Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

Streamflow forecasting is of great importance in water resources management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time series data on river flows, weather patterns, and other relevant factors, machine-learning models can learn patterns and relationships to present predictions about future river flows. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray, in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time series data were used for the model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt-Winters' method. The model's forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resources management and flood warnings.

This paper presents a repository of image databases where the image features are augmented with embedded vector field (VF) features to enhance the machine learning (ML) and increase classification statistics. Six VFs are formulated and used for the purpose of embedding. Three of them generate real shaped singular points (SPs): springing, sinking and saddle. The other three VFs generate seven kinds of SPs which include the real shaped SPs and four complex shaped SPs: repealing and attracting (out and in) spirals; clockwise and counterclockwise orbits (center). Also, the paper describes the VF singularities, their shapes, and locations according to the image objects. Then, the present work defines the mappings between the SPs’ shapes if the VFs are separately embedded into the same image. Thus, the paper infers which VFs are most appropriate for embedding into a particular image database such that the augmented SPs shapes enhance the ML classification. Examples of images with different embedded VFs are shown, in the text, to support and validate the theoretical conclusions. The contributions of the paper are: a description of the SPs location in an image; the mappings between the SPs of the different VFs; the definition of an imprint of an image in a VF; the generation of databases’ imprints. The advantage of classifying an image database with embedded VFs is that the new database enhances ML and improves the classification statistics if compared to the classification of the original image database with the same ML classifier. Furthermore, images with embedded SPs create a useful augmentation to the original image databases.

The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

Background: Time-series biosignal data, representative of a physiological process, is often applied to time-sensitive machine learning applications that benefit from acceleration. Medical and research applications that process biosignal data in real-time may utilize new hardware architecture by switching from CPU to GPU devices to take advantage of the data processing speedups. In order to utilize machine learning kernels that are typically employed by a CPU on a GPU, the machine learning kernel must be reimplemented using custom compilers that can take advantage of GPU architecture. Objectives: The primary objective is to evaluate the speed of CPU-based machine learning algorithms commonly employed in biosignal process- ing and compare the speedup improvements obtained through GPU acceleration. Methods: A systematic search was conducted across multiple databases to identify studies employing GPU acceleration in biosignal processing. Inclusion and exclusion criteria are defined for GPU accel- eration studies. In this literature review, 12 studies of GPU kernel development for traditionally CPU-based kernels are analyzed. Results: It is found that a positive speedup occurs when using GPU kernels over traditional CPU-based algorithms in all instances. The speedup of GPU over CPU performance ranges between 1.87 to 27018.27 times faster. Conclusions: This review will contribute to the understanding of the role of GPU kernel development in biosignal processing, providing insights into performance improvements obtained by current GPU kernel development. The results indicate that GPU kernel development is a plausible direction to obtain real-time biosignal-based systems.

1) Background: The screening of preeclampsia (PE) and intrauterine growth restriction (IUGR) represents a constant challenge for obstetricians. The aim of this study was to determine and compare the predictive performance of 4 machine learning-based algorithms for the prediction of PE, IUGR, and their association in a cohort of singleton pregnancies; (2) Methods This prospective study was conducted at a tertiary maternity hospital in Romania, and included 210 pregnancies that underwent first trimester screening. We included clinical and paraclinical data into 4 machine learning-based algorithms decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and calculated their predictive performance; (3) Results: RF performed the best when used to predict PE, IUGR, and its subtypes, as well as the association between PE and IUGR. The overall predictive performance of DT for all these disorders was inferior to RF, NB, and SVM. Both SVM and NB had similar accuracy for the prediction of PE, while NB performed better than SVM for the prediction of IUGR; (4) Conclusions: Machine-learning-based algorithms could be useful for the prediction of ischemic placental disease and need to be validated on large cohorts of patients.

Financial bubble prediction has been a significant area of interest in empirical finance, garnering substantial attention in the literature. This study aimed to detect and forecast financial bubbles in the Vietnamese stock market from 2001 to 2021. The PSY procedure, which involves a right-tailed unit root test to identify the existence of financial bubbles, was employed to achieve this goal. Machine learning algorithms were then utilized to predict real-time financial bubble events. The results revealed the presence of financial bubbles in the Vietnamese stock market during 2006-2007 and 2017-2018. Additionally, the empirical evidence supported the superior performance of the Random Forest and Artificial Neural Network algorithms over traditional statistical methods in predicting financial bubbles in the Vietnamese stock market.

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

Consumer food environments have transformed dramatically in the last decade. The number of food outlets has increased, and a large proportion of the UK population now purchase food from a takeaway or restaurant at least once a week. Despite these developments, national spending on food control has reduced and many Local Authorities struggle to meet health inspection targets. This work presents a data driven approach to enhance current inspection processes with a view to reduce consumer risk of foodborne illness whilst eating outside the home. We explore the utility of three machine learning algorithms to predict non-compliant food outlets in England and Wales as defined by Food Hygiene Rating Scheme scores >= 2. Using socio-demographic, business type and urbanness features we experiment with under and over sampling strategies at five ratios to address problems of class imbalance in the dataset prior to analysis. We find that Synthetic Minority Over Sampling Technique alongside a Random Forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves 84% of total non-compliant outlets in a test set of 92,595 (sensitivity=0.843, specificity=0.745, precision=0.274). We discuss the utility of machine learning algorithms to prioritise high risk establishments for inspection by Local Authority officials and make recommendations for weighting outcomes to improve their appropriateness in an applied setting.

In this brief letter we report our initial results on the application of deep-learning to the massive MIMO channel estimation challenge. We show that it is possible to estimate wireless channels and that the possibility of mitigating pilot-contamination with deep-learning is possible given that the leaning model underwent an extensive training-phase and that it has been presented with a large number of different channel conditions.

Abstract: This study examines the legal implications of implementing wearable technology in professional sports, focusing on European football leagues. As the adoption of wearable devices in sports continues to grow, it raises significant legal concerns regarding privacy, data protection, and athlete rights. The research employs a mixed-methods approach, combining qualitative analysis of legal frameworks and quantitative survey data from 150 professional footballers across top European leagues. Key findings reveal that 78% of surveyed athletes express concerns about the potential misuse of their biometric data collected through wearable devices (p < 0.001). The study identifies three primary legal challenges: (1) data ownership and control, (2) informed consent and voluntary participation, and (3) potential discrimination based on biometric data in contract negotiations. A comparative analysis of regulatory approaches in different European countries shows significant variations, with only 40% having specific legislation addressing wearable technology in sports.The research also explores the intersection of wearable technology with existing sports law, including doping regulations and fair play principles. A chi-square test indicates a significant association between athletes' awareness of legal rights and their willingness to use wearable technology (χ2 = 15.7, p < 0.01).This study contributes to the growing body of literature on sports law and technology by providing empirical evidence and legal analysis specific to the European football context. The findings have important implications for policymakers, sport’s governing bodies, and athletes' unions in developing comprehensive frameworks to address the legal challenges posed by wearable technology in professional sports.

Ipfs and public peer-to-peer (P2P) networks were adopted to make AI calculations more decentralized. Taking AI workloads over a decentralized network could bring better fault tolerance, guarantee data safety, and increased privacy. This research explores the challenges of centralized AI, such as data confidentiality, scalability, and accessibility, while discussing the promise of decentralized AI. Combining IPFS with decentralized systems improves scalability, data protection, and fault tolerance.

