Neural Network Learning Algorithm Research Articles

Predicting money laundering sanctions using machine learning algorithms and artificial neural networks

ABSTRACT This article used machine learning (ML) and artificial neural network (ANN) algorithms to predict the likelihood of a country being sanctioned by the Basel Institute on Governance for not adhering to anti-money laundering (AML) standards. Data for this paper came from the Basel AML Index and the World Bank. The results showed that the logistic regression and support vector machine (SVM) classifiers had the highest performance and balanced accuracy scores in sanction prediction. Additionally, these two algorithms also had the highest precision, specificity, and F1 scores, indicating that they were robust in their predictions of money laundering sanctions. In contrast to the ML classifiers, the ANN model had the highest sensitivity and receiver operating characteristic scores for money laundering sanctions. The strongest predictors of sanctions are financial transparency, political and legal risks, unemployment rate, and money laundering and terrorist financing risks. These findings reinforce the potential practical applications of ML and ANN models in predicting sanctions.

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

AbstractConcrete which is the most commercialized construction material and thus it plays a key role in this era of development and hence its evolution is of utmost importance and therefore the evolution of the quality of concrete to that of its highly evolved type namely, ultra‐high performance concrete (UHPC) is undeniably the boon to this sector. Though, the correlations between the technical characteristics of UHPC and the composition of its mixture are complicated, nonlinear, and complex to characterize using standard statistical techniques. This paper is intended to use both deep neural network and ensemble machine learning algorithms namely gradient boosting, extreme gradient boosting, random forest regressor, extra tree regressor, and voting regressor trained with an 810 UHPC mixture collections with 15 input variables to predict its compressive strength. After adjusting a regression model, the prediction efficiency and generalization ability of the developed models are validated using a number of performance parameters. It was established that all employed models performed better at forecasting result, the extra tree regressor was the most accurate followed by extreme gradient boosting.

Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm

Training of dendritic neuron model artificial neural networks is generally achieved by using nonlinear least square methods. The distribution of random error terms is ignored in training algorithms although error terms are random variables. Maximum likelihood estimators can be obtained for dendritic neuron model artificial neural networks by using some indefinite symmetric probability distributions. In this study, statistical learning algorithms are proposed for dendritic neuron model artificial neural networks. Maximum likelihood estimators for dendritic neuron model artificial neural networks are obtained by using Normal, Cauchy, Logistic, Gumbel and Laplace distributions. The Sine cosine algorithm is used for maximization of the likelihood function under error terms that have Normal, Cauchy, Logistic, Gumbel and Laplace distributions. The proposed learning algorithms are applied to Istanbul Stock Exchange time series data sets. At the end of the analysis of application results, the performance of the proposed method is statistically better than well-known deep and shallow artificial neural networks.

Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

— The difficulties to drive away the durian farm threatens animals such as wild boars, monkeys, foxes, and squirrels during nighttime often experienced by durian farmers. Therefore, the Pro Durian application is proposed that allows farmers to identify durian threats through a camera phone with an alert feature activation when the system detects an animal to drive away those animals. The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. The classification accuracies reached 80% in detecting the animal’s images. Keywords— Durian Farm, Recognition Image, TensorFlow lite, Android Studio, Convolution Neural Network

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

Dynamic evolution of changbaishan volcanism in Northeast China illuminated by machine learning

Previous studies propose that there is a mantle upwelling that generated the Cenozoic basalts in Changbaishan. However, the dominant source and mechanism of the mantle upwelling remains highly debated. Here we apply machine learning algorithms of Random Forest and Deep Neural Network to train models using global island arc and ocean island basalts data. The trained models predict that Changbaishan basalts are highly influenced by slab-derived fluid. More importantly, the fluid effect decreases with no (87Sr/86Sr)0 and εNd(t) changes between 5 Ma and 1 Ma, then enhances with increasing εNd(t) and decreasing (87Sr/86Sr)0 after 1 Ma. We propose that a gap opened at about 5 Ma and the hot sub-slab oceanic asthenosphere rose through the gap after 1 Ma, generating the basalts enriched in fluid mobile elements and with the addition of depleted mantle component derived from the sub-slab oceanic asthenosphere.

Transport Stream Optimization Based on Neural Network Learning Algorithms

The article discusses a description of the practical application of traffic flow management automation technologies and the use of artificial intelligence to solve traffic optimization problems, traffic monitoring, describes its goals, tasks, indicates the necessary functionality of the traffic monitoring automation system. The defining performance indicators of the use of automated traffic control systems are described. Various methods of calculations used in the control, regulation, analysis and management of traffic flows are demonstrated. The gradations of service levels and levels of convenience in estimating throughput are considered. Analyzed various criteria for the regulation and control of road and transport situations in general.

