Artificial Intelligence Neural Networks Research Articles

Sensors and Sensing Devices Utilizing Electrorheological Fluids and Magnetorheological Materials-A Review.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluids (MRF), magnetorheological elastomers (MRE) and magnetorheological plastomers (MRP). To easily understand the history of sensing research using these two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensors, tactile devices, strain sensors, wearable bending sensors, magnetometers, display devices and flux measurement sensors. After briefly describing what has been reviewed in a conclusion, several challenging future works, which should be undertaken for the practical applications of sensors or/and sensing devices, are discussed in terms of response time and new technologies integrating with artificial intelligence neural networks in which several parameters affecting the sensor signals can be precisely and optimally tuned. It is sure that this review article is very helpful to potential readers who are interested in creative sensors using not only the proposed smart materials but also different types of smart materials such as shape memory alloys and active polymers.

Economic and technological modernization and transformation of Russia in the context of a new reality

Purpose: through the prism of new knowledge generated by the author, to explore possible ways of modernization and transformation of Russia in the conditions of the new reality: conducting the special military operation, the sharp confrontation with the collective West, the series of sanctions and counter-sanctions, the new economic course – supply-side economics, the Concept of technological development until 2030 and development of neural network artifi cial intelligence. The article is a natural continuation and an integral part of the research conducted by the author over several decades and dedicated to finding ways to a future without crises and destructive wars for Russia and the global world. Methods: the article uses a complex of general scientifi c methods, including analysis, generalization, comparison, etc. The research is based on the results of the author’s previous works and is relies on the methodological tools formed in them – new knowledge, a new scientific paradigm (NSP). Results: the author analyzed the state of Russia in the context of the need to gain economic and technological sovereignty. It is shown that there remains a lack of eff ective ways out of the deepest crisis and understanding the future in economic, scientifi c and technological aspects. Analysis of Russian and foreign publications through the prism of new knowledge made it possible to once again prove that the future of the world does not lie in the development of artifi cial intelligence, but in the transition to a new development paradigm (NDP) and comprehensive human development in a new model of life. Conclusions and Relevance: the conclusions of the author’s previous research on the need to transition to a new development paradigm were confi rmed. New evidence has been obtained that Russia, in the conditions of the new reality that began in February 2022, has become even closer to switching to NDP, the meaning and goal of which is the person himself, within the framework of which his highest need is satisfi ed – to become perfect in the physical, intellectually and spiritually.

Prediction and effectiveness study of fracture distribution based on an artificial neural network technology and production performance data detection method: a case study of Ordovician carbonate formation in the Tahe oilfield, Tarim Basin

To solve the difficulty of predicting the spatial distribution of fracture linear density and clarify the controlling influence of tectonic factors on the effectiveness of fractures. This article combines core, conventional logging, imaging logging, and 3D seismic volume data and uses artificial intelligence neural network technology combined with the sequential Gaussian method to establish a linear density volume prediction model for fractures in the study area. In addition, production dynamic data are applied to verify the effectiveness of the fractures. The results show that the artificial intelligence neural network technique is highly applicable with an accuracy of up to 76.8% in predicting the fracture linear density. The fracture linear density is controlled by tectonic activity, and the fracture linear density increases sharply in the areas adjacent to the main faults, the fracture superposition site, and the intersection site of multiple faults, with the fracture linear density reaching 8.65 fractures per meter in study areas. The effective distance of fracture connectivity is mostly concentrated between 550 and 670 m. When the difference in fracture density along the structural ridge is between 2 and 4 fractures/meter, the effectiveness of the fractures is strong.

