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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Forecasting and modeling on average rainwater and vapor pressure in Chelyabinsk Russia using deep learning models

Forecasting and modeling on average rainwater and vapor pres...

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6th Smart Cities Symposium (SCS 2022)

作者： M. Abotaleb T. Makarovskikh A. Ali Subhi H. Alkattan A.O. Adebayo Department of System Programming South Ural State University Chelyabinsk Russia University of Diyala Diyala Iraq Information Communication and Technology Department Lagos State University Lagos Nigeria

Machine learning technique is full-fledged as a boosting sector to develop modeling and forecasting of complex time series observations in the present environment. This study made an attempt to inspect the future performance of rainfall data and vapor in Chelyabinsk by using a machine learning technique. The data series is divided into a training set (60%) and a test set (40%) for model developing a validation purpose. We further developed deep learning models such as, LSTM, BILSTM, GRU and compared on the basis of ME, RMSE, MAE, MPE, MAPE, ACF1 on the training data set. For testing data set, we compared these deep learning models based on RMSE. LSTM model acts as a superior machine learning model over BILSTM and GRU in this data series. Forecasting performance of these three models significantly at par. This finding may be significant to build a strong literature of the Chelyabinsk weather's forecast, which can be helpful for policy makers and researchers. Also, we strongly believe that, this work could be used as a literature adaptation of machine learning technique for complex time series over statistical models.

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Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata

arXiv

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arXiv 2023年

作者： Wesolowski, Jacek Wieczorkowski, Robert Wójciak, Wojciech Programming Coordination of Statistical Surveys and Registers Department Statistics Poland Aleja Niepodleglości 208 Warsaw00-925 Poland Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland

The optimum sample allocation in stratified sampling is one of the basic issues of survey methodology. It is a procedure of dividing the overall sample size into strata sample sizes in such a way that for given sampling designs in strata the variance of the stratified π estimator of the population total (or mean) for a given study variable assumes its minimum. In this work, we consider the optimum allocation of a sample, under lower and upper bounds imposed jointly on sample sizes in strata. We are concerned with the variance function of some generic form that, in particular, covers the case of the simple random sampling without replacement in strata. The goal of this paper is twofold. First, we establish (using the Karush-Kuhn-Tucker conditions) a generic form of the optimal solution, the so-called optimality conditions. Second, based on the established optimality conditions, we derive an efficient recursive algorithm, named RNABOX, which solves the allocation problem under study. The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm, a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository. Copyright © 2023, The Authors. All rights reserved.

关键词： Sampling

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

SSRN

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

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Materials Today: Proceedings 2022年 56卷 2213-2216页

作者： Boddu, Raja Sarath Kumar Karmakar, Partha Bhaumik, Ankan Nassa, Vinay Kumar Vandana Bhattacharya, Sumanta Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India Government of West Bengal Bikash Bhawan Salt Lake W.B. Kolkata India Dept. of Applied Mathematics With Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Computer Science Engineering South Point Group of Institutions Sonepat Haryana India Department of Mathematics Dasmesh Khalsa College Zirakpur India Department of Science and Technology and Biotechnology MAKAUT India

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic

关键词： COVID-19

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Food Resources in Food System technology: Bifunctional Food System technology Based on Pickering Emulsions 6

Food Resources in Food System Technology: Bifunctional Food ...

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6th Smart Cities Symposium, SCS 2022

作者： Potoroko, Irina Kadi, Ammar Paymulina, Anastasia Bagale, Uday Abotaleb, Mostafa El-Kenawy, El-Sayed M. Department of Food and Biotechnology South Ural State University Chelyabinsk454080 Russia Siberian Federal Scientific Center of Agro-Bio Technologies of the Russian Academy of Sciences Novosibirsk region Krasnoobsk Russia Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (纸本)9781839538544

The development of domestically generated food additives based on eco-technologies with specified functional-technological and bioactive qualities is now a significant focus of contemporary study. Pickering emulsions are more stable both physically and chemically, making them a highly promising method of introducing biologically active chemicals into the body. The purpose of this research was to determine if ultrasound could be used to create a bioactive colloidal Pickering emulsion system using flax cellulose or brown algal polysaccharides as a stabilizer. Pickering emulsions stabilized with fucoidan, sodium alginate, or flax cellulose, based on a lipid fraction of sunflower oil and cinnamon oil blend, or a lipid fraction of linseed oil, were applied to the subjects, who were then subjected to ultrasound treatment at 630W/l for 16 min (4 min on, 3 min off) at a controlled temperature of 50 °C. The results obtained demonstrate the effectiveness of using ultrasonic exposure to create bioactive Pickering emulsion colloidal systems. Fucoidan stabilizes pickering emulsion, with the highest AOA values (5.120±0.005 DPPH, %) and a rise in AOA values in linseed oil-based emulsion samples from 30% to 100% in comparison to emulsion samples based on a blend of lipid phases. The viscoelastic characteristics of Pickering emulsions with various oil fractions vary dramatically for the integrated structure-forming components. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Flax

