Use Of Artificial Intelligence Techniques Research Articles

Water Quality Classification and Machine Learning Model for Predicting Water Quality Status—A Study on Loa River Located in an Extremely Arid Environment: Atacama Desert

Water is the most important resource for human, animal, and vegetal life. Recently, the use of artificial intelligence techniques, such as Random Forest, has been combined with other techniques, such as models of logical–mathematical reasoning, to generate predictive water quality models. In this study, a rule-based inference technique to generate water quality labels is described, using historical physicochemical parameter data on seven water monitoring stations in Loa River, collected by the Chilean Ministry of the Environment. Next, a predictive model of water quality status was created, using Random Forest, physicochemical parameters, and expert knowledge. The validation of Random Forest results is described using three quality indicators from the machine learning model: accuracy (acc), precision (p), and recall (r). This paper describes dataset preparation, the refinement of the threshold values used for the physicochemical parameters most significant in the class, and the predictive model labeling water quality. The models obtained yielded the following mean values: acc = 0.897, p = 89.73, and r = 0.928. The ML model reported here is novel since no previous studies of this kind predict the water quality of Loa River, located in an extremely arid zone. This study also helps to create specific knowledge to predict freshwater quality.

Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol

BackgroundVariability and inaccuracies in the diagnosis of prostate cancer, and the risk of complications from invasive tests, have been extensively reported in the research literature. To address this, the use of artificial intelligence (AI) has been attracting increased interest in recent years to improve the diagnostic accuracy and objectivity. Although AI literature has reported promising results, further research is needed on the identification of evidence gaps that limit the potential adoption in prostate cancer screening practice.MethodsA systematic electronic search strategy will be used to identify peer-reviewed articles published from inception to the date of searches and indexed in CINAHL, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Registries including Cochrane Central Register of Controlled Trials, ClinicalTrials.gov and International Clinical Trials Registry Platform (ICTRP) will be searched for unpublished studies, and experts were invited to provide suitable references. The research and reporting will be based on Cochrane recommendations and PRISMA guidelines, respectively. The screening and quality assessment of the articles will be conducted by two of the authors independently, and conflicts will be resolved by a third author.DiscussionThis systematic review will summarise the use of AI techniques to predict the need for prostate biopsy based on clinical and demographic indicators, including its diagnostic accuracy and readiness for adoption in clinical practice.Systematic review registrationPROSPERO CRD42022336540

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Over the past 2decades, scientific evidence has strongly supported the use of low-radiation dose chest computed tomography (CT) as a screening technique for lung cancer. This approach has resulted in a significant reduction in mortality rates by enabling the detection of early-stage lung cancer amenable to potentially curative treatments. Regarding diagnosis, there are also novel methods under study, such as liquid biopsy, identification of the pulmonary microbiome, and the use of artificial intelligence techniques, which will play a key role in the near future. At present, there is a growing trend towards less invasive surgical procedures, such as segmentectomy, as an alternative to lobectomy. This procedure is based on 2recent clinical trials conducted on peripheral tumors measuring less than 2cm. Although these approaches have demonstrated comparable survival rates, there remains controversy due to uncertainties surrounding recurrence rates and functional capacity preservation. With regard to adjuvant therapy, immunotherapy, either as a monotherapy or in conjunction with chemotherapy, has shown encouraging results in resectable stages of locally advanced lung cancer, demonstrating complete pathologic responses and improved overall survival.After surgery treatment, despite the lack of solid evidence for long-term follow-up of these patients, clinical practice recommends periodic CT scans during the early years.In conclusion, there have been significant advances in lung cancer that have improved diagnostic techniques using new technologies and screening programs. Furthermore, the treatment of lung cancer is increasingly personalized, resulting in an improvement in the survival of patients.