A novel deep learning‐based bioacoustic approach for identification of look‐alike white‐eye (Zosterops) species traded in wildlife markets

The songbird trade crisis in East and South East Asia has been fuelled by high demand, driving many species to the brink of extinction. This demand, driven by the desire for songbirds as pets, for singing competitions and for prayer animal release has led to the overexploitation of numerous species and the introduction and spread of invasive alien species and diseases to novel environments. The ability to identify traded species efficiently and accurately is crucial for monitoring bird trade markets, protecting threatened species and enforcing wildlife laws. Citizen scientists can make major contributions to these conservation efforts but may be constrained by difficulties in distinguishing ‘look‐alike’ bird species traded in markets. To address this challenge, we developed a novel deep learning‐based Artificial Intelligence (AI) bioacoustic tool to enable citizen scientists to identify bird species traded in markets. To this end, we used three major avian vocalization databases to access bioacoustic data for 15 morphologically similar White‐eye (Zosterops) species that are commonly traded in Asian wildlife markets. Specifically, we employed the Inception v3 pre‐trained model to classify the 15 White‐eye species and ambient sound (i.e. non‐bird sound) using 448 bird recordings we obtained. We converted recordings into spectrogram (i.e. image form) and used eight image augmentation methods to enhance the performance of the AI neural network through training and validation. We found that recall, precision and F1 score increased as the amount of data augmentation increased, resulting in up to 91.6% overall accuracy and an F1 score of 88.8% for identifying focal species. Through the application of bioacoustics and deep learning, this approach would enable citizen scientists and law enforcement officials efficiently and accurately to identify prohibited trade in threatened species, making important contributions to conservation.

Enhancing performance of multi-pressure evaporation organic Rankine Cycle/Supercritical Carbon Dioxide Brayton cycle through genetic algorithm and Machine learning optimization

The Supercritical Carbon Dioxide Brayton combined cycle is one of the most important ways to enhance the performance of the cycle. Since the considerable exergy loss resulting from large temperature differences in the heat exchangers of combined cycles, the multi-pressure evaporation concept was adopted from Organic Rankine cycle research. Two novel multi-pressure evaporation configurations, namely parallel evaporation Brayton combined cycle and series evaporation Brayton combined cycle, are designed with the purpose of enhancing SCO2 Brayton cycle performance. The performance of the two new cycles was compared with that of the baseline cycle. Moreover, Printed Circuit Heat Exchangers were employed to improve the overall compactness of the cycles. The results from model calculations were utilized to train an Artificial intelligence neural network model using machine learning techniques which allowed to simplify the cycle and improves the computational speed. Subsequently, genetic algorithm was employed to conduct multi-objective optimization on the three cycles. Finally, a detailed exploration was conducted from the perspective of thermal and exergy to elucidate the reasons for the advantages and differences between the two new cycles compared to the baseline cycle. The results indicate that the series Brayton combined cycle exhibits the highest performance, and the combined cycle of multi-pressure evaporation helps to improve the performance of the combined cycle. Compared to the baseline cycle, the combined cycle efficiency of both series and parallel combined cycles has increased from 45.54% to 46.18% and 46.29%, respectively, while the bottom cycle efficiency has improved from 3.81% to 4.93% and 5.03%. The ratio of heat exchange area to net output power increased from 0.6051 to 0.6614 and 0.6740 for the parallel and series cycles, respectively. Thermal analysis shows that, the fundamental purpose of changing the layout is to improve the specific power per unit of working medium in the cycle. Exergy analysis shows that, the two new combined cycles improve cycle efficiency by reducing exergy losses in the intermediate heat exchanger. Compared to the parallel version, the series version's staged compression allows for a lower temperature difference in intermediate heat exchanger 1, resulting in smaller exergy losses and higher cycle efficiency. This work will contribute to an enhanced understanding of Supercritical Carbon Dioxide combined cycles within the academic community and the introduction of multi-pressure evaporation will further improve the performance of these important cycles.

RECENT ADVANCES AND APPLICATIONS OF DEEP LEARNING (DL) IN THE ACCOUNTING PROFESSION

In recent years, the accounting profession has undergone a transformative revolution with the help of rapid advancement in Deep Learning (DL) technologies. Deep Learning, a subset of Artificial Intelligence (AI) has emerged as a powerful tool in the world of finance and accounting, offering unprecedented opportunities for automating tasks, improving accuracy and uncovering valuable insights. This groundbreaking technology has found a multitude of applications in accounting, ranging from automating repetitive data entry tasks to enhancing fraud detection, financial forecasting and risk management. Deep Learning driven by Artificial Intelligence Neural Networks (AI-NN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and more is reshaping the landscape of financial analysis, auditing, prevention of frauds, risk management and much more. Through these advancements, we gain not only efficiency but also a new perspective on the role of accountants in an increasingly data-driven and digitally interconnected world.

