Neurological disorders are among the leading causes of both physical and cognitive disabilities worldwide, affecting approximately 15% of the global population. This study explores the use of machine learning (ML) and deep learning (DL) techniques in processing Electroencephalography (EEG) signals to detect various neurological disorders, including Epilepsy, Autism Spectrum Disorder (ASD), and Alzheimer’s disease. We present a detailed workflow that begins with EEG data acquisition using a headset, followed by data preprocessing with Finite Impulse Response (FIR) filters and Independent Component Analysis (ICA) to eliminate noise and artifacts. Furthermore, the data is segmented, allowing the extraction of key features such as Bandpower and Shannon entropy, which improve classification accuracy. These features are stored in an offline database for easy access during analysis, to be then applied for both ML and DL models, systematically testing their performance and comparing the results to prior studies. Hence, our findings show impressive accuracy, with the random forest model achieving 99.85% accuracy in classifying autism vs. healthy subjects and 100% accuracy in distinguishing healthy individuals from those with dementia using Support Vector Machines (SVM). Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis.

Epilepsy is a widespread neurological disorder affecting approximately 50 million people worldwide, significantly impacting quality of life and placing a heavy burden on healthcare systems. Early and reliable seizure detection remains a critical challenge, often hindered by limited availability of high-quality electroencephalogram (EEG) data and the suboptimal performance of existing classification methods. In this work, we propose a novel two-stage framework that addresses both data scarcity and classification accuracy. The first stage involves generating synthetic EEG signals that realistically mimic epileptic patterns using the Variable Structure Model Neuron (VSMN) with multidendrites, providing an effective means of data augmentation. In the second stage, we introduce a hybrid LSTM-VSMN model, where the VSMN activation function is integrated within the Long Short-Term Memory (LSTM) network gates, replacing conventional activations such as tanh. This integration improves the model’s ability to capture complex temporal dependencies in EEG sequences. To the best of our knowledge, this is the first study to leverage VSMN both for EEG signal synthesis and as an activation function within a deep recurrent neural network for seizure detection. The proposed model is rigorously evaluated against conventional activation functions, achieving an accuracy of 98.16% in single-fold validation and 97.59% under 3-fold cross-validation. Furthermore, it achieves a Mean Absolute Error (MAE) as low as 0.0241 and a Mean Absolute Percentage Error (MAPE) of 2.41%, substantially outperforming baseline approaches. These results demonstrate the effectiveness of the hybrid LSTM-VSMN architecture in enhancing automated seizure detection, offering a promising tool for clinical decision support and real-time monitoring applications.

This paper presents an object detection model for logistics-centered objects deployed and used by autonomous warehouse robots. Using the Robot Operating System (ROS) infrastructure, our work leverages the set of provided models and a dataset to create a complex system that can meet the guidelines of the Autonomous Mobile Robots (AMRs). We describe an innovative method, and the primary emphasis is placed on the Logistics Objects in Context (LOCO) dataset. The importance is on training the model and determining optimal performance and accuracy for the implemented object detection task. Using neural networks as pattern recognition tools, we took advantage of the one-stage detection architecture YOLO that prioritizes speed and accuracy. Focusing on a lightweight variant of this architecture, YOLOv4 Tiny, we were able to optimize for deployment on resource-constrained edge devices without compromising detection accuracy, resulting in a significant performance boost over previous benchmarks. The YOLOv4 Tiny model was implemented with Darknet, especially for its adaptability to ROS Melodic framework and capability to fit edge devices. Notably, our network achieved a mean average precision (mAP) of 46% and an intersection over union (IoU) of 50%, surpassing the baseline metrics established by the initial LOCO study. These results demonstrate a significant improvement in performance and accuracy for real-world logistics applications of AMRs. Our contribution lies in providing valuable insights into the capabilities of AMRs within the logistics environment, thus paving the way for further advancements in this field.