APPRECIATING THE AI REVOLUTION: EMPOWERING EDUCATIONAL RESEARCHERS THROUGH AI TOOLS FOR WRITING RESEARCH ARTICLES

The use of AI-based technologies in the process of researching and writing up findings has garnered a lot of attention in the world of education. This research investigates the use of AI techniques in the field of educational research as well as the repercussions of doing so. The purpose of this article is to offer advice and suggestions to help researchers navigate the changing environment of research writing in the age of artificial intelligence (AI). The advantages of using AI tools are investigated in this study. Some of these benefits include higher research efficiency, enhanced research quality, expanded accessibility, and chances for skill development. Additionally, it covers ethical concerns such as the privacy of data and the biases introduced by algorithms. In addition, the research highlights the need of striking a balance between the automation that can be achieved through AI and the knowledge that can only be obtained from humans. This means ensuring that AI tools supplement critical writing abilities rather than replacing them. The study suggests that researchers become familiar with AI tools, evaluate the capabilities of such tools, and choose the tools that are the most fit for certain research objectives. This will help researchers fill the shortcomings that were observed. In addition to this, it recommends the formulation of standards for the ethical use of AI, as well as the encouragement of activities that build skills, such as critical thinking and academic writing, to be practiced concurrently with the utilization of AI technologies. In addition, the research highlights the significance of promoting cooperation and the exchange of information within the research community, advocating for accessibility and fairness, and continually analyzing and adjusting to improvements in artificial intelligence technology. Educational researchers may successfully exploit artificial intelligence (AI) technologies in research writing, improve research methods, and contribute to the growth of the field if they adopt the recommendations offered in this study and implement them.

Impact of Artificial Intelligence in Improving the Effectiveness of Accounting Information Systems

The current study examines how artificial intelligence (AI) enhances the performance of a company’s public accounting information systems (AIS). A quantitative approach was used to accomplish this goal and show how well it fits with reality within the scope of public accounting. Primary data was gathered through the use of a questionnaire and analysed with SPSS-22. To ensure the constructs' validity and trustworthiness, a chronbach's alpha and an exploratory factor analysis were performed. The hypothesis was examined via regression analysis. The research found that the use of AI techniques greatly contributed to the improvement of public accounting information system. The research suggests that organisations should coordinate the activities of intelligent systems with their financial targets.

An Artificial Intelligence Driven Review of Literature on the Detection of COVID-19 Disease from Radiographic Images

Abstract: Pandemic of COVID-19 has attained significance in the field of research and innovation. After the arrival of various variants of covid 19, it demands more research for the identification and analysis of features. Medical investigations are found to be risky and costly. And so, the use of artificial intelligence techniques become more popular for the classification of covid 19 features. Such AI techniques perform recognition with accuracy and time effective in order to control diagnosis. The use of AI for COVID-19 detection has emerged as a promising solution. The proposed review of literature performs the analysis of the effectiveness of the AI models such as CNN and Inception-ResNet-V2, for the identification of COVID-19 from the radiographic scans. While these models have shown promising results, it is important to use them as an additional tool to assist medical professionals, rather than as a replacement. Ethical considerations such as reliability, transparency, and fairness must also be considered in the development of these models. Additionally, the data used to train and test these models should be diverse, representative, and validated to avoid potential biases and improve generalization capabilities. Further research and collaboration between medical professionals and AI experts are needed to advance the management and deployment of AI models for the detection of COVID-19.

Artificial intelligence and analysis of body position to determine the speed of the strike in Karate

The purpose of the study. The techniques of artificial intelligence in educational hardware and software are able to deduce knowledge and skills, and the importance of the study is due to the use of artificial intelligence techniques in analyzing the situation of skill performance, the use of artificial intelligence techniques in the system of skill analysis of the skills under study, the link of artificial intelligence with kinetic variables For the skills under study and the study aimed to apply artificial intelligence techniques in skill analysis, building an artificial intelligence system from motor variables and motor performance. Materials and methods. The study samples The researcher determined the study population of karate players by choosing the study sample in a random way from karate players in Kafr El-Sheikh Sports Club. Results. Performance through data and recommendations came to use artificial intelligence techniques in various games and areas of physical education. Conclusions. Artificial intelligence techniques work on skill analysis through programming neurons. Deep learning gives indications of improving performance through data. The use of artificial intelligence techniques in various games and areas of physical education

