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Chemical processes are vital in various industries but are often complex and nonlinear, making accurate modeling essential. Traditional linear approaches struggle with dynamic behaviour and changing conditions. This paper explores the advantages of the new theory of fractional neural networks (FNNs), focusing on applying fractional activation functions for continuous stirred tank reactor (CSTR) modeling. The proposed approach offers promising solutions for real-time modeling of a CSTR. Various numerical analyses demonstrate the robustness of FNNs in handling data reduction, achieving better generalization, and sensitivity to noise, which is crucial for real-world applications. The identification process is more generalized and can enhance adaptability and improve industrial plant management efficiency. This research contributes to the growing field of real-time modeling, highlighting its potential to address the complexities in chemical processes.

Gears are widely recognized as crucial components for power connections and transmission owing to their substantial loading capacity, transmission efficiency, and consistent power output. Given their complexity, they are prone to faults and require continuous condition monitoring (CM). The study proposes an innovative approach that combines signal analysis with data-driven techniques to monitor gear conditions accurately. By employing a shallow neural network (NN) model, which incorporates statistical time features and principal component analysis (PCA), this strategy achieves precise detection of seven distinct levels of gear wear. The feature generation technique utilizes a sliding and overlapping window to extract 15 statistical time features from the tri-axial vibration signals of the gearbox. The PCA has been applied to the feature set (FS) to retain relevant features, avoid overfitting during model training, and visualize the underlying structure of the training data. A comparative analysis with other leading classification methods, applied to both the original and PCA-reduced feature sets reveals that the shallow NN model, particularly when using the PCA-reduced FS, stands out for its exceptional performance. It achieves a remarkable test accuracy of 99.1% on experimental data having a linear time complexity, highlighting the model’s practicality for real-world applications.

In this paper, a series of novel activation functions is presented, which is derived using the improved Riemann–Liouville conformable fractional derivative (RLCFD). This study investigates the use of fractional activation functions in Multilayer Perceptron (MLP) models and their impact on the performance of classification tasks, verified using the IRIS, MNIST and FMNIST datasets. Fractional activation functions introduce a non-integer power exponent, allowing for improved capturing of complex patterns and representations. The experiment compares MLP models employing fractional activation functions, such as fractional sigmoid, hyperbolic tangent and rectified linear units, against traditional models using standard activation functions, their improved versions and existing fractional functions. The numerical studies have confirmed the theoretical observations mentioned in the paper. The findings highlight the potential usage of new functions as a valuable tool in deep learning in classification. The study suggests incorporating fractional activation functions in MLP architectures can lead to superior accuracy and robustness.

This research explores the application of the Riemann–Liouville fractional sigmoid, briefly RLFσ, activation function in modeling the chaotic dynamics of Chua’s circuit through Multilayer Perceptron (MLP) architecture. Grounded in the context of chaotic systems, the study aims to address the limitations of conventional activation functions in capturing complex relationships within datasets. Employing a structured approach, the methods involve training MLP models with various activation functions, including RLFσ, sigmoid, swish, and proportional Caputo derivative PCσ, and subjecting them to rigorous comparative analyses. The main findings reveal that the proposed RLFσ consistently outperforms traditional counterparts, exhibiting superior accuracy, reduced Mean Squared Error, and faster convergence. Notably, the study extends its investigation to scenarios with reduced dataset sizes and network parameter reductions, demonstrating the robustness and adaptability of RLFσ. The results, supported by convergence curves and CPU training times, underscore the efficiency and practical applicability of the proposed activation function. This research contributes a new perspective on enhancing neural network architectures for system modeling, showcasing the potential of RLFσ in real-world applications.

Coronavirus disease (COVID-19) was confirmed as a pandemic disease on February 11, 2020. The pandemic has already caused thousands of victims and infected several million people around the world. The aim of this work is to provide a Covid-19 infection screening tool. Currently, the most widely used clinical tool for detecting the presence of infection is the reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less sensitive and requires the resource of specialized medical personnel. The use of X-ray images represents one of the latest challenges for the rapid diagnosis of the Covid-19 infection. This work involves the use of advanced artificial intelligence techniques for diagnosis using algorithms for classification purposes. The goal is to provide an automatic infection detection method while maximizing detection accuracy. A public database was used which includes images of COVID-19 patients, patients with viral pneumonia, patients with pulmonary opacity, and healthy patients. The methodology used in this study is based on transfer learning of pre-trained networks to alleviate the complexity of calculation. In particular, three different types of convolutional neural networks, namely, InceptionV3, ResNet50 and Xception, and the Vision Transformer are implemented. Experimental results show that the Vision Transformer outperforms convolutional architectures with a test accuracy of 99.3% vs 85.58% for ResNet50 (best among CNNs). Moreover, it is able to correctly distinguish among four different classes of chest X-ray images, whereas similar works only stop at three categories at most. The high accuracy of this computer-assisted diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

This paper presents a neural network model to estimate arterial blood pressure (ABP) waveforms using electrocardiogram (ECG) and photoplethysmography (PPG) signals and its first two order mathematical derivatives (PPG′, PPG″). In order to achieve this objective, a lightweight and optimized neural network architecture has been proposed, made of Conv1D and BiLSTM layers. To train the network, the UCI Database “Cuff-Less Blood Pressure Estimation Data Set” has been used, which contains ECG and PPG signals together with the corresponding ABP waveform data; then the first two PPG derivatives have been computed. Four different configurations and parameter sets have been tested to choose the best structure and set of parameters. Additionally, various batch sizes, numbers of BiLSTM layers, and the presence of a maximum pooling layer have been tested. The best performing model achieves a mean absolute error of around 2.97, which is comparable to the state-of-the-art methods. Results prove deep learning techniques can be effectively used for non-invasive cuffless arterial blood pressure estimation. The lightweight and optimized model can be effectively used for continuous monitoring of blood pressure, which has significant clinical implications. Further research can focus on integrating the proposed model with wearable devices for real-time blood pressure monitoring in daily life.

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.

This paper presents an estimation of the parameters for a Double Layer Super Capacitor (DLC) that is modelled with a two-branch circuit. The estimation is achieved using a constrained minimization technique, which is developed off-line and uses a single constraint to write the matrix equation. The model is algebraically manipulated to obtain a matrix equation, and a signal processing system is developed to prepare the signals for the identification algorithms. The proposed method builds on the results obtained using an unconstrained ordinary least-squares (OLS) technique. The method is tested both in simulation and experimentally, using a specially-designed experimental rig. A current ramp input is used to generate the corresponding output voltage and its derivatives. The results obtained from the constrained off-line minimization algorithm are compared with those obtained using a traditional off-line estimation method. The discussion of the results shows that the proposed method outperforms the traditional estimation technique. In summary, this paper contributes to the field of DLC parameter estimation by introducing a new off-line constrained minimization technique. The results obtained from the simulations and experimental rig demonstrate the effectiveness of the proposed method with two of three parameters showing relative errors less than 5%.

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally.

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.