Artificial Intelligence Neural Networks Research Articles

Artificial intelligence neural network and fuzzy modelling of unsteady Sisko trihybrid nanofluids for cancer therapy with entropy insights.

The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy generation suspended in Darcy-Forchheimer porous medium. Electro Magneto Hydro Dynamics (EMHD), non-linear thermal radiation and exponential and thermal- space dependent heat source/sink coefficients are considered with the intent of conceiving an Runge-Kutta-Fehlberg method with shooting procedures integrated with a combination of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Reptile Search Algorithm (RSA). Then, ANFIS-RSA, is used to predict the Nusselt number, skin friction co-efficient in radial and tangential velocities. Reliable self-similarity variables have reduced a non-linear partial differential set of equations into an ordinary differential equation. According to the empirical evidence, Sisko fluid parameter rises the radial velocity whereas for magnetic field and Darcy-Forchheimer the azimuthal and axial velocities visualizations decreasing trend, respectively. The entropy generation and Bejan number rises for electric and radiation effects. Also, ANFIS-RSA indicates that the model attained a high level of precision in terms of radial velocity (98.13%), tangential velocity (98.18%) and Nusselt number (98.91%). Thus, the longer rendering of the nanoparticles used here might, makes them potentially helpful for regulating the therapeutic impact in the management and treatment of cancer.

Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review

The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization of equipment idle time. In this context, this study aims to characterize the current knowledge about the challenges of the biopharmaceutical industry in the application of prescriptive maintenance, which derives from predictive maintenance, in the context of I4.0. To achieve this, a systematic review of the literature was carried out in the scientific knowledge bases IEEE Xplore, Scopus, Web of Science, Science Direct, and Google Scholar, considering works such as Reviews, Article Research, and Conference Abstracts published between 2018 and 2023. The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. There are significant advances in this area including the implementation of more elaborate algorithms used in artificial intelligence neural networks, the advancement of the use of decision support systems as well as the collection of data in a more structured and intelligent way. It is concluded that for the adoption of prescriptive maintenance in the pharmaceutical industry, issues such as the definition of data entry and analysis methods, interoperability between “shop floor” and corporate systems, as well as the integration of technologies existing in the world, must be considered for I4.0.

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study

ObjectiveThe purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist. Materials and MethodsThe diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation. ResultsThe differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p < 0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst. ConclusionAI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.

Influence of video instruction on soccer kick velocity in young players

Soccer is a sport that necessitates proficiency in motor control and cognitive abilities, with kicking serving as a fundamental action. Improving kicking performance is vital for player advancement, leading to the inquiry: “How can video instruction promptly optimize kicking technique in young players?”. This research aimed to investigate the immediate effects of an instructional video on the kinematic variables of kicking to induce an acute alteration in the movement pattern. A cohort of 26 male youths, aged between 10 and 15 years, partook in the study and were segregated into an instruction group (IG) and a control group (CG). The IG received exposure to a video containing specific kicking instructions, while the CG viewed a video lacking such guidance. Both groups were recorded performing penalty kicks before and after viewing the video. Kinematic analysis was carried out utilizing the OpenPose artificial intelligence neural network for pose detection and subsequent 3D reconstruction. The primary discovery was that the length of the last stride (LS) notably increased in the IG following the instructional video. This outcome indicates that video instructions hold promise in promptly enhancing particular aspects of kicking technique in young soccer players.

Enhanced MPPT controller for partially shaded PV systems using a modified PSO algorithm and intelligent artificial neural network, with DSP F28379D implementation

Enhanced MPPT controller for partially shaded PV systems using a modified PSO algorithm and intelligent artificial neural network, with DSP F28379D implementation

Regulating Tax Avoidance Driven by AI Neural Networks: Strengths and Weaknesses of General Anti-Avoidance Regimes in South Africa and Elsewhere

This article explains the ever-growing use of artificial intelligence (AI) in tax and assesses the strength and weaknesses of general anti-avoidance rule regimes to address tax avoidance driven by AI. Proactive steps by regulators are desirable. Recommendations are formulated underlining the need for international coordination and an “all of government” approach.

The Potential of Neural Networks in Teacher Preparation for Foreign Language Classes and Educational Content Creation

Artificial intelligence (AI) technologies are now being extensively used in various fields of human activity, including science and education. Neural networks, which are AI algorithms, are widely used in language learning. The relevance of this study is due to the need to study the potential of neural networks in the process of preparing a teacher of higher school for foreign language classes. Using neural networks, the university teacher demonstrates the ability not only to choose teaching materials but also to adapt them to the real learning environment, as well as to create new materials, taking into account various available resources and educational needs. The use of neural networks helps to optimize the process of teacher’s preparation for classes. This study aims to analyze the didactic potential of Twee and Perplexity AI neural networks, as well as to examine their use in the preparation for foreign language classes and the creation of educational content for students in various fields of professional training. The research methods we applied involved analysis, comparison, generalization of scientific literature, observation, study of experience and products of activity, analysis of educational literature, modeling of educational process. The scientific novelty of the study is the development of methods of using Twee and Perplexity AI neural networks in the preparation of a set of exercises on the formation of main types of speech activity among non-linguistic students. The conclusion can be drawn that it is necessary to further develop the methodology of teaching foreign languages with the help of artificial intelligence technologies which would help in generating the unique educational content and a set of communication exercises. Twee and Perplexity AI neural networks are effective pedagogical and didactic tools in teacher preparation for foreign language classes. Keywords: artificial intelligence, Twee and Perplexity AI neural networks, creation of educational content, methods of foreign language teaching, AI technologies application