Understanding the Extent of Automation and Process Transparency Appropriate for Public Services

Many countries are exploring the potential of artificial intelligence (AI) to improve their operations and services, and China is no exception. However, not all AI techniques or automation approaches are suitable for every government service or process since transparency and accountability are paramount in the public sector. In this context, automation via expert systems (ES) is still a vital complement or even an alternative to AI techniques, because they can be more easily audited for potential biases. This paper analyzes the smart examination and approval (SEA) process use in China and explores how different forms of automation could be better options for certain services or specific processes within services, considering their level of transparency as an important characteristic. Based on these results, the authors argue that governments could consider hybrid approaches combining, for example, machine learning, for verification processes, and ES, which are more easily auditable, to make final decisions on individual cases. They also propose a classification of services by considering the extent of automation and process transparency needed. The classification considers a hybrid approach such as SEA, but also include other alternatives such as the exclusive use of AI techniques, as well as traditional online delivery and face-to-face procedures.

A systematic review on artificial intelligence applications in architecture

Since the advent and usage of artificial intelligence approaches in architecture, a significant number of studies have focused on integrating technological solutions to architectural issues. Artificial intelligence applications in architectural design range from intelligent material design to architectural plan solutions. The ubiquity and distribution of research in this field, as well as the rising use of artificial intelligence techniques to solve design challenges, require an analytical classification of the essential literature review. This article presents a descriptive and analytical review of the work on artificial intelligence applications in architecture. A strong review has been made that identifies and addresses the gaps in artificial intelligence and architecture; and the literature review is transformed into statistical plots. The study's findings indicate a growing interest in artificial intelligence in the field of architecture. There is a need for novel research to be conducted in these areas using advanced technology and techniques.

The use of Artificial Intelligence Techniques in Policing

The use of Artificial Intelligence Techniques in Policing

Simulation of Solar Based Smart Grid System Using Artificial Neural Network and Fuzzy Controller

To promote the economy and reliability of the energy trading systems, the use of interconnected smart grids is encouraging. A distributed energy management plan for the interconnected operation of the smart grid that maximizes the resident intake of renewable energy is required during operation. On the client side, possibilities and actions are being discussed in the research papers to incorporate the renewable energy sources. In this paper, the use of Artificial Intelligent Techniques to manage energy or power supply to meet the electricity demand of customers is illustrated. Simulation has been done using wind and solar power supply to manage the load demand for the client side in a smart grid system. Smart Grid has been simulated for all these energy sources to be used with the forecasted electric power requirements. All the required energy demand can be identified from the forecast data, allowing smart grids to deliver better results. For this purpose solar panels are first used in smart grids with the help of artificial neural networks and fuzzy controllers so that load shifts can be done easily and efficiently. Simulation work has been done in MATLAB/Simulink.

Time Series and Data Science Preprocessing Approaches for Earthquake Analysis

Time series are frequently used today to analyze data that changes over time and to predict future trends. Usage areas of time series data include many applications such as financial market forecasts, weather forecasts, sales forecasts, medical diagnostics and stock management. Among the methods, there are techniques such as autoregressive integration, moving average, long-short-term memory neural network, time series condensation, wavelet transform and Frequency Domain. These techniques are chosen depending on the characteristics of the time series data and their intended use. For example, the ARIMA model is used for variable variance and non-stationary time series, while the LSTM model may be more suitable for capturing long-term dependencies. In this article, it has been tried to prove that time series based artificial intelligence systems can be built on fault movements, which are very difficult to predict on earthquake time series data, and it is quite possible to get useful results. In particular, deep learning methods are among the prominent methods in the article. Deep learning methods are used to detect complex structures and analyze large datasets to produce accurate results. These methods include multilayer perceptrons, long-short-term memory neural network, and radial-based function network. It is also emphasized that factors such as the selection of features used in earthquake prediction, data preprocessing, feature engineering and correct model selection are also important. As a result, the use of artificial intelligence techniques on earthquake time series data has great potential in estimating earthquake risk. Deep learning methods perform better, especially for large datasets, and more accurate results can be obtained with the right model selection. However, factors such as data preprocessing and feature selection also need to be considered.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44-0.99, 0.63-1.00, and 0.73-0.96, respectively, with AUCs of 0.73-0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

