This research presents the development and implementation of information technology for monitoring and analyzing segments of a mobile operator's stores using clustering methods. The study addresses a pertinent issue in marketing and business optimization, namely the enhancement of strategies for the network of mobile communication stores.
Nowadays wind energy is one of the most important and promising sources of environmentally clean renewable energy. Wind turbine blades are among the most expensive components. Depending on the size, their manufacturing costs range between 10 % and 20 % of total manufacturing costs. Moreover, the size of blades has increased in recent years, leading to greater efficiency and energy production, but presenting higher failure probability.
This article presents the design, development, and evaluation of a specialized program for analyzing, developing aggregations of this data, and visualizing large volumes of data. The main goal of this program is to simplify data processing, speed up their analysis, and make it easier to write code for problems with large amounts of data. To achieve this goal, machine learning is used, as well as two repositories.
The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis.
Recently, deep learning technologies, namely Neural Networks [1], are attracting more and more attention from businesses and the scientific community, as they help optimize processes and find real solutions to problems much more efficiently and economically than many other approaches. In particular, Neural Networks are well suited for situations when you need to detect objects or look for similar patterns in videos and images, making them relevant in the field of information and measurement technologies in mechatronics and robotics.
A new approach to inpainting problems that combines domain decomposition methods (DDM) with deep neural networks (DNN) to solve partial differential equations (PDE) is presented. First, this article examines different
The development of machine learning and deep learning (ML/DL) change the skills expected by society and the form of ML/DL teaching in higher education. This article proposes a formal system to improve ML/DL teaching and, subsequently, the graduates' skills. Our proposed system is based on the quality assurance (QA) system adapted to teaching and learning ML/DL and implemented on the model suggested by Deming to continuously improve the QA processes.
Gender recognition from voice is a challenging problem in speech processing. This task involves extracting meaningful features from speech signals and classifying them into male or female categories. In this article, was implemented a gender recognition system using Python programming. I first recorded voice samples from both male and female speakers and extracted Mel-frequency cepstral coefficients (MFCC) as features. Then trained, a Support Vector Machine (SVM) classifier was on these features and evaluated its performance using accuracy, precision, recall, and F1-score metrics.
As a result of the research, one of the approaches to building an intelligent information system based on the recommendation of products to users with a solution to the cold start problem is described and modeled. The conducted research takes into account the advantages and disadvantages of the meth- ods, as well as their compatibility, when combining them, which is an important factor for the speed of the system and the efficiency of the algorithm.
This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it.