has led us to employ interval type-2 fuzzy sets. We defined five footprints of uncertainty for each physiological signal to cover its whole range and employed instance selection technique to optimize the rule base. We develop a rule-based fuzzy system that employs the fewest feasible number of rules for the purpose of emotion recognition. In order to enhance the comprehensibility of the system, we have also utilized a minimal set of features in the construction of the rules. This study implemented on physiological signals from the K-EmoCon dataset. Obtained results shows that the proposed method outperform other existing method with lower computational complexity.

关键词： Knowledge engineering Emotion recognition Fuzzy sets Uncertainty Surveillance Medical services Physiology Distance measurement Older adults Fuzzy systems

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