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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Alnahdi, Noha Alnanih, Reem Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The reliability of software interfaces has become increasingly recognized as a crucial factor impacting the quality, manageability, and efficiency of various software systems. Traditional approaches to software reliability assessment often overlook interface-level reliability, focusing primarily on system-level metrics. However, the growing emphasis on user-centered design has amplified the demand for reliable and user-friendly software interfaces. This paper introduces a novel information model for evaluating the reliability of software interface design, which is crucial for enhancing assessment approaches in software engineering. The proposed model integrates usability testing and reliability attributes early in the software development life cycle (SDLC), particularly in the interface design phase, to enhance the system's overall performance and user experience. The paper describes the current state of models and methods used to evaluate software reliability at the interface design level, outlining the problems that the proposed new model aims to solve. It presents the theoretical basis for the model and provides information on its integration into the SDLC. Analytical examples are used to corroborate the proposed model, demonstrating its usefulness in the early project stages and its ability to improve software interface reliability and practicality. The paper also discusses challenges in adoption and suggests areas for future theoretical and empirical research. By developing a more comprehensive perspective on software quality, this work paves the way for practical, reliable, and user-friendly interfaces across various software domains. © 2024 The Authors.

关键词： Data reliability

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2023 International Conference on computer science, information technology and Engineering, ICCoSITE 2023

作者： Rani, Larissa Navia Yuhandri, Yuhandri Universitas Putra Indonesia YPTK Information System Computer Science Faculty Padang Indonesia Universitas Putra Indonesia YPTK Information Technology Computer Science Faculty Padang Indonesia

ISBN: (纸本)9798350320954

In this study aimed to measure the level of similarity between two logos, both those that look different and those that look the same. This can be realized by forming a logo image database that is stored in a logo image database derived from various existing logo image data. This research uses 4 logo images as data testing and 210 image data for the database as data training. All of the logo images used come from the Ministry of Law and Human Rights of the Republic of Indonesia (Kemenkumham RI) West Sumatra Regional Office. The size images are color images with a pixel size of 320 x 320 pixels, the purpose of which is for the process of dimensional uniformity of the images to be studied. This research uses Content Base Image Retrieval (CBIR) method to search for images from a large image database than using Convolutional Neural Network (CNN) type Residual Network (ResNet-18) Architecture to get the similarity score accurately. The result of this implementation is the formation of an automatic distribution of training images and validation images with 147 training image data values (70%) and 63 validation images (30%) of the 210 existing images. The result of this research is producing the algorithm to implement the method and the tool software application to measure the similarity of logo images. The accuracy of this tool is 93.65% with a total of 84 iterations. © 2023 IEEE.

关键词： Network architecture

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Impact of Skin Lesion Segmentation Labeling Errors on Convolutional Neural Networks 66

Impact of Skin Lesion Segmentation Labeling Errors on Convol...

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66th International Symposium ELMAR, ELMAR 2024

作者： Sojo, Robert Bencevic, Marin Habijan, Marija Galic, Irena Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9798350375411

Accurate segmentation of skin lesions is critical for dermatological diagnosis and treatment. This study investigates the impact of label errors on the performance of convolutional neural networks in skin lesion segmentation. We conducted experiments using ground truth and simulated erroneous labels to train models and analyzed the effects of the errors using regression analysis. To simulate errors, we assume that errors are more likely to occur in more complex border regions. In addition, we add parameters to bias the errors towards either false positives or false negative regions. The overall effect of the false positive and false negative errors on the Dice coefficient is deceptively low. However, when we observe the false negative or false positive rate of the predictions, we see that they are significantly influenced by the bias of the label errors. In addition, label false positives have a more severe impact on prediction results than label false negatives. We recommend focusing on minimizing label false positives and suggest moving beyond mean Dice Score Coefficients for model evaluation. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Ensuring User Privacy in the Digital Age: A Quality-Centric Approach to Tracking and Data Protection 19

Ensuring User Privacy in the Digital Age: A Quality-Centric ...

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19th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2024

作者： Teze, Vitalijs Nazaruka, Erika Faculty of Computer Science Information Technology and Energy Riga Technical University 10 Zunda Embarkment Riga Latvia

ISBN: (纸本)9789897586965

In the contemporary digital landscape, the pervasive practice of user tracking and the consequent erosion of data protection present significant challenges to user privacy. This paper introduces 'Privacy Risk Assessor' a software tool designed to evaluate and enhance online privacy. Addressing the dynamics of user tracking, the tool analyses websites for privacy-threatening metrics in the context of existing tracking mechanisms. Employing a methodological approach, the tool's architecture enables efficient processing and adaptability to tracking techniques and privacy regulations. The research focuses on key metrics of quality attributes including security, usability, trust, reliability, and performance, providing actionable insights into privacy risks. An evaluation was conducted on a dataset of 492 Latvian websites, with an emphasis on diverse privacy-related factors. The study revealed insights into prevalent privacy practices and underscored the tool's effectiveness in real-world scenarios. The 'Privacy Risk Assessor' stands out for its possibility to be integrated into web development process, offering developers and end-users ability to proactively measure potential privacy threats. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Data privacy

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IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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computers, Materials & Continua 2024年第2期78卷 2245-2267页

