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4th International Conference on Advancements in computing, ICAC 2022

作者： Bandara, H.M.A.P. Charles, J. Lekamge, L.S. Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sri Lanka

ISBN: (纸本)9798350398090

The rapid proliferation of internet-based technology has made the sharing economy the next e-commerce business model. Recently, sharing economy lodging platforms have gained a significant market share in the tourism and lodging industry. Tourism and hospitality industries are now being significantly disrupted by Airbnb, an online lodging platform. For businesses and customers who utilize these accommodation platforms, online reviews serve as quality indicators, affecting their decisions to make a transaction. Sentiment analysis and text mining can be used to analyze these online reviews to identify various factors embedded in them that can influence how guests perceive lodging in the sharing economy. Peer-to-peer accommodation platforms can benefit from analyzing these aspects since they can utilize the results to streamline their operations and give customers better services. Current research on this domain has only identified a limited number of important factors, such as trust, quality, security, price, cleanliness, and indoor environmental quality. However, there can be many other factors that can affect the accommodation experience. These factors would require further attention. Therefore, in this study a dataset pertaining to the Airbnb platform was considered which contained a total of 401 964 review comments. Word cloud, frequency distribution, and topic modeling were used as data analysis techniques to identify various factors affecting accommodation experience. Results indicate that factors including location, safety, host-guest interaction, amenities, proximity to restaurants and transit options, and apartment uniqueness can be primarily taken into account to give superior services to their clients. © 2022 IEEE.

关键词： Sentiment analysis

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Personalized Food Recommendations: A Machine Learning Model for Enhanced Dining Choices 14

Personalized Food Recommendations: A Machine Learning Model ...

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14th IEEE International Conference on Control System, computing and Engineering, ICCSCE 2024

作者： Al-Hubaishi, Mohammed Ali, Musab A. M. Tahir, Nooritawati Md Halic University Department of Computer Engineering Istanbul Turkey University of Technology and Applied Sciences College of Computing and Information Sciences Oman International Information Technology University Department of Computer Engineering and Information Security Kazakhstan Universiti Teknologi MARA Malaysia Universiti Teknologi MARA College of Engineering Malaysia Riyadh Saudi Arabia

ISBN: (纸本)9798350364507

This research introduces an innovative approach to enhance dining experiences through a personalized meal suggestion system. Our method leverages decision tree-based machine learning to predict user preferences by analyzing various attributes. The aim is to assist individuals in making optimal food choices at restaurants, thereby enriching their culinary journey. QR codes are integrated into an Android restaurant application, facilitating convenient access to the food menu. User inquiries are efficiently managed through a MySQL database accessible across multiple platforms, including a Java GUI, an Android app, and a dedicated website catering to both user and administrator roles. © 2024 IEEE.

关键词： Adversarial machine learning

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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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Survival analysis for the identified cancer gene subtype from the co-clustering algorithm

Survival analysis for the identified cancer gene subtype fro...

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2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Machap, Logenthiran Moorthy, Kohbalan Tunku Abdul Rahman University College Johor Department of Computing and Information Technology Segamat Malaysia Universiti Malaysia Pahang College Johor College of Computing and Applied Science Pahang Malaysia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

Cancer gene subtype information is significant for understanding tumour heterogeneity. The early detection of cancer and subsequent treatment can be lifesaving. However, it is hard clinically and computationally to detect cancer and its subtypes in their early stages. Therefore, we extend the analysis and results from Machap et al. (2019), to include the Kaplan-Meier survival analysis with the integration of gene expression and clinical features data. There are two cancer datasets used for the analysis: breast cancer and glioblastoma multiforme. The luminal type was the common subtype of breast cancer, showing a higher survival rate. Whereas the Proneural subtype in glioblastoma multiforme has a little longer survival rate than the other three subtypes. These molecular differences between subtypes have been shown to correlate very well with clinical features and survival parameters to help understand the disease and develop better therapeutic targets. © 2022 IEEE.

关键词： Gene expression

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Single-atom nanozymes for electrochemical sensing oxidative stress biomarkers

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Trends in Chemistry 2025年第6期7卷 333-344页

作者： Mani, Veerappan Tostado-Blazquez, Guillermo Ramalingam, Vinoth Salama, Khaled Nabil Thuwal23955-6900 Saudi Arabia Centre for Advanced Measurement Research & Health Translation Department of Pure and Applied Chemistry University of Strathclyde GlasgowG1 1XL United Kingdom School of Computing Engineering and Technology Robert Gordon University Garthdee Road AberdeenAB10 7GJ United Kingdom

Developing reliable and low-cost sensors for oxidative stress biomarkers has gained significant interest for understanding and managing chronic diseases. Recently, single-atom nanozymes (SANs) with atomically dispersed metal sites and unique metal–nitrogen–carbon (M–N–C) structures have emerged as promising redox enzyme mimetics, offering superior catalytic efficiency and specificity, and with exceptional sensing capabilities for oxidative stress biomarkers such as hydrogen peroxide (H2O2), nitric oxide (NO), glutathione (GSH), and uric acid (UA). This review discusses key progress, challenges, and future directions in SANs for electrochemical determination of oxidative stress biomarkers, aiming to guide future research toward practical and impactful solutions in healthcare and disease management. © 2025 Elsevier Inc.

