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Smart MobiNet:A Deep Learning Approach for Accurate Skin Cancer Diagnosis

computers, Materials & Continua 2023年第12期77卷 3533-3549页

作者： Muhammad Suleman Faizan Ullah Ghadah Aldehim Dilawar Shah Mohammad Abrar Asma Irshad Sarra Ayouni Department of Computer Science Bacha Khan UniversityCharsaddaPakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Computer Studies Arab Open UniversityMuscatOman School of Biochemistry and Biotechnology University of the PunjabLahorePakistan

The early detection of skin cancer,particularly melanoma,presents a substantial risk to human *** study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning ***,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and *** address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive *** model utilizes a multi-scale feature extraction methodology by using various convolutional *** ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this *** data augmentation approaches are implemented to address the issue of model *** this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer *** findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of ***,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at *** model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.

关键词： Deep learning Smart MobiNet machine learning skin lesion melanoma skin cancer classification

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Critical Evaluation of the Role of Cloud systems and Networking in the Security and Growth of the Business Market 2

Critical Evaluation of the Role of Cloud Systems and Network...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Fakhouri, Hussam N. Alhadidi, Basim Alsharaiah, Mohammad A. Al Naddaf, Heba Alomoush Data, Ashraf Saleh University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman Jordan Al-Balqa Applied University Department Of Computer Information Systems Salt19117 Jordan Al-Ahliyya Amman University Data Science And Artificial Intelligence Department Amman Jordan Damascus University Faculty Of Media Deprtment Of Electronic Media Damascus Syria University Of Petra Faculty Of Information Technology Science And Artificial Intelligence Department Amman Jordan

ISBN: (纸本)9798350394962

This paper aims to assesses the multifaceted role of cloud systems in stimulating and protecting business market growth, with the goal of exploring the transformative benefits of cloud computing, such as lower costs, increased flexibility, improved data security, efficient collaboration, and greater integration, while also highlighting the challenges and risks associated with it. These include data protection, legal concerns, and compliance, which are especially critical given the growing incidence of cybersecurity risks. Furthermore, the paper dives into the many types of cloud services, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), outlining their distinct benefits, potential downsides, and applicability in various business situations. It also looks at new trends and solutions, particularly the rise of hybrid cloud systems. © 2024 IEEE.

关键词： Infrastructure as a service (IaaS)

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Arduino-Based Gas Leakage Monitoring and Treatment System 1

Arduino-Based Gas Leakage Monitoring and Treatment System

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Muhammad, Faiq Rafi Putra, Alfa Satya Aribowo, Arnold Pelita Harapan University Faculty Of Computer Science Computer Systems Department Tangerang Indonesia Pelita Harapan University Faculty Of Computer Science Information Systems Department Tangerang Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The use of gas in daily life is very important, especially in household life where it has become a major need. There are risks related to gas usage to the user and the community, such as gas leakage, which can be fatal and can cause fire to break out. Therefore, a system is needed to monitor and safely handle gas leakage. This paper discusses a gas leak monitoring and treatment system that uses gas sensor to detect gas concentration level, solenoid valve to open and close the gas valve, LCD to monitor and display gas concentration level, an exhaust fan to dispose gas from the room, buzzer as warning alarm, push-button switch to manually open gas valve after it has been closed, and Cayenne platform to send email notification to the user when gas leakage is detected. These devices are controlled by an Arduino Mega microcontroller board. From the system design and testing results, it can be concluded that the Arduino-based gas leakage monitoring and treatment system is working as expected in safely handling gas leakages. The success rate of gas sensor, solenoid valve, buzzer, push-button, and exhaust fan testing is 100%, and the success rate of email notification testing is 80%. © 2022 IEEE.

关键词： Solenoid valves

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Step Size Control in Evolutionary Algorithms for Neural Architecture Search 16th

Step Size Control in Evolutionary Algorithms for Neural Arch...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Nieber, Christian Mota Dias, Douglas Naredo Garcia, Enrique Ryan, Conor Department of Computer Science and Information Systems University of Limerick Limerick Ireland Department of Computer Science & Applied Physics Atlantic Technological University Galway Ireland Departamento de Ciencias Básicas Universidad del Caribe Cancun Mexico

ISBN: (纸本)9789897587214

This work examines how evolutionary Neural Architecture Search (NAS) algorithms can be improved by controlling the step size of the mutation of numerical parameters. The proposed NAS algorithms are based on F-DENSER, a variation of Dynamic Structured Grammatical Evolution (DSGE). Overall, a (1+5) Evolutionary Strategy is used. Two methods of controlling the step size of mutations of numeric values are compared to Random Search and F-DENSER: Decay of the step size over time and adaptive step size for mutations. The search for lightweight, LeNet-like CNN architectures for MNIST classification is used as a benchmark, optimizing for both accuracy and small architectures. An architecture is described by about 30 evolvable parameters. Experiments show that with step size control, convergence is faster, better performing neural architectures are found on average, and with lower variance. The smallest architecture found during the experiments reached an accuracy of 98.8% on MNIST with only 5,450 free parameters, compared to the 62,158 parameters of LeNet-5. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Evolutionary Algorithms LeNet Lightweight CNNs MNIST Neural Architecture Search Step Size Control

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When Left Behind in Multi-User Virtual Reality: Clues to Indicate the Absence of Users

When Left Behind in Multi-User Virtual Reality: Clues to Ind...

