The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate method...
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An approach for automated knowledge extraction and decision-making from medical images through a workflow for preprocessing of incoming X-ray images, analysis, classification and evaluation of the results is presented...
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A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network sec...
A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network security has grown in importance as a research topic. The usage of intrusion detection systems is a well-known strategy for safeguarding computer networks. This paper proposes an anomaly detection method that blends rule-based and machine-learning-based methods. In order to construct the appropriate rules, a genetic algorithm is utilized. Principal component analysis is used to extract the relevant features aimed to improve the performance. The suggested method is validated experimentally using the KDD Cup 1999 dataset, which meets the requirement of using appropriate data. The proposed method is applied to detect and analyze four types of attacks in a well-known benchmark dataset: Neptune, Ipsweep, Pod, and Teardrop, utilizing Support Vector Machine, Decision Tree, and Naive Bayes algorithms. After testing the characteristics specified in the training phase, the data is classified into attack categories and normal behavior during the machine learning phase.
The integration and detection of AI (Artificial Intelligence) in a variety of fields, primarily education, are examined in this paper. With an emphasis on virtual assistants and their uses, it explores the potential a...
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ISBN:
(数字)9798350376449
ISBN:
(纸本)9798350376456
The integration and detection of AI (Artificial Intelligence) in a variety of fields, primarily education, are examined in this paper. With an emphasis on virtual assistants and their uses, it explores the potential and constraints of such technology. A study was conducted with students at the Technical University of Sofia, to assess their ability to distinguish between texts written by humans and AI. The results showcase that learners need to develop their critical analysis skills evidenced by their mixed expertise.
Scientific and scientific-technical information is a valuable tool for the development of education, technology and society as a whole. The increase in the volume of information and the development of information netw...
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Nowadays, quality is the most important attribute that creates value. It is considered as the competitive advantage of private and public organizations by which they differentiate their products or services. Quality a...
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Speech is a natural way of communicating between human beings and as such, it triggers an interest of transforming it to a way of interaction with a computer as well. Once it is converted into a sequence of words, it ...
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The Matura exam is the final national examination that high school students in many countries must pass to be eligible for admission to a university. This paper discusses the key factors that have the most impact in p...
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Predicting student success is an important task in educational institutions, as it allows for targeted interventions and support systems to enhance educational outcomes. This paper explores the use of SHAP (SHapley Ad...
Predicting student success is an important task in educational institutions, as it allows for targeted interventions and support systems to enhance educational outcomes. This paper explores the use of SHAP (SHapley Additive exPlanations) model-agnostic method in understanding and interpreting student success prediction. The predictive model was built using Multi-Layer Perceptron neural network algorithm on a large public dataset. By shedding light on the underlying factors driving student success, this research contributes to the advancement of data-driven decision-making in education.
A particular problem in using artificial intelligence techniques in the sensor grid is the high power consumption. Remote sensors are usually limited by the amount of power available, thus our general goal is to minim...
A particular problem in using artificial intelligence techniques in the sensor grid is the high power consumption. Remote sensors are usually limited by the amount of power available, thus our general goal is to minimize it while preserving accurate experimentation results. Using emerging technologies like spiking neural networks and neuromorphic hardware, we can create sensor grids that perform data processing with preserving low power consumption. In this paper, we demonstrate a remote node with integrated visual sensor that works on less than 10mW of energy, while performing continuous scene monitoring, object detection and classification. The system has intelligent power management and the ability to send data wirelessly.
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