Intelligent Data Analysis Algorithms Based on Neural Network Models of Machine Learning

ABSTRACTIntroduction:This study aimed to evaluate the usefulness of machine learning (ML) and neural network (NN) models versus traditional statistical methods for estimating biochemical recurrence (BCR) in men following robot-assisted radical prostatectomy (RARP).Methods:Patients who underwent RARP from November 2011 to July 2022 were taken in the study. Patients with BCR were assigned to Group 2, whereas those without BCR were placed in Group 1. Preoperative and postoperative parameters, together with demographic data, were recorded in the database. This study used one NN, the radial basis function NN (RBFNN), and two ML approaches, the K-nearest neighbor and XGboost ML models, to predict BCR.Results:Following the application of exclusion criteria, 516 patients were deemed eligible for the study. Of those, 234 (45.3%) developed BCR, and 282 (54.7%) did not. The results showed that the median follow-up period was 24 (15–42) months, and the median BCR diagnosis was 12.23 ± 15.58 months. The area under the curve (AUC) for the Cox proportional hazard analysis was 0.77. The receiver-operating characteristic curves (AUCs) for the XGBoost and K closest neighbor models were 0.82 and 0.69, respectively. The RBFNN’s AUC was 0.82.Conclusions:The classical statistical model was outperformed by XGBoost and RBFNN models in predicting BCR.

High-performance thermal interface materials (TIMs) have attracted persistent attention for the design and development of miniaturized nanoelectronic devices; however, a large number of potential new materials exist to form these heterostructures and the explorations of their thermal properties are time consuming and expensive. In this work, we train several supervised machine learning (ML) and artificial neural network (ANN) models to predict the interfacial thermal resistance (R) between graphene and hexagonal boron-nitride (hBN) with only the knowledge of the system temperature, coupling strength between two layers, and in-plane tensile strains. The training data were obtained by high-throughput computations (HTCs) of R using classical molecular dynamics (MD) simulations. Four different ML models, i.e., linear regression, polynomial regression, decision tree and random forest, are explored. A pair of one dense layer ANNs and another pair of two dense layer deep neural networks (DNNs) are also investigated. It is reported that the DNN models provide better R prediction results compared to the ML models. The thermal property predictions using HTC and ML/ANN models are applicable to a wide range of materials and open up new perspectives in the explorations of TIMs.

Row detection in agricultural applications has commonly used Hough transform techniques and traditional signal processing based approaches relating to machine vision. There are various learning based methods available that are capable of producing similar results in terms of detection. In this paper, a neural network based algorithm is developed, and we compare the Hough transform and a machine learning implementation with the proposed approach to determine which would be the most appropriate in a real-time application given a variety of factors including computational performance, accuracy, and environmental variability. Compared to the learning based approaches which rely on training data, Hough transform based detection relies on a variety of processes, including binarization and denoising, which are not required to be explicitly implemented in the machine learning or neural network models. Additionally, to add another layer of diversity to the three possible solutions examined is the consideration for color input data. The Hough transform method and the neural network model implemented both require color input data while the machine learning model relies on texture features instead of color to make its classification predictions. Compared to the traditional image understanding techniques, autonomous vehicles face challenges due to similarities in color and texture between the crops and their surroundings. Therefore, the algorithm is developed to overcome such challenges. Preliminary results show that the neural network model developed was found to offer the most versatility compared to traditional methods and the highest accuracy on the order of 97% for this application across several different input conditions.

The integration of offshore wind power into modern electricity grids and markets is critically dependent on the accuracy of short-term power forecasts. High-quality forecasts support grid stability, facilitate market participation, and reduce balancing costs. However, generating reliable offshore wind power forecasts remains a significant challenge due to the inherently volatile nature of offshore wind regimes, which are characterized by rapid power ramps and complex meteorological patterns. Additionally, power curtailments caused by grid congestion further complicate the forecasting task by introducing inconsistencies between actual generation potential and observed outputs. This study presents the development and evaluation of advanced offshore wind power forecasting models using machine learning techniques, like gradient boosting methods (Linear Regression, XGBoost) and neural network-based approaches (Multilayer Perceptrons (MLPs)). A comprehensive analysis of input features, including numerical weather prediction (NWP) data, production values of past time steps and extrapolated curtailment signals from real wind speed measurements, is conducted to understand their impact on model performance. The paper addresses methods to pre-process data affected by curtailment and explores techniques to improve the model's ability to distinguish between physical variability (i.e. wind speed) and grid-induced limitations, such as grid congestions. Through a series of experiments using real-world offshore wind farm data, the study quantifies the forecasting skill of the proposed machine learning models under various scenarios, including high-ramp conditions and periods of curtailment. Results demonstrate that, with appropriate feature engineering and data treatment, models like XGBoost and MLP can achieve high forecast accuracy, offering a promising approach for operational forecasting in offshore environments. These findings highlight the potential of machine learning, particularly ensemble and neural network models, to enhance the precision and reliability of offshore wind power forecasts—ultimately supporting the secure and efficient integration of offshore wind energy into the power system. This work was carried out within the Femtec Innovation Lab, a program targeted towards female STEM students to connect them to partners from industry and science. Through interdisciplinary project work this fosters collaborative working with the main focus of increasing the participation and representation of women in STEM fields.

