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Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

computers, Materials & Continua 2022年第3期70卷 5949-5965页

作者： K.S.Bhuvaneshwari K.Venkatachalam S.Hubalovsky P.Trojovsky P.Prabu Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Sceince CHRIST(Deemed to be University)Bangalore560074India

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and *** on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating ***,the large volume of data is generated and transferred through network,the security and performance are remained an ***(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network *** performance and loss are still an issue because of the features space grows while detecting the *** this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and *** proposed system includes three phases such as preprocessing,feature selection and *** the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization *** second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks *** clustering loss and network loss can be reduced with the optimization *** evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation *** experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

关键词： Intrusion detection system binning normalization deep clustering convolutional neural network information gain dragonfly optimizer

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Predicting Survival HIV/AIDS Patients Using Machine Learning Methods: Artificial Neural Network and Decision Tree Models

Predicting Survival HIV/AIDS Patients Using Machine Learning...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Ndabarushimana, Ladislas Ngom, Papa Ndekou Tandong, Paul Python Diongue, Abdou Ka Banzubaze, Emmanuel University Cheikh Anta Diop of Dakar Department of Mathematics and Computer Science Dakar Senegal University Gaston Berger of Saint Louis Department of Applied Mathematics Saint Louis Senegal National Institute of Public Health Department of Paraclinical Sciences Bujumbura Burundi

ISBN: (纸本)9798350327816

In Burundi, Anti Retroviral Therapy(ART) helps to control infected people living with Immunodeficiency Virus/Acquired Immune Deficiency Syndrome(HIV/AIDS), but the rate of deaths is still high. This paper presents a model for predicting the survival of HIV/AIDS patients treated in health institution' Association Nationale de Soutien aux Seropositifs et Malades du SIDA' (ANSS) in Burundi. Algorithms of Artificial Neural Network(ANN) and Decision Tree(DT) were used for predicting the survival of People Living With HIV(PLWHIV) under the ANSS health care departments. The results show that the average probability of survival of patients at ANSS institution was 90.2% for an average follow-up period of 5 years. The developed model will help ANSS institution and the Ministry of Health to define effective management strategies for these HIV/AIDS patients. © 2023 IEEE.

关键词： ANSS Artificial Neural Network Burundi Decision Tree HIV/AIDS survival prediction

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Proposal Yager Ranking Function to Solve the Fuzzy Transportation Model via Trapezoidal Fuzzy Number 3

Proposal Yager Ranking Function to Solve the Fuzzy Transport...

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3rd International Conference on mathematics and applied science, ICMAS 2022

作者： Mitlif, Rasha Jalal Hussein, Iden Hasan Department of Applied Sciences Branch of Mathematics and Computer Applications University of Technology Baghdad Iraq Department of Mathematics College of Science of Woman University of Baghdad Baghdad Iraq

Transportation theory is the name given to the study of optimal transportation (OT) and resource allocation in mathematics and economics. In any discipline of science and technology, engineering, medicine, and management, uncertainty plays a critical role. Fuzzy mathematics (FM) and fuzzy logic (FL) are used in Artificial Intelligence (AI) to process natural language and are frequently employed in decision making (DM). In this paper, using three ranking function to treat the fuzzy transportation model, two of them are Yager and Maleki ranking functions and the other is proposal and suggest depend up on of Yager ranking function based on trapezoidal fuzzy membership function when the total demand, cost of transportation, and the total amount of product (supply) are fuzzy, then the fuzzy model shrinkage to traditional or crisp model. Finally solving these models to the real data that we got it from The General Company for Food Products Abo ghreeb / Iraq. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

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Towards Lingua Franca on the Patmos Processor

Towards Lingua Franca on the Patmos Processor

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Ehsan Khodadad Luca Pezzarossa Martin Schoeberl Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

Real-time embedded systems demand higher reliability than any other computer systems. These systems require special modeling paradigms to satisfy time constraints. This paper introduced a design method by combining T-CREST, a time-predictable multi-core hardware, with Lingua Franca, a coordination framework that generates deterministic time-predictable code. We executed a Lingua Franca piece of software on T-CREST platform and performed preliminary experiments demonstrating its correct functionality.

