Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and motor decline. Early detection remains challenging, as traditional neuroimaging and neuropsychological assessments often fail to capture subtle, preclinical changes. Recent advances in digital health and artificial intelligence (AI) offer new opportunities to identify non-invasive biomarkers of cognitive impairment. In this study, we propose an AI-driven framework for early AD based on handwriting motion data captured using a sensor-integrated Smart Pen. The system employs an inertial measurement unit (MPU-9250) to record fine-grained kinematic and dynamic signals during handwriting and drawing tasks. Multiple machine learning (ML) algorithms—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (kNN)—and deep learning (DL) architectures, including one-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-BiLSTM network, were systematically evaluated. To address data scarcity, we implemented a Sim-to-Real Domain Adaptation strategy, augmenting the training set with physics-based synthetic samples. Results show that classical ML models achieved moderate diagnostic performance (AUC: 0.62–0.76), while the proposed hybrid DL model demonstrated superior predictive capability (accuracy: 0.91, AUC: 0.96). These findings underscore the potential of motion-based digital biomarkers for the automated, non-invasive detection of AD. The proposed framework represents a cost-effective and clinically scalable informatics solution for digital cognitive assessment.

This article explores the methods of processing and analyzing big data in order to improve the accuracy and efficiency of machine learning (MO) models. The main focus is on classification problems, the effectiveness of algorithms such as XGBoost, the support vector machine (SVM), ensemble methods, as well as systems for working with big data, including Hadoop and Apache Spark. The key stages of working with data are described: cleaning, normalization, selection of features, which is critically important for building stable models on large amounts of data. Accuracy, completeness, F-measure, and AUC-ROC metrics were used to evaluate the effectiveness of the algorithms, which made it possible to conduct a comparative analysis and identify the most productive approaches. Special attention is paid to the application of MO in the context of organizational innovations, including the tasks of classification, forecasting the success of innovations and innovation portfolio management. Recommendations on the choice of technologies and algorithms for various data types and scales are presented, and prospects for integrating distributed computing platforms with MO algorithms to achieve scalable and efficient solutions are discussed.

Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to modulate movement speed and force–parameters often disrupted in cognitive impairments like AD. Participants included 20 patients diagnosed with AD and 18 healthy controls, matched in age and educational levels. Data collection involved tasks such as sentence rewriting, figure redrawing, and digit rewriting, processed using CoolTerm software at a sampling rate of 18 Hz. Descriptive statistics revealed that the AD group exhibited lower mean values for gyroscope and acceleration data, indicating slower and less variable movements compared to the control group. T-tests confirmed significant differences (p < 0.001) across all measured parameters between the AD and control groups. The results support the potential of the Robo-pen as a non-invasive, cost-effective diagnostic tool for early detection of AD. By capturing subtle neuromotor changes, the Robo-pen facilitates earlier diagnosis and timely intervention, potentially altering the disease trajectory and improving patient outcomes. This study marks a significant advancement in the early detection of AD, highlighting the Robo-pen’s promise as a transformative tool in neurodegenerative disease diagnosis and management.

The transparent and efficient evaluation of scientific and technical projects is important for allocating public research funding. This paper analyzes the is.ncste.kz platform, the national information system of the National Center for State Scientific and Technical Expertise (NCSSTE) of Kazakhstan, which supports the lifecycle from application submission through expert review and council decision to appeals and monitoring. The documented workflow is compared, using official sources, with the National Science Foundation (NSF, United States), Horizon Europe (European Union), and the New Energy and Industrial Technology Development Organization (NEDO, Japan), and is interpreted through the DeLone and McLean information systems success model. For this one case, five standard models are specified and linked through stated interfaces: M/M/c queueing, integer linear programming, composite scores with Cronbach’s alpha and intraclass correlation, an absorbing Markov chain, and a Cox proportional hazards model that is proposed but not estimated. The models use assumed parameters and are not calibrated with operational data, so the results illustrate the calculations only, and transfer to other platforms is outlined but not tested. A SWOT analysis ranks proposed improvements with an explicit scoring rule and compares blockchain with simpler integrity mechanisms.