Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

Wind energy has the possibility for bringing out energy in a very constant and sustainable manner being a notable and eligible source. Although, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and therefore, the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which will also aid in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day to day wind speed info using machine learning and deep learning algorithms. A way is put forward by us to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term these algorithms may be used for wind generation rate with historical relation to wind speed info. Index Terms: Wind turbine,machine learning algorithm

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

In the exploration and production of fossil resources, the characterization of rock facies is critical for estimations of rock physical properties, such as porosity and permeability, and for reservoir detection and simulation. We propose a new machine learning (ML) algorithm for characterizing rock facies using a convolutional neural network (CNN) with feature engineering and data padding strategies. In the new ML algorithm, we extend rock feature data from 1-dimensional “profile” to 2-dimensional maps by padding the original dataset. The 2-dimensional padded rock facies map enables the CNN to capture the inherent geological features while keeping the local continuities. In this new ML algorithm, we only need a simple CNN design and structure to efficiently achieve accurate classification of rock facies. We test the feasibility of applying this new algorithm using a verifiable well logging dataset from the Panoma gas field in southwest Kansas. The results show that our new ML algorithm with a simple CNN structure has achieved higher accuracy in classifications of rock facies in comparison with the CNN results of the 2016 SEG ML contest. This new ML algorithm has application potential in automatic rock facies characterization with high accuracy and efficiency.

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

Deep learning and its application in geochemical mapping

Caring health from childhood is a most important challenge. To date, machine learning (ML) algorithms have been introduced to several fields of knowledge, while in education it is a novel perspective. This review aims to evaluate the effectiveness of ML applications on data registered by inertial measurement units collected from preschool to secondary education children's physical activity during school-hours. Furthermore, the review aims to explore how ML is used to process and interpret this data for outcomes like motor competence, physical activity intensity, sedentary behavior and academic/developmental indicators. Following PRISMA guidelines, we systematically searched PubMed, Web of Sciences, SCOPUS, SPORTDiscus and ProQuest Central databases. 13 studies met the inclusion criteria, covering preschool to secondary education settings across multiple countries. The methodological quality ranged from moderate to high (11-17/18 MINORS points). ML algorithms, mainly Random Forest, Support Vector Machines, Gradient Boosting and Convolutional Neural Networks, were successfully applied to classify or predict various outcomes such as motor competence, physical activity intensity, sedentary behavior and developmental or academic indicators. Reported accuracies ranged from approximately 70% to 99%, demonstrating the strong potential of wearable sensor data combined with ML to objectively monitor and assess school-related physical activity.

Maize kernel traits such as kernel length, kernel width, and kernel number determine the total kernel weight and, consequently, maize yield. Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield. There are a few methods that allow the extraction of ear and kernel features through image processing. We evaluated the potential of deep convolutional neural networks and binary machine learning (ML) algorithms (logistic regression (LR), support vector machine (SVM), AdaBoost (ADB), Classification tree (CART), and the K-Neighbor (kNN)) for accurate maize kernel abortion detection and classification. The algorithms were trained using 75% of 66 total images, and the remaining 25% was used for testing their performance. Confusion matrix, classification accuracy, and precision were the major metrics in evaluating the performance of the algorithms. The SVM and LR algorithms were highly accurate and precise (100%) under all the abortion statuses, while the remaining algorithms had a performance greater than 95%. Deep convolutional neural networks were further evaluated using different activation and optimization techniques. The best performance (100% accuracy) was reached using the rectifier linear unit (ReLu) activation procedure and the Adam optimization technique. Maize ear with abortion were accurately detected by all tested algorithms with minimum training and testing time compared to ear without abortion. The findings suggest that deep convolutional neural networks can be used to detect the maize ear abortion status supplemented with the binary machine learning algorithms in maize breading programs. By using a convolution neural network (CNN) method, more data (big data) can be collected and processed for hundreds of maize ears, accelerating the phenotyping process.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals. This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting CVD. The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

