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On the Effectiveness of Kolmogorov—Arnold Networks for Enhanced Oil Recovery Prediction in Polymer Flooding

On the Effectiveness of Kolmogorov—Arnold Networks for Enha...

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algorithms, Computing and Artificial Intelligence (ACAI), international conference on

作者： Samson Dawit Bekele Yerzhan Kenzhebek Timur Imankulov Department of Computer Science Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Enhanced oil recovery techniques like polymer flooding are vital for maximizing hydrocarbon extraction, but predicting recovery factors remains challenging due to complex reservoir dynamics. Traditional artificial neural networks offer predictive power but lack interpretability, limiting their practical utility in engineering applications. this study introduces Kolmogorov-Arnold Networks (KANs) as an interpretable alternative for modeling recovery factors in polymer flooding. Using a synthetically generated dataset of over 160,000 samples with key reservoir parameters, we trained KAN models through a series of experiments, varying network architectures, grid sizes, learning rates, and optimizers (Adam and LBFGS) to evaluate performance and interpretability. the best-performing KAN achieved a Test Mean Squared Error (MSE) of 0.000597 and a Test coefficient of determination $(\boldsymbol{R}^th)$ of 0.902 with only 1,885 parameters, closely matching the performance of a previous deep neural network model that used 43,265 parameters (Test $\mathbf{R}^{\mathbfth}$ of 0.908). While KANs did not surpass the DNN in predictive accuracy, they offered comparable results with significantly reduced complexity and enhanced transparency. the modular structure and learnable activation functions of KANs provide insights into the decision-making process. this work contributes to the EOR field by demonstrating that KANs can effectively model complex reservoir behaviors while being transparent in their decision making.

关键词： Analytical models Adaptation models Accuracy Oils Decision making Artificial neural networks Predictive models Reservoirs complexity theory Polymers

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Attacks Against Machine Learning Systems: Analysis and GAN-based Approach to Protection 7th

Attacks Against Machine Learning Systems: Analysis and GAN-b...

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7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Kotenko, Igor Saenko, Igor Lauta, Oleg Vasiliev, Nikita Iatsenko, Dmitry 14th Line 39 St. Petersburg199178 Russia Admiral Makarov State University of Maritime and Inland Shipping 5/7 Devinsky st. Saint-Petersburg198035 Russia Saint-Petersburg Signal Academy 3 Tikhoretsky prospect Saint-Petersburg194064 Russia Southern Federal University Rostov-on-Don Russia

ISBN: (纸本)9783031437915

the rapid increase in the number of artificial intelligence applications has increased the importance of machine learning (ML) systems. the data used and the trained ML models is of great interest to competitors, while evasion and disabling of ML systems are of interest to intruders. this paper presents an analysis of existing attacks against ML systems, taking into account the stages of training, the use of ML algorithms and the operation of the information infrastructure. the classification of attacks against ML systems is proposed, the classes of attacks that are most relevant and dangerous for ML systems are identified. the analysis of possible measures to counter attacks against ML systems is carried out. It allows us to propose an approach to their protection based on the integrated usage of Neural-Clearance and Jpeg-Compression technologies, as well as a generative-adversarial network in the form of a hybrid neural network in which the autoencoder plays the role of a generator, and the classifier is a discriminator. An experimental evaluation of the proposed approach was carried out, which confirmed its high efficiency. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cybersecurity

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A Novel Extracellular Spike Detection Algorithm Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

A Novel Extracellular Spike Detection Algorithm Based on Imp...

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Canhong Long Zuozhi Liu School of Mathematics & Statistics Guizhou University of Finance and Economics Guiyang China School of Mathematics Northwest University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Precisely discerning spike signals amidst noise presents a significant and intricate challenge. To address this issue, we propose a new detection algorithm for extracellular spikes based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), aiming to achieve high accuracy and robust spike detection. Firstly, the extracellular recording signal is decomposed using CEEMDAN to obtain multiple intrinsic mode functions (IMFs). Subsequently, by analyzing the sample entropy of these IMFs, the important information hidden in the recorded signal can be prevented. Finally, the high-entropy noise components are discarded using wavelet soft thresholding. Experimental results from both simulated and real data demonstrate that the proposed method significantly outperforms existing methods by a large margin.