Automated Detection of Abnormal Optical Coherence Tomography B-scans Using a Deep Learning Artificial Intelligence Neural Network Platform.

Automated Detection of Abnormal Optical Coherence Tomography B-scans Using a Deep Learning Artificial Intelligence Neural Network Platform.

Introduction to Photonic Neural Networks

The article briefly discusses: the development of photonic neural networks of artificial intelligence, the principles of construction and stages of development of photonic neural networks. The types of training of artificial intelligence neural networks, the advantages of such networks, as well as the scope of their application for creating more efficient artificial intelligence systems based on the photonic approach are described.

Estimation of Lightning Flash Rate in Precipitation Features by Applying Shallow AI Neural Network Models to the GMI Passive Microwave Brightness Temperatures

AbstractThe satellite‐constellation passive‐microwave Brightness Temperature (TB) observations, with global coverage, and more additions from upcoming CubeSats, have been mainly used in surface precipitation retrievals. However, these observations can also be used to indicate the intensity of convective systems. This study attempts to relate Global Precipitation Mission (GPM) Microwave Imager (GMI) TBs to Geostationary Lightning Mapper (GLM) lightning flashes by using 4 years (02/2018–04/2022) of GPM Precipitation Feature (PF) database. GMI TBs are collocated to the GLM lightning counts, and to the ERA5 reanalysis 2‐m air temperature in PFs. Three Artificial Intelligent Neural Network Models (AI‐NN) are trained to classify PFs producing lightning in a 20‐min window respectively for land, ocean, or coast. The flash rate of the determined Lightning producing PFs (LPFs) is then quantified by three other AI‐NNs, each trained for one of the three regions. Though the models clearly capture the global geographical distribution of LPFs with a Probability Of Detection over 90%, high False Alarm Rates are found, ranging from 49.9% over land to 91.5% over the ocean. The importance of TB at each passive microwave channel varies regionally, corresponding to the different microphysical properties in various types of precipitation systems. The global lightning distribution is derived by applying the AI models to global PFs and is well compared to the lightning climatology from Lightning Imaging Sensor and Optical Transient Detector. This suggests that the use of passive microwave TBs can help to fill the gaps in lightning monitoring thanks to their global coverage.

Retracted: Research on Tourism Resource Evaluation Based on Artificial Intelligence Neural Network Model

Retracted: Research on Tourism Resource Evaluation Based on Artificial Intelligence Neural Network Model

Isogeometric Convolution Hierarchical Deep-learning Neural Network: Isogeometric analysis with versatile adaptivity

We are witnessing a rapid transition from Software 1.0 to 2.0. Software 1.0 focuses on manually designed algorithms, while Software 2.0 leverages data and machine learning algorithms (or artificial intelligence) for optimized, fast, and accurate solutions. For the past few years, we have been developing Convolution Hierarchical Deep-learning Neural Network Artificial Intelligence (C-HiDeNN-AI), which enables the realization of Engineering Software 2.0 by opening the next-generation neural network-based computational tools that can simultaneously train data and solve mechanistic equations. This paper focuses on solving partial differential equations with C-HiDeNN. Still, the same neural network can be used for training and calibration with experimental data, which will be discussed in a separate paper. This paper presents a computational framework combining the C-HiDeNN theory with isogeometric analysis (IGA), called Convolution IGA (C-IGA). C-IGA has five key features that advance IGA: (1) arbitrarily high-order smoothness and convergence rates without increasing degrees of freedom; (2) a Kronecker delta property that enables direct imposition of Dirichlet boundary conditions; (3) automatic and flexible global/local mesh-adaptivity with built-in length scale control and adjustable radial basis functions; (4) ability to handle irregular meshes and triangular/tetrahedral elements; and (5) GPU implementation that speeds up the program as fast as finite element method (FEM). Mathematically, we prove that both IGA and C-IGA mappings are equivalent, and by taking a special design and modified anchors as nodes, C-IGA degenerates to IGA. We demonstrate the accuracy, convergence rates, mesh-adaptivity, and performance of C-IGA with several 1D, 2D, and 3D numerical examples. The future applications of C-IGA from topology optimization to product manufacturing with multi-GPU programming are discussed.

