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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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A UAV Object Detection Algorithm Based on Improved YOLOX 5

A UAV Object Detection Algorithm Based on Improved YOLOX

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5th International Conference on Intelligent Robotics and control engineering, IRCE 2022

作者： Wang, Shiyu Xu, Guowei Song, Qingzeng Yang, Zhenhao Xue, Yongjiang College of Control Science and Engineering Tiangong University Tianjin China College of Computer Science and Technology Tiangong University Tianjin China College of Material Science and Engineering Tiangong University Tianjin China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

In aerial photography, UAV will cause small objects and mutual occlusion between objects, bringing enormous challenges to the current one-stage object detection algorithm. In order to solve the above problems, this paper proposes a YOLOX-TBP one-stage object detection algorithm. Based on YOLOX-l, the algorithm adds a prediction head to detect objects of different scales to improve the detection effect of small objects. At the same time, the partial convolution of the backbone network is replaced by Transformer to improve the global information of the model, and a weighted bi-directional feature pyramid network (BiFPN) is introduced to improve the multi-scale feature fusion effect through the method of self-learning weights. The final experiments show that the MAP of the YOLOX-TBP model tested on the VisDrone-DET 2021 dataset reaches 40.27%, which is 0.84% higher than the previous SOTA method (DBNet). © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Uncertainty-Aware Path Planning with Multiple Constraints for Autonomous Vehicles Based on Nonlinear Reduced-Order Model Predictive control

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IEEE Transactions on Consumer Electronics 2024年

作者： Badiei, Mohammad Hossein Dashtaki, Saeed Mohammadi Vafamand, Navid Piran, Md. Jalil University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Shiraz University Department of Power and Control Engineering Shiraz Iran Sejong University Department of Computer Science and Engineering Korea Republic of

In this paper, we present a Risk-Aware Nonlinear Reduced-Order Model Predictive control framework that utilizes real-time state estimation, reduced-order modeling, and optimization for reliable and efficient path planning with low energy consumption. Our method employs filtering techniques to estimate the system's state by correcting for process and measurement noise, providing an accurate representation of the vehicle's position and environment. A reduced-order model predicts future states based on the estimated input, while the optimizer adjusts control inputs to meet trajectory and safety constraints, such as avoiding obstacles. By incorporating adapted filtering techniques and optimized cost functions, our approach minimizes computational overhead while maintaining precise control over vehicle navigation. In the experiments, we introduce communication and process noises, allowing the robot to operate without knowing the precise location of obstacles and resulting in potential near-collisions. This involves redefining the constrained optimal control problem for a reduced-order model, addressing input constraints amidst uncertainties. Our method achieves over 94% success in collision-free navigation within the first 50 steps, while maintaining precise trajectory alignment and adapting to environmental changes. It also improves prediction speed up to 15% per cycle compared to NMPC and demonstrates 91% overall effectiveness in avoiding collisions and reaching the target. © 1975-2011 IEEE.

关键词： Cost functions

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A Completed Gaussian Extended Binary Pattern for Texture Image Classification 23

A Completed Gaussian Extended Binary Pattern for Texture Ima...

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Proceedings of the 2nd International Conference on Signal Processing, computer Networks and Communications

作者： Xiaochun Xu Weizheng Lin Dingrong Chen Nanling Su Leinuo Tang Changxu Cai College of Computer and Control Engineering Minjiang University China International Digital Economy College Minjiang University China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance.

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CNFPRNet: Innovative Fusion of GPT-2 Language Model and Natural Language Generation Model for Personalized Educational Recommendations 6th

CNFPRNet: Innovative Fusion of GPT-2 Language Model and Natu...

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6th International Conference on computer Science and Educational Informatization, CSEI 2024

作者： Li, Chao Xu, Zixuan Jing, Weipeng College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9789819637379

In this paper, we propose a novel AI-based model that combines a GPT-2 language model with a natural language generation component to generate Customized Narrative Fragments for Personalized Recommendations(CNFPR) in education. Our approach builds on the existing literature on AI-based learning content generation and learning pathway augmentation, but introduces a new component that leverages the power of GPT-2 and an innovative Conceptual Definition Extractor to generate high-quality natural language text. By integrating this component into our model, we aim to create a more personalized and engaging learning experience for students, one that is tailored to their individual needs and preferences. Experimental results show that compared with the Zero-shot evaluation with GPT-2, our CNFPRNet model demonstrates a 23.5% increase in ROUGE scores. This improves the accuracy of recommended content and enables teaching resources to better meet the individual needs of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Report generators

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers 49

A Formation Control for Multi-Agent Systems for Event-Trigge...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Asada, Daiki Nakagawa, Takuya Hoshi, Yoshikatsu Oya, Hidetoshi Nagai, Shunya Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University The Department of Computer Science Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan

ISBN: (纸本)9798350331820

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs). © 2023 IEEE.