PurposeThere is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment of various medical conditions in ophthalmology and beyond. Applications of ML for ocular neoplasms are in early development and this review synthesizes the current state of ML in ocular oncology.MethodsWe queried PubMed and Web of Science and evaluated 804 publications, excluding nonhuman studies. Metrics on ML algorithm performance were collected and the Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. We report the results of 63 unique studies.ResultsResearch regarding ML applications to intraocular cancers has leveraged multiple algorithms and data sources. Convolutional neural networks (CNNs) were one of the most commonly used ML algorithms and most work has focused on uveal melanoma and retinoblastoma. The majority of ML models discussed here were developed for diagnosis and prognosis. Algorithms for diagnosis primarily leveraged imaging (e.g., optical coherence tomography) as inputs, whereas those for prognosis leveraged combinations of gene expression, tumor characteristics, and patient demographics.ConclusionsML has the potential to improve the management of intraocular cancers. Published ML models perform well, but were occasionally limited by small sample sizes owing to the low prevalence of intraocular cancers. This could be overcome with synthetic data enhancement and low-shot ML techniques. CNNs can be integrated into existing diagnostic workflows, while non-neural networks perform well in determining prognosis.

Brain tumors are formed due to the abnormal growth of the cells that get multiplied and become in uncontrollable perspective. Tumors can damage brain cells by pressing down the skull, which consociate begins in, which negatively affects human health. In advanced stages, a brain tumor is a more dangerous infection that cannot be relieved. Brain tumor detection and early prevention are necessary in today's world. An extreme learning machine (ELM) is a widely adopted algorithm in machine learning. It is proposed to use classification models in brain tumor imaging. This classification is based on the techniques implemented: Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). CNN efficiently solves the convex optimization problem and is faster, requiring less human effort. The algorithmic architecture of GAN uses two neural networks, pocking one against the other. These networks are implemented in various fields to classify brain Tumor images. The present study mainly aims to introduce a new proposed classification system of preschool children brain imaging with Hybrid Convolutional Neural Networks and with the techniques of GAN. The proposed technique is compared with the existing hybrid-CNN and hybrid-GAN techniques. The outcomes are encouraging because the loss is deduced, and the accuracy facet increases. The proposed system achieved a training accuracy of 97.8% and a validation accuracy of 89%. The outcomes of the studies show that ELM in the platform of GAN for preschool children brain imaging classification has achieved higher predictive performance than the traditional classification mechanisms in increasingly complex scenarios. Time elapsed for training brain images samples finds inference value for training samples and time elapsed value increased by 28.9855%. Approximation ratio for cost based on probability, finding Approximation ratio for low probability range is increased by 88.1%. The combination of CNN, GAN and hybrid-CNN, hybrid-GAN, and hybrid CNN + GAN, compared with the proposed hybrid system, increased Detection latency for low range learning rate by 3.31%.

PurposeTo develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI (CMR).Materials and MethodsThis retrospective study included cine, late gadolinium enhancement (LGE), and T1 mapping examinations from two hospitals. The training set included 2329 patients (34 089 images; mean age, 54.1 years; 1471 men; December 2017 to March 2020). A hold-out test set included 531 patients (7723 images; mean age, 51.5 years; 323 men; May 2020 to July 2020). CNN models were developed to detect two mitral valve plane and apical points on long-axis images. On short-axis images, anterior and posterior right ventricular (RV) insertion points and left ventricular (LV) center points were detected. Model outputs were compared with manual labels assigned by two readers. The trained model was deployed to MRI scanners.ResultsFor the long-axis images, successful detection of cardiac landmarks ranged from 99.7% to 100% for cine images and from 99.2% to 99.5% for LGE images. For the short-axis images, detection rates were 96.6% for cine, 97.6% for LGE, and 98.7% for T1 mapping. The Euclidean distances between model-assigned and manually assigned labels ranged from 2 to 3.5 mm for different landmarks, indicating close agreement between model-derived landmarks and manually assigned labels. For all views and imaging sequences, no differences between the models’ assessment of images and the readers’ assessment of images were found for the anterior RV insertion angle or LV length. Model inference for a typical cardiac cine series took 610 msec with the graphics processing unit and 5.6 seconds with central processing unit.ConclusionA CNN was developed for landmark detection on both long- and short-axis CMR images acquired with cine, LGE, and T1 mapping sequences, and the accuracy of the CNN was comparable with the interreader variation.Keywords: Cardiac, Heart, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Feature Detection, Quantification, Supervised Learning, MR Imaging Supplemental material is available for this article. Published under a CC BY 4.0 license.

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84–0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85–0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81–0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81–0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83–0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.