A novel online monitoring system of frequency oscillations based intelligence phasor measurement units

In this paper, frequency oscillation has been present as it is an important issue in power systems, especially when it is joined to the new trend to use alternative energies as an energy source, as well as to be a big risk for the inter-connection of the modern electric power networks. Wind farm acts as alternative energy and connected to two buses on the Iraqi power system. Because the low-frequency oscillation monitoring needs be accurate and fast, the main objective is to propose a novel online monitoring system consisting of phasor measurement unit's (PMU) with artificial intelligence neural network (PMU-NN). The location of the phasor measurement units has been optimized using (graph-theoretic procedure algorithm) and the function for the artificial intelligence (NN) is radial basis function (RBFNN). The data information from phasor measurement units is the inputs to the artificial intelligence system then predictions are made Information on low-frequency oscillation (target). The MATLAB toolboxes (PSAT & NN) used to obtain results. Finally, from the results, the validity of the proposed (PMU-NN) system has been proven and tested on the Iraqi power grid (24 bus) in several cases and several places on the network and the comparison was made with the analysis model.

Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival.

Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice. A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.

Retracted: Research and Implementation of Intelligent Evaluation System of Teaching Quality in Universities Based on Artificial Intelligence Neural Network Model

Retracted: Research and Implementation of Intelligent Evaluation System of Teaching Quality in Universities Based on Artificial Intelligence Neural Network Model

Research on Coronary Heart Disease Indicators——Based on Machine Learning Model

Coronary atherosclerotic heart disease is a common and high-risk disease in clinical cardiology. The leading factor of this kind of heart disease is the atherosclerotic lesion of arteria coronary, which can lead to narrow or choke of the vascellum cavity and musculus cardiacus ischemia, anoxia or thanatosis. In this research, a clinical prediction model through artificial intelligence neural network is built, for instance, K-Nearest Neighbor Classifier, Nonlinear SVM, Random Forest, Gradient Boost, XG Boost, Ridge and so on to analyze the influence of each prediction variable on Coronary atherosclerotic heart. It can be observed that using the K-Nearest Neighbor Classifier algorithm the prediction accuracy of breast cancer reaches 90.16%.

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture.

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sensors of these systems must be transmitted to a back-end server for calculation. With an increase in the demand for the Internet of Things, problems such as higher cost of bandwidth and overload of server computing are faced when using the aforementioned systems. To reduce the server workload, certain computing tasks must be offloaded from cloud servers to edge computing platforms. In this study, a bedside monitoring system based on neuromorphic computing hardware was developed to detect bedside falls and sleeping posture. The artificial intelligence neural network executed on the back-end server was simplified and used on an edge computing platform. An integer 8-bit-precision neural network model was deployed on the edge computing platform to process the thermal image captured by the thermopile array sensing element to conduct sleep posture classification and bed position detection. The bounding box of the bed was then converted into the features for posture classification correction to correct the posture. In an experimental evaluation, the accuracy rate, inferencing speed, and power consumption of the developed system were 94.56%, 5.28 frames per second, and 1.5 W, respectively. All the calculations of the developed system are conducted on an edge computing platform, and the developed system only transmits fall events to the back-end server through Wi-Fi and protects user privacy.

A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies.

The increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10-19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.