Chattogram is the port city as the busiest port of Bangladesh is there. Geographically, it lies besides the Bay-of-Bengal and Karnaphuly river. The city is currently stuck with urban flood and water pollution. Drainage congestion results in irregular flow of water which then ended in urban flood. Urban flood carries huge number of pollutants from the drains to the other water bodies. This study aims to investigate drainage congestion as the causal factor of urban flood and urban flood is the cause of water pollution. This study also aims to assess the impacts of water pollution in the study area. The findings highlight that drainage congestion causes irregular water discharge and overflow of water. A short time to rain results as the urban flood in the study area. The water of drainage system gets mixed with the urban flood which then carried into the karnaphuly river, lakes, khals, canals, ponds etc. The pollutants from the drain cause the water pollution. Study found that the drainage water is much more polluted in comparison to normal water quality parameters. The amount of pollutant is much more because of drainage congestion as the congested water gather much pollutants than the natural flow of water. Statistical analysis also found the significant correlation between drainage congestion and water pollution. However, the residents of the study area are the much more sufferers of the water pollution. They fall a victim to Cholera, Typhoid, hepatitis B, Dysentery, Dengue, Chikonguniya etc. water related diseases. Impacts on urban neighborhood as impacts on aesthetic beauty, impacts on drinking water, impacts on human habitat, impacts on environment, infrastructural impact, spread out of diseases among the residents etc. were found in this study. Impacts on urban environment as impacts on soil, impacts on atmosphere, impacts on human habitat, impacts on human health, impacts on road and other infrastructures etc. were investigated as well. This research also came up with the possible solution to mitigate the impacts of urban flood and water pollution in the study area.

Background: Since the introduction of the SLICC criteria in 2012, biopsy-proven lupus nephritis (LN) has been the only independent diagnostic criterion for systemic lupus erythematosus (SLE). This was reaffirmed by the EULAR/ACR in 2019, emphasizing the importance of renal biopsy in LN. However, the current classification lacks specific histopathological criteria for defining LN. This study describes the histological findings of patients with LN, compares them with those of other glomerular diseases, and evaluates their diagnostic accuracy. Methods: This retrospective cohort included 731 kidney biopsies. The patients were divided into two groups: a LN group and a control group comprising patients with membranous nephropathy, IgA nephropathy, membranoproliferative glomerulonephritis, pauci-immune glomerulonephritis, and proliferative glomerulonephritis. Sensitivity and specificity analyses were conducted for various histopathological features. Results: We identified five features strongly correlated with LN: mesangial proliferation, subendothelial deposits, C1q staining ≥ 1+, dominant IgG, and ≥ 4 positive immunofluorescence elements. Combined, these features yielded an area under the ROC curve of 0.94 (95% CI: 0.91–0.95). In membranous nephropathy, histological features such as mesangial deposits, C1q positivity, and ≥ 4 positive immunofluorescence elements effectively distinguished class V LN from non-lupus membranous nephropathy, with an area under the ROC curve of 0.85 (95% CI: 0.76–0.93). Conclusion: The combination of mesangial proliferation, subendothelial deposits, C1q staining ≥1+, dominant IgG, and ≥ 4 positive immunofluorescence elements offer good accuracy for diagnosing renal involvement in SLE. In the absence of pathognomonic features, combined criteria are valuable diagnostic tools, particularly when other SLE criteria are lacking.

Background. A direct clinical and morphological comparison of non-COVID myocarditis than was diagnosed before the SARS-CoV-2 pandemic and post-COVID myocarditis has not been performed so far. Purpose: to compare morphological activity, toll-like receptors expression and response to the immunosuppressive therapy in patients with non-COVID and post-COVID myocarditis. Metodhs. Seventy-seven patients (52 male and 25 female, 48.7±11.7 y.o.) with biopsy proven myocarditis, NYHA class 2 or more heart failure, ejection fraction (EF) <45% were included. A history of myocardial infarction, verified cardiomyopathies, systemic autoimmune diseases, viral DNA in the myocardium, except parvovirus B19, were exclusion criteria. The right ventricular endomyocardial biopsy with hematoxylin and eosin, Van Gieson stains, PCR for virus (herpes viruses, parvovirus B19, adeno-, enteroviruses and SARS-CoV-2), immunohistochemical study for CD3, CD45, CD68, CD20, nucleocapsid / spike proteins of SARS-CoV-2 and in addition toll-like receptors (TLR) type 4 and 9 expression (in 38 patients) were performed. The steroids (methylprednisolone 24-40 mg per day), azathioprine, mycophenolate mofetil were prescribed. The mean follow-up was 16 [6; 30] month. Results. In 45 patients, myocarditis was diagnosed before the SARS-CoV-2 pandemic (giant-cell in one case and lymphocytic in the others). Other 32 patients had post-COVID myocarditis positive for RNA or/and proteins of SARS-CoV-2 (giant-cell in one case, eosinophilic in three cases and lymphocytic in the others). There were no differences in age, NYHA class, CRP and anti-heart antibodies level, echocardiographic parameters (mean EF 30.2±7.8 vs 28.7±6.7%), parvovirus B19 positivity (22 vs 34%), doses of methylprednisolone (24-40 mg/day) and death / transplantation rate (11.1 vs 9.4%). Differences between non-COVID vs post-COVID myocarditis focused on higher CD3, CD 45*, TLR 4 (4+ vs 6+), TLR 9 (0 vs 2+) expression and better response to therapy [10% or more increase in EF in 53 vs 86%* of patients, mean EF (43.9±12.3 vs 49.8±7.6%*) by the end of follow-up], *p<0.05. Conclusion. Post-COVID myocarditis is characterised by different morphological types, higher morphological activity, tendency to higher TLR expression and the best response to the immunosupressive therapy in comparison with non-COVID myocarditis.

This study, based on proactive motivation theory, explores the relationship between perceived overqualification, digital leadership, and constructive deviant behavior, as well as generational differences. Using data collected in two waves from 596 employees, the results show that perceived overqualification positively influences constructive deviant behavior, and digital leadership enhances this effect. Employees born in the 1980s are most influenced by both factors, followed by those born in the 1990s, where the effect of digital leadership is less significant. The study suggests that effectively utilizing digital leadership and recognizing generational differences in perceived overqualification can help organizations better manage overqualified talent, improving employee satisfaction and productivity.

Introduction Meningioma is the second most common primary intracranial tumor of the central nervous system. in Sudan, meningioma is the most common primary brain tumor, among Sudanese patients, Surgical total excision of meningioma offers a better survival, however, Chemotherapy has largely been unsuccessful for meningioma treatment, and therefore, refractory and recurrence meningioma treated with palliative surgery and radiotherapy. Monosomy of chromosome 22 is the utmost common genetic alteration among meningioma and many genes were investigated and described in the development of this tumor of which , NF2 hSNF5/INI1gene, P53. In addition to this, Molecular signal pathways in meningioma, and that contribute to the survival, proliferation, self-renewal, and differentiation properties of normal stem cells which are abnormally activated and nominated as cancerous stem cell (CSCs). Objectives 1/ To identify the possible pathway of candidate genes in both setting and whole genes hypothesis free approach 2/ To assess the presence of mutations in candidate genes among histological subtypes of meningioma in Sudan. Material and methods This is a prospective cross-sectional study done at the National Center of Neurological Sciences (NCNS) Khartoum, from 2018 to 2021. The study included all cerebral tumors that were radio- logically diagnosed preoperatively as meningioma. Ethical approval was taken from Institute of Endemic disease, Khartoum University, and written consent was obtained Three DNA extracts were randomly selected from 20 meningioma tissue samples and processed for WES. All genes harboring exonic and splice site variations stratified into SNVs and INDELs sets were prepared. The 1000G online database was used to explore novel mutations per 3 samples. SIFT and PolyPhen-2 programs were used to predict possible impact of amino acids substitution. To address meticulous genes interaction and particular centrality, all shared genes within the class INDEL and SNVs were used as input on Network Analysis to predict protein- protein interaction and functional gene ontology. Finally, shared mutations within genes of centrality were detected via Sanger sequencing and mutations were correlated between histological sub-types and recurrenceThree non meningioma brain tumor used as control. Results WES analysis revealed high number for mutational signature within 2 classes (INDEL and SNVs) per 3 samples, the findings of 1000 genome online database showed number of novel mutations; 5618 in fibrous , 6032 in meningothelial while in recurrent fibrous sample 5930. Network analysis of shared genes per each class (INDEL, SNVs), showed UBC is the most protein of remarkable degree centrality over thousand of genes. Whereas, all shared genes per 3 samples within the 2 classes (INDEL and SNVs) revealed Akt1 with the highest degree of centrality and betweenness. Akt1 rs17846829was detected in 20% of the samples, while CD44 stemness gene revealed mutation A>G (rs9666607) in all meningioma samples (100%), this mutation was associated with 60% of the recurrence. Conclusions Nevertheless, the present study, highlight how such complex molecular processes might contribute to tumorogenesis, in attempt to shed light over thousands of genes and multifaceted network analysis rather than a solitary gene which is challenging question.