An Overview of the Recent Advances in Composite Materials and Artificial Intelligence for Hydrogen Storage Vessels Design

The environmental impact of CO2 emissions is widely acknowledged, making the development of alternative propulsion systems a priority. Hydrogen is a potential candidate to replace fossil fuels for transport applications, with three technologies considered for the onboard storage of hydrogen: storage in the form of a compressed gas, storage as a cryogenic liquid, and storage as a solid. These technologies are now competing to meet the requirements of vehicle manufacturers; each has its own unique challenges that must be understood to direct future research and development efforts. This paper reviews technological developments for Hydrogen Storage Vessel (HSV) designs, including their technical performance, manufacturing costs, safety, and environmental impact. More specifically, an up-to-date review of fiber-reinforced polymer composite HSVs was explored, including the end-of-life recycling options. A review of current numerical models for HSVs was conducted, including the use of artificial intelligence techniques to assess the performance of composite HSVs, leading to more sophisticated designs for achieving a more sustainable future.

Recent Trends in The Use of Artificial Intelligence Techniques and Their Impact on Investment, Development and Growth for Developed and Developing Countries Under Corona Pandemic

This research aims to identify the modern trends in the use of artificial intelligence technology and its impact on investment, development and growth for countries in light of the Corona pandemic, as well as to identify the concept of artificial intelligence technology, and the concept of investment, and the importance of this research stems from the fact that it discusses the modern technology that enters all fields of science, which is Artificial intelligence technology, which has applied in most of the sciences, if not all, as the use of artificial intelligence contributes to advancing the economy forward. The historical and descriptive analytical method is used in this study which is appropriate to the nature of the study. One of the most important findings of the research is that the advanced technologies and advanced in the science of artificial intelligence will provide a lot of investment, development and growth opportunities for countries. Keywords: investment, economic development, artificial intelligence growth, the economy.

The Current Status of Secondary Use of Claims, Electronic Medical Records, and Electronic Health Records in Epidemiology in Japan: Narrative Literature Review

Real-world data, such as claims, electronic medical records (EMRs), and electronic health records (EHRs), are increasingly being used in clinical epidemiology. Understanding the current status of existing approaches can help in designing high-quality epidemiological studies. We conducted a comprehensive narrative literature review to clarify the secondary use of claims, EMRs, and EHRs in clinical epidemiology in Japan. We searched peer-reviewed publications in PubMed from January 1, 2006, to June 30, 2021 (the date of search), which met the following 3 inclusion criteria: involvement of claims, EMRs, EHRs, or medical receipt data; mention of Japan; and published from January 1, 2006, to June 30, 2021. Eligible articles that met any of the following 6 exclusion criteria were filtered: review articles; non-disease-related articles; articles in which the Japanese population is not the sample; articles without claims, EMRs, or EHRs; full text not available; and articles without statistical analysis. Investigations of the titles, abstracts, and full texts of eligible articles were conducted automatically or manually, from which 7 categories of key information were collected. The information included organization, study design, real-world data type, database, disease, outcome, and statistical method. A total of 620 eligible articles were identified for this narrative literature review. The results of the 7 categories suggested that most of the studies were conducted by academic institutes (n=429); the cohort study was the primary design that longitudinally measured outcomes of proper patients (n=533); 594 studies used claims data; the use of databases was concentrated in well-known commercial and public databases; infections (n=105), cardiovascular diseases (n=100), neoplasms (n=78), and nutritional and metabolic diseases (n=75) were the most studied diseases; most studies have focused on measuring treatment patterns (n=218), physiological or clinical characteristics (n=184), and mortality (n=137); and multivariate models were commonly used (n=414). Most (375/414, 90.6%) of these multivariate modeling studies were performed for confounder adjustment. Logistic regression was the first choice for assessing many of the outcomes, with the exception of hospitalization or hospital stay and resource use or costs, for both of which linear regression was commonly used. This literature review provides a good understanding of the current status and trends in the use of claims, EMRs, and EHRs data in clinical epidemiology in Japan. The results demonstrated appropriate statistical methods regarding different outcomes, Japan-specific trends of disease areas, and the lack of use of artificial intelligence techniques in existing studies. In the future, a more precise comparison of relevant domestic research with worldwide research will be conducted to clarify the Japan-specific status and challenges.