关键词： Embedded systems Source coding Network-on-chip Software Real-time systems Software reliability Time factors

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Development of Machine Learning Based Fraudulent Website Detection Scheme 5

Development of Machine Learning Based Fraudulent Website Det...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Chen, Shyh-Wei Chen, Po-Hsiang Tsai, Ching-Tsorng Liu, Chia-Hui Tunghai University Department of Computer Science Taichung City Taiwan Chinese Culture University Department of Applied Mathematics Taipei Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

The development of mobile computing and e-commerce has greatly changed traditional transactions and grown online shopping. People are buying and selling goods on websites or social platforms. However, there are many malicious and counterfeit products on fraudulent websites to deceive consumers and make high improper profits. Due to the obvious increase in the number of such fraudulent websites, it is difficult to identify and detect these websites by manual inspection. In order to solve this problem, we propose an intelligent detection mechanism by using a machine learning approach to classify fraudulent websites. We use data set containing 300 legitimate websites, 300 fraudulent websites, and 15 features for training. In two machine learning algorithms, Random Forest and Deep Neural Networks, we divided the training set and the test set in a ratio of 8:2. Finally, the prediction is compared with the previous research results. The experimental results show that the RF accuracy of the random forest algorithm is 99.3% which is better than other deep neural networks algorithms. © 2022 IEEE.

关键词： Deep neural networks

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Faster, Deterministic and Space Efficient Subtrajectory Clustering

arXiv

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arXiv 2024年

作者： van der Hoog, Ivor van der Horst, Thijs Ophelders, Tim Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Information and Computing Sciences Utrecht University Netherlands Department of Mathematics and Computer Science TU Eindhoven Netherlands

Given a trajectory T and a distance ∆, we wish to find a set C of curves of complexity at most , such that we can cover T with subcurves that each are within Fréchet distance ∆ to at least one curve in C. We call C an (, ∆)-clustering and aim to find an (, ∆)-clustering of minimum cardinality. This problem variant was introduced by Akitaya et al. (2021) and shown to be NP-complete. The main focus has therefore been on bicriterial approximation algorithms, allowing for the clustering to be an (, Θ(∆))-clustering of roughly optimal size. We present algorithms that construct (, 4∆)-clusterings of O(k log n) size, where k is the size of the optimal (, ∆)-clustering. We use O(n log2 n + n · (k + ) log n) space and O(kn3 log4 n) time. Our algorithms significantly improve upon the clustering quality (improving the approximation factor in ∆) and size (whenever ∈ Ω(log n)). We offer deterministic running times comparable to known expected bounds. Additionally, we give a near-quadratic improvement upon the dependency on n in the space usage. © 2024, CC BY.

关键词： Approximation algorithms

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AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional Subspaces

AdaSub: Stochastic Optimization Using Second-Order Informati...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： João Victor Galvão Da Mata Martin S. Andersen Department of Applied Mathematics and Computer Science Technical University of Denmark

We introduce AdaSub, a stochastic optimization algorithm that computes a search direction based on second-order information in a low-dimensional subspace that is defined adaptively based on available current and past information. Compared to first-order methods, second-order methods exhibit better convergence characteristics, but the need to compute the Hessian matrix at each iteration results in excessive computational expenses, making them impractical. To address this issue, our approach enables the management of computational expenses and algorithm efficiency by enabling the selection of the subspace dimension for the search. Our code is freely available on GitHub, and our preliminary numerical results demonstrate that AdaSub surpasses popular stochastic optimizers in terms of time and number of iterations required to reach a given accuracy.

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Inventory Model for Imperfect Deteriorating Items Under Fuzzy Environment 11

Inventory Model for Imperfect Deteriorating Items Under Fuzz...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Pandey, Jayanti Tripathi Jain, Muskan School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Department of Mathematics Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The aim of this study is to introduce the application of fuzzy numbers in an Economic Order Quantity (EOQ) model for defective items under deterioration. In practice, the fuzziness of parameters tends to reduce over time compared to the theoretical assumptions, which justifies the use of fuzzy numbers. The proposed model starts with crisp approach which later involves the fuzzification of inventory parameters, such as demand rate, holding cost, deterioration rate, purchase cost, screening cost, and ordering cost, to account for uncertainty and imprecision. The triangular fuzzy numbers are utilized to represent these parameters, and a fuzzy model is developed accordingly. To defuzzify the model, Yager's ranking index is adopted, and a detailed comparison is made between crisp and general fuzzy solutions. The model is validated through numerical examples and graphical illustrations, followed by a thorough sensitivity analysis to gain a deeper comprehension of how modifications in parameters affect the optimal solution. © 2024 IEEE.

关键词： Deterioration

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Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series

Multi-View Self-Supervised Learning For Multivariate Variabl...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Thea Brüsch Mikkel N. Schmidt Tommy S. Alstrøm Department of Applied Mathematics and Computer Science Technical University of Denmark

Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariate time series data, the set of input channels often varies between applications, and most existing work does not allow for transfer between datasets with different sets of input channels. We propose learning one encoder to operate on all input channels individually. We then use a message passing neural network to extract a single representation across channels. We demonstrate the potential of this method by pretraining our model on a dataset with six EEG channels and then fine-tuning it on a dataset with two different EEG channels. We compare models with and without the message passing neural network across different contrastive loss functions. We show that our method, combined with the TS2Vec loss, outperforms all other methods in most settings.

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