For the first time, this communication is intended to perform the possible biological role of natural compound Hypericin, in the Crystal Structure of dual-specificity tyrosine phosphorylation regulated kinase 2 (DYRK2), by Molecular and Dynamic Simulation. From Docking analysis by Autodock Vina and Autodock 4, Hypericin shows a significant ability to binds to DYRK2 protein, an important receptor for cell growth and development. Indeed, from docking studies Hypericin reports a binding energy value of about -13 kcal/mol and Estimated Inhibition Constant (Ki)of ca 1nM . Regarding MD simulation.From our results, this theoretical research it could has excellent future implications in the fight against cancer disease.

Considering the prevalence of cropping pattern in many Saffron cultivation regions, the continuous cropping obstacle caused the decline in growth and development. These effects justify the necessity of studying agronomic strategies to promote plant quality and production. Herein, the mono cropping, intercrop of Paulownia and Saffron, and intercrop of Soybean and Saffron patterns have been performed. The metabolites of Saffron under three cropping patterns were determined and identified using by an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. Uni and multivariate statistical analysis were applied to examine the difference between groups and select the significant differential accumulated metabolites. The results showed that a total of 315 compounds were significantly changed in three comparison groups. Phenylalanine metabolism, linoleic acid metabolism, and flavonoid biosynthesis pathway has been greatly enriched by the changed metabolites. The phenyl lactate, trans-2-Hydroxy-cinnamate, γ-Linolenate, 9-oxo-octadecadienoic acid, 13-0xo-0ctadecadienoic acid, 9,12,13-TriHOME of those metabolites showed significant differential accumulation in Saffron under three cropping patterns. And the accumulation of safranal and picrocrocin related compounds, such as Crocetin, Crocin showed a similar variation regularity. These indicate that there may be a close internal relationship between the cropping pattern and the biosynthesis of metabolites related to endogenous substances and Saffron quality formation.

Enzyme catalysis has traditionally been used by various human cultures for creating value, long before its basic concepts have been uncovered, by preparing useful products through the trans-formation of raw materials available from natural resources. Tremendous scientific and techno-logical progress has been accumulated globally in understanding what constitutes an enzyme, what reactions enzymes can catalyze, and how to search, develop, apply and improve enzymes to make desired products. The exquisite properties of enzymes as nature's preferred catalysts, such as high selectivity, diversity and adaptability, enable their optimal work, whether in single reactions or in multiple reactions. Excellent opportunities for resource efficient manufacturing of compounds needed are provided by the actions of enzymes working in reaction cascades and pathways within the same reaction space, like molecular robots along a production line. Enzyme catalysis plays an increasing role for industrial innovation and responsible production in various areas, such as green and sustainable chemistry, industrial or white biotechnology. Sources of inspiration can be current manufacturing or supply chain challenges, the treasure of natural enzymes or the opportunities of engineering tailor-made enzymes. Making best use of the power of enzyme catalysis is essential for changing the way how current products are manufactured, how renewable biobased resources can replace fossil-based resources, and how the safety, health environment aspects of manufacturing processes can be improved towards cleaner and more sustainable production.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. The research uses advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. The findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. Oil revenues also play a critical role, reflecting the country's economic reliance on oil. The study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. These findings also support the importance of consistent and predictable income sources for sustaining consumption. Additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

The normative and safeguarding role of ethics and law in the development of biomedical science and technology, as well as their importance in promoting the progress of the industry, analyzed the key role of ethics and law in the field of biomedical science and technology, by restricting scientific research behavior, protecting the rights and safety of subjects, providing a solid guarantee for scientific research activities, and promoting the healthy development of biomedical science and technology. At the same time, the positive role of ethics and law in promoting international cooperation and exchange of biomedical science and technology is also discussed, which will inject strong impetus into the global development of biomedical science and technology. In addition, through case analysis, the paper demonstrates the key role of ethical review in the field of biomedical science and technology, as well as the importance of law in maintaining market order, protecting subjects' rights and interests, and fairness and justice. The successful practice of ethics and law in the development of biomedical science and technology is emphasized, which proves that following ethical principles and legal framework is the key factor to promote the progress of biomedical science and technology. This paper explores the important role of ethics and law in the development of biomedical science and technology, and provides valuable reference and guidance for practitioners in the industry. By following ethical principles and legal norms, we will jointly promote the prosperity and development of biomedical science and technology, and contribute to the common progress of human health.

This article addresses the emergence of a niche for sustainable agricultural production in the province of García Rovira, Colombia. It examines the intervention of the Colombian planning regime, which implements agendas for sustainable territorial development. However, these agendas are not adequately aligned with provincial realities, resulting in a productive decline. From a multi-level perspective, a niche of production linked to the landscape is identified, highlighting sustainable practices and innovation. The study seeks to understand how planning influences the emergence of such niches and what factors boost their transition to sustainability. Through a mixed methodology, growing sectors are highlighted, and the profiles of the producers involved are outlined. The main conclusion is that the planned intervention is inefficient, as the niche of sustainable production emerges without its clear influence. Therefore, it is proposed to re-evaluate planning using alternative analysis frameworks to detect emerging productive dynamics, serving as a basis for future plans.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

Understanding mechanisms of ageing remains a complex challenge for biogerontologists, but recent adaptations of evolutionary ageing theories offer a compelling lens in which to view both age-related molecular and physiological deterioration. Ageing is commonly associated with a progressive loss of biochemical processes and degeneration of molecular material, however, the mechanisms of diminishing function are not simply a result of aberrant expression of these pathways. Natural selection pressures are at their highest in youthful periods to select genes advantageous to maximising reproductive capacity. After sexual maturation, selective pressure diminishes, subjecting individuals to maladaptive pleiotropic gene function once beneficial for developmental growth, but pathogenic later in life. Due to this selective ‘shadowing’ in ageing, mechanisms to counter hyperfunctional genes are unlikely to evolve within a given species. These genetic ‘run-on’ programmes are not dysfunctional per se and, in fact, remain highly robust. Thus, interventions capable of optimising gene function during the ageing process represent as attractive therapeutic modalities. Caenorhabditis elegans offers a unique model to explore high-throughput screening of interventions to combat hyperfunctional pathways involved in ageing. However, the majority of research to date employ whole-life vs late-life pathway manipulation of developmental/growth-related systems to claim ageing attenuation, despite significant impairments in early-life fitness and discordance with healthspan. Here, we discuss data supporting hyperfunctional changes at a global molecular and functional level in C. elegans, and how classical lifespan-extension mutants alter these dynamics. We later discuss the relevance of such mutant models for exploring mechanisms of ageing, highlighting that post-reproductive gene manipulation represents a far more translatable approach for C. elegans research not routed within pleiotropic constraints. More so, lifespan has become a routine readout for quantifying ‘ageing’, however, we make the argument that a compression of gerospan vs lifespan extension is a more meaningful goal for anti-ageing regimes with current knowledge. Similarly, mechanisms governing lifespan are distinct from those regulating health, and slowing senescent pathology accumulation appears more applicable for the abstraction to human ageing.

Abstract Introduction: Poor structural quality of health facilities for tuberculosis (TB) diagnosis and treatment contributes to unfavorable health outcomes among TB patients. Improving the quality of healthcare services helps to avert TB-related morbidity. Despite these facts, the level of structural quality of health facilitieSs to deliver TB services is not in line with what the national TB and leprosy program (NTLP) recommends. Hence, the present study was conducted to assess the structural quality of health facilities to deliver quality TB services to TB patients among public health facilities in the Sidama region.Objectives: To determine the quality of the structural domain of TB services at public health facilities in the Sidama region of southern Ethiopia.Methods: An institution-based cross-sectional study was conducted from June to July 2023 among randomly selected public health facilities in the Sidama region. We included 40 randomly selected public health facilities that delivered TB services in the study area. Data was collected by experienced data collectors using facility audits, clinical observation checklists, structured questionnaires, and in-depth interviews. An Open Kit Mobile (ODK) application was used to collect data and exported to SPSS version 26. All statistical analysis was done using SPSS version 26.Results: The overall mean quality score for the structural dimension was 50.25 among the 40 health facilities audited in the study area.Conclusion: The quality of TB services regarding structural dimensions was poor in the study area. Therefore, any strategies must give careful attention to increasing the comprehensive structural dimension of quality TB services.

