Application Of Artificial Intelligence Techniques Research Articles

A Survey on Application of Artificial Intelligence Techniques for Prognostics

A Survey on Application of Artificial Intelligence Techniques for Prognostics

Applications of Artificial Intelligence Techniques for trajectories optimization in robotics mobile platforms

Different control methods for the autonomous navigation of robots have been designed during industry 4.0, several works use Simultaneous Localization And Mapping (SLAM) or path planning systems for trajectory tracking, however, there are different restrictions when it is required to avoid obstacles and reconfigure parameters in real-time. The present work shows an algorithm based on the use of Deep Q-Networks (DQN) and Reinforcement Learning, where the model is in charge of maximizing the reward while executing actions on the robot and in turn extracts information about its position and obstacles within the simulated environment. A series of experiments were carried out for the configuration of the algorithm whose results validate the operation of the network, showing that the robot learns through exploration, exploiting the knowledge learned from previous scenarios. Using a simulated environment allowed the DQN network to compute complex functions due to the randomness, leading to higher autonomous learning performance over other control methods.

Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)

Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview.

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

HYBRID MACHINE LEARNING MODEL APPLIED TO PHASE INVERSION PREDICTION IN LIQUID-LIQUID PIPE FLOW

One of the current challenges in two-phase flow is the characterization of phase inversion in the oil and gas industry. Empirical and semi-empirical models have been developed by several researchers, allowing limited predictions through correlations. Recently, models obtained with application of artificial intelligence techniques, such as artificial neural networks, have become a promising alternative to identify flow patterns and their transition boundaries. This work's aim is to develop a hybrid model that identifies the phase inversion transition from oil-in-water to water-in-oil flow in vertical pipes. It is based on recent models found in the literature and logistic regression models based on artificial neural networks, for which information was obtained from the literature. The proposed hybrid model achieved an RMSE ≈ 0.0834, thus being an efficient contribution to the identification of phase inversion in oil-water two-phase flow.

Artificial neural networks applications in partially shaded PV systems

Renewable energy sources have attracted attention in the last few years as an efficient and sustainable alternative to conventional fossil fuels. Among these sources, solar power emerges as an abundant and feasible energy resource for powering various forms of energy-demanding sectors, such as industrial applications and transportation. Solar photovoltaic (PV) systems directly transmute the energy in the solar electromagnetic radiation to electrical energy. However, a significant problem in solar PV systems is partial shading. A noticeable energy loss happens when a small portion of the PV system is subject to shading. There has been increasing attention to applying Artificial Intelligence (AI) techniques to mitigate partial shading. One of the most promising AI techniques is Artificial Neural Networks (ANNs) used extensively in analysing partially shaded PV systems. This work reviews the applications of ANNs in various aspects of partially shaded PV systems. The application of ANNs in Maximum Power Point Tracking (MPPT), fault detection, fault mitigation, system modelling, and performance optimization of solar PV systems undergoing partial shading are summarized and discussed. Finally, future research directions are presented to further improve these techniques and move them toward practical application.

Recognizing human functional patterns in IADLs through hand detection and artificial intelligence.

Cognitive impairment is one of the characteristics shown by patients with Alzheimer's dementia (Montenegro et al., 2020; Kelley & Petersen, 2007). This affects the executive functions of most of these patients from the early stage (Guarino et al., 2019) and consequently, have difficulty in carrying out their daily activities (Dubbelman et al., 2020). These activities require precise coordination of the person's movements and interaction with their environment, so instrumental activities of daily living (IADLs) are predictors of such impairment (Barberger-Gateau & Dartigues, 1993), as several research studies have shown(Brovelli et al., 2017). Therefore, assessing the performance of IADLs by analyzing human biomechanical markers such as hand movements is of great interest. This, together with the application of artificial intelligence techniques, in particular deep learning, will allow the development of technological tools for the creation of an automated model to support the early diagnosis of Alzheimer's disease. This work was inspired by the development of Alejandro Acosta et al, where human biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern (Acosta-Franco et al., 2021). To improve the accuracy of the baseline model, we propose a hand detection algorithm based on a probabilistic approach considering skin color as a descriptor. To test the proposed algorithm, we used the dataset of egocentric videos containing the performance of IADL activities, which is organized into four classes, based on the prehensile patterns of the hands: force and precision, and on the kinematics of the instruments: displacement and manipulation. With the improvement made to the base model we obtained an accuracy higher than 74% in recognizing movement patterns of displacement and manipulation of objects, and a good prediction of the depth of the anatomical planes. Our improved model obtained a good percentage of recognition accuracy for the instrument's kinematics, which can help in the development of technological tools for the creation of an automated model to support the diagnosis of early Alzheimer's disease.

