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International Joint Conference on Neural Networks (IJCNN)

作者： Lidia Ghosh Dipanjan Konar Amit Konar Atulya K. Nagar Dept. of Computer Science and Engineering Institute of Engineering and Management Kolkata Dept. of Electronics and Communication Engineering Institute of Radio Physics and Electronics Calcutta University Kolkata India ETCE Department Artificial Intelligence Laboratory Jadavpur University Kolkata India Dept. of Math and Computer Science Liverpool Hope University UK

This paper proposes a generic architecture of a recurrent fuzzy inferential neural network realized with Petri Nets. The proposed recurrent topology allows revision or updates of fuzzy singleton memberships of inferences, which may lead to limit cycles (sustained periodic oscillations) or stability of fuzzy inferences. Determining stability in such recurrent fuzzy neural network requires adaptation of fuzzy memberships for all propositions mapped at places of the Petri Net in parallel. The network is said to have attained stability, if after $k$ updates of memberships of the propositions, the steady-state values are reached for all the propositions of the network, where $k$ denotes the number of transitions in the Petri net. If fuzzy memberships of the propositions do not converge after $k$ updates, then the network yields sustained oscillations in memberships (called Limit Cycles). Detection of stability or limit cycles in such network requires users to wait for $k$ steps of fuzzy membership updates at all places of the network. To avoid the computational overhead for $k$ updating of memberships in the entire network, this paper makes an attempt to determine the condition of stability or limit cycles using Lyapunov stability theorem before the network is invoked for fuzzy membership updating and inference generation. The results of the analysis envisage that the condition of stability depends on the topological architecture and initial assignment of memberships at the places. The condition derived can be checked to test possible stability in the network. In case the condition for stability is not attainable, the network is expected to have limit cycles, which too can be detected without updating of memberships.

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Federated Heuristic Optimization Based on Fuzzy Clustering and Red Fox Optimization Algorithm

Federated Heuristic Optimization Based on Fuzzy Clustering a...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Dawid Polap Katarzyna Prokop Gautam Srivastava Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Dept. of Math and Computer Science Brandon University Brandon MB Canada

Heuristic algorithms are dependent on many coefficients like the number of iterations or individuals. However, quite often these algorithms move individuals toward the best in the population. Based on this observation, we propose the idea of federated heuristics. The proposed idea is to initially distribute individuals into certain intervals. Then, it performs a specified number of iterations of the algorithm to identify the potentially best intervals. Sorted intervals (in relation to the best-adapted individual) make it possible to separate the appropriate size of the population in each of them. Moreover, these clusters are merged by a fuzzy algorithm due to a decrease in their numbers. The more significant the interval, the greater the number of individuals and iterations allocated in these areas. As a consequence, several instances of the selected heuristic algorithm are triggered, which can divide the best individual. The proposed technique was described using the red fox algorithm and tested at a classic set of functions with different parameters of the used heuristic.

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Fuzzy Analysis for Consensus in Federated Learning with Simulated Heuristic Attacks

Fuzzy Analysis for Consensus in Federated Learning with Simu...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Dawid Połap Gautam Srivastava Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Dept. of Math and Computer Science Brandon University Brandon MB Canada

A large number of objects participating in the voting can be an advantage as well as a disadvantage. In the case of decentralized federated learning, adding the model to the aggregation is preceded by a vote. The choice of voters and their results can be falsified through various attacks such as dataset poisoning. In this paper, we propose a fuzzy consensus analyzing the results of individual voters regarding the aggregation of a given model. The consensus is based on a fuzzy controller that selects the most reliable models for aggregation. For this reason, it uses image-modifying heuristics and quick evaluations of incoming results. If a decision is made that a selected client is unreliable several times, it is blocked to reduce the number of performed operations. The proposed system was tested on selected tasks related to image classification. The results were discussed and compared to evaluate the proposed system.