This review explores the transformative impact of sensor technologies in early disease detection. It highlights recent advancements in nanotechnology-based sensors, particularly focusing on their applications in identifying disease biomarkers at early stages. The integration of these sensors with artificial intelligence for predictive analytics in healthcare is also discussed. Applications in cancer, infectious diseases, and chronic disease management underscore their diverse utility. Challenges in sensor technology adoption and future directions, including multiplexed sensors and wearable devices, are examined. This review underscores sensors' potential in enhancing disease management and patient outcomes.

Computed tomography (CT) is a widely employed imaging modality with diverse clinical applications in pediatric and adult populations, offering numerous advantages. Despite several alternatives to imaging modalities with ionising radiation, CT scans have demonstrated persistent global popularity, contributing to a substantial amount of effective radiation doses for patients. This exposure results in hazards, including stochastic effects that amplify the radiation-induced cancer risk proportionally to the radiation dose received. CT-related radiation comprises a substantial portion of cumulative effective doses within diagnostic radiology, specifically impacting younger patient cohorts undergoing repeated investigations for chronic diseases over time. Scientific literature over recent decades has explored various methods to mitigate radiation doses during medical imaging. One notable strategy involves implementing low-dose and ultralow-dose CT protocols, maintaining diagnostic efficacy in acute and non-acute medical and surgical conditions. Utilising innovative image reconstruction algorithms, such as iterative reconstruction and emerging deep-learning algorithms based on artificial intelligence, plays a pivotal role in achieving an approximately 80% reduction in patient radiation compared to standard doses. This review article discusses the significance of low-dose protocols across a spectrum of acute and chronic medical conditions affecting different body systems.

Molecular docking, a computational method predicting how molecules bind to target proteins, is employed in this study to explore Polydatin's interactions with Ras-related protein Rab (Rab) proteins using the Mcule Database. This database validates the docking results and offers insights into the pharmacological properties of Polydatin. Notably, specific Rab proteins like Rab-5A, HRas, and Rab-18 exhibit robust interactions with Polydatin, indicated by favorable binding energies.These findings illuminate the potential roles of Polydatin in therapeutic and biological contexts, emphasizing its interaction with Rab proteins. The Mcule Database enhances the credibility of the study, providing additional perspectives on Polydatin's applications. The observed interactions, particularly with key proteins like HRas, present a promising avenue for modulating cellular signaling pathways. This modulation holds therapeutic potential, potentially addressing conditions associated with pathway overactivity, including specific cancer types.

The assessment of Histone deacetylases in conjunction with Curcumin, conducted through the Mcule Database employing the Autodock Vina algorithm, involves an examination of molecular interactions. This analysis includes the calculation of binding energies, revealing the strength and stability of the interactions between Curcumin and various Histone deacetylase targets. The comparison encompasses diverse aspects such as binding energies, specific binding sites, interactions across different targets, structural details, and potential implications for biological activity. The results imply that Curcumin shows heightened affinity for Histone deacetylase 4 in comparison to other investigated histone deacetylases.

This article explores the cutting-edge advancement of gypsum and cement building boards infused with shape-stabilized n-octadecane, an organic phase change material (PCM). The primary focus is on improving energy efficiency and providing electromagnetic interference (EMI) shielding capabilities for contemporary buildings. The research investigates the integration of these materials into construction materials, using red-mud Carbon Foam as a stabilizer for n-octadecane (OD@CCF). Various analyses, including microstructural examination, porosity assessment, and additive dispersion, were conducted using X-ray microtomography and density measurements. Thermal conductivity measurements demonstrated the enhancement of composite boards as the OD@CCF content increased, while mechanical tests indicated an optimal additive content of up to 20%. The thermal-regulated capabilities of these advanced panels were evaluated in a custom-designed room model equipped with a homemade environmental chamber, illustrating a consistent temperature environment during heating and cooling cycles. The incorporation of OD@CCF into cement boards exhibited improved thermal energy storage properties. Moreover, the examined composite boards displayed efficient electromagnetic shielding performance within the frequency range of 3.2–7.0 GHz, achieving EMI values of approximately 18 and 19.5 dB for gypsum and cement boards, respectively, meeting the entry-level value required for commercial applications.

Vaccines trigger a complicated immunological response that includes B and T cells, with B cells producing antibodies. SARS-CoV-2 immunity weakens over time after vaccination. Discovering key changes in antigen-reactive antibodies over time after vaccination could help improve vaccine efficiency. In this study, we collected data on blood antibody levels in a cohort of healthcare workers vaccinated for COVID-19 and obtained 73 antigens in samples from four groups according to the duration after vaccination, including 104 unvaccinated healthcare workers, 534 healthcare workers within 60 days after vaccination, 594 healthcare workers between 60 and 180 days after vaccination, and 141 healthcare workers over 180 days after vaccination. An efficient machine learning based framework containing four feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and maximum relevance minimum redundancy) and four classification algorithms (decision tree, k-nearest neighbor, random forest, and support vector machine) was designed to screen out essential antigens. Several efficient classifiers with weighted F1 value around 0.75 were constructed. This study revealed that S1+S2, S1.mFcTag, S1.HisTag, S1, S2, Spike.RBD.His.Bac, Spike.RBD.rFc, and S1.RBD.mFc were most highly ranked among all features, where S1 and S2 are the subunits of Spike, and the suffixes represent the tagging information of different recombinant proteins. Meanwhile, the classification rules were extracted from the optimal decision tree to explain quantitatively the roles of antigens in the classification. This study identified antibodies associated with decreased clinical immunity based on populations with different time spans after vaccination. These antibodies have important implications for maintaining long-term immunity to SARS-CoV-2.

Introduction: Academic success is primary goal of every student. It is described as the extent to which a student has successfully achieved his or her short and long-term educational goals. Several factors have been established to predict academic performance of students. Machine learning techniques have been employed in predicting students’ performance, but it has not been prevalent in developing countries like Nigeria and most studies did not consider class teachers’ end-of-the-year rating. Aim: The aim of this work is to develop a Radial Basis Function Neural Network (RBFNN) for prediction of secondary school students’ performance. Materials and Methods: We obtained data from school repository containing students’ raw score and classteachers’ rating from year one to year six. The data was labelled into pass or fail given the actual outcome of their examinations. Subjects were categorized into Mathematics, English and major, depending on the student’s specialization. Class-teachers’ ratings were also included in the dataset. The preprocessed dataset was used to train the RBFNN model. The impact of Principal Component Analysis (PCA) was also measured. Results: We set up four experiments in order to achieve our aim. The best result gave the sensitivity of 93.49%, specificity of 75%, accuracy of 86.59% and an AUC score of 94%. Other experiments gave a relatively low performance. Conclusion: This study helps students to get a projection of academic success even before sitting for the examination. This will also help parents and counsellors in knowing the direction of their counseling to each student. Teachers and parents should pay attention to class teacher ratings of the students as this is discovered to affect the prediction accuracy of their examination success.