Evolutionary Augmentation Policy Optimization for Self-Supervised Learning

Self-supervised Learning (SSL) is a machine learning algorithm for pretraining Deep Neural Networks (DNNs) without requiring manually labeled data. The central idea of this learning technique is based on an auxiliary stage aka pretext task in which labeled data are created automatically through data augmentation and exploited for pretraining the DNN. However, the effect of each pretext task is not well studied or compared in the literature. In this paper, we study the contribution of augmentation operators on the performance of self supervised learning algorithms in a constrained settings. We propose an evolutionary search method for optimization of data augmentation pipeline in pretext tasks and measure the impact of augmentation operators in several SOTA SSL algorithms. By encoding different combination of augmentation operators in chromosomes we seek the optimal augmentation policies through an evolutionary optimization mechanism. We further introduce methods for analyzing and explaining the performance of optimized SSL algorithms. Our results indicate that our proposed method can find solutions that outperform the accuracy of classification of SSL algorithms which confirms the influence of augmentation policy choice on the overall performance of SSL algorithms. We also compare optimal SSL solutions found by our evolutionary search mechanism and show the effect of batch size in the pretext task on two visual datasets.

Identification and detection of brain tumour using deep learning-based classification MRI applied using neural network and machine learning algorithm

Identification and detection of brain tumour using deep learning-based classification MRI applied using neural network and machine learning algorithm

Identification and Detection of Brain Tumour Using Deep Learning-based Classification MRI Applied Using Neural Network and Machine Learning Algorithm

Identification and Detection of Brain Tumour Using Deep Learning-based Classification MRI Applied Using Neural Network and Machine Learning Algorithm

An Analysis of Prediction of Students’ Results Using Deep Learning

Many efforts have already been carried out for education mining in the past. Many techniques and models are already developed to predict and identify students’ performance, learning behavior, status, and education level. However, there is no exact solution for the student’s result prediction since it is affected by the level of the student, the field of study, the location of the data collection, different sizes and nature of data, etc. Different research shows that there can be up to 10% difference in the accuracy of results with and without a feature selection process. Thus, the proposed model designs a better model for student result prediction using feature selection and deep learning techniques. The proposed dissertation task compares and analyzes Correlation-based Feature Selection (CFS), Chi-Square ([Formula: see text]), Genetic Algorithm (GA), Information Gain (IG), Maximum Relevance Minimum Redundancy (mRMR), ReliefF, and Recursive Feature Elimination (RFE) feature selection techniques with the Classification and Regression Tree (CART), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) machine learning algorithms. In the proposed model, a feature selection process CFS and prediction using CNN is recommended. The recommended model (CFS–CNN) is tested with a primary dataset collected from bachelor-level students. The recommended model provides improved performance compared to old techniques. The major contribution of the proposed dissertation is to design a better model for the prediction of students’ results using demographic data and past examination results.

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning algorithms. We implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that categorizes the network traffic into normal and attacks traffic. In this work, interpolation data preprocessing is used to compute the missing values. Also, the imbalanced data problem is solved using a synthetic data generation method. Extensive experiments have been implemented to compare the performance results of the proposed model (CNN+LSTM) with a basic model (CNN only) using both balanced and imbalanced dataset. Also, with some state-of-the-art machine learning classifiers (Decision Tree (DT) and Random Forest (RF)) using both balanced and imbalanced dataset. The results proved the impact of the balancing technique. The proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy (92.10%) compared with the basic CNN model (89.90%) and the machine learning (DT 88.57% and RF 90.85%) models. Moreover, comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

Research advances in simple and compact optical imaging techniques

Computational imaging enables optical imaging systems to acquire more information with miniaturized setups. Computational imaging can avoid the object-image conjugate limitation of the imaging system, and introduce encoding and decoding processes based on physical optics to achieve more efficient information transmission. It can simultaneously increase the amount of information and reduce the complexity of the system, thereby paving the way for miniaturizing imaging systems. Based on computational imaging, the simple and compact optical imaging techniques are developed, which is also called simple optics. To develop miniaturized optical imaging elements and integrated systems, simple optics utilizes the joint design of optical system and image processing algorithms, thereby realizing high-quality imaging that is comparable to complex optical systems. The imaging systems are of small-size, low-weight, and low-power consumption. With the development of micro-nano manufacturing, the optical elements have evolved from a single lens or a few lenses, to flat/planar optical elements, such as diffractive optical elements and metasurface optical elements. As a result, various lensless and metalens imaging systems have emerged. Owing to the introduction of encoding process and decoding process, an optical imaging model is developed to represent the relationship between the target object and the acquired signal, from which the computational reconstruction is used to restore the image. In the image restoration part, the algorithms are discussed in three categories, i.e. the classic algorithm, the model-based optimization iterative algorithm, and the deep learning (neural network) algorithm. Besides, the end-to-end optimization is highlighted because it introduces a new frame to minimize the complexity of optical system. In this review, the imaging techniques realized by simple optics are also discussed, such as depth imaging, high-resolution and super-resolution imaging, large field of view imaging, and extended depth of field imaging, as well as their important roles in developing consumer electronics, unmanned driving, machine vision, security monitoring, biomedical devices and metaverse. Last but not least, the challenges and future developments are prospected.

