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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Li, Yinyin Jiang, Tao Gu, Hanzhu Lu, Weibing Wu, Qingguo Yu, Yongtao Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Air Quality Index (AQI) is an important indicator for determining good or bad air quality. The accurate and efficient prediction of AQI plays a positive role in promoting the management of air pollution. However, current algorithms for predicting AQI usually do not comprehensively consider the effects of pollutant factors and meteorological factors on the prediction performance. Therefore, taking pollutant factors and meteorological factors as the basis of the model study, a CNNLSTM-Attention hybrid model is proposed. The CNN-LSTM module is used to obtain the air quality-related features, and the attention mechanism is introduced to weigh and sum the output of the LSTM in order to obtain the final attention-weighted features. The results show that the model has better performance than the single model for the prediction of the air quality index. © 2023 IEEE.

关键词： Air quality

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A Semi-Anchoring Guided High-Resolution Capsule Network for Vehicle Logo Recognition

A Semi-Anchoring Guided High-Resolution Capsule Network for ...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Wang, Lanfang Yu, Zhenyang Yu, Yongtao Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

As a unique identity reflecting the manufacturer of the vehicle, the vehicle logo information plays an important role in many transportation-related applications. However, due to the challenges of size variations, shape and form diversities, deformations, occlusions, and complex scenarios, it is still not an easy task to realize highly accurate vehicle logo recognition from images. This paper proposes a novel semi-anchoring guided high-resolution capsule network (SAGHR-CapsNet) for vehicle logo recognition. First, constructed with a multibranch high-resolution capsule network architecture functioned with repeated multiresolution feature fusion for feature extraction, the SAGHR-CapsNet can extract semantically strong and spatially accurate feature representations at each scale. Second, designed with a capsule-based efficient self-attention mechanism for feature semantic promotion, the SAGHRCapsNet functions excellently to attend to channel-wise informative features and target-oriented spatial features. Finally, adopted with a semi-anchoring guided strategy for vehicle logo recognition, the SAGHR-CapsNet performs promisingly to simultaneously improve the processing efficiency and guarantee the recognition accuracy. Intensive quantitative evaluations and comparative analyses on two large-scale data sets demonstrated the applicability and superiority of the SAGHR-CapsNet in vehicle logo recognition tasks. © 2023 IEEE.

关键词： Vehicles

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Application of Lightweight DSC-NET Network Model in Histopathological Images of Lung Cancer and Colon Cancer

Application of Lightweight DSC-NET Network Model in Histopat...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： An, Wenlong Zong, Hui Zhou, Heng Wang, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Classification of histopathological images is a fundamental task in the workflow of pathological diagnosis. Due to the complexity of pathological images, it is particularly important to use deep learning to improve diagnostic efficiency. This paper designs a lightweight network model DSC-NET, which consists of multi-scale feature stitching Multi-Conv, coordinate attention CA. The improved selfcalibrated convolution MSC-Conv is composed of selfcalibrated convolution SC-Conv, coordinate attention CA and depthwise DW. In addition, the DSC-NET network model converts the 1×1 convolutional layer (Conv) in the Block module into a linear layer (Linear), which reduces the computational complexity of the model while maintaining the ability of the convolution operation to capture local features. The research in this paper adopts the lung cancer and colon cancer datasets and adds Gaussian noise to these datasets to simulate the equipment shooting situation and evaluate the lightweight DSC-NET network model. Through quantitative comparisons with previous state-of-the-art methods, our experimental results demonstrate that the proposed method achieves superior accuracy. Furthermore, our method stands out with a smaller parameter count and significantly lower FLOPs, highlighting its efficiency and computational advantages. It has important potential to assist pathologists in pathological diagnosis. © 2023 IEEE.

关键词： Convolution

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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A Surface Defect Detection Method Based on Attention Mechanism for Workpieces

A Surface Defect Detection Method Based on Attention Mechani...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Qiu, Junlin Shen, Yongshan Feng, Wanli Zhang, Haodong Sun, Keke Qin, Yuxin Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

With the rapid development of image processing technology in recent years, faced with the issues of low detection accuracy and missed detections in the process of surface defect detection on workpieces, we propose a workpiece surface defect detection method based on attention mechanism. The model uses EfficientDet-d0 as the baseline network, mixes the Fused-MBConv structure and MBConv structure in EfficientNetv2 network as part of the feature extraction network, and uses the convolutional attention module CBAM to focus on the information of space and channel direction at the same time, and the Hardmish activation function is used in the structure. Introducing a fast spatial pyramid pooling module (SPPF) at the top of the feature extraction network increases the network's depth and enhances its expressive power. The extracted features are fed into the Improved-BiFPN (Bidirectional Feature Pyramid Network) for feature enhancement, improving the model's ability to detect defects of different sizes. Experimental results demonstrate that our proposed surface defect detection method for workpieces outperforms other advanced detectors. © 2023 IEEE.