Genomic alterations in chronic myeloid leukaemia patients who failed second generation tyrosine kinase inhibitor

The practise of prescribing chronic myeloid leukaemia (CML) patients with indefinite tyrosine kinase inhibitors (TKIs) has gone uncontested, and the capacity of TKIs to eliminate the CML clone is also still unknown. Although the vast majority of CML patients do respond to TKIs, nonetheless, resistance may develop either de novo or during treatment. TKI resistance pathways are commonly classified as BCR-ABL1-dependent or BCR-ABL1-independent. The molecular evolution causal for this subset of individuals to lose molecular remission is still unknown. In this study, our goal was to explore the molecular mechanisms involved in resistance to TKI in patients who failed second-generation TKIs in order to identify potential genetic signatures and pathways that lead to TKI resistance. A total of 34 samples (2 responder and 32 non-responder) were subjected to whole-transcriptomic analysis. mRNA gene expression, gene fusion and single nucleotide variation (SNV) were determined using Illumina DRAGEN Bio-IT Platform pipeline. A preliminary results for a total of 386 differentially regulated genes (DEGs) were identified, with 148 genes up-regulated and 238 genes down-regulated in the non-responder group. The DEGs were enriched in histone acetylation/deacetlylation (HDACs/HATs), DNA methylation, and RNA polymerase regulation pathway. Further analyses demonstrated that CCCDC32-CBX3 fusion is significantly associated with non-responder. Techniques with enhanced sensitivity such as next generation sequencing and the use of artificial intelligence techniques coupled with the development of mathematical modelling and computational prediction methods could reveal the underlying mechanism of drug resistance and facilitate the design of more effective treatment strategies for improving drug efficacy in CML patients. This preliminary analysis found that individuals who failed second-generation TKIs express a potentially unique genetic signature. Analysis using larger sample size is necessary to validate these findings.

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to select appropriate treatment options and to predict patients' conditions. There has been a high amount of data collected by biomedical sensors during the continuous monitoring process of patients in the ICU, so the use of artificial intelligence techniques in automatic LoS estimation would improve patients' care and facilitate the work of healthcare personnel. In this work, a novel methodology to estimate the LoS using data of the first 24 h in the ICU is presented. To achieve this, XGBoost, one of the most popular and efficient state-of-the-art algorithms, is used as an estimator model, and its performance is optimized both from computational and precision viewpoints using Bayesian techniques. For this optimization, a novel two-step approach is presented. The methodology was carefully designed to execute codes on a high-performance computing system based on graphics processing units, which considerably reduces the execution time. The algorithm scalability is analyzed. With the proposed methodology, the best set of XGBoost hyperparameters are identified, estimating LoS with a MAE of 2.529 days, improving the results reported in the current state of the art and probing the validity and utility of the proposed approach.

Future trends in organic flour milling: the role of AI

<abstract> <p>The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.</p> </abstract>

Fog Computing in Healthcare: Systematic Review

Healthcare has seen many advances in sensor technology, but with recent improvements in networks and the addition of the Internet of Things, it is even more promising. Current solutions to managing healthcare data with cloud computing may be unreliable at the most critical moments. High response latency, large volumes of data, and security are the main issues of this approach. The promising solution is fog computing, which offers an immediate response resistant to disconnections and ways to process big data using real-time analytics and artificial intelligence (AI). However, fog computing has not yet matured and there are still many challenges. This article presents for a computer scientist a systematic review of the literature on fog computing in healthcare. Articles published in six years are analysed from the service, software, hardware, information technologies and mobility with autonomy perspectives. The contribution of this study includes an analysis of recent trends, focus areas and benefits of the use of AI techniques in fog computing e-health applications.