Artificial Intelligence Neural Networks Research Articles

The Application of Artificial Intelligence – Artificial Neural Networks – in Wastewater Treatment

Wastewater treatment is essential because it reduces the pollutant in the water, promotes the water quantity, and protects the ecosystem from harmful and toxic elements in wastewater. Many uncertainties appear in wastewater treatment systems since the natural condition is complex, and the technology of wastewater treatment is limited. Artificial Intelligence (AI) is a novel and influential technology assisting with complicated work, including modeling. The advantages of AI are evident in wastewater treatment because of the high accuracy, which leads to cost, energy, and material saving. This article mainly focuses on introducing Artificial Intelligence in wastewater treatment, displaying the application of Artificial Intelligence Neural Networks in wastewater treatment, and analyzing the advantages and problems. Overall, the research demonstrates that applying Artificial Intelligence in wastewater treatment provides a promising future with benefits, such as cost-saving and high accuracy.

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

The Application Research of Artificial Intelligence Neural Network in the Posture Analysis of Six Joints of Piano Player and Arm

The Application Research of Artificial Intelligence Neural Network in the Posture Analysis of Six Joints of Piano Player and Arm

Verification of Convolutional Neural Network Cephalometric Landmark Identification

Introduction: The mass-harvesting of digitized medical data has prompted their use as a clinical and research tool. The purpose of this study was to compare the accuracy and reliability of artificial intelligence derived cephalometric landmark identification with that of human observers. Methods: Ten pre-treatment digital lateral cephalometric radiographs were randomly selected from a university post-graduate clinic. The x- and y-coordinates of 21 (i.e., 42 points) hard and soft tissue landmarks were identified by 6 specialists, 19 residents, 4 imaging technicians, and a commercially available convolutional neural network artificial intelligence platform (CephX, Orca Dental, Hertzylia, Israel). Wilcoxon, Spearman and Bartlett tests were performed to compare agreement of human and AI landmark identification. Results: Six x- or y-coordinates (14.28%) were found to be statistically different, with only one being outside the 2 mm range of acceptable error, and with 97.6% of coordinates found to be within this range. Conclusions: The use of convolutional neural network artificial intelligence as a tool for cephalometric landmark identification was found to be highly accurate and can serve as an aid in orthodontic diagnosis.

Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.

At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy. In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366-0.7398) in external validation. The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models

Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation.

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer.

Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones

This paper presents edge machine learning (ML) technology and the challenges of its implementation into various proof-of-concept solutions developed by the authors. Paper presents the concept of Edge ML from a variety of perspectives, describing different implementations such as: a tech-glove smart device (IoT embedded device) for controlling teleoperated robots or an UAVs (unmanned aerial vehicles/drones) that is processing data locally (at the device level) using machine learning techniques and artificial intelligence neural networks (deep learning algorithms), to make decisions without interrogating the cloud platforms. Implementation challenges used in Edge ML are described and analyzed in comparisons with other solutions. An IoT embedded device integrated into a tech glove, which controls a teleoperated robot, is used to run the AI neural network inference. The neural network was trained in an ML cloud for better control. Implementation developments, behind the UAV device capable of visual computation using machine learning, are presented.

Stochastic numerical computing to study MHD micropolar nanofluidic system along a rotating disk in the presence of partial slip conditions

The goal of investigation is to look at how a rotating disc affects the motion of magnetohydrodynamics micropolar nanofluid flow model (MHD-MPNFFM) in applied magnetic field and partial slip condition. Using suitable transformations, the PDEs of considered flow system are converted to ODEs. These obtained non-linear ODEs are then processed using the computational power of Lobatto IIIA approach to obtain a dataset of Artificial Intelligence neural networks model backpropagated with Bayesian Regularization procedure (AINM-BBRP) for variation of velocity slip parameters, magnetic field parameter, material parameters, Brownian motion parameter, thermophoresis parameter and Lewis number. To verify the current findings, a contrast to past study is included. Graphical representations of regression, efficiency, fit curve, error bars, and trained state analysis are presented using nftool. The velocity, temperature, microrotation, and concentration profiles were computed, and the findings were presented through graphs and tables. This is observed that velocity and microrotation or spin components are degraded by the slip action and magnetic field. Brownian motion parameter, thermophoresis parameter, and Lewis number all have rising temperature fields, however Brownian motion parameter and Lewis number have decreasing concentration profiles. The accuracy achieved in terms of relative error demonstrates the validity and significance of the solution process.

Vehicle Driving Risk Prediction Model by Reverse Artificial Intelligence Neural Network.

