Application Of Artificial Intelligence Techniques Research Articles

Application of artificial intelligence techniques in textile wastewater decolorisation fields: A systematic and citation network analysis review

AbstractThis study reviewed 155 journal articles to examine how artificial intelligence techniques are being applied in textile coloration and related fields. Distribution of the reviewed articles was assessed in terms of the type of journals, year of publication, methods, and research background. Based on the citation network analysis method, an objective approach, CitNetExplorer and VOSviewer are used to identify the clusters. It is found that artificial intelligence techniques are mainly three‐layer artificial neural networks with different designs, which are mainly used in textile wastewater decolorisation fields, including wastewater treatment and colour fading, such as colour prediction and real‐time monitoring of textile wastewater, dye removal efficiency, and dye degradation. Finally, the future research direction and limitations of this article are put forward.

Machine learning based on laboratory data for disease prediction

Objective: to review domestic and foreign literature on the issue of machine learning methods applied in medical information systems (MIS), to analyze the accuracy and efficiency of the technologies under study, their advantages and disadvantages, the possibilities of implementation in clinical practice.Material and methods. The literature search was performed in the PubMed/MEDLINE databases covering the period from 2000 to 2020 (using groups of keyphrases: "machine learning", "laboratory data", "clinical events", "prediction diseases"), CyberLeninka ("machine learning", "laboratory data", "clinical events", "prediction diseases" Russian keyphrases combinations) and Papers With Code ("clinical events", "prediction diseases", "electronic health record"). After reviewing the full text of 30 literature sources that met the selection criteria, the 19 most relevant articles were selected.Results. An analysis of sources that describe the application of artificial intelligence techniques to obtain predictive analytics, taking into account information about patients, such as demographic, anamnestic, and laboratory data, the data of instrumental studies, information about existing and former diseases available in MIS, was performed. The existing ways of predicting adverse medical outcomes using machine learning methods were considered. Information about the significance of the used laboratory data for constructing high-precision predictive mathematical models is presented.Conclusion. Implementation of machine learning algorithms in MIS seems to be a promising tool for effective prediction of adverse medical events for wide application in real clinical practice. It corresponds to the global trend in the development of personalized medicine based on the calculation of individual risk. There is an increase in the activity of research in the field of predicting noncommunicable diseases using artificial intelligence technologies.

Application of artificial intelligence techniques in incremental forming: a state-of-the-art review

Application of artificial intelligence techniques in incremental forming: a state-of-the-art review

The Effectiveness of Utilising Modern Artificial Intelligence Techniques and Initiatives to Combat COVID-19 in South Korea: A Narrative Review

All nations are facing significant challenges and difficulties in the face of an intense pandemic that controls human life, and many things have changed in life, which led to the doubling of the global economy and the difficulty of life for many of the world's inhabitants. At present, the nations are seeking to control the spread of the epidemic in them, as every country uses plans to prevent this pandemic. This article presents South Korea's experience in employing modern artificial intelligence techniques in facing COVID-19. Besides, the use of artificial intelligence techniques and their great role in analysing images of people with COVID-19 disease are discussed, and how they are used to save many lives of the victims. The data obtained in this article is from a group of studies, social networking sites, and news on TV channels in the first period of 2020, which are collected and summarised in this article. This study summarises the role of South Korea in facing the pandemic through the application of artificial intelligence techniques to detect the disease, tracking the injured through GPS and how an intelligent quarantine is established, diagnosing and examining the damaged remotely, applying an electronic platform to publish information about how to avoid infection and the use of robots to assist diagnose disease and use drones for sterilisation purposes.

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee effective resource provisioning. Objective: To tackle these challenges, future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on CPU utilization since it is considered a leading cause of the Quality of Service dropping. Methods: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that try to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and Conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for their strength in classification are also effective for prediction.