This is break direction on oxygen sources and conveyance methodologies for COVID-19 treatment. It has been adjusted from WHO and UNICEF's specialized details and direction for oxygen treatment gadgets, which is important for the WHO clinical gadget specialized arrangement, 1 and depends on current information on the circumstance in China and different nations where cases have been distinguished. This direction is proposed for wellbeing office chairmen, clinical leaders, acquisition officials, arranging officials, biomedical architects, foundation engineers and strategy producers. It portrays how to: measure oxygen interest, to distinguish oxygen sources that are accessible, and select suitable flood sources to best react to COVID-19 patients' requirements, particularly in low-and-center pay nations. WHO will refresh these suggestions as new data opens up. Coronavirus pandemic spurred fake interest for oxygen gas chambers for clinical use - both at emergency clinics and inquisitively, for home use by patients. A few patients and surprisingly sound people investigate the conceivable outcomes and likely benefits of utilizing oxygen from chambers for private utilization. Be that as it may, this isn't continuously protected, and sufficient safety measures are to be taken, bombing which there can be fatalities. This paper investigates the significance of keeping up satisfactory degrees of oxygen levels appropriate for human utilization. It advises the clinical use and the advantages and disadvantages of putting away oxygen chambers at home. The investigation likewise addresses lawful and administrative perspectives. The investigation's discoveries can help people settle on an educated choice on the protected use regarding oxygen gas. Further, it cautions on the expanded significance of guidelines and limiting access and use. This paper aims at designing an oxygen level monitoring technique in an oxygen cylinder. The amount of oxygen present inside the oxygen cylinder is very vital information when such cylinder is in use for supply of oxygen to a critical patient. The amount of oxygen present inside the cylinder is measured by the pressure at the outlet nozzle. The pressure is measured using a high precision MEMS Pressure Sensor. The output of the MEMS pressure sensor is voltage of the order milli. An amplifier is used to amplify this milli volt signal. A microcontroller is used in cascade to process the signal and display the pressure of oxygen cylinder.

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for the clinicians. The aim of this study was to evaluate and compare the predictive performances of machine-learning based models for the prediction of preeclampsia, and its subtypes; (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients’ clinical and paraclinical characteristics were evaluated in the first trimester, and were included in 4 machine learning based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed; (3) Results: early-onset PE was best predicted by DT (accuracy: 94.1%), and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%), and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%; (4) The machine learning-based models could be useful tools for PE prediction in the first trimester of pregnancy.

The lunar calendar is often overlooked in time series data modelling, despite its importance in understanding seasonal patterns as well as economic, natural phenomena, and consumer behavior. This study aims to investigate the effectiveness of the lunar calendar in modelling and forecasting rainfall levels using various machine learning methods. The methods employed included Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally pro-vided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems, while also opening new opportunities for further research into lunar calendar applications across various domains.

Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

Identifying skeletal remains has been and will remain a challenge for forensic doctors and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study proposes a machine learning method to determine gender starting from morphometric analysis of L1-L5 lumbar vertebrae in a modern Romanian population. The purpose of the present study was to observe whether by using the ML method there is a good predictability of gender in forensic identification based on parameters obtained from the metric analysis of the lumbar spine specific to the Romanian population. This paper offers two models of ML, RF and XGB, each with its own characteristics, and presenting different performance, random forest having the best. For both, we used two metrics (accuracy and roc_auc), the latter being the most used to highlight model performance. The L1-L5 lumbar vertebrae exhibit sexual dimorphism and can be used in gender estimation. Machine learning is more accurate in determining gender than discriminatory function analysis.

Based on orbital angular momentum (OAM) properties of Laguerre Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and Machine-learning detection method. In the encoding process, the intensity profile for data encoding is generated based on selection of p and ℓ indices, while the decoding process is performed using support vector machine (SVM) algorithm. Two different decoding models based on SVM algorithm are tested to verify the robustness of optical encoding model, finding a BER =10−9 for 10.2dB of signal-to-noise ratio in one of the SVM models.

Background/Objectives: Accurate prediction of stock prices is an extremely challenging task because of factors such as political conditions, global economy, unexpected events, market anomalies, and relevant companies’ features. In this work, the random forest has been used to forecast the prices of the four major Greek systemic banks Methods/Analysis: We make use of a set of financial variables based on intraday data: (i) Open stock price, (ii) High stock price, (iii) Low stock price, and (iv) Close stock price of a particular Greek systemic bank. Results/Findings: The variables used here are crucial in predicting systemic banks' stock closing prices. These provide a better prediction of the next day's closing price of the bank series. Novelty /Improvement: To our knowledge, this is the first study that employs machine learning techniques in Greek systemic banks.

Melanoma is considered the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in prognosticating this type of cancer. With the emergence of new therapeutic strategies for metastatic melanoma that have shown improvement in patient survival, we developed a transfer learning-based biomarker discovery model that could help in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results reveal that the genes we found show consistency with other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set, and our methods found novel biomarker genes as well. Our ensemble model achieved Area Under the Receiver Operating Characteristic (AUC) of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB). We also assessed the potential sources of bias for our model and confirmed some of them by the model's performance.

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

From the various perspectives of Machine Learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the Early Detection (ED) of anoma-lies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision-making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLM) operate with a focus on ED in a multidisci-plinary manner and specifically how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVM), De-cision Trees (DT), Random Forests (RF), Naive Bayesian Classifier (NB), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBM) and Stacking Ensemble Models (SEM). It was identified that MLMs for ED in multiple areas under SBM and SEM implementation achieved accuracies greater than 80% and 90%, respectively. n fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.

Deep learning techniques, such as convolutional neural networks (CNN), generative adversarial networks (GAN), and graph neural networks (GNN), have over the past decade changed the ac-curacy of prediction in many diverse fields. In recent years, the application of deep learning tech-niques in computer vision tasks in pathology demonstrated extraordinary potential in assisting clinicians, automating diagnosis, and reducing costs for patients. Formerly unknown pathologi-cal evidence, such as morphological features related to specific biomarkers, copy number varia-tions, and other molecular features, were also able to be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their appli-cations in pathology.

The combination of new-generation information technology and manufacturing technology has resulted in major and profound impact on future development paradigm of manufacturing. It is challenging for existing methods to conduct a multidimensional trend exploration related to machine tool domain, which is the basis of virtually everything in manufacturing. In this paper, we proposed an integrating approach framework combined topic models, bibliometric, trend analysis and patent analysis to mine insightful information about future development from multi-source data related to machine tool, such as papers, grants, patents and news. Specifically, papers and grants provided two different perspectives to explore the current focuses and future trends in machine tool research. Furthermore, the future technology development of machine tool was investigated through patents analysis. Finally, news related to machine tool industry in recent years was analyzed to analyze future machine tool business mode. The integration of the above various analytical methods and the multi-dimensional mining of literatures enabled the analysis of the future development of machine tool domain systematically from multi-perspectives which include research, technology development and industry. The conclusions obtained in this paper is beneficial to different communities of machine tool in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.

Coastal cliffs erode in response to short- and long-term environmental changes but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in 3-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face were compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. Controlling and curing diabetes in its early stages is the most significant technique to avoid its effects of diabetes. However, lack of awareness and expensive clinical tests are the primary reasons to skip clinical diagnosis and take preventive methods in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, the study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes. Because clinical features are not always known to all people from lower-income countries. To find the best fit supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. The study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for growing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

Several sun models suggest the radioactive balance where the concentration of greenhouse gases and the albedo effect are related to the Earth's surface temperature. There is a considerable increment of greenhouse gases due to anthropogenic activities. Climate change correlates with this alteration in the atmosphere and an increase in surface temperature. Efficient forecasting of climate change and its impacts of 1.5°C global warming above pre-industrial levels could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted the temperature change in the coming years. The global community has to create a model that can realize good predictions to ensure the best way to deal with the warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the present work. The FTT can solve problems such as the faint young sun paradox. In addition, we use different machine learning models to evaluate our method and compare the experimental prediction and results.

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. Supervised classification machine learning models are applied in this paper. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed nationwide random telephone surveys to gauge the public’s attitudes and actions towards disaster preparedness. However, these surveys may lack generalizability in certain affected locations due to low response rates or areas not covered by the survey. To address this issue and enhance the comprehensiveness of disaster preparedness assessments, we develop a framework that seamlessly integrates machine learning and simulation. Our approach leverages machine learning algorithms to establish relationships between public attitudes towards disaster preparedness and demographic characteristics. Using Monte Carlo simulation, we generate datasets that incorporate demographic information of the affected location based on government-provided demographic distribution data. The generated dataset is then input into the machine learning model to predict the disaster preparedness attitudes of the affected population. We demonstrate the effectiveness of our framework by applying it to Miami-Dade County, where it accurately predicts the level of disaster preparedness. With this innovative approach, relief agencies can have a clearer and more comprehensive understanding of public disaster preparedness.