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Detecting and Mitigating Adversarial Perturbations to Improve E-Commerce Security

Detecting and Mitigating Adversarial Perturbations to Improv...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Usman Tariq Fehmi Jaafar Yasir Malik Dept. of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Saudi Arabia Dept. d’Informatique et de mathématique Université du Québec à Chicoutimi Canada Dept. of Computer Science Bishop’s University Canada

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

E-commerce platforms face the critical challenge of adversary events, including fraudulent transactions and fake reviews, which can lead to significant financial and reputational damage. Addressing this, our research introduces a hybrid Deep Learning model, tailored for the detection of such adversarial activities. This innovative approach leverages spatial and sequential data processing capabilities, enhancing the identification of subtle adversarial manipulations across diverse e-commerce contexts. Our findings indicate a high detection rate of 93 percent for adversarial attacks, with precision, recall, and Matthews Correlation Coefficient metrics underscoring the model’s efficacy. This work significantly contributes to e-commerce security by advancing the robustness of detection systems against a spectrum of adversarial threats, including account takeovers and deceptive reviews. While demonstrating a notable improvement over existing methods, our research also acknowledges the potential for evasion by sophisticated attacks, highlighting areas for future work in enhancing model resilience. This balance of innovation and critical insight provides a solid foundation for further advancements in the field of e-commerce security

关键词： Deep learning Ethics Reviews Robustness Data models Electronic commerce Security Fake news Faces Business

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Neuro-heuristic Pallet Detection for Automated Guided Vehicle Navigation

Neuro-heuristic Pallet Detection for Automated Guided Vehicl...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Silesian University of Technology Faculty of Applied Mathematics Kaszubska 23 Gliwice44-100 Poland Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada

ISBN: (纸本)9781665480451

Automated guided vehicles (AGV) allow for the automation of operations in warehouse environments. From an application point of view, vehicles can use sensors to move and perform a variety of tasks, including moving objects. In this paper, we focus on the analysis of the environment and the preparation of data for vehicle navigation. The proposed solution is based on two paths of action. In the first, the image of the room is processed by heuristics to locate the robot's target - the palette. The found pattern allows one to locate the destination as well as create a mask. The mask can be used to train the U-Net network. When a network is trained, the use of heuristics for pallet location can be omitted. Locating the target allows the image to be processed to obtain an AGV navigation map. The proposed solution based on heuristics and U-Net networks has been described and tested in simulations to indicate the potential of the proposed approach. © 2022 IEEE.

关键词： Navigation

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LDPSA: A Large Dataset of Persian Sentiment Analysis

LDPSA: A Large Dataset of Persian Sentiment Analysis

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International Conference on Web Research (ICWR)

作者： Ali Nazarizadeh Taha Samavati Sina Mansouri Minoo Sayyadpour Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Faculty of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Dept. Math and Computer Science Kharazmi University Tehran Iran

ISBN: (数字)9798350394986

ISBN: (纸本)9798350394993

Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-dept. analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian Sentiment Analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253,368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper.

关键词： COVID-19 Sentiment analysis Electronic publishing Gold Data analysis Pandemics Reliability

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EXISTENCE OF MINIMAL SURFACES IN INFINITE VOLUME HYPERBOLIC 3-MANIFOLDS

arXiv

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arXiv 2025年

作者： Coskunuzer, Baris Huang, Zheng Lowe, Ben Pallete, Franco Vargas UT Dallas Dept. Math. Science RichardsonTX75080 United States Department of Mathematics The City University of New York Staten IslandNY10314 United States Department of Mathemaics University of Chicago ChicagoIL60637 United States France

The existence of embedded minimal surfaces in non-compact 3-manifolds remains a largely unresolved and challenging problem in geometry. In this paper, we address several open cases regarding the existence of finite-area, embedded, complete, minimal surfaces in infinite-volume hyperbolic 3-manifolds. Among other results, for doubly degenerate manifolds with bounded geometry, we prove a dichotomy: either every such manifold contains a closed minimal surface or there exists such a manifold admitting a foliation by closed minimal surfaces. We also construct the first examples of Schottky manifolds with closed minimal surfaces and demonstrate the existence of Schottky manifolds containing infinitely many closed minimal surfaces. Lastly, for hyperbolic 3-manifolds with rank-1 cusps, we show that a broad class of these manifolds must contain a finite-area, embedded, complete minimal *** Codes 53A10, 57M50 © 2025, CC BY.

