Artificial Neural Network Technology Research Articles

Enhancing Speech-to-Text and Translation Capabilities for Developing Arabic Learning Games: Integration of Whisper OpenAI Model and Google API Translate

This study tackles language barriers in computer-mediated communication by developing an application that integrates OpenAI’s Whisper ASR model and Google Translate machine translation to enable real-time, continuous speech transcription and translation and the processing of video and audio files. The application was developed using the Experimental method, incorporating standards for testing and evaluation. The integration expanded language coverage to 133 languages and improved translation accuracy. Efficiency was enhanced through the use of greedy parameters and the Faster Whisper model. Usability evaluations, based on questionnaires, revealed that the application is efficient, effective, and user-friendly, though minor issues in user satisfaction were noted. Overall, the Speech Translate application shows potential in facilitating transcription and translation for video content, especially for language learners and individuals with disabilities. Additionally, this study introduces an Arabic learning game incorporating an Artificial Neural Network using the CNN algorithm. Focusing on the “Speaking” skill, the game applies to voice and image extraction techniques, achieving a high accuracy rate of 95.52%. This game offers an engaging and interactive method for learning Arabic, a language often considered challenging. The incorporation of Artificial Neural Network technology enhances the effectiveness of the learning game, providing users with a unique and innovative language learning experience. By combining voice and image extraction techniques, the game offers a comprehensive approach to enjoyably improving Arabic speaking skills.

Data is the fuel of generative artificial intelligence

TThe aim of the article is to present the generative technology of artificial intelligence and a general analysis of its achievements and technological limitations. In turn, the hypothesis is as follows: data is the main factor ensuring the development of generative AI. The identified research niche includes stimulants of this development, the need to clarify the concept of generative AI and the indication of the most frequently used sources and methods of obtaining data. At the beginning, the concept and genesis of artificial intelligence were explained, which was necessary to move on to further considerations regarding the analyzed generative artificial intelligence. The self-learning mechanisms used by this type of artificial intelligence are discussed, consisting mainly in the analysis of specific data sets, thanks to which computers learn things that have not been known before. The advantages of generative artificial intelligence were also indicated, including increased productivity, greater efficiency and high creativity. Part of the attention was also paid to the limitations of this technology, including inaccurate and fuzzy output information, uncritical analyzes and often biased conclusions, copyright infringement and high requirements for memory and computing power of computers. Two factors have been identified that have recently stimulated the development of generative intelligence, i.e. the growing computing power of computers and easy access to large data sets. The main subject of considerations are large data sets, dynamically and uncontrollably self-replicating in Internet servers. Today, data resources are collected very efficiently, including: in Big Data, Cloud Computing and Internet of Things technologies, and their use is almost cost-free and publicly available. It was enough to improve the technology of artificial neural networks, expand multi-layer machine learning and create deep learning for generative intelligence to gain enormous momentum and become a symbol of the beginning of the 21st century. The whole investigation was summarized with a synthetic ending.

Formation of Bachelor`s Skills in the Field of Machine Learning and Intelligent Data Analysis

The article presents the content of the educational disciplines “Workshop on Python Programming” and “Fundamentals of Artificial Intelligence”, included in the module “Data Analysis. Machine learning. Artificial Intelligence”, implemented in Ural universities. Modern teachers note that when preparing the next generation of teachers, the priority should be their acquisition of experience in using artificial intelligence tools in teaching. The objectives of mastering the module are developing students’ skills in the field of neural network mathematical modeling, developing knowledge about basic data analysis algorithms, development of skills in visualization and interpretation of data to solve applied problems using object-oriented programming technologies and artificial neural network technologies. As part of project activities, students gain experience in data mining by completing research projects (for example, a project on comparative assessment of the effectiveness of multilayer perceptron learning algorithms) and applied projects (for example, on the development of a neural network system for predicting the success of schoolchildren’s research work; on the development of a neural network system for predicting student attendance training sessions). At USPU, software systems have been developed in Python that implement the presented neural network prognostic systems. Educational and methodological materials for the disciplines of the module include video lectures and presentations for them, laboratory work (including those based on GoogleColab notebooks), datasets, test sets, theory questions and practical tasks for exams and tests. The effectiveness of the developed teaching methodology is confirmed by the results of a pedagogical experiment. Keywords: neural network technologies, data mining, predictive systems, machine learning, content of the academic discipline

