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Examining Wave Evolution through Traveling Wave Solutions of the Extended Benney-Luke Equation for a Physical Perspective

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International Journal of Theoretical Physics 2025年第6期64卷 1-14页

作者： Yokuş, Asıf Durur, Hülya Duran, Serbay Department of Mathematics Faculty of Science Firat University Elazig 23100 Turkey Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan 75000 Turkey Department of Mathematics and Science Education Faculty of Education Adıyaman University Adıyaman 02040 Turkey

In this study, the classical Benney-Luke equation has been expanded by adding a coefficient to the term representing diffusion, thus making the model more suitable for physical realities. This modification aims to provide a deeper understanding of fluid behavior by uncovering the mysteries hidden within the model's coefficients. With the extended equation, it is possible to model complex wave behavior in fluid systems such as shallow water wave dynamics more flexibly and accurately. The traveling wave solutions are obtained by analytical expansion method and compared with existing solutions in the literature and certain constraint conditions are taken into account to ensure the physical validity of the solutions. It is observed that the obtained solutions exhibit a stable structure with constant profile and amplitude, soliton-like behavior and consistent propagation in infinite space–time. In addition, the effects of diffusion on the wave dynamics are studied in detail and the stability of the solution is evaluated by mathematical analysis. When the solutions are compared with the existing results in the literature, it is seen that the proposed model offers a wider parameter space and allows a more realistic representation of physical phenomena. In this respect, the study makes significant contributions in terms of both theoretical depth and physical meaning by surpassing the previous approaches in the literature and paves the way for more accurate modeling of fluid systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Exact solution Modified (1/G′)-expansion method Shallow water waves Soliton behavior Stability analysis

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack 1

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kurra, Sai Suchith Rayana, Likhitha Yoosuf, Mohamed Sirajudeen School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

ISBN: (数字)9789819752003

ISBN: (纸本)9789819751990

With the increasing complexity of the cyber-attacks, traditional intrusion systems have become inadequate in effectively detecting and preventing network attacks. Modern machine learning techniques have the capability of preventing sophisticated and aggressive cyber-attacks and have emerged as a promising approach to enhance the accuracy and efficiency of the intrusion detection. This research paper provides a comprehensive analysis and evaluation of the integration of machine learning and deep learning algorithms into intrusion detection system with respect to the Distributed Denial of Service (DDoS) attack. The study begins by presenting an overview of intrusion detection systems, highlighting their importance and significance in safeguarding network infrastructure. Subsequently, it explores supervised machine learning algorithms and deep learning models, such as Random Forest tree, Support Vector Machines (SVM), ResNet and TabNet. The paper presents the pros and cons of existing IDS models with respect to real-time processing, scalability, false positive and false negative results, and IDS vulnerabilities. The above-mentioned algorithms have been trained and evaluated with CICIDS-2017 dataset. The performance metrics used to evaluate the IDS models are accuracy, precision, F1 score, training time of each model, and adaptability to evolving attack patterns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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An Efficient Quantifier Elimination Procedure for Presburger Arithmetic 51

An Efficient Quantifier Elimination Procedure for Presburger...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Haase, Christoph Krishna, Shankara Narayanan Madnani, Khushraj Mishra, Om Swostik Zetzsche, Georg Department of Computer Science University of Oxford United Kingdom Department of Computer Science & Engineering IIT Bombay India Kaiserslautern Germany Department of Mathematics IIT Bombay India

ISBN: (纸本)9783959773225

All known quantifier elimination procedures for Presburger arithmetic require doubly exponential time for eliminating a single block of existentially quantified variables. It has even been claimed in the literature that this upper bound is tight. We observe that this claim is incorrect and develop, as the main result of this paper, a quantifier elimination procedure eliminating a block of existentially quantified variables in singly exponential time. As corollaries, we can establish the precise complexity of numerous problems. Examples include deciding (i) monadic decomposability for existential formulas, (ii) whether an existential formula defines a well-quasi ordering or, more generally, (iii) certain formulas of Presburger arithmetic with Ramsey quantifiers. Moreover, despite the exponential blowup, our procedure shows that under mild assumptions, even NP upper bounds for decision problems about quantifier-free formulas can be transferred to existential formulas. The technical basis of our results is a kind of small model property for parametric integer programming that generalizes the seminal results by von zur Gathen and Sieveking on small integer points in convex polytopes. © Christoph Haase, Shankara Narayanan Krishna, Khushraj Madnani, Om Swostik Mishra, and Georg Zetzsche.

关键词： Integer programming

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Optimizing Supply Chain Operations in the Electronics Industry Using Machine Learning and Integer Linear Programming 3rd

Optimizing Supply Chain Operations in the Electronics Indust...

