Application Of Artificial Intelligence Methods Research Articles

Machine learning and IoT Based Robotic Solution for Home Wrist Rehabilitation through Motion Capture Enabled Architecture

The present study investigates the effectiveness of an integrated approach that combines machine learning, robotic rehabilitation, and traditional methods for wrist rehabilitation among the hemiparesis population. Subjects with robotic rehabilitation at the four-week intervention period showed the ball’s significant difference in the flexion and extension angles and demonstrated the better overall outcomes from the robotic intervention . The machine learning models, in particular, the artificial neural networks, provided the prediction of rehabilitation outcomes and demonstrated the high accuracy, high precision, high recall, and high F1 score. This study supports the idea of the beneficial effects of combining robotic rehabilitation with machine learning algorithms and supports the notion that the two benefits could bring high synergistic effects above their individual potentials. The report also contributes to the development of the rehabilitation science of wrists, given that few studies have explored the applications of artificial intelligence methods or robotic rehabilitation beyond the context of feasibility reports. The findings of the current research could contribute useful insights for the decision-making practices of therapists and healthcare providers, also given the reliance on large data contributed by the previous interventions. Future study could investigate the long-term results, the cost-effectiveness of the approaches, and assist in developing the potential results of customization for individual patients.

Model Predictive Control of Magnetic Helical Swimmers in Two-Dimensional Plane

Magnetic micro/mini-swimmers have a great potential application in biomedical research and have gained broad attention. Recent research studies focus on the automatic control methods of magnetic micro/mini-swimmers, such as artificial intelligence methods. However, their autonomous manipulation remains a challenge since they are subject to various disturbances from the external environment and model uncertainties. The current methods employ a status observer to estimate these disturbances and uncertainties. In this paper, we apply a data-driven technique that utilizes the nonlinear approximation ability of neural networks (NNs). To be specific, a flexible structure of NNs, Broad Learning System (BLS), is employed to model the input-output mapping relationship between the direction of the rotating magnetic field and the swimming direction of the helical miniature swimmer. Then, according to the dual-variable decoupling control, a levitation controller is formulated and a path following controller is proposed based on a planar three-degree-of-freedom model of magnetic helical swimmers, which is inspired by the model of wheeled mobile robots. Simulations and experiments are conducted to quantitatively validate the proposed control method using different planar paths. The experiment results show that the mean absolute error of the path following control is about 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the body length which is less than 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm$</tex-math> </inline-formula> . Our proposed control method provides a preliminary study to alleviate the impact of disturbances and uncertainties on the control performance of magnetic micro/mini-swimmers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is mainly motivated by the potential application of artificial intelligence methods in magnetic micro/mini-robot community, especially for the applications that reject disturbances and uncertainties. In practice, a path planner is employed to compute a pre-defined reference path that connects the start and the targeted locations. Then our control method is applied to the magnetic helical swimmer and it is guided to follow the reference path. Tackling external disturbances and model uncertainties is still challenging during the control progress. Neural network-based technique is an intuitive approach since their nonlinear modeling ability and generalization are suitable for estimating these disturbances and uncertainties. As for training the neural-network model, the training data about the angle parameters should be recorded by manual control of the helical swimmer. These angle parameters define the rotating axis of the uniform rotating magnetic field and the self-rotating axis of the magnetic helical swimmer. According to the planar motion model, the optimal controller is formulated using the feedback distance error and angle error, and the sum of the control signal and the prediction of the compensating model is used as the final control input. Our electromagnetic coil system features easy operation and configuration of cameras or other sensors. Simulations and experiments validate the performance of the neural network-based compensating method and the proposed optimal control method using magnetic micro/mini-swimmers.

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

Robust machine learning models: linear and nonlinear

AbstractArtificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and robustness. This is a problem in regulated industries, as authorities aimed at monitoring the risks arising from the application of Artificial Intelligence methods may not validate them. No measurement methodologies are yet available to jointly assess accuracy, explainability and robustness of machine learning models. We propose a methodology which fills the gap, extending the Forward Search approach, employed in robust statistical learning, to machine learning models. Doing so, we will be able to evaluate, by means of interpretable statistical tests, whether a specific Artificial Intelligence application is accurate, explainable and robust, through a unified methodology. We apply our proposal to the context of Bitcoin price prediction, comparing a linear regression model against a nonlinear neural network model.

