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computational intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computer Systems Science & Engineering 2023年第12期47卷 3127-3144页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

关键词： computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification

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Privacy-Enhanced Data Deduplication computational intelligence Technique for Secure Healthcare Applications

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Computers, Materials & Continua 2022年第2期70卷 4169-4184页

作者： Jinsu Kim Sungwook Ryu Namje Park Department of Convergence Information Security Graduate SchoolJeju National University63294Korea Master’s Program in Future Strategy Korea Advanced Institute of Science and Technology34141Korea Department of Computer Education Teachers CollegeJeju National University63294Korea

A significant number of cloud storage environments are already implementing deduplication technology.Due to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is required.As storage capacity increases,additional storage solutions are required.By leveraging deduplication,you can fundamentally solve the cost problem.However,deduplication poses privacy concerns due to the structure itself.In this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve it.In the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is maintained.In addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing attacks.The proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

关键词： computational intelligence cloud multimedia data deduplication

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Predicting the effects of selected reservoir petrophysical properties on bottomhole pressure via three computational intelligence techniques

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Petroleum Research 2023年第1期8卷 118-129页

作者： Emmanuel E.Okoro Samuel E.Sanni Tamunotonjo Obomanu Paul Igbinedion Department of Petroleum Engineering University of Port HarcourtRivers StateNigeria Department of Chemical Engineering Covenant UniversityOtaNigeria Department of Petroleum Engineering Federal Polytechnic of Oil and GasBonny IslandNigeria Department of Petroleum Engineering Covenant UniversityOtaNigeria

This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables.A total of 15,633 data sets were collected from Volvo field in Norway,and after screening the data,a total of 9161 data sets were used to develop apt computational intelligence models.The data were randomly divided into three different groups:training,validation,and testing data.Two case scenarios were considered in this study.The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables,while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties(porosity and permeability).Each of the two scenarios involved as implied in the first scenario,the use of three(3)heuristic search optimizers to determine optimal model architectures.The optimizers were allowed to choose the optimal number of layers(between 1 and 10),the optimal number of nodal points(between 10 and 100)for each layer and the optimal learning rate required per task/operation.the results,showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001,although this became successively slower as the leaning rate decreased.With the chosen model configuration,the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets.Comparing the three heuristic search optimizers based on minimum MSE,RMSE,MAE and highest coefficient of determination(R^(2))for the actual and predicted values,shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately

关键词： computational intelligence Bottomhole pressure Petrophysical properties Heuristic search optimizer Volvo field data

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computational intelligence in Remote Sensing Image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable performance.Evolutionary computing and deep learning are important branches of computational intelligence.Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration.This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning.In the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed.The application of deep learning in remote sensing image registration is also discussed.At the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： computational intelligence evolutionary computation neural network deep learning remote sensing image registration

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computational intelligence Determines Effective Rationality

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International Journal of Automation and computing 2008年第1期5卷 63-66页

作者： Edward P.K.Tsang Centre for computational Finance and Economic Agents University of EssexColchester CO4 3SQUK

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.

关键词： Rationality bounded rationality computational intelligence economics computational intelligence determines effective rationality （CIDER） theory.

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Comprehensive Overview on computational intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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Chinese Journal of Mechanical Engineering 2017年第4期30卷 782-795页

作者： Wan Zhang Min-Ping Jia Lin Zhu Xiao-An Yan School of Mechanical Engineering Southeast UniversityNanjing 211189 China

computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

关键词： computational intelligence Machinerycondition monitoring Fault diagnosis Neural networkFuzzy logic Support vector machine - Evolutionaryalgorithms

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Advances in medical decision support systems for diagnosis of acute graft-versus-host disease: molecular and computational intelligence joint approaches

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Frontiers in Biology 2011年第4期6卷 263-273页

作者： Maurizio FIASCHÉ Maria CUZZOLA Giuseppe IRRERA Pasquale IACOPINO Francesco Carlo MORABITO DIMET University“Mediterranea”of Reggio CalabriaItaly Transplant Regional Center of Stem Cells and Cellular Therapy “A.Neri”Reggio CalabriaItaly School of Hematology University of MessinaItaly

Acute graft-versus-host disease (aGVHD) is a serious systemic complication of allogeneic hematopoietic stemcell transplantation (HSCT) causing considerable morbidity and mortality. Acute GVHD occurs when alloreactivedonor-derived T cells recognize host-recipient antigens as foreign. These trigger a complex multiphase process thatultimately results in apoptotic injury in target organs. The early events leading to GVHD seem to occur very soon,presumably within hours from the graft infusion. Therefore, when the first signs of aGVHD clinically manifest, thedisease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated.So, it comes as no surprise that progress in treatment based on clinical diagnosis of aGVHD has been limited in the past30 years. It is likely that a pre-emptive strategy using systemic high-dose corticosteroids as early as possible couldimprove the outcome of aGVHD. Due to the deleterious effects of such treatment particularly in terms of infection riskposed by systemic steroid administration in a population that is already immune-suppressed, it is critical to identifybiomarker signatures for approaching this very complex task. Some research groups have begun addressing this issuethrough molecular and proteomic analyses, combining these approaches with computational intelligence techniques,with the specific aim of facilitating the identification of diagnostic biomarkers in aGVHD. In this review, we focus on theaGVHD scenario and on the more recent state-of-the-art.We also attempt to give an overview of the classical and noveltechniques proposed as medical decision support system for the diagnosis of GVHD.

关键词： computational intelligence gene selection GVHD machine learning personalized modelling wrapper

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Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

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Journal of Computer and Communications 2024年第3期12卷 160-183页

作者： Hend S. Salem Mohamed A. Mead Ghada S. El-Taweel Department of Computer Sciences Suez Canal University Ismailia Egypt

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

关键词： Big COVID-19 Data Machine Learning Hyperparameter Optimization Particle Swarm Optimization computational intelligence

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Advances in simulated modeling of vibration systems based on computational intelligence

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Progress in Natural Science:Materials International 2002年第12期 3-8页

作者： LEE Heow Pueh LIM Siak Piang LEE Kwok Hong Department of Mechanical Engineering Institute of High Performance Com-puting National University of Singapore Singapore Singapore 117528 Singapore 119260

computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors’ own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of

关键词： computational intelligence artificial neural network fuzzy system evolutionary computation vibration system.

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computational intelligence Methods for Information Understanding and Information Management

Computational Intelligence Methods for Information Understan...

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The Fourth International Conference on Information and Management Sciences

作者： Wlodzislaw Duch Norbert Jankowski Krzysztof Grabczewski Department of Informatics Nicolaus Copernicus UniversityTorunPolandDepartment of Computer ScienceSchool of Computer EngineeringNanyang Technological UniversitySingapore Department of Informatics Nicolaus Copernicus UniversityTorunPoland Department of Informatics Nicolaus Copernicus UniversityTorunPoland

＜正＞ Information management relies on knowledge acquisition methods for extraction of knowledge from data. Statistical methods traditionally used for data analysis are satisfied with predictions, while understanding of data and extraction of knowledge from data are challenging tasks that have been pursued using computational intelligence （CI） methods. Recent advances in applications of CI methods to data understanding are presented, implementation of methods in the GhostMiner data mining package [1] developed in our laboratory described, new directions outlined and challenging open problems posed. To illustrate the advantages of different techniques, a single dataset is exposed to the many-sided analysis.

关键词： data understanding knowledge extraction decision support data mining computational intelligence machine learning neural networks feature extraction decision trees.

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