Neurocognition-inspired design with machine learning

AI-generated content (AIGC) in the context of dermoscopic image analysis describes the application of artificial intelligence (AI) approaches to produce synthetic images for training and enriching machine learning (ML) methods. In dermatology, where dermoscopic images are essential in skin lesion detection, AIGC helps overcome the challenges of inadequate datasets and class imbalances. By employing advanced generative complex systems like Generative Adversarial Networks (GANs), AIGC will simulate different dermoscopic conditions by generating realistic-view synthetic images. These AI-generated images help to enhance the training dataset, contributing to the ML models with many widespread and varied sets of instances. This aids in increasing the ability of the model to precisely predict and generalize while handling new, unnoticed dermoscopic images. Deep learning (DL) methods, specifically convolutional neural networks (CNNs), can provide a massive breakthrough in an extensive range of computer vision (CV) tasks of complex systems, predominantly by exploiting large-scale annotated datasets. This paper develops a GAN-based Synthetic Medical Image Augmentation for the imbalanced Dermoscopic Image Analysis (GANSMIA-CDIA) method. The GANSMIA-CDIA technique’s primary target is exploiting the GAN model for synthetic image generation to handle class imbalance data problems. In addition, the GANSMIA-CDIA technique diagnoses the melanoma using the optimal DL model. The GANSMIA-CDIA technique applies a contrast enhancement process for the noise eradication process. The GANSMIA-CDIA technique follows a feature fusion method comprising different DL methods such as MobileNetV2, AlexNet, ResNet50, and Inceptionv3 to learn feature patterns in the preprocessed images. Meanwhile, the Social Network Search (SNS) technique is utilized for the hyperparameter tuning process. Also, the bidirectional long short-term memory (BiLSTM) technique is implemented to detect and classify melanoma. A series of simulations were performed on the standard dermoscopic image dataset to evaluate the performance of the GANSMIA-CDIA technique. The experimental values indicate the excellence of the GANSMIA-CDIA technique over existing techniques.

Cardiovascular diseases (CVDs) remain a leading cause of global mortality, underscoring the urgent need for early and accurate diagnosis. Electrocardiograms (ECGs) are a widely used, noninvasive tool for detecting cardiac abnormalities, but manual interpretation can be time- consuming and prone to error. This study explores advanced machine learning (ML) and deep learning (DL) methods to improve the detection of CVDs from ECG images, leveraging their ability to analyze complex patterns with high precision. Models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and ensemble frameworks are utilized, alongside traditional ML models like XGBoost, CatBoost, and LightGBM. The performance of these models is evaluated based on precision, recall, F1-score, and accuracy. Results show that DL models, particularly an ensemble of CNN and ViT, achieve superior accuracy , outperforming standalone ML methods. Furthermore, an ensemble of XGBoost, CatBoost, and LightGBM achieves competitive results, while Optuna-tuned XGBoost further enhances ML performance. The findings highlight the potential of combining ML and DL techniques for robust and reliable CVD detection in ECG images. This research demonstrates the promise of AI-driven solutions in advancing medical diagnostics and supports the integration of automated systems for early detection of cardiovascular diseases.

Domain name generation algorithm (DGA) classification is an essential but challenging problem. Both feature-extracting machine learning (ML) methods and deep learning (DL) models such as convolutional neural networks and long short-term memory have been developed. However, the performance of these approaches varies with different types of DGAs. Most features in the ML methods can characterize random-looking DGAs better than word-looking DGAs. To improve the classification performance on word-looking DGAs, subword tokenization is employed for the DL models. Our experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking DGAs. We then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the DGAs. Results show that the integrated scheme outperformed existing ML and DL methods, and also the subword DL methods.

The root cause of the seizure is a sudden abnormal excessive electrical discharge in the brain and it is an Epileptic Seizure (ES) when such abnormal electrical activity arises particularly for epilepsy. Recognizing ES is crucial for effective treatment as it often repeats and can lead to serious outcomes. Since epilepsy is a neurological issue, detecting ES by analyzing brain signals is the preferred method, with Electroencephalogram (EEG) being the most reliable approach for this purpose. Different Machine Learning (ML) and Deep Learning (DL) methods are extensively used in ES detection from EEG signals. Existing methods first extract features from EEG signals using different methods and then classify ES using appropriate ML/DL methods. This study investigates ML-based ES detection where feature extraction from decomposed EEG signals using various methods and integrating the extracted features to classify ES are the main attractions. Empirical Mode Decomposition (EMD) is employed to systematically break down EEG signals into Intrinsic Mode Functions (IMFs), with the earlier IMFs containing more information than the later ones. From the initial six IMFs, three distinct features named Fluctuation index (F), Variance (V) and Ellipse Area (EA) of the second-order difference plot are extracted. Neural Network (NN), the well-known ML method, is employed in this study for ES classification from extracted F, V and EA features individually and integrating these (i.e., F + V + EA). The experimental analysis is conducted on the benchmark CHB-MIT dataset and the integrated feature set shows promising performance over individual feature sets. The proposed NN-based ES detection with integrated decomposed features outperforms prominent existing methods, showing an accuracy of 99.80%

Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive comparison of machine learning and deep learning methods is conducted for the classification of robot execution failures using data acquired from force-torque sensors. Three different feature engineering approaches are proposed. The first is a Baseline approach that includes 90 raw time-series features. The second is the Domain-6 approach, which consists of 6 basic statistical features per sensor (36 in total). The third is the Domain-12 approach, which comprises 12 comprehensive statistical features per sensor (72 in total). The domain features include the mean, standard deviation, minimum, maximum, range, slope, median, skewness, kurtosis, RMS, energy, and IQR. In total, ten classification algorithms are evaluated, including eight machine learning methods and two deep learning models: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Trees (DT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM-LGBM), as well as a One-Dimensional Convolutional Neural Network (CNN-1D) and Long Short-Term Memory (LSTM). For traditional machine learning algorithms, 5 × 5 nested cross-validation is used, whereas for deep learning models, 5-fold cross-validation with a 20% validation split is employed. To ensure statistical reliability, all experiments are repeated over 30 independent runs. The experimental results demonstrate that feature engineering has a decisive impact on classification performance. In addition, regardless of the feature set, the highest accuracy (93.85% ± 0.90) is achieved by the Naive Bayes classifier using the Baseline features. The Domain-12 feature set provides consistent improvements across many algorithms, with substantial performance gains. The results are reported using accuracy, precision, recall, and F1-score metrics and are supported by confusion matrices. Finally, permutation feature importance analysis indicates that the skewness features of the Fx and Fy sensors are the most critical variables for failure detection. Overall, these findings show that time-domain statistical features offer an effective approach for robot failure classification.

Because EEG-based identity requires a large amount of training data when training a classification model, and the collection of EEG signals requires a lot of time and effort. Therefore, we hope to perform data augmentation on the EEG data used for identity. Generative adversarial networks have achieved great success in image generation, but the raw EEG signals are not in the form of images. Therefore, we process the EEG signal into an EEG topomap with stronger spatial feature representation, and use a spatial feature-based generative adversarial network image augmentation method (SF-GAN). To verify the generality of our proposed method, we use real EEG topomap samples processed from two different EEG datasets, BCI Competition IV 1 and BCI Competition IV 2a, to train SF-GAN to generate augmented samples for training identity classification model. The proposed method can use smaller real samples to expand the training set of identity, reduce the data dependence on real samples, and reduce the time of data collection to a certain extent. And it is proved by experiments that the data generated by this method can further improve the training effect of the classification model.

ObjectivePituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs.MethodsThe clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods.ResultsExperimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%.ConclusionsIn this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.

<p class="0abstract">The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.<em> </em><strong></strong></p>

Understanding the relationship between rosemary odor and mental workload through deep learning.

Imagined speech is a process in which a person imagines words without saying them. Electroencephalogram (EEG)-based brain–computer interface (BCI) systems help in automatically identifying imagined speech to facilitate persons with severe brain disorders. Extracting meaningful information from the raw EEG signal is a challenging task due to the nonstationary nature of EEG signals. Decomposing a signal into several sub-bands (SBs) using rational dilation wavelet transform (RADWT) requires selecting predefined factual parameters, which is an arduous task. The main objective of this study is to propose an adaptive RADWT method capable of decomposing EEG signals by adaptively selecting the tuning parameters and classifying the EEG signals into distinct categories. The optimum tuning parameters of RADWT are obtained using particle swarm optimization (PSO) and used to decompose the EEG signals into several SBs. Several statistical features are elicited from each SB and used to input six different machine learning (ML) algorithms. This work uses a 64-channel EEG dataset recorded from 15 healthy people for three categories: long words, short words, and vowels. The performance of the proposed AISR system is evaluated using seven performance evaluation metrics: accuracy, recall, precision, Cohen’s kappa, F1-score, and area under the curve. The proposed system achieved the average classification accuracies of 87.26% ± 1.12%, 89.23% ± 0.95%, 95.5% ± 0.68%, and 92.16% ± 0.83% for long words, short–long words, short words, and vowels, respectively. When compared with the existing state-of-the-art, the proposed non-parametric decomposition approach and the Bagging algorithm achieved a 3%–5% improvement. The performance of the proposed method is validated using an open-access dataset.

Photometric redshifts (photo- z ’s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Vera C. Rubin Observatory, and the Nancy Grace Roman Space Telescope. Previous work has shown that image-based deep learning photo- z methods produce smaller scatter than photometry-based classical machine learning (ML) methods on the Sloan Digital Sky Survey (SDSS. Main Galaxy Sample, a test bed photo- z dataset. However, global assessments can obscure local trends. To explore this possibility, we used a self-organizing map (SOM) to cluster SDSS galaxies based on their ugriz colors. Deep learning methods achieve lower photo- z scatter than classical ML methods for all SOM cells. The fractional reduction in scatter is roughly constant across most of color space with the exception of the most bulge-dominated and reddest cells where it is smaller in magnitude. Interestingly, classical ML photo- z ’s suffer from a significant color-dependent attenuation bias, where photo- z ’s for galaxies within an SOM cell are systematically biased towards the cell’s mean spectroscopic redshift and away from extreme values, which is not readily apparent when all objects are considered. In contrast, deep learning photo- z ’s suffer from very little color-dependent attenuation bias. The increased attenuation bias for classical ML photo- z methods is the primary reason why they exhibit larger scatter than deep learning methods. This difference can be explained by the deep learning methods weighting redshift information from the individual pixels of a galaxy image more optimally than integrated photometry.