International Journal of Human Computing Studies

Using the Capabilities of the AutoCAD Program to Solve Metric and Position Issues

Anvar E. Zhabbarov — 2024-02-23

The article proposes to “compare” the solution of metric and positional problems in engineering graphics, that is, to perform it in practice with the help of computer programs after doing it manually based on theoretical knowledge. Theoretical knowledge teaches how to read drawings, how to imagine objects spatially depending on their representation, what operations are necessary to solve the problem, and introduces the laws and rules. Computer graphic programs, on the other hand, ensure that the drawings are done quickly, cleanly and accurately.

Human Stress Detection Through Sleep by Using Machine Learning

Rajasekaran G — 2024-04-07

An individual's capacity to learn, concentrate, make sound decisions, and solve problems is all profoundly affected by stress. Recently, researchers in the fields of computer science and psychology have begun to focus on stress detection and modelling. Affective states, the sensation of the underlying emotional state, are used by psychologists to quantify stress. Human stress classification has mostly relied on user-dependent models, which can't adapt to different users' needs. This necessitates a substantial amount of effort from new users as they train the model to anticipate their emotional states. Urgent action is required to address prevalent childhood mental health concerns, which, if left untreated, can progress to more complex forms. Analysis of medical data and problem diagnosis are now areas where machine learning approaches shine. After running Features on the complete set of characteristics, we were able to minimise the number of attributes. We compared the accuracy of the chosen set of attributes on several ML methods.

The Subject of This Study is the Multiphase Flow of a Compressible Liquid in a Porous Medium, Specifically Focusing on Classification

Bustanov A. Khudaykul — 2024-05-17

Modeling the flow of two-phase compressible fluids through porous media is very pertinent to a broad spectrum of physical and technical applications. The study focuses on reservoir modeling and oil and gas production, which require the use of advanced numerical methods to ensure efficiency. The objective is to achieve a numerical solution to this model by integrating finite element and finite volume approaches. This involves generating velocity values at the boundary of the finite volume grid cells based on point pressure values at KE nodes.

Cloud Technology as a New Approach for Effective Education

Aminov I.B. — 2024-05-15

One of the tasks of the education system in modern society is to provide each person with free and open access to education throughout his life, taking into account his interests, abilities and needs. The article considers the possibilities of using cloud technologies in education, and also presents the main examples of modern services built on the basis of cloud computing technology for education.

Analysis of Real-Time Video for the Detection of Fire Using OpenCV

M. Gandhi M. Gandhi — 2024-06-25

Because of the wide range of colours and textures present in visual landscapes, fire detection is a challenging undertaking. To get over this issue, several fire image categorization methods have been suggested; nevertheless, the majority of these systems depend on rule-based methods or characteristics that are manually created. Develop and propose an innovative technique for fire picture detection using deep convolution neural networks. Adaptive piece-wise linear units are utilised in the network's hidden layers in place of conventional rectified linear units or tangent functions. In addition, we will generate a fresh, compact dataset of fire photos to use for model training and evaluation. Increasing the amount of training images available through the use of conventional data augmentation methods and generative adversarial networks helps alleviate the overfitting issue that arises from training the network on a small dataset. In this study, we compare and contrast two methods for measuring the geometrical features of wildland fires: one that uses image processing to identify colours, and the other that uses Mk2ethods. Presented here are two novel rules and two novel detection methods that make use of an intelligent combination of the rules; their respective performances are then evaluated. About 270 million non-fire pixels and 200 million fire pixels taken from 500 wild terrain photos taken under different imaging conditions are used to run the benchmark. Color and presence of fire are used to classify pixels as fire, whereas average intensity of the associated image is used to classify pixels as non-fire. Because of this, the future of Metrologic systems for detecting fires in unstructured environments looks bright thanks to this technology.