关键词： Empirical mode decomposition Accuracy Noise Entropy Robustness Recording complexity theory Extracellular Detection algorithms

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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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Proceedings of the 7th international conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Research on Online Firepower Distribution of Multi-bomb Cooperative Anti-ship Based on MADRL 7

Research on Online Firepower Distribution of Multi-bomb Coop...

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7th international conference on Control and Robotics Engineering, ICCRE 2022

作者： Yang, Lizhao Wan, Kaifang Tang, Jianqiang Li, Jinliang Chen, Junzong Northwestern Polytechnical University School of Electronics and Information Xi'an China 29th Research Institute of China Electronics Technology Corporation Science and Technology on Electronic Information Control Laboratory Chengdu China 29th Research Institute of China Electronics Technology Corporation Electromagnetic Space Operations and Applications Laboratory Chengdu China Beijing China

ISBN: (纸本)9781665468404

In modern naval warfare, the attack and defense between anti-ship missile and target ship has always been an important problem. Whether the local ship formation can be attacked quickly and effectively has become the winning point of leading naval warfare. Anti ship missile with its high precision, high range, high damage rate has become one of the main weapons in today's maritime operations. However, due to the limited damage ability of single missile, multi missile cooperative attack mode is often adopted in actual combat. therefore, how to reasonably distribute the firepower of anti-ship missile is often the key problem in modern naval warfare. In this paper, a new method based on deep Q-value network is proposed to solve the fire allocation strategy of anti-ship missile. Unlike the traditional algorithms in this field, DQN algorithm can automatically solve the optimal fire allocation strategy by interacting with the environment without relying on any prior information of the enemy. the algorithm fully gives full play to the computational ability of neural network and the ability of decision-making of reinforcement learning, solves the problem that the traditional algorithm can not realize rapid decision-making, and also solves the problem that the traditional reinforcement learning algorithm cannot traverse because of the large state space. the experimental results show that the deep Q-value network algorithm gives the best fire allocation scheme with the best damage effect under different formation and training times. © 2022 IEEE.

关键词： Deep learning

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Research on an Adaptive RRT Path Planning Algorithm Based on Regional Sampling

Research on an Adaptive RRT Path Planning Algorithm Based on...

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Computer Information Science and Application Technology (CISAT), international conference on

作者： Yu Su Hongbing Li Wenbin Gong Honghao Chen Yutao Jiang Boxuan Long College of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China College of Electronic and Information Engineering Chongqing Three Gorges University Wanzhou Chongqing China College of Computer Science and Technology Changchun University of Science and Technology Changchun Jilin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

An enhanced RRT path planning algorithm based on regional sampling is proposed with the goal of addressing the issues of inadequate guidance, discontinuous sampling, and unsatisfactory path in RRT algorithm path search in complicated situations. the improved algorithm first enhances the algorithm’s guidance and avoids unnecessary sampling by using a target bias strategy and a regional sampling strategy. Secondly, an adaptive step size strategy based on obstacle factors is adopted to dynamically adjust the step size by detecting the complexity of the environment around the expansion direction. Finally, the produced path is trimmed and optimized. According to simulation results, the revised algorithm reduces running time, path length, and number of path nodes by 68.65%, 18.56%, and 85.45%, respectively, in comparison to the RRT algorithm. the enhanced approach reduces the running time, path length, and number of path nodes by 14.06%, 12.91%, and 82.97%, respectively, in comparison to the Bias RRT algorithm.

关键词： Information science Adaptive systems Navigation Heuristic algorithms Simulation Routing Path planning complexity theory Convergence

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Genetic NEAT-Based Method for Multi-Class Classification

Genetic NEAT-Based Method for Multi-Class Classification

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algorithms, Computing and Artificial Intelligence (ACAI), international conference on

作者： Abdallah Alfaham Stijn Van Raemdonck Siegfried Mercelis IDLab - Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium Faculty of Applied Engineering University of Antwerp Antwerp Belgium

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Advances in the field of NeuroEvolution (NE) highlighted the potential of applying genetic and evolutionary mechanisms to Machine Learning (ML) problems while simultaneously alleviating the need for manual neural architecture design. the NeuroEvolution of Augmenting Topologies (NEAT) is a prominent NE method shows competitive performance in the field of reinforcement learning while its performance is not as efficient if we apply it to supervised multi-class problems. this may be attributed to the multiple objectives of the problem which hinders the learning process in NEAT. In this study, we introduce a novel method C-NEAT which is developed to address this issue and enhance the performance of NEAT without changing its core implementation. C-NEAT seeks to learn and classify different classes by creating and using a container holds the best genomes i.e. networks where each one of them focuses on learning a specific class label of the problem. Each organism in NEAT which is a unit contains the evolved genome and its fitness value is assigned automatically to a specific class based on its index in the population. During the evolution process, the container will keep updating itself and store the best evolved genomes for these classes. through this, C-NEAT will focus on recognizing each class label and it will preserve the learning progress, thus ensuring higher learning efficiency.