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Padhi, Spandan Marken, Gagandeep Data Science Department of Mathematics Chandigarh University Gharuan India Department of Computer Science Engineering Chandigarh University Gharuan India

ISBN: (纸本)9789819719600

Efficient operations, within the supply chain are vital for the electronics industry. In this study, we focus on optimizing the supply chain network of a hypothetical company ABC Electronics, an entity crafted for the purposes of this research paper. The company faces challenges related to production, inventory management, and transportation logistics. To address these challenges we employ machine learning (ML) and integer linear programming (ILP) techniques. Our primary objectives include enhancing demand predictions through ML, optimizing production schedules and inventory using ILP, and devising strategies for transportation logistics. Our research makes a contribution toward reducing costs and improving efficiency at ABC Electronics enabling them to stay competitive in the electronics market. Furthermore, our findings have implications, for industry standards, sustainability practices, and customer satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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computer Modeling in engineering & sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1443-1457页

作者： Chen, Ziheng Silvestri, Fabrizio Tolomei, Gabriele Wang, Jia Zhu, He Ahn, Hongshik Stony Brook University Department of Applied Mathematics and Statistics Stony BrookNY11794 United States Sapienza University of Rome Department of Computer Engineering The Department of Computer Science Rome00185 Italy Xi'An Jiaotong-Liverpool University Department of Intelligent Science Suzhou215000 China Rutgers University-New Brunswick Department of Computer Science PiscatawayNJ08854 United States

Counterfactual examples (CFs) are one of the most popular methods for attaching post hoc explanations to machine learning models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood;thus, they are hard to generalize for complex models and inefficient for large datasets. This article aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task. We then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. In addition, we develop a distillation algorithm to extract decision rules from the DRL agent's policy in the form of a decision tree to make the process of generating CFs itself interpretable. Extensive experiments conducted on six tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both the classification and regression tasks. Finally, we show the ability of our method to provide actionable recommendations and distill interpretable policy explanations in two practical real-world use cases. © 2020 IEEE.

关键词： Reinforcement learning

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Emerging Trends in Social Networking Systems and Generation Gap with Neutrosophic Crisp Soft Mapping

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computer Modeling in engineering & sciences 2023年第8期136卷 1759-1783页

作者： Muhammad Riaz Masooma Raza Hashmi Faruk Karaaslan Aslıhan Sezgin Mohammed M.Ali Al Shamiri Mohammed M.Khalaf Department of Mathematics University of the PunjabLahorePakistan Department of Mathematics CankırıKaratekin UniversityCankırıTurkey Department of Mathematics and Science Education Amasya UniversityAmasyaTurkey Department of Mathematics Faculty of Science and ArtsKing Khalid UniversityMuhayl AssirSaudi Arabia Department of Mathematics and Computer Faculty of ScienceIbb UniversityIbbYemen Higher Institute of Engineering and Technology King Marriott AlexandriaEgypt Faculty of Engineering Arab Academy for Science&Technology and Maritime Transport(AASTMT)Aswan BranchEgypt

This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental *** define NCS-mapping and its inverse NCS-mapping between two *** develop a robust mathematical modeling with the help of NCS-mapping to analyze the emerging trends in social networking systems(SNSs)for our various *** investigate the advantages,disadvantages,and natural aspects of SNSs for five *** the changing of the generations,it is analyzed that emerging trends and the benefits of SNSs are increasing day by *** suggested modeling with NCS-mapping is applicable in solving various decision-making problems.

关键词： Neutrosophic crisp soft set operations of NCSSs NCS-mapping inverse NCS-mapping social networking systems decision-making

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An Advanced Clustering with Toeplitz and Circulant Matrices and a Comparison Against Spectral Clustering, and K-Means 3

An Advanced Clustering with Toeplitz and Circulant Matrices ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Saragadam, Sneha Rohan, Gamidi Manideep, Thotapalli Sri Surya Jayan, Sarada Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331518592

In an effort to increase computing efficiency, this work presents an improved spectral clustering technique that approximates the affinity matrix using Toeplitz and Circulant matrices. To preserve the quality of the clustering, we take advantage of the spectral characteristics of the similarity graph's Laplacian matrix. The Iris flower, Wine Quality, and Mushroom datasets are used to assess the approach. The following six clustering methods are contrasted: PCA combined with either spectral or K-means clustering, K-means clustering (with and without matrix approximation), and spectral clustering (with and without matrix approximation). The Silhouette Score, Fl score, precision, recall, and Adj usted Rand Index (ARI) are used to evaluate performance. The suggested method produces similar results to traditional methods with a significant reduction in computational cost, according to the results. In order to fully investigate the advantages of these matrices, architectures in spectral clustering are explored. © 2024 IEEE.

关键词： K-means clustering

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Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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