Риски и вызовы при внедрении искусственного интеллекта в систему высшего образования

<p style="text-align: justify;">The purpose of this article is to study possible risks and challenges in the higher education system that arise in connection with the growing speed of implementation of technologies, where artificial intelligence (AI) and neural network technologies are already part of its structure. The analysis is carried out with the aim of predicting the future nature of higher education, as well as exploring changes in the entire teaching paradigm under the influence of AI. The article identifies a number of expected problems when teaching students, in the pedagogical process, organizing their independent work, as well as in the management and administration of higher educational institutions when introducing AI capabilities into the process of higher education. The question is raised about the impact of restrictions on the development of Russian science, namely the disastrous consequences for its development due to the monopolization of AI algorithms and the subjective approach to managing a neural network by an organized technocratic group. In the course of analyzing the intersections of opposing issues of problematic issues and the positive dynamics of the influence of AI on the educational system of higher education, a vector has been set for further research on this topic.</p>

Navigating the biomechanical landscape: Enhanced methods for drug analysis

In drug analysis and detection, chemiluminescence is a standard technical method based on the idea that the concentration of observed substances in the chemical detection system and the chemical luminescence intensity establish a linear quantitative relationship under certain conditions. Bio-mechanical landscape (BML), which uses bioengineered systems to simulate essential elements of the human tumour microenvironment, is already enhancing cancer biology and aiding clinical translation. The majority of illicit substances have dangerous cardiovascular side effects that might range from an irregular heartbeat to a heart attack. Cardiovascular disease, including blocked veins and bacterial infections of the blood arteries and heart valves, may also result from injecting illicit narcotics. The challenging characteristics of such a cardiovascular disease using drug analysis are heart failure, low blood pressure and headaches. Hence, in this research, drug analysis has improved wearable body sensor network-enabled artificial neural network (WBSN-ANN) technologies for cardiovascular disease. Medicine and drug development because they provide information on patient status in the clinical context and vital information on pharmacodynamics activity, effectiveness, and safety throughout the research process. The process results in a research-intensive organization finding a novel chemical or biological compound and developing it into a medicine that patients can legally utilize for drug discovery and development.

Intelligent RCS Extrapolation Technology of Target Inspired by Physical Mechanism Based on Scattering Center Model

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction.

Synthesis and pharmacological activity of various organic and inorganic salts of phenyl derivatives of imidazobenzimidazole

Introduction: The creation of new opioid analgesics that are devoid of the main undesirable effects – euphoria, respiratory depression, tolerance – is an important task for the treatment of pain syndrome. Eighteen salts of 9-pyrrolidinoethyl-(1a), 9-piperidinoethyl-(1b), and 9-morpholinoethyl-2-(4-fluorophenyl)benzo[d]imidazo[1,2-a]imidazole (1c) were synthesized and tested for kappa agonist and analgesic activity. Materials and Methods: Specific analgesic activity studies were conducted using models of nociceptive pain in the “Hot/Cold-plate” and ”Plantar Test” tests, and in neuropathic pain against the background of sciatic nerve ligation. The relationship between the physicochemical and pharmacological properties of the compounds was studied in silico using quantum chemistry methods and artificial neural network technology. Results: The most active compound identified was 4-(2-(2-(4-fluorophenyl)-benzo[d]imidazo[1,2-a]imidazol-9-yl)ethyl)morpholine dihydrochloride (1c.2HCl). EC50, acute toxicity, and conditional therapeutic index values were calculated, which were 38.8 times superior to the kappa agonist U50488. It was established that compound 1c.2HCl exhibits a dose-dependent analgesic effect in the “Hot/Cold-plate” test, which is six times superior to that of butorphanol, maintaining a statistically significant effect for six hours in the “Plantar test” and without causing the formation of tolerance with a ten-day administration. In models of neuropathic pain syndrome, 1c.2HCl with a fourteen-day administration significantly reduced tactile and cold allodynia by three and 1.6 times, respectively, and was not inferior in activity to gabapentin. The kappa-opioidergic mechanism of the antinociceptive action of 1c.2HCl compound was confirmed. Quantum chemistry methods and artificial neural network technologies have shown that the high level of kappa-opioid agonist activity of hydrochlorides, in contrast to other salts, results in lower total energy production; therefore, there is a significant increase in energy during the formation of a salt supramolecular complex. Conclusion: The most active compound was identified – 1c.2HCl, for which kappa-opioid activity in vitro and analgesic properties were revealed in various in vivo models. It has been shown that the compound does not cause the formation of tolerance and is not toxic.