关键词： Linear matrix inequalities

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Relay-projection microscopic telescopy

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Light(Science & Applications) 2025年第4期14卷 1143-1155页

作者： Wenjun Yi Shuyue Zhu Meicheng Fu Nan Gu JunliQi Siyu Liu Mengjun Zhu Ping Wang Xin Chen Yi Zhang Hongyu Zhang Yao Xu Junyi Du Peng Xiong Zhaohua Dong Luobing Dong Qiong Liu Xiujian Li College of Science National University of Defense TechnologyChangshaChina Xi’an Satellite Control Center Xi’anChina School of Information and Electronics Engineering Hunan City UniversityYiyangChina College of Electronic Science National University of Defense TechnologyChangshaChina The School of Computer Science and Technology Xidian UniversityXi’anChina Senior Department of Otolaryngology Head and Neck Surgery the 6th Medical Center of Chinese PLA General HospitalChinese PLA Medical SchoolBeijingChina

The fundamental trade-off between spatial resolution and imaging distance poses a significant challenge for current imaging techniques,such as those used in modern biomedical diagnosis and remote ***,we introduce a new conceptual method for imaging dynamic amplitude-phase-mixed objects,termed relay-projection microscopic telescopy(rPMT),which fundamentally challenges conventional light collection techniques by employing non-line-ofsight light collection through square-law relay-projection *** successfully resolved tiny features measuring 2.76μm,22.10μm,and 35.08μm for objects positioned at distances of 1019.0 mm,26.4 m,and 96.0 m,respectively,from single-shot spatial power spectrum images captured on the relay screen;these results demonstrate that the resolution capabilities of rPMT significantly surpass the Abbe diffraction limit of the 25 mm-aperture camera lens at the respective distances,achieving resolution improvement factors of 7.9,25.4,and *** rPMT exhibits long-distance,wide-range,high-resolution imaging capabilities that exceed the diffraction limit of the camera lens and the focusing range limit,even when the objects are obscured by a scattering *** rPMT enables telescopic imaging from centimeters to beyond hundreds of meters with micrometer-scale resolution using simple devices,including a laser diode,a portable camera,and a diffusely reflecting *** contemporary high-resolution imaging techniques,our method does not require labeling reagents,wavefront modulation,synthetic receive aperture,or ptychography scanning,which significantly reduce the complexity of the imaging system and enhance the application *** method holds particular promise for in-vivo label-free dynamic biomedical microscopic imaging diagnosis and remote surveillance of small objects.

关键词： light collection techniques Imaging Distance Non Line Sight Relay Projection Microscopic Telescopy remote sensingherewe imaging techniquessuch biomedical diagnosis Spatial Resolution

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Multi-Attention Gate Based U-net for Retinal Vessel Segmentation 12

Multi-Attention Gate Based U-net for Retinal Vessel Segmenta...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Zhu, Yu Li, Rui Teng, Shenghua Cao, Xinrong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China College of Computer and Control Engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350310153

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE-DB1, demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Blood vessels

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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A Network Combining CNN and Transformer for Blind Image Super-Resolution 12

A Network Combining CNN and Transformer for Blind Image Supe...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Zhang, Shuhao Li, Zuoyong Teng, Shenghua Zeng, Kun Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining CNN and transformer named NCCT for kernel estimation. By modeling local and non-local image priors simultaneously, NCCT outperforms other blind SR methods in terms of kernel estimation accuracy. Moreover, we design a network module named RRFDB for constructing lightweight blind SR network, which runs faster and achieves comparative SR performance with fewer parameters compared with other state-of-the-art blind SR methods. © 2022 IEEE.

关键词： Optical resolving power

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