AI, Volume 6, Issue 5 (May 2025) – 6 articles

Due to range of factors in the development stage, generative artificial intelligence (AI) models cannot be completely free from bias. Some biases are introduced by the quality of training data, and developer influence during both design and training of the large language models (LLMs), while others are introduced in the text-to-image (T2I) visualization programs. The bias and initialization at the interface between LLMs and T2I applications has not been examined to date. This study analyzes 770 images of librarians and curators generated by DALL-E from ChatGPT-4o prompts to investigate the source of gender, ethnicity, and age biases in these visualizations. Comparing prompts generated by ChatGPT-4o with DALL-E’s visual interpretations, the research demonstrates that DALL-E primarily introduces biases when ChatGPT-4o provides non-specific prompts. This highlights the potential for generative AI to perpetuate and amplify harmful stereotypes related to gender, age, and ethnicity in professional roles. Full article

Effective anomaly detection is essential for realizing modern and secure industrial control systems. However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communication protocols, and given industrial requirements. Many components of an industrial control system allow direct integration, while others are designed as closed systems or do not have the required performance. At the same time, the effective usage of available resources and the sustainable use of energy are more important than ever for modern industry. Therefore, in this paper, we present a modular and hybrid concept that enables the integration of efficient and effective anomaly detection while optimising the use of available resources under consideration of industrial requirements. Because of the modular and hybrid properties, many functionalities can be outsourced to the respective devices, and at the same time, additional hardware can be integrated where required. The resulting flexibility allows the seamless integration of complete anomaly detection into existing and legacy systems without the need for expensive centralised or cloud-based solutions. Through a detailed evaluation within an industrial unit, we demonstrate the performance and versatility of our concept. Full article

(1) Background: In recent years, Transformer-based models have dominated the time-series forecasting domain, overshadowing recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). While Transformers demonstrate superior performance, their high computational cost limits their practical application in resource-constrained settings. (2) Methods: In this paper, we reconsider RNNs—specifically the GRU architecture—as an efficient alternative to time-series forecasting by leveraging this architecture’s sequential representation capability to capture cross-channel dependencies effectively. Our model also utilizes a feed-forward layer right after the GRU module to represent temporal dependencies, and aggregates it with the GRU layers to predict future values of a given time-series. (3) Results and conclusions: Our extensive experiments conducted on different real-world datasets show that our inverted GRU (iGRU) model achieves promising results in terms of error metrics and memory efficiency, challenging or surpassing state-of-the-art models on various benchmarks. Full article

Background/Objectives: The implementation of artificial intelligence-based systems for disease detection using biomedical signals is challenging due to the limited availability of training data. This paper deals with the generation of synthetic EEG signals using deep learning-based models, to be used in future research for training Parkinson’s disease detection systems. Methods: Linear models, such as AR, MA, and ARMA, are often inadequate due to the inherent non-linearity of time series. To overcome this drawback, long short-term memory (LSTM) networks are proposed to learn long-term dependencies in non-linear EEG time series and subsequently generate synthetic signals to enhance the training of detection systems. To learn the forward and backward time dependencies in the EEG signals, a Bidirectional LSTM model has been implemented. The LSTM model was trained on the UC San Diego Resting State EEG Dataset, which includes samples from two groups: individuals with Parkinson’s disease and a healthy control group. Results: To determine the optimal number of cells in the model, we evaluated the mean squared error (MSE) and cross-correlation between the original and synthetic signals. This method was also applied to select the length of the hidden state vector. The number of hidden cells was set to 14, and the length of the hidden state vector for each cell was fixed at 4. Increasing these values did not improve MSE or cross-correlation and unnecessarily increased computational complexity. The proposed model’s performance was evaluated using the mean-squared error (MSE), Pearson’s correlation coefficient, and the power spectra of the synthetic and original signals, demonstrating the suitability of the proposed method for this application. Conclusions: The proposed model was compared to Autoregressive Moving Average (ARMA) models, demonstrating superior performance. This confirms that deep learning-based models, such as LSTM, are strong alternatives to statistical models like ARMA for handling non-linear, multifrequency, and non-stationary signals. Full article

(1) Background: The misuse of transformation technology using medical images is a critical problem that can endanger patients’ lives, and detecting manipulation via a deep learning model is essential to address issues of manipulated medical images that may arise in the healthcare field. (2) Methods: The dataset was divided into a real fundus dataset and a manipulated dataset. The fundus image manipulation detection model uses a deep learning model based on a Convolution Neural Network (CNN) structure that applies a concatenate operation for fast computation speed and reduced loss of input image weights. (3) Results: For real data, the model achieved an average sensitivity of 0.98, precision of 1.00, F1-score of 0.99, and AUC of 0.988. For manipulated data, the model recorded sensitivity of 1.00, precision of 0.84, F1-score of 0.92, and AUC of 0.988. Comparatively, five ophthalmologists achieved lower average scores on manipulated data: sensitivity of 0.71, precision of 0.61, F1-score of 0.65, and AUC of 0.822. (4) Conclusions: This study presents the possibility of addressing and preventing problems caused by manipulated medical images in the healthcare field. The proposed approach for detecting manipulated fundus images through a deep learning model demonstrates higher performance than that of ophthalmologists, making it an effective method. Full article

Advances in continuous glucose monitoring (CGM) technologies and wearable devices are enabling the enhancement of automated insulin delivery systems (AIDs) towards fully automated closed-loop systems, aiming to achieve secure, personalized, and optimal blood glucose concentration (BGC) management for individuals with diabetes. While model predictive control provides a flexible framework for developing AIDs control algorithms, models that capture inter- and intra-patient variability and perturbation uncertainty are needed for accurate and effective regulation of BGC. Advances in artificial intelligence present new opportunities for developing data-driven, fully closed-loop AIDs. Among them, deep reinforcement learning (DRL) has attracted much attention due to its potential resistance to perturbations. To this end, this paper conducts a literature review on DRL-based BGC control algorithms for AIDs. First, this paper systematically analyzes the benefits of utilizing DRL algorithms in AIDs. Then, a comprehensive review of various DRL techniques and extensions that have been proposed to address challenges arising from their integration with AIDs, including considerations related to low sample availability, personalization, and security are discussed. Additionally, the paper provides an application-oriented investigation of DRL-based AIDs control algorithms, emphasizing significant challenges in practical implementations. Finally, the paper discusses solutions to relevant BGC control problems, outlines prospects for practical applications, and suggests future research directions. Full article