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Applying SVR-PPSO, SVR-SSO, and SVR-BBO to estimate california bearing capacity of stabilized pond ash using admixtures

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8311-8327页

作者： Teng, Wei Li, Yan Sun, Hongxing Chen, Haojie School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Information Technology Nanchang Vocational University Jiangxi Nanchang China School of Intelligent Construction Luzhou Vocational and Technical College Sichuan Luzhou China

In the present study, three hybrid models include support vector regression-salp swarm optimization (SVR-SSO), support vector regression-biogeography-based (SVR-BBO), and support vector regression-phasor particle swarm optimization (SVR- PPSO) was applied to forecast pond ash's CBR value modified with lime sludge (LS) and lime (LI). In the developed models, five variables were selected as inputs. It can result that the developed integrated models have R2 bigger than 0.9952. It means the agreement between observed and forecasted values by hybrid models is mainly similar to represent the highest accuracy. In both the training and testing stages, PSO-SVR results from better performance than the BBO-SVR model, with R2, RMSE, MAE, and PI equal to 0.9983, 0.6439, 0.3181, and 0.0081 for training data, and 0.9975, 0.7319, 0.4135, and 0.0141 for testing data, respectively. So, by considering the OBJ index, the OBJ value for PSO-SVR is 12.966, lower than BBO-SVR at 16.9957. Therefore, the PSO-SVR model outperforms another model to estimate the CBR of pond ash modified with LI and LS, consequently being recognized as the proposed model that makes it to be used for practical applications. © 2024 - IOS Press. All rights reserved.

关键词： Lime

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Efficient Road Segmentation Techniques with Attention-Enhanced Conditional GANs

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SN computer science 2024年第1期5卷 176页

作者： George, Glenn Varghese Hussain, Mohammad Shahil Hussain, Rizwan Jenicka, S. School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore India

Road segmentation from aerial images is a challenging yet crucial task, underpinning significant applications in urban planning, navigation, and transportation systems. In this study, we employ a conditional Generative Adversarial Network (GAN) architecture that synergistically integrates the strengths of Attention U-Net and PatchGAN to address this task. The Attention U-Net, serving as the generator, is trained on the publicly available Massachusetts Roads dataset, with an emphasis on critical regions while concurrently disregarding the irrelevant ones, thereby enhancing the accuracy of road segmentation. Simultaneously, the PatchGAN discriminator ensures the generation of sharp, high-quality segmentations. Through this cooperative approach, we have achieved an overall accuracy of 98.2%, a recall of 82.30%, a precision of 78.66%, an Intersection over Union (IoU) of 67.19%, and an F1 score of 80.44% on our dataset. While these results are promising, they also highlight areas for improvement, particularly in reducing false positives and enhancing the identification of all road pixels, underscoring the potential for future refinements in this research domain. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Aerial image analysis Attention U-Net Conditional generative adversarial network (CGAN) Deep learning-artificial neural network Road segmentation

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Uncovering the Hidden Patterns of the COVID-19 Global Pandemic: An in-Depth Data Analytics Approach

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SN computer science 2024年第8期5卷 981页

作者： Sarobin, M. Vergin Raja Rathore, Jinen Mishra, Rajat Moolya, Vinay Vittal Seth, Yash School of Computer Science and Engineering Vellore Institute of Technology TN Chennai 600127 India

COVID-19 is a highly infectious respiratory illness caused by the novel coronavirus. It was first identified in Wuhan, China in December 2019 and has since spread globally, infecting and killing a vast number of people, leading to a worldwide pandemic. The pandemic has left the world in disarray. We wished to apply data analytics and regression models to understand and study the data – OwiD (Our World in Data) real time covid dataset - to analyse and draw trends and factors that led to the widespread of the virus. Doing so, allows us to identify key factors and trends that played a vital role in the rapid spread of the virus. We can thus determine the underlying hidden patterns of key factors. This will help provide a better understanding and determine the potential reasons COVID-19 took the world by storm with its fast-paced spread. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Data analytics OWiD (Our World in Data) Pandemic Regression Virus

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MGOD: Multi-Granular Outlier Detection with Clustlier Analysis

MGOD: Multi-Granular Outlier Detection with Clustlier Analys...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Qingsheng Zhao, Mingjie Ji, Yuzhu Luo, Xiaopeng Zhang, Yiqun Zhang, Yue Guangdong University of Technology School of Computer Science and Technology China Guangzhou Huali College School of Computer Engineering China Guangdong Polytechnic Normal University School of Computer Science China