The popularity of private cars has brought great convenience to citizens' travel. However, the number of private cars in society is increasing yearly, and the traffic pressure on the road is also increasing. The number of traffic accidents is increasing yearly, and the vast majority are caused by small private cars. Therefore, it is necessary to improve the traffic safety awareness of drivers and help car manufacturers to design traffic risk prediction systems. The Backpropagation neural network (BPNN) algorithm is used as the technical basis, combined with the MATLAB operation program, to simulate the driving process of the car. Dynamic predictive models are built to predict and analyze vehicle safety risks. Multiple experiments found that: (1) in various simulations, the simulation driving process of MATLAB is more in line with the actual car driving process; (2) the error between BPNN and the actual driving prediction is within 0.4, which can meet the actual needs. Predictive models are optimized to deploy and predict in various traffic situations. The model can effectively prompt risk accidents, reduce the probability of traffic accidents, provide a certain degree of protection for the lives of drivers and passengers, and significantly improve the safety of traffic roads.

Intelligent Computing of Levenberg-Marquard Technique Backpropagation Neural Networks for Numerical Treatment of Squeezing Nanofluid Flow between Two Circular Plates

This study presents new techniques based on the artificial intelligence neural network with Levenberg-Marquardt Scheme with backpropagation (ANN-LMS). The boundary value problem BVP is obtained from the governing equations of the flow model. Along with ANN-LMS, the semianalytical method namely the optimal homotopy analysis method (OHAM) is used for validating the results. ANN-LMS optimized the absolute error and increased the accuracy of the solution. The effect of physical parameters is discussed with the help of plots and tables.

Analysis of Ideological and Political Classroom Based on Artificial Intelligence and Data Mining Technology

With the continuous innovation of science and technology, the teaching system at home and abroad has also been reformed and optimized in combination with artificial intelligence, big data, and other technical means. As the main part of teaching activities, students’ emotional change in classroom activities is one of the important indicators that affect their learning effect and comprehensive learning achievement. Accurate analysis and judgment of students’ emotional changes are the main basis for teachers to evaluate teaching quality and improve teaching methods. Based on the above situation, this paper uses artificial intelligence and data mining technology to explore the emotional change process and influencing factors of students in ideological and political teaching classroom. Firstly, the artificial intelligence neural network algorithm is used to construct the face recognition model to improve the problem of low accuracy in the traditional recognition process. A dynamic minimum algorithm is designed to improve the operation speed of students’ emotion analysis. Finally, data mining technology is used to obtain students’ behavior data in the big data environment to provide data support for students’ emotional analysis model in ideological and political class. The results show that the student emotion analysis model based on artificial intelligence and data mining technology can be applied and run in various teaching environments.

Wireless Sensor Modeling Optimization Algorithm Based on Artificial Intelligence Neural Network

With the development and progress of society, science and technology have entered the field of view of scholars at home and abroad. As the science and technology with the biggest potential in recent years, wireless sensor network has been involved in many scientific fields. This paper aims to study the wireless sensor modeling optimization algorithm of artificial intelligence neural network. In this paper, a WSN data fusion algorithm (DFRMP) based on regression model prediction is proposed, and the four algorithms of SLR, PAQ, TINA, and DFRMP are compared. The experimental results of this paper show that when the mean square error and mean absolute error are not much different, the data transmission rate of DFRMP algorithm is the smallest. When the absolute error threshold is 1, the data transfer rates of these four algorithms are 0.033, 0.0327, 0.035, and 0.017, respectively. This shows that the DFRMP algorithm proposed in this paper has superior performance.

Application of Nonlinear Fractional Differential Equations in Computer Artificial Intelligence Algorithms

Abstract In order to study the application of nonlinear fractional differential equations in computer artificial intelligence algorithms. First, the concept, properties and commonly used neural network models of artificial neural network are introduced, the domestic and foreign status quo of the application of fractional calculus theory to neural network technology is described. Then, the definition, properties and numerical calculation methods of fractional calculus theory are introduced in detail. Then, based on the analysis of artificial intelligence neural network algorithm, the theory of fractional differentiation is introduced, construct BP neural network based on fractional order theory. The Sigmoid function is used as the node function of the neural network, and the actual data is used as the sample set, train a fractional-order network. Finally, by training the network, summarize the change of the two parameters a and p in the function, the impact on the training of the entire network, and make a simple comparison with the fractional order neural network based on the sigmoid function. Experiments show that a variable-order iterative learning algorithm is proposed and applied to the training of neural networks, the results show the feasibility of this algorithm and its advantages in convergence speed and convergence accuracy.

IGBT Fault Prediction Combining Terminal Characteristics and Artificial Intelligence Neural Network.

