Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermore, various uses in the real world, Data distributions in intrusion detection systems, for example, are non-stationary, which produce concept drift over time or non-stationary learning. The word "concept drift" is used to describe the process of changing one's mind about something in an online-supervised learning scenario, the connection between the input data and the target variable changes over time. We define adaptive learning, classify existing concept drift strategies, evaluate the most typical, distinct, and widely used approaches and algorithms, describe adaptive algorithm assessment methodology, and show a collection of examples, all of this is based on the assumption that you have a basic understanding of supervised learning. The survey examines the various aspects of concept drift in a comprehensive manner in order to think about the current fragmented "state-of-the-art". As a result, which intends to give scholars, industry analysts, and practitioners a comprehensive introduction to idea drift adaptability.
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A field experiment is conducted to study the effect of different levels of peat (0, 25, 50, 75, and 100 Mg ha-1 to uncropped and cropped soil to wheat. Soil samples are taken in different period of time (0, 3, 30, 60, 90, 120, and 180 days after cultivation to determine (NaHCO3-Exteractable P at 3 different depths (0-10, 10-20, and 20-30 cm). Field Experiment is conducted in a randomized complete block design (RCBD) with four replicates. Wheat, Al-Rasheed variety, is cultivated as a testing crop. The entire field is equally dived in two divisions. One of the two divisions is cultivated to wheat and the second is left uncropped. The effect of five levels of peat namely 0, 25, 50, 75, 100 Mg ha-1 is investigated. Soils are fully analyzed
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ان من اهم القضايا التي تثيرها المعرفة البشرية في تجلياتها، وتعبيراتها المفاهيمية، تكمن في مدى تأصلها وانتمائها الى البنى والتشكيلات الموضوعية (في مستوياتها التاريخية) التي تسعى لتفسيرها وادراكها ومضاهاتها. فالينبوع الذي يغرف منه الفكر مادته هو الكيان الاجتماعي المتموضع خارج الوعي والايدولوجيا.
ان قدرة الوعي على ادراك الواقع الموضوعي بخصائصه العامة يشكل الشرط الضروري لاكتساب الوعي ل
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Polish Academy of Sciences
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o
... Show MoreBotnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
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This work presents a five-period chaotic system called the Duffing system, in which the effect of changing the initial conditions and system parameters d, g and w, on the behavior of the chaotic system, is studied. This work provides a complete analysis of system properties such as time series, attractors, and Fast Fourier Transformation Spectrum (FFT). The system shows periodic behavior when the initial conditions xi and yi equal 0.8 and 0, respectively, then the system becomes quasi-chaotic when the initial conditions xi and yi equal 0 and 0, and when the system parameters d, g and w equal 0.02, 8 and 0.09. Finally, the system exhibits hyperchaotic behavior at the first two conditions, 0 and 0, and the bandwidth of the chaotic
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Rotating fan shaft system was investigated experimentally and theoretically to study its dynamic performance. The type of oil used for the bearing was taken in consideration during the experimental program .Three types of oil were used, SAE 40, SAE 50 and degraded oil. During the experiments, the fan blades stagger angle was changed through angles (20˚, 30˚, 40˚, and 50˚). The shaft rotational speed also changed in the range of (0-3000 rpm). All these parameters have investigated for two cases (balanced and unbalanced fan). The performance parameters of the fan were found experimentally by measuring the fan, volume flow rate, Reynolds and Strouhal numbers, efficiency and pressure head. Analytical part was also represented to prepare
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