Intelligent fault detection of photovoltaic panel using neural networks

This research primarily aims to leverage artificial neural network technology for diagnosing power output issues in photovoltaic (PV) panels stemming from fluctuations in solar irradiance and temperature. The proposed diagnostic approach relies on constructing a reference model that captures the expected normal operating behavior of the PV panel under fault-free conditions. This reference model is then compared against the actual power output, and the difference, known as the residual, is analyzed to detect potential faults. The neural network is trained using real-world data inputs like solar irradiance and temperature measurements, with the sole output being the power produced by the PV panel. Through training, the neural network learns to map the complex non-linear relationships between environmental inputs and expected power output, effectively modeling the PV system's intrinsic behavior under healthy conditions. Results demonstrate the neural network-based approach's remarkable ability to diagnose faults with high accuracy while avoiding potential non-linear complications. This intelligent monitoring system provides a reliable protocol for early fault detection through training on actual measurements, eliminating the need for complex mathematical models. Consequently, it streamlines the maintenance process by negating intricate procedures to identify PV panel issues. The neural network effectively learns the mapping between environmental conditions and expected power output through exposure to real-world data during training. By analyzing deviations from this learned mapping, represented by the residual signal, the approach can reliably detect anomalies indicative of faults or performance degradation in the PV system. This data-driven nature allows the system to adapt to site-specific characteristics and capture non-linear effects without explicit modeling.

Summary of Research on Highway Bridge Vehicle Force Identification

Vehicle force identification is one of the core technical problems to be solved urgently in the management of transportation infrastructure, and it has also been a research hotspot in recent years. To promote the application of vehicle force identification technology on bridges and explore its development direction, the development status of indirect vehicle force identification methods based on bridge response is reviewed during this study. The basic theories of two major methods, including bridge weigh-in-motion (BWIM) and moving force identification (MFI), are described in detail in this study, and then, the key technical principles of bridge force identification are revealed. Secondly, the development status of BWIM in recent years is reviewed from three aspects, including test accuracy, applicability and test efficiency. Combined with a variety of theories, the current status of MFI is analyzed from the establishment of the solution to the equation. Finally, the development direction of an artificial neural network and machine vision technology are prospected in this study. The BP neural network has good self-learning ability and self-adaptive ability, but the algorithm needs to be improved. The identification method based on machine vision represents the current development direction in vehicle force identification, with great potential.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

Active suspension for all-terrain vehicle with intelligent control using artificial neural networks

The automotive industry focuses on developing advanced protection and stability control systems, particularly for suspension and steering, to enhance vehicle comfort, luxury, and safety. This research presents an intelligent controller for all-terrain vehicle (ATV) suspension systems based on Artificial Neural Network (ANN) technology. The controller leverages ANN capabilities to optimize system performance. MATLAB simulations were conducted to evaluate its effectiveness under various disturbances. A comparative analysis compared the ANN regulator, classic ANFIS regulator, and passive performance in different disturbance scenarios. The simulation results demonstrate exceptional performance of the ANN-based controller in displacement reduction, speed, acceleration, and robustness. The controller effectively mitigates disturbances, enhancing overall suspension system performance. These findings highlight the advantages of employing ANN technology in ATV suspensions. This research contributes to intelligent control systems advancement in the automotive industry, specifically in ATV suspensions. The demonstrated improvements have the potential to enhance passenger comfort, vehicle stability, and safety across terrains. By implementing ANN-based controllers, automotive manufacturers can optimize suspension systems, leading to improved vehicle performance. Several indicators, including RMSE, MRE, and R2, were utilized to test and validate the models. The R2 values for the three quality parameters ranged from 0.989 to 0.999, indicating a high level of consistency in the predictions made by the ANN, a "5-12-1" structure is employed. The results of this study add to the expanding body of knowledge endorsing the efficacy of ANNs in simulating and optimizing quarter-vehicle dynamics.