The insulated gate bipolar transistor (IGBT) is widely utilized in the transportation, power, and energy domains because of its high input impedance and minimal on-voltage drop. IGBTs are frequently used in industrial applications for lengthy periods of time, collecting fatigue damage and eventually aging and failing, which can result in system shutdown and financial losses in severe circumstances. As a result, a study into the IGBT's reliability is extremely important. Fault prediction technology, which is an important aspect of reliability research, may analyze device state through changes in terminal parameters, anticipate aging trends, and issue early warnings at thresholds to avoid significant safety issues caused by IGBT aging failures. Therefore, the appropriate end parameters are selected as aging characteristic parameters, and fault prediction is performed. Therefore, this paper has carried out research on the IGBT fault prediction technology that integrates the terminal characteristics and artificial intelligence neural network. The main research contents include the following: (1) this paper starts from the basic principle of IGBT and the structure of its device and analyzes its failure mode on the failure of IGBT. The characteristic parameter of collector-emitter turn-off peak voltage value is selected for IGBT fault prediction, and the aging data of NASA PCoE Research Center is used to verify that the characteristic parameter can be used for fault prediction. (2) In view of the shortcomings of traditional fault forecasting methods, this paper proposes to use deep learning time series forecasting methods for fault forecasting. The LSTM is theoretically analyzed, and the prediction network is built. The experimental results show that the LSTM network model can improve the accuracy of IGBT fault prediction, with fewer parameters and higher prediction efficiency.

Performance Optimization of Atomic Layer Deposited HfOx Memristor by Annealing With Back-End-of-Line Compatibility

Hafnium oxide (HfO_<i>x</i>) memristor has attracted enormous attention due to its high performance and back-end-of-line (BEOL) compatibility, thus providing a novel approach to implementing artificial intelligence neural networks. In this work, great performance optimization of HfO_<i>x</i> memristor has been achieved by using atomic layer deposition (ALD) method and post-metal annealing (PMA) process, in which both procedures are with low-temperature budget (&#x003C; 300 &#x00B0;C) and are compatible with CMOS BEOL process. The device exhibits forming-free, high yield, good linearity, fast speed and non-volatile characteristics. Besides, the device conductance can be well modulated by using the most desired pulse protocol, namely the identical pulse with same pulse amplitude and width. More than 3bit stable conductance states have been obtained, indicating its great potential in practical memristor neuromorphic computing system.

O-204 Non-invasive AI image analysis unlocks the secrets of oocyte quality and reproductive potential by assigning ‘Magenta’ scores from 2-dimensional (2-D) microscope images

Abstract Study question Can an Artificial Intelligence (AI) software tool, utilizing 2-D image analysis of mature oocytes, prospectively correlate an oocyte score to utilizable blastocyst development? Summary answer Oocyte Magenta scores show a statistically significant difference in blastocyst development between the highest (7.1-10) and lowest (1.0-4.0) scored oocytes [46.1% vs 26.6%; p &amp;lt; 0.005]. What is known already Unlike sperm (WHO 2010) or embryos (Gardner blastocyst grading), there is no validated visual oocyte scoring system used in clinical practice. Embryologists have been unsuccessful in correlating oocyte morphological features to reproductive potential. A valuable oocyte scoring system should be able to correlate higher scores with improved embryological outcomes. Although not possible by the human eye, a non-invasive oocyte AI assessment tool (Magenta) has accomplished this feat in retrospective studies. This study applied the Magenta network at two IVF clinics in real-time; representing one of the few prospective AI studies in our field, and the only one focusing on oocytes. Study design, size, duration This prospective, multi-center study was conducted from September - November 2021 by TRIO Fertility (Toronto, Canada) and CARE Fertility (Sheffield and Nottingham, UK), utilizing the oocyte AI image analysis tool, Magenta. Magenta was created with a convolutional neural network trained on 16,373 oocyte images and corresponding outcomes. Inclusion criteria was all IVF-ICSI patients who consented to participate without severe male factor (testicular or epididymal sources). Results are based on 392 images of oocytes (46 patients). Participants/materials, setting, methods Non-invasive, light microscope images were taken of mature oocytes post-denudation, prior to ICSI, utilizing an image capture software. Images were uploaded and analyzed by Magenta, scoring each oocyte on a scale of 1-10, and remained in a blinded folder to the IVF clinics. De-identified patient outcomes were collected to analyze blastocyst development correlation with Magenta scores. Oocytes were handled as per good laboratory practice, without extended periods outside the incubator or disruption to standard protocols. Main results and the role of chance Oocyte images were analyzed by Magenta to score each oocyte on a scale of 1-10. There was a total of 46 patients representing 392 oocytes from both TRIO (26, 280) and CARE (20, 112). The scoring spectrum was divided into 3 tiers (1.0-4.0: 188 oocytes; 4.1-7.0: 128 oocytes; 7.1-10: 76 oocytes). A utilizable blastocyst was defined as a Gardner grade of 2BB or greater on Day 5 or 3BB or greater by Day 6 of embryo development and of adequate quality for transfer, freezing or PGT-A biopsy. The blastocyst development (positivity) rate was 26.6% (1.0-4.0), 32.0% (4.1-7.0) and 46.1% (7.1-10), with mean Magenta scores of 2.4, 5.5 and 8.2, respectively. The lowest and highest tier of Magenta scores were accordingly found to have the lowest and highest blastocyst rates, which was statistically significant (p-value &amp;lt; 0.005) by a Two-Proportions Z-test. Overall, oocytes that developed into a utilizable blastocyst had a higher mean Magenta score (5.0) than oocytes that did not develop into a utilizable blastocyst (4.3); (p-value &amp;lt;0.05) by a Welch’s Two Sample t-test. Limitations, reasons for caution Sample size is currently limited for this ongoing, prospective study. Therefore, additional male factor (non-surgical sperm sources) and possible poor images have not been removed from the current analysis. Furthermore, AI neural network accuracy is restricted by the amount of data it is trained on. Wider implications of the findings Magenta has enabled visual oocyte assessments that will provide IVF-ICSI patients with insights into their oocyte quality; resulting in counselling benefits and the ability to make more informed, personalized decisions regarding future treatment plans. AI will inevitably improve the IVF process and prospective validation studies are critical in its evolution. Trial registration number Not applicable.