关键词： Biomarkers

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Evaluation of Spatiotemporal Fetal Cardiac Imaging Using Deep Learning Techniques

Evaluation of Spatiotemporal Fetal Cardiac Imaging Using Dee...

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International Health Informatics Conference, IHIC 2022

作者： Nidhi, Dipak Kumar Srivastav, Khushboo Heikkonen, Jukka Kanth, Rajeev Tribhuvan University IOE Pulchowk Campus Kathmandu Nepal Kathmandu Nepal Department of Computing University of Turku Turku Finland School of Information Technology Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9789811990892

Fetal echocardiography is a standard diagnostic tool used to evaluate and monitor fetuses with a defective heart associated with a number of fetal conditions. Deep learning is a machine learning technology which can perform specific tasks with specific goals. Deep learning techniques are used to evaluate fetal cardiac ultrasound cine loops and improve the evaluation of fetal abnormalities. Convolutional neural network along with recurrent neural network was applied in this study as CNN + LSTM, CNN + GRU, and 3D CNN for the processing and classification of ultrasonographic cine loops into various classes. The models were able to sort the fetal cardiac cine loops into five standard views with 92.63%, 94.99%, and 82.69% accuracy, respectively. Furthermore, the models were able to diagnose Tricuspid atresia (TA) and Hypoplastic left heart syndrome (HLHS) with an accuracy of 94.61%, 91.99%, and 86.54%, respectively. These deep learning-based algorithms were found to be effective tools for evaluating and monitoring normal and abnormal fetal heart cine loops. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heart

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Extremum-Preserving Correction of the Nine-Point Scheme for Diffusion Equation on Distorted Meshes

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Advances in applied Mathematics and Mechanics 2023年第2期15卷 402-427页

作者： Wang Kong Zhenying Hong Guangwei Yuan Zhiqiang Sheng Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjingJiangsu 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjingJiangsu 211106China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100871China

In this paper,we construct a new cell-centered nonlinear finite volume scheme that preserves the extremum principle for heterogeneous anisotropic diffusion equation on distorted *** introduce a new nonlinear approach to construct the conservative flux,that is,a linear second order flux is firstly given and a nonlinear conservative flux is then constructed by using an adaptive method and a nonlinear weighted *** new scheme does not need to use the convex combination of the cell-center unknowns to approximate the auxiliary unknowns,so it can deal with the problem with general discontinuous *** results show that our new scheme performs more robust than some existing schemes on highly distorted meshes.

关键词： Extremum-preserving correction diffusion equation distorted mesh nine-point scheme

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Measuring Electromagnetic Performance and Configuration of Networks in High Interference Environments 15

Measuring Electromagnetic Performance and Configuration of N...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Khan, A.Z. Panwar, Neeraj Rayaguru, N.K. Shelke, Priya M. Giri, Rakesh Kumar Jaishankar, P. Yeshwantrao Chavan College of Engineering Applied Physics Department Nagpur India University Gehu Dehradun Department of School of Computing India Vel Tech Rangarajan Dr sagunthala R & D Institute of Science and Technology Department of Eee India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Sunrise University Department of Computer Science & Engineering Alwar India Sastra Deemed to be University Department of School of Civil Engineering India

ISBN: (纸本)9798350370249

Measuring the electromagnetic overall performance and configuration of networks in high-interference environments can be challenging. In those environments, the indicators of hobby can be infected with unwanted heritage indicators, i.e., noise, leading to degraded overall performance and misguided size readings. This paper investigates the most reliable techniques for measuring networks' electromagnetic performance and configuration in these problematic environments, utilizing introducing a unique multistage framework that combines sign-processing strategies with some community parameters. In particular, the framework includes four degrees: sign reception, sensitivity analysis, pass-correlation, and community configuration optimization-an analysis of the results of varying environmental conditions on sign reception and sensitivity change into conduct. A cross-correlation approach has evolved to decide the extent of correlation among one-of-a-kind alerts and to locate the specific configurations of the particular networks. Sooner or later, an optimization set of rules was carried out to locate the network's optimum configuration. The final result of this research is a reliable method for measuring the electromagnetic performance and configuration of networks in high-interference environments. The proposed technique is anticipated to help researchers and engineers better understand the electromagnetic behaviors of complicated networks in actual-world scenarios. © 2024 IEEE.

关键词： Configuration Electromagnetic Environmental Performance Technique

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Forward-backward-forward dynamics for bilevel equilibrium problem

arXiv

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arXiv 2024年

作者： Mittal, Kanchan Gautam, Pankaj Vetrivel, V. Department of Mathematics Indian Institute of Technology Madras Chennai India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee India

We introduce a forward-backward-forward (FBF) algorithm for solving bilevel equilibrium problem associated with bifunctions on a real Hilbert space. This modifies the forward-backward algorithm by relaxing cocoercivity with monotone and Lipschitzness. Further, we present the FBF dynamical system and investigate the generated trajectory’s existence, uniqueness and weak convergence. We illustrate the proposed method for equilibrium problem under saddle point constraint. © 2024, CC BY.

关键词： Dynamical systems

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