作者： Ashraf S.Mashaleh Noor Farizah Binti Ibrahim Mohammad Alauthman Mohammad Almseidin Amjad Gawanmeh School of Computer Sciences Universiti Sains MalaysiaPenangMalaysia Department Computer Center Al-Balqa Applied UniversitySaltJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science Tafila Technical UniversityTafilaJordan College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic *** IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection *** paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT *** logic addresses IoT threat uncertainties and ambiguities *** component settings are optimized using PSO to improve *** methodology allows for more complex thinking by transitioning from binary to continuous *** of expert inputs,PSO data-driven tunes rules and membership *** study presents a complete IoT botnet risk assessment *** methodology helps security teams allocate resources by categorizing threats as high,medium,or low *** study shows how CICIoT2023 can assess cyber *** research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

关键词： IoT botnet detection risk assessment fuzzy logic particle swarm optimization(PSO) cybersecurity interconnected devices

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Power System Dynamic Properties after Implementation of PSS and Inertia Decrease

Power System Dynamic Properties after Implementation of PSS ...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kljajić, Ružica Marić, Predrag Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789531842976

Low inertia of the power system combined with low damping impairs the power system's ability to cope with disturbances that can significantly affect system stability. In such a case, in addition to speed or frequency deviations of the generator, low-frequency oscillations also occur and suitable ways must be found to mitigate them. In this paper, an oscillatory stability as well as the time domain generator's speed deviations were observed in an IEEE 14-bus system in four simulation scenarios after improving the system damping by implementing a Power System Stabilizer (PSS) and the system inertia was changed by integrating wind farm with fully rated converter interfaced wind generators. © 2024 IEEE.

关键词： Damping

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Ensemble Model for Detection and Classification of Brain Tumors 4

Ensemble Model for Detection and Classification of Brain Tum...

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4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Bhagyalaxmi, K. Dwarakanath, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Ramapuram India Srm Institute of Science and Technology Department of Information Technology Tamilnadu Ramapuram India

ISBN: (纸本)9798331529635

Brain tumors, characterized by abnormal cell growth within the brain, present significant challenges for early detection and accurate classification due to their complex and heterogeneous nature. Manual evaluation of MRI scans, though critical, is often laborintensive and prone to errors, highlighting the need for advanced, automated diagnostic methods. Convolutional Neural Networks (CNNs) have demonstrated strong potential in this domain by effectively learning hierarchical features from raw medical images to detect and classify brain tumors. However, single CNN models often encounter difficulties in handling the variability in tumor appearance, such as differences in shape, size, and intensity. To address these challenges, this research proposes an ensemble model that integrates pretrained CNN architectures-ResNet50, Inception, and Xception. The ensemble technique strengthens the adaptability and accuracy of brain tumor identification and categorization by incorporating the advantages of these distinct models. The proposed model was trained and evaluated using a large dataset of 5,316 MRI images, representing a range of brain tumor types, including gliomas, pituitary, meningiomas, and cases no tumor. With a high classification accuracy of 99.39%, our ensemble model proved to be useful in enhancing the reliability of diagnoses and assisting surgeons in making decisions on the treatment of brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent System for Intrusion Detection Based on Machine Learning 6

Intelligent System for Intrusion Detection Based on Machine ...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Ragab, Mohamed Ibrahim Fouad, Khaled M. School of Information Technology and Computer Science Nile University Giza Egypt New Mansoura University and Nile University Faculty of Computer Science and Engineering Mansoura Egypt

ISBN: (纸本)9798350378511

In today's digital age, security challenges threaten user privacy as networks face increased vulnerability to malicious attacks due to large data volumes. Intrusion Detection Systems (IDS) play a crucial role in identifying cyber-attacks and protecting system resources and users. This study utilizes machine learning classifiers (MLC) to analyze the NSL-KDD dataset, optimizing by preprocessing to remove irrelevant features. System performance is assessed using four attribute subsets, comparing model accuracy across DoS, Probe, U2L, and R2L attack classes to determine the best algorithm for each class. Using Random Forest with 20 features successfully achieved an accuracy of up to 99 % in intrusion detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring the Need for XAI for Managing Celiac Disease in Healthcare Sector in Saudi Arabia 15

Exploring the Need for XAI for Managing Celiac Disease in He...

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作者： Alzahrani, Amal Alnanih, Reem Alsaedi, Olaa A. Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The integration of Artificial Intelligence (AI) tools, particularly chatbots, in the healthcare sector has significantly enhanced patient care and has the potential to reduce healthcare costs, allowing for the reallocation of resources to other priority areas. The worldwide incidence of celiac disease ranges from 0.7% to 1.4%, is notably more prevalent in Saudi Arabia, where the rate reaches 3.2%. The high prevalence of celiac disease in Saudi Arabia underscores the necessity for healthcare technologies tailored to this population, including AI tools and chatbots that are essential for effective disease management. The success of these technologies depends on patients' trust and understanding, which are crucial for their adoption and use. Explainable AI (XAI) plays a pivotal role in this context, as it provides clear and justifiable explanations of AI-driven decisions, thereby enhancing patients' trust and understanding in AI system. This study aimed to investigate whether individuals with celiac disease perceive a need for explanations offered by Explainable AI (XAI) when using chatbots to manage their medical condition. A cross-sectional study was conducted online among Saudi Arabian celiac patients from May 2024 to June 2024. Statistical methods, including the Chi-Square test, percentage analysis, and rank analysis, were used to analyze the collected data. The questionnaire, distributed online via various social media platforms in Saudi Arabia, ensured widespread accessibility and active participation from the target population. The findings indicate a significant demand for personalized dietary assistance, as evidenced by a high chi-square score of 18.61 for personalized diet tracking needs. XAI has the potential to enhance chatbots by not only monitoring eating habits but also by providing personalized recommendations and explaining the rationale behind these suggestions. The study supports the hypothesis that celiac patients require explainable and personalize

关键词： Nutrition

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