ЖАСАЛМА ИНТЕЛЛЕКТ БИЛИМ БЕРҮҮДӨ

The article discusses the use of artificial intelligence in education and its progressiveness, as well as problems in the use of artificial intelligence at present time. Contains answers to the question: “Is it necessary to use artificial intelligence in education?” It also contains information about the emergence and development of artificial intelligence. The opinion about artificial intelligence in education, namely in higher educational institutions as an academic discipline, was considered. The place of artificial intelligence in education, the negative sides and the positive sides were considered. The article also contains a mathematical model or the mathematical basis of artificial intelligence. Information about the positive and negative effects of artificial intelligence or Chatgpt on people is included. Included is detailed information about ChatGPT, one of the artificial intelligence neural network, as well as information on how to use ChatGPT.

Particle tracking, recognition and LET evaluation of out-of-field proton therapy delivered to a phantom with implants

Objective. This study aims to assess the composition of scattered particles generated in proton therapy for tumors situated proximal to some titanium (Ti) dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumor. Approach. A therapeutic conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants inserted in the target volume (made of Ti and plastic, respectively). The scattered radiation resulted during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence neural networks (AI NN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0° and 60°. Results. Using AI NN, 3 classes of particles were identified: protons, electrons & photons, and ions & fast neutrons. Placing a Ti implant in the beam’s path resulted in predominantly electrons and photons, contributing 52.2% of the total number of detected particles, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without metal inserts, respectively. The LET spectra were derived for each group of particles identified, with values ranging from 0.01 to 7.5 keV μm−1 for Ti implants/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions. Significance. This method, complemented by directional maps, holds the potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixed field, and enhancing in this way the LET calculation.

Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing

IntroductionHepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.MethodThe GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.ResultsAn ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.ConclusionThe consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

Islanded micro-grid under variable load conditions for local distribution network using artificial neural network

ABSTRACT This research proposes an intelligent artificial neural network (ANN) controller designed for a micro-grid featuring solar photovoltaic, wind, and Battery Energy Storage (BES) systems, integrated with a shunt voltage source converter. The primary goal of this proposed method is to minimize Total Harmonic Distortion (THD) and ensure a stable Distribution Line Commutation Voltage (DLCV) amidst load variations, achieving a rapid settling period. The STF not only facilitates phase synchronization but also effectively separates harmonic and fundamental components. This research contributes to advancing micro-grid control strategies, particularly in enhancing stability and reducing distortion during variable operating conditions. Based on an analysis of the four test cases, it is evident that the proposed strategy lowered the THDs to 4.63%, 3.45%, 3.05%, and 4.70% for each test case, as well as the PF to a level that was similar. The proposed controller shows better results compared with other controllers like Sliding Mode Controller (SMC) and the Proportional-Integral Controller (PIC).

Simplified methods for the design of landfill double composite liners using neural network

Double composite liners (DCLs) have been widely used in landfills to protect the surrounding environment. This study aims to develop simplified empirical equations for calculating breakthrough times of DCLs based on analytical equations or experimental data. The artificial intelligence neural network called Group Method of Data Handling (GMDH) type neural network was used to perform equation simplification. New empirical equations in polynomial formats are obtained by a layer-summation method and a series of numerical experiments based on analytical solutions for contaminant transport in double composite liners. The accuracy of empirical equations is demonstrated by comparing them with the existing solutions and numerical results. The performance of four types of DCLs were then investigated. The mean absolute percentage errors (MAPEs) for each type of DCLs with different leachate heads and soil liner thicknesses are all lower than 10%. Additionally, a trend for the improvement of the GMDH equation accuracy with the increase of Δh1 is observed. The presented equations can perform well in high leachate head conditions (e.g. &gt; 5 m) where DCLs are required.

Predicting microfluidic droplet diameters in glass capillary devices using machine learning

Using actual experimental data of single emulsion diameters generated from glass capillary microfluidic devices, we report predictions for droplet diameters in both dripping and jetting regimes using an artificial intelligence neural network. This is the first time that glass capillary microfluidic experimental data has been employed to train a neural network and predict droplet diameters for these particular devices. The neural network inputs are fluid properties and co-flow geometries of glass capillary devices. Specifically, the inputs are orifice width, capillary spacing, flow rate ratio, inner and outer capillary number, and the regime, dripping or jetting. The experimental data set consists of 800 points; 80% of the data is for training the model and 20% is for testing. Droplet sizes generated with single emulsion devices varied from 161 microns to 1085 microns in diameter, with most droplet sizes in the dripping regime. Three model algorithms – Neural Network, Linear Regression, and Support Vector Regression – were compared for droplet diameter prediction. A neural network performed best in the dripping regime with an accuracy of 91.1%. Linear regression performed the best of the three models in the jetting regime with a 92.6% accuracy. The support vector regression, with a 10-fold cross validation average score of 0.278, was outperformed by the neural network and linear regression models with 10-fold cross validation average scores of 0.623 and 0.604, respectively. Finally, we have also successfully generated a graphic user interface that predicts microfluidic droplet diameters for glass capillary devices using the neural network.

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.