SHM system for anomaly detection of bolted joints in engineering structures

In this article, the elastic wave propagation phenomenon is applied as the basis for a system designed to detect anomalies in the pre-tensioned connections of engineering structures. A series of laboratory tests were carried out on a steel frame. This approach, where the connection is a fragment of the complex structure, and not an isolated part, enabled us to analyse behaviours similar to those in real engineering structures. The problem of the similarity of signals corresponding to different types of anomalies was considered. Thanks to the application of principal component analysis and a procedure employing a combination of artificial neural networks, the extraction of the most significant features of the registered signals was possible. The proposed procedure proved to be very sensitive and accurate for the proper detection of anomalies, as well as for the determination of their location and type, even if the loosening of bolts was very slight (i.e., invisible to the naked eye). The most important element of the presented approach is the fact that the combination of the assessment of the condition of local parts along with the application of artificial intelligence techniques can result in a Structural Health Monitoring (SHM) system that allows for the global assessment of structural integrity.

A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units.

The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.

Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases.

Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.

Overview of current state of research on the application of artificial intelligence techniques for COVID-19.

BackgroundUntil now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task.MethodologyThis review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models.ResultsIn this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences.ConclusionsThe AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

Artificial Intelligence Techniques in Smart Grid: A Survey

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

Session-based news recommendations using SimRank on multi-modal graphs

Recommender systems are among the most widespread applications of artificial intelligence techniques. For instance, news recommender systems serve users in managing the overload of information they come across when accessing news portals. Obviously, in the news domain time-awareness of recommendation approaches are crucial. However, most of these approaches missed to consider user sessions, which group the items that a user interacted with. In this paper, we study the problem of session-based recommendations by running SimRank on time-evolving heterogeneous graphs. In particular, we construct a dynamic heterogeneous multi-partite graph and adjust SimRank to run on it by using different (i) sliding time window sizes, (ii) sub-graphs used for model learning and (iii) sequential article weighting strategies. We evaluate our algorithms on two real-life datasets, and we show that our method outperforms other state-of-the-art methods in terms of accuracy and diversity. The significance and impact of this work is important because it introduces to the research community of expert and intelligent systems, for the first time, a stream-based version of SimRank algorithm, which is able to run over time-evolving graphs.

System Identification of Coupled Electric Drive Using Machine Learning Algorithms

System identification is behaviour of the system and hence nonlinear system identification is important. Machine learning which the application of artificial intelligence technique are programs that can learn and adapt to the changes without human interference. These are implemented in such a way that they try to find the relationship between the inputs and the target. Hence machine learning algorithms such as gradient descent based linear regression and neural networks are implemented for a coupled electric drive system. The simulation results are then analysed for their performance indices. the method of developing mathematical relationship between the input and output variables. Linear models cannot capture the non-linear

A Review of Artificial Intelligence in Serious Game for Public Health

Serious games used in public health for purposes including training, learning, prediction, coaching, diagnostic, rehabilitation and supporting. However, the serious game focuses mainly on user experience and graphical application, but less attention on applying artificial intelligence (AI). By enhancing artificial intelligence, it will have the capability to solve issues, especially in the field of public health. This article discussed a review of the use of artificial intelligence in a serious game for public health. The main idea of this paper is to gather all the related articles and create a trend analysis of the use of Artificial Intelligence (AI) in a serious game for public health. The related articles were applied to Artificial intelligence (AI) in the area of decision-making. The final section discussed the new trend and the future of Artificial intelligence using current AI techniques.

An application of artificial intelligence techniques in prediction of birds soundscape impact on tourists’ mental restoration in natural urban areas

The characteristics of birds' sounds assume a primary role in tourists' mental restoration and stress recovery. The aim of this research is the evaluation of birds' sound composition in mental restoration of urban tourists to develop a decision support system as a practical tool. In this order, the recorded sounds of six birds were composed (57 composed sounds) and the human perception approach was used to assess the impact of sounds on the urban tourist's mental restoration. The MLP (Multi-Layer Perceptron), RBFNN (Radial Basis Function Neural Network) and SVM (Support Vector Machine) models were developed for mental restoration prediction in different birds' sound compositions. The results indicated that RBFNN model output (R2 training = 0.89, and R2 test = 0.85) has the best accuracy compared to the MLP and SVM models in prediction of birds' soundscape score in natural urban areas. According to the sensitivity analysis, the values of White eared Bulbul (Pycononotus leucotis), Great Tit (Parus major), House Sparrow (Passer domesticus), Laughing Dove (Spilopelia senegalensis), White Wagtail (Motacilla alba), and Eurasian Magpie (Pica pica) are prioritized respectively that influence the RBFNN model outputs. In practice, the designed environmental decision support system tool is applied by urban planners, managers, psychoacoustic researchers, and landscape architects to predict the landscape score in different birds' habitats.

