Prostate cancer remains one of the most prevalent malignancies among men worldwide and achieving an accurate and timely diagnosis is essential for guiding appropriate treatment decisions and improving patient outcomes. In recent years, Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, has been increasingly applied to clinical, imaging, and histopathological data to enhance diagnostic accuracy. These approaches have shown strong potential in detecting complex patterns within medical datasets that may not be easily captured using traditional statistical methods. Nevertheless, despite the growing number of AI-based diagnostic models proposed in the literature, their adoption in routine clinical practice remains limited. This is largely due to concerns related to transparency, interpretability, reliability, and the extent to which clinicians can understand and trust automated decision-making systems. To address these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising direction aimed at improving the interpretability of predictive models without compromising performance. In this study, an XAI-based framework for prostate cancer diagnosis is developed by integrating ensemble learning with post-hoc explanation techniques. Multiple classifiers, including decision trees, support vector machines, and artificial neural networks, were combined within an ensemble strategy to leverage their complementary strengths and enhance overall predictive capability. To ensure model transparency, explanation methods such as SHAP (Shapley Additive Explanations) and feature importance analysis were employed to quantify and illustrate the contribution of each variable to the final prediction. The experimental results demonstrate that the proposed ensemble XAI framework achieves superior diagnostic accuracy compared with individual classifiers while also providing clear and clinically meaningful explanations of its decisions. These findings highlight the value of incorporating explainability into machine learning models to strengthen clinician trust, support responsible AI deployment, and enable more informed and reliable decision-making in prostate cancer management.

The paper is devoted to the optimization of data structure in classification and clustering problems by mapping the original data onto a set of ordered feature vectors. When ordering, the elements of each feature vector receive new numbers such that their values are arranged in non-decreasing order. For update structure, the main volume of computational operations is performed not on multidimensional quantities describing objects, but on one-dimensional ones, which are the values of objects individual features. Then, instead of a rather complex existing algorithm, the same simplest algorithm is repeatedly used. Transition from original to ordered data leads to a decrease in the entropy of data distribution, which allows us to reveal their properties. It was shown that the classes differ in the functions of feature values for ordered object numbers. The set of these functions displays the information contained in the training sample and allows one to calculate class of any object in the test sample by values of its features using the simplest total probability formula. The paper also discusses the issues of using ordered data matrix to solve problems of partitioning a set into clusters of objects that have common properties.

The educational sector’s technological evolution led to quick market growth of immersive learning tools which specifically benefit students of Science Technology Engineering and Mathematics fields. Augmented Reality (AR) creates an innovative method for colleges to display interactive digital content on actual environments thus generating potential progress in student learning and retention of complex STEM concepts. The research dedicates itself to examining how AR technologies improve student attention and memory functions particularly within STEM academic settings. AR facilitates cognitive psychology and multimedia learning theoretical frameworks to identify the educational effects which occur when using this technology. The effectiveness of AR educational was evaluated by analyzing cases along with implementing classroom projects and running cognitive assessment tests across different types of learning spaces. This paper makes several vital contributions to educational innovation research, because it focuses on practical application alongside technology integration and its impact on learning methods.

The rapid advancements in large language models (LLMs) have led to an exponential increase in survey papers, making it challenging to systematically track and analyze their evolving taxonomy. This study employs graph representation learning combined with classical machine learning techniques to model and interpret the structural evolution of LLM-related survey papers. By constructing attributed graphs that capture topic distributions and interconnections, we provide a data-driven framework to explore research trends in this domain. A dataset of 241 survey papers published between July 2021 and January 2024 is analyzed to identify thematic developments and interdisciplinary relationships. The results highlight key areas of specialization, including the emergence of prompting science, multimodal models, and domain-specific applications in finance, education, and law. Co-occurrence analysis of survey topics reveals strong interconnections between core LLM research and fields such as software engineering, hardware architecture, and evaluation methodologies. These findings demonstrate the increasing specialization of LLM research and its growing integration across multiple disciplines. By leveraging graph-based methodologies, this study offers a structured approach to understanding the LLM survey landscape, facilitating efficient navigation of existing literature and identification of emerging research directions. The insights presented contribute to a more comprehensive understanding of the field’s trajectory, assisting researchers and practitioners in engaging with the latest developments in LLM research.

