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An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid El-Shafai Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images

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Interpretable Roadside 2D Object Detection Based on Axial Attention

Interpretable Roadside 2D Object Detection Based on Axial At...

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International Conference on computer and Information Science (ACIS)

作者： Fan Wu Shaojing Song Jinyan Hu Faculty of Intelligent Manufacturing and Control engineering Shanghai Polytechnic University Shanghai China College of Computer and Informational Engineering Shanghai Polytechnic University Shanghai China

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

Environmental perception plays a crucial role in the operation of autonomous vehicles. Recent studies have relied on single vehicle cameras for perception due to the lack of roadside view dataset, but this approach provides limited perception information. To address this limitation, there is a growing interest in studying roadside perception to complement vehicle-side perception information. However, existing methods face challenges in accurately recognizing and locating small objects or objects under occluding conditions in the roadside. And autonomous vehicles require millisecond-level decision-making for safe driving, yet many advanced object detection methods struggle to meet the need for high-speed reasoning. Additionally, deep learning models often make black-box decisions without providing explanations or understanding of their decision-making process. To tackle these issues, this study proposes an interpretable 2D object detection method for roadside based on the YOLO V8 with Axial Attention replacing the CNN network within the network architecture. The trained model is then input into HiResCAM to visualize the decision-making process of the roadside detection model. Experimental results on DAIR-V2X-I and Rope3D demonstrate that our proposed method not only improves mean Average Precision (mAP) but also enhances image processing speed by 1.5 to 2 times compared to existing methods. By visualizing the decision-making process of the roadside detection model, it make autonomous vehicle perception more transparent and credible.

关键词： YOLO Deep learning Visualization Information science Image recognition Face recognition Computational modeling Decision making Network architecture Autonomous vehicles

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MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction 39

MSR: A Multifaceted Self-Retrieval Framework for Microscopic...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hong, Dongsheng Chen, Chao Li, Xujia Wang, Shuhui Lin, Wen Liao, Xiangwen College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong College of Computer and Control Engineering Minjiang University China

ISBN: (纸本)157735897X

The microscopic cascade prediction task has wide applications in downstream areas like “rumor detection”. Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of “infected” users while neglecting those of “uninfected” users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent “behavioral preferences” and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Taxonomies

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Efficient Deep Learning Modalities for Object Detection from Infrared Images

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computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDM SPIHT IoT

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Interval Observer Design for Multi-Rate Systems with Multiplicative Noises

Interval Observer Design for Multi-Rate Systems with Multipl...

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Chinese Automation Congress (CAC)

作者： Kaijie Cheng Xin Li Liangkuan Zhu Qige Hong College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China

An interval observer is designed for multi-rate systems with multiplicative noises in this paper. First, a multi-rate sampling system is investigated. At the same time, the presence of multiplicative noise would couple with the multi-rate sampling mechanism, which brings a significant challenge for the estimate of the system states. To address this problem, some arithmetic rules are proposed to separate the noise-contaminated terms, and the multi-rate system is transformed into an equivalent system with a uniform sampling period using the lifting technique and augmentation methods. Based on this, an interval observer is developed to estimate the bounds of the system states, sufficient conditions to ensure the effectiveness of the interval observer, and the input-to-state stability (ISS) of the estimation error dynamics are derived. Finally, simulations verify the proposed method.

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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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Improved SqueezeNet-Based Method for Automotive Paint Film Defect Detection

Improved SqueezeNet-Based Method for Automotive Paint Film D...

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Artificial Intelligence and Digital Technology (ICAIDT), International Conference on

作者： Shengnan Yan Yihui Shen School of Computer and Control Engineering Minjiang University Fujian China College of Arts and Sciences Fujian Medical University Fujian China

ISBN: (数字)9798350386905

ISBN: (纸本)9798350386912

To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovatively suggests a classification method combining Selective Kernel Networks (SKNet) with SqueezeNet model. Initially, the DDIM is used for dataset expansion, followed by an evaluation of the similarity between generated and captured paint film defect images using multiscale structural similarity (MS-SSIM). The training and generation effects of DDIM are then compared with those of DCGAN. In the SqueezeNet model, a Selective Kernel module is added following the fire7 module to enhance the model’s attention mechanism. The results show that all types of paint film defect images generated by DDIM have MS-SSIM indices above 0.64, with most exceeding 0.7. The combined approach of Selective Kernel Networks(SKNet) and SqueezeNet outperforms other attention mechanisms, achieving an accuracy above 96.3%. The method demonstrates promising prospects for paint film defect detection, enhancing identification efficiency and accuracy while reducing detection costs, and is applicable to mobile or embedded devices.