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Advanced IOT Traffic Light Control System

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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QUANTUM RANDOM WALKS AND QUANTUM OSCILLATOR IN AN INFINITE-DIMENSIONAL PHASE SPACE

arXiv

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arXiv 2024年

作者： Busovikov, Vladimir Pechen, Alexander Sakbaev, Vsevolod Department of Mathematical Methods for Quantum Technologies Steklov Mathematical Institute of Russian Academy of Sciences Gubkina Str. 8 Moscow Russia University of Science and Technology MISIS Leninskiy prosp. 4 Moscow Russia Ivannikov Institute for System Programming of RAS Alexander Solzhenitsyn Str. 25 Moscow Russia

We consider quantum random walks in an infinite-dimensional phase space constructed using Weyl representation of the coordinate and momentum operators in the space of functions on a Hilbert space which are square integrable with respect to a shift-invariant measure. We study unitary groups of shift operators in the phase space and averaging of such shifts by Gaussian vectors, which form semigroups of self-adjoint contractions: we find conditions for their strong continuity and establish properties of their generators. Significant differences in their properties allow us to show the absence of the Fourier transform as a unitary transformation that implements the unitary equivalence of these compressive semigroups. Next, we prove the Taylor formula for a certain special subset of smooth functions for shifting to a non-finite vector. It allows us to prove convergence of quantum random walks in the coordinate representation to the evolution of a diffusion process, as well as convergence of quantum random walks in both coordinate and momentum representations to the evolution semigroup of a quantum oscillator in an infinite-dimensional phase space. We find the special essential common domain of generators of semigroups arising in averaging of random shift operators both in position and momentum representations. The invariance of this common domain with respect to both semigroups allows to establish properties of a convex combination of both generators. That convex combination are Hamiltonians of infinite-dimensional quantum oscillators. Thus, we obtain that a Weyl representation of a random walk in an infinite dimensional phase space describes the semigroup of self-adjoint contractions whose generator is the Hamiltonian of an infinite dimensional harmonic *** Codes 28C20, 81V73, 43A32, 37K06 Copyright © 2024, The Authors. All rights reserved.

关键词： Hilbert spaces

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Communication acceleration of local gradient methods via an accelerated primal-dual algorithm with inexact prox 22

Communication acceleration of local gradient methods via an ...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Abdurakhmon Sadiev Dmitry Kovalev Peter Richtárik King Abdullah University of Science and Technology Thuwal Saudi Arabia and Moscow Institute of Physics and Technology Dolgoprudny Russia and Institute for System Programming of the Russian Academy of Sciences Research Center for Trusted Artificial Intelligence Moscow Russia King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781713871088

Inspired by a recent breakthrough of Mishchenko et al. [2022], who for the first time showed that local gradient steps can lead to provable communication acceleration, we propose an alternative algorithm which obtains the same communication acceleration as their method (ProxSkip). Our approach is very different, however: it is based on the celebrated method of Chambolle and Pock [2011], with several nontrivial modifications: i) we allow for an inexact computation of the prox operator of a certain smooth strongly convex function via a suitable gradient-based method (e.g., GD, Fast GD or FSFOM), ii) we perform a careful modification of the dual update step in order to retain linear convergence. Our general results offer the new state-of-the-art rates for the class of strongly convex-concave saddle-point problems with bilinear coupling characterized by the absence of smoothness in the dual function. When applied to federated learning, we obtain a theoretically better alternative to ProxSkip: our method requires fewer local steps (O(k1/3) or O(k1/4), compared to O(k1/2) of ProxSkip), and performs a deterministic number of local steps instead. Like ProxSkip, our method can be applied to optimization over a connected network, and we obtain theoretical improvements here as well.

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Optimal Control of Dengue-Chikungunya Co-infection: A Mathematical Study 4th

Optimal Control of Dengue-Chikungunya Co-infection: A Mathem...

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4th IFIP TC 12 International Conference on Intelligence Science, ICIS 2020

作者： De, Anupam Maity, Kalipada Panigrahi, Goutam Maiti, Manoranjan Department of Applied Sciences Haldia Institute of Technology Haldia West Bengal India Department of Mathematics Mugberia Gangadhar Mahavidyalaya Bhupatinagar West Bengal India Department of Mathematics National Institute of Technology Durgapur Durgapur West Bengal India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore West Bengal India

ISBN: (纸本)9783030748258

In the present work, we developed a mathematical model for dengue-Chikungunya co-infection to analyze the disease transmission dynamics and interrelationship. We considered the essence of time dependent optimal control with the help of Pontryagin’s maximum principle. The result of diverse combinations of control measures is analyzed and shown graphically. © 2021, IFIP International Federation for Information Processing.

关键词： Co-infection Dengue-Chikungunya Optimal control

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