In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.

The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.

Skin cancer is a serious and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate diagnosis are critical for successful treatment and improved patient outcomes. In recent years, deep learning has emerged as a powerful tool for medical image analysis, including the diagnosis of skin cancer. The importance of using deep learning in diagnosing skin cancer lies in its ability to analyze large amounts of data quickly and accurately. This can help doctors make more informed decisions about patient care and improve overall outcomes. Additionally, deep learning models can be trained to recognize subtle patterns and features that may not be visible to the human eye, leading to earlier detection and more effective treatment. The pre-trained Visual Geometry Group 16 (VGG16) architecture has been used in this study to classification of skin cancer images, and the images have been converted into other color scales, there are named: 1) Hue Saturation Value (HSV), 2) YCbCr, 3) Grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 84.242%. The dataset has also been evaluated with other popular architectures and compared. The performance of VGG16 with images of each color scale is analyzed. In addition, feature parameters have been extracted from the different layers. The extracted layers were felt with the VGG16 to evaluate the ability of the feature parameters in classifying the disease.

Farcana has developed a smart a gaming input device, that, apart from being a tool for the gamer to use in the process of gameplay, is also a suitable tool to collect biomedical information about the gamer, which after analysis by the artificial intelligence (AI) system allows informing the gamer about whether the individual is in a state of tilt. Tilt itself is a poor emotional state of the individual that appears due to the latter’s inability to control one’s emotions in the process of gameplay. The gamer can be either winning or losing, yet the fact that he/she can neither control nor even acknowledge the emotional state is tilt. The latter is an immense factor of impact on the overall success of the individual in the sphere of gaming and one’s rating in cybersport. This paper has analyzed numerous studies and patents on the topic at hand. The available literature has provided the necessary insight on the topic of tilt and why it is important to help the gamer acknowledge one’s state, especially given deteriorating results. Also, we have proposed a framework for the AI system for tilt recognition.

This paper proposes two new algorithms for classifying objects with categorical attributes. These algorithms are derived from the assumption that the attributes of different object classes have different probability distributions. One algorithm classifies objects based on the distribution of the attribute frequencies, and the other classifies objects based on the distribution of the pairwise attribute frequencies described using a matrix of pairwise frequencies. Both algorithms are based on the method of invariants, which offers the simplest dependencies for estimating the probabilities of objects in each class by an average frequency of their attributes. The estimated object class corresponds to the maximum probability. This method reflects the sensory process models of animals and is aimed at recognizing an object class by searching for a prototype in information accumulated in the brain. Because these matrices may be sparse, the solution cannot be determined for some objects. For these objects, an analog of the k-nearest neighbors method is provided in which for each attribute value, the class to which the majority of the k-nearest objects in the training sample belong is determined, and the most likely class value is calculated. The efficiencies of these two algorithms were confirmed on five databases.

Gifted students have different ways of learning. They are characterized by a fitful level of attention and intuitive reasoning. In order to distinguish gifted students from normal students, we conducted an experiment with 17 pupils, willing participants in this study. We collected different types of data (gender, age, performance, initial average in math and EEG mental states) in a web platform called NetMath intending for the learning of mathematics. We selected ten tasks divided into three difficulty levels (easy, medium and hard). Participants were invited to respond to top-level exercises on the four basic operations in decimals. Our first results confirmed that the student's performance has no relation with age. A younger 9-year-old student achieved a higher score than the group with an average of 68.18%. This student can be considered as a gifted one. The gifted students can be also characterized by a mean value of attention (around 60%). They also can be defined by slightly weaker values of their mental states of attention and workload in comparison with the weak pupils.