1270-P: A Neural Network Model for the Prediction of Comorbid Cardiometabolic Diseases—Proof of Concept Using UK Primary Care Data

Background and Aims: Cardiometabolic comorbidities frequently develop in individuals with hypertension, type 2 diabetes, or hypercholesterolemia. When present in combination, the risks of vascular disease are additively increased. Methods: We created an artificial intelligence neural network to forecast the development of additional comorbidities when a single cardiometabolic disorder was present. The neural network was first taught diagnostic criteria to reproduce current clinical guidelines with 95% accuracy and, in a second step, trained on the outcome of anonymized electronic medical records of 48225 adults from South London primary care clinics, where 6127 had hypertension, 3001 had type 2 diabetes, and 5630 were treated for hypercholesterolemia. Among these cohorts, 2142, 1482 and 724 developed a second morbidity within a 3 year follow up period. Results: Prediction of an initial comorbidity had a sensitivity of 0.94±0.14, 0.85±0.32, 0.83±0.36 and specificity of 0.87±0.24, 0.90±0.19, 0.93±0.14 up to 1, 2 and 3 years prior to the diagnosis respectively. A second comorbidity was then predicted with a sensitivity of 0.63±0.1, 0.63±0.1, 0.52±0.21 for patients with hypertension who developed type 2 diabetes and 0.76±0.09, 0.70±0.08, 0.85±0.13 for patients with type 2 diabetes who developed hypertension. Interpretation: This proof-of-concept analysis demonstrates the utility of a neural network to predict the development of one or more comorbidities based on data routinely collected in primary care. At the level of individual patients knowledge of the probability of acquiring additional comorbidities could provide opportunities for prevention. Non-pharmacological and specific pharmacological interventions could be directed at avoiding or postponing additional cardiometabolic risk factors. Disclosure A.Krentz: None. L.S.Brunschwig: Employee; Metadvice SA. Y.Dibner: Employee; Metadvice. H.Michel: None. A.Jaun: Board Member; Metadvice.

The natural magnesite efficacy on arsenic extraction from water and alkaline influence on metal release in water

Arsenic (As) removal studies were carried out through batch experiments to investigate the performance of the locally available calcined magnesite mineral rocks from Tanzania. Natural water from a stream source in Tanzania and the prepared synthetic water at the laboratory were used for the studies. Parameters such as initial As concentration, calcined magnesite dosage, contact time and pH were evaluated for As removal using an overhead rea×2 shaker. Arsenic concentration was reduced from 5.3 to 1.1 mg/L As(V) at 180 min when 0.5 g/L calcined magnesite was applied to a synthetic water sample, whereas the concentration of 117 μg/L As(V) and 5.2 μg/L As(III) was reduced to below 0.1 μg/L in natural water. An increase in calcined magnesite dosage resulted in increased As removal up to below 0.01 mg/L. The calcined magnesite raised the pH of the water sample from 6.8 to 10 when the applied dosage increased between 0.002 g/L and 0.05 g/L. The pH was constant at around 10 even when the amount of 0.05 g/L was added 2000 times. Despite the high pH, the amount of magnesium released in water was low. The calcination of magnesite at 500 °C increased surface area by 4 times as compared to the natural magnesite and X-ray diffraction showed presence of MgCO3 phase as the dominant phase at this temperature. The reaction kinetics of As removal on 0.5 g/L calcined magnesite fitted with the pseudo-second-order (R2 = 0.96). Reaction isotherm was strongly fitted with Freundlich isotherm (R2 = 0.98). Linear regression and artificial intelligence neural network showed the As removal was influenced by both contact time and pH. Arsenic can be removed from As water using calcined magnesite and will be suitable for water treatment around gold mining areas.

A Deep Study on a Particle-Water Coupled Fast Induction Heating System

Abstract A particle induction heating method to promote electrical heating performance for water is studied in this paper. The method sinks metal particles (we chose Nickel particles) inside water, and uses a radio frequency (RF) oscillating magnetic field to heat the particle bed. The heat generated on the surface of particles by electrical eddy current further raises the temperature of water. Our experimental results show that this thermal process has a remarkably high heating rate owing to the huge heat dissipation area of particles (in some experiment cases, water boils in seconds, even though the volume ratio of particle to water is only 1:89). To describe the physical nature of the process, a mathematical model is proposed. And our numerical simulation results generated from the model agree very well with the experimental data. However, some parameters used in the model are not easily obtainable for engineering application. Thereafter, to predict the temperature for the heating process, at the end of this work, artificial intelligent neural network architecture is further proposed.