Dissolved Gas Analysis and Application of Artificial Intelligence Technique for Fault Diagnosis in Power Transformers: A South African Case Study

In South Africa, the growing power demand, challenges of having idle infrastructure, and power delivery issues have become crucial problems. Reliability enhancement necessitates a life-cycle performance analysis of the electrical power transformers. To attain reliable operation and continuous electric power supply, methodical condition monitoring of the electrical power transformer is compulsory. Abrupt breakdown of the power transformer instigates grievous economic detriment in the context of the cost of the transformer and disturbance in the electrical energy supply. On the condition that the state of the transformer is appraised in advance, it can be superseded to reduced loading conditions as an alternative to unexpected failure. Dissolved gas analysis (DGA) nowadays has become a customary method for diagnosing transformer faults. DGA provides the concentration level of various gases dissolved, and consequently, the nature of faults can be predicted subject to the concentration level of the gases. The prediction of fault class from DGA output has so far proven to be not holistically reliable when using conventional methods on account of the volatility of the DGA data in line with the rating and working conditions of the transformer. Several faults are unpredictable using the IEC gas ratio (IECGR) method, and an artificial neural network (ANN) has the hindrance of overfitting. Nonetheless, considering that transformer fault prediction is a classification problem, in this work, a unique classification algorithm is proposed. This applies a binary classification support vector machine (BCSVM). The classification precision is not reliant on the number of features of the input gases dataset. The results indicate that the proposed BCSVM furnishes improved results concerning IECGR and ANN methods traceable to its enhanced generalization capability and constructional risk-abatement principle.

Application of Artificial Intelligent Techniques for Power Quality Improvement in Hybrid Microgrid System

The hybrid AC-DC microgrid (MG) has gained popularity recently as it offers the benefits of AC and DC systems. Interconnecting AC-DC converters are necessary since the MG has both DC and AC sub-grids. Adding an extra harmonic adjustment mechanism to the interlinking converters is promising because non-linear AC loads can worsen the quality of the voltage on the AC bus. The interlinking converters’ primary function is to interchange real and reactive power between DC and AC sub-grids, so the typical harmonic controlling approach implemented for active power filters (APFs) might not be appropriate for them. When the MG’s capacity is high, it is desirable that the switching frequency be lesser than the APFs. The performance of harmonic correction or even system stability may suffer at low switching frequencies. In this study, a harmonic compensating technique appropriate for hybrid AC-DC interlinking converters with lower switching frequencies is planned. The suggested strategy, modeling techniques, stability analysis, and a thorough virtual impedance design are discussed in this work.

Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

Due to their specific characteristics, innovation projects are developed in contexts with great volatility, uncertainty, complexity, and even ambiguity. Project management has needed to adopt changes to ensure success in this type of project. Artificial intelligence (AI) techniques are being used in these changing environments to increase productivity. This work collected and analyzed those areas of technological innovation project management, such as risk management, costs, and deadlines, in which the application of artificial-intelligence techniques is having the greatest impact. With this objective, a search was carried out in the Scopus database including the three areas involved, that is, artificial intelligence, project management, and research and innovation. The resulting document set was analyzed using the co-word bibliographic method. Then, the results obtained were analyzed first from a global point of view and then specifically for each of the domains that the Project Management Institute (PMI) defines in project management. Some of the findings obtained indicate that sectors such as construction, software and product development, and systems such as knowledge management or decision-support systems have studied and applied the possibilities of artificial intelligence more intensively.

Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions.

Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, the design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources, sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This article presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.

