Applicability, scope, and threshold determination of the cotton water stress characterization index: Prediction based on machine learning algorithms and validated by field experiments

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer’s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It’s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Abstract:As part of a larger study to elucidate the presence of hydrated minerals on asteroid surfaces, we are developing a robust taxonomic classification system using spectroscopic observations in the vicinity of 3 &amp;#956;m. We have constructed a Python algorithm to identify band centers and band depths near 3 &amp;#181;m for a set of normalized, thermally-corrected asteroid spectra for use to serve as inputs to Python&amp;#8217;s Scikit-Learn library of Machine Learning (ML) algorithms. We anticipate a thorough investigation of both Principal Component Analysis and ML (supervised, unsupervised, and Artificial Neural Network) techniques to assess which technique is likely to be better suited for classifying the 3-&amp;#181;m data. At this writing, we have run tests using Python&amp;#8217;s Agglomerative clustering ML algorithm to examine possible clustering scenarios. These initial steps have given us some familiarity with the mechanics of using ML on the 3-&amp;#181;m dataset as well as serving to identify some possible pitfalls or cul-de-sacs. Presented here are the preliminary results we have obtained.Introduction:Although various techniques have been used, asteroid classification has typically been done via Principal Component Analysis (PCA: [1,2]). PCA is a statistical technique that reduces the dimensionality of a dataset by identifying the most important parameters within a dataset based on their variance. Parameters that exhibit the greatest amount of variance are considered to be of greater importance while parameters with the least amount of variance are considered to be of lower importance. While the PCA technique produces better visualizations of the data by reducing the dimensionality of a dataset, the PCA technique comes with some drawbacks. Disadvantages such as its dependence on scale and information loss due to the orthogonal property of PCA can cause interpretation of PCA results to prove to be a more critical and time-consuming process. Therefore, exploring other means of classification may prove to be worthwhile.Machine Learning (ML) algorithms have had a significant impact on the way in which data is analyzed and interpreted, and have already proven to be a powerfully reliable resource in the field of planetary science. Accordingly, the application of ML to an asteroid taxonomy has the potential to be more efficient, objective, and easy-to-implement than PCA. ML algorithms can be supervised, in which the program &amp;#8220;learns&amp;#8221; from training data and is able to classify new inputs, or unsupervised, in which the program analyzes the dataset to determine patterns such as clusters. [3] used an Artificial Neural Network (ANN, a subset of ML) to classify asteroids, work followed up by [4]. Recent explorations of supervised ML for asteroid taxonomy are promising, and have applied training sets from existing databases to new visible and/or NIR photometric data (e.g. [5,6,7]).We seek to explore the benefits of ML algorithms, as well as compare and contrast to the PCA technique, in the production of an asteroid taxonomy. Our initial exploration has utilized a set of normalized, thermally-corrected asteroid spectra in the vicinity of 3 &amp;#181;m. We have identified band centers and band depths and served this parameter space as inputs to Python&amp;#8217;s Agglomerative clustering ML algorithm.Methodology:Thermal corrections of the asteroid spectra were performed via a forward model that uses a modified version of the Standard Thermal Model (STM: [8]). The forward model treats the beaming parameter as a free parameter adjusting its value for each iteration of the STM until it converges onto a value that yields expected long-wavelength continuum behavior. Spectra were then normalized to unity at a wavelength of 2.3 &amp;#181;m, followed by identification of band centers and band depths near 3 &amp;#181;m using both polynomial and Gaussian fits. In addition, band depths were measured at wavelengths of 2.9 &amp;#181;m and 3.2 &amp;#181;m to gather more information on asteroid band shapes. Lastly, the aforementioned calculated spectral features were input into Python&amp;#8217;s Agglomerative clustering algorithm to determine which asteroid spectra shared similar features.Summary:As part of a larger investigation to better understand hydrated mineralogies as they apply to asteroids, we have begun work towards developing a quantitative taxonomic framework derived from asteroid spectra in the wavelength range from 2.0-4.0 &amp;#181;m. Our exploration thus far of Python&amp;#8217;s Agglomerative clustering algorithm has proven to be fruitful. Minor changes to the parameterization of this algorithm can yield very different results, which naturally can lead to different interpretations. The Agglomerative clustering algorithm is one of many the powerful ML algorithms we will explore against the PCA technique, all of which we will be discussing in our presentation.