Modeling the proof strength of pipe steels tempered with the application of artificial intelligence methods

The paper demonstrates the results of modeling the proof strength in pipe steels improved by tempering heat treatment. The main types of models used in this study are described, and information about the pros and cons of different approaches to modeling the target variable is summarized. Empirical equations relating hardness to yield strength and tensile strength are given. The role of the parameter n in these equations is indicated. The reasons for choosing the applied set of independent variables in the models are explained. The distribution of the target variable in the data sample is shown, and information about the feature space used for each of the models is provided. A general description of the source data is given. The structure of the main data sample is studied by the DBSCAN clustering method and the t-SNE dimension reduction algorithm. The reason for splitting the sample into clusters is substantiated in the context of reducing the spread of the predicted value of proof strength. The effectiveness of splitting the sample is estimated by using the measure of the spread of n. Various regression models for predicting yield strength are compared. It is shown that the regression model based on gradient boosting over the decision trees (LightGBM) has the smallest prediction error among the models considered. The permutation significance of the features of the model with the smallest prediction error is determined, the calculated significance of the features being compared with that from the metallurgical theory. The validity of the obtained prediction models is evaluated in view of the significance of the features and the metric estimate used in this study. The hypothesis of using a proxy variable (n) obtained from theoretical calculations as a predictor in the yield strength prediction model is tested. It is demonstrated that the application of the grouping method together with the parameter n makes it possible to obtain satisfactory prediction results on a smaller feature space.

The application of artificial intelligence methods in examining elementary school students' academic cheating on homework and its key predictors

The application of artificial intelligence methods in examining elementary school students' academic cheating on homework and its key predictors

APPLICATION OF THE DECISION TREE ALGORITHM TO EVALUATE THE RESULTS OF CHROMATOGRAPHIC ANALYSIS OF TRANSFORMER OIL

Power oil-filled transformer is the main link in the process of conversion and transmission of electrical energy in electrical networks at almost all voltage classes. At the moment in our country more than 50% of them are operated with significant excess of service life, which increases the requirements to their proper technical control and full-fledged diagnostics of all its main components.&#x0D; &#x0D; The aim of the research is to consider the ways of simplification and automation of the process of technical diagnostics of power oil-filled equipment by means of application of artificial intelligence methods, namely the decision tree algorithm for evaluation of the results of chromatographic analysis of transformer oil.&#x0D; &#x0D; Materials and Methods. As input data, the results of chromatographic analysis of dis-solved gases in transformer oil conducted from December 11, 2009 to December 12, 2020 for two power transformers of voltage class 110 kV installed in the power system of one of the regions of our country were considered and analyzed. As a software application used for diagnosing the technical condition of a power transformer using artificial intelligence methods, we selected the free application Deductor Academic 5.3 Build 0.46.&#x0D; &#x0D; Research results. The article considers the application of a promising method (decision tree algorithm) of interpretation of data obtained as a result of chromatographic analysis of dissolved gases in transformer oil. The analysis data were processed by means of artificial intelligence methods, the result of which was the reliability and accuracy of determining the technical condition of the power oil-filled transformer.&#x0D; &#x0D; Conclusions. According to the results of the study, a decision tree algorithm is proposed for the implementation of artificial intelligence in solving the problem of power transformer diagnostics using chromatographic analysis results.