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2022 International Conference on Cyberworlds, CW 2022

作者： Wolfel, Matthias Basing, Lara-Marie Deuchler, Jonas Karlsruhe University of Applied Sciences Faculty of Computer Science and Business Information Systems Germany University of Hohenheim Faculty of Business Economics and Social Sciences Germany

ISBN: (数字)9781665468145

ISBN: (纸本)9781665468145

Recently, with the increasing popularity of immersive virtual reality, synchronous communication via avatars in virtual worlds has become more popular. In contrast to asynchronous communication - like social media platforms - people expect others to be present during embodied, virtual interactions. However, for a variety of reasons, this is not always the *** some communication media provide a good indicator that the other is still present, such as live images of the conversation partner in video conferencing, not all media do provide this information naturally, e.g., when the interlocutor is silent on the phone. In immersive multi-user virtual reality, a wrong and misleading signal may even be provided when an avatar is still shown while its owner is absent. In many situations, an assumed presence of the other who is actually no longer there can lead to confusion. In the presented empirical study, we show that users in shared immersive environments prefer an indicator when a person is absent and that the preferred way of visualization depends on the use case. © 2022 IEEE.

关键词： Virtual reality

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Advancing Text-to-Image Generation: A Comparative Study of StyleGAN-T and Stable Diffusion 3 under Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 85卷 784-800页

作者： Sadek, Mohamed G. Hassan, A.Y. Diab, Tamer O. Abdelhafeez, Ahmed Benha Engineering Faculty Egypt Electrical Engineering Department Benha Engineering Faculty Benha University 13511 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Recent advances in generative models have revolutionized the technology employed for image synthesis quite significantly, and two paradigms—GANs and diffusion-based models—are leading the pack of innovation. This paper outlines an extensive comparison and analysis of some of the best models across both paradigms, namely StyleGAN-T, DF-GAN, AttnGAN, and BigGAN on the GAN side and Stable Diffusion 3 (SD3), DALL·E 3, Midjourney v6, and Imagen 2 on the diffusion side. We systematically inspect the architectural design, training protocols, text-conditioning processes, and domain adaptability of each model, highlighting how they address text-to-image generation challenges differently. Through qualitative and quantitative measurements—such as FID, CLIP Score, human preference surveys, and compositional accuracy, the work reveals performance tradeoffs concerning speed, control, creativity, semantic alignment, and photorealism. We use the Neutrosophic Set model to select the best model based on these evaluation matrices. We have different scores for each model based on evaluation matrices. So, the neutrosophic set is used to overcome the uncertainty information. We use the COPRAS method to rank the models and select the best one based on the evaluation matrix weights. © 2025, University of New Mexico. All rights reserved.

关键词： Photointerpretation

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Ethical Guidelines for the Application of Generative AI in German Journalism

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Digital Society 2025年第1期4卷 1-43页

作者： Hofeditz, Lennart Jung, Anna-Katharina Mirbabaie, Milad Stieglitz, Stefan Faculty of Business Administration and Economics Hochschule Niederrhein – University of Applied Sciences Mönchengladbach Germany Faculty of Computer Science University of Duisburg-Essen Duisburg Germany Faculty of Information Systems and Applied Computer Science University of Bamberg Bamberg Germany Faculty of Economics and Social Sciences University of Potsdam Potsdam Germany

Generative Artificial Intelligence (genAI) holds immense potential in revolutionizing journalism and media production processes. By harnessing genAI, journalists can streamline various tasks, including content creation, curation, and dissemination. Through genAI, journalists already automate the generation of diverse news articles, ranging from sports updates and financial reports to weather forecasts. However, this raises ethical questions of high relevance for media organizations and societies especially when genAI is used for more sensitive topics and at larger scale. To not jeopardize trustworthiness in journalistic organizations, it is important that the use of genAI in journalism is guided by moral principles. We therefore conducted 18 interviews with researchers and practitioners with expertise in AI-based technologies, journalism, and ethics from a German perspective in order to identify guidelines for the ethical use of genAI in media organizations. We derived requirements for the ethical introduction of genAI and actionable guidelines which explain how decision makers in media organizations should address ethical principles for the use of AI in the news production life cycle, in order to contribute to trustworthiness of journalistic organizations and products.

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Modeling of COVID-19 outbreak in Gaza Strip using SEIR model

Modeling of COVID-19 outbreak in Gaza Strip using SEIR model

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

作者： Radwan, Akram M. Mortaja, Waleed Alzaq, Husam University College of Applied Sciences Information Technology Faculty Gaza Palestine University College of Applied Sciences Engineering & Smart Systems Faculty Gaza Palestine Istinye University Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350320954

The novel coronavirus (COVID-19) pandemic is a major global health threat that was spreading very quickly around the world. Many mathematical models are being formulated to study the spread of COVID-19 and to predict its evolution, in order to help mitigating future outbreaks. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is considered a prevalent model that describes various diseases outbreaks including COVID-19. The model divides the individuals into four groups;susceptible, exposed, infectious, recovered. To predict the evolution of the pandemic using the SEIR model, we first estimate the parameters as well as initial conditions. In this work, we model the spreading of COVID-19 pandemic using the SEIR in Gaza Strip to identify the better fitting model for forecasting future spread. The data required for estimation was collected between March 1st, 2021 and December 31st, 2021. The simulation results using SEIR model showed a significant model fitting, as there is no considerable difference from the real data compared to the respective model values. We also study the severity of the pandemic by identifying two important parameters;the basic reproduction rate (Ro), which determines the speed of the spread of COVID-19;and the infection fatality ratio (IFR) which estimates the proportion of deaths among all infected individuals. We obtained a value of Ro = 0. 89. IFR value is ranged between 0.079% and 0.085%, which is less than the global average of about 0.15%. © 2023 IEEE.

关键词： COVID-19

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