关键词： Feature extraction

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Safety Helmet Detection Algorithm for Complex Scenarios Based on PConv-YOLOv8

Safety Helmet Detection Algorithm for Complex Scenarios Base...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Wang, Yuanyuan Jiang, Feilong Li, Yazhou Zhang, Haiyan Wang, Meifeng Yan, Shaofeng Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Detecting safety helmets in complex environments is challenging due to issues like occlusion and lighting variations. Addressing the issues of slow detection speed and low object detection accuracy in complex environments with the YOLOv8 model, this paper introduces a lightweight safety helmet detection model, called PConv-YOLOv8, that is suitable for real-time applications in complex environments. Our method incorporates the PConv (Partial Convolution) module into the YOLOv8 model, reducing the complexity of the feature extraction network while enhancing feature representation accuracy. It also incorporates SimAM attention to extract and enhance the most relevant features by evaluating their similarity. Additionally, it considers category imbalance and positional regression in the target detection task, enhancing the model's performance in target category identification and positional localization. Moreover, we propose the Wise-Distribution Focal Loss function to improve bounding box selection accuracy and enhance the model's robustness. This paper introduces the Wise-Distribution Focal Loss method, which enhances the performance of target category recognition and location localization by improving the accuracy of bounding box selection and increasing the robustness of the overall model. The experimental results demonstrate that the method proposed in this paper achieves a 125% improvement in detection speed and a 1.8% increase in mAP0.5 compared to the YOLOv8 model. © 2023 IEEE.

关键词： Convolution

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CNN-Based Colon Cancer Recognition Model 3

CNN-Based Colon Cancer Recognition Model

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ba Alawi, Abdulfattah E. Bozkurt, Ferhat Taiz University Software Engineering Faculty of Engineering and It Taiz Yemen Ataturk University Computer Engineering Faculty of Engineering Erzurum Turkey

ISBN: (纸本)9798350305333

Digital pathology is being used extensively for the diagnosis of tumors. Disappointingly, the existing approaches are still constrained whenever confronted with a resolution, a size of images, and a lack of extensively cleaned datasets. Further, the recognition accuracy mostly does not reach high scores. In terms of Deep Learning (DL) approaches' capacity to handle extensive applications, such an approach appears to be an absorbing solution for both categorization and tissue segmentation in histopathology images. The present study concentrates on the application of deep learning models in the classification of the context of histopathology data and the recognition of colon cancer. In this, cutting-edge Fully Convolutional Network (CNN) models such as DenseNet121, EfficientNetB7, EfficientNetB1, EfficientNetB2, and DenseNet201 have been evaluated for the recognition of Colon Cancer. The assessment of the algorithms used for the proposed CNN-based colon cancer detection model ensures reliable classification findings with EfficientNetB2 attaining up to 99.9994% in terms of accuracy. © 2023 IEEE.

关键词： Diagnosis

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Mitigating Ransomware in Government-Managed Institutions: A Global Critical Information Infrastructure Perspective

Mitigating Ransomware in Government-Managed Institutions: A ...

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20th International Conference on Cyber Warfare and Security, ICCWS 2025

作者： Mashila, Musiwalo Sithungu, Siphesihle Philezwini Lebea, Khutso Academy of Computer Science and Software Engineering Faculty of Science University of Johannesburg South Africa

ISBN: (纸本)9781917204316

This paper examines the escalating ransomware threats faced by government-managed educational institutions, focusing on their vulnerabilities, case studies, and mitigation strategies. With the adoption of Bring Your Own Device (BYOD) policies, schools increasingly expose their networks to cyber risks, making them attractive targets for cybercriminals. Case studies, including attacks on the Los Angeles Unified School District and the University of California, San Francisco, illustrate the profound impact of ransomware incidents and the diverse responses of institutions. Effective cybersecurity measures are crucial, emphasizing the need for prioritized spending, comprehensive security training, and advanced detection and response strategies. The role of government is also vital, as it develops legislation, guidelines, and funding opportunities to enhance educational cybersecurity. Recommendations include technical measures to secure networks and collaborative educational initiatives to share best practices. Ultimately, this research study underscores the necessity of continuous adaptation and government support in fortifying defences against ransomware threats. By fostering a cybersecurity awareness and resilience culture, educational institutions can better protect sensitive data and ensure the safety of their operations in an increasingly tricky digital landscape. © 2025 Proceedings of the International Conference on Information Warfare and Security (ICCWS). All rights reserved.

关键词： Cyber attacks

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Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Model 3

Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Mo...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ba Alawi, Abdulfattah E. Bozkurt, Ferhat Baturalp, Faruk Software Engineering Taiz University Faculty of Engineering and It Taiz Yemen Computer Engineering Ataturk University Faculty of Engineering Erzurum Turkey

ISBN: (纸本)9798350305333

With the rapid growth of software and networks, the rate of cyber-attacks has increased rapidly. As a result, the demand for a dependable and suitable Intrusion Detection System (IDS) solution for safeguarding devices and networks has become essential. Nevertheless, in order to accurately detect the activities of new kinds of crimes, particularly tasking-step incidents, an effective IDS requires an accurate and up-to-date dataset. In this study, MSCAD is used which comes with multi-step attack tasking: the initial attack is for password-cracking type, and the subsequent one is a volume-driven Distributed Denial of Service (DDoS) attack. The dataset used (MSCAD) contains five types of internet attacks including Port Scan Traffic, App-based DDoS, Volume-based DDoS, Web Crawling, and Password Cracking (Brute Force). Nine algorithms including Gaussian Naive Bayes, Bournuli Naive Bayes, Decision Tree, K-Nearest Neighbors, Catboost, XGB, and Random Forest were employed as classifiers. The accuracy rate reached over 99.9% in terms of accuracy and AUC-ROC. © 2023 IEEE.

关键词： Intrusion detection

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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