关键词： Supervised learning Genomics Computer architecture Reinforcement learning Containers Search problems Classification algorithms complexity theory Topology Standards

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Self-Attention Approach for Inter-class Similarities of Grocery Product Classification

Self-Attention Approach for Inter-class Similarities of Groc...

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international conference on Informatics and Computational Sciences (ICICoS)

作者： Marvelius Putra Kenardi Steven the Rissa Rahmania Computer Science Department School of Computer Science Bina Nusantara University Bandung Campus Jakarta Indonesia

ISBN: (数字)9798350375886

ISBN: (纸本)9798350375893

Artificial Intelligence plays a crucial role throughout the ages, specifically in smart retail. However, it is still flawed when differentiating similar patterns in various products. this paper aims to classify and analyze grocery products using ResNet50 alongside a self-mechanism algorithm. the dataset is developed internally and focuses on 1 brand of a product with 8 variations representing the product’s flavor. there are a total of 4000 images which are split into train, validation, and test. An experiment is then carried out by implementing a self-attention block after the backbone of the ResNet50 architecture. the performance result is evaluated by accuracy, loss, and confusion matrix. the results of the experiment yield an accuracy value of 96.8% and loss of 2%. this indicates that the combination of deep learning has the potential in supporting classify similar products in smart retail.

关键词： Deep learning Accuracy Image color analysis Computational modeling Computer architecture complexity theory Classification algorithms

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Greek Parliament Members on Instagram: A study on building a political image 7

Greek Parliament Members on Instagram: A study on building a...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media conference, SEEDA-CECNSM 2022

作者： Mylona, Ifigeneia Amanatidis, Dimitrios Dossis, Michael International Hellenic University Dept. of Management Science and Technology Kavala Greece University of Western Macedonia Hellenic Open University Dept. of Informatics School of Science and Technology Patras Kastoria Greece University of Western Macedonia Dept. of Informatics Kastoria Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

this paper focuses on examining how Greek MPs leverage Instagram and carry out a twofold investigation that addresses two main questions: Do politicians prefer to communicate over Instagram their private or public life moments? How does the use of Instagram by Greek politicians affect their perception by the public? To validate our findings, we construct a dataset on Greek MPs' attributes and proceed with its statistical description and visualization. A hierarchical, multiple linear regression framework is employed to model the number of MPs posts with respect to various predictor variables. Data is gradually introduced, creating a series of models of increased complexity and deducing whether the added predictors account for any more variance, thus either accepting or dropping the respective independent variables. Acquired models can subsequently be compared with ANOVA to determine the significance of the models' change. © 2022 IEEE.

关键词： Multiple linear regression

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Turing Machines with Two-Level Memory: A Deep Look into the Input/Output complexity 28th

Turing Machines with Two-Level Memory: A Deep Look into the ...

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28th international Computing and Combinatorics conference (COCOON)

作者： Ma, Hengzhao Li, Jianzhong Gao, Xiangyu Gao, Tianpeng Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9783031221040;9783031221057

the input/output complexity, which is the complexity of data exchange between the main memory and the external memory, has been elaborately studied by a lot of former researchers. However, the existing works failed to consider the input/output complexity in a computation model point of view. In this paper we remedy this by proposing three variants of Turing machine that include external memory and the mechanism of exchanging data between main memory and external memory. Based on these new models, the input/output complexity is deeply studied. We discuss the relationship between input/output complexity and the other complexity measures such as time complexity and parameterized complexity, which is not considered by former researchers. We also define the external access trace complexity, which reflects the physical behavior of magnetic disks and gives a theoretical evidence of IO-efficient algorithms.

关键词： Computational theory Computational model Input/output complexity

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