Predicting Performance Measurement of Residential Buildings Using an Artificial Neural Network

Application Earned Value Management (EVM) as a construction project control technique is not very common in the Republic of Iraq, in spite of the benefit from EVA to the schedule control and cost control of construction projects. One of the goals of the present study is the employment machine intelligence techniques in the estimation of earned value; also this study contributes to extend the cognitive content of study fields associated with the earned value, and the results of this study are considered a robust incentive to try and do complementary studies, or to simulate a similar study in alternative new technologies. This paper is aiming at introducing a novel and alternative method of applying Artificial Intelligence Techniques (AIT) for earned value management of the construction projects through using Artificial Neural Networks (ANN) to build mathematical models to be used to estimate the Schedule Performance Index (SPI), Cost Performance Index (CPI) and to Complete Cost Performance Indicator (TCPI) in Iraqi residential buildings before and at execution stage through using web-based software to perform the calculations in the estimation quickly, accurately and without effort. ANN technique was utilized to produce new prediction models by applying the Backpropagation algorithm through Neuframe software. Finally, the results showed that the ANN technique shows excellent results of estimation when it is compared with MLR techniques. The results were interpreted in terms of Average Accuracy (AA%) equal to 83.09, 90.83, and 82.88%, also, correlation coefficient (R) equal to 90.95, 93.00, and 92.30% for SPI, CPI and TCPI respectively. Doi: 10.28991/cej-2021-03091666 Full Text: PDF

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

BackgroundMore than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care.ObjectiveThis study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.MethodsWe searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer.ResultsWe identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures.ConclusionsAI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.

Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations

The Sustainable Development Goals (SDGs) present the emerging need to explore new ways of AgriFood production and food security as ultimate targets for feeding future generations. The study adopts a Design Science methodology and proposes Artificial Intelligence (AI) techniques as a solution to food security problems. Specifically, the proposed artefact presents the collective use of Agricultural Technology (AgriTech) drones inspired by the biomimetic ways of bird swarms. The design (artefact) appears here as a solution for supporting farming operations in inaccessible land, so as unmanned aerial devices contribute and improve the productivity of farming areas with limited capacity. The proposed design is developed through a scenario of drone swarms applying AI techniques to address food security issues. The study concludes by presenting a research agenda and the sectoral challenges triggered by the applications of AI in Agriculture.

Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence

Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named “rover”), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover traveling speed were studied. To assess the effectiveness of the spraying coverage, for each run several water-sensitive papers were placed throughout the vegetation. Compared to the commonly distributed mixture volume (1000 L/ha), the prototype is able to reduce up to 55% of product sprayed, but still assure an excellent crop coverage.

Application of Artificial Intelligence Techniques in Prediction of Energetic Performance of a Hybrid System Consisting of an Earth-Air Heat Exchanger and a Building-Integrated Photovoltaic/Thermal System

Abstract In this study, an attempt is made to assess the feasibility of several machine learning techniques for forecasting the energetic performance of a hybrid renewable energy unit consisting of an earth-air heat exchanger (EAX) and a building-integrated photovoltaic thermal (BPV/T) unit. The unit provides preheating/precooling of outdoor air in cold/warm days and generates electricity throughout the year. The employed methods are artificial neural network (ANN), support vector machine network (SVMN), and fuzzy network (FN). These techniques are employed to develop a relationship between the input and output parameters of the unit. The annual total energy output of the unit is taken as the essential output of the unit, while the input parameters were the length, depth, and width of the BPV/T unit, the air mass flowrate, and length and diameter of the EAX unit. The results indicated that all the methods are successful at the prediction of the annual total energy output of the unit; however, the SVMN outperforms other methods in the test phases where the non-trained data sets are examined. Finally, it is demonstrated that the SVMN model can successfully predict the output for any arbitrary combination of the inputs within the training intervals.

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.