With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services...
详细信息
DC traction motors are widely used in all branches of urban economy. They began to be used in industry about a hundred years ago with the advent of the first calculation methods. When designing a DC motor, it is impor...
详细信息
The report proposes a dynamic routing method for Self-healing Networks (ShN). The method takes into account the specific features of ShN. During routing, the flow of service information is reduced. The search for the ...
详细信息
Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comp...
详细信息
作者：
Oleksandra BulgakovaViacheslav ZosimovAutomation
Robotics and Computer Programming P.M Platonov Educational and Scientific Institute of Computer Engineering Odesa National University of Technology Odesa Ukraine
This paper presents the impact of external factors on consumer purchasing behavior in e-commerce. It examines how variables such as time of day, weather conditions, economic fluctuations, social influences, cultural t...
详细信息
ISBN:
(数字)9798331542634
ISBN:
(纸本)9798331542641
This paper presents the impact of external factors on consumer purchasing behavior in e-commerce. It examines how variables such as time of day, weather conditions, economic fluctuations, social influences, cultural trends, seasonal events, and others can influence online purchasing decisions. Using data from an e-commerce website, a model was built using GIA GMDH to analyze the impact of these factors on consumer purchasing behavior. The study found significant correlations between seemingly minor external factors and consumer behavior, highlighting the importance of holistic analysis for companies to adapt their strategies and improve e-commerce performance.
The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed ...
详细信息
ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes.
It has great importance to provide the highest accuracy from fingerprint identification and verification systems, which have a large number of biometric features. Fingerprint recognition systems are more widely utiliz...
详细信息
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 Io...
详细信息
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.
The article proposes a method of zero-watermarking for colored RGB bitmap images. This algorithm can be used to protect property rights to multimedia resources in which it is impossible to use ordinary watermarks due ...
详细信息
ISBN:
(数字)9798350384499
ISBN:
(纸本)9798350384505
The article proposes a method of zero-watermarking for colored RGB bitmap images. This algorithm can be used to protect property rights to multimedia resources in which it is impossible to use ordinary watermarks due to the impossibility of changing the image (such as medical data where high accuracy is required and the appearance of artifacts is unacceptable) or due to the presence of a higher distortion of the container. This algorithm is based on DWT, SVD and operations in the field by modulo 256. The software implementation of the algorithm was developed (using the Accord and *** libraries to implement DWT, SVD). The software implementation of the algorithm was tested. This algorithm has advantages in the form of normalization of the average error, but it needs further research to improve its performance.
In this paper, we explored approaches that improve the performance of ensemble bagging classifiers for identifying the state of a computer system. The following algorithms are considered: Ensemble pruning, Advanced Vo...
In this paper, we explored approaches that improve the performance of ensemble bagging classifiers for identifying the state of a computer system. The following algorithms are considered: Ensemble pruning, Advanced Voting Algorithms, Dynamic Voting Strategies, Confidence Calibration, Adaptation through Meta-Features and Meta-Learning, which allow improving the voting procedure of the bagging meta-algorithm. Artificial data was generated as initial data, which complicates the classification task and contains an increased amount of noise. In the Google Collab environment, software models of algorithms have been developed, and their quality has been assessed. It was found that the use of the Ensemble pruning and Adaptation through Meta-Features algorithm is the most qualitative. In addition, the Ensemble pruning algorithm reduces the number of basic ensemble classifiers, and, as a result, increases the efficiency of computer system identification. Based on the results of the study, a method for identifying a computer system was proposed through the integrated use of a bagging classifier and an optimization procedure based on the Ensemble pruning algorithm.
暂无评论