Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals.

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomic nervous system dysfunction, which can be assessed through heart activity analysis. Heart rate variability (HRV) has shown promise as a potential biomarker for diagnostic support and early screening of those conditions.

Adaptive information-constrained mapping for feature compression in edge AI and federated systems.

This article explores the problem of efficient feature compression in distributed intelligent systems with limited resources, particularly within the context of Edge AI and Federated Learning. The relevance of this study is driven by the growing need to reduce communication overhead under conditions of unstable Quality of Service, limited bandwidth, and high heterogeneity of input data. The scientific novelty lies in the development of a consistent entropy-regularised compression model that combines variational latent mapping, non-negativity-constrained projection design, and stochastic-Boolean transformation of the feature space.

On the Baltimore Light RailLink into the quantum future.

In the current era of noisy intermediate-scale quantum (NISQ) technology, quantum devices present new avenues for addressing complex, real-world challenges including potentially NP-hard optimization problems. Acknowledging the fact that quantum methods underperform classical solvers, the primary goal of our research is to demonstrate how to leverage quantum noise as a computational resource for optimization. This work aims to showcase how the inherent noise in NISQ devices can be leveraged to solve such real-world problems effectively.

Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions.

Education is crucial for the growth of effective life skills and the allocation of needed resources. Higher education institutions are adopting advanced technologies, such as artificial intelligence (AI), to enhance traditional teaching methods. Predicting academic performance has become increasingly important, improving university rankings and expanding student opportunities.

HyperMask: Adaptive hypernetwork-based masks for continual learning.

Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. Many continual learning (CL) strategies are trying to overcome this problem. One of the most effective is the hypernetwork-based approach.

Modified Couple Stress Theory for nonlinear primary resonance of FG-GPLRC cylindrical shells in thermal environment.

A modified couple stress theory (MCST)-based microshell model for functionally graded graphene platelets reinforced composite (FG-GPLRC) is proposed for the first time to investigate the nonlinear forced vibration behavior of reinforced microshells subjected to extreme temperatures. To achieve this, the effective elastic modulus is derived using the modified Halpin-Tsai model, while the rule of mixtures is applied for density, Poisson's ratio, and thermal expansion coefficients. The first-order shear deformation theory (FSDT) and von Karman strains are considered, and nonlinear governing partial differential equations (PDEs) are derived using Hamilton's principle, which accounts for size effects and initial stresses induced by the thermal environment.

An Analytical Framework for Optimizing the Renewable Energy Dimensioning of Green IoT Systems in Pipeline Monitoring.

The increasing demand for sustainable and autonomous monitoring solutions in critical infrastructure has driven interest in Green Internet of Things (G-IoT) systems. This paper presents an analytical and experimental framework for designing energy-efficient, self-sustaining pipeline monitoring systems that leverage renewable energy harvesting and low-power operation techniques. We propose a hybrid approach combining solar energy harvesting with energy-saving strategies such as adaptive sensing, duty cycling, and distributed computing to extend the lifetime of IoT nodes without human intervention.

Transformation trees - Documentation of multimodal image registration.

Multimodal image registration plays a key role in creating digital patient models by combining data from different imaging techniques into a single coordinate system. This process often involves multiple sequential and interconnected transformations, which must be well-documented to ensure transparency and reproducibility. In this paper, we propose the use of transformation trees as a method for structured recording and management of these transformations.

Explainable AI for lung cancer detection via a custom CNN on CT images.

Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer frequently have a bad prognosis because of late-stage detection, which severely limits treatment options and decreases survival rates.

What is the doggest dog? Examination of typicality perception in ImageNet-trained networks.

Due to the emergence of numerous model architectures in recent years, researchers finally have access to models that are diverse enough to properly study them from the perspective of cognitive psychology theories, e.g. Prototype Theory.

Free vibration characteristics of trapezoidal nanoplate rested on viscoelastic substrate with arbitrary boundary conditions using differential quadrature method.

This study investigates the free vibration analysis of trapezoidal nanoplate resting on viscoelastic foundation based on first order shear deformation theory (FSDT) incorporating nonlocal elasticity theory, using differential quadrature (DQ) method. The nanoplate's governing equations of motion together with various associated boundary conditions have been discretized applying a mapping DQ method in the spatial domain. Then the complex natural frequencies of the trapezoidal nanoplates obtained by solving the eigen value matrix equation.

Quantification of system resilience through stress testing using a predictive analysis of departure dynamics in a [Formula: see text] queue with multiple vacation policy.

The study investigates the departure counting process in a finite-buffer queueing system with batch arrivals and multiple vacation policy, focusing on quantifying system resilience through stress testing and predictive analysis. A representation for the mixed double transform of the number of departures up to a fixed time moment is obtained in explicit form by applying an analytic approach based on integral equations and linear algebra. We perform a comparative analysis of numerical calculations and simulations made in OMNeT++ Discrete Event Simulator.

Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.

Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitive Impairment (MCI). However, the progressive nature of AD makes it challenging to identify its causal factors. Modern diagnostic workflows for AD use cognitive tests, neurological examinations, and biomarker-based methods, e.

Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions.

Large Language Models (LLMs), like GPT and BERT, have significantly advanced Natural Language Processing (NLP), enabling high performance on complex tasks. However, their size and computational needs make LLMs unsuitable for deployment on resource-constrained devices, where efficiency, speed, and low power consumption are critical. Tiny Language Models (TLMs), also known as BabyLMs, offer compact alternatives by using advanced compression and optimization techniques to function effectively on devices such as smartphones, Internet of Things (IoT) systems, and embedded platforms.

Image steganalysis using active learning and hyperparameter optimization.

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this paper introduces an innovative approach that combines active learning and off-policy Deep Reinforcement Learning (DRL) to improve image steganalysis with minimal labeled data.

How Accurate Can 2D LiDAR Be? A Comparison of the Characteristics of Calibrated 2D LiDAR Systems.

The utilization of 2D Light Detection and Ranging (LiDAR) measurements does not always provide the precision needed to accurately determine the motion range or recalibrate the position of Autonomous Guided Vehicles (AGVs). Consequently, it is essential to employ filtering and calibration methods to enhance the precision and accuracy of measurements derived from 2D LiDAR. The article proposes a multi-sectional calibration (MSC) method incorporating a median filtration (MF) phase to enhance the measurement accuracy of 2D LiDAR.

Collision Avoidance Mechanism for Swarms of Drones.

This article presents a new approach to collision avoidance in drone swarms, designed for operations in large drone swarms and dynamic environments. The mechanism uses distributed communication, where drones share information about their positions and planned trajectories to predict and avoid collisions. The proposed mechanism enables drones to autonomously cooperate and maintain safe distances in complex scenarios.

Cascading quantum walks with Chebyshev map for designing a robust medical image encryption algorithm.

The secure storage and transmission of healthcare data have become a critical concern due to their increasing use in the diagnosis and treatment of various diseases. Medical images contain confidential patient information, and unauthorized access to or modification of these images can have severe consequences. Chaotic maps are commonly used for constructing medical image cipher systems, but with the growth of quantum technology, these systems may become vulnerable.

Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.

Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as intrusiveness and delayed reaction times. This research addresses these gaps by leveraging facial analysis and state-of-the-art machine learning techniques to develop a real-time, non-intrusive DDD system.

Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in different areas from the norm (extrasystole). Arrhythmias can disrupt the balanced circulation, leading to serious complications like heart attacks, strokes, and sudden death. Medical devices like electrocardiography (ECG) and Holter monitors are commonly used for diagnosing and monitoring cardiac rhythm disorders.

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network.

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range.

Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks.

Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network's lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN's lifetime.