Utilization of synthetic minority oversampling technique for improving potato yield prediction using remote sensing data and machine learning algorithms with small sample size of yield data

Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study

6P Urine spectroscopy coupled with artificial intelligence: Proof of concept for a new diagnostic tool to detect gynaecological cancers

BackgroundKidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression.MethodsWe searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms’ accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model.ResultsFifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84–0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I2) of (0.87, 0.84–0.90, [I2 99.0%]) and a weak sensitivity of (0.68, 0.58–0.77, [I2 99.7%]) in predicting kidney disease deterioration. And the the results of subgroup analysis indicated that ML algorithm’s AUC for predicting CKD prognosis was 0.82 (0.79–0.85), with the pool sensitivity of (0.64, 0.49–0.77, [I2 99.20%]) and pool specificity of (0.84, 0.74–0.91, [I2 99.84%]). The ML algorithm’s AUC for predicting IgA nephropathy prognosis was 0.78 (0.74–0.81), with the pool sensitivity of (0.74, 0.71–0.77, [I2 7.10%]) and pool specificity of (0.93, 0.91–0.95, [I2 83.92%]).ConclusionTaking advantage of big data, ML algorithm-based prediction models have high accuracy in predicting kidney disease progression, we recommend ML algorithms as an auxiliary tool for clinicians to determine proper treatment and disease management strategies.

Nowadays, facial expression analysis (FEA) is becoming an important application on various fields such as medicine, education, entertainment and crime analysis because it helps to analyze where no verbal communication is possible. FEA is being done after face recognition and depends on the feature extraction of how efficiently it is generated. Therefore, classification plays a vital role to acquire the necessary output to analyze the correct expression. In addition, machine learning (ML) and deep learning algorithms are useful to classify the data as system requires either structured-like text or unstructured-like images and videos perhaps to analyze the expression, and image input is preferred by the system as well because the face image consists of a kind of information like texture of organized features, age, gender and shape which cannot be described properly by the textual annotation to a corresponding image. The system can be done in different ways: either it can apply the deep learning algorithms on raw data, or can apply ML algorithms on the preprocessed images based on the user requirement. This chapter discusses the challenges and potential ML algorithms and efficient deep learning algorithms to recognize the automatic expression of humans to prop up with the significant areas such as human computer interaction, psychology in medical field, especially to analyze the behavior of suspected people in crowded areas probably in airports and so on. In recent years, ML algorithms had become very popular in the field of data retrieval to improve its efficiency and accuracy. A new state-ofthe- art image retrieval called ML algorithms plays an imperative role to decrease the gap semantically between the user expectation and images available in the database. This chapter presents a comprehensive study of ML algorithms such as supervised, unsupervised and a sequence of both. Furthermore, the demonstration of various ML algorithms is used for image classification, and of clustering which also represents the summary and comparison of ML algorithms for various datasets like COREL and face image database. Finally, the chapter concludes with the challenges and few recommendations of ML algorithms in image retrieval.

