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A Transfer Learning-Based Framework for Enhanced Classification of Perceived Mental Stress Using EEG Spectrograms

IEEE Access 2025年 13卷 89266-89286页

作者： Khan, Sheharyar Noorani, Sadam Hussain Frnda, Jaroslav Rauf, Usman Arsalan, Aamir Saeed, Sanay Muhammad Umar University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan HITEC University Department of Computer Science Taxila47050 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Žilina01026 Slovakia Fatima Jinnah Women University Department of Software Engineering Rawalpindi46000 Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies. © 2013 IEEE.

关键词： Noninvasive medical procedures

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On the Optimization of User Allocation in Heterogeneous 5G Networks Using DUDe Techniques 14

On the Optimization of User Allocation in Heterogeneous 5G N...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Tsachrelias, Konstantinos Gkamas, Apostolos Katsigiannis, Chrysostomos-Athanasios Bouras, Christos Kokkinos, Vasileios Pouyioutas, Philippos University of Patras Computer Engineering and Informatics Department Patras Greece University Ecclesiastical Academy of Athens Athens Greece University of Nicosia Computer Science Department Nicosia Cyprus

ISBN: (纸本)9798350335385

In previous years, 3G and 4G cellular homogeneous networks configured with macro Base Stations (BSs) relied only on the downlink signal, even though transmission power and interference levels differ significantly between uplink and downlink. In 5G Heterogeneous Networks (HetNet), a new generation which have multiple BSs of different types such as Femto BS and Macro BS, there is the possibility of choosing to receive the data from one BS and transmit them to a different BS, and therefore decoupling the uplink and downlink. Especially, the increasing demand for faster, more reliable, and efficient connectivity has made the optimization of User Equipment (UE) allocation in 5G networks a crucial task. To tackle the challenges posed by heterogeneous 5G networks, this study compares and evaluates the performance of Downlink/Uplink Decoupling (DUDe) and traditional Downlink/Uplink Coupled (DUCo) user allocation approaches. Simulation results show that DUDe UE allocation outperforms DUCo by providing improved network performance and more efficient utilization of network resources in diverse network conditions. These findings have important implications for the design and optimization of 5G networks and provide valuable insights for researchers and practitioners in the field. © 2023 IEEE.

关键词： 5G mobile communication systems

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Increasing the RES hosting capacity of the cyprus distribution system

Increasing the RES hosting capacity of the Cyprus distributi...

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CIRED 2024 Vienna Workshop

作者： Phivos Therapontos Rafail Konstantinou Charalambos A. Charalambous Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9781837241224

The Renewable Energy Sources (RES) penetration in the power system of cyprus has dramatically increased over the last years. As a result, the system is already facing significant challenges limiting the RES hosting capacity of the Distribution Network (DN). The major limitation factors are network congestion and voltage security. In this paper, alternative solutions for increasing the RES hosting capacity in DNs are reviewed, and a methodology is introduced to evaluate their effectiveness. More specifically, different inverter settings and an advanced centralised voltage control from power transformers are used to mitigate voltage-related issues, while network reinforcements and upgrading the operating voltage are considered for further increase in hosting capacity. The solutions and evaluation methodology are tested using a real MV network of the cyprus distribution system and the strategic plan of the cyprus Distribution System Operator (DSO) for maximizing RES hosting capacity is outlined.

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A Dynamic Security Assessment Tool for Modern Power Systems with Massive RES Penetration

A Dynamic Security Assessment Tool for Modern Power Systems ...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Phivos Therapontos Charalambos A. Charalambous Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Modern power systems are facing significant challenges due to the massive penetration of renewable energy sources (RES). Recently, issues related to frequency security, system strength, and excessive fault levels have been experienced in some power systems. Classical dynamic security assessment (DSA) tools have not been developed to identify and analyze these new challenges that are critical in low-inertia grids. In this paper, a DSA tool has been developed that evaluates frequency security, system strength, and maximum fault levels to allow for secure system planning and operation. Furthermore, to increase the accuracy of the DSA tool, inverter-based resources’ (IBRs’) protections and capabilities have been incorporated in the DSA tool. A real-time operation and an operational planning application of the DSA tool are showcased using the islanded cyprus Power System.

