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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Dhasmana, Gulshan Ravi, K.S. Nayana Yadav, M. Desai, Ishan Shankar, Rajmani Oracle Cloud Engineering Data Center NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Aj Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks, which are increasingly targeted by sophisticated cyber-attacks. To address these issues, new methodologies that combine real-time monitoring and adaptive security mechanisms are needed. Cyber Twin technology, an innovative extension of digital twin technology, presents a promising solution by creating AI-driven digital replicas of IoT devices and software systems for continuous security monitoring and management. This paper introduces a Cyber Twin technology Framework for AI-driven real-time software security in IoT ecosystems. The framework employs advanced AI models, including Convolutional Neural Networks (CNNs) for anomaly detection and Generative Adversarial Networks (GANs) for synthetic data generation to simulate potential attack scenarios. A dynamic reinforcement learning module is integrated to optimize threat response strategies based on evolving threat patterns. By creating real-time digital replicas of IoT components, the Cyber Twin framework continuously monitors device behaviors, identifies anomalies, and autonomously initiates mitigation actions. The system is evaluated in a simulated IoT environment with over 500 interconnected devices. Experimental results demonstrate that the Cyber Twin framework achieved a 99.2% detection accuracy in identifying cyber threats, with a false positive rate of 1.3%. The dynamic response module reduced incident response time by 35% compared to traditional methods, enhancing the system's ability to neutralize potential threats in real-time. The use of GAN-based synthetic data also enabled proactive defense strategies, reducing attack success rates by 40% during testing. The Cyber Twin technology Framework provides a robust solution for real-time software security in complex IoT ecosystems. By leveraging A

关键词： Generative adversarial networks

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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The propagation of Quasi-static region during granular impact

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Particuology 2023年第12期83卷 1-7页

作者： Moyu Yang Ran Li Yingnan Xiu Qiang Zeng Xiaoyan Ye Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Center for Particle-Laden Turbulence Key Laboratory of Mechanics on Disaster and Environment in Western China attached to the Ministry of Education of ChinaDepartment of Mechanics and Engineering ScienceSchool of Civil Engineering and MechanicsLanzhou UniversityLanzhou730000China

In this paper,through experiments and DEM simulations,it is found that there is a ring-shaped region named Quasi-static Region between the particles expanding outward and the particles collapsing inward during the impact ***-static Region is always generated from the impact point at the same time,and then spreads out at a uniform *** the propagation of Quasi-static Region,the velocity of the particles in the particle extend region varies linearly in space and *** the change rate is only related to the properties of the particle *** simulation results show that the particle flow in Quasi-static Region is elastic-inertial flow,while the particle flow expanding outward and collapsing inward is inertial flow.

关键词： Impact Quasi-staticregion Propagation Shearflow

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Efficient Acoustic-Optical Fiber Coupling with Wire Horn Structure

Efficient Acoustic-Optical Fiber Coupling with Wire Horn Str...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Wang, Ruixuan Theis, Logan Homa, Daniel Wang, Anbo Pickrell, Gary Center for Photonics Technology The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Center for Photonics Technology Department of Material Science and Engineering Virginia Tech BlacksburgVA United States

This paper reports on a wire horn structure to enhance acoustic-optical fiber coupling. Compared with the conventional schemes, it was proved to enhance the ultrasonic wave peak-to-peak amplitude coupling efficiency b... 详细信息

ISBN: (纸本)9781957171296

关键词： Coupling efficiency Fiber Bragg gratings Fiber optic sensors Mode conversion Optical fibers Tunable lasers

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Efficient Acoustic-Optical Fiber Coupling with Wire Horn Structure

Efficient Acoustic-Optical Fiber Coupling with Wire Horn Str...

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2023 Laser Applications Conference, LAC 2023

关键词： Ultrasonic waves

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Formal Modeling of Road Network-Based Autonomous Vehicle Validation Scenarios With Intersections and Pedestrians

Formal Modeling of Road Network-Based Autonomous Vehicle Val...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Kutlu, Ilke Goss, Quentin Akinci, Tahir Cetin Ilhan Akbas, Mustafa İstanbul Technical University Department of Electrical Engineering İstanbul Turkey Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science FL United States University of California Riverside Center for Environmental Research and Technology RiversideCA United States

ISBN: (纸本)9798350367942

Autonomous vehicles (AVs) are an exciting emerging technology that reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, increasing driving quality and safety. Validation testing of AVs provides the assurance of quality and safety, and is also an integral part of new AV research and development. Following in suite of their predecessor, i.e. non-autonomous vehicles, scenario-based validation testing is a safe, time and cost effective means of testing these complex, costly, and safety critical systems. In this paper we propose an open-source implementation of a scenario-based AV validation testing approach for AVs in traffic environments with pedestrians and intersections. Furthermore, we utilize the approach to implement scenarios of previously unimplementable AV scenarios from the Atomic Blocks dataset. © 2024 IEEE.

关键词： Automobile testing

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The Optimal Shape of the Surface Hardening Head Obtained by Numerical Modeling 15

The Optimal Shape of the Surface Hardening Head Obtained by ...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Mihaela Cornelia Codrean, Marius Gordan, Mircea Codrean, Mihaela Oproescu, Mihai Novac, Ovidiu Constantin Slovac, Francisc University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Computers and Electrical Engineering University of Pitesti Faculty of Electronics Communication and Computer Science Department of Elecytronics Pitesti Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania

ISBN: (纸本)9798350321388

This paper presents some aspects of the numerical modeling performed in order to study the process of induction heating applied to a workpiece, using Flux 2D and FEMM (Finite Element Method Magnetics), two software tools specifically designed for electromagnetic simulations. The paper presents a significant technical finding regarding the optimal shape of the surface hardening head. Various shapes of the inductor were analyzed, focusing on solving the eddy current problem using the commercial program FEMM. The induction heating process was numerically simulated using the FLUX 2D program for a semi-finished punch made of OLC 45 steel, specifically for hot surface heat treatment. The simulations were conducted utilizing the program's simulation capabilities. © 2023 IEEE.

关键词： Induction heating

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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