Humans are a vision-dominated species, and what we see depends on where we look. Therefore, eye movements (EM) are essential to our interactions with the environment, and experimental findings show EM is affected in neurodegenerative disorders (ND). It could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to determine if changes in EM-evoked responses can tell us about ND, such as Alzheimer’s (AD) and Parkinson’s Disease (PD) progression in different stages. In the present review, we have analyzed the results of neurological, psychological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with Machine Learning (ML) methods. Described predictive algorithms are using various approaches, including Granular Computing, Naive Bayes, Decision Trees/Tables, Logistic Regression, C-/LinearSVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are also navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes. In conclusion: EM parameters with clinical symptoms are powerful precision instruments that, in addition to predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22^nd January 2020, till 26^th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

This paper investigates the use of deep learning techniques to perform energy demand forecasting. Specifically, the authors have adapted a deep neural network originally thought for image classification and composed of a convolutional neural network (CNN) followed by a multilayered fully connected artificial neural network (ANN). The convolutional part of the network was fed with a grid of temperature forecasting data distributed in the area of interest in order to extract a featured temperature. The subsequent ANN is then fed with this calculated temperature along with other data related to the timing of the forecast. The proposed structure was first trained and then used in a real setting aimed to provide the French energy demand forecast using ARPEGE forecasting weather data. The results show that the performance of this approach is in the line of the performance provided by the reference RTE subscription-based service, which opens the possibility to obtain high accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.

This paper presents a system based on pedestrian dead reckoning for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple device. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth's magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the SST turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

Differentiated thyroid cancer (DTC), comprising of papillary and follicular thyroid cancers, is the most prevalent type of thyroid malignancy. Accurate prediction of DTC is crucial for improving patient outcomes. Machine learning (ML) offers a promising approach to analyze risk factors and predict cancer recurrence. In this study, we aimed to develop predictive models to identify patients at an elevated risk of DTC recurrence based on 16 risk factors. We developed six ML models and applied them to a DTC dataset. We evaluated the ML models using Synthetic Minority Over-Sampling Technique (SMOTE) and with hyperparameter tuning. We measured the models’ performance using precision, recall, F1 score, and accuracy. Results showed that Random Forest consistently outperformed the other investigated models (KNN, SVM, Decision Tree, AdaBoost, and XGBoost) across all scenarios, demonstrating high accuracy and balanced precision and recall. The application of SMOTE improved model performance and hyperparameter tuning enhanced overall model effectiveness.

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news, and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news, and the various ways of automatically detecting it, this article provides its own experiments. These experiments were done on four different datasets, one that was made just for this article, and using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive-aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting existing terrestrial fiber infrastructure. Employing a Waveplate model, we integrate real ground displacement data from seven earthquakes, magnitudes ranging from four to six, to simulate the strains within fiber cables and collect large set of light’s polarization evolution data. These simulations help to enhance a Machine-Learning model that is trained and validated to detect Primary waves arrivals that precede earthquakes’ destructive Surface waves. The validation results show that the model achieves over 95% accuracy. The Machine-Learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates by a triangulation method and calculate fibers to epicenter distance. This setup allows early warning generation to municipalities close to epicenter location, and progressing to those further away. The model testing shows 98% of accuracy in detecting primary wave and one-second of detection time, affording nearby areas 21-seconds to take countermeasures, and an extended 57-seconds in more distant areas.

Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial to accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, comparing their efficiency with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design.

The use of credit cards and online banking is expanding exponentially. As more individuals use debit cards, credit cards, and internet banking, the likelihood of falling victim to various forms of fraud also rises. Due to ignorance, credit card company customers have provided their card details, personal information, and one-time password to an unknown bogus caller on several occasions in the recent past. One of the biggest issues facing financial institutions and customers alike is credit card theft. It may result in large financial losses and harm to the financial institution's image. While there are several ways to identify credit card fraud, one of the best is machine learning.By using past data, machine learning algorithms may be trained to recognize trends and abnormalities that point to fraud. The state-of-the-art in machine learning for credit card fraud detection is reviewed in this study. Numerous research on the same subject have been conducted in the past with a variety of well-known machine-learning techniques. However, with a grasp of the notion of hybrid machine learning, we will now examine how well hybrid machine learning performs on the same problem statement.

In this study, we present a novel approach to estimate the Hurst exponent of time series data using a variety of machine learning algorithms. The Hurst exponent is a crucial parameter in characterizing long-range dependence in time series, and traditional methods such as Rescaled Range (R/S) analysis and Detrended Fluctuation Analysis (DFA) have been widely used for its estimation. However, these methods have certain limitations, which we sought to address by modifying the R/S approach to distinguish between fractional Lévy and fractional Brownian motion, and by demonstrating the inadequacy of DFA and similar methods for data that resembles fractional Lévy motion. This inspired us to utilize machine learning techniques to improve the estimation process. In an unprecedented step, we train various machine learning models, including LightGBM, MLP, and AdaBoost, on synthetic data generated from random walks, namely fractional Brownian motion and fractional Lévy motion, where the ground truth Hurst exponent is known. Meaning that we can initialize and create these stochastic processes with a scaling Hurst/scaling exponent which, is then used as the ground truth for training. Furthermore, we perform the continuous estimation of the scaling exponent directly from the time series, without resorting to the calculation of the power spectrum or other sophisticated preprocessing steps, as done in past approaches. Our experiments reveal that the machine learning-based estimators outperform traditional R/S analysis and DFA methods in estimating the Hurst exponent, particularly for data akin to fractional Lévy motion. Validating our approach on real-world financial data, we observe a divergence between the estimated Hurst/scaling exponents and results reported in the literature. Nevertheless, the confirmation provided by known ground truths reinforces the superiority of our approach in terms of accuracy. This work highlights the potential of machine learning algorithms for accurately estimating the Hurst exponent, paving new paths for time series analysis. By marrying traditional finance methods with the capabilities of machine learning, our study provides a novel contribution towards the future of time series data analysis.

Within this paper, a machine learning algorithm is used to investigate the importance of certain setpoints and parameters in the filtration processes of a large-scale water treatment facility. Previously, a model for the filtration process based on Run-to-Run Control was proposed and tested against sample data from the treatment plant, but it was quickly found that such a model was incompatible for successfully computing setpoints of operation which minimize the energy cost of running the filtration systems. The machine learning model described herein is an attempt to elucidate the importance of the available data on the filtration systems and to identify the most important variables that influence the filtration run time.

In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website's visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website's source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website's performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize recent advances in machine learning to accurately and timely detect different anomalies and condition monitoring. This paper addresses this issue by evaluating different machine learning techniques and schemes and showing how to apply these approaches to solve anomaly detection and detect faults on photovoltaic components. For this, we apply distinct state-of-the-art machine learning techniques (AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest) to detect faults/anomalies and evaluate their performance. These models shall identify the PV system's healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such complex solution space.

Electromyographic (EMG) signals provide information about a person's muscle activity. For hand movements, in particular, the execution of each gesture involves the activation of different combinations of the forearm muscles, which generate distinct electrical patterns. Conversely, the analysis of these muscle activation patterns, represented by EMG signals, allows recognizing which gesture is being performed. In this study, we aimed to implement an automatic identification system of hand or wrist gestures based on supervised Machine Learning (ML) techniques. We trained different computational models and determined which of these showed the best capacity to identify six hand or wrist gestures and generalize between different subjects. We used an open access database containing recordings of EMG signals from 36 subjects. Among the results obtained, we highlight the performance of the Random Forest model, with an accuracy of 95.39%, and the performance of a convolutional neural network with an accuracy of 94.77%.