Research on spacecraft in orbit perception based on artificial neural networks and digital twin technology using grating arrays

In order to improve the safety of spacecraft, the research on artificial neural network and digital twin technology based on, to our best knowledge, a novel fiber Bragg grating (FBG) sensor array is proposed for intelligent sensing monitoring of spacecraft on-orbit collisions. Femtosecond FBG arrays were fabricated on the novel oxide-doped fiber by point-by-point writing technique. The femtosecond FBG is analyzed using the time-dependent perturbation theory of quantum mechanics. The FBG array can achieve high-temperature measurement of 1100 °C and large strain measurement of 15000 µε. The sensing arrays were deployed on the surface of the spacecraft. Constructed the multi-layer perceptron neural network structure and convolutional neural network structure. 1200 samples were trained. Conducted model accuracy testing. The accuracy rate is above 98%, and accuracy verification has been implemented. The digital twin model was designed based on various data such as strain and temperature of the spacecraft structure under impact monitored by FBG sensors. A precise mapping has been formed between the physical entities of spacecraft and digital twins. Empower spacecraft with functions such as self-monitoring, judgment, and response. To ensure the stable and safe operation of spacecraft.

A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.

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.

Robustness of classifier systems using the optimal voting rule and neural network

In connection with the development of artificial neural network (ANN) technologies, it is of interest to compare the robustness of classifier systems using the optimal voting rule and ANN. Robust algorithms are required to have high (though not the highest) efficiency in the case of planned situations, and acceptable efficiency in case of predetermined deviations from the plan (model). The robustness of the optimal voting rule and the neural network to the inaccuracy of computing the posteriori probabilities of partial solutions of the combined classifiers was investigated. It was found that in calculating the posterior probabilities with the error the neural network has provided a better performance than the optimal voting rule, showing the property robustness to the inaccuracy of input data. The use of classifiers utilizing ANN allows to increase robustness of algorithms in the signal receivers.

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions.

This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study's novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry.

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

Design and optimization of the Teaching evaluation system based on artificial intelligence

Abstract In today’s highly developed information society, the construction work of educational informatization has become the focus of the future development of major universities. This paper combines artificial neural networks and UML software modeling technologies to design a teaching evaluation system using artificial neural network technology. The system requirements are analyzed from three aspects: system feasibility, functional requirements, and neural network evaluation function. The proposed system performance requirements are based on these three aspects. The neural network approximation problem can be solved using the gradient descent method based on the BP neural network model. After a series of operations, such as data preprocessing, the Design of the teaching evaluation system is completed, realizing the joint role of teaching and neural network evaluation module, and at the same time, proposing a system optimization decision for the teaching quality aspect. The teaching evaluation system designed in this study passed the test as the average response time of the whole system was 94.366 seconds when the number of users was 100, and there was no phenomenon such as transaction unresponsiveness. After the use of the teaching system, the feedback from the students’ use shows that the number of computer science students who mastered more than 80% of the system concepts is 42, and the rest of the knowledge is concentrated in the number of people who mastered 40% to 80%.

Green Marketing Performance Statistics Based on AHP and BP Neural Network Models

In order to promote the development of circular economy, starting from green marketing is the best means. Combining artificial neural network technology and hierarchical analysis subjective evaluation method, the key factors affecting green marketing performance were analyzed by constructing an evaluation index system for green marketing performance statistics, and the relative weights of each performance indicator were calculated by using analytic hierarchy process (AHP) and the relative importance ranking was given. Then the evaluation results were introduced into the neural network model as input variables. The evaluation results are trained and tested by using the inverse inverse propagation neural network. Finally, the simulation results show that the model can not only reduce the defects of subjective randomness, but also improve the predictability, accuracy and effectiveness of green marketing performance statistics. The calculation results are accurate, the method is feasible, and the errors are controllable, which has theoretical scientific significance and practical guiding significance.