Estimation of body weight from morphological measurements in Boer goats with the application of artificial neural networks and some regression models

In this study, examination of the characteristics of body measurements affecting the body weight of Boer goats and the estimation of the body weight were investigated. To examine their body morphological features, 400 live animals were taken into consideration. The morphological measurements taken from the goats in the study were body weight (BW), body length (BL), heart girth (HG), withers height (WH), rump height (RH), rump length (RL), ear length (EL) and head with (HW) respectively. These animals were between 1-6 years old; 112 of them were male and 288 of them were female. Multiple regression, ridge regression and artificial neural networks (ANN) methods were applied to estimate the body weight. In the prediction of body weight as a dependent variable, the ANNs predictive model produced high predictive performance. Mean square error (MSE), mean absolute error (MAD) and mean absolute percent error (MAPE) statistics were used to determine model performance. Using the Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) learning algorithm, the body features that had the greatest impact on body weight were determined. Comparison of the predictive performance of the put forward model against both multiple regression and state of the ridge regression methods showed that the artificial neural networks outperformed both competing models by achieving the least values for MAD, MSE and MAPE in both training and testing data sets. The results of artificial neural networks were promising and accurate in the prediction of the body weight of goats.

Online course resource recommendation based on deep learning

In order to further improve the online learning experience and effect of courses for learners, this study, with the help of deep learning neural network algorithm, based on the characteristics of deep learning algorithm such as good processing performance and strong data adaptability, carries out research for online course students' preferences, learner characteristics and other fields, and constructs a course resource recommendation technical algorithm based on deep learning. Through algorithm experiments, it is found that the performance of NCF model is improved by 57.7% compared with that of CF model in the algorithm with RMSE as evaluation index. The performance under MAE evaluation index is improved by 58.5%, indicating that the integration of the deep learning neural network algorithm can better improve the online course recommendation effect and enhance students' online learning experience.

Small-signal Modeling of InP HBT based on PSO-ELM Neural Network

Extreme Learning Machine (ELM) is a new learning algorithm for single-hidden layer feedforward neural network, which has been widely used in lots of fields. However, it still has the insufficiency of randomly determining the hidden layer threshold and output weight, which leads to ill-conditioned output. In order to avoid the risk of decreasing prediction accuracy caused by this possibility, the ELM is optimized using particle swarm algorithm. A Particle Swarm Optimization (PSO) enhanced ELM algorithm is proposed to accurately model the small-signal properties of InP Heterojunction Bipolar Transistors (HBTs). PSOELM algorithm solves the problem of unstable prediction data caused by random determination of input weights in ELM. Comparing the modeling effects of the PSO-ELM model and the ELM model under different bias conditions for a 1 μm×15 μm InP HBT, it is proved that the PSO-ELM algorithm has better consistency with the measured data.

Statistical Analysis with Machine and Neural Learning-Based Model on Cardiovascular Diseases and Stroke Prediction

Several risk factors, such as hypertension, hyperlipidemia, and an irregular heart rhythm, make an early diagnosis of cardiovascular disease challenging. Reducing cardiac risk calls for precise diagnosis and therapy. Clinical practice in the healthcare business is likely to evolve in tandem as a result of advancements in machine learning. Therefore, scientists and doctors need to acknowledge machine learning's significance. The fundamental purpose of this research is to a reliable analyzing Risk Factors for Cardiovascular Disease method that makes use of machine learning. Classifying well-known cardiovascular datasets But, on the other hand, is a job for state-of-the-art machine learning techniques and neural network algorithms. Several statistical and visualization indicators were used to assess the efficacy of the suggested approaches and to determine the optimal machine-learning and neural-network approach. Using these modeling methods acquired high and accurate accuracy on stroke and heart disease prediction.

Comparative Analysis of Deep Learning Models on Brain Tumor Segmentation Datasets: BraTS 2015-2020 Datasets

Deep Learning neural networks have shown applicability in segmentation of brain tumor images.This research have been carried for comprehensive review of several deep learning neural networks. The datasets included in this study are standard datasets Multimodal Brain Tumor Segmentation (BraTS). This paper has summarized the performance of various deep learning neural network algorithms on BraTS datasets. Algorithms have been compared and summarized against the baseline models with specific attributes like dice score, PPV and sensitivity. It has been found that out of the different models applied on the BraTS 2015 dataset GAN in the year 2020 algorithm is showing better results on this data set. GAN architecture termed RescueNet gave the best segmentation results in terms of 0.94 dice score and 0.88 Sensitivity. This has been also observed that models used cascaded deep learning models had independent deep learning models at each stage which had no correlation among the stages which can cause class imbalance. Further it have found that the Attention models tried to solve problem of class imbalance in the brain tumor segmentation task. This work also found that existing CNN’s is having overfitting issues. For this ResNet models can add a rapid connect bounce relationship parallel to the layers of CNN to accomplish better outcomes for the brain tumor segmentation task.

SYMBIOSIS OF MEDICAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE: NEW OPPORTUNITIES IN ONCOLOGY

SYMBIOSIS OF MEDICAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE: NEW OPPORTUNITIES IN ONCOLOGY

An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition.