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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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Document Image Shadow Removal via Frequency information-Oriented Network 27th

Document Image Shadow Removal via Frequency Information-Ori...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Fan Zhou, Xinyue Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Li, Guantin Man, Wang Wu, Yun School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Removing shadows from document images can significantly improve the Quality of Experience (QoE) and boost the performance of the downstream document analysis and recognition tasks. However, existing methods still have limited generalization ability on complex document images and are prone to disrupt the image details. To address this issue, we consider the different shadow types that impact the image content on different frequency sub-bands. This motivates us to exploit frequency-domain information and further design a Frequency information-oriented Deshadow Network (FID-Net). The proposed FID-Net mainly uses two elaborated modules, named Frequency Feature Extractor (FFE) and a Frequency Feature Refinement (FFR). FFE can generate low/high-frequency features through adaptively decomposing spectra of the shadow image. After that, FFR further refines both frequency features with mutual information operations. With the proposed key designs, extensive experimental results on the commonly used benchmarks demonstrate that the proposed method can learn discriminative shadows and achieve favorable performance against state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Switchable photothermal conversion efficiency for reprogrammable actuation

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npj Flexible Electronics 2023年第1期7卷 70-78页

作者： Yongcheng He Haojun Liu Jiajia Luo Nuo Li Lihua Li Puxian Xiong Jiulin Gan Zhongmin Yang State Key Laboratory Luminescent Materials and Devices Institute of Optical Communication MaterialsSpecial Glass Fiber and Device Engineering Technology Research and Development Center of Guangdong ProvinceGuangdong Provincial Key Laboratory of Fiber Laser Materials and Applied TechniquesSouth China University of Technology510641 GuangzhouChina Future Institute of Technology School of Information and Optoelectronic Science and EngineeringSouth China Normal University510515 GuangzhouChina

Reprogrammable soft matter brings flexibility to soft robots so that they can display various motions,which is flourishing in soft ***,the reprogramming of photoresponsive materials used in soft robots is time-consuming using existing *** this study,we promote a strategy for rapid reprogramming via switchable photothermal conversion efficiency(PCE).The liquid crystalline elastomers doped with semiconductor bismuth compounds(Bi-LCE)used in this work exhibited large photothermal actuation with over 35%shrinkage in 5 s at high PCE state,which demonstrated little deformation at low PCE ***,the material was capable of being reprogrammed up to 10 times,with only 20 min required for one PCE reversible *** on this switchable PCE effect,the same Bi-LCE film displayed various shape changes through different programmable ***,a reprogrammable hollow tube made of PCE reprogrammable materials could tune the diameter,cross-section configuration,and surface morphology,which was crucial for microfluidics *** materials provide endless possibilities for reusability and sustainability in robotics.

关键词： programming consuming bismuth

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NLOS Error Suppression Method based on UWB Indoor Positioning 20

NLOS Error Suppression Method based on UWB Indoor Positionin...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhang, Qing Cheng, Xiao-Chuan Wang, Ke-Xin Cao, Zhi-Wei Hong, Yong Harbin Engineering University College of Intelligent Systems Science and Engineering China Harbin Engineering University Sanya Nanhai Innovation and Development Base China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China

ISBN: (纸本)9798350320831

In order to improve the accuracy of Ultra-wide Band (UWB) indoor positioning in Non-line of Sight (NLOS) environment, a UWB indoor positioning algorithm based on Received Signal Strength Indication (RSSI) is proposed to suppress NLOS errors and improve positioning accuracy. Based on the signal strength received by the base station, the NLOS error is discriminated, and based on the discriminated results, the measurements that are less affected by the NLOS error are weighted by a power-square algorithm based on the RSSI difference, the measurements that are more affected by the NLOS error are rejected, and the other measurements are calculated using a power-square weighting algorithm for the residuals to obtain the final positioning results. The experimental results show that the algorithm can effectively suppress NLOS errors and improve localisation accuracy in the NLOS environment. © 2023 IEEE.

关键词： Indoor positioning systems

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Face Forgery Detection Algorithm Based on Improved MobileViT Network 8

Face Forgery Detection Algorithm Based on Improved MobileViT...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Wang, Tiantian Lu, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou014010 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou014010 China Inner Mongolia University of Technology Institute of Information Engineering Hohhot010051 China

ISBN: (纸本)9798350302455

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. Therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization. © 2023 IEEE.

关键词： National security

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Angle-resolved photoemission study of NbGeSb with non-symmorphic symmetry

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Chinese Physics B 2024年第2期33卷 464-470页

作者：马欢谭宁吴徐传李满王义炎路洪艳夏天龙王善才 Department of Physics Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing 100872China Beijing Key Laboratory of Opto-Electronic Functional Materials&Micro-Nano Devices Renmin University of ChinaBeijing 100872China School of Information Network Security People’s Public Security University of ChinaBeijing 100038China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Physics and Physical Engineering Qufu Normal UniversityQufu 273165China

We investigate the electronic structure of NbGeSb with non-symmorphic *** employ angle-resolved photoemission spectroscopy(ARPES)to observe and identify the bulk and surface states over the Brillouin *** utilizing high-energy photons,we identify the bulk Fermi surface and bulk nodal line along the direction X–R,while the Fermi surface of the surface state is observed by using low-energy *** observe the splitting of surface bands away from the high-symmetry point *** density functional theory calculations on bulk and 1 to 5-layer slab models,as well as spin textures of NbGeSb,verify that the band splitting could be attributed to the Rashba-like spin–orbit coupling caused by space-inversion-symmetry breaking at the *** splitted surface bands cross with each other,forming two-dimensional Weyl-like crossings that are protected by mirror *** findings provide insights into the two-dimensional topological and symmetry-protected band inversion of surface states.

关键词： non-symmorphic symmetry nodal line splitting of surface bands angle-resolved photoemission spectroscopy

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Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision 29

Speaker Clustering in Textual Dialogue with Pairwise Utteran...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Su, Zhihua Zhou, Qiang State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China

We propose a speaker clustering model for textual dialogues, which groups the utterances of a multi-party dialogue without speaker annotations, so that the actual speakers are identical inside each cluster. We find that, without knowing the speakers, the interactions between utterances are still implied in the text, which suggest the relations between speakers. In this work, we model the semantic content of utterance with a pre-trained language model, and the relations between speakers with an utterance-level pairwise matrix. The semantic content representation can be further instructed by cross-corpus dialogue act modeling. The speaker labels are finally generated by spectral clustering. Experiments show that our model outperforms the sequence classification baseline, and benefits from the auxiliary dialogue act classification task. We also discuss the detail of determining the number of speakers (clusters), eliminating the interference caused by semantic similarity, and the impact of utterance distance. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Study of a Hybrid System (PV-Battery-Grid) for Electric Vehicle Charging Stations 13

Study of a Hybrid System (PV-Battery-Grid) for Electric Vehi...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Cheikh, Ghizlene Haba, Cristian-Gyozo Bentounes, Hadj Adda University of Abdel Hamid Ibn Badis Science and Technology Faculty Renewable Energies Laboratory and Electrical Systems Mostaganem Algeria Gheorghe Asachi Technical University of Iaşi Department of Electrical Engineering Iaşi Romania University of Abdel Hamid Ibn Badis Science and Technology Faculty CDSEE Laboratory Mostaganem Algeria

ISBN: (纸本)9798350356199

Electric mobility is a growing trend, necessitating innovative and efficient systems to support Electric Vehicle (EV) charging infrastructure. This paper presents a robust hybrid photovoltaic (PV)-battery-grid system that integrates renewable energy with smart energy management to address intermittency and grid dependence. The system uses a Maximum Power Point Tracking (MPPT) controller to optimize energy extraction from PV panels, ensuring optimal utilization even under variable weather conditions. It also incorporates a battery storage unit and three-phase grid connectivity for uninterrupted power for EV charging. The study uses real-world data from Iasi, Romania, focusing on some representative EV models. The system's ability to adapt to fluctuating energy demands and optimize distribution between PV generation, battery storage, and the grid is confirmed through dynamic simulations. The inclusion of three-phase rectification improves power stability and equipment longevity. The proposed hybrid system can reduce dependency on fossil fuels, enhance energy security, and provide a scalable framework for future EV charging infrastructures. © 2024 IEEE.

关键词： Charging stations

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Large spontaneous valley polarization and anomalous valley Hall effect in antiferromagnetic monolayer Fe2CF2

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Physical Review B 2024年第2期110卷 024416-024416页

作者： San-Dong Guo Liguo Zhang Yiwen Zhang Ping Li Guangzhao Wang School of Electronic Engineering State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Key Laboratory of Extraordinary Bond Engineering and Advanced Materials Technology of Chongqing School of Electronic Information Engineering

Superior to ferromagnetic (FM) materials, antiferromagnetic (AFM) materials do not have any net magnetic moment and are robust to external magnetic perturbation with ultrahigh dynamic speed. To achieve spontaneous valley polarization and anomalous valley Hall effect (AVHE) in AFM materials is of great significance for potential applications in spintronics and valleytronics. Here, we predict an A-type AFM monolayer Fe2CF2 with large spontaneous valley polarization. Monolayer Fe2CF2 has zero Berry curvature in momentum space but nonzero layer-locked hidden Berry curvature in real space, which provides the basic conditions for the realization of AVHE. Because Fe2CF2 possesses the combined symmetry (PT symmetry) of spatial inversion (P) and time reversal (T) symmetry, the spin is degenerate, which prevents the AVHE. An out-of-plane external electric field can be used to produce spin splitting due to the introduction of layer-dependent electrostatic potential, and then layer-locked AVHE can be realized in Fe2CF2. Moreover, the spin order of spin splitting can be reversed when the direction of electric field is reversed. It is proved that the AVHE can be achieved in Janus Fe2CFCl without external electric field due to intrinsic built-in electric field. Our works provide an AFM monolayer with excellent properties to realize AVHE.

关键词： Electronic structure Hall effect Magnetic order Valley degrees of freedom Valleytronics Antiferromagnets Density functional calculations

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