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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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A Guaranteed Cost Approach to Dynamic Output Feedback control for Neutral-Type Markovian Jumping Stochastic Systems

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Journal of Systems Science & Complexity 2021年第4期34卷 1487-1500页

作者： LI Yanbo KAO Binghua XIE Jing KAO Yonggui School of Information and Statistics Guangxi University of Finance and EconomicsNanning 530003China Department of Mathematics Harbin Institute of Technology(Weihai)Weihai 264209China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520China

This paper is devoted to investigating the dynamic output feedback(DOF)control problem of Markovian jump neutral-type stochastic systems with a guaranteed cost *** of the state and measurement equations contain time ***-dependent DOF controllers are first designed such that the closed-loop system is asymptotically stable in mean-square and an adequate performance level of this system is ***,sufficient conditions for the solvability of this problem are derived in the form of linear matrix inequalities(LMIs).A numerical example is presented to reveal the effectiveness of our findings.

关键词： Guaranteed cost control linear matrix inequality Markovian jump neutral stochastic systems output feedback control time delay

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Efficient multimodal cancelable biometric system based on steganography and cryptography

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Iran Journal of Computer Science 2023年第2期6卷 109-121页

作者： Salama, Gerges M. El-Gazar, Safaa Nassar, Rana M. El-Shafai, Walid Khalaf, Ashraf A. M. El-Banby, Ghada M. Hamed, Hesham F. A. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia 61111 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh 11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 84428 Saudi Arabia

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performan

关键词： BSVD Cancelable biometrics DRPE MFCCs

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Triggered Mechanism for Strict Feedback Systems under Unknown control Directions 14

Triggered Mechanism for Strict Feedback Systems under Unknow...

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14th Asian control Conference, ASCC 2024

作者： Liu, Guilong Wu, Hongda Yang, Yongliang Xie, Wei Zhang, Weidong School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581598

This paper presents an event-based asymptotic tracking control scheme designed for strict feedback systems with unknown control directions. The control directions directly determine how the controller acts on the system, thus impacting the system's stability and convergence speed. When the control directions are unknown, it presents a significant challenge in controller design. To tackle this issue, the Nussbaum-type gains are introduced. However, this method increases communication overhead. Hence, an event-triggered mechanism is introduced to alleviate this burden. Additionally, to achieve and maintain desired target states, satisfying control requirements and performance demands, this paper improves the event-triggered mechanism to achieve asymptotic tracking. Finally, the feasibility and effectiveness of the proposed design are validated through a simulation example. © 2024 Asian control Association.

关键词： Feedback

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On the Gaussian Filtering for Nonlinear Dynamic Systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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27th International Conference on information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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Adaptive Deep Reinforcement Learning for Critical Boundary Scenario Generation 43

Adaptive Deep Reinforcement Learning for Critical Boundary S...

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43rd Chinese control Conference, CCC 2024

作者： Zhou, Junjie Wang, Lin Wang, Xiaofan Meng, Qiang Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai202400 China Ministry of Education of China Key Laboratory of System Control and Information Process Shanghai202400 China National University of Singapore Department of Civil and Environmental Engineering Singapore117576 Singapore Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai202400 China

ISBN: (纸本)9789887581581

Critical boundary scenarios play a vital role in the comprehensive testing of autonomous vehicles, as they can significantly expedite testing processes and reduce economic and time costs. To tackle the intricate, multidimensional, and diverse nature of intelligent driving scenarios, this paper proposes an innovative bi-level adaptive deep reinforcement learning (BADRL) approach aimed at generating authentic and diverse critical boundary scenarios. Leveraging naturalistic driving data, background agents are trained to exhibit naturalistic and realistic driving behaviors using a neural-based naturalistic driving behavior model. To address the intricacies of multi-interaction, high-dimensional environments, the scenario complexity model is introduced to evaluate the relative complexity between the traffic environment and the tested autonomous vehicle in real time. By integrating the scenario complexity model, the naturalistic driving behavior learning, intelligent driving testing, and critical boundary scenario generation are concatenated together to form a closed loop. BADRL facilitates the upper-level neural network in learning to adaptively increase the complexity of test scenarios, which are then fed into lower-level models to optimize the behavior of principal traffic participants, thereby generating naturalistic and critical boundary test scenarios. Extensive simulations were carried out to verify the efficacy of the BADRL technique in complex intersection environments. Results indicate that the BADRL approach boosts the efficiency of critical boundary scenario generation by approximately 10% compared to the state-of-the-art method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deep reinforcement learning

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Automatic Pavement Type Recognition based on Mobile Deep Learning 4

Automatic Pavement Type Recognition based on Mobile Deep Lea...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Murasaki, Reiya Matsushima, Kousuke National Institute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Institute of Technology Kurume College Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9781665419048

Pavement deformation detection is an essential task in order to maintain driving safety. In recent years, convolutional neural networks (CNN), one of the machine learning methods, have been widely studied for pavement condition detection. In this paper, we propose a method that can achieve optimal pavement deformation detection in each region, assuming a mobile terminal. © 2022 IEEE.

关键词： Pavements

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Investigating Crowdsourced Neural Radiance Maps for Autonomous Vehicles 2nd

Investigating Crowdsourced Neural Radiance Maps for Autonom...

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2nd International Conference on Cognitive Mobility, COGMOB 2023

作者： Zhu, Morui Szász, Erik Szántó, Mátyás Vaitkus, Márton Sörös, Gábor Department of Control Engineering and Information Technology Budapest University of Technology and Economics Budapest Hungary Nokia Bell Labs Budapest Hungary

ISBN: (纸本)9783031876196

We present a crowdsourcing and mapping pipeline that enables the generation of accurate neural radiance field (NeRF) maps from camera feeds of autonomous vehicles. We generate synthetic image feeds from the CARLA driving simulator, we compare three different models (Nerfacto, TensoRF, Instant-NGP), and evaluate the accuracy of the reconstructions based on the number of acquisition vehicles and trajectory variations. We also demonstrate interactive visualization of the NeRF maps in a virtual reality headset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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Computer Systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Visual SLAM in Dynamic Environments using Multi-lens Omnidirectional Camera 4

Visual SLAM in Dynamic Environments using Multi-lens Omnidir...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Yamasaki, Shoya Matsushima, Kousuke National Insutitute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Insutitute of Technology Kurume College Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9781665419048

Many studies relate to Visual SLAM (Simultaneous Localization and Mapping) systems have been conducted for automatic driving and assisted driving systems. In this paper, we propose the Visual SLAM system combining multi-lens omnidirectional camera and object recognition. The proposed method can reduce some noises caused by moving objects, such as vehicle, pedestrian, bicycle, and so on, in the road environment. We also propose a robust SLAM system against tracking loss by improving the tracking thread. Since a multi-lens omnidirectional camera system can view not only the front but also the left and right sides of the scene, it is possible to obtain effective feature points even in scenes with little texture. Therefore, it provides better SLAM results than monocular camera systems. © 2022 IEEE.

关键词： Textures

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