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Optical dromions for complex Ginzburg Landau model with nonlinear media

applied Mathematics(A Journal of Chinese Universities) 2023年第1期38卷 111-125页

作者： Syed T.R.Rizvi Aly R.Seadawy Muhammad Younis S.O.Abbas Abdul Khaliq Department of Mathematics COMSATS University IslamabadLahore CampusPakistan Mathematics Department Faculty of ScienceTaibah UniversityAl-Madinah Al-Munawarah 41411Kingdom of Saudi Arabia Punjab University College of Information Technology University of the PunjabLahore Pakistan Mathematics Department Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahorePakistan

This manuscript studies the optical dromions with beta derivative(BD)applied to the Complex Ginzburg Landau equation(CGLE)with Kerr law,parabolic law,cubic quintic septic law and quadratic cubic *** obtain bright dromians by using the sine-cosine method(SCM).We will also obtain domain walls with the assistance of Bernoulli equation approach(BEA).Constraint conditions are also listed.

关键词： CGLE optical dromians non-Kerr laws

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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INITIAL VALUE PROBLEMS FOR NONLINEAR NEUTRAL DELAY Ψ-CAPUTO FRACTIONAL HYBRID DIFFERENTIAL EQUATIONS WITH BOUNDED DELAYS

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Journal of Mathematical Sciences (United States) 2025年 1-11页

作者： Malghi, Hayat Taqbibt, Abdellah El Omari, M’hamed Qaffou, Abdelaziz Hilal, Khalid Department of Mathematics Applied Mathematics and Scientific Computing Laboratory Faculty of Sciences and Technology Beni Mellal Sultan Moulay Slimane University P.O. Box 523 Beni Mellal 23000 Morocco

In this work, we explore the existence of solutions to an initial value problem for nonlinear neutral delay Ψ-Caputo fractional hybrid differential equations with bounded delays. The existence results are established using fixed point theorems. To demonstrate the practical applicability of our findings, we provide an illustrative example. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Fixed point Hybrid differential equation Neutral delay differential equation Ψ-Caputo derivative

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Smart IoT-Based Solution for Emergency Assistance during Sandstorms and Landslides 10

Smart IoT-Based Solution for Emergency Assistance during San...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Regula, Thirupathi Hoqani, Warda Al Cheriyan, Sunitha Reddy, Sharmila K. Kumar, Naveen Tazeen, Shazia Kiran, Kumar S. G. College of Computing and Information Sciences University of Technology and Applied Science Department of It Muscat Oman Vaagdevi Engineering College Department of Cse Telangana India Nit Department of Cse Telangana Warangal India

ISBN: (纸本)9798331532895

Natural disasters, which are caused by changes in the climate and geography, pose a major threat to human life and economic stability. These events, which include cyclones, tsunamis, landslides, and earthquakes, can result in serious ecological damage as well as fatalities. The speed at which climate change is occurring increases the unpredictability of such disasters, especially in vulnerable regions like deserts and forests. This study offers a solution to the challenges of rescuing those who are stranded by such catastrophes, where traditional rescue methods and communication may not be effective. The system uses Internet of Things (IoT) and mobile phone technologies to send location information and real-time alerts through Global Positioning System (GPS) and Geographic Information System (GIS), enabling the timely location of a person or vehicle for effective emergency response. This approach aims to improve disaster preparedness and survival rates in remote, catastrophe-prone areas. © 2024 IEEE.

关键词： Landslides

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Social mining for sustainable cities: thematic study of gender-based violence coverage in news articles and domestic violence in relation to COVID-19

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Journal of Ambient Intelligence and Humanized computing 2023年第11期14卷 14631-14642页

作者： Manzoor, Muhammad Asad Hassan, Saeed-Ul Muazzam, Amina Tuarob, Suppawong Nawaz, Raheel Department of Computer Science Information Technology University 346-B Ferozepur Road Lahore Pakistan Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom Department of Applied Psychology Lahore College for Women University Lahore54000 Pakistan Faculty of Information and Communication Technology Mahidol University Salaya Thailand Department of Operations Technology Events and Hospitality Management Manchester Metropolitan University Manchester United Kingdom

We argue that social computing and its diverse applications can contribute to the attainment of sustainable development goals (SDGs)—specifically to the SDGs concerning gender equality and empowerment of all women and girls, and to make cities and human settlements inclusive. To achieve the above goals for the sustainable growth of societies, it is crucial to study gender-based violence (GBV) in a smart city context, which is a common component of violence across socio-economic groups globally. This paper analyzes the nature of news articles reported in English newspapers of Pakistan, India, and the UK—accumulating 12,693 gender-based violence-related news articles. For the qualitative textual analysis, we employ Latent Dirichlet allocation for topic modeling and propose a Doc2Vec based word-embeddings model to classify gender-based violence-related content, called GBV2Vec. Further, by leveraging GBV2Vec, we also build an online tool that analyzes the sensitivity of Gender-based violence-related content from the textual data. We run a case study on GBV concerning COVID-19 by feeding the data collected through Google News API. Finally, we show different news reporting trends and the nature of the gender-based violence committed during the testing times of COVID-19. The approach and the toolkit that this paper proposes will be of great value to decision-makers and human rights activists, given the prompt and coordinated performance against gender-based violence in smart city context—and can contribute to the achievement of SDGs for sustainable growth of human societies. © 2022, Crown.

关键词： COVID-19

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Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces

Effectiveness of Machine Learning Algorithms on Battling Cou...

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2023 International Research Conference on Smart computing and Systems Engineering, SCSE 2023

作者： Gunawardhana, H.M.K.T. Kumara, B.T.G.S. Rathnayake, Kapila Jayaweera, Prasad M. Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Sri Lanka Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Sri Lanka Faculty of Applied Sciences University of Sri Department of Computer Science Jayawardanapura Sri Lanka

ISBN: (纸本)9798350341454

For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study's findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces. © 2023 IEEE.

关键词： Electronic commerce

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the university of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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On the System Performance of Mobile Edge computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： Mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

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Tube-based Model Reference Adaptive Control for Vibration Suppression of Active Suspension Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 728-731页

作者： Yashar Mousavi Alireza Alfi Ibrahim Beklan Kucukdemiral Afef Fekih IEEE the Department of Applied Science School of ComputingEngineering and Built EnvironmentGlasgow Caledonian University the Faculty of Electrical Engineering Shahrood University of Technology the Electrical and Computer Engineering Department University of Louisiana at Lafayette

Dear editor,With the developments of industrial automation in recent years,vehicle suspension systems have received a great deal of attention in industry and academia due to their critical role in the chassis performance of vehicles[1].The suspension system is expected to guarantee the vehicle’s maneuverability and provide satisfactory ride comfort by absorbing the vibrations arising from the road surface excitations and ensuring road-holding capability and suspension *** by the desirable performance of the model reference adaptive control(MRAC)approach,various literature studies have investigated its performance in diverse linear and nonlinear practical systems[2].

关键词： editor Active guarantee

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