Machine Learning in Engineering Analysis and Design: An Integrated Fuzzy Neural Network Learning Model

Nailfold capillaroscopy (NFC) is a well-established, highly specialized, non-invasive method for assessing microcirculation and represents the “gold standard” in rheumatology for distinguishing primary from secondary Raynaud’s phenomenon (RP). Through analysis of capillary structures in the nailfold region, clinicians can identify diff erent patterns (scleroderma, scleroderma-like, and non-specifi c). Despite its clinical signifi cance, image interpretation remains challenging due to subjectivity and the requirement for substantial examiner expertise. In recent years, artifi cial intelligence (AI) has off ered novel solutions through the automated recognition and analysis of capillary structures. Machine learning and deep learning approaches have demonstrated high eff ectiveness in detecting abnormalities, classifying capillary patterns, and predicting the risk of systemic sclerosis. Projects such as CAPI-Detect, along with various other machine learning, deep learning, and neural network models (DenseNet-121, Effi cientNet-B0, ResNet-34, NFC-Net), have shown that AI can identify novel quantitative parameters not accessible through traditional visual assessment and can increase the objectivity and reproducibility of results up to 90%. Most studies in this fi eld focus on capillaroscopic patterns in systemic sclerosis (SSc); however, pilot studies in juvenile dermatomyositis, diabetes mellitus, and hypertension further highlight the applicability of these technologies. Some AI models are even capable of distinguishing onychomycosis, nail psoriasis, and subungual melanoma. Machine learning and deep learning models based on image analysis (Vision Transformer, ViT) appear to represent an additional valid system for the early and rapid interpretation of NFC images and morphological biomarkers in systemic sclerosis (SSc), integrating EULAR-validated algorithms for distinguishing scleroderma from non-scleroderma patterns with artifi cial intelligence (AI). Beyond its diagnostic applications, AI also holds potential in disease monitoring, assessment of therapeutic response, and the development of personalized treatment strategies. The processing of digital images through validated machine learning algorithms, neural networks, and related approaches is aligned with the priorities of the National Health Strategy 2030 for digitalization and the development of eHealth (Policy 2.5). The establishment of a National Digital Platform for Medical Diagnostics is envisaged, aimed at supporting all medical specialties. This platform will be integrated with the National Health Information System and the electronic patient record, with the primary objective of improving the quality of healthcare services. The integration of AI into medical practice opens new opportunities to support early diagnosis and improved management of patients with microangiopathy, facilitating timely detection and the prevention of complications, with the ultimate aim of ensuring better quality of life and preserved functional capacity.

Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement properties, and as a bridge connecting classical computation and quantum computation. Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks. In this Letter, we present autoregressive matrix product states (AMPS), a tensor network model combining matrix product states from quantum many-body physics and autoregressive modeling from machine learning. Our model enjoys the exact calculation of normalized probability and unbiased sampling. We demonstrate the performance of our model using two applications, generative modeling on synthetic and real-world data, and reinforcement learning in statistical physics. Using extensive numerical experiments, we show that the proposed model significantly outperforms the existing tensor network models and the restricted Boltzmann machines, and is competitive with state-of-the-art neural network models.

Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge. For predicting diabetes, this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory (BiLSTM) network with a bidirectional gated recurrent unit (BiGRU). A robust DL model, developed by combining various datasets with weighted coefficients, dense operations in the connection of deep layers, and the output aggregation using batch normalization and dropout functions to avoid overfitting. The goal of this hybrid model is better generalization and consistency among various datasets, which facilitates the effective management and early intervention. The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks. The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 -0.310 during training and 0.034 -0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.

