Application Of Artificial Intelligence Models Research Articles

A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis

Abstract This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field. The current review examines various studies that using AI techniques, including machine learning (ML), deep learning (DL), and neural networks (NNs), in surface chemistry and catalysis. It reviews the literature on the application of AI models in predicting adsorption behaviours, analyzing spectroscopic data, and improving catalyst screening processes. It combines both theoretical and empirical studies to provide a comprehensive synthesis of the findings. It demonstrates that AI applications have made remarkable progress in predicting the properties of nanostructured catalysts, discovering new materials for energy conversion, and developing efficient bimetallic catalysts for CO2 reduction. AI-based analyses, particularly using advanced NNs, have provided significant insights into the mechanisms and dynamics of catalytic reactions. It will be shown that AI plays a crucial role in surface chemistry and catalysis by significantly accelerating discovery and enhancing process optimization, resulting in enhanced efficiency and selectivity. This mini-review highlights the challenges of data quality, model interpretability, scalability, and ethical, and environmental concerns in AI-driven research. It highlights the importance of continued methodological advancements and responsible implementation of artificial intelligence in catalysis research.

Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.

Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another. This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging. PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images. This review collected 102 works published between April 2017 and August 2023. Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test. Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P < .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions. Our review is limited by the study design and model heterogeneity. Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.

Unraveling variations and enhancing prediction of successful sphincter-preserving resection for low rectal cancer: A post hoc analysis of the multicenter LASRE randomized clinical trial.

Accurate prediction of successful sphincter-preserving resection (SSPR) for low rectal cancer enables peer institutions to scrutinize their own performance and potentially avoid unnecessary permanent colostomy. The aim of this study is to evaluate the variation in SSPR and present the first artificial intelligence (AI) models to predict SSPR in low rectal cancer patients. This was a retrospective post hoc analysis of a multicenter, noninferiority randomized clinical trial (LASRE, NCT XXXXXX) conducted in 22 tertiary hospitals across China. A total of 604 patients who underwent neoadjuvant chemoradiotherapy (CRT) followed by radical resection of low rectal cancer were included as the study cohort, which was then split into a training set (67%) and a testing set (33%). The primary end point of this post hoc analysis was SSPR, which was defined as meeting all the following criteria: (1) sphincter-preserving resection; (2) complete or nearly complete TME, (3) a clear CRM (distance between margin and tumor of 1mm or more), and (4) a clear DRM (distance between margin and tumor of 1mm or more). Seven AI algorithms, namely, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGB), light gradient boosting (LGB), decision tree classifier (DTC), random forest (RF) classifier, and multilayer perceptron (MLP), were employed to construct predictive models for SSPR. Evaluation of accuracy in the independent testing set included measures of discrimination, calibration, and clinical applicability. The SSPR rate for the entire cohort was 71.9% (434/604 patients). Significant variation in the rate of SSPR, ranging from 37.7% to 94.4%, was observed among the hospitals. The optimal set of selected features included tumor distance from the anal verge before and after CRT, the occurrence of clinical T downstaging, post-CRT weight and clinical N stage measured by magnetic resonance imaging. The 7 different AI algorithms were developed and applied to the independent testing set. The LR, LGB, MLP and XGB models showed excellent discrimination with AUROC values of 0.825, 0.819, 0.819 and 0.805, respectively. The DTC, RF and SVM models had acceptable discrimination with AUROC values of 0.797, 0.766 and 0.744, respectively. LR and LGB showed the best discrimination, and all 7 AI models had superior overall net benefits within the range of 0.3-0.8 threshold probabilities. Finally, we developed an online calculator based on the LGB model to facilitate clinical use. The rate of SSPR exhibits substantial variation, and the application of AI models has demonstrated the ability to predict SSPR for low rectal cancers with commendable accuracy.

Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.

The Application of Artificial Intelligence Models for Food Security: A Review

Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.

Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models

Quality syngas production with higher moles of hydrogen and methane are the primary objective of gasification process which is dependent upon the process parameters and composition of biomass. However, it is always a costly and time-consuming task to get the optimum biomass composition and process parameters for quality syngas production. In this research, artificial intelligence (AI) algorithms have been applied for high quality syngas prediction with better moles fractions of hydrogen and methane using hydrothermal gasification (HTG). Comparative analysis of Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Gradient Boost Regressor (GBR), Extreme Boost Regressor (XGB), and Random Forest Regressor (RFR) based algorithms have been done to select an optimal one. Ultimate analysis of biomass and process input parameters inlcuding temperature, pressure, percentage solid content of biomass, and resident time have been used as an input parameter for prediction models. Final comparative results of these AI models conclude that XGB has a better prediction result as compared to other with coefficient of determinant (R2) and mean square errors ranges from 0.85 to 0.95 and 0.008–0.01, respectively. Furthermore, process temperature and the resident time are the most contributing factors in mole fractions of hydrogen and methane. Higher hydrogen and oxygen contents in the biomass, significantly contributes to the production of quality syngas.

The Application of Artificial Intelligence Models in Commercial Banks − Opportunities and Threats

One of the main sectors that makes heavy use of the development of advanced computational methods is the banking sector. The goals of our research are as follows: 1) to compare scientific and regulatory approaches to defining artificial intelligence (AI) and machine learning (ML), 2) to propose AI and ML definitions for regulatory purposes that allow us to clearly state if a given method is AI/ML or not, 3) to compare the complex quantitative methods applied in banking in terms of complexity and interpretability in order to provide a clear classification of methods to the interested parties (practitioners and management), 4) to propose a possible approach towards the further development of quantitative methods in the areas of required strict interpretability. Our literature review focuses on the definitions of AI/ML applied by scientists and regulators, as well as the proposals of application of complex quantitative solutions in different banking domains. We propose practical definitions of AI and ML based on the current state of the art and requirements of clarity in the banking industry (a very limited risk appetite regarding regulations non‑compliance) and compare quantitative methods applied in different banking domains. For regulatory purposes, we propose general and inclusive definitions of AI and ML which allow for a clear classification of specific methods. In the case of strict requirements towards the interpretability of applied methods, we propose a gradual and controlled increase in the complexity of existing solutions. Therefore, we propose the differentiation of quantitative methods in terms of interpretability and complexity. We also think that the definitions of AI/ML in further regulations should make it possible to clearly say whether particular approaches are AI/ML. Our research is directed to policymakers, practitioners, and executives related to the banking sector.

Locally private estimation of conditional probability distribution for random forest in multimedia applications

The application of artificial intelligence models to raw multimedia data is susceptible to various data inference attacks, posing a significant risk in terms of sensitive input information leakage. Most of the existing studies on privacy-preserving multimedia applications based on artificial intelligence, focus on a single intelligent model and thus have various limitations. In this paper, we attempt to directly perturb the core component of many multimedia intelligent models in Bayesian networks and deep learning. That is, we apply conditional probability distribution estimation to guarantee the privacy of the models. At first, we present the formal problem formulation of private conditional probability distribution estimation and apply it to random forest for task classification in multimedia applications. Then, we design a simple perturbation approach called NaivePrivDistEst, to add noise to all the elements of probability estimation in random forest. Next, we present an improved approach called FastLRG, that utilizes the taxonomy tree to discretize the continuous attributes, thereby combining the attribute features to improve the prediction accuracy of random forest. Finally, we perform extensive experiments to evaluate the performance of random forest based on the proposed estimation algorithms. The experimental results indicate that the proposed models have better performance compared with existing private decision trees.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ОЦЕНКЕ И ПРОГНОЗИРОВАНИИ ЭКОНОМИЧЕСКОГО СОСТОЯНИЯ ОРГАНИЗАЦИИ

The article is devoted to the effective application of artificial intelligence models in various fields, including in the field of assessing the economic condition, which can increase the speed of data processing, deepen the degree of data analysis and reduce labor costs, thereby increasing the effectiveness of monitoring the economic condition of organizations. To predict the economic state of an organization, you can use the mathematical theory of catastrophes, which is a research program for studying and predicting the instability of various systems.

Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.

A pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical treatment. The model was designed using ensemble deep learning (DL). A total of 172 anterior segment images of pterygia were obtained from the Jiangxi Provincial People's Hospital (China) between 2017 and 2022. They were divided by a senior ophthalmologist into the non-surgery group and the surgery group. An artificial intelligence model was then developed based on ensemble DL, which was integrated with four benchmark models: the Resnet18, Alexnet, Googlenet, and Vgg11 model, for detecting the pterygium that requires surgical treatment, and Grad-CAM was used to visualize the DL process. Finally, the performance of the ensemble DL model was compared with the classical Resnet18 model, Alexnet model, Googlenet model, and Vgg11 model. The accuracy and area under the curve (AUC) of the ensemble DL model was higher than all of the other models. In the training set, the accuracy and AUC of the ensemble model was 94.20% and 0.978, respectively. In the testing set, the accuracy and AUC of the ensemble model was 94.12% and 0.980, respectively. This study indicates that this ensemble DL model, coupled with the anterior segment images in our study, might be an automated and cost-saving alternative for detection of the pterygia that require surgery.

Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed.

Myocardial fibrosis detection using kernel methods: preliminary results from a cardiac magnetic resonance study

Abstract Funding Acknowledgements Type of funding sources: None. Background Asserting the presence of myocardial fibrosis from cardiac magnetic resonance (CMR) images is sometimes a complex task, even for experienced cardiac imagers. The application of artificial intelligence models to the evaluation process can be of help for enhancing diagnostic accuracy. Purpose In this work, we tested two different Machine Learning (ML) algorithms, namely kernel methods with Support Vector Machine (SVM) and Convolutional Neural Network (CNN) to a cohort of consecutive CMR studies. The goal was a binary classification task, aimed to identify myocardial scar (present/absent). Methods Dataset consisted of 642 CMR scans, equally divided into healthy and fibrosis-affected. Raw DICOM files were preprocessed through an automated pipeline, in order to retrieve only short-axis post contrast acquisitions. Heart regions were then individuated using a YOLO network, in order to remove the background and focus only on data of interest. Finally, for each subject 10 slices were extracted through interpolation, and all images resized to 128 by 128 pixels. Dataset was divided into training and test sets, with proportions 80%-20%. Results The first analysis was based on state-of-the-art CNN models, pre-trained on the ImageNet dataset. The training of the models was optimized using "reduce learning rate on the plateau", "early stopping", and standard data augmentation techniques. Experiments showed that CNNs-based models led to poor performances. The second attempt was based on kernel methods and SVM. Before feeding the input to the algorithm, dimensionality reduction was implemented using a Principal Component Analysis retaining 99% of the variance. The resulting 335 features were passed as input to an SVM, testing different kernels (e.g. Linear, Gaussian, Cossim). Models were trained and optimized using a Grid Search with a 5-fold Cross-Validation. The best SVM configuration displayed an accuracy of 68% and a sensitivity of 60%. Improved results could be obtained using state-of-the-art Multiple Kernel Learning algorithms, leading to 71% accuracy and sensitivity of 72% (with a 12% increment with respect to the previous algorithm). Conclusions Our preliminary results of this study showed the possibility for a ML system to learn to identify myocardial fibrosis, directly from raw CMR images. In particular, kernel methods were able to achieve good and significant results, even with small amounts of data, suggesting room for improvement. In future works, we plan to furtherly explore kernel methods to improve the results and to build an end-to-end solution for cardiac imagers.

