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.
The current study aims to develop a proposed educational program based on augmented reality (AR) technology, in addition to assessing its effectiveness in developing research and historical imagination skills of the Humanities Track's female students at the secondary stage, as well as assessing the correlative and predictive relationships between the amount of growth for the two dependent variables. To achieve this, a secondary school in the city of Makkah Al-Mukarramah was chosen, and an available random sample of (30) female students from the study population was selected. The quasi-experimental approach was followed by this study, particularly one group design. In addition, two tools were used to collect study data, namely: a test of
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This research aims to apply the Performance Focused Activity Based Costing System in the consultant office of Al-Khwarizmi College of Engineering at the University of Technology for the purpose of measuring the cost of consulting services provided by these offices in order to reduce costs and their reflection in achieving profits. For the purpose of calculating costs accurately, and to test the hypothesis of the research, the research was applied in the office of the consultant of the College of Engineering Al-Khwarizm - University of Baghdad through the financial statement
... Show MoreThe expansion of the social media environment has created its own linguistic realities which involve more colloquial communication and practical employment of language. This research focuses on nominalizations in detail, which are originally formed words that have been changed for a noun role. These nominalizations are examined within the context of Facebook posts. The research aims to discover the various nominalizations used and how often they appear in a large sample of data from Public Facebook Posts Corpora. Computational linguistics opened new fields of study and enabled researchers to study large amounts of data easily, making it easier to identify patterns. Two computational methods of identifying nominalization in a large dataset w
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Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin
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This paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that
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Photoacoustic is a unique imaging method that combines the absorption contrast of light or radio frequency waves with ultrasound resolution. When the deposition of this energy is sufficiently short, a thermo-elastic expansion takes place whereby acoustic waves are generated. These waves can be recorded and stored to construct an image. This work presents experimental procedure of laser photoacoustic two dimensional imaging to detect tumor embedded within normal tissue. The experimental work is accomplished using phantoms that are sandwiched from fish heart or blood sac (simulating a tumor) 1-14mm mean diameter embedded within chicken breast to simulate a real tissue. Nd: YAG laser of 1.064μm and 532nm wavelengths, 10ns pulse duration, 4
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