With the fast growth of Internet of Things (IoT), there is an increasing security impact, e.g., IoT environments has become the advanced target of complex cyber-attack like Distributed Denial of Service (DDoS) attack. Most conventional Intrusion Detection Systems (IDSs) fail to scale with the heterogeneity, scale, and dynamism of IoT networks. To overcome these challenges, in this paper, we develop a smart hybrid intrusion detection framework that can effectively utilize the power of Machine Learning (ML) and Neural Network (NN) models, namely: cascades backpropagation neural network (CPBNN) and convolutional neural network (CNN) to improving accuracy and adaptability of detection in IoT environments. The proposed system employs a two-layer detection structure where the CPBNN model is used to detect abnormal patterns in the network packet level and identify the abnormal behaviors of packet sequences, whereas the CNN model performs deep feature extraction and classification to predict the abnormality as well as the nature of the abnormality. We develop this hybrid architecture to work well under IoT level large-scale deployments without incurring software overhead. The system has been evaluated on the KDDTest-21 benchmark dataset with the usual metrics, including accuracy, precision, recall and F1-score. Experiments results prove the CNN’s performance with 90% and CPBNN with 82% which valid the efficiency of the proposed method in detecting IoT related security threats. These results suggest the ability of intelligent hybrid IDS systems not only to defend in a pro-active manner using ML and neural networks in synergy, but also to use the adversary's strength against itself.

Recently, a lot of emphasis has been placed on Artificial Intelligence (AI) and Machine Learning (ML) algorithms in medicine and the healthcare industry. Cardiovascular disease (CVD), is one of the most common causes of death globally, and Electrocardiogram (ECG) is the most widely used diagnostic tool to investigate this disease. However, the analysis of ECG signals is a very difficult process. Therefore, in this work, automated classification of ECG data into five different arrhythmia classes is proposed, based on MIT-BIH dataset. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Deep Learning (DL) models were used. The black-box nature of these complex models imposes the need to explain their outcomes. Hence, both Permutation Feature Importance (PFI) with Gradient-Weighted Class Activation Maps (Grad-CAM) interpretability techniques were investigated. Using the K-Fold cross-validation method, the models achieved an accuracy of 97.1% and 98.5% for CNN and LSTM, respectively.

This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neuroma. A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included. Clinical data and raw features from four MRI sequences (T1-weighted, T2-weighted, T1-weighted contrast enhancement, and T2-weighted-Flair images) were analyzed. Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features. Nomogram, machine learning, and convolutional neural network (CNN) models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate model performance. A total of 1050 radiomic parameters were extracted, from which 13 radiomic and 3 clinical features were selected. The CNN model performed best among all prediction models in the test set with an area under the curve (AUC) of 0.89 (95% CI, 0.84-0.91). CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma. As such, CNN modeling may serve as a potential decision-making tool for neurosurgery.

Plants are an integral part of human life, and the ability to identify a fruit plant from its leaf image is both fascinating and challenging. Advances in image processing and pattern recognition have made it feasible to perform plant identification using digital images. Machine learning (ML) and convolutional neural network (CNN) models have demonstrated strong capabilities in handling texture-related features in image processing tasks, including segmentation. In this Paper, we present an approach that utilises ML and CNN models, including AlexNet, Inception, ResNet, LeNet, VGG Net, MobileNet, DenseNet, and GoogLeNet. These models are employed for classifying fruit plants through leaf images, achieving promising performance on leaf image datasets. Among the evaluated CNN models, MobileNet achieved the highest performance with 94.81% training, 99.57% validation, and 99.44% test accuracy, outperforming all others. LeNet, AlexNet, and ResNet also showed strong results above 93%, while DenseNet, GoogLeNet, and VGGNet achieved moderate accuracy. Inception performed the weakest, confirming MobileNet as the most efficient and reliable model for fruit plant leaf classification.

The classification process is also used in artificial intelligence (AI), which is intelligence created by a computer, so that it can mimic actions like humans in general and can capture events that occur in the surrounding environment. Seeing the very high development of the international coffee trade, it can be concluded that if there is a type of coffee that has the best quality, it will be sought after by coffee importing countries. Batik is cloth that is painted using canting and liquid wax to form motifs or patterns that have high artistic value. Batik has a variety of unique and distinctive patterns that reflect the region where the batik motif originates. One area that has batik motifs in Indonesia is batik in the North Coastal Region of Java Island. In this area, various batiks are produced according to the characteristics of the area itself. Regarding information related to the introduction of types of batik motifs, perhaps it comes from people who have batik skills and batik craftsmen who best understand batik as a whole, while the general public does not really know about batik motifs. Because batik has different motifs and batik motifs in some areas have motifs that are almost uniform but not the same. One of the technologies that can be used is deep learning. The purpose of this study is to propose a Convolutional Neural Network (CNN) and Transfer Learning (TL) model to be implemented in an intelligent system for the process of image classification of coffee bean types. The method used in this study is the PSOCNN transfer learning model VGG16 From the results of tests carried out on 2 models, namely the CNN model, the PSOCNN transfer learning model VGG16. it was found that the highest accuracy was obtained when classifying batik motives images using the PSOCNN-transfer learning model VGG16, which was 83%. The level of accuracy that is increased when compared to the usual CNN model indicates that the use of transfer learning has a good effect on the level of accuracy obtained. Although an increase of 6% is significant, but with this increase it opens up opportunities to increase even higher by using other transfer learning models

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.