Breast cancer is one of the most prevalent and life-threatening diseases worldwide, making accurate early diagnosis critical for improving patient outcomes. This study investigates the effectiveness of logistic regression models for breast cancer detectionusing the Wisconsin Breast Cancer (Diagnostic) Dataset. Various optimization techniques, including Ordinary Least Squares, Gradient Descent, Newton’s Method, L2 Regularization, Stochastic Gradient Descent (SGD), and GridSearch-based fine-tuning, were applied to determine their impact on model performance. Standard evaluation metrics—Accuracy, Precision, Recall, and F1-score—were used to compare methods. The results demonstrate that GridSearch-based fine-tuning consistently yields the highest overall performance, highlighting the importance of systematic hyperparameter optimization in enhancing model robustness and predictive accuracy. These findings emphasize that even classical models like logistic regression can achieve state-of-the-art results in medicaldata analysis when combined with appropriate optimization strategies.
This article analyzes the scientific foundations and practical effectiveness of applying machine learning and deep learning methods for the automatic detection and severity assessment of dysarthria. Dysarthria is a common symptom of neurological disorders such as Parkinson’s disease, cerebrovascular accidents, cerebral palsy, and multiple sclerosis, leading to impairments in articulatory, prosodic, and resonant components of speech. Due to the subjectivity and time consumption of traditional clinical diagnostics, the demand for automated speech processing systems is increasing. In this study, the UA-Speech and TORGO datasets were employed, and classical machine learning models such as RandomForest, XGBoost, LightGBM, SVM, LogisticRegression, and MLPClassifier were compared with deep learning approaches based on CNN architectures. The models were optimized using GridSearchCV, while the pronounced data imbalance was addressed with ADASYN and class weight techniques. Key acoustic features such as MFCC, Mel spectrograms, and pitch were used to construct the feature space. The results indicate that machine learning models can achieve high accuracy in detecting dysarthria symptoms, while hybrid approaches combining CNNs provide improved overall performance. This research lays the groundwork for developing automated early dysarthria diagnostic systems and contributes to the advancement of technologies aimed at clinical practice.
Big data clustering remains challenging: the Minimum Sum-of-Squares Clustering (MSSC) problem underlying K-means is NP-hard, and existing methods either reach poor local minima or require prohibitive metaheuristic hybrids. We target arbitrarily tall data: a fixed feature space may contain arbitrarily many, possibly infinitely many, observations, while the algorithm accesses only finite random samples. We propose Big-means++, an algorithm achieving scalability and global-search quality by curating inputs to MSSC optimization on big data. It orchestrates local K-means refinements into a data-native global search for big data clustering. Rather than optimizing the full-data MSSC objective, Big-means++ traverses sample-induced surrogate landscapes. Each sample defines a distinct empirical MSSC approximation with a perturbed local-optimum structure, turning sample-to-sample variation into a global-search mechanism. Unlike Big-means, a flowing-incumbent strategy propagates centroid state across empirical landscapes through K-means refinements on fresh samples without rollback to a best-so-far solution. This increases mobility and favors stable, high-quality configurations across approximations of the full-data structure. A new shaking mechanism varies sample size geometrically, broadening the surrogate landscapes explored across resolution scales, accounting for cluster imbalance, and improving solution quality. A competitive multi-agent system asynchronously explores independent sampled landscapes, transforming diverse stochastic trajectories into collective search intelligence. Automatic convergence detection stops each agent after attaining a high-quality solution but before further search risks degrading it, while providing a universal speed-quality control. Experiments on 22 datasets against 11 competing algorithms demonstrate the effectiveness, efficiency, and robustness of Big-means++.
The article presents a comprehensive review of international systems for the evaluation of scientific and technical projects with a detailed analysis of the experience of four key regions – the United States, the European Union, Japan and China. Institutional models for organizing the evaluation, principles for selecting and evaluating projects, and mechanisms for preventing conflicts of interest are considered. Particular attention is paid to the criteria used, including scientific novelty, potential socio-economic significance, innovativeness and compliance with strategic priorities. The aim of the study is the analysis of the processes of digitalization of the evaluation, including the introduction of automated systems for selecting experts, the use of publication activity databases and the integration of tools for assessing scientific contribution. A comparison of the functionality of modern international digital platforms, such as OpenAIRE, Elsevier Expert Lookup, ResearchGate and Publons, is carried out, identifying their advantages and limitations. Based on comparative analysis, the strengths of national systems are highlighted: focus on public significance, anonymization and twoway feedback, interim monitoring of projects, strategic focus on priority areas and the use of artificial intelligence. Based on the results of the study, recommendations are proposed for Kazakhstan, including the creation of a national integrated digital platform for expertise using artificial intelligence and blockchain technologies, as well as the introduction of elements of anonymization and interim control. The proposed measures are aimed at increasing transparency, reducing timeframes and improving the quality of expert decisions in the field of scientific and technological progress.
This paper proposes a methodological approach for evaluating the temporal stability of emotional states in multimodal data. Unlike previous studies, we introduce a multi-level formalization (signal →function →latent space →prediction) and a set of indicators including the number of class switches, episode duration, and the Hurst exponent. Stability is assessed at the level of trajectories in a polar space interpreted on the basis of Plutchik’s model. The study proposes a system of metrics describing temporal stability and provides a comparative analysis of these metrics across several multimodal architectures. The evaluation protocol includes speaker-independent splits, standard metrics (F1/UAR, CCC/MAE), and temporal stability indicators. As an addi-tional validation step, a text-only pilot experiment was conducted on a balanced small subset of the MELD dataset. The proposed evaluation approach makes it possible to characterize the temporal stability properties of models more accurately. The practicalvalue of the work lies in improving the predictability and interpretability of behavior in human-computer interaction systems and assistive technologies.