Research on Structured Extraction Method for Function Points Based on Event Extraction

Software size is a significant input for software cost estimation, and the implementation of software size estimation dramatically affects the results and efficiency of cost estimation. Traditionally, the software size estimation is implemented by strictly trained experts and is more labor-intensive for large software projects, which is relatively expensive and inefficient. Function Point Analysis is a widely used method for software size estimation, supported by several international standards. We propose a structured and automated function point extraction method based on event extraction in natural language processing to address the problem of complex and inefficient manual recognition for function point recognition. This approach has been validated in 10 industrial cases. Experimental results show that our method can identify more than 70% of the function points, which significantly improves the efficiency of Function Point Analysis implementation. This paper could be a guide on the application of artificial intelligence techniques to software cost estimation.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.

Breast cancer is the most common disease among women, with over million new diagnoses each year worldwide. About of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Application of an Innovative Convolutional/LSTM Neural Network for Computing Resource Allocation in NFV Network Architectures

An innovative resource allocation framework for virtualized network environments and based on the application of Artificial Intelligence techniques is proposed and investigated. It integrates the needed processing capacity prediction procedure and the allocation one which determines the capacity under-allocation or over-allocation needed to minimize the resource allocation and Quality of Service degradation costs. The proposed solution is based on: i) a monitoring procedure in which the processing capacities required by virtual instances are periodically monitored; ii) an integrated allocation/prediction procedure in which the processing capacities to be allocated to the virtual instances are evaluated in time intervals successive to the monitoring periods. This second procedure uses a Convolutional/Long Short Term Memory neural network whose loss function is defined so as to minimize an overall cost dependent on both the cloud resource allocation and Quality of Service degradation costs. We evaluate the proposed solution in backbone and metropolitan traffic and network scenario. We show how in the traffic scenario of an Italian Mobile Operator in Milan zone, the proposed solution far outperforms the not integrated classical solution in which the capacity prediction and allocation procedures are separately performed. Furthermore its performance are very near to the one of an oracle that optimizes the capacity over/under dimensioning parameter.

Prediction and uncertainty quantification of ultimate bond strength between UHPC and reinforcing steel bar using a hybrid machine learning approach

The composite action of the reinforcing bars in the UHPC involves complex and nonlinear mechanisms. Inadequate knowledge of their interaction may lead to insufficient bond resistance in the concrete structures. The behavior of reinforcing steel bars in UHPC is generally determined by conducting pull-out tests. However, these tests need amounts of time and cost. A soft computing technique is an alternative approach that exterminates the need to conduct pull-out tests. This study presents the application of artificial intelligence technique in predicting ultimate bond strength (UBS) between ultrahigh-performance concrete (UHPC) and steel reinforcement bars and performs uncertainty quantification of such values. A total of 333 pull-out test data points were collected. A hybrid machine learning (ML) model, improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) was developed to predict the UBS between UHPC and reinforcing bars. The efficacy of the IEPANN was evaluated against other ML models such as particle swamp optimization hybridized artificial neural network (PANN), support vector regression (SVR), and multiple linear regression (MLR). The IEPANN model was benchmarked against three design codes, including CEB-FIP, AS3600, EC2, and four empirical models. The result obtained based on R = 0.973, MAE = 1.880, RMSE = 2.595, MAPE = 7.708, and RI = 0.944 shows that the IEPANN model can predict UBS between UHPC and reinforcing steel bars with acceptable accuracy. The uncertainty of the UBS was quantified with geometrical and material configuration randomness based on Monte Carlo simulation. The simulation result shows that the yield strength of steel rebar is the most sensitive, while the embedment length is the least sensitive to the bond strength. Systematic assessment of uncertainties in UBS in the design of reinforced concrete members may lead to a higher level of confidence and enhance construction reliability.