ISBN: (纸本)9798350386226

Unsupervised Outlier Detection (UOD) is crucial for the analysis of biomedical and health data with undesirable outliers. However, the complex distribution of real data often brings difficulties to UOD where the "masking effect", i.e., only a small number of densely distributed outliers (also called clustliers) can collectively mask themselves from being detected, is particularly challenging. Another difficulty derived from this is how to distinguish clustliers from small clusters. Therefore, we propose a novel Multi-Granular Outlier Detector (MGOD). It first partitions the dataset into subsets with natural neighbor topological relationships to circumvent the non-trivial neighbor range setting. Then it effectively detects both clustliers and isolated samples (also called scatliers) based on a newly designed anomaly score. The score comprehensively takes into account the density and connectivity of samples to reflect different extents and types of abnormality. It turns out that MGOD is accurate and highly interpretable. The performance of MGOD is also robust to the involved hyper-parameters, which are easy to set. Comprehensive evaluations have been conducted to compare seven counterparts on 15 datasets, most of which are biomedical datasets. The results of significance tests confirm the effectiveness and superiority of MGOD. The source code is opened at https://***/r/MGOD-C531. © 2024 IEEE.

关键词： Unsupervised learning

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Hyperspectral imagery quality assessment and band reconstruction using the prophet model

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CAAI Transactions on Intelligence technology 2025年第1期10卷 47-61页

作者： Ping Ma Jinchang Ren Zhi Gao Yinhe Li Rongjun Chen National Subsea Centre School of ComputingEngineering and TechnologyRobert Gordon UniversityAberdeenUK School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhouChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina

In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land *** study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet *** introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global *** metric effectively captures the intricate noise and distortions inherent in the HSI ***,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality *** validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are *** a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is *** authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed *** addition,the authors assess the classification accuracy utilising these reconstructed *** various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band ***,the authors’approach obviates the need for manually prefiltering of noisy *** comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.

关键词： band reconstruction band quality hyperspectral image(HSI) prophet model

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

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Intelligent Automation & Soft Computing 2023年第1期35卷 567-580页

作者： M.P.Karthikeyan E.A.Mary Anita Department of Computer Science and Engineering R.M.K.Engineering CollegeChennai601206India Department of Computer Science and Engineering School of Engineering and TechnologyChrist UniversityBengaluru560029India

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid *** Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye *** can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic *** that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level *** model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst *** input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of *** detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable ***,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose ***,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

关键词： Retinal fundus images exudate diabetic retinopathy SVM ANFIS morphological component analysis

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Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks

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Soft Computing 2023年第5期27卷 2739-2750页

作者： Javadpour, Amir Rezaei, Samira Sangaiah, Arun Kumar Slowik, Adam Mahmoodi Khaniabadi, Shadi School of Computer Science and Technology Guangzhou University Guangzhou510006 China Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands School of Computing Science and Engineering VIT University Vellore India Department of Industrial Engineering and Management National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland Department of Computer Science and Engineering USM Engineering Campus Pulau Pinang George Town14300 Malaysia

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Exploring the Impact of Deep Learning Models on Lane Detection Through Semantic Segmentation

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SN computer science 2024年第1期5卷 139页

作者： Kumar, Sunil Pandey, Ankur Varshney, Sudeep School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering Manipal University Jaipur Jaipur India Department of Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

Due to advancements in the deep learning technology, object detection has become significantly important for lane detection and vehicle detection. In recent times, lane detection has become more popular as it plays a significant role in traffic surveillance compared to other object detection technology. However, these strategies have several intrinsic flaws which need to be addressed. Traditional-based techniques still suffer from the challenges of the effectiveness and accuracy, whereas a complex convolutional layer is a challenge for deep learning-based strategies. A parameter selection issue affects the majority of the available lane detection algorithms, which further contributes to their unsatisfactory detection performance. In this study, we provide an effective lane detection method based on semantic segmentation to identify lane lines in a high-dimensional dataset by adding vertical spatial properties and contextual driving information. This paper employs two created frames—feature merging block and information exchange block—to identify unclear and obstructed lane lines more effectively. The simulations have been carried out for the proposed model on TUSimple and CULane datasets which resulted with 94.42% accuracy, 93.47% precision, 92.85% recall, and 93.17% F1-score. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Lane detection Occluded lane lines Semantic segmentation

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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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