Understanding the distribution of electrical potential within neurons is critical for advancing our comprehension of neuronal signaling and communication. Neurons, the fundamental units of the nervous system, rely on complex electrochemical processes to transmit information. The intricate structure of neurons, especially those with multiple dendrites, plays a crucial role in how these electrical signals are generated, propagated, and integrated. Despite significant progress in neuroscience, accurately modeling the electrical potential within neurons with elaborate dendritic architectures remains a challenge. This article introduces a novel approach to modeling the electrical potential in multi-dendritic neurons using Bessel functions, which offers a more precise and detailed representation of these processes. The proposed method involves solving the electric potential diffusion equation in cylindrical coordinates, a mathematical framework that naturally aligns with the geometry of dendrites. The radial and axial components of the solution are expressed using Bessel functions and sinusoidal functions, respectively. Bessel functions are particularly well-suited for this purpose due to their ability to describe waveforms in cylindrical systems, making them ideal for capturing the spatial variations in electrical potential within the cylindrical shape of dendrites. By leveraging this mathematical approach, we obtain a complete representation of the potential distribution across the neuron, from the soma (cell body) through the dendrites to the synaptic terminals. This model accurately captures the spatial variations of electrical potential in different regions of the neuron, including areas with complex dendritic arborizations, which are branching structures that significantly influence the neuron’s electrical characteristics. Simulation results underscore the effectiveness of this approach in reproducing realistic neuronal behavior. The model successfully mimics the way electrical signals propagate and interact within dendritic structures, providing crucial insights into the underlying mechanisms of signal integration and transmission in neurons.

Accurate classification of electrocardiogram (ECG) beats remains a major challenge due to noise, inter-patient variability, and spectral distortions that degrade signal quality, particularly in real-time monitoring and embedded system scenarios. To address these limitations, this paper proposes ECG-MSEE-Net, a novel deep learning model for ECG beat classification that integrates a convolutional neural network (CNN), an adaptive spectral weighting mechanism based on Maximum Spectral Energy Entropy (MSEE), and a Transformer encoder. The CNN extracts discriminative local morphological features, while the MSEE-based module adaptively weights ECG segments according to their spectral quality, enhancing robustness against noisy or atypical signals. Additionally, the Transformer encoder captures long-term temporal dependencies to further optimize classification performance. The proposed model is evaluated on the MIT-BIH Arrhythmia dataset using a patient-independent protocol. Experimental results demonstrate an accuracy of 98.74%, an F1-score of 92.19%, and strong generalization capabilities, even in the presence of noise. Comparisons with state-of-the-art methods confirm that ECG-MSEE-Net effectively handles spectral anomalies and achieves competitive performance. Furthermore, validation on an embedded platform (Raspberry Pi) highlights its potential for real-time ECG monitoring and connected healthcare applications.

Understanding how brain activity varies across neurological and neurodevelopmental disorders requires tools capable of revealing patterns hidden in complex electroencephalographic (EEG) data. Conditions such as epilepsy, Alzheimer’s disease, dementia, and autism exhibit distinct alterations in neural oscillations and connectivity, which remain difficult to interpret in real time; therefore, this study proposes an interactive interface for intuitive exploration and analysis of disease-specific EEG dynamics. The system integrates classical signal processing techniques and computational modeling to extract spectral features, inter-electrode coherence, and spatial activation patterns, which are visualized through spectrograms, topographic maps, and connectivity graphs that update continuously. In addition, a web-based platform is incorporated to enable clinicians and technicians to store and manage patient information, including diagnosis, severity level, number of recordings, sampling frequency, recording duration, and acquisition dates, supporting structured data organization and longitudinal monitoring. The results demonstrate that the interface captures meaningful differences between disorders, with epileptic patterns showing strong synchronization and burst activity, while neurodegenerative conditions exhibit spectral slowing and reduced connectivity. Overall, the proposed framework provides an effective and accessible tool for EEG visualization, combining interactive analysis with clinical data management to support research, education, and potential clinical applications.