关键词： Renewable energy sources Adaptation models Accuracy Power system dynamics Real-time systems Planning Security Protection Faces

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Optimized Image Dehazing using Dark Channel Prior with Minimum Fusion and Improved Atmospheric Light Handling

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IEEE Access 2025年 13卷 100720-100730页

作者： Hussain, Ahmad Iqbal, Mansoor Aslam, Sheraz Alahmari, Saad Alkharashi, Abdulwahab University of Wah Department of Computer Science Cantt Wah Pakistan CTL eurocollege Department of Computer Science Limassol 3077 Cyprus Cyprus University of Technology Department of Electrical Engineering Computer Engineering and Informatics Limassol 3036 Cyprus Northern Border University Applied College Department of Computer Science Arar Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Computer Science Saudi Arabia

Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments. © 2013 IEEE.

关键词： Atmospheric Light Estimation Dark Channel Prior Image Dehazing Minimum Fusion Technique Visual Clarity

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Overview of Load Disaggregation Models, Methods and Hardware 59

Overview of Load Disaggregation Models, Methods and Hardware

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59th International Universities Power engineering Conference, UPEC 2024

作者： Lancranjan, Mircea N. Muresan, Alexandru Jurj, Dacian I. Berciu, Alexandru Polycarpou, Alexis Czumbil, Levente Micu, Dan D. Technical University of Cluj-Napoca Department Electrical Engineering Cluj-Napoca Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (纸本)9798350379730

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated. © 2024 IEEE.

关键词： AI Data Cleaning Energy Efficiency Energy Signature Load Disaggregation Load Monitoring NILM

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Cerebral cortex contributions to Parkinson's disease tremor:what exactly generates the tremor?

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Neural Regeneration Research 2025年第12期20卷 3535-3536页

作者： Manuel Bange Hao Ding Muthuraman Muthuraman Informatics for Medical Technology Institute of Computer ScienceUniversity AugsburgAugsburgGermany Department of Neurology Neural Engineering with Signal Analytics and Artificial Intelligence(NESA-AI)University Clinic WürzburgWürzburgGermany

Pathological tremor is one of the cardinal symptoms in Parkinson's disease (PD).Tremor is comprised of involuntary,rhythmic,a nd oscillating movements that can vary according to the circumstances under which they occur,the body parts that are involved,and the frequency at which they *** example,tremors can be mild to severe,are stress sensitive,and can affect arms,legs,or the head (Dirkx and Bologna,2022).

关键词： tremor cortex exactly

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Enhancing Solar Farm Operations: Machine Learning for Equipment Fault Detection and Classification

Enhancing Solar Farm Operations: Machine Learning for Equipm...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Christofides, Nicholas School of Engineering LondonSE1 0AA United Kingdom Department of Engineering and Construction London United Kingdom Frederick University Department of Electrical Engineering Computer Engineering and Informatics Cyprus

ISBN: (纸本)9798350376067

Photovoltaic (PV) energy is considered one of the most widespread renewable sources. In this study, the vulnerability of solar PV systems to various faults, leading to potential performance degradation, has been addressed. A robust fault detection and classification strategy is proposed, employing Gradient Boosting Machine Learning (ML) algorithms (Light Gradient Boosting Method (LGBM), Categorical Boosting (Cat-boost), and AdaBoost). The methodology involved formulating a comprehensive PV system to create a synthetic fault database, utilizing diverse features. To simulate normal conditions and various fault scenarios, a MATLAB/Simulink-based PV System is developed. The optimization of hyperparameters of ML algorithms has been achieved by grid search optimization technique, resulting in enhanced performance, and reduced computational cost/time. The study involved multiple independent runs on ML algorithms and the application of Principal Component Analysis (PCA) for dimensionality reduction in the context of fault classification to assess the accuracy and consistency. Cross-validation is implemented to ensure the generalization capability of the ML algorithms to unseen data. Comparison has been established with Random Forest (RF) algorithms to show the performance accuracy of ML in fault diagnosis of PV systems. © 2024 IEEE.

关键词： Dimensionality reduction

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

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Neural Computing and Applications 2025年第17期37卷 10851-10873页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

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