关键词： Training Accuracy Attention mechanisms Noise reduction Data augmentation Data models Object recognition

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Smooth Switched Flight control Design of Fixed-wing Aircraft for Heavy Payload Airdrop

Smooth Switched Flight Control Design of Fixed-wing Aircraft...

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Chinese control and Decision Conference, CCDC

作者： Ye Liang Yifei Dong Yuejiang Han Tianyu Gao Yuting Ma Baoqing Yang Lixian Zhang College of Computer and Control Engineering Northeast Forestry University Harbin China School of Astronautics Harbin Institute of Technology Harbin China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the issue of flight control of fixed-wing aircraft (FwA) in heavy payload airdrop tasks, where smooth control commands are considered for actuators. The case that heavy payloads are mounted out of the symmetry plane is taken into account for dynamic modeling of the FwA, where the dynamic model is split into different modes corresponding to various situations of mounted payloads. Considering the dramatic variations of mass and center of gravity, a multi-equilibria switched system (MESS) is formulated for flight control, where the stable (unstable) modes correspond to the cases of symmetry (unsymmetry) mounting to ensure that the airdrop is with small rolling/yawing angles. By inserting the smooth portion, a smooth switched controller and the corresponding dwell time condition is obtained for MESS to tackle the flight control problem in heavy payload airdrop of FwA, which has not been considered before. The effectiveness and advantages of the proposed controller are demonstrated via simulations.

关键词： Switched systems Actuators Atmospheric modeling Switches control systems Stability analysis Aircraft

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3D Object Tracking with Reduced Cumulative Error on Sparse Point Clouds

3D Object Tracking with Reduced Cumulative Error on Sparse P...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Chen, Qi Zhu, Yanping Zhu, Dong Jiang, Aimin Changzhou University School of Computer Science and Ai Changzhou China Changzhou University School of Microelectronics and Control Engineering Changzhou China Hohai University College of Internet of Things Engineering Changzhou China

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

In 3D target tracking, the cumulative error generated in the sparse point cloud environment makes tracking more difficult, which is a key issue to track the target stably to allow the lost target to be retrieved. This paper proposes a Bayesian theorem-based 3D single-target tracking algorithm to improve the subsequent tracking performance by reducing the cumulative error caused by low-quality prior information. First, we use Bayes' theorem to classify the prior information to obtain the maximum a posteriori probability, and second, we suppress the proposals with poor prior information to re-duce the cumulative error. Finally, the algorithm proposed in this paper is applied to the dataset, and the study shows an improvement of about 23% in real-time performance over the PTT-Net algorithm and excellent performance in several tracking metrics. © 2022 IEEE.

关键词： Target tracking

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Attitude Tracking of Near-ground Quadrotor: A Switched Linear Parameter Varying Approach

Attitude Tracking of Near-ground Quadrotor: A Switched Linea...

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Chinese control and Decision Conference, CCDC

作者： Ye Liang Zefeng Lin Tianyu Gao Yifei Dong Yihang Ding Lixian Zhang College of Computer and Control Engineering Northeast Forestry University Harbin China School of Astronautics Harbin Institute of Technology Harbin China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper is devoted to the attitude tracking problem of a near-ground quadrotor, where the aerodynamic force of propellers is impaired under the ground effect. An altitude-dependent dynamic model for attitude control of the quadrotor is formulated with consideration of actuator dynamics, upon which an attitude tracking system for near-ground quadrotor is obtained, instead of only considering the altitude control problem via altitude-dependent total lift force in the past studies. By selecting the altitude as a scheduling variable, the attitude tracking control system is further described via a switched linear parameter varying scheme, which has not been considered before. The stability condition and the H ∞ performance are obtained, then an altitude-dependent attitude controller is designed, where the effectiveness and advantages are demonstrated via simulations.

关键词： Actuators Attitude control Propellers Force Switches Aerodynamics Stability analysis

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