Application of artificial intelligence method in adaptive antenna system

The requirements for adaptive antenna systems in modern and future wireless networks of the fifth (5G) and sixth (6G) generations are analyzed. The block diagram of the adaptive antenna system is presented and the basic principle of its operation is described. It is proposed to improve the block diagram of a modern adaptive antenna system by integrating an artificial intelligence module into it. The principle of interaction of the artificial intelligence module with the adaptive antenna system in the block diagram is shown and described. One of the methods of artificial intelligence (machine learning), the intelligent agent, is described and its mathematical model is presented. The possibility of applying the considered method in the cellular environment of a wireless communication network to improve the operation of an adaptive antenna system is shown. An example of the operation of an artificial intelligence module as part of an adaptive antenna system using an intelligent agent method is given. It is shown that, using the machine learning method, an intelligent agent within a single wireless communication cell can create a certain knowledge system capable of understanding and learning, taking into account the patterns of subscribers’ movement within the cell and predicting the direction of movement of a particular subscriber terminal. The resulting knowledge system is formed in an artificial intelligence module, which is included in the block diagram of a modern adaptive antenna system proposed in this paper, and can potentially be used to more accurately control the directional pattern of an adaptive antenna system. The idea proposed in this paper potentially allows us to develop the concept of a smart antenna, as well as to improve the characteristics of adaptive antenna systems, namely, to increase the energy efficiency of these systems by more accurately realizing the directivity characteristics and intelligent control of the radiation pattern petals using artificial intelligence.

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

To promote sustainable growth and minimize the greenhouse effect, rice husk fly ash can be used instead of a certain amount of cement. The research models the effects of using rice fly ash as a substitute for regular Portland cement on the compressive strength of concrete. In this study, different machine-learning techniques are investigated and a procedure to determine the optimal model is provided. A database of 909 analyzed samples forms the basis for creating forecast models. The derived models are assessed using the accuracy criteria RMSE, MAE, MAPE, and R. The research shows that artificial intelligence techniques can be used to model the compressive strength of concrete with acceptable accuracy. It is also possible to evaluate the importance of specific input variables and their influence on the strength of such concrete.

Application of artificial intelligence methods to model the effect of grass curing level on spread rate of fires

Artificial intelligence (AI) enables new approaches to fire behaviour models of operational relevance, including prescribed burns. This is particularly important in modelling of processes that are poorly understood, such as live fuel's effect on fire propagation. The objective of this study was to apply AI algorithms to quantify the effect of the proportion of dead fuels in a senescing grassland, the curing level, on reducing the rate of fire spread relative to the fully cured condition. We applied three different machine learning (ML) models, regression trees, support vector regression (SVR) and Gene expression programming (GEP), two ensemble ML methods, Random Forest and GEP Forest, and non-linear regression analysis to an experimental fire dataset. Results show SVR and GEP as the best ML methods to model the curing level impact on fire spread. No differences in model fit were observed between the best ML methods and non-linear regression analysis.

Application of Artificial Intelligence Methods and Evaluation Models for Breast Cancer Diagnosis and Prognosis

Cancer is a primary global health concern, and accurate diagnosis and prognosis are crucial in patient care. This paper applies the deep learning algorithm CNN to detect and prognosis breast cancer. The results of the conducted experiment are assessed through rigorous evaluation frameworks, ensuring clinical relevance and applicability. The performance of the developed CNN model enhanced the accuracy, efficiency, and reliability of the breast cancer diagnosis and prognosis.

Application of artificial intelligence methods in the detection of agricultural plots to produce spatialized indicators of indirect pesticide exposure

Application of artificial intelligence methods in the detection of agricultural plots to produce spatialized indicators of indirect pesticide exposure

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.

Artificial intelligence systems in surgery: A review of opportunities, limitations, and prospects