Intelligent voltage and frequency control of islanded micro-grids based on power fluctuations and communication system uncertainty

Control of critical micro-grid factors such as voltage/frequency is complex and challenging in power distribution networks that include micro-grids. Therefore, providing an efficient and optimal control framework to maintain stability and control the critical factors of the network significantly affects the development of micro-grids goals. The present study proposed the intelligent voltage/frequency control scheme in primary and secondary micro-grids. Micro-grid voltage/frequency restoration was planned based on the PSO-Fuzzy control algorithm in the primary control. In addition to optimizing the members of the fuzzy controller, the PSO optimization algorithm was also used to calculate the voltage/ frequency deviation based on the voltage error factors and the violation of the deviation from the frequency reference. The PSO-Fuzzy structure was provided to optimize hybrid energy storage units' charge and discharge thresholds. Moreover, an intelligent artificial neural network algorithm was presented based on Lyapunov stability equations and a communication system platform to compensate for the voltage/frequency deviation in the secondary control. The proposed algorithm had optimal and efficient performance in different scenarios, such as the effect of load dynamic and its type, power fluctuations, and communication system delays.

Research on energy-saving packaging design based on artificial intelligence

Background:Low-carbon economy is an economic model based on low energy consumption, low pollution and low emission. Energy-saving packaging design is an idea that guides the practice reform and development under the guidance of consumers’ green psychology. It takes the green design concept of modern eco-environmental protection and other development concepts as the main standard of packaging design application practice. Objective:In order to change this situation, we should combine people’s formed green psychology, so as to find a way of green packaging design. Methods:This paper discusses the application and development of energy-saving packaging design. Based on artificial intelligence (AI) technology, it realizes the combination of market and green environmental protection design, and improves product utilization and production efficiency. For judging the type of intelligent energy-saving packaging, the confidence weight database decision algorithm is adopted, and the basic principle is to assume that AI neural network forms the energy consumption vector. Conclusion:For the long-term benefit of human survival, packaging designers should re-examine the relationship between design and natural environment, reduce the pollution components in packaging design, take environmental protection as the top priority of design, and establish a design concept that conforms to ecological ethics. Intelligent packaging technology realizes the integration and management of product packaging information, which is conducive to people’s better understanding of products and is of great significance to the development of low-carbon product packaging.

The Use of Big Data Combined with Artificial Intelligence Neural Network Technology in Urban Spatial Evaluation System.

This exploration aims to promote the development of urbanization in China and improve the utilization rate of urban resources. First, intensive theory and spatial economics are studied. Next, an input-output urban spatial evaluation system is established based on intensive theory and data envelopment analysis (DEA). Then, deep learning (DL) is adopted for optimization, and an urban space evaluation system based on DL is proposed. Finally, the reliability level of the urban space evaluation system is tested. The results show that the model's input and output index α values are above 0.9, and the overall reliability level is higher than 0.9, indicating that the urban space evaluation system has a high reliability. The training results of the DL model show that the mean absolute error (MAE) of model prediction decreases gradually with the increase of training time and training times. When the training lasts for 5 min, each index' MAE is basically stable between 0.22 and 0.23, and the evaluation accuracy is obvious. The urban space evaluation system based on DL has higher evaluation accuracy, reaching 83.40%. Therefore, this exploration can provide research experience for promoting the effective utilization of urban resources and provide a reference for formulating an urbanization evaluation index system suitable for China's national conditions.