Faulty scenarios in sour water treatment units: Simulation and AI-based diagnosis

Fault Detection and Diagnosis (FDD) is a Process System Engineering (PSE) area of great importance, especially with increased process automation. It is one of the chemical engineering fields considered promising to Artificial Intelligence (AI) application. FDD systems can be useful to supervise Sour Water Treatment Units (SWTU) behavior, as they are chemical processes that present operational difficulties when disturbances occur. SWTU remove contaminants from sour water (SW) streams generated through petroleum processing, consisting mainly of small amounts of H2S and NH3. They are considered one of the primary aqueous wastes of refineries and cannot be disposed of due to environmental regulations. However, no previous studies focused on the development of FDD systems for SWTU exist and works on its dynamics are scarce. Hence, the present work proposes to study the dynamic simulated behavior of an SWTU and develop an FDD system applying AI techniques with hyperparameters optimization. The simulation was performed in Aspen Plus Dynamics® and ran to create normal operation and six relevant faults, including occurrences in the process (e.g., inundation and fouling) and sensors. FDD was performed through data classification, and results were evaluated mainly by accuracy and confusion matrices. Even after variable reduction, FDD was satisfactory with over 87.50% accuracy in all AI techniques. RF and SVM with linear and Gaussian kernels presented the best results, with over 93% of accuracy in training and testing, and had the shortest computing times. The second column’s sump level proved to be the most relevant variable for fault identification.

Application of artificial intelligence to corelate food formulations to disease risk prediction: a comprehensive review.

Clinicians and administrators are applying Artificial Intelligence (AI) Techniques widely as the promising results of their applications in the healthcare have been established. The meaningful impact of the AI applications will be limited unless it is coherently applied with human diagnosis and inputs from specialist clinician. This will help to address limitations and take advantage of the promises of the AI techniques. Machine Learning is one of the AI technique that finds high relevance in the medicine and health care. This review provides an overall glimpse of current practices and research outcomes of the application of the AI techniques in the healthcare and medical practices. It further describes Machine Learning Techniques in disease prediction and scope for food formulations for combatting disease.

Challenges And Solution of Artificial Intelligence in Cyber Security

A computer can completely perform like a person thanks to the advancement of information technology. A machine that responds and performs like a human brain is required for AI, an amazing feature of information technology. Touch and speech are recognized by the system as qualities that can be used to operate potential daily operations without human intervention. Artificial intelligence, on the other hand, is the study of an intelligence "agent" that effectively completes a task while taking the state of the environment into account. The majority of computing systems are created to fulfil certain functions based on the context, applying special traits drawn from components of human nature that exist inherently. Using approaches and understanding of higher-level human-inspired elements, judgement, and emotions flow working, artificial intelligence frequently collaborates with mankind to fix the problem. Artificial intelligence is intelligence based on machines as opposed to human intelligence. A single person cannot manage the complexity of activities and the volume of information needed to secure cyberspace without significant automation. But in order to adequately defend against security risks, technology and software with conventional fixed implementations are challenging to design. AI machine learning and simplification techniques can be used to treat this issue. After reviewing the available artificial intelligence software for cyber security, we may assume that useful applications currently exist. They employ neural networks to first secure the perimeter and many other cyber security domains. On the other hand, it was evident that certain cyber security issues could only be effectively solved by the application of artificial intelligence techniques. This study paper's objective is to assess the existing difficulties posed by artificial intelligence for cyber security. The research report will include ground-breaking suggestions for AI in cyber security.

A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events

Soil temperature is a fundamental parameter in water resources and irrigation engineering. A cost-effective model that can accurately forecast soil temperature is urgently needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature predictions. In the present study, attempts are made to deliver a comprehensive and detailed assessment of the performance of a wide range of AI approaches in soil temperature prediction. In this regard, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to more advanced AI techniques, such as multi-layer perceptron and deep learning, are taken into account. Meanwhile, great varieties of land and atmospheric variables are applied as model inputs. A sensitivity analysis was conducted on input climate variables to determine the importance of each variable in predicting soil temperature. This examination reduced the number of input variables from 8 to 7, which decreased the simulation load. Additionally, this showed that air temperature and solar radiation play the most important roles in soil temperature prediction, while precipitation can be neglected in forecast AI models. The comparison of soil temperature predicted by different AI models showed that deep learning demonstrated the best performance with R-squared of 0.980 and NRMSE of 2.237%, followed by multi-layer perceptron with R-squared of 0.980 and NRMSE of 2.266%. In addition, the performance of developed AI models was evaluated in extremely hot events since heat warnings are essential to protect lives and properties. The assessment showed that deep learning and multi-layer perceptron methods still have the best prediction. However, their R-squared decreased to 0.862 and 0.859, and NRMSE increased to 6.519% and 6.601%, respectively.