Artificial intelligence technologies are increasingly being applied in a variety of medical disciplines. After reviewing 278 publications from 1985 to 2023, 99 articles were selected from the databases elibrary, PubMed, Medline, WoS, Nature, Springer, and Wiley J Database to present the main approaches and a modern picture of the application of artificial intelligence methods and technologies in pediatric surgery and intensive care. The article examines many facets of artificial intelligence systems for medical uses, namely, computer decision support systems or supporting the surgeon throughout the surgical intervention procedure. Computer analysis of 3D visualization and 3D anatomical modeling of images obtained from computed tomography and magnetic resonance imaging investigations can be used to plan operations. Because of the possibilities of sufficiently accurate 3D models and methods for organs and pathological processes, various methodologies and software tools for preoperative planning and intraoperative support of surgical intervention have been developed. Computer (technical) vision analyzes high-quality medical images and interprets them in multimodal three-dimensional images for computer diagnoses and operations under visual control, including augmented reality methods. Robotic surgery involves manipulators, including remotely controlled ones, and intellectualized complexes that independently perform specific actions of the second assistant surgeon. In intensive care, artificial intelligence technologies are being investigated to merge data from bedside monitors and other information about patients conditions to identify critical situations and control mechanical ventilation. Simultaneously, several obstacles impede the adoption of artificial intelligence in surgery. The nature and standardization of the initial data required for their integration, taking into consideration atypical cases, the possibility of bias in the sample used, and the transparency of the decision-making process in machine learning models are examples. The explanation of solutions presented in machine learning models and the transition to full-fledged validation of the systems being built define the prospects for developing and using artificial intelligence systems.

Using AI Tools to Develop Training Materials for Aviation: Ethical, Technical, and Practical Concerns

A key potential advantage of modern training technologies relates to the ability to use them to automate aspects of training. Although in the early stages of adoption within the aviation industry, artificial intelligence (AI) tools and methods have many promises for training design, development, delivery, and assessment. We applied the use-case technology-mapping framework (UCTM) to identify and analyze how automation and AI technologies may be used within the flightcrew training design pipeline, integrating perspectives from relevant literature, informal discussions with stakeholders, and workshops with domain experts. Our preliminary findings highlight current/near-future applications of AI methods and tools in the training design pipeline. Here, we discuss ethical/legal, technical, and practical considerations for flightcrew training. We urge practitioners and researchers in the aviation human factors community to engage in this discussion and to conduct empirical research that will allow for a positive use of the technology across applications, including in aviation training. Practical Takeaways/Applications. • We present a wide range of potential use cases of AI in flightcrew training. • We discuss the ethical/legal, technical, and practical implications of automation and AI. • This information may inform future training processes and practices in the aviation domain.

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures.

Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient's potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.

Application of artificial intelligence methods for determination of transients in the power system

Numerous factors, including sudden load reductions, switching transient loads, lightning strikes, and malfunctions of control devices, can result in overvoltage. Overvoltage can harm associated power supply components and result in insulation failure, electronic component damage, flashovers, etc. A machine learning technique called a "neural network" estimates computation results that depend on a lot of inputs. For a variety of reasons, neural networks have recently been used to manage and optimize the power system. This paper presents an artificial neural network (ANN)-based approach to determining overvoltages in power systems. To simulate overvoltages, many simulations were performed in Electromagnetic Transient Program (EMTP). Variations of parameters of interest that have an influence on overvoltages were made using JavaScript that was connected to EMTP models. The extraction of characteristic parameters from overvoltage waveshape is a demanding task, and it was conducted in MATLAB, as was a overvoltage classification methodology based on neural networks. Results were presented and discussed.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Automated detection of hippocampal sclerosis using real-world clinical MRI images.

Hippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task. Our aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS. We proposed a deep learning algorithm called "HS-Net" to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility. The HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method. Our computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.

A Rule Based Theorem Prover: an Introduction to Proofs in Secondary Schools

The introduction of automated deduction systems in secondary schools face several bottlenecks. Beyond the problems related with the curricula and the teachers, the dissonance between the outcomes of the geometry automated theorem provers and the normal practice of conjecturing and proving in schools is a major barrier to a wider use of such tools in an educational environment. Since the early implementations of geometry automated theorem provers, applications of artificial intelligence methods, synthetic provers based on inference rules and using forward chaining reasoning are considered to be more suited for education proposes. Choosing an appropriate set of rules and an automated method that can use those rules is a major challenge. We discuss one such rule set and its implementation using the geometry deductive databases method (GDDM). The approach is tested using some chosen geometric conjectures that could be the goal of a 7th year class (approx. 12-year-old students). A lesson plan is presented, its goal is the introduction of formal demonstration of proving geometric theorems, trying to motivate students to that goal