International Journal of Computer (IJC)

Application of International Standards to Improve Competitiveness in the Gaming Industry

Andrei Saprykin — 2025-01-03

This review article explores the importance of international standards in optimizing processes and enhancing the competitiveness of companies in the rapidly growing market for video games. The author delves into the existing standards created by renowned organizations such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE). Among the standards examined are ISO/IEC 25010, which covers systems and software quality models, ISO/IEC 33020, which assesses process capability, ISO/IEC 29110, which outlines lifecycle profiles for small businesses, IEEE 2861 for evaluating and optimizing mobile gaming performance, and ISO/IEC/IEEE 29119 for software testing. The article highlights the key features of these standards and explains how they contribute to process optimization, quality improvement, and enhanced user experience (UX). It also addresses the risks associated with implementing these standards and suggests strategies to minimize or eliminate them.

Hybrid Skin Lesion Detection Integrating CNN and XGBoost for Accurate Diagnosis

Adekunle O. Ajiboye — 2025-01-10

Skin cancer, particularly melanoma, remains one of the most challenging medical conditions due to its rapid progression and high mortality rate when not detected early. The growing prevalence of skin cancer highlights a significant problem in medical diagnostics: the need for automated, accurate, and efficient classification systems that can aid dermatologists in diagnosing various types of skin lesions. This issue is exacerbated by the imbalance in available datasets, underrepresentation of certain lesion classes, and a lack of generalizable diagnostic tools, ultimately impacting patient outcomes and healthcare efficiency.

This study aimed to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) for feature extraction and XGBoost for classification to address the problem of skin lesion classification. This study's guiding conceptual framework was applying deep learning techniques combined with ensemble models to enhance classification accuracy and model interpretability.

The study utilized the HAM10000 dataset, comprising 10,015 dermatoscopic images across seven skin lesion classes. Dynamic resampling based on power analysis ensured class balance by selecting 158 samples per class. Image preprocessing techniques, such as resizing, hair removal, and Gaussian blurring, were applied to standardize the data. The CNN model extracted hierarchical features, while the XGBoost model performed classification on these features. The research methodology involved a quantitative approach using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to evaluate the model’s effectiveness.

The results demonstrated that the CNN-XGBoost hybrid model achieved superior classification performance with an accuracy of 86.46% on the test dataset, outperforming the standalone CNN model. The hybrid model effectively addressed class imbalance and exhibited high discriminatory power across all lesion classes, as confirmed by an average ROC-AUC score of 0.98.

The study concludes that the hybrid CNN-XGBoost model holds significant potential for assisting dermatologists in early skin lesion detection and improving diagnostic accuracy. Recommendations for future research include validation using diverse datasets, incorporating clinical metadata, and enhancing model interpretability for real-world deployment. These findings contribute to advancing AI-driven healthcare solutions, offering promising implications for dermatological diagnostics and patient care.

Implementation of machine learning in Android Applications

Vladislav Terekhov — 2025-01-20

The introduction of machine learning into Android applications based on the Java platform allows you to significantly expand the functionality of mobile applications, improving the user experience and increasing the efficiency of data processing. The use of various libraries, such as TensorFlow Lite and ML Kit, gives developers flexible tools for integrating machine learning models. This allows you to implement image recognition, text analysis, and user segmentation functions, providing a more personalized service. However, developers face challenges related to the limitations of computing resources of mobile devices, which require optimization of models to work in conditions of low power consumption and limited RAM. Nevertheless, machine learning on Android shows high development prospects, contributing to the creation of more intelligent and adaptive mobile solutions.