This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries that are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures that our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.

In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.

This paper describes a morphological analysis of the Kazakh language for Kazakh-English statistical machine translation through changing the compound words of Kazakh language, and explores the effect of using the modified input on translation quality with a large number of training sentences. Word alignment problem would become more serious for translation from morphologically rich language such as Kazakh to morphologically simple one such as English, due to the problem of data sparseness on translation word forms in many different morphological variants. We present our investigations on unsupervised Kazakh morphological segmentation over newspaper corpus and compare unsupervised segmentation against rule-based language processing tools. In our experiments, the results show that our proposed method can improve word alignment and translation quality.

In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L). Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.

This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network's status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.