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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 Multi-constraint 3D Mesh Models Deformation Method

A Multi-constraint 3D Mesh Models Deformation Method

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

作者： Zhang, Chuangchuang Chen, Xiaobing Lin, Jianchu Guo, Shuxin Wang, Rongrong Gu, Yinxi Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

Aiming at the problems of too many control vertices and difficult operation of the traditional free deformation technique, a multi-constraint 3D mesh models deformation method is proposed. Firstly, the input model is embedded into a sparse tetrahedral control mesh, and the positional relationship between the model and the control mesh is established using tetrahedral coordinates. Secondly, skeleton nodes are set on the model to generate skeleton segments, control mesh vertices are bound to the skeleton segments, the control mesh is deformed by manipulating the rotation and translation of the skeleton nodes, and finally the new positions of the model vertices are computed based on the control mesh. Since the skeleton drives a sparse tetrahedral control mesh, the accuracy of model deformation will be reduced, resulting in a series of problems in the deformed model. In order to optimize the model deformation, this paper sets the influence range of the control mesh on the model deformation by the weight of the constraint region, adds point constraints to the control mesh vertices to set the change range of this vertex, and finally calculates the correction factor according to the stretch constraints and volume constraints, and optimally adjusts the position of the control meshes vertices. The experimental results show that this method can make up for the problems of the traditional free deformation technique, which is complicated and unintuitive to operate, and improve the accuracy of deforming the model. © 2023 IEEE.

关键词： Mesh generation

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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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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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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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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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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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A Social Distancing Framework Based on GPS and Bluetooth Empowered by Feature-based Machine Learning Algorithm

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 118-127页

作者： Khan, Muhammad Adnan Rehman, Abdur Abbas, Sagheer Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immunization against the disease. In this case, social distancing is thought to be an effective barrier against the spread of the contagion virus as the risk of virus transmission can be reduced by avoiding direct contact with people. Thus, the goal of this research is to develop and improve an AI (Artificial Intelligence) system architecture for social distance monitoring. The framework could also use the GPS (Global Positioning System) to recognize human separation through cell phones. The transition learning framework is also applied to improve the consistency of the existing system. In this manner, the detection system uses a pre-trained technique that takes a Bluetooth dataset and location-sharing dataset to link to an additional level. In an attempt to approximate social distancing breaches among people, we used Bluetooth technology along with GPS distance estimation and set a threshold. To predict if the distance value exceeds the required social distance standard, a violation threshold is calculated and then it sends an alarm to every individual who is not maintaining social distancing. In response, the individual who breaks the social distance limit is also monitored using a detection approach. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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A survey of Detecting Flakiness in Automated Test Regression Suite 21

A survey of Detecting Flakiness in Automated Test Regression...

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21st International Learning and Technology Conference, L and T 2024

作者： Elgazzar, Mohamed Hossny, Eman Omara, Fatma A. Faculty of Computers and Informaiton Software Engineering Dept Cairo Egypt Faculty of Computers and Informaiton Computer Science Dept Cairo Egypt

ISBN: (纸本)9798350393569

Recently, Flaky Tests have gotten the attention of the research community. These tests not only waste time and resources, but also undermine the reliability of the automated tests and hence the software systems are affected. So, the detection of Flaky Tests is very important and has a wide audience from both industry and academia. In addition, the research work is very promising having a great potential for the contribution that maps to a true value added to the up-to-date industry projects. Although lots of effort has been done to detect Flakiness in the software systems, the majority of the existed works focuses on different techniques in isolation. The aim of the work in this paper is to overlook the existed flakiness detection techniques, try to analyze them, and hold a comparison, indicating the proper context for each one. Most of the existed flakiness detection techniques are done on unit tests and library/technical projects. According to the comparative study among these techniques, it is found gaps and wide potential for contribution. On the other hand, flakiness detection techniques can be clustered under three main headers;First, study the phenomena under different testing levels by trying to focus on projects with integration, system, and end-toend tests. Second, study the phenomena under different Project types and data sets by studying business projects with user interfaces and mobile applications and so on. Third, study the phenomena toward a faster delivery cycle by studying flake tests in the context of parallel execution and Continuous Integration and Continuous Deployment (CI/CD). We can conclude that not all detection techniques have the same potential where some techniques are very competent while others still struggle to achieve considerable efficiency. © 2024 IEEE.

关键词： User interfaces

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation 21

Smart Glove with Mobile Application to Detect Static Arabic ...

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21st International Learning and Technology Conference, L and T 2024

作者： Elshareif, Mohamed Elgilani Eltahir Ahmad Alias, Nurul Aswani Jomhari, Nazean Sofian, Hazrina University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350393569

The use of assistive technology in the field of education is now a common practice in today's tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise. © 2024 IEEE.

关键词： Assistive technology

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