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Strong Laws for Generalized Normal Distribution

SSRN

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SSRN 2022年

作者： Wen, Luliang Zhuang, Ziyang Department of Mathematics Foshan University Foshan528000 China The Hong Kong University of Science and Technology Dept. Math. Kowloon Hong Kong

Let X be a random variable with the generalized normal distribution, i.e., its p.d.f. is given by (Formular presented) for (Formular presented), and s > 0. Let {Xn, n ≥ 1} be a sequence of independent random variables with the common distribution as X. The paper shows that (Formular presented) and the cluster set of the sequence {(Xn − µ)/(log n)1/s, n ≥ 2} is [−σ, σ] with probability one. Furthermore,(Formular presented) The above results extend those related to the normal distribution. © 2022, The Authors. All rights reserved.

关键词： Random variables

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CNN-based Community Detection: A Comparison Study

CNN-based Community Detection: A Comparison Study

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Abdelfateh Bekkair Slimane Oulad-Naoui Slimane Bellaouar Rababe Roumaissa Krimat Alla Haddaoui Dept. of Mathematics and Computer Science Lab. des Mathématiques et Sciences Appliquées (LMSA) Université de Ghardaia Algeria

Community detection is indispensable for comprehending the structural organization and functional modules of complex networks. Convolutional neural networks (CNNs) have emerged as powerful instruments for graph data analysis in recent years. In this paper we start by providing a classification of CNN community detection approaches in networks based on node and edge transformations. Then we conduct a comparative analysis of these two categories. We assess the performance of the two CNN models on diverse network datasets using normalized mutual information a widely accepted evaluation metric. According to the findings of the study, CNN models can identify cohesive communities within networks. Moreover, they demonstrate that the node-based transformation method is more straightforward than the edge-based transformation method, while still producing satisfactory community detection results. This indicates that the node-based transformation method simplifies community detection and requires fewer computational resources.

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IoT Anomaly Detection Using a Multitude of Machine Learning Algorithms

IoT Anomaly Detection Using a Multitude of Machine Learning ...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Maria Balega Waleed Farag Soundararajan Ezekiel Xin-Wen Wu Alicia Deak Zaryn Good Dept. of Math and Computer Science Indiana University of Pennsylvania Indiana PA U.S.A Dept. of Computer Science University of Mary Washington Fredericksburg VA U.S.A

The security of IoT (Internet of Things) systems is crucial yet challenging. Anomaly detection can help assess and improve the security of these devices and systems. The detection of anomalous traffic can be performed with the use of machine learning algorithms. Gradient Boosting is a Machine Learning (ML) technique that handles both regression and classification problems and uses decision tree algorithms to produce a prediction model. eXtreme Gradient Boosting (XGBoost) is a unique implementation of Gradient Boosting that has shown very good performance and outcomes in various problems. In this paper, XGBoost’s classification abilities are examined when applied to the adopted IoT-23 dataset to see how well anomalies can be identified and what type of anomaly exists in IoT systems. Moreover, the results obtained from XGBoost are compared to other ML methods including Support Vector Machines (SVM) and Deep Convolutional Neural Networks (DCNN). The classification results were assessed based on accuracy, precision, recall, and various other performance metrics. Our experimental results showed that XGBoost outperformed both SVM and DCNN achieving accuracies up to 99.98%. In addition, XGBoost proved to be the most efficient method with respect to execution time.

关键词： Support vector machines Performance evaluation Machine learning algorithms Predictive models Boosting Prediction algorithms Security

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