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, can provide fast, accurate, and large-scale discrimination of genotypes in the field, which is crucial for improving the efficiency of breeding programs. Despite their importance, studies aimed at accurately classifying sorghum hybrids using spectral variables as input sets in ML models are still scarce in the literature. Against this backdrop, this study aimed: (I) to discriminate sorghum hybrids based on canopy reflectance in different spectral bands (SB) and vegetation indices (VIs); (II) to evaluate the performance of ML algorithms in classifying sorghum hybrids; (III) to evaluate the best dataset input for the algorithms. A field experiment was carried out in the 2022 crop season in a randomized block design with three replications and six sorghum hybrids. At 60 days after crop emergence, a flight was carried out over the experimental area using the Sensefly eBee real time kinematic. The spectral bands (SB) acquired by the sensor were: blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), Rededge (735 nm, RE_735) e NIR (790 nm, NIR_790). From the SB acquired, vegetation indices (VIs) were calculated. Data were submitted to ML classification analysis, in which three input settings (using only SB, using only VIs, and using SB + VIs) and six algorithms were tested: artificial neural networks (ANN), support vector machine (SVM), J48 decision trees (J48), random forest (RF), REPTree (DT) and logistic regression (LR, conventional technique used as a control). There were differences in the spectral signature of each sorghum hybrid, which made it possible to differentiate them using SBs and VIs. The ANN algorithm performed best for the three accuracy metrics tested, regardless of the input used. In this case, the use of SB is feasible due to the speed and practicality of analyzing the data, as it does not require calculations to perform the VIs. RF showed better accuracy when VIs were used as an input. The use of VIs provided the best performance for all the algorithms, as did the use of SB + VIs which provided good performance for all the algorithms except RF. Using ML algorithms provides accurate identification of the hybrids, in which ANNs using only SB and RF using VIs as inputs stand out (above 55 for CC, above 0.4 for kappa and around 0.6 for F-score). There were differences in the spectral signature of each sorghum hybrid, which makes it possible to differentiate them using wavelengths and vegetation indices. Processing the multispectral data using machine learning techniques made it possible to accurately differentiate the hybrids, with emphasis on artificial neural networks using spectral bands as inputs and random forest using vegetation indices as inputs.

Heart disease is one of the most dangerous and largest killers in the world. Identifying heart disease early has a vast positive effect on patient outcomes and their quality of life. In this research, we try to identify heart disease using machine learning (ML) algorithms. ML algorithms have the highest probability of success if they work on a data set with extensive and diverse information about the given problem - in this case heart disease. There are multiple types of ML algorithms to test, so we can try many different ones on the data, making the results more precise. Even if there are many different algorithms to test, each machine-learning solution can yield different results depending on the dataset used and our target goals. The main goal of this study is to find the differences between individual ML algorithms being used in our specific case: which ML algorithm, or combination of algorithms, is appropriate to detect heart disease with high accuracy? The ML algorithms used in this research are the Naive Bayes Classifier, the Random Forest classifier, and the Support Vector Machine (SVM) algorithm. Furthermore, this study generates insights into these ML algorithms – if a particular algorithm’s model performs better than another on the dataset, analyzing this difference can help us understand what makes the model more suitable for diagnostic screening. Changing models’ hyperparameters or their pre-processing techniques allows for a more robust and reliable model that can be readily incorporated into a healthcare environment.

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model–agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

To comply with the advanced smart grid operations such as Artificial Intelligence (AI) based Distributed Generation (DG) Integration and Load schedules, learning of future load and supply availability is inevitable. Specifically, the use of Big Data analytics and prediction is very crucial as they have changed the paradigm of Conventional Grid operations. Since last two decades, research on Load demand (PT) forecasting is on high pedestal. And there has been more than a dozen Machine Learning (ML) algorithms reported in the literature. But, features/predictors selection was always a critical call in any ML based prediction. Not only that effective comparison and choice between numerous ML algorithms has always been a research challenge. To adress the said challenges, this article presents the load forecasting of a domestic load center using Feed Forward Artificial Neural Networks (FF-ANN) and nineteen different ML algorithms trained by the combination of weather and time stamp features/predictors. ML algorithm driven MATLAB-SIMULINK prediction model designed and developed can predict the Load demand for any given date if weather parameters are fed to it. In adition, an extensive comparison between different ML algorithms in terms of training time, prediction speed, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), R2, Training Time in seconds and Prediction Time in Obs/Sec presented paves a way for researchers in selecting right ML algorithm for load forecasting problem concerning domestic load centers. Among all ML algorithms trained and tested, Rotational Quadratic Gaussian Process Regression (RQ-GPR) ML algorithm is witnessed to be with higher accuracy and lower RMSE. MATLAB 2018b licensed user added with Statistics and ML Tool box is used for the whole implementation.KeywordsLoad forecastingMachine learning algorithmsGaussian process regressionSupport vector regressionTree regressionEnsembled algorithmsArtificial neural networks

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. The "College of Wisconsin Breast Cancer Dataset", which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%. When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.