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

The quality of wine depends upon the quality of the grapes, which, in turn, are affected by different viticulture aspects and the climate during the grape-growing season. Obtaining wine professionals’ judgments of the intrinsic qualities of selected wine products is a time-consuming task. It is also expensive. Instead of waiting for the wine to be produced, it is better to have an idea of the quality before harvesting, so that wine growers and wine manufacturers can use high-quality grapes. The main aim of the present study was to investigate the use of machine learning aspects in predicting wine quality and to develop a pipeline which represents the major steps from vineyards to wine quality indices. This pipeline outputs the predicted yield, values for basic parameters of grape juice composition, values for basic parameters of the wine composition, and quality. We also found that the yield could be predicted because of input data related to the characteristics of the vineyards. Finally, through the creation of a web-based application, we have investigated the balance berry yield and wine quality. Using these tools further developed, vineyard owners should be able to predict the quality of the wine they intend to produce from their vineyards before the grapes are even harvested.

The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023. First of all, a static analysis is proposed aimed at identifying trends relating to mental health in the Italian regions with indication of the regional gaps. Subsequently, a clustering with k-Means algorithm is proposed. Below is a comparison of 11 machine learning algorithms for predicting the performance of the mental health index. Finally, the article offers some economic policy suggestions. The results are critically discussed in light of the scientific literature.

Radiomics refers to the extraction and analysis of a wide range of medical imaging features in a non-invasive and cost-effective manner to comprehensively characterise tumours. In this study, machine learning models combining radiomics and clinical factors were developed to predict retroperitoneal lymph node metastasis in testicular germ cell tumours (TGCTs), with the aim of reducing unnecessary treatment in this group of young patients. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. After segmenting 273 retroperitoneal lymph nodes using dedicated radiomics software, we developed machine-learning prediction models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). For each classifier, we developed a radiomics-only, clinical-only, and combined radiomics-clinical prediction model. The models’ performances were evaluated using the area under the receiver operating characteristic curves (AUCs). The RF-based combined clinical and radiomic model showed the most robust performance in predicting LNM with an area under the curve (AUC) of 0.95 (±0.03; 95% CI), accuracy 87%, precision 89%, recall 86% and F1 score 87%, followed by the LGBM model with an area under the curve (AUC) of 0.93 (±0.05; 95% CI), accuracy 83%, precision 87%, recall 80% and F1 score 82%. Decision curve analysis demonstrated the clinical utility of our proposed RF-based combined clinical–radiomics model. Our study has identified reliable and predictive machine-learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine-learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment. Multi-centre validation is required to provide high-quality evidence for clinical application.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used ML algorithms such as NB (Naive Bayes), LR (Logistic Regression), ANN (Approximate Nearest Neighbor algorithms search), XGboost, RF (random forest), decision trees, and SVM (Support Vector Machine) to compare with BERT, a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT (Bidirectional Encoder Representations from Transformers) classifier was slightly higher than that of the NB, LR, ANN, XGboost, RF, decision trees, and SVM classifiers, indicating that the BERT classification method was better.

Porous materials can be characterized by well-trained neural networks. In this study, fibrous paper-type gas diffusion layers were trained with artificial data created by a stochastic geometry model. The features of the data were calculated by means of transport simulations using the Lattice–Boltzmann method based on stochastic micro-structures. A convolutional neural network was developed that can predict the permeability and tortuosity of the material, through-plane and in-plane. The characteristics of real data, both uncompressed and compressed, were predicted. The data was represented by reconstructed images of different sizes and image resolutions. Image artifacts are also a source of potential errors in the prediction. The Kozeny–Carman trend was used to evaluate the prediction of permeability and tortuosity of compressed real data. Using this method, it was possible to decide if the predictions on compressed data were appropriate.

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used Support Vector Machine (SVM), and Bidirectional Encoder Representations from Transformers (BERT), a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT classifier was slightly higher than that of the SVM classifier, indicating that the BERT classification method was better.

Background: Machine learning has revolutionized situational awareness during disaster management by classifying, clustering, and predicting impacted locations and people. Despite its importance, no review has been conducted on machine learning tools for food assistance efforts during emergency or non-emergency situations. The purpose of this scoping review is to address that gap. Methods: Keywords were defined within the concepts of food assistance and machine learning. After the database searches, PRISMA guidelines were followed to perform a partnered, two-round scoping literature review. Text mining and Latent Dirichlet Allocation topic modeling algorithms were used to determine trends. Results: 28 articles met criteria and were included in the analysis. The types of study designs included: model development (42.9%), non-study (i.e., text and opinion) (28.6%), qualitative research (14.3%), case study (10.7%), and meta-analysis (3.6%). There were no quantitative studies. The machine learning tools’ main functions were improving SNAP programs (32.1%), detecting needs and resources (25%), predicting food insecurity (21.4%), and situational awareness of current food insecurity issues (21.4%). None of these studies took place during a disaster or explicitly addressed emergency mitigation, preparedness, or recovery. All of the studies were in early phases of development and implementation. Conclusion: Machine learning tools for improving situational awareness, resource allocation, policymaking, and prediction have the potential to improve food assistance, but there is a lack of implementation and evaluation during all disaster phases. Also needed is more formative work on generating food-related queries and defining variables and features of food security.

The importance of knowing the advance time in drip irrigation lines lies in its precision, as this influences the proper management of water and nutrients delivery. The mathematical calculation to determine the advance time is based on the general hydraulic flow equation, which relates the fluid velocity and the cross-sectional area of the pipe to the volume of water flowing through it. However, in irrigation pipes, where the flow is a mixture of dripper properties, its pressure, the approach to solving the advance time is different, as it is the sum of individual advance times for each segment. To address this issue, Python 3.11 was used along with various libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn to create a modeling environment and run algorithms that could predict outcomes. A program was developed that calculates the advance time for each segment of the drip line using partial velocities, and a dataset was generated that was used to train and test machine learning models. Several machine learning algorithms such as Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests were implemented to predict the advance time. Additionally, SciPy optimize was used to obtain multivariable equations that describe the advance time in drip irrigation lines. Results showed that the dripper flow has the greatest influence on the advance time, followed by the diameter and distance. Decision Tree and SVM models had the best accuracy with scores above 98%. Equations were found to calculate the advance time in the complete drip line and in 95% of its length, with coefficients of determination close to 99.33%. This study demonstrated the importance of understanding the relationship between dripper parameters and travel time in drip irrigation, as well as the utility of machine learning and optimization tools for predicting and modeling this phenomenon.

Appeals to governments for implementing basic income are contemporary. The theoretical backgrounds of the basic income notion, only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize the vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligent algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by perdition of vulnerable to future poverty households? In this paper, we utilize a multidimensional and longitudinal welfare data comprising one and a half million individual data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes.

Shear velocity logs are crucial in the oil and gas industry for assessing subsurface mechanical properties, including rock stiffness, shear strength, and seismic wave propagation, essential for optimizing hydrocarbon exploration and production strategies. However, obtaining shear velocity logs conventionally is expensive and time-consuming, especially when drilling additional wells solely for this purpose. With the recent boom in machine learning algorithms adoption across various scientific domains, it proved to be an extremely valuable tool for numerous applications in the oil and gas industry. It makes use of the readily available large datasets collected over decades and leverages this data to train powerful, data-driven models, reducing the reliance on empirical relationships that usually have poor generalization. This study follows this approach and presents the use and comparison of machine learning algorithms for predicting shear velocity logs from conventional and readily available logs in the Ahnet field, Algeria. Ultimately, this study aims to enhance reservoir assessment and optimize hydrocarbon recovery processes, potentially reducing exploration costs and improving oil and gas production decision-making in the region.

Transfer learning has proven to be a valuable technique for deploying machine learning models on edge devices and embedded systems. By leveraging pre-trained models and fine-tuning them on specific tasks, practitioners can effectively adapt existing models to the constraints and requirements of their application. In the process of adapting an existing model, a practitioner may make adjustments to the model architecture, including the input layers, output layers, and intermediate layers. In this study, we examine the effects of these adjustments on the runtime and energy performance of an edge processor performing inferences. Based on our observations, we make recommendations for how to adjust convolutional neural networks during transfer learning to maintain runtime performance. We observe that the Edge TPU is generally more efficient than a CPU at performing inferences on convolutional neural networks, and continues to outperform a CPU as the depth and width of the convolutional network increases. We explore multiple strategies for adjusting the input and output layers of an existing model and demonstrate important performance cliffs for practitioners to consider when modifying a convolutional neural network model.