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solutions to conserve renewable water resources. Understanding the aquifer status using numerical models is time-consuming and also is associated with inherent uncertainty; therefore, in recent decades, the application of artificial intelligence methods to predict water table fluctuations has significantly gained momentum. In this study, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine (SVM) methods were utilized to predict groundwater level (GWL) with 1-, 2-, and 3-month lead time in Tehran-Karaj plain. Several input scenarios were developed considering groundwater levels, average temperature, total precipitation, total evapotranspiration, and average river flow on a monthly interval. The four error criteria, the correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE), were the basis to evaluate the models. Results showed that all the applied methods could provide acceptable GWL prediction, but the ANFIS was the most accurate. However, the ANFIS model showed slightly better performance by yielding R = 0.98 for the training stage and R = 0.98 for the testing stage in the P84 observation well and the second combination of inputs and 1-month lead time. The outcomes also revealed that all the approaches mentioned above could appropriately predict GWL for the leading time of 1 and 2months, but the models provided unsatisfactory results for a 3-month leading time.

Application of Artificial Intelligence Models to Predict the Tensile Strength of Glass Fiber-Modified Cemented Backfill Materials during the Mine Backfill Process

Cemented backfill coal mining technology is gradually becoming a key technology for green mining of coal resources. And cemented backfill materials generally have congenital defects such as poor crack resistance, poor durability, and high brittleness, which restrict the promotion and application of cemented backfill coal mining technology. Due to the complex stress environment of in situ stress, mining stress, water pressure, and gas pressure, cemented backfill materials need to have good mechanical properties, and glass fiber is usually used to mix into cemented backfill materials to improve its performance, but there are many problems including complex testing process, high cost, and long time-consuming in the study of mechanical properties of glass fiber-modified cemented backfill materials (GFCBM) by laboratory tests. Consequently, this study proposed and compared four artificial intelligence models to forecast the tensile strength of GFCBM. Firstly, the laboratory tests of tensile properties of GFCBM under different influence factors were implemented to supply the prediction model with dataset. The input variables are aeolian sand content, cement content, glass fiber length, and glass fiber content, and the output variable is the tensile strength of GFCBM. The correlation coefficient ( R ), mean absolute error (MAE), and root mean square error (RMSE) are selected to assess the estimated performance of the hybrid intelligent model. The results indicate that the four hybrid artificial intelligence models show a latent capacity for forecasting the tensile strength of GFCBM, and according to the order from high to low, the prediction ability of the four prediction models is as follows: ABC-SVM, GA-SVM, SSA-SVM, and DE-SVM, and the corresponding R values are 0.9555, 0.9539, 0.9413, and 0.9359, respectively. The research findings are beneficial to promote the application of cemented backfill coal mining technology.

Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders

In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.

Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends

Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review.

Simple SummaryOral cancer is characterized by high morbidity and mortality, since the disease is typically in an advanced locoregional stage at the time of diagnosis. The application of artificial intelligence (AI) techniques to oral cancer screening has recently been proposed. This scoping review analyzed the information about different machine learning tools in support of non-invasive diagnostic techniques including telemedicine, medical images, fluorescence images, exfoliative cytology and predictor variables at risk of developing oral cancer. The results suggest that such tools can make a noninvasive contribution to the early diagnosis of oral cancer and we express the gaps of the proposed questions to be improved in new investigations.The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.

Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey

Reference evapotranspirationETo is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly meanETo tover Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regressionMLR, random forestRF, and extreme learning machineELM. Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such asMLRandRF. Moreover, among the 10 statistical measures, the uncertainty at 95%U95indicator showed an excellent ability to select the best and most efficient forecast model. The superiority ofELMin the prediction of mean monthlyETo overMLRandRFapproaches is illustrated in the reduction of theU95parameter to 49.02% and 34.07% forRFandMLRmodels, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate theETo using the simplest and cheapest meteorological and geographical variables with acceptable accuracy.