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches. A simple code for the adaptive control algorithm used here can be downloaded from: https://github.com/alexscheinker/ES_adaptive_optimization

Human often cross their legs unconsciously while sitting, which can lead to issues like shifting the center of gravity, lower back pain, decreased blood flow and pelvic distortion. Detecting unconscious leg-crossing is important for maintaining correct posture. In this study, we explored the detection of leg-crossing postures using machine learning on data from body pressure distribution sensors. Collected 180 seconds of pressure data from 4 male subjects (25.8 ± 6.29 y.o.) in three conditions: no leg crossing, right leg crossed, and left leg crossed. Seven classifiers, including SVM and RF, were evaluated using Accuracy, Recall, Precision, and Specificity. As a result, SVM was difficult to classify.

This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It focuses on analyzing the interplay between regional marginal prices (LMP) and carbon emissions within electricity markets. Leveraging a dataset that encompasses hourly LMP and carbon emissions data across various regions of New York State, the paper explores how market designs and operational strategies influence carbon output. The analysis utilizes neural networks to simulate and predict the effects of different market scenarios on carbon emissions, highlighting the role of LMP, loss costs, and congestion costs in environmental policy effectiveness. The results underscore the potential of system engineering to provide a holistic framework that integrates market dynamics, policy adjustments, and environmental impacts, thereby offering actionable insights into optimizing market designs for reduced carbon footprints. This approach not only enhances the understanding of the complex interactions within electricity markets but also supports the development of targeted strategies for achieving sustainable energy transitions.

Efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. Manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4-12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of Supine (97.7%), Sitting (93.5%) and Prone (89.9%) position. Moreover, using data ablation experiments and analysing the SHAP values, the study assessed the importance of various groups of features from both time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification.

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of a terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and solution based on prediction of the weight mask by an encoder-decoder architecture, subse-quently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. Each sample corresponds to a 10 by 10 meter area of the stream channel and adjacent land. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. The proposed solution was compared with existing methods for measuring WSE in small streams using a drone. The results indicate that the solution outperforms previous photogrammetry-based methods and is second only to the radar-based method, which is considered the most accurate method available. The solution is characterized by a high degree of explainability and generalization.

In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 to C3), this research leverages machine learning (ML) techniques for effective UAV identification. The study primarily focuses on the impact of data augmentation methods—pitch shifting, time delays, harmonic distortion, and ambient noise integration—on classifier performance. These techniques aim to mimic real-world acoustic variations, thus enhancing the classifier's robustness and practical applicability. Results indicate that moderate levels of augmentation significantly improve classification accuracy. However, excessive application of these methods can negatively affect performance. The study concludes that sophisticated acoustic data augmentation can substantially enhance ML-driven drone detection, providing a versatile and efficient tool for managing drone-related security risks. This research contributes to UAV detection technology, presenting a model that not only identifies but also categorizes drones, underscoring its potential for diverse operational environments.

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses (NSSRN) dataset and predict nurse turnover using machine learning (ML) algorithms. Four ML algorithms, namely logistic regression (LR), random forests (RF), decision tree (DT), and extreme gradient boosting (XGBoost), are applied to the SMOTE-enhanced dataset. The data is randomly split into an 80% training set and a 20% validation set. Eighteen carefully selected variables from the NSSRN database serve as predictive features, and the machine learning model identifies feature importance concerning nurse turnover. The study includes a performance comparison based on metrics such as Accuracy, Precision, Recall (Sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests (SMOTE_RT) exhibit the most robust predictive power, both in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). XGBoost, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, EHR/EMR usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable ML algorithms for nurse turnover prediction.

Neutral facial expression recognition is of great importance in various domains and applications. This study introduces a data-centric approach for neutral facial expression recognition, presenting a comprehensive study that explores different methodologies, techniques, and challenges in the field to foster a deeper understanding. The results show that data augmentation plays a crucial role in improving dataset performance. Additionally, the study investigates different model architectures and training techniques to identify the most effective approach, with the InceptionV3 model achieving the highest accuracy of 72%. Furthermore, the research examines the influence of preprocessing methods on the performance of both InceptionV3 and a simplified CNN model. Interestingly, the results indicate that preprocessing techniques positively affect the performance of the simpler CNN model but negatively impact the InceptionV3 model. The implemented system, used to evaluate the findings, demonstrates promising results, correctly classifying 77% of neutral expressions. However, there are still areas for improvement. Creating a specialized dataset that includes both neutral and non-neutral expressions would greatly enhance the accuracy of the system. By addressing limitations and implementing suggested improvements, neutral facial expression recognition can be significantly enhanced, leading to more effective and accurate results.

Machine Learning techniques can be used to identify whether deficits in cognitive functions contribute to antisocial and aggressive behavior. This paper initially presents the results of tests conducted on delinquent and non-delinquent youths to assess their cognitive functions. The dataset extracted from these assessments, consisting of 37 predictor variables and one target. was used to train three algorithms that aim to predict whether the data corresponds to that of a young offender or a non-offending youth. Prior to this, statistical tests were conducted on the data to identify characteristics that exhibited significant differences in order to select the most relevant features and optimize the prediction results. Additionally, other feature selection methods, such as Boruta, RFE, and Filter, were applied, and their effects on the accuracy of each of the three machine learning models used (SVM, RF, and KNN) were compared. 80% of the data were utilized for training, while the remaining 20% were used for validation. The best result was achieved by the K-NN model trained with 19 features selected by the Boruta method, followed by the SVM model trained with 24 features selected by the filter method.

Wet granulation is a frequent process in the pharmaceutical industry. As a starting point for numerous dosage forms, the quality of the granulation not only affects subsequent production steps but also impacts the quality of the final product. It is thus crucial and economical to monitor this operation thoroughly. Here, we report on identifying different phases of a granulation process using a machine learning approach. The phases reflect the water content which in turn influences the processability and quality of the granule mass. We used two kinds of microphones and an acceleration sensor to capture acoustic emissions and vibrations. We trained convolutional neural networks (CNNs) to classify the different phases using transformed sound recordings as the input. We achieved a classification accuracy of up to 90 % using vibrational data and an accuracy of up to 97 % using the audible microphone data. Our results indicate the suitability of using audible sound and machine learning to monitor pharmaceutical processes. Moreover, since recording acoustic emissions is contactless, it readily complies with legal regulations and present Good Manufacturing Practices.

The communication link between the base station and the mobile sensor networks, such as multi-agent systems for collaborative tasks which include ground mobile robots and drones, is crucial for localization and success of tasks in indoor environments. The Power-domain Non-Orthogonal Multiple Access (P-NOMA) is an emerging multiplexing technique that enables the base station to accumulate signals for different agents using the same time-frequency channel. The environment information such as distance from the base station is required at the base station to calculate communication channel gains and allocate suitable signal power to each agent. The accurate estimate of the position for power allocation of P-NOMA in a dynamic environment is challenging due to changing location of the end-agent and shadowing. In this paper, we take advantage of the two-way Visible Light Communication (VLC) link to: (1) estimate the position of the end-agent in a real-time indoor environment based on the signal power received at the base station and (2) allocate resources using the Simplified Gain Ratio Power Allocation (S-GRPA) scheme with the look-up table method. In addition, we use the Euclidean Distance Matrix (EDM) to estimate the location of the end-agent whose signal was lost due to shadowing.

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

Background and Objective: Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20-80.4 % of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related with low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem. Methods: This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with type 2 diabetes were included in the study. In the data pre-processing stage, firstly outlier/excessive observation analyses were performed LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first level classifiers. Tree ensemble classifiers, namely ADA, AGBoost, and SGB, were used as second level classifiers. Results: After SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the after SMOTE data. The testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when SMOTE is used, can be preferred to correct class imbalance. Conclusions: SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when SMOTE was applied, the diagnostic accuracy decreased in some models, but the sensitivity increased